Poster Abstracts

1- Understanding Cell Context in Digital Pathology by Combining Mathematical Modeling and Deep Learning

Shahira Abousamra, Stanford University, (CSBC)

Within the tumor microenvironment, cell context, defined here as the arrangement of different cell types and their spatial co-localization, has been shown to have prognostic and predictive significance. While the cell context is essential for analysis, it has not been explicitly incorporated into modern learning frameworks. By combining spatial statistics and cell topology with deep learning, we propose novel methods to explicitly model cell context and improve current state-of-the-art analysis in computational pathology. First, we consider the cell classification task, i.e., classify cell types in histopathology images. While existing methods mostly focus on the morphology and texture of individual cells, pathologists often infer cell types from their local spatial context, i.e neighboring cells. We use spatial statistics to describe this local context. Our method, MCSpatNet, utilizes multi-task learning to learn features that integrate both a cell’s appearance and its spatial context. Using MCSpatNat, we demonstrate a boost in the performance on cell classification benchmarks. Second, we observe how cells form structural patterns, where these patterns are defined as clusters and holes. We represent these structural patterns using topological and spatial descriptors in a deep generative model. This approach enables us to generate high quality multi-class cell layouts conditioned on spatial and topological characteristics. The generated layouts can serve data augmentation needs to improve performance in different downstream tasks. In summary, by combining mathematical modeling and deep learning, our work paves the way for more advanced modeling of the tumor microenvironment and a better understanding of its complexities and underlying interactions.

Other Authors: Shahira Abousamra, David Belinsky, John Van Arnam, Felicia. Allard, Eric Yee, Rajarsi Gupta, Tahsin Kurc, Dimitris Samaras, Joel Saltz, and Chao Chen.

2-  Physical and functional mapping of the p53 mutation-driven interactome

Nadia Arang , UCSF , (CSBC)

The TP53 gene is mutated in nearly half of all human cancers. In contrast to other tumor suppressors, mutations in TP53 are commonly missense among a subset of hotspot mutations. Many mutations are localized to the DNA-binding domain of the protein resulting in altered structural interfaces and protein accumulation. The mechanism driving a gain-of-function phenotype, and oncogenic properties in p53-mutant cancers is not yet well-understood. Thus, we hypothesize that the accumulation of p53 and gain-of-function mediators playing an essential role in oncogenesis may yield selective therapeutic targets in p53-mutant cancer cells. Here, we profiled the protein-protein interactions (PPI) of p53 and its mutant forms using affinity-purification mass spectrometry (AP-MS) in two distinct cellular models. Across both contexts, we find unique mutation-driven interactions with novel proteins across distinct functional groups. Moreover, application of tools that integrate the usage of AlphaFold reveal the structural basis for a subset our AP-MS-derived PPIs enabling the prioritization of putative interactions. Future directions include validation of unique PPIs across different cancer models and therapeutic targeting in p53-mutant cancer types.

Other Authors: Arang, Nadia; Forget, Antoine; Chantousi, Nefeli; O'Leary, Patrick; Chen, Huadong; Diolaiti, Morgan; Ortega, Fabian E; Guiley, Keelan Z; Ashworth, Alan; Braun, Ben; Bashir, Ali; Bandyopadhyay, Sourav; Diaz-Florez, Ernesto; Krogan, Nevan

3- Massively parallel base editing screen to unlock cancer susceptibility across the PI3K-AKT-mTOR pathway in breast cancer.

Ronald Babu, UCSF

Somatic mutations in PIK3CA are linked to approximately 40% of breast cancer cases, making it a critical factor in the second most common cancer among women in the United States. Despite extensive cancer genome sequencing efforts cataloging various PIK3CA gene variants—ranging from germline mutations to somatic and variants of uncertain significance (VUS)—the functional relevance of these variants to breast cancer progression remains poorly understood. Breast cancer is often driven by mutations in the PI3K-AKT-mTOR signaling pathway, which can lead to either gain or loss of function and has been a focal point for therapeutic intervention. Numerous clinical efforts have targeted this pathway with drugs acting upstream or downstream; however, the emergence of drug resistance, often due to additional somatic mutations, highlights the urgent need for a comprehensive functional mapping of these variants. To address this challenge, we conducted a massively parallel base-editing screen to systematically map loss-of-function (LOF) and gain-of-function (GOF) variants in the PIK3CA-Akt1-mTOR pathway. Using cytidine base editors (CBEs) and adenine base editors (ABEs), we introduced specific mutations in key genes (PIK3CA, Akt1, mTOR, and PTEN) and evaluated their roles in cancer progression. This study provided a comprehensive functional map of genetic mutations within the PI3K-AKT-mTOR pathway and offered critical insights into how these mutations contribute to cancer susceptibility. The resulting cancer dependency map will serve as a valuable resource for guiding therapeutic strategies and developing targeted treatments based on genetic variant profiles.

Other Authors: Martin Gordon, Atoshi Banerjee, Kirsten Obernier, Nevan Krogan

4- Cerebrospinal fluid cell-free RNA sequencing in cancer patients with leptomeningeal metastases

Giuseppe Barisano , Stanford University, (MetNet)

Leptomeningeal metastases (LM) occur when cancer cells invade the pia and arachnoid mater and are typically characterized by a poor prognosis. Clinical trials for LM treatments are lacking, partly due to challenges in LM diagnosis, as Magnetic Resonance Imaging or cytology on cerebrospinal fluid (CSF) have low sensitivity for LM. Moreover, the number of cells in the CSF are very few and LM cannot be biopsied, making the available tissue samples to develop new treatments severely limited. Therefore, novel robust and sensitive biomarkers for LM are needed to advance clinical trials and better characterize LM. Here we optimized a clinically feasible protocol for CSF cell-free RNA sequencing of LM and verified the robustness of the gene expression profiles under different pre-analytical conditions. We then evaluated the clinical use of CSF cell-free RNA sequencing to diagnose LM in non-small cell lung cancer (NSCLC) and breast cancer (BC) patients and to gain novel insights on the progression mechanisms of cancer cells in the brain leptomeninges and potential treatment response. Our test-retest data shows high robustness and reproducibility of the gene expression profiles identified with our protocol. Controls, NSCLC, and BC patients with LM clustered separately on principal component analysis. Expression of frequently studied NSCLC- and BC-associated genes were detected in the CSF samples of the corresponding patients. These results show feasibility towards identifying molecular mechanisms underlying LM and may represent a novel, robust diagnostic approach for LM in clinical trials and multi-center collaborations.

Other Authors: Maxine Umeh-Garcia, Rukayat Taiwo, Daniel Herrick, Charlotte Weixel, Pablo Nunez, Sophia Chernikova, Thy Trinh, Seunghyun Lee, Melanie Hayden-Gephart

5- Evaluating RAS signaling in triple-negative breast cancer

Ana Calizo , Johns Hopkins University , (MetNet)

Background Triple-negative breast cancer (TNBC) is characterized by the absence of hormone receptors and is associated with poor outcomes. One of the major causes of mortality is metastasis, which initiates with invasion. The lab has previously reported KRAS as required for collective and disseminated invasion. Although RAS mutations are rare in breast cancer, alternative ways contribute to pathway dysregulation, like gene amplification. Specific mechanisms of RAS signaling during invasion require further investigation. Methods C31-Tag is a genetically-engineered mouse model for TNBC. Primary tumors were either formalin-fixed, or frozen for RNA and DNA extraction. Remainder of the tumor was used for organoid generation, embedded in collagen for invasion, then fixed. Downstream applications include immunofluorescence, qPCR, or western blot. Results Primary tumors demonstrated p-ERK+ cells at the tumor-stromal interface. Further stains confirmed p-ERK localized to SV40+ or vimentin+ cells. Primary sections demonstrated amplification and increased copy number of the KRAS gene in a subset of C31 tumors. Invasive organoids showed heterogeneity in p-ERK+ cells. An ERK reporter expressed in normal mammary glands exhibited enrichment of ERK activity in some cells during branching. Conclusion Expression of p-ERK in a subset of SV40+ and vimentin+ cells suggest a role for tumor cells in mediating ERK activity. Tumors from our mouse model demonstrate KRAS amplification, which is also documented in patient cases of TNBC. While invasive organoids showed inconclusive patterns of p-ERK, we hope that a kinase translocation reporter can reveal complexities of ERK dynamics and its role in cell fate specification and invasion programs.

Other Authors: Andrew Ewald

6- Meta-DiSCO: A statistical framework for identifying differential spatial colocalization between cell types in spatial -omics

Jake Chang , Stanford University , (CSBC)

Advancements in spatial -omics technologies have improved the accessibility and resolution of spatially-resolved single-cell data. However, there is still a dearth of statistical methods that leverage both spatial information and single-cell resolution to uncover meaningful spatial patterns and potential biomarkers. We introduce Meta-DiSCO, a meta-analysis statistical framework to identify differential spatial cell-type colocalization from multiple images, patients, and conditions. Under this framework, we modify a metric from the geospatial sciences literature, the colocation quotient, to estimate the degree to which certain cell-type pairs are spatially colocalized. We demonstrate the utility of this framework on data simulated from spatial point processes and apply the method to multiplexed immunofluorescent images of head and neck squamous cell carcinoma tumors. Meta-DiSCO successfully detects and discovers known and novel spatial colocalization patterns in primary, node-positive tumors. Here, we demonstrate its utility for hypothesis-generation towards the discovery of spatial biomarkers and tissue microenvironment interactions.

Other Authors: Rohit Khurana, Perla Molina, Weiruo Zhang, Lu Tian, Sylvia Plevritis

7- Deconvolution of leptomeningeal metastasis landscape using an internal carotid injection model of brain metastasis

Sophia Chernikova , Stanford University, (MetNet)

Leptomeningeal disease (LMD), a specific form of central nervous system (CNS) metastasis in which cancer cells infiltrate leptomeninges, is rapidly fatal due to poor detection and no effective treatment. Treatment of LMD is challenged by the diffuse nature of the disease, low specificity of detection, poor drug availability in CNS and high neurotoxicity. LMD is a very heterogeneous disease: LMD tumors vary in size and may be found at various locations throughout the neuroaxis. Co-existence of leptomeningeal spread and parenchymal metastases in not uncommon. MRI can also be normal in LMD setting; in this case the diagnosis is based on the identification of cancer cells in cerebrospinal fluid. This spatial and morphological heterogeneity of LMD may ultimately be responsible for challenges in diagnosis and treatment. Mechanistic and translational studies are hampered by the lack of adequate in vivo models that recapitulate the consecutive steps of LMD progression separate from systemic disease. In this study, we showed that a commonly used mouse model brain metastasis, which utilizes internal carotid (ICA) injection, results in leptomeningeal spread. The ICA injection of multiple tumor cell lines faithfully recapitulated the stages, heterogeneity, and spatial distribution of human LMD. Even cell lines with a demonstrated clear preference for growth in brain parenchyma had a measurable population of cells within the leptomeninges. Furthermore, single-cell RNA sequencing analysis revealed distinct subpopulations within the leptomeningeal cancer cell population. The leptomeningeal spread should be considered when interpreting the data from the ICA model that is widely used to study brain metastasis.

Other Authors: Yuelong Wang, Sai Saroja Kolluru, Jiaojiao Deng, Griffin G Hartmann, Stephanie J Andersen, Sheila Tsau, Hriday P. Bhambhvani, Dina Polyak, Thy T. H. Trinh, Eli Johnson, Samantha Colomb, Kerriann Casey, Robert C Jones, Sheela Crasta, Ian D. Connolly, G-One Ahn, Siyuan Zhang, Julien Sage, Seema Nagpal, Stephen R. Quake, J. Martin Brown, Melanie Hayden Gephart

8- The role of Osteoclast-like tumor hybrid cells in bone metastasis

Chih-Wei Chou , UT Health San Antonio, (MetNet)

Tumor hybrid cells (THCs) are generated through cell-cell fusion and exhibit both characteristics of parental macrophages and cancer cells. Osteoclasts are derived from multiple fusions of the RANKL-stimulated macrophages and secrete hydrolases to digest the bone. To study the role of THCs in bone microenvironment, we injected mouse prostate, lung, or breast cancer cells into mouse femurs. We found that typical multi-nuclei osteoclasts with a ruffled border and multiple tiny podosomes were lined on the bone lesions accosted by the cancer cells. However, we also found a group of mono- or di-nucleated osteoclast-like THCs (OTHCs) aligned on the bone lining, expressing both osteoclast and cancer markers. These findings were recapitulated through the in vitro co-culture of osteoclast precursors and cancer cells. Therefore, we hypothesize that cancer cells can educate osteoclast differentiation through a paracrine action; moreover, cancer cells may overcharge osteoclast function through fusion with them. This aberrant fusion event shifted the paracrine to autocrine functions of cancer cells and increased the bone digestion functions of OTHCs. In addition, multi-omics analysis will be conducted to further explore the gene expressions and functions of the OTHCs in mouse models and human patient samples to investigate the role of OTHCs in bone metastasis.

Other Authors: Tim Hui-Ming Huang, Chia-Nung Hung

9- A transcriptional analysis of image-localized high-grade glioma biopsies reveals a meaningful graph of tumor development

Lee Curtin, Mayo Clinic, (CSBC)

High-grade glioma (HGG) portends dismal survival, owing in part to its intra- and inter-patient heterogeneity. Samples of HGG collected for clinical care are useful for diagnosis, but fail to capture the spatial disease landscape. Anatomical imaging provides a view of the overall tumor, but limited insight into cell-level variation by itself. By analyzing image-localized biopsies, we can better understand subpopulation ecologies and interactions that may then be exploited for future therapeutic benefit. We collected 202 image-localized multi-regional biopsies from 58 patients to characterize the molecular heterogeneity of HGG. Samples were run through Monocle, a reverse graph embedding algorithm that groups samples into states and orders them along developmental trajectories. Gene-set enrichment analyses were implemented to determine each state’s distinct pathway expression. CIBERSORTx, a deconvolution algorithm, was used to predict relative abundances of 7 normal, 6 glioma, and 5 immune subpopulations for each sample. CIBERSORTx-predicted abundances were then overlaid on the Monocle graph, both of which were investigated with respect to imaging-defined regions, treatment status, and patient metadata. Monocle classified HGG into four main states along a three-pronged trajectory, which connects with key imaging regions of the disease. The states showed distinct CIBERSORTx-population ecologies and gene-set expression consistent with the literature. Sex differences were observed amongst immune pathways between different Monocle states. These algorithms and image-localized biopsies reveal a low-dimensional trajectory and ecologies that help us understand the development and evolution of HGG. Characterizing the in vivo diversity of subpopulations within HGG is important for treatment stratification and ultimate patient benefit.

