Abstract A007: Integrated molecular and functional characterization of the intrinsic apoptotic machinery identifies therapeutic vulnerabilities in glioma

Introduction: The tumor immune microenvironment (TME) is a strong prognostic factor in breast cancer with higher proportions of tumor infiltrating lymphocytes associated with longer survival. Features of the breast TME vary by race/ethnicity and are linked to disparities in outcomes. In 2021, we performed the first comprehensive investigation of the effect of germline variants on immune traits associated with survival. Leveraging pan-cancer data from The Cancer Genome Atlas (TCGA), we identified 33 heritable immune traits and >1500 SNPs associated with the TME. We hypothesize that racial/ethnic disparities in cancer outcomes may be partially driven by germline SNPs that differentially mediate the TME. Here, we assess the differential expression of heritable immune traits and prevalence of immune-associated germline SNPs across genetic ancestry groups in TCGA breast cancer cohort. Lastly, we assess whether these heritable TME features alone are robust predictors of genetic ancestry. Methods: Utilizing expression-based signatures of immune traits and germline SNP data in TCGA breast cancers (n=986), we investigated the distribution of heritable immune traits and immune-associated germline variants across genetic ancestry groups (European EUR=762, African AFR=163, Asian ASN=45, Amerindian AMR n=16). Differential expression of heritable traits across genetic ancestries were assessed using Kruskal-Wallis test. Proportions of genotypes across genetic ancestries were compared using multiple pair-wise Fisher’s exact tests. P-values were adjusted for multiple testing, and significance defined at adj. p<0.05. Multi-class prediction of EUR, AFR and ASN genetic ancestry was performed using random forest machine learning models. Cross-validation was performed in 75% of samples and 25% used for independent validation. Immune traits and/or germline SNPs were used as features and model performance was evaluated using mean balanced accuracy (Acc). Results: Expression of 33% of heritable immune traits varied by genetic ancestry in breast cancer (adj. p<0.05). Enrichment of eosinophils, natural killer, Th17 and T central memory cells were lower while expression of Interferon, PD-1, and antigen-presentation signatures were higher in AFR vs. EUR. Enrichment of Th2 cells was higher in ASN vs. EUR, while cytotoxic cells was higher in AMR vs. EUR. Moreover, 31% of immune-associated SNPs had significant differences in genotype proportions between at least two ancestry groups. Prevalence of 454 SNPs varied between AFR and EUR and included SNPs associated with CD8 T cells and Interferon signaling. Immune-associated germline SNPs (Acc=0.96) and immune traits+germline SNPs (Acc=0.99) were robust predictors of EUR, AFR and ASN ancestry. Conclusion: Prevalence of heritable components of the TME varied across genetic ancestries. By identifying which features of the TME that drive treatment responses are mediated by heritable germline factors, we can further elucidate mechanisms that underlie disparities in treatment outcomes. Further validation of these findings is underway. Citation Format: Rosalyn W Sayaman, Denise M Wolf, Christina Yau, Vesteinn Thorsson, Mohamad Saad, Donglei Hu, Scott Huntsman, Davide Bedognetti, Michael J Campbell, Jennifer Rosenbluth, Elad Ziv. Differential prevalence of immune-associated germline variants across genetic ancestry groups may partially underlie racial and ethnic disparities in breast cancer outcomes [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B042.