Other Authors: Kamila Bond, Kyle W. Singleton, Leland S. Hu, Nhan L. Tran, Osama Al-Dalahmah, Pamela R. Jackson, Peter Canoll, Kristin R. Swanson

10- Spatial transcriptomics identifies unique tumor and microenvironment pathomic programs that are associated with the lung premalignancy and adenocarcinoma continuum

Yibo Dai, The University of Texas MD Anderson Cancer Center

Background: Lung adenocarcinoma (LUAD) is one of the most prevalent and lethal cancer types in the US, yet our understanding of the transition of normal-appearing tissue (NAT) to adenomatous lung premalignant lesions (aPMLs) and LUADs is very dismal. This study was designed to fill this void by systemically analyze the pathologic continuum of NAT > aPML > LUAD using spatial transcriptomics (ST). Methods: Visium spatial profiling was performed on 56 samples from 25 patients with paired aPMLs and LUADs. Non-negative matrix factorization was conducted on Visium data to identify transcriptional programs for each sample. Then, clustering of sample-based programs was performed to define consensus metaprograms across the cohort. Results: Eight distinct metaprograms (MP1~8) were identified, distinguishing stromal (MP2), myeloid (MP4), lymphoid (MP6), and epithelial (MP3, MP5) compartments. Furthermore, we also identified metaprograms representing the lung capillary bed (MP7), stressed cellular state (MP8), and mosaic cellular patterns (MP1). The metaprograms correlated well with pathological annotations and captured fine tissue structures like lymphoid aggregates. LUADs and aPMLs showed distinct MP profiles, with MP3 and MP6 highly abundant in LUADs, and MP1, MP4, MP5 and MP7 more abundant in aPMLs. Specifically, we observed positive correlations between MP3 and MP6, as well as between MP4 and MP5 in sample distributions, indicating co-evolution of tumor and immune microenvironment during disease progression. Conclusions: In summary, this study provided a comprehensive molecular and cellular landscape of aPML and LUAD, and served as a vital basis for deeper mechanistic studies in the future aiming at early disease interception.

Other Authors: Fuduan Peng, Ansam Sinjab, Sujuan Yang, Minyue Chen, Tieling Zhou, Alejandra G. Serrano, Guangsheng Pei, Yunhe Liu, Yanshuo Chu, Yang Liu, Jiahui Jiang, Kai Yu, Ruiping Wang, Jiping Feng, Zahraa Rahal, Lorena I. Gomez Bolanos, Guangchun Han, Kyung Serk Cho, Akshay Basi, Avrum Spira, Steven Dubinett, Luisa M. Solis, Stephen Swisher, Mingyao Li, Junya Fujimoto, Ignacio I. Wistuba, Kadir Akdemir, Jared Burks, Humam Kadara, Linghua Wang

11- Mapping the Spatial Biology of Kaposi Sarcoma (KS) in Skin: Insights into Tumor Growth and Cell Behavior

Arun Das , University of Pittsburgh, (CSBC)

Despite extensive research, on the 30th anniversary of the discovery of Kaposi Sarcoma-associated Herpesvirus (KSHV), the spatial biology of Kaposi Sarcoma (KS) remains understudied. In this study, we used single-cell spatial transcriptomics from 49 KSHV-infected patients and 3 healthy controls to investigate KSHV spatial virology within the skin. Our study uncovers the composition of spatial cellular niches, correlated with KSHV latent and lytic gene expression, as well as spatial features such as cell-type proximity and overall cell density. We identify three primary tumor-associated niches: Niche 0, enriched in fibroblasts, endothelial cells, dendritic cells, and macrophages, providing structural support and vascularization; Niche 1, located at the boundary between the tumor and the surrounding stroma, containing a mix of fibroblasts, endothelial cells, and KS tumor cells; and Niche 2, tumor core, dominated by proliferating KS tumor cells. Control samples are composed almost entirely of Niche 0, while early-stage (patch) lesions show some presence of Niche 1. Plaque-stage lesions display an increase in Niche 1, and nodular-stage lesions exhibit nearly equal distributions of Niches 0, 1, and 2. Differential expression analysis reveals that Niche 0 shows upregulation of SFRP2, LUM, CXCL12, FBLN1, and PTGD5, promoting extracellular matrix organization and angiogenesis. In Niche 1, FABP4, CDH5, and LYVE1 are differentially expressed, potentially facilitating lymphangiogenesis and lipid metabolism. Niche 2 shows upregulation of FSCN1, KSHV.ORF71, GNG11, KSHV.ORF72, and PROX1, reflecting active tumor proliferation and viral latency. Finally, Niches 4 to 7, representing various normal epidermal and dermal skin layers, are free of KSHV infection.

Other Authors: Wen Meng, Shou-Jiang Gao, Yufei Huang

12- Metastatic potential and clonal dynamics of isolated subpopulations within TNBC cells

Carolina De Santiago , University of Texas at Austin, (CSBC)

Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer, marked by high metastatic rates and significant intra-tumor heterogeneity. In this study, we employed a novel genetic cell tracking tool, ClonMapper, and single-cell RNA sequencing (scRNA-seq) to identify and characterize the origins of invasive subpopulations within TNBC. Genetic barcodes were stably integrated into HCC1806 cells, creating a population with approximately 1,000 distinct barcoded clones. By utilizing a transwell assay, migratory cells were isolated for further expansion, generating stable biological replicates. Clonal abundance sequencing of these replicates revealed a significant reduction (over 8-fold) in the number of unique barcodes. Scratch and transwell assays confirmed that these replicates had increased bidirectional migration and invasion rates. Leiden clustering of scRNA-seq data showed that the invasive subpopulations have a distinct transcriptomic profile compared to the parental cells. Gene expression analysis highlighted increased epithelial-mesenchymal transition (EMT) in these invasive populations, with activation of signaling pathways such as FAK-Rac and Rho-Rac. Further analysis identified at least two unique clusters among the isolated populations. By tracking the genetic barcodes, we can trace the origins of the most prevalent clones and elucidate the signaling pathways driving the emergence of these invasive subpopulations.

Other Authors: Amy Brock

13- Active transcription and epigenetic reactions synergistically regulate meso-scale genomic organization in cancer cells

Monika Dhankhar , University of Pennsylvania, (MetNet)

In interphase nuclei, chromatin forms dense domains of characteristic sizes, but the influence of transcription and histone modifications on domain size is not understood. We present a theoretical model exploring this relationship, considering chromatin-chromatin interactions, histone modifications, and chromatin extrusion. We predict that the size of heterochromatic domains is governed by a balance among the diffusive flux of methylated histones sustaining them and the acetylation reactions in the domains and the process of loop extrusion via supercoiling by RNAPII at their periphery, which contributes to size reduction. Super-resolution and nano-imaging of five distinct cell lines confirm the predictions indicating that the absence of transcription leads to larger heterochromatin domains. Furthermore, the model accurately reproduces the findings regarding how transcription-mediated supercoiling loss can mitigate the impacts of excessive cohesin loading. Our findings shed light on the role of transcription in genome organization, offering insights into chromatin dynamics and potential therapeutic targets.

Other Authors: Aaysuh Kant, Vivek Shenoy

14- Foscarnet Treatment Alleviates Cytomegalovirus-Induced Immune Suppression in Brain Metastases

Wenjuan Dong , Houston Methodist Research Institute , (Both)

Metastatic tumor cells develop unique molecular features that aid their growth and survival in secondary sites like the brain. Using RNA sequencing, we analyzed paired primary tumors and brain metastases from breast cancer patients and identified 371 up-regulated genes in brain metastatic tumors compared to primary tumors (P<0.05; log2 FC>1 or <-1). Reactome pathway analysis revealed significant enrichment of the Human Cytomegalovirus (HCMV) early and late events among these up-regulated genes. As a neurotropic virus, CMV proteins and/or nucleic acids are present in about 98% of brain metastases from colon and breast cancers and 99% of glioblastomas. However, the role of CMV in shaping the tumor microenvironment for cancer cell survival and growth in the brain remains unclear.

To investigate this, we employed spatial profiling technologies to analyze brain metastases from CMV-positive and CMV-negative patients. We found higher expressions of CD163, CD66b, CD14, and CD68 in CMV-positive samples, indicating increased M2 microglial activation, which is linked to poorer prognosis in glioma patients.

We further evaluated the effects of the antiviral agent Foscarnet on CMV-positive brain metastases using a triple-negative breast cancer (TNBC) mouse model and found that it significantly extended survival in CMV-infected mice compared to vehicle-treated controls. Single-cell spatial profiling demonstrated that Foscarnet substantially improved the immunosuppressive environment in CMV-positive brain metastases.

Given that CMV is frequently reactivated during chemotherapy or radiation therapy in brain metastasis patients, our findings highlight the potential benefits of incorporating anti-CMV therapies in clinical practice for CMV-positive brain metastatic patients.

Other Authors: Wenjuan Dong1, Puri Akshjot2, Jianting Sheng1,3, Shan Xu1, Matthew Vasquez4, Bill Chan1, Neha Lakka1, Jenny Chang2,5, Hong Zhao1,2,4,5, and Stephen T.C. Wong1,2,3,4,6. 1 Systems Medicine and Bioengineering Department, Houston Methodist Neal Cancer Center, Houston, TX. 2 Houston Methodist Neal Cancer Center, Houston, TX. 3 Department of Radiology, Weill Cornell Medicine, Cornell University, NY, NY. 4 Advanced Cellular and Tissue Microscopy Shared Resource, Houston Methodist Research Institute and Houston Methodist Neal Cancer Center, Houston, TX. 5 Department of Medicine, Weill Cornell Medicine, Cornell University, NY, NY. 6 Department of Biomedical Engineering, Texas A & M University, College Station, TX

15- Highly-resolved single-cell atlas of human breast cancer to explore tumor heterogeneity

Christina Ennis, Boston University School of Medicine, (CSBC)

Single cell transcriptomics (scRNAseq) remains the method of choice to quantify and annotate cellular heterogeneity in the tumor and its microenvironment (TME), yet most studies are under-powered to associate changes in tumor heterogeneity with phenotypes such as tumor subtype, grade, and patient age, among others. In this study, we created an integrated atlas of human breast cancer (BC), the largest resource of its kind, totaling > 700,000 cells across 129 patients, and optimized computational methods to benchmark integration performance, and robustly perform hierarchical cell type annotation. By combining single profiles, a higher-resolution annotation of immune, stromal, and epithelial cell types was achieved. Further, using this integrated atlas, generalized linear mixed effect model (GLMM)-based analysis identified significant changes in immune and stromal cell type abundances associated with tumor grade. These changes in TME heterogeneity were not discernible or had effects in the opposite direction when the analysis was limited to individual BC studies, highlighting the need for atlas-based mega-analysis approaches. This highly-resolved integrated scRNAseq BC atlas will be a valuable resource for hypothesis-driven analyses of tumor heterogeneity including our own ongoing analysis of metabolic comorbidities in BC.

Other Events: Andrew Chen, Lina Kroehling, Stefano Monti, Christina Ennis

16- Utilizing spatial transcriptomics to dissect tumor-immune crosstalk in the brain metastasis microenvironment

Nicole Eskow, NYU School of Medicine, (MetNet)

Immune checkpoint inhibitors have revolutionized the treatment of brain metastasis (BM), demonstrating intracranial response rates and durability not previously seen with other therapies. However, little is known about how metastatic tumors interact with the unique microenvironment of the brain to either support or hinder the efficacy of immune-based therapies. Using serial intracardiac and intracarotid injection techniques, we developed and characterized six syngeneic models of melanoma and breast cancer BM, providing us with a novel platform to study the BM-immune interface. We are building a spatial transcriptomic (10X Visium) atlas of brains collected from these mice, as well as sham-injected controls. We found that astrocytes and myeloid cells comprise the majority of intratumoral infiltrate in our models, suggesting that these populations may play a direct role in tumor cell communication. Using the R package CellChat to explore possible cell-cell communication networks, we found various myeloid-driven signaling pathways and inflammatory signatures (such as chemokine and complement signaling) to be upregulated directly within tumors. Currently, we are utilizing monoclonal antibodies in our BM mouse models to determine the therapeutic potential of blocking the tumor-specific signaling cascades discovered in our analyses. We are also collecting data from human BM samples to further support the therapeutic relevance of our findings. Overall, our work thus far has revealed several cellular populations and pathways that may influence tumor-immune crosstalk in the BM microenvironment, enabling discovery of novel targets to promote anti-tumor immunity in BM.

Other Authors: Sorin Shadaloey, Amanda Flores Yanke, Elif Tugce Karasu, Orlando Aristizabal, Eva Hernando

17- Merging machine learning and landscape ecology to identify and define treatment resistant niche

Chandler Gatenbee , Moffitt Cancer Center, (CSBC)

Multiplexed images provide the opportunity to paint a finely detailed picture of the interactions between different cell types and environmental resources (oxygen, glucose, etc..), aiding our understanding of what happens within the tumor during initiation, progression, metastasis, response to treatment, etc… While such images offer the potential to perform spatial analyses with well defined phenotypes/genotypes, their high dimensionality can make it challenging to extract relevant information. We propose this can be overcome by leveraging the power of species distribution models (SDM), an approach from landscape ecology that seeks to identify the niche that support species of interest. While SDMs often use classic machine learning (ML) methods, such as boosted regression trees, recent efforts to improve the interpretability of deep learning models now make it possible to apply the SDM approach to models previously considered “black boxes”. Herein, we have developed a SDM based on vision transformers, which are particularly well suited to the quadrat count data typically fed into SDMs. In an early-stage study aimed at revealing markers of tumor persistence, we train the SDM to identify post-therapy tumor fragments in breast cancer. Our preliminary results indicate that GLUT-1 is most predictive of tumor persistence. Consistent with previous work, our findings suggest the resistant cancer cells may be experiencing a “tragedy of the commons”, which can potentially be exploited with adjuvant therapy. While much work remains, we believe this preliminary analysis highlights the power of integrating modern ML methods with ecological approaches to identify predictive biomarkers and treatment targets.

Other Authors: Dana Ataya, Robert A. Gatenby, Alexander R.A. Anderson

18- Absolute copy number determination of chromosomal passenger complex regulators in triple-negative breast cancer

Monserrat Gerardo-Ramirez , University of Virginia, (CSBC)

NO ABSTRACT

Other Authors: Catalina Alvarez-Yela, Sarah M. Groves, Kevin A. Janes, P. Todd Stukenberg.

19- 3D chromatin remodeling associated with acquisition of basal signatures in breast cancer

Rosela Golloshi, Johns Hopkins School of Medicine , (MetNet)

Next steps include the generation of relevant spatiotemporal features that allow the design of agent-based models (ABMs) to represent the dynamics of TIME which can inform experimental combination treatments of targeted inhibitors and immunotherapy in cancer models.

Other Authors: Elana J. Fertig, Andrew Ewald

20- Revealing the Biophysics of Lamina-Associated Domain Formation by Integrating Theoretical Modeling and High-Resolution Imaging

Zixian Guo, University of Pennsylvania, (MetNet)

Chromatin interactions with the nuclear lamina (NL) form lamina-associated domains (LADs), crucial for gene repression and maintaining cellular identity. LADs are characterized by increased histone methylation level and are physically and chemically tethered to the NL by proteins like LAP2β. Here, we develop a phase-field model of chromatin organization that incorporates chromatin-chromatin interactions, chromatin-lamina affinity, and the kinetics of methylation and acetylation. Our model predicts the size and shape of peripheral heterochromatin domains and reveals that the strength of chromatin-lamina interactions drives LAD morphological regulation. Analyzing super-resolution images of hMSCs, we identify a heterogeneous, bimodal distribution of chromatin-lamina affinities. We find that soft substrate environments increase LAD thickness, linked to contractility-dependent increased nuclear localization of HDAC3, enhancing chromatin-lamina affinity and histone methylation. These findings are validated for in-vitro nuclei under the alternation of chemo-mechanical cues such as contractility inhibition and substrate stiffening. In tendinosis, a condition marked by collagen degeneration, similar increases in LAD thickness are observed, aligning with our model's predictions. Our findings emphasize the microenvironment's role in genome organization and offer insights into cellular responses to developmental cues, cancer metastasis, and degenerative diseases.

Other Authors: Monika Dhankhar, Aayush Kant, Ramin Basir, Rohit Joshi, Su Chin Heo, Robert L. Mauck, Melike Lakadamyali, Vivek B. Shenoy

21- Spatial Analysis of Immune-Related Adverse Events Using Novel Animal Models in Myocarditis and Neurotoxicity

Kun Han, Houston Methodist Research Institute, (Both)

Immune related adverse events (irAEs) have emerged as a significant challenge in immune checkpoint blockade (ICB) therapy. To better understand the mechanisms underlying these conditions and develop effective treatments, we established two novel animal models of myocarditis and neurotoxicity. Our studies revealed that Type 3 T cells are critical in the pathogenesis of both irAEs. We performed spatial transcriptomic analysis on the myocarditis mouse model and identified distinct functional cardiac regions with characteristically enriched signaling pathways. Type 3 T cells , NK cells, and DCs largely infiltrated into the heart tissue, which could be eliminated by a selected rescue drug. However, there were no significant changes in three memory T cell subtypes (central memory T cells, effector memory T cells, and tissue-resident memory T cells) upon treatment, suggesting the potential role in long-term cardiac damage and potential recurrence. Additionally, we observed certain activated metabolic and epigenetic pathways in these memory T cells, implying that their collaborative role in myocarditis. We are conducting similar spatial analysis on the neurotoxicity model. Beyond cell profiling, we applied our Spatial Single Cell Crosstalk modeling tool, S2C2, on spatial transcriptomics data from these irAEs models to delineate key immune-related pathways activated in the immune-tumor microenvironment of these models of myocarditis and neurotoxicity, aiming for a systematic understanding of the underlying mechanisms. In conclusion, the presented systems immunology strategy based on two novel irAEs models provides a comprehensive platform for studying adverse events during cancer immunotherapy, potentially mitigating irAEs and improving the efficacy of ICB therapy.

Other Authors: Lin Wang, Zheng Yin, Ju young An, Youker Keith, Stephen Wong

22- Purine biosynthesis tunes estrogen responses in breast cancer

Dina Hany , DBDS/Stanford University, (CSBC)

Two-thirds of breast cancers express estrogen receptor α (ERα), on which they are dependent for growth and survival. Targeting ERα with specific antagonists, such as tamoxifen and fulvestrant have substantially improved the survival of patients. However, about 40% of ERα+ tumors acquire resistance to therapy, which represents a major clinical problem. A switch of growth towards estrogen-independence remains poorly understood. We performed a genome-wide CRISPR/Cas9 knockout screen to discover new genetic determinants of the response of breast cancer to endocrine therapy. PAICS, an enzyme involved in the de novo biosynthesis of purines, appeared as one of the top hits whose loss of function sensitizes the cells to tamoxifen. We found that increased expression of PAICS can shift the growth of ERα-dependent breast cancer to be estrogen-independent and tamoxifen-resistant. Some mechanistic insights revealed that this can be mediated by increased cAMP-activated protein kinase A and mammalian target of rapamycin activities. Finally, we propose PAICS as a novel drug target in combination with ERα for the efficient and potentially safe treatment of ERα+ breast cancer. Check our publication in Science Advances: DOI: 10.1126/sciadv.add3

Other Authors: Dina Hany, Vasiliki Vafeiadou, and Didier Picard Département de Biologie Moléculaire et Cellulaire, Université de Genève, Switzerland

23- Promotion of metastasis by apoptotic cells

Mark Headley, Fred Hutch Cancer Center, (MetNet)

Tumor metastasis requires tumor cells to separate from a primary tumor, survive within the hostile environment of the vasculature, arrest at a distant site, extravasate, and proliferate within a distinct tissue. Each of these steps imposes severe restrictive bottlenecks on circulating tumor cells (CTC), and the vast majority of CTCs die during the metastatic cascade. Despite clinical evidence correlating circulating dying cells with metastatic progression, the effect of dying cells on CTC dissemination is unknown. To address this, we used intravenous metastasis models in which tumor cells were injected alone or in combination with dying cells, and observed that the presence of apoptotic cells, but not cells dying by other mechanisms, increased lung metastasis. We found that the co-localization of apoptotic cells with CTCs enhanced the survival of CTCs during the period between vascular arrest and extravasation. We attribute this effect to the promotion of coagulation by apoptotic cells, a crucial early contributor to CTC survival. During apoptosis, the membrane lipid phosphatidylserine flips to the outer leaflet; this exposure of phosphatidylserine on the cell surface increases the activity of the coagulation initiator Tissue Factor. The pro-metastatic effect of apoptotic cells is abrogated by administration of the anticoagulant Heparin, by knocking out Tissue Factor on apoptotic cells or by blocking phosphatidylserine. We demonstrate a previously unappreciated role for apoptotic cells in facilitating metastatic dissemination by creating CTC-supportive emboli through their procoagulant activity.

Other Authors: Cassidy Hagan, Annelise G. Snyder, Emily Park, Andrew Oberst

24- scMINER: an information-theoretic framework for hidden driver inference from single-cell transcriptomics data

Siarhei Hladyshau, St. Jude Children's Research Hospital

The main challenge in the analysis of single-cell transcriptomic data is reliable inference of biological signal from sparse and stochastic data. To address this challenge, we developed the scMINER framework – single-cell mutual information-based network engineering ranger. We use an information-theoretic approach to perform unsupervised cell clustering, infer transcription factors and signaling regulatory networks, and identify hidden drivers from single-cell data. scMINER performs better than many state-of-the-art algorithms (Seurat, SC3, Scanpy, scVI, and scDeepCluster) in clustering. The accuracy of the transcription factor (TF) genome-scale regulatory network inferred by scMINER is significantly higher when compared to networks inferred by alternative methods like GENIE3, GRNBoost2, and PIDC. In addition to the inference of TF regulatory network, scMINER provides an option to identify signaling genes (SIG) regulatory network, which is a unique feature of our approach. Based on the derived regulatory network, we introduce a way to estimate gene activity, which represents regulation on the protein level and helps to overcome the limitations of sparse gene expression signals. In terms of activity-based analysis, scMINER outperforms SCENIC, another popular approach for regulon activity analysis.

Other Authors: Qingfei Pan 1,* , Siarhei Hladyshau 1,* , Xiangyu Yao 1,* , Liang Ding 1,* , Jiayu Zhou 1 , Lei Yan 1 , Xinran Dong 1,2 , Yogesh Dhungana 1,3 , Hao Shi 4 , Chenxi Qian 1 , Chad Burdyshaw 5 , Joao Pedro Veloso 1 , Alireza Khatamian 1 , Zhen Xie 1,6 , Isabel Risch 1,4 , Xu Yang 1 , Xin Huang 1 , Michael Rusch 1 , Michael Brewer 5 , Koon-Kiu Yan 1 , Hongbo Chi 4 , Jiyang Yu 1 1 Department of Computational Biology, St. Jude Children’s Research Hospital 2 Children’s Hospital of Fudan University 3 Graduate School of Biomedical Sciences, St. Jude Children’s Research Hospital 4 Department of Immunology, St. Jude Children’s Research Hospital 5 Department of Information Services, St. Jude Children's Research Hospital 6 Department of Physiology, University of Tennessee Health Science Center * These authors contributed equally

25- Elucidating Compound Mechanism of Action and Polypharmacology with a large-scale perturbational profile compendium

Lucas ZhongMing Hu, Columbia University, (CSBC)

Although major target proteins are available for many specific oncology drugs, drug efficacy and toxicity are mediated by more than single, high-affinity drug target. Rather, the pharmacologic properties of a drug are the result of its complex polypharmacology, as mediated by poorly characterized lower-affinity targets, as well as tissue-specific secondary effectors that are largely undetectable by traditional assays. The goal of this study is to address critical unresolved questions in cancer pharmacology by characterizing the proteome-wide MoA of oncology drugs as a critical, yet highly elusive step to accurately predict their clinical efficacy and toxicity. To elucidate proteome-wide, drug-mediated changes in protein activity (MoA), we generated genome-wide drug perturbation profiles from >50 cancer cell lines representing distinct tumor subtypes, selected as high-fidelity models of patients in clinical cohorts, as well as primary patient-derived tumor cells. Here we report on the analysis of the first 23 cell lines following perturbation using >700 clinically relevant oncology drugs. The corresponding PanACEA database represents the largest resources of functionally annotated, genome-wide perturbational profiles for clinically relevant drugs. VIPER-based analysis of this resource elucidated the effect of each individual drug on the activity of ~6,500 regulatory and signaling proteins. Analyses of these data elucidated functional relationship between drugs and group drugs into functionally distinct modules, thus providing insights into both MoA and polypharmacology. We validated many of our predictions using experimental assays and structure-based simulation. Our analyses also resulted many effective drug inhibitors for cancer dependent transcription (co-)factors, previously considered undruggable, that were experimentally validated.

Other Authors: Eugene Douglass, Mikko Turunen, Sergey Pampou, Adina Grunn, Ron Realubit, Albert A. Antolin, Alexander L.E. Wang, Hai Li, Prem Subramaniam, Prabhjot S Mundi, Charles Karan, Mariano Alvarez, Andrea Califano

26- Assessment of E-cadherin dynamics and metastatic trajectories in a mouse model of luminal breast cancer

Ryan Huizar, Johns Hopkins Medical School, (MetNet)

E-cadherin, a key cell adhesion protein, exhibits a complex and context-dependent role in metastasis, though its precise function remains unclear. Using a combination of imaging and transcriptional analysis in a luminal breast cancer mouse model (MMTV-PyMT), we investigate the heterogeneity and dynamic regulation of E-cadherin. We identify two distinct cellular states within primary tumors, termed mEcad-Lo and mEcad-Hi, based on differential subcellular localization of E-cadherin. The mEcad-Lo state is characterized by increased invasiveness and an epithelial-mesenchymal transition (EMT) transcriptional profile, while the mEcad-Hi state correlates with enhanced proliferation. Notably, cells are capable of transitioning between these states in response to environmental cues. Bulk RNA-sequencing of the mEcad-Lo and mEcad-Hi populations reveals distinct transcriptional signatures linked to invasion and spatial organization within primary tumors. mEcad-Lo populations show enriched expression of EMT and angiogenesis-related transcripts. Inhibition of angiogenic pathways reduces cancer cell invasion, underscoring the importance of these pathways in the invasive behavior of cancer cells in isolation of the endothelium. Moreover, we identify upregulation of notch-delta family members in mEcad-Lo populations, specifically in perivascular epithelial cells. Knockdown of these pathways enhances EMT heterogeneity ex vivo, inducing a strongly mesenchymal phenotype, suggesting that notch-delta signaling maintains a hybrid epithelial/mesenchymal (E/M) state in this model of luminal breast cancer. Collectively, these findings underscore the multifaceted role of E-cadherin in metastasis and provide insights into the epithelial state transitions that cells undergo during the metastatic cascade.