Functional precision medicine (FPM) integrates patient-derived preclinical models, molecular profiling, and drug sensitivity testing to advance personalized cancer treatments. This approach uses live patient tumor samples to conduct real-time functional assays, exposing them to a range of therapeutic agents to determine which treatments may be most effective. FPM enables a more tailored and potentially effective treatment strategy, particularly for complex cancers such as invasive breast carcinoma, which encompasses a spectrum of molecularly and clinically diverse diseases. Some subtypes, such as triple-negative breast cancer, metastatic or recurrent disease, and rare histologies, pose substantial treatment challenges. To address these complexities, we previously developed a resource of 37 hard-to-treat breast cancer patient-derived xenografts (PDXs) (Savage et al. 2020 - PMID: 32546838), demonstrating that PDXs preserve the molecular heterogeneity and multi-drug chemoresistance of the primary tumors. Expanding on this work, we have developed 35 new PDX models, including four derived from metaplastic breast cancers (MpBC), a rare and aggressive subtype characterized by minimal response to standard chemotherapy. We evaluated the molecular landscape of PDX and patient tumors, leveraging comprehensive omics data from this dataset. For each model, we evaluated potential vulnerabilities and the pattern of genomic alteration and expression profiles. Spatial transcriptomics of MpBC cases in our cohort was also evaluated (Visium - 10X technology) and we observed that the distinct transcriptomic populations overlap with the spatially defined histological features. To make our data accessible to the scientific community, we build a repository of rare breast cancer models, complete with detailed clinical, molecular, and pathological annotations, in addition to drug sensitivity profiles that may predict patient responses. By combining live biobanking and advanced profiling techniques, we identified tumor vulnerabilities, enabling the prioritization of drugs targeting these specific weaknesses. This multi-faceted approach represents a promising path to improving patient outcomes in difficult-to-treat breast cancer subtypes, helping to tailor preclinical testing and develop more effective therapies. Citation Format: Hellen Kuasne, Anne Marie Fortier, Sandrine Busque, Paul Savage, Constanza Martinez Ramirez, Anie Monast, Nebras Koudieh, Atilla Omeroglu, Lara Richer, Jamil Asselah, Nathaniel Bouganim, Francine Tremblay, Ari Nareg Meguerditchian, Sarkis Meterissian, Morag Park. Functional precision medicine in challenging breast cancer subtypes: Developing preclinical models for tailored therapy in complex and rare histologies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B028.

Sensitivity to therapeutic agents is impacted both by genetic heterogeneity between patients or among clones within a tumor and by heterogeneity of epigenetic cell states. Prior work demonstrates that human pancreatic tumors comprise malignant cells in a mixture of multiple cell states with distinct genetic dependencies, leading to a reservoir of chemoresistance across the tumor; targeting just one or two malignant cell states is insufficient to achieve durable therapeutic responses. One approach to overcome this challenge is to identify agents with efficacy against malignant cells in multiple cell states. However, in the absence of genetic distinctions between cell states, identifying therapeutic vulnerabilities for each malignant cell state present in a patient’s tumor in real time remains a challenge. OncoTreat is a CLIA certified algorithm that matches drugs to patients based on tumor gene expression profiles. The approach utilizes regulatory network analysis, a systems biology framework that transforms gene expression data into regulatory protein activity profiles, in a manner that facilitates the identification of master transcriptional regulators of cell state. We carried out a Phase 1 clinical trial of the OncoTreat framework to examine feasibility for implementing RNA-based precision medicine in patients with metastatic pancreatic ductal adenocarcinoma (2nd line and beyond). Patient-derived xenografts were established using a protocol for generating tumor slices from subjects prior to 1st line therapy, and selected agents matched by OncoTreat were assessed co-clinically while subjects received standard of care regimens, with the potential for subsequent treatment with successful agents upon progression. Two subjects in the study matched to selenexor, an XPO1 inhibitor that is FDA approved for multiple myeloma. Co-clinical studies of selinexor in patient-derived xenograft models generated from the matched subjects showed prolonged survival compared to control regimens. Single-cell RNA sequencing analysis of these tumors found that the activity of master regulators confirmed that the activity of selinexor-responsive master regulators was inverted in response to treatment in vivo, reducing the fraction of malignant cells in the tumors. Strikingly, selenexor treatment was associated with significant inhibition of RAS/MAPK signaling, suggesting a potential novel role for selinexor targeting Ras. Together, these data indicate a potential therapeutic vulnerability for a subset of PDAC patients that can be predicted by a CLIA-certified RNA-based precision medicine platform. Citation Format: Alvaro Curiel Garcia, Lorenzo Tomassoni, Tanner C. Dalton, Amanda R. Decker-Farrell, Carmine F. Palermo, Daniel R. Ross, Stephen A. Sastra, Urszula N. Wasko, Isabel M. Goncalves, Prabhot Mundi, Basil S. Bakir, Rachael A. Safyan, Hanina Hibshoosh, Gulam A. Manji, Andrea Califano. RNA-based precision medicine predicts sensitivity to selinexor in select pancreatic ductal adenocarcinoma patients [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr IA016.