Other Authors: James McCann, Andrew Ewald

27- Phagocytosis-initiated tumor hybrid cells acquire a c-Myc-mediated quasi-polarization state for immunoevasion and distant dissemination

Chia-Nung Hung, UTHSCSA, (MetNet)

While macrophage phagocytosis is an immune defense mechanism against invading cellular organisms, cancer cells expressing the CD47 ligand send forward signals to repel this engulfment. Here we report that the reverse signaling using CD47 as a receptor additionally enhances a pro-survival function of prostate cancer cells under phagocytic attack. Although low CD47expressing cancer cells still allow phagocytosis, the reverse signaling delays the process, leading to incomplete digestion of the entrapped cells and subsequent tumor hybrid cell (THC) formation. Viable THCs acquire c-Myc from parental cancer cells to upregulate both M1- and M2-like macrophage polarization genes. Consequently, THCs imitating dual macrophage features can confound immunosurveillance, gaining survival advantage in the host. Furthermore, these cells intrinsically express low levels of androgen receptor and its targets, resembling an adenocarcinoma-immune subtype of metastatic castration-resistant prostate cancer. Therefore, phagocytosis-generated THCs may represent a potential target for treating the disease.

Other Authors: Chih-Wei Chou, Chia-Nung Hung, Cheryl Hsiang-Ling Chiu, Xi Tan, Meizhen Chen, Chien-Chin Chen, Moawiz Saeed, Che-Wei Hsu, Michael A. Liss, Chiou-Miin Wang, Zhao Lai, Nathaniel Alvarez, Pawel Osmulski, Maria E. Gaczynska, Li-Ling Lin, Veronica Ortega, Nameer B. Kirma, Kexin Xu, Zhijie Liu, A. Pratap. Kumar, Josephine A. Taverna, Gopalrao V. N. Velagaleti, Chun-Liang Chen, Zhao Zhang, and Tim Hui-Ming Huang Department of Molecular Medicine, University of Texas Health Science Center, San Antonio, TX

28- Modeling tumor immune microenvironment in Brain Metastases using 3D assembloids

Shruti Jain , Stanford University, (MetNet)

Brain metastases are highly aggressive, treatment resistant malignancies with debilitating neurological sequelae and a grave prognosis. Microglia, brain’s native immune cells, play a key role in metastases progression by interacting with other cell types in brain tumor microenvironment (TME) including metastatic cancer cells, astrocytes, neurons etc. Targeting microglia and it’s cross talk with other cell types in brain TME have great therapeutic potential. Cancer cells subvert the anti-tumorigenic functions of microglia and reprogram them to create an immunosuppressive TME that facilitates the tumor progression. However, the mechanisms by which cancer cells reprogram microglia from anti-tumorigenic to pro-tumorigenic remain unclear mainly due to (i) our inability to isolate human tumor-associated microglia; (ii) our inability to model the tumor-microglia interactions within the TME, and (iii) lack of models that allow manipulation of microglia while still reliably recapitulating the human disease. To overcome these critical challenges, I have developed a novel human induced pluripotent stem cell (hiPSC) derived 3D assembloid model. Using this tool we model the complex functional brain tumor-microglia interactions and their cross talk with other CNS cell types ( for e.g. neurons; astrocytes). Using multiomic approach we demonstrate increased migration of microglia towards cancer cells in 3D assembloids is mediated via secreted cytokines as observed in patient tumor samples. In conclusion, this work has direct translational potential as our 3D assembloid model provides a powerful tool to study tumor-immune/tumor-neuro-immune axis in brain metastases and can be used to test the effective therapeutic strategies targeting these interactions in brain metastases.

Other Authors: Wanhua Li2, Sofia Tosoni1, Thy Trang Hoang Trinh1, Marius Wernig2, Melanie Hayden Gephart1* 1Department of Neurosurgery, Stanford School of Medicine, Stanford, CA 94305 ; 2Department of Pathology, Stanford School of Medicine, Stanford, CA; *corresponding author

29- Organ-specific macrophages may lead to suppressive tumor microenvironment in metastatic pancreatic cancer

Yu-Lan Kao, Washington University in St. Louuis, (MetNet)

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a five-year survival rate of just 13%. The disease is frequently diagnosed at the metastatic stage, with the liver being the most common site of metastasis, accounting for approximately 80% of cases, followed by the lung at 20%. Macrophages play a crucial role in the development of tumor resistance mechanisms, particularly by promoting immunosuppression. Previous studies have demonstrated that liver macrophages can induce systemic immunosuppression by driving T cell apoptosis in a mouse model of melanoma liver metastasis. In this study, we employed single-nuclei RNA sequencing and multiplex immunohistochemistry staining to characterize immune cells in different organs from human samples, including primary pancreatic tumors, metastatic livers, and lungs. Our findings reveal that macrophages in liver metastases exhibit upregulation of the hypoxia pathways and downregulation of interferon signaling and antigen presentation pathways compared to the primary PDAC. Moreover, we observed a reduced density of T cells in liver metastases compared to both primary tumors and lung metastases, which is consistent with the poorer prognosis associated with liver metastasis. In contrast, lung metastases are linked to better patient outcomes, as supported by existing literature. These results suggest that the liver exhibits a less immunologically active environment compared to the lung. Further research needs to be conducted to elucidate the mechanisms by which macrophages and other cells within the tumor microenvironment contribute to immune suppression. Understanding these mechanisms may identify specific macrophage populations in the liver as potential therapeutic targets.

Other Authors: Varun Shenoy1, Blake E. Sells1, Alyssa Weinstein1, Liang-I Kang1,6,7, Brett L Knolhoff1, Paul M. Grandgenett2, Michael A. Hollingsworth3,4, Ryan C. Fields5,7, Li Ding1,7,8,9, David G. DeNardo1,6,8* Affiliations: 1Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA 2Eppley Institute for Research in Cancer and Allied Diseases, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE 68198 USA 3Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE 68198 USA 4Fred & Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, 68198 USA 5Department of Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA 6Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA 7Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, USA 8McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, USA 9Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA

30- Identifying protein systems under selection in the tumor genome

Mark Kelly, UC San Diego, (CSBC)

Cancer genomes are impacted by complex patterns of genetic mutations which often differ greatly from tumor to tumor. A longstanding hypothesis is that this heterogeneity stems from selective pressure on a common set of protein systems that can each be dysregulated by myriad genetic events. To define the ensemble of systems under selection in a lung adenocarcinoma, we develop Cancer Systems Recovery by Maximum Posterior Probability (CanSRMaPP), a general statistical model that factors genetic alteration frequencies into a mixture of pressures on specific genes and systems, while accounting for genome-wide mutagenic processes. The result is a low-dimensional projection of the cancer genome that maintains utility while greatly simplifying cancer omics analyses. We further show that CanSRMaPPgeneralizes across distinct patient cohorts and that the final set of systems parsimoniously models cancer molecular phenotypes. CanSRMaPP presents a general toolbox for predictive pathway mapping in cancer, with implications to other polygenic diseases.

Other Authors: Burcak Otlu, Trey Ideker

31- Local colocalization landscape of primary tumors reveals spatial differences between tumor edge and center in head and neck cancer

Rohit Khurana, Stanford University, (CSBC)

Through recent technological advances, spatial biology has emerged as a new field that is revolutionizing our understanding of diverse cellular landscapes, such as the tumor microenvironment (TME). Highly multiplexed imaging platforms like CODEX create detailed tissue maps, situating proteomic data within a spatial context at single-cell resolution. To quantify spatial associations in these datasets, statistical metrics like the colocation quotient (CLQ) are commonly used to assess significant cell pair colocalization across samples; however, these measures provide point estimates for the entire spatial domain, thus failing to capture local heterogeneity. Borrowing from advances in statistical geography, we introduce the local indicator of colocation quotient (LCLQ) as a new, viable metric for spatial biology datasets that constructs per-cell spatial embeddings reflecting proximity to neighboring cell types. After generating CODEX data on a tissue microarray of specimens from 78 head and neck cancer patients, we used the LCLQ to reconstruct the cell pair colocalization landscape between the tumor center and edge. The resulting findings underscore LCLQ’s versatility in assessing local associations at the single-cell level.

Other Authors: Rohit Khurana, Jacob A. Chang, Weiruo Zhang, Sylvia K. Plevritis Affiliation: Department of Biomedical Data Science, Stanford University

32- The CSBC Research Center – Spatiotemporal Tumor Analytics (ST-Analytics) – investigates the mechanisms behind the observed durability of a combination of immune checkpoint blockade (ICB) followed by a MAPK inhibitor (MAPKi) for treating melanoma.

Juho Kim, Institute for Systems Biology, (CSBC)

We are currently building models for phenotyping H&E regions through integration with spatial chromatin accessibility assessed via DBiT ATAC-seq (deterministic barcode in tissue sequencing for transposase-accessible chromatin). First, image super-pixels are generated corresponding to DBiT tixels (spots). The tile-level super pixels are encoded as a vector using pathology foundation models. The encodings can be used for segmentation of tissue regions with spatial DBiT ATAC-seq signals, and the phenotyping of H&E regions. Based on this representation, we can obtain a smoother and more continuous representation of tissue, instead of the discrete nature of DBiT. In parallel, we also integrate H&E and mIHC images from adjacent sections of murine tumors through whole slide image registration. After creating tiled data, mIHC tiles are processed to provide machine learning targets; whether a particular cell type is observed. Machine-vision deep learning models are being trained to detect the presence of immune cells, starting with CD8 T cells. These predictions will be correlated to previously predicted areas of lymphocyte infiltration producing a well calibrated model that can be applied in H&Es integrated with DBiT data.

Next steps include the generation of relevant spatiotemporal features that allow the design of agent-based models (ABMs) to represent the dynamics of TIME which can inform experimental combination treatments of targeted inhibitors and immunotherapy in cancer models.

Other Authors: Juho Kim, Chong Xia, Muna Yase, Shuo Wang, Sai Manikonda, Pelin Garbioglu, Mohitveer Kahlon, Jianjun Jiang, Yin Tang, Sarah Li, David L Gibbs, Boris Aguilar, Heber L Rocha, Paul Macklin, Claudia M Ludwig, James R Heath, Wei Wei, Vésteinn Thorsson

33- Hypoxia-induced histone methylation and NF-κB activation in pancreas cancer fibroblasts promotes EMT-supportive growth factor secretion

Matthew Lazzara, University of Virginia, (CSBC)

The pancreatic ductal adenocarcinoma (PDAC) tumor microenvironment contains hypoxic tissue subdomains and cancer-associated fibroblasts (CAFs) of multiple subtypes that play tumor-promoting and -restraining roles. Here, we demonstrate that hypoxia promotes an inflammatory-like CAF phenotype and that hypoxic CAFs selectively promote epithelial-mesenchymal transition (EMT) in PDAC cancer cells through growth factor-mediated cell crosstalk. By analyzing patient tumor single-cell transcriptomics and conducting an inhibitor screen, we identified two EMT-inducing growth factors secreted by hypoxic CAFs. We further found that reactive oxygen species-activated NF-κB cooperates with hypoxia-dependent histone methylation to promote growth factor expression in hypoxic CAFs. In lineage-traced autochthonous PDAC mouse tumors, hypoxic CAFs resided preferentially near hypoxic, mesenchymal cancer cells. However, in orthotopic tumors engineered with hypoxia fate-mapped CAFs, once-hypoxic re-oxygenated CAFs lacked a spatial correlation with mesenchymal cancer cells. Thus, hypoxia promotes reversible CAF-malignant cell interactions that drive EMT through druggable signaling pathways.

Other Authors: Karl M. Kowalewski, Sara J. Adair, Anne Talkington, Jason Wieder, Jason R. Pitarresi, Kia Perez-Vale, Sepideh Dolatshahi, Rosalie Sears, Ben Z. Stanger, Todd W. Bauer

34- Proteomic stratification of heterogeneous cell migration

William Leineweber, Stanford University, (CSBC)

Cancer cells can adopt heterogeneous migration behaviors to facilitate metastatic progression. Many genes are implicated in migration heterogeneity, yet understanding how migration-related protein proteins interact to produce heterogeneous migration behaviors remains poorly understood. To address this question, this study linked live-cell tracking with end-point spatial proteomics to identify molecular states underlying migration heterogeneity. U2OS osteosarcoma stably expressing the FUCCI cell cycle reporter system were tracked for 24 hours on a Leica DMI8 widefield microscope. Immediately after, cells were fixed and highly-multiplexed immunofluorescence imaging was performed. Unlike marker panels intended to identify cell subtypes, a custom panel was developed to identify different modes of cell migration. Even within one cell line, unsupervised clustering identified cells matching profiles akin to epithelial, mesenchymal, and amoeboid migration states. Further analysis of how single cell trajectories correlate to these cell states, as well as how cell cycle dynamics impact both migration and molecular cell states are in progress. Results from these analyses will shed new light on the “go vs. grow” hypothesis by directly testing how cell speed, persistence, and overall invasion correspond to cell cycle stage and progression. Overall, this experimental approach represents an important bridging between functional measurements and end-point omics technologies that will be crucial to gain actional insights amid the boom in multiomics.

Other Authors: Alice Finkelstein, Emma Lundberg

35- RIG-I-dependent type I interferon mediates tumor-intrinsic inflammation in breast cancer 3D culture

Katherine Liu, Stanford University, (MetNet)

The type I interferon pathway, initially recognized as a crucial element in the antiviral immune response, has been extensively studied in the context of cancer. In breast cancers, activation of the type I IFN signaling pathway has been shown to induce apoptosis, but can also contribute to the evolution of drug resistance and metastasis. The dual role of type I IFN in breast cancers remains incompletely understood. Recent studies suggest that the impact of type I IFN on cancer therapy response is highly variable, contingent upon the strength, duration, and source of IFN signaling stimuli. We sought to leverage our scalable 3D spheroid cell model to investigate the role of tumor-intrinsic type I IFN in breast cancers. Through CRISPR-interference screening, we discover that perturbation of the type I IFN pathway increases growth in 3D breast cancer spheroids relative to 2D monolayers. RNA-seq analysis further demonstrates an inflammatory profile in 3D spheroids, indicative of tumor-intrinsic type I IFN. Spatial transcriptomic analysis of breast cancer patient samples reveals an enrichment of the same tumor-intrinsic IFN inflammatory signature in immune-desert tumor cell niches. To dissect the mechanism driving this response, we assessed several potential innate immune sensors and identified retinoic acid-inducible gene-I (RIG-I) as the necessary upstream effector. RIG-I is a nucleic acid sensor that triggers innate immune response by inducing type I IFN transcription. RIG-I agonists have been shown to induce cancer cell death and are currently undergoing evaluation in multiple clinical trials. These insights shed light on the potential to harness innate immune signaling to effectively target and eliminate tumors.

Other Authors: TBD

36- Indirect selection for enhanced phenotypic plasticity underlies the link between therapy resistance and metastatic colonization.

Andriy Marusyk , Moffitt Cancer Center, (CSBC)

Acquired resistance to targeted therapies remains inevitable in advanced, metastatic cancers. Acquisition of therapy resistance is typically attributed to a single genetic or non-genetic mechanism. However, our recent studies revealed a more complex scenario of a resistance continuum, where progression toward resistance is mediated by the acquisition of multiple cooperating expression-level and mutational changes. Interestingly, acquired resistance to a specific drug was commonly associated with an enhanced ability to adapt to unrelated stressors, including the enhanced metastatic colonization potential, through plasticity-mediated phenotypic “reprogramming.” To explain this link, we hypothesized that, under a scenario of multiple intermediate steps between persistence and resistance, direct selection for reduced drug sensitivity leads to an indirect selection for non-specific adaptability potential. Following the in-silico proof of principle validation of the feasibility of this hypothesis, we set to examine its biological relevance. To this end, we generated multiple single-cell derived isolates from a therapy-naïve cell line model of ALK+ lung cancers. We found that despite similar short-term sensitivity to ALK inhibitors (ALKi), individual isolates differed in their ability to develop resistance over time. Importantly, similar to our observations in cells with acquired therapy resistance, therapy-naïve clones with enhanced ability to acquire ALKi resistance exhibited an enhanced ability to adapt to unrelated pharmacological and physical stressors. Our studies indicate that the link between therapy resistance and metastatic disease can be mediated by enhanced phenotypic plasticity, i.e., the ability to adapt to new contexts by rewiring gene expression networks.

Other Authors: Alicia Tivoli, Shirali Obul, Matthew Froid, Virginia Turati

37- Investigation of Integrin Targeting: Interpreting Drug Response Profiles

Nicole Mattson, UC San Diego, (CSBC)

Cancer continues to be a devastating disease, with thousands of patients succumbing to it annually. The path to developing effective therapies is often blocked by stringent approval processes, particularly concerning patient safety. To navigate these challenges, we propose targeting pathways with existing therapeutic approvals in other diseases, ensuring drug safety and tolerance. A compelling target is the integrin family of proteins, successful in treating Inflammatory Bowel Diseases and Cardiovascular Disease. Our project focuses on a novel integrin inhibitor, cpd_AV2, comparing its efficacy against the leading integrin-targeting cancer therapeutic, Cilengitide. Although Cilengitide is well-tolerated, it has not significantly reduced cancer burden. In comparison, cpd_AV2 demonstrates rapid and potent cancer cell cytotoxicity where Cilengitide fails. Offering an opportunity to develop cpd_AV2 as a new integrin-targeted therapeutic. To elucidate cpd_AV2's superior cancer cell toxicity, we analyzed the sensitivity of hundreds of cell lines. We found that cpd_AV2 treatment leads to a population of sensitive and resistant cells, whereas Cilengitide did not show strong sensitivity profiles. Leveraging these genotypes of different cell populations, we built explainable machine learning models that identify biomarkers predicting sensitivity to cpd_AV2. These biomarkers are immediately clinically relevant, facilitating the identification of patients most likely to benefit from this therapy. Additionally, our explainable model will reveal the mechanism of action of cpd_AV2 as a therapeutic option in cancer. This project offers an innovative dual approach of biomarker identification and mechanistic discovery which positions cpd_AV2 as a promising therapeutic option in cancer.

Other Authors: Nicole Mattson, Trey Ideker, Stephanie Fraley, Chun-Wei Chen

38- Identifying the genetic interactions between metastatic breast tumor cells and the lung microenvironment

Anna Michmerhuizen, University of North Carolina, (CSBC)

Metastatic breast cancer has progressed to grow in critical organs and is the primary cause of death for women with triple negative breast cancer (TNBC), which presents a significant clinical challenge. Gene expression-based subtyping indicates that most TNBCs fall into the Basal-like (70%) or Mesenchymal/Claudin-low (10-20%) subtypes. We and others have previously shown that metastatic behavior is associated with molecular subtype with the basal-like and claudin-low subtypes metastasizing primarily to the lung and brain, HER2-enriched to the liver, and luminal subtypes to the bone. Using data from >2000 human primary tumors that have progressed to metastases, we performed supervised machine learning using elastic net regression to identify genomic features predictive of lungspecific metastasis. In the successful model, features indicating a higher likelihood of lung metastasis included innate immunity cell types (Eosinophils, Mast Cells), features of vascularity (VEGF pathway, vascular content signature), and tumor cell intrinsic features (NRAS). Conversely, high expression of ER+/luminal features, including an ESR1-active signature and PGR gene expression, was predictive of a lower likelihood of lung metastases. To experimentally validate these findings, we are testing mouse models for their metastatic potential. Out of the 11 mouse models tested, 7 are lung metastatic, and 4/7 have high NRAS expression. Elastic net is also being used to build computational models to predict metastasis to additional organ sites including liver, brain, and bone. Finally in vivo CRISPR-based functional studies are underway to validate genetic drivers promoting metastasis to the lung, which may lead to novel opportunities for therapeutic interventions.

Other Authors: Tulay Yilmaz-Swenson, Kevin R. Mott, Charles M. Perou

39- Analysis of intratumoral myeloid-T cell communication networks at single-cell resolution reveals mechanisms of combinatorial immunotherapy

Kathryn Miller-Jensen, Yale University, (CSBC)

Responses to immunotherapy and other treatments involve complex rewiring of cell-cell communication networks in tissues. Putative communication networks can be inferred from single-cell RNA-sequencing (scRNA-seq) data, but current strategies do not effectively manage cell-to-cell heterogeneity within cell types. To address this, we developed a computational strategy to analyze myeloid-T cell communication networks from scRNA-seq data at single-cell resolution following different combinations of immunotherapy treatments. We used our approach to determine how agonistic CD40 (CD40ag) alters cell-cell communication alone and in combination with immune checkpoint blockade (ICB). We discovered subsets of tumor-associated macrophages (TAMs) communicating with T cells along distinct axes that are differentially targeted by CD40ag versus ICB, and we experimentally validated our findings with fluorescent imaging and spatial transcriptomics. Importantly, we found that combining CD40ag with ICB promotes the establishment of coordinated TAM-T cell and dendritic cell-T cell interactions that are known to promote anti-tumor immunity in other contexts. Overall, our approach efficiently uncovered critical cell-cell communication axes within heterogeneous cell types that underlie immunotherapeutic efficacy and resistance.

Other Authors: Kate Bridges, Gabriela A. Pizzurro, Alev Baysoy, Janani P. Baskaran, Ziyan Xu, Koonam Park, Harriet Kluger, Rong Fan, Susan M. Kaech, Marcus W. Bosenberg

40- Tumor-draining lymph nodes are sites of continued melanoma evolution

Tara Muijlwijk , NYU Langone Health, (MetNet)

Lymph node (LN) metastasis is an early indicator of aggressive disease in melanoma, where lymphatic spread may both provide a mechanism for ongoing tumor evolution and dissemination and suppress systemic immune responses. Despite emerging interest in the role of LN metastasis on systemic tumor progression, the tumor-intrinsic and -extrinsic parameters that determine risk for regional spread remain largely unknown. To begin to fill this knowledge gap, we paired spatial transcriptomics (10x Visium) with highly multiplexed immunohistochemistry (Akoya Phenocycler) on archived human biospecimens to deconvolve the heterogenous genomic, transcriptomic, and microenvironmental programs associated with tumor evolution in the lymphatic basin. We hypothesized melanoma cells and the immune microenvironment co-evolve to form LN metastases. We established a cohort of formalin-fixed paraffin-embedded (FFPE) sections of primary cutaneous melanomas (n=5) and paired tumor-draining, metastatic LNs (n=7) from five stage III patients collected upon initial diagnosis. We inferred their gene copy number variations from transcriptomic data to establish clonal lineage trees and maps of genetic clones in patient-matched primary melanoma and LN tissues. These data will allow us to understand better how different genetic clones expand and are selected during metastasis and to order these genetic lesions in time for subsequent transcriptional and phenotypic analyses. Our data reveal that LN metastases have an increased clonal heterogeneity relative to their paired primary tumors, indicating that the LN could serve as a site for continued genomic evolution. Our data also suggests that LN colonization enables continued tumor evolution, possibly leading to immunological adaptation and systemic disease progression.

Other Authors: Katherine S. Ventre1, Robert Stagnitta1, Tara Muijlwijk1, Shi Qiu1, Iman Osman1,2, Markus Schober1,2, Amanda W. Lund1,2 1Ronald O. Perelman Department of Dermatology, 2Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York, NY

41- Transcriptomic Analyses and Novel Organoid and Xenograft Modeling Reveals Potential Drug Sensitivities in a KRAS G12V-Mutant Colorectal Cancer Brain Metastasis”

Emon Nasajpour, Stanford University, (MetNet)

Brain metastases (BM) occur in 2.3% of patients with colorectal cancer (CRC), which is the second leading cause of cancer deaths worldwide. Due to the rarity of CRC-originating BM (CRC-BM) there are limited clinical guidelines for their treatment. Despite aggressive treatment approaches, including surgery and chemo-irradiation, patients with CRC-BM have a very poor median overall survival of only 5.3 months, highlighting the need for better therapeutic approaches. Next-generation sequencing has identified KRAS mutations in ~40% of metastatic CRC. Although therapies targeting mutant KRAS and the downstream MAPK pathway components have been developed, their clinical translation is hampered by the dearth of accurate preclinical models for CRC-BMs. In this study, we generated a patient-derived organoid (PDO) model from a surgical sample of a KRAS G12V mutant BM originating from a CRC. When injected orthotopically into immunocompromised mice, these PDOs consistently formed tumors, indicating stable malignant potential. The PDOs and derived xenografts retained the KRAS G12V mutation, epithelial and mesenchymal marker expression, accurately mirroring the original BM. Spatial transcriptome analysis of the patient BM discovered tumor epithelial cells with six distinct functional features, characterized by enhanced MAPK signaling through KRAS G12V, upregulated oxidative stress response in endoplasmic reticulum and protein folding, cells of the tumor stroma, cells involved in epithelial-to-mesenchymal transition, cells responsible for metastasis and progression, and cells with upregulated cell cycle activity. Bulk transcriptome analyses revealed gene expression differences between PDO and xenograft, which reflected in vitro growth conditions. Pathway analyses revealed that transcripts common to PDO, xenografts, and patient BM were enriched in protein folding in the endoplasmic reticulum and the unfolded protein response pathway. Pharmacologic inhibition of the unfolded protein response pathway efficiently reduced the viability of the PDO, suggesting that the PDO may reflect the clinical vulnerabilities of the patient BM in preclinical testing.

Other Authors: Dena Panovska1#, Emon Nasajpour1#, Yao Lulu Xing1#, Alexa Gwyn2, Daniella Morales3, Suhani Chaudary3, Anuja Sathe4, Caitlynn To-Duyen Tran3, Cara Coleen Rada5, Michitaka Nakano5, Pardes Habib1, Leif Rabin1,6, Steven D. Chang1, Calvin Kuo5, Jeffrey J Nirschl7, Hannes Vogel7, Hanlee Ji4, Melanie Hayden Gephart1, Claudia K. Petritsch1*

42- Evolutionary strategies in treatment resistant cancers: NK cell immunotherapy targeting immunogenic cell stress responses to DNA damaging agents

Hannah Newman, Moffitt Cancer Center, (CSBC)

Evolution-informed therapies exploit ecological and evolutionary consequences of drug resistance to inhibit the expansion of treatment-resistant populations and prolong time to progression. One strategy, termed an evolutionary double-bind, uses an initial therapy to elicit a specific adaptive response by cancer cells, which is then selectively targeted by follow-on therapy. Here we examine the combination of DNA damaging agents (DDA) and natural killer (NK) cell-based immunotherapy as a quantifiable double-bind strategy. Radiotherapy and chemotherapy induce lethal DNA damage; however, cancer cells adapt by upregulating DNA damage response pathways. While this evolutionary strategy increases resistance to DDA, it also upregulates immunogenic stress response which increases expression of NK cell ligands. Here we investigated a possible evolutionary double bind in which cancer cells that survive DDA are exposed to NK cells. Using multiple cancer cell lines, we show cells treated with DDA have altered expression of ligands recognized by NK cells including MICA/B, PVR, PVRL2, and HLA-E. In addition, we demonstrate these cells have altered susceptibility to NK cell-mediated killing after therapy. Using isogenic models of radiation-resistance and targeted therapy resistance, we demonstrate that resistant cells maintain upregulated NK cell ligands with a 2-fold increase in sensitivity to NK cell mediated killing. These dynamics were framed mathematically, and model simulations predicted optimal tumor control would be achieved through initial RT rapidly followed by NK-based immunotherapy; subsequent experiments confirmed predictions. We conclude that DDA and NK cell-based immunotherapy produces an evolutionary double bind that can be exploited in heterogenous tumors to limit treatment resistance.

Other Authors: Jeffery West, Andrew Ojeda, Veronica Estrella, Mark Robertson-Tessi, Joel Brown, Cliona O’Farrelly, Alexander Anderson, Kimberly Luddy, Robert Gatenby

43- Habitat fragmentation in the cancer ecosystem: exploiting treatment induced ecological collapse to prevent recurrence.