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 145 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, differential abundance analyses identified significant changes in immune and stromal cell type composition 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 comorbidities in BC. Citation Format: Andrew D Chen, Lina Kroehling, Christina Ennis, Gerald Denis, Stefano Monti. Highly-resolved integrated single cell atlas of human breast cancer enables hypothesis-driven analysis of tumor heterogeneity [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr A029.

Background: Gastrointestinal (GI) cancer accounts for over one-third of cancer-related deaths, yet early detection remains challenging. Micronuclei (MN) are cytoplasm-resident subcellular structures containing damaged chromosome segments, serving as a hallmark of genomic instability. Although elevated MN frequency in hematopoietic cells has been observed in patients with solid tumors, their role in disease progression remains elusive. We developed a technique (WO2021/228246 A1) that enables the purificationand characterization of MN DNA (MN-DNA) from erythrocytes in peripheral blood, unveiling the distinct genomic profiles in MN-DNA from GI cancer patients compared to healthy donors (HDs). Specific genomic regions within MN-DNA exhibited significant changes in read counts in GI cancer patients, which we termed as tumor-associated MN-DNA (taMN-DNA) features. This study evaluates the potential of taMN-DNA features for the early detection of GI cancer, including colorectal (CC), gastric (GC) and esophageal (EC) cancer, while exploring the impact of early-stage solid tumors on genomic instability in hematopoietic cells. Methods: Peripheral blood (1–2 mL) was collected from 1753 participants, including 987 HDs and 766 GI cancer patients (CC: 420, GC: 282, EC: 64). Participants were randomly divided into training, validation and independent test cohorts in a 7:2:1 ratio, maintaining similar cancer types, gender and age distribution. MN-DNA was extracted from erythrocytes, sequenced, and analyzed to identify taMN-DNA features. Machine learning algorithms were applied using these features for cancer detection. Results: In this study, over half the cancer cohort were diagnosed at early-stage: TNM stage 0-I accounted for 40.57%, stage II for 23.32%, stage III for 29.25% and stage IV for 6.87%. The pan-cancer detection model achieved an overall sensitivity of 88.2% across various cancer types, with a specificity of 95.4%. It accurately identified the tumor tissue of origin with an overall accuracy of 91.9% at 95.4% specificity, including sensitivities of 90.8% for CC, 91.7% for GC, and 100% for EC. Notably, for early-stage cancers (TNM stage 0-II), the model demonstrated exceptional performance, with sensitivities of 97.4% for CC, 94.7% for GC, and 100% for EC, at 95.4% specificity. Furthermore, our findings suggest that the significant increase of DNA damage in hematopoietic cells during tumor progression, contributing to the formation of taMN-DNA features. Conclusions: This pilot study demonstrates the potential of taMN-DNA features as a functional biomarker for early detection of GI cancer. The abundance of erythrocytes in peripheral blood provides a non-invasive, accessible source for MN-DNA enrichment. Our study also reveals a remote regulation of genomic instability in bone marrow hematopoietic cells by early-stage solid tumors, offering new insights into the development of effective GI cancer screening tests, precision diagnostics, and therapeutic strategies. Research sponsor: Timing Biotech. Citation Format: Haobo Sun, Xingyun Yao, Yuehua Han, Yurong Jiao, Xiangxing Kong, Chengcheng Liu, Qingqu Guo, Fei Meng, Honghao Liang, Xiuqin Fan, Jun Li, Xiaofei Gao. Micronuclei DNA enables early detection of gastrointestinal cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr A018.