Andrew Ojeda, Moffitt Cancer Center , (CSBC)

Remarkable advances in therapies have led to effective first line treatments for most cancers. However, curative outcomes remain uncommon because evolution of resistance almost inevitably results in treatment failure and tumor progression. In the language of evolutionary biology, an effective cancer therapy, by inducing death in large fractions of the cancer population, causing a “race to extinction” similar to dynamics observed in species extinctions. However, cells that survive therapy can form the nidus of a new population that can ultimately replace the original - termed “evolutionary rescue”. Preliminary observations following neoadjuvant therapy find surviving population typically persist in small islands of cancer cells. Evolutionarily, this resembles habitat fragmentation typically observed after an ecosystem is disrupted due to reduction in habitat quality, competition for limited resources, and more vulnerable populations. Importantly, habitat fragmentation leaves the surviving population highly vulnerable to extinction. Thus, we hypothesize surviving cancer cells, are highly susceptible to potential eradication by a second perturbation that exploits vulnerabilities of habitat fragmentation. We have reproduced the habitat fragmentation observed in clinical samples in genetically engineered murine MMTV-PyMT mice. Treatment given at all stages of tumor development induced isolated tumor fragments within the tumor border. Image analysis of clinical and murine fragmented tumors revealed unique ecological traits supporting survival of individual fragments. We use this model to investigate the microenvironmental and cellular/molecular factors that govern cancer survival to therapies. This information will determine ways to prevent evolutionary rescue and give us insight on refining current treatment protocols to improve patient outcomes.

Other Authors : Veronica Estrella, Hannah Newman, Chandler Gatenbee, Kimberly Luddy, Kenneth Tsai, Joel Brown, Robert Gatenby

44- Interface-guided phenotyping of coding variants in the transcription factor RUNX1

Kivilcim Ozturk, UC San Diego

Understanding the functional impact of single amino acid substitutions in cancer driver genes remains an unmet need. Perturb-seq provides a tool to investigate the effects of individual mutations on cellular programs by measuring their transcriptional consequences. Here, we developed an approach to functionally assess variant impact in single cells. We deployed ScalablE fUnctional Screening by Sequencing (SEUSS), a Perturb-seq style technique, to generate and assay mutations at physical interfaces of the Runt-related transcription factor 1 (RUNX1). We measured the impact of 115 mutations on RNA profiles in single myelogenous leukemia cells and used the profiles to categorize mutations into three functionally distinct groups: wild-type (WT)-like, loss-of-function (LoF)-like and hypomorphic, that were validated in orthogonal assays. LoF-like variants dominated the DNA-binding site and were recurrent in cancer; however, recurrence alone did not predict functional impact. Hypomorphic variants shared characteristics with LoF-like but favored protein interactions, promoting gene expression indicative of nerve-growth-factor response and cytokine recruitment of neutrophils. Accessible DNA near differentially expressed genes frequently contained RUNX1-binding motifs. Finally, we identified the functional impact of 16 documented variants of uncertain significance and trained a classifier to predict 103 more. Overall, our work demonstrates the power of transcriptional profiling in single cells to assess the functional impact of missense mutations on cellular programs and provides a scalable method for coding variant impact phenotyping.

Other Authors: Rebecca Panwala, Jeanna Sheen, Kyle Ford, Nathan Jayne, Andrew Portell, Dong-Er Zhang, Stephan Hutter, Torsten Haferlach, Trey Ideker, Prashant Mali, Hannah Carter

45- Elucidating the Role of E-cadherin in Breast Cancer Metastasis

David Padilla, Johns Hopkins University School of Medicine, (MetNet)

Metastasis allows primary cancer cells to invade the surrounding stroma and establish secondary cancer in a distant organ. During invasion, cell-cell adhesion molecules must change to allow cells to invade the stroma. Using invasive ductal carcinoma (IDC) models, the Ewald lab showed that E-cadherin suppresses invasion but is required for metastasis. E-cadherin, a calcium-dependent epithelial protein mediating cell-cell interactions (adherens junctions) and interacting with catenins and the actin cytoskeleton, maintains epithelial homeostasis. However, which protein-protein interactions mediate E-cadherin's function in metastasis remains unclear. E-cadherin is a classical cadherin composed of five extracellular cadherin domains (EC1-5), one transmembrane domain (TM), and one cytoplasmic domain (CD). This study aims to investigate the role of E-cadherin domains in metastasis. Prior studies have described proteolytic cleavage products of E-cadherin, but not all have been studied in metastasis. First, we assessed E-cadherin cleavage in human breast cancer cell lines and organoids derived from the genetically engineered mouse model PyMT in 2D and 3D assays. In 2D assays, monolayers of the human breast cancer lines MCF7 and MDA-MB-231 were plated and analyzed by Western blot and immunofluorescence (IF). For 3D assays, PyMT organoids were embedded in collagen and analyzed via Western blot and IF. In 2D and 3D assays, samples were treated with different protease inhibitors. Our findings show that E-cadherin is proteolytically cleaved in the cell line MCF7, as reported. Likewise, E-cadherin cleavage is conserved in the murine model PyMT. Thus, the PyMT mouse model is suitable for evaluating each E-cadherin cleavage product in metastasis.

Other Authors: Andrew Ewald

46- Organ-specific evolution of the tumor microenvironment in metastatic breast cancer

Seongyeol Park, Stanford Cancer Institute, (MetNet)

Metastasis is a critical stage in the progression of cancer, generally resulting in incurable disease. The dynamics of this process and the determinants of colonization at different organ sites are largely unknown. Breast cancer is an exemplar of organotropism, providing an opportunity to understand differences in the tumor microenvironment (TME) across metastatic sites and the impact of immunotherapy. To address this question, we generated a multi-modal, longitudinal data set, including spatial proteomics (Multiplexed Ion Beam Imaging, MIBI) from metastatic triple-negative breast cancer (mTNBC) patients enrolled in the TONIC clinical trial (NCT02499367), and their accompanying primary tumor. We obtained approximately 400 tissue samples from 110 patients, encompassing various organs (e.g. lymph node, liver, skin, and intestine), time points (e.g. primary and metastasis, before and during nivolumab treatment). This unique and comprehensive set of data enabled us to examine the organ-specific development of the TME and its effects on immunotherapy. Through unsupervised clustering of spatial data, we identified five distinct TME groups within mTNBC. These TME groups exhibit varying response rates to immunotherapy. We observe frequent changes in TME group composition between pre- and post-treatment specimens. In addition, the composition of TME groups varied among metastatic organs. Generally, metastatic tissues had more unfavorable TME groups compared to primary breast tissues, with the exception of the lymph nodes. Our findings emphasize the dynamic nature of the TME in mTNBC and broaden our understanding of site-specific metastasis toward the goal of developing more effective treatment strategies for this aggressive disease.

Other Authors: Noah Greenwald, Kathleen Houlahan, Jolene Ranek, Aziz Khan, Michael Angelo, Marleen Kok, Christina Curtis

47- A multimodal spatial-omics atlas of the course of non-malignant lung identifies proinflammatory and targetable cues that promote progression to lung adenocarcinoma

Fuduan Peng, UT MD Anderson Cancer Center

Background: Events that mediate transition of normal-appearing tissue (NAT) to adenomatous premalignant lesions (aPMLs) and further to locally invasive lung adenocarcinoma (LUAD) are very poorly narrated. Methods: Matching NATs, aPMLs and LUADs of 25 patients were analyzed by spatial transcriptomics (ST, n=56), RNA-sequencing of fixed single cells (scFFPE-seq, n=75), and whole exome sequencing (WES, n=79). A subset of tissues was also analyzed by sequential immunofluorescence (seqIF, COMET) and subcellular spatial gene [removed]Xenium, n=41). Results: We performed multimodal spatial omics analysis of epithelial tissues along the NAT-aPML-LUAD continuum. In total, we analyzed over 450,000 spots by Visium ST, 700,000 cells by scFFPE-seq and more than 1.75 million cells using Xenium. aPMLs and LUADs showed distinct spatial transcriptional patterns, lineage plasticity and cell states. Seven key metaprograms with unique spatial mappings showed early proinflammatory phenotypes in aPMLs along with other programs that underlie malignant conversion of premalignancy. Both LUAD and aPML regions highly expressed signatures of KRT8+ alveolar intermediate cells (KACs). KACs denoted transitionary states between NATs and aPMLs or LUADs. Spatial cell neighborhood analysis consistently showed that KACs were proximal to proinflammatory macrophages and fibroblasts. Multimodal analysis of a tobacco lung carcinogenesis mouse model showed that KACs displayed increased NF-kB signaling and expression of the Il1b receptor Il1r1. Importantly, treatment with IL-1 or macrophage co-culture enhanced growth of KAC-enriched organoids and IL-1 neutralization in vivo led to KAC regression. Conclusions: Our spatial omics analyses identify unique and targetable cellular states and interactions that underlie transition of normal tissue to LUAD.

Other Authors: Ansam Sinjab, Sujuan Yang, Minyue Chen, Yibo Dai, Tieling Zhou, Alejandra G. Serrano, Guangsheng Pei, Jianlong Liao, Yunhe Liu, Yanshuo Chu, Yang Liu, Jiahui Jiang, Kai Yu, Ruiping Wang, Jiping Feng, Zahraa Rahal, Lorena I. Gomez Bolanos, Guangchun Han, Kyung Serk Cho, Warapen Treekitkarnmongkol, Akshay Basi, Avrum Spira, Steven Dubinett, Luisa M. Solis, Stephen Swisher, Mingyao Li, Junya Fujimoto, Ignacio I. Wistuba, Kadir Akdemir, Jared Burks, Linghua Wang, Humam Kadara

48- Mechanisms linking Drp1 and mitochondrial fission to tumor growth in KRas-driven colon cancer.

Daniel Phipps , University of Virginia, (CSBC)

Colorectal cancer (CRC) develops in a complex metabolic environment. Approximately 40% of CRC patients carry a deleterious mutation in KRAS, a potent driver of tumor cell metabolism. Oncogenic KRas signaling promotes dynamic structural changes in mitochondrial networks via ERK-mediated activation of the mitochondrial fission GTPase Drp1. CRC cell lines exhibit a highly fragmented mitochondrial phenotype, but the mechanisms that link aberrant mitochondrial fission to tumor cell growth and metabolism are poorly understood. We hypothesize that KRas-driven Drp1 activation initiates specific metabolic changes to promote tumorigenesis within the complex energetic environment of the colon. These specific changes in metabolism, however, can create vulnerabilities that could potentially be exploited therapeutically. We plan to test this hypothesis in three ways. First, we are implementing complementary inducible Drp1 knockout systems to determine the requirement of Drp1 for proliferation and metabolic reprogramming in vitro; second, we are collaborating with the laboratory of Jason Papin to construct and test mathematical models of CRC metabolism and identify unique metabolic dependencies; and third, we are developing a genetically engineered mouse model to determine how Ras-driven activation of Drp1 impacts CRC tumorigenesis in vivo. Collectively, these approaches will enable us to determine how mitochondrial fission and KRas-driven metabolic reprogramming coalesce to promote CRC tumor cell metabolism and proliferation and to identify specific vulnerabilities that arise in CRC cells with highly fragmented mitochondrial networks. Ultimately, we seek to identify novel therapeutic targets to limit the growth and metastatic potential of tumors that arise in the colon.

Other Authors: William Shao, Jennifer A. Kashatus, Jason A. Papin, David F. Kashatus

49- Paired MALDI-MSI and cyclic immunofluorescence captures metabolic signatures for spatially resolved immune cell populations in vivo.

Veronika Pister, MIT, (CSBC)

Understanding why some tumors respond to immunotherapy while others do not remains a significant challenge. Metabolic reprogramming of the tumor microenvironment (TME) is a hallmark of cancer that contributes to immune evasion and tumor persistence. One key factor influencing these varied responses is the altered availability of nutrients within the TME. Cancer cells can manipulate the TME by secreting metabolites that suppress immune function or by outcompeting immune cells for essential nutrients. Traditional studies of these phenomena have been constrained by either limited spatial resolution or the necessity of analyzing bulk tissue samples, which often mask critical insights. We addressed these limitations through in vivo microdelivery of small-molecule therapeutics, which ensures consistent recruitment of immune cells at precise time points. This approach allows for near single-cell resolution measurements of metabolism within the native TME using spatial metabolomics techniques such as MALDI mass spectrometry imaging (MSI). In the MMTV-PyMT mouse model of breast cancer, which closely mirrors complex immune cell dynamics, we combined MALDI-MSI with cyclic immunofluorescence on adjacent tissue sections to correlate proteomic and metabolomic profiles. Segmentation of MSI data enabled us to map metabolic landscapes specific to different immune cell types. Notably, we identified two distinct metabolic clusters associated with tissue-resident and infiltrating myeloid cells. Specific metabolic pathways, such as aspartate and purine metabolism, were upregulated in macrophage populations, highlighting the potential of metabolite markers to reveal cell types. These findings underscore the promise of spatial metabolomics for understanding immune-tumor interactions and better tailoring immunotherapy strategies.

Other Authors: Zuzana Tatarova, Jenna Blum, Noel Park, Shawn Davidson, Ernest Fraenkel, Oliver Jonas

50- Pro-inflammatory macrophages primed through CD40-agonist can reprogram the melanoma microenvironment to overcome checkpoint inhibitor resistance

Gabriela Pizzurro, Yale University, (CSBC)

Therapy resistance with immune checkpoint blockade (ICB) remains a significant clinical challenge. One strategy to restore anti-tumor activity aims to target tumor-associated macrophages (TAMs). Our previous results in the YUMMER1.7 melanoma model demonstrated that early treatment (day 7 post-inoculation) with agonistic CD40 (CD40ag) overcomes PD-1 resistance, primarily via activation of IL-12-secreting CCR7+ dendritic cells. We hypothesized that responses of myeloid subsets to CD40ag and, therefore, treatment efficacy is impacted by tumor stage and immune tumor microenvironment (TME) composition. To test this, we compared single-dose ICB (a-PD-1+a-CTLA-4), efficacious when applied early, but no longer so when delaying treatment to day 11. Adding CD40ag to single-dose ICB treatment reversed the lost efficacy, inducing more than 80% overall survival. But this effect decayed with treatment initiation deferral, defining conditions of successful TME reprogramming versus therapy resistance. Delayed CD40ag treatment induced rapid myeloid recruitment to the TME, and upregulated pro-inflammatory markers in a CD40+Ly6C+ TAM subset, with high iNOS, CCL3 and TNF expression, which propagated for over a week. Monocyte transfer experiments revealed that this activation happened both in newly infiltrating and resident TAMs. Interestingly, in tumor-bearing Rag-/- mice, CD40ag-treated TAMs displayed an immediate pro-inflammatory response, at both early and delayed timepoints, but only transiently, suggesting different TAM-T-cell interactions over time. We collected scRNAseq on CD40ag-treated tumors to understand the temporal differences in the melanoma TME. By integrating transcriptomic, flow cytometry and in vivo functional data, we will determine changes in myeloid cells associated with successful immune responses as well as potentially limiting immunotherapy.

Other Authors: Kate Bridges, Anasuya Dighe, Harriet Kluger, Susan Kaech, Marcus W. Bosenberg, and Kathryn Miller-Jensen

51- Using clinical data and mathematical modeling to generate decision support for evolutionary therapy in cancer

Mark Robertson-Tessi, Moffitt Cancer Center, (CSBC)

Cancer is an eco-evolutionary system that adapts to evade therapy. As such, static treatment regimens eventually fail in many advanced cancers. A personalized, proactive, and dynamic approach to therapy has the potential to improve outcomes. To explore clinical feasibility of such approaches, we developed a clinical trial (NCT04343365), the Evolutionary Tumor Board (ETB), which uses eco-evolutionary theory and predictive modeling to assist clinical decision making for individual cancer patients (n=24, ongoing). We developed a virtual patient framework for guiding dynamic evolutionary therapy for enrolled patients. The framework relies on detailed data curation and imaging measurements for each patient, and mathematical modeling incorporates multi-lesion tumor growth, treatment response, and evolution of resistance. Models are calibrated by historical datasets of similar patients, clinical trial data, and the patient’s longitudinal data. A “Phase i trial” approach accounts for prediction uncertainty, and information is delivered via a software interface. The ETB has provided novel and useful decision support for the patients and oncologists. At the same time, our efforts show that there are both challenges and opportunities in predicting and personalizing therapy, particularly in the context of real-time clinical care. Our approach can be applied in other diseases where dynamic decision support is applicable.

Other Authors: Joel Brown, Maria Biernacki, Kimberly Luddy, Rikesh Makanji, Joaquim Farinhas, Jill Gallaher, Andriy Marusyk, Robert Gatenby, Damon Reed, Christine Chung, Alexander Anderson

52- MelOD: The Melanoma Omics Dashboard for multimodal data exploration

Paul Sastourne-Haletou, NYU Langone Health, (MetNet)

Melanoma research increasingly relies on integrating diverse omics datasets, including bulk RNA sequencing, single-cell RNA sequencing, and proteomics, to reveal the molecular mechanisms behind cancer progression and treatment response. However, exploring and interpreting such complex data can be challenging, especially for researchers without advanced computational skills. To address this, we introduce MelOD (Melanoma Omics Dashboard), a comprehensive RShiny application that facilitates interactive analysis and visualization of melanoma omics data. MelOD provides a user-friendly interface that combines various data types, allowing researchers to examine melanoma's molecular landscape from multiple perspectives. The application features customizable visualization tools and supports dynamic data exploration, enabling users to generate hypotheses and extract meaningful biological insights. By democratizing access to advanced bioinformatics tools and promoting real-time data interaction, MelOD aims to accelerate discovery and improve our understanding of melanoma biology.

Other Authors: Adam Walker Markus Schober Eva Hernando Kelly Ruggles

53- Evolutionarily distinct types of disseminated cancer cells shape metastatic progression.

Lornella Seeneevassen, Albert Einstein college of medicine, (MetNet)

Disseminated cancer cells (DCCs) accumulate in secondary organs, forming a diverse population ranging from early (e-DCCs) to late (l-DCCs) stages. e-DCCs and l-DCCs can enter dormancy before reawakening to form metastases. The interaction between e-DCCs and l-DCCs and its impact on their fate remains unclear. Primary tumor size is a poor prognosis indicator, but the reasons are not entirely clear. We hypothesized that l-DCCs accumulating in the lungs as tumors get bigger may influence dormant e-DCCs to awaken. In the MMTV-HER2WT mouse model, e-DCCs and l-DCCs co-localization in the lungs leads to e-DCCs exiting dormancy and forming metastases more efficiently, indicating that l-DCCs may awaken eDCCs. Using Pro-Code barcoding technology, we found that early and late primary lesions exhibit low phenotypic heterogeneity, most metastasis cells originate from e-DCCs, and metastases have distinct clonal distributions unlike their primary lesions. RNAseq data from mosaic (early plus late cells) metastases and computational analysis using literature validated ligand-receptor pairs identified Thrombospondin1-Syndecan1 as a potential pair for e-DCC-l-DCC communication. Recombinant thrombospondin1 promotes proliferation of e-DCC HER2+ cells in vitro, an effect negated by Syndecan1 knock-down. Thrombospondin1 induces the expression of mesenchymal and epithelial markers in e-DCCs, suggesting a switch to a hybrid state, also observed when e-DCCs are co-cultured with l-DCCs. Interestingly, l-DCCs also respond to e-DCCs by expressing more mesenchymal markers. We propose that l-DCCs secrete at least thrombospondin-1, which binds to Syndecan-1 in e-DCCs, leading to their awakening. e-DCC-derived metastases may have specific druggable targets, offering new treatment avenues for metastatic disease.

Other Authors: Carolina Rodriguez-Tirado1,2, Lornella Seeneevassen2,3,4, Uliana Berseneva1,2, Luisanna Pia5, Luis E. Valencia Salazar 2,3,4., Brian Brown5, Julio Aguirre-Ghiso2,3,4 and Maria Soledad Sosa1,2 1Department of Microbiology and Immunology, Albert Einstein College of Medicine, Montefiore, Bronx, NY 10461, USA. 2Department of Oncology, Albert Einstein College of Medicine, Montefiore, Bronx, NY 10461, USA. 3 Department of Cell Biology, Albert Einstein College of Medicine, Montefiore, Bronx, NY 10461, USA. 4 Cancer Dormancy and Tumor Microenvironment Institute, Albert Einstein College of Medicine, Bronx, NY 10461, USA. 5 Genetics/Genomics Science, Icahn School of Medicine, Mount Sinai, New York, NY 10029, USA.

54- Cancer mutations converge on protein assemblies to predict resistance to replication stress

Akshat Singhal, UC San Diego, (CSBC)

Rapid proliferation is a hallmark of cancer associated with sensitivity to therapeutics that cause DNA replication stress (RS). Many tumors exhibit drug resistance, however, via molecular pathways that are incompletely understood. Here, we develop an ensemble of predictive models that elucidate how cancer mutations impact the response to common RS-inducing (RSi) agents. The models implement recent advances in deep learning to facilitate multidrug prediction and mechanistic interpretation. Initial studies in tumor cells identify 41 molecular assemblies that integrate alterations in hundreds of genes for accurate drug response prediction. These cover roles in transcription, repair, cell-cycle checkpoints, and growth signaling, of which 30 are shown by loss-of-function genetic screens to regulate drug sensitivity or replication restart. The model translates to cisplatin-treated cervical cancer patients, highlighting an RTK–JAK–STAT assembly governing resistance. This study defines a compendium of mechanisms by which mutations affect therapeutic responses, with implications for precision medicine.

Other Authors: Xiaoyu Zhao, Trey Ideker

55- Tumor-draining lymph nodes are sites of continued melanoma evolution

Rob Stagnitta, NYU Langone , (MetNet)

Lymph node (LN) metastasis is an early indicator of aggressive disease in melanoma, where lymphatic spread may both provide a mechanism for ongoing tumor evolution and dissemination and suppress systemic immune responses. Despite emerging interest in the role of LN metastasis on systemic tumor progression, the tumor-intrinsic and -extrinsic parameters that determine risk for regional spread remain largely unknown. To begin to fill this knowledge gap, we paired spatial transcriptomics (10x Visium) with highly multiplexed immunohistochemistry (Akoya Phenocycler) on archived human biospecimens to deconvolve the heterogenous genomic, transcriptomic, and microenvironmental programs associated with tumor evolution in the lymphatic basin. We hypothesized melanoma cells and the immune microenvironment co-evolve to form LN metastases. We established a cohort of formalin-fixed paraffin-embedded (FFPE) sections of primary cutaneous melanomas (n=5) and paired tumor-draining, metastatic LNs (n=7) from five stage III patients collected upon initial diagnosis. We inferred their gene copy number variations from transcriptomic data to establish clonal lineage trees and maps of genetic clones in patient-matched primary melanoma and LN tissues. These data will allow us to understand better how different genetic clones expand and are selected during metastasis and to order these genetic lesions in time for subsequent transcriptional and phenotypic analyses. Our data reveal that LN metastases have an increased clonal heterogeneity relative to their paired primary tumors, indicating that the LN could serve as a site for continued genomic evolution. Our data also suggests that LN colonization enables continued tumor evolution, possibly leading to immunological adaptation and systemic disease progression.

Other Authors: Katherine S. Ventre1, Robert Stagnitta1, Tara Muijlwijk1, Shi Qiu1, Iman Osman1,2, Markus Schober1,2, Amanda W. Lund1,2 1Ronald O. Perelman Department of Dermatology, 2Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York, NY

56- Linking Transcriptional State to Metastatic Fate in Melanoma using the LARRY Barcoding System

Rachel Tan, New York University, (MetNet)

Metastasis is responsible for approximately 90% of cancer deaths and significantly impacts patient outcomes. Melanoma is a prime example of a highly metastatic tumor. Melanoma features transcriptionally heterogeneous cell states, but their role during melanoma progression and metastasis has not been well defined. By coupling cell barcoding technology with scRNA-seq, transcriptional cell states can be linked to metastatic dissemination. A human melanoma short-term culture (12-273BM) was infected with lentivirus carrying pLARRY-GFP, labelling each cell with one of approximately 240,000 unique barcodes. pLARRY-GFP-12-273BM cells were injected intradermally in NSG mice. After 7-10 weeks, the intradermal tumors were surgically resected to allow time for further metastasis development. Melanoma cells from both primary tumors and their metastases were subjected to scRNA-seq. 8 flank tumors were sequenced, of which 5 resulted in visible macrometastases and 3 did not. In the 5 tumors with macrometastases, the metastasizing clone (i.e., present in metastases) was not always the most dominant clone in the primary tumor, suggesting additional selection mechanisms during metastasis. scRNA-seq profiling revealed intratumoral heterogeneity, with cells exhibiting multiple transcriptional states, from melanocytic to neural-crest-like. Notably, we identified a cluster overrepresented in metastatic tumors , enriched in pathways related to extracellular matrix organization. Using InferCNV, we observed a selection of clones carrying specific copy number variations during metastasis. By further analysis and functional studies of these cell states, we aim to further refine their association with metastatic potential. Overall, by uncovering novel determinants of melanoma dissemination, we hope to advance novel therapeutic strategies against metastasis.

Other Authors: Alcida Karz, Maria A. Gomez Munoz, Pietro Berico, Eva Hernando

57- Mapping protein-protein interaction remodeling following chemotherapy in triple-negative breast cancer.

Richa Tiwari, UCSF, (CSBC)

In precision medicine, machine learning models are often “black boxes,” predicting phenotypes from genotypes without knowing the underlying mechanisms. To arm genotype-phenotype models with the insights of associated molecular mechanisms, the current study involves the generation of comprehensive maps of cellular structure/function in triple-negative breast cancer (TNBC) context in response to chemotherapy. Leveraging endogenous tagging (endo-tag) based APMS and SEC coupled mass spectrometry (SEC-MS), we aim to generate differential protein-protein interaction maps focusing on approximately 100 most frequently altered cancer-associated genes, spanning various chromatin modifier classes. Endo-tag-APMS offers high-resolution PPI network characterization, while SEC-MS provides a global view of interactome perturbations. Our SEC-MS data for the MDA-MB-468 cell line provides unique insights into the dynamics of chromatin remodeling complexes in response to paclitaxel or vorinostat treatment, impacting 50% of the proteins of interest. Concurrently, our initial endo-tag APMS-based differential PPI mapping unveils significant rewiring of interactome upon drug treatment for specific targets including HDAC3, HDAC2, BRPF1, USP7, etc. Importantly, comparative analysis indicates that SEC-MS-identified complexes could be recapitulated by endo-tag APMS, especially regarding high-confidence interactors. Ultimately, integrating large-scale physical interactomics data with high-throughput imaging and genetic perturbation data aims to facilitate the development of interpretable machine learning models tailored for predicting therapeutic responses in cancer.

Other Authors: Richa Tiwari, Joonwon Kim, Atoshi Banerjee, Gwendolyn Jang, Monita Muralidharan, Helene Foussard, Anjan Venkatesh, Alejandro Chavez, Antoine Forget and Nevan Krogan.

58- Extraction and whole transcriptome sequencing of cell free RNA from human cerebrospinal fluid

Maxine Umeh Garcia, Stanford University, (MetNet)

Cerebrospinal fluid (CSF) – which circulates the central nervous system, significantly interfacing with brain tumor tissues and carrying tumor cell-free nucleic acids — provides a critically understudied biospecimen to understand brain metastases (BrM) and leptomeningeal disease (LMD) biology. We have banked, and have ready-access to, 300+ CSF samples from patients with BrMs and LMD, many of whom were treated at Stanford Hospital (2012 – 2024). We then leveraged a combination of total RNA extraction and whole transcriptome RNA sequencing to interrogate gene expression patterns present in CSF cell-free (cf) RNA – a method we term CSF-Seq. CSF-Seq has revealed expression profiles in LMD CSF not found in CSF of non-LMD cancers (gliomas) and non-cancer controls (hydrocephalous). These results serve as a starting point for identifying key gene expression signatures associated with development of LMD and BrM progression.