Background: Hepatocellular Carcinoma (HCC) is a fatal malignancy which carries variable responses to standard treatments. Recent advances in genetic research have provided a clearer image of some mutations driving HCC development, which gave an opportunity for targeted therapies like (Wnt/B-catenin) pathway inhibitors to intervene. CTNNB1 (B-catenin) gene mutation, which is present in 20-40% of HCC cases, was found to affect behavior of the tumor and its response to treatment and was linked with a better overall prognosis and may be a therapeutic target for HCC treatment. We aim to further investigate the impact of CTNNB1 mutations on treatment response and overall survival (OS) in patients with HCC. Methods: We used cBioPortal for Cancer Genomics server to gather data of 2453 samples from 5 studies conducted between 2018 and 2024. The data were used to analyze the prevalence of CTNNB1 mutation in HCC patients, its impact on survival rates, and how it compares to other common mutations like TP53. Survival outcomes for HCC patients who received immunotherapy as part of their treatment regimen were compared between CTNNB1 and TP53 mutations. Kaplan-Meier survival curves were generated for median OS analysis and compared by using log-rank test. All statistical comparisons were considered significant if p < 0.05. Results: Our study cohort included patients with HCC who received immunotherapy as part of their treatment. Males constituted 64.2% of the study population, while the remainder were females. More than half of our study subjects were white constituting about 55.7% of our cohort. The prevalence of TP53 and CCTNNB1 mutations was 540 and 174, respectively. Patients with TP53 mutation had a median OS of 25.15 months (95% CI: 22.61 - 28.15) compared to 39.78 months (95% CI: 32.56 - 53.33) among CTNNB1 patients. The difference was statistically significant (p=0.00314). Conclusion: Our study suggests that CTNNB1 mutations in HCC are linked to a better median OS compared to TP53 mutations, which aligns with current literature data. This also suggests that therapies targeting the Wnt/B-catenin pathway may represent promising therapeutic outcomes for HCC patients. However, further research is crucial to validate these findings and assess the clinical implications of CTNNB1 mutation status to provide personalized treatment plans and ultimately improve patient care. Citation Format: Bayan Khasawneh, Saifudeen Abdelrahim, Abdullah Esamil, Ebtesam Al-Najjar, Maen Abdelrahim. CTNNB1 mutations in hepatocellular carcinoma: Prognostic significance and superior survival outcomes in patients treated with immunotherapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr A037.

Timely and precise identification of effective treatments is crucial for improving patient outcomes and reducing healthcare costs, particularly in cancer care. This challenge is amplified in rare, recurrent, and hard-to-treat cancers due to their genetic complexity and tumor heterogeneity. Dr. Gujral will discuss his lab’s innovative approach to revolutionize cancer treatment through the development of a clinically integrated, data-driven platform for predicting drug responses. By analyzing patient biopsy samples collected within hours of surgery and applying advanced artificial intelligence algorithms, this platform rapidly screens thousands of potential therapies, delivering actionable insights in under a week. The goal is to bring this system into everyday medical care, making cancer treatment faster, more effective, and more affordable for everyone. Citation Format: Taran Gujral. Harnessing patient biopsies and AI for rapid, personalized cancer therapy discovery [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr IA030.

Cell surface proteins offer significant cancer therapeutic potential attributable to their accessible membrane localization and central role in cellular signaling. Despite this, their promise remains largely untapped due to the technical challenges inherent to profiling cell surface proteins. Here, we employed N-glycoproteomics to analyze 85 patient-derived xenografts (PDX), constructing Glyco PDXplorer – an in vivo pan-cancer atlas of cancer-derived cell surface proteins. We developed a target discovery pipeline to prioritize proteins with favorable expression profiles for immunotherapeutic targeting and validated FAT2 as a head and neck squamous cancer (HNSC) enriched surface protein with limited expression in normal tissue. Functional studies revealed that FAT2 is essential for HNSC growth and adhesion through regulation of surface architecture and integrin-PI3K signaling. Chimeric antigen receptor (CAR) T cells targeting FAT2 demonstrated potent anti-tumor activity in HNSC models. This work lays the foundation for developing FAT2-targeted therapies and represents a pivotal resource to inform therapeutic target discovery for multiple cancers. Citation Format: Thomas Kislinger. Pan-cancer N-glycoproteomic atlas of patient-derived xenografts uncovers FAT2 as a therapeutic target for head and neck cancers [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr IA026.

The evasion of antitumor immunity is a defining characteristic of cancer. STING, a proposed innate immune signaling adapter, plays a crucial role in eliciting antitumor immunity by integrating innate detection and adaptive immune surveillance. STING functions as an intrinsic tumor suppressor but is often downregulated in many human cancer types, and the mechanism by which STING exhibits intrinsic antitumor functions remains unclear. Here, we find that high STING impairs tumor cells growth by inhibiting both aerobic glycolysis and oxidative phosphorylation (OXPHOS) independently of its innate immune action. Knockout of STING in DLBCL cell lines leads to increased mitochondria membrane potential (MMP) and ATP generation, and decreased apoptosis. Mechanistically, STING inhibits hexokinase II (HK2) to obstruct its hexokinase function, and inhibits ATP5a which is a catalytic subunit of the mitochondrial ATP synthase complex responsible for the synthesis of ATP.Consequently, STING inhibits HK2 and ATP5a to limit both tumor aerobic glycolysis and OXPHOS, and enhance antitumor immunity in vivo. This work demonstrates that STING acts as a cell-intrinsic metabolic checkpoint that limits aerobic glycolysis and OXPHOS to exert antitumor functions. These findings hold significant implications for the advancement of STING-based treatment strategies to enhance antitumor immunotherapy. Citation Format: Rui Wang. STING is a cell-intrinsic metabolic checkpoint restricting aerobic glycolysis and OXPHOS by targeting HK2 and ATP5a [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B005.

The innate immunity cGAS–STING signaling has been extensively studied to boost lymphocytes infiltration for tumor inhibition/eradication. However, DLBCL cells often cope with, take advantage, and get addicted to chronic STAT3 and NF-κB inflammatory state for survival. Little is known whether this chronic STAT3 and NF-κB inflammatory state in DLBCL is cGAS or STING dependent. In this study, we examined the relationship of cGAS–STING and STAT3, NF-κB in DLBCL. Different from STING low or negative, cGAS is usually highly expressed in many cancer types. Here we discovered a cancer promoting role of cGAS in DLBCL through IL6-STAT3 and NF-κB signaling. Firstly, we showed that cGAS was highly expressed in both ABC and GCB subtypes of DLBCL. Secondly, we discovered that it is cGAS, not STING knockout, that induced cell cycle arrest, decreased proliferation rate and induced apoptosis in DLBC cell lines in vitro. cGAS knockout lead to decreased tumor growth in xenograft tumor models in vivo. Furthermore, data from TCGA show that cGAS was closely correlated with many tumor-promote genes. Pan-cancer analysis reveal that cGAS is overexpressed and predicts poor prognosis in many cancer types. Mechanically, it is cGAS, not STING knockout, that lead to downregulation of STAT3 and NF-κB signaling both in vitro and in vivo. Suppression of cGAS consequently deprives DLBCL from this pro-inflammatory niche for growth, which showed a therapeutic target in DLBCL treatment. Citation Format: Rui Wang, shuling Liu. cGAS promote DLBCL survival through STAT3 and NF-κB signaling [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B004.

T cell exhaustion is a major driver of immune checkpoint blockade (ICB) resistance and clinically effective strategies to prevent or reverse T cell exhaustion to restore ICB sensitivity are lacking. CD38, an ecto-enzyme involved in NAD+ catabolism, is highly expressed in exhausted CD8+ T cells in human melanoma, yet its role in T cell exhaustion remains to be elucidated. Here we show that CD38+CD8+ T cells are enriched during tumor progression and following unsuccessful ICB treatment and are strongly associated with ICB resistance in melanoma. Chronic TCR activation and type I interferon stimulation upregulate CD38 in human T cells, leading to mitochondrial dysfunction and reduced anti-tumor activity. Disrupting CD38 restored cellular NAD+ pools and T cell bioenergetics, leading to improved T cell proliferation, enhanced effector function and elevation in T cells “stemness” markers that are associated with increased response to ICB in human melanoma. Importantly, targeting CD38 in a cohort of patient-derived organotypic tumor spheroid (PDOTS), human living tumor explants, with intact immune compartment, demonstrates that CD38-directed therapies can overcome ICB resistance in clinically resistant human melanoma, an effect that is furthered enhanced by supplementation with NAD+. These results emphasize the need for further preclinical and clinical evaluation of CD38 directed therapies in melanoma and underscore the importance of NAD+ as a vital metabolite to enhance those therapies. Citation Format: Or-Yam Revach, Angelina Cicerchia, Ofir Shorer, Boryana Petrova, Seth Anderson, Joshua Park, Chen Lee, Arnav Mehta, Samuel Wright, Niamh McNamee, Aya Tal-Mason, Giulia Cattaneo, Payal Tiwari, Hongyan Xie, Johanna Sweere, Li-Chun Cheng, Natalia Sigal, Elizabeth Enrico, Marisa Miljkovic, Shane Evans, Ngan Nguyen, Mark Whidden, Ramji Srinivasan, Matthew Spitzer, Yi Sun, Tatyana Sharova, Aleigha Lawless, William Michaud, Martin Rasmussen, Jacy Fang, Claire Palin, Feng Chen, Xinhui Wang, Cristina Ferrone, Donald Lawrence, Ryan Sullivan, David Liu, Uma Sachdeva, Debattama Sen, Keith Flaherty, Robert Manguso, Lloyd Bod, Manolis Kellis, Genevieve Boland, Keren Yizhak, Jiekun Yang, Naama Kanarek, Moshe Sade-Feldman, Nir Hacohen, Russell Jenkins. Overcoming resistance to immunotherapy by targeting CD38 in human tumor explants [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B034.