Other Authors: Pablo Perez1, Rukayat Taiwo1, Giuseppe Barisano1, Daniel Herrick1, Thy Trinh1, Seunghyun Lee1, Elias Spiliotopoulos2, Vaibhavi Shah1, Thuy Ngo2, Melanie Hayden Gephart1 1Department of Neurosurgery, School of Medicine, Stanford University, Stanford CA 2Knight Cancer Institute, Oregon Health and Science University, Portland, OR

59- Deciphering the mechanism of immune evasion by dormant early disseminated breast cancer cells

Luis Valencia Salazar, Albert Einstein College of Medicine, (MetNet)

Metastases cause most cancer-related deaths in solid cancers. Early disseminated cancer cells (eDCCs) can remain dormant for decades before forming lethal metastases. Current immunotherapies, aimed at preventing macro-metastasis growth, are partially effective but we hypothesize that they may fail to target dormant eDCCs. Using an MMTV-HER2 breast cancer mouse model, we compared immune infiltration in lungs, spleen, and blood of mice at early lesion (EL) and primary tumor (PT) stages to wild-type (WT) mice. We found increased immune infiltration and CD8+ T cells in EL mice lungs, with antigen-exposed CD8+ T cells elevated in all profiled organs of EL and PT mice. However, exhausted CD8+ T cells increased only in the lungs of EL and PT mice compared to WT mice. CD8+ T cells are inactivated by the lung microenvironment before primary tumor establishment, explaining why their depletion didn't awaken dormant eDCCs. PD-L1 blockade also didn't affect eDCC frequency. Profiling revealed eDCCs secrete immune-suppressive molecules like TGFb1/2, IFNb and Galectin-1 (Gal1), possibly explaining CD8+ T cell ineffectiveness. Interestingly, depleting NK cells enhanced metastatic outgrowth, while depleting Gal1 suppressed metastasis development and reduced Ki67+ eDCCs without eradicating them. This suggests NK cells, not CD8+ T cells, may affect eDCC dormancy, and Gal1 might shield eDCCs from immune detection. Our findings emphasize the need to understand how dormant eDCCs are recognized by the immune system to design new metastasis prevention strategies.

Other Authors: Wei Zheng, Erik Guillen, Deisy Segura-Villalobos, Gregoire Lauvau , Julio A. Aguirre-Ghiso

60- pyVIPER: Unleashing Scalable and Rapid Insights from Single-Cell RNASeq Data for Cancer Research

Alessandro Vasciaveo, Columbia Univ DSB, (CSBC)

Single-cell sequencing has transformed cancer research by enabling the study of tumor heterogeneity and the complex interactions within the tumor microenvironment at an unprecedented resolution. However, the low signal-to-noise ratio in single-cell RNA sequencing (scRNASeq) poses challenges for quantitatively deciphering key cancer processes. We have demonstrated that gene regulatory network (GRN) analysis can address this by identifying Master Regulator (MR) proteins, using the VIPER algorithm, to pinpoint critical drivers of cancer and its microenvironment. As scRNASeq datasets from patient-derived tumors grow in size and complexity, there is a pressing need for scalable tools capable of analyzing datasets with hundreds of thousands of cells. To meet this need, we introduce pyVIPER, a highly efficient and scalable Python toolkit designed to assess MR activity in large-scale scRNASeq datasets. pyVIPER facilitates tumor and microenvironment analysis by supporting multiple enrichment algorithms, advanced data transformations, and a novel data structure for GRN manipulation. With seamless integration into publicly available state-of-art scRNASeq tools, as well as machine learning libraries, pyVIPER provides the ideal platform for analyzing tumor-derived samples. Benchmarking shows that pyVIPER reduces runtime from hours to minutes for large datasets, compared to previous tools, enabling a fast and comprehensive analysis of cancer and tumor microenvironment dynamics at single-cell resolution. This advancement makes VIPER-based analysis accessible for virtually any large-scale single-cell dataset from patient tumors, offering new insights into cancer biology and therapeutic targets.

Other Authors: Alexander L.E. Wang+, Zizhao Lin+, Luca Zanella+, Lukas Vlahos, Miquel Anglada Girotto, Aziz Zafar, Heeju Noh, Andrea Califano* and Alessandro Vasciaveo*

61- Spatiotemporal mapping of melanoma-draining lymph nodes reveals a protective innate alarm system ahead of metastasis

Katherine Ventre , NYU , (MetNet)

Despite increasing data that lymph node (LN) metastasis is immunosuppressive, local LN tumor-immune interactions remain poorly understood. Here, we map the immunological impact of metastasis on human and mouse LN. We performed high-dimensional (40-plex) immunofluorescence and Visium 10X spatial transcriptomics (ST) on adjacent sections from sentinel LNs of 12 patients with stage III cutaneous melanoma. Imaging captured lesions from single cells to macrometastases. We hypothesized these represent independent metastatic events, supported by InferCNV analysis; we therefore used lesion size and clonal projections to model a metastatic trajectory. Individual melanoma cells localized to the subcapsular sinus (SCS) and clustered with macrophages, IL-33-producing lymphatic endothelial cells (LECs), and mast cells. Larger lesions did not associate with this network. Multimodal integration analysis of paired real-time longitudinal scRNAseq and ST of tumor-draining LN from an inducible mouse melanoma model (TyrCreERT2;BrafV600E;Pten-/-) indicated that the innate leukocyte composition of the SCS shifts before metastasis. Mast cell quantity and LEC Il33 expression both peaked in the pre-metastatic niche. NicheNet analysis on predicted SCS populations revealed significant Tnf signaling in the pre-metastatic niche but not after macrometastasis. Mast cells are the primary producer of Tnf in our data, and their Tnf expression peaks pre-metastasis. We therefore propose a model by which lymphatic drainage from the primary tumor induces SCS LEC IL-33 production before metastasis. IL-33 induces mast cell TNF production, coordinating a type I inflammatory response that attempts to protect against metastasis. Reinforcement of this network may minimize metastasis and preserve the anti-tumor immune function of the LN.

Other Authors: KatherinRob Stagnatti, Itai Yanai, Iman Osman, Markus Schober, Amanda W Lund

62- Deciphering Genetic Markers of BRAF Inhibitor Sensitivity and Resistance

Patrick Wall, UC San Diego, (CSBC)

Precision BRAF inhibitors have been transformative in treatmenting of tumors with activating BRAF mutations. However, responses to these inhibitors are shaped by additional mutations across the tumor genome whose effects are generally unknown. To decipher these effects, we developed interpretable deep neural networks that evaluate mutations in 702 cancer-related genes to predict responses of 1,244 cancer cell lines to six targeted BRAF inhibitors. Models inspect each mutation to determine whether it is a driver of sensitivity, resistance, or is neutral. By integrating systems biology information, our models reveal how mutations interact to alter broader molecular pathways. We validated detailed genetic mechanisms for dabrafenib, which selectively binds BRAF with activating V600 point mutations. Dabrafenib models identified functional differences in BRAF mutations at key structural residues modulating drug efficacy. These models extended to identifying molecular indicators of melanoma patients treated with BRAF inhibitors. Beyond the drug target, models recognized activating vs. inactivating mutations in BCL2, a key cell death regulator, induce divergent molecular pathways that render cells sensitive or resistant to kinase inhibitors independent of BRAF mutation status. These predicted BCL2 biomarkers translate to lymphoma patients treated with rituximab. These findings provide a mechanistic framework for discovering somatic mutation biomarkers of drug sensitivity and resistance, which refine and expand the molecular indications in personalized cancer treatment.

Other Authors: Trey Ideker

63- Targeting Brain Metastases with iPSC-based Vaccination

Lin Wang, Stanford University, (MetNet)

It is estimated that between 10% and 30% of cancer patients will develop brain metastases (BMs) during their disease's progression1. Annually, up to 300,000 people in the U.S. alone are diagnosed with this condition2. Presently, prevention and treatment strategies for BMs are far from satisfactory. Pluripotent stem cells, or iPSCs, have been shown to exhibit surface markers similar to those on many types of cancer cells3-5. A previous study demonstrated that mice vaccinated with their own irradiated iPSCs plus a Toll-like receptor 9 (TLR-9)-based adjuvant named CpG, four weeks before the tumor implantation, displayed significant tumor regression and survival prolongation at the experiment's conclusion in comparison to mice in the placebo group6. These results suggest that an iPSC-based vaccine could have significant potential in preventing various types of cancer malignancies. Notably, immune checkpoint inhibitors that target programmed cell death protein 1 (PD-1) have been shown to modulate the immune response, even in patients with BMs7,8. Accordingly, we hypothesize that an iPSC-based vaccine presenting multiple Tumor Specific Antigens (TSAs) or Tumor Associated Antigens (TAAs) to the immune system, when combined with a PD-1 inhibitor, could restore T cell activity, thereby stimulating a robust anti-tumor response against BMs.

Other Authors: Yang Zhou, Renesmee Kuo, Rohit Verma, Joseph C Wu

64- Systems biology analysis identifies the synthetic lethality between p80 and VHL in clear cell renal cell carcinoma

Zhen Xie, St Jude Children's Research Hospital, (Both)

Patients with advanced clear cell renal cell carcinoma (ccRCC) exhibited poor survival outcomes. Inactivation of the von Hippel Lindau (VHL) tumor suppressor gene appears in over 80% of ccRCC tumors, presenting a potential vulnerability for alternative ccRCC treatments. Our study aims to leverage VHL synthetic lethality (SL), where perturbing VHL and its SL gene simultaneously render cell death, to eliminate tumor cells in VHL-deficient patients. To identify VHL SL, we analyzed a comprehensive cohort with 274 RNA-Seq profiles from 97 ccRCC patients. To reconcile the discrepancy between the measured RNA expression and the protein activity, the latter being more relevant to physiological processes, we employed a systems biology framework to reverse-engineer a ccRCC-specific gene regulatory network from TCGA-KIRC, obtaining high-fidelity regulons for 5724 TFs and 20764 signaling proteins at isoform level. This permits accurate protein activities inference with NetBID2 (data-driven network-based Bayesian inference of drivers). We identified p80, an understudied enzyme with significantly elevated activity in VHL-deficient ccRCC, suggesting potential SL relation with VHL that was validated in ccRCC cell lines and mouse models. Knockout p80 presented a tumor suppression effect in VHL-deficient ccRCC, which was subsequently reversed by re-introducing VHL. Mechanistically, integration analysis of transcriptomic and proteomics profiles indicated that p80 knockdown reconfigured mitochondrial homeostasis in VHL-deficient ccRCC, coinciding with AP-MS based p80 interactome enriched with energy metabolism associated proteins. In summary, we report p80, a mitochondrial regulator, as a synthetic lethal gene of VHL and opening a novel avenue for the development of next-generation therapeutics for ccRCC.

Other Authors: Cheng Zhang, Shanshan Yu, Qingfei Pan, Qing Zhang, Jiyang Yu

65- Tumor classification and deconvolution in liquid biopsies using FLEXseq

Jingru Yu, Stanford University, (MetNet)

Methylome profiling is an emerging clinical tool for tumor classification and liquid biopsies. Here, we developed FLEXseq (Fragment Ligation EXclusive methylation sequencing), a genome-wide methylation profiler that enriches and sequences the adjacent flanks of CCGG motifs in DNA fragments. FLEXseq strongly correlates (Pearson's r = 0.97) with whole genome bisulfite sequencing (WGBS) while enriching 18-fold. To demonstrate the broad applicability of FLEXseq, we analyzed 244 specimens across cells, body fluids, plasma, and formalin-fixed paraffin-embedded (FFPE) tissues. We conducted tumor classifications based on machine learning and cell type deconvolution while leveraging their disparate reference datasets. In a case-control study of 106 cerebrospinal fluids (CSF), the overall classification accuracy was 98%. All classified CSF cases of lung adenocarcinoma (n = 20), B cell lymphomas (n = 20), and breast carcinoma (n = 5) were accurate. FLEXseq offers a cost-efficient, base-pair resolution methylome with the potential to be a diagnostic tool for tissue and liquid biopsies.

Other Authors: Lauren S. Ahmann, Yvette Y. Yao, Angus Toland, Alicia Snowden, Chandler Ho, Netanel Loyfer, Tommy Kaplan, Hannes Vogel, Linlin Wang, Brooke Howitt, Brittany Holmes, Alarice C. Lowe, Wei Gu

66- Spatial community deconvolution of transcriptomic expression profiles via integration of single-cell RNA sequencing and multiplexed imaging with MONTAGE

Weiruo Zhang, Stanford University, (CSBC)

Single-cell omics profiling modalities, such as RNA sequencing and in-situ multiplexd imaging, offer high-resolution molecule profiling of the tissue but each has limitations. While single-cell RNA sequencing (scRNA-seq), quantifies the whole transcriptome, it could introduce cell compositional bias through cell dissociation. In-situ multiplexed imaging retains spatial context without cell dissociation but limited to a number of markers. We present a machine learning framework, MONTAGE, integrating these two omics data to build a spatial-signature matrix that encodes spatial properties through gene expressions. The MONTAGE signature matrix enables transferring spatial properties obtained from a small cohort of single-cell data to vast amount of public bulk transcriptomic expressions or sequencing-based spatial transcriptomics through deconvolution. MONTAGE thus allows associating the transferred spatial properties with clinical information of public data for disease studies. We applied MONTAGE on a study cohort of 10 whole-slide head and neck cancer (HNSCC) samples with scRNA-seq and in-situ multiplexed immunofluorescence imaging to build a spatial-signature matrix capturing spatial communities with cell-type colocalizations. After transferring the spatial communities to large-scale public datasets, we identified and validated a spatial community with fibroblast-macrophage colocalization significantly associated with HNSCC progression.

Other Authors: Weiruo Zhang, Zinaida Good, Serena Chang, Marc A. Baertsch, Saumyaa Saumyaa, Nikolay Samusik, Rachel Hildebrand, Christina S. Kong, Quynh-Thu Le, Andrew J. Gentles, John B. Sunwoo, Garry P. Nolan, Edgar G. Engleman, Sylvia K. Plevritis