The need for personalized medicine in oncology is widely accepted but translating this important concept into clinical practice has been challenging. Recognizing the inherent limitations of sequencing base, inference methods for predicting anti-cancer treatment responses, functional assays (e.g., organoids and PDX models) have been developed; however, these approaches also have significant limitations including high cost and time required to establish the models, low “take rates”, and destruction of the native tumor microenvironment (TME). To overcome these challenges, EMPIRI developed a proprietary novel 3D ex vivo tumor culture method from patient samples called E-slices that enables rapid, personalized drug sensitivity testing in a clinically actionable time frame. E-slice is a faithful model of individual cancer patient’s tumor, including the TME. It is a proprietary 3D tissue culture platform that is generated by making thin sections of intact, fresh tumor tissues from each cancer patient. Importantly, the E-slice method has been shown to accurately predict individual patient treatment responses to chemotherapies and targeted therapies in two small retrospective clinical studies. In addition, because E-slices retain tumor-infiltrating immune cells and spatial topography of all cell types in their native TME, E-slices sustain immune cell survival and cellular phenotypes, enabling immunotherapy response measurements and biomarker discoveries ex vivo. Specifically for immunotherapy response measurements, key differentiators of the E-slice platform from other ex vivo systems are: 1) E-slice is generated from fresh patient tumor tissues with tumor-trained and -resident immune cells and not artificially introduced PBMCs used in most other systems; 2) E-slices are cultured serum-free, chemically defined medium that does not artificially activate or suppress the immune system; 3) E-slice has been validated by single-cell RNA-sequencing to maintain immune cells in their native state up to 8 days ex vivo; 4) E-slices can be used to detect secreted protein and metabolic biomarkers in the conditioned media pre- and post-treatment for biomarker discovery; and 5) E-slice can measure human-specific immunotherapy responses ex vivo in 8 days. In summary, we present a novel ex vivo 3D human tumor tissue culture platform that faithfully replicates and retains each patient tumor’s TME and can enhance immunotherapy development pipelines and clinical deployment. Citation Format: Viridiana Leyva-Aranda, Jose Maldonado, Thomas D Gallup, Dave Gallup, Kyuson Yun. A novel functional precision oncology ex vivo culture platform: E-slice with intact TME for immunotherapy and other cancer therapy response measurements from human tumors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B036.

My research program employs a pipeline of functional approaches for analysis of primary cells from patients with acute myeloid leukemia (AML) and other hematologic malignancies. This platform aims to identify the druggable landscape across hematologic malignancies and prioritize novel combination therapies. The pipeline is comprised of gene knockdown (e.g. siRNA, CRISPR/Cas) and small-molecule libraries to interrogate the genes, pathways, cellular processes, and microenvironmental crosstalk that govern cancer cell growth and drug response, and these functional screens are complemented with deep sequencing. Simultaneous application of these functional approaches with omic data has accelerated our understanding of druggable pathways that contribute to neoplasia. One example of this platform has been our Beat AML 1.0 platform in which we have assessed ex vivo drug sensitivity, whole exome sequencing, and RNA-sequencing for a cohort of nearly 1,000 AML patient samples, which provides a functional genomic landscape for this disease. We have also utilized genome-wide CRISPR screens with overlay of key therapeutic agents in cell line models, and coupling of these genome-wide screen data with the large patient sample dataset has illuminated a diversity of determinants of therapeutic response. In this lecture, I will provide an overview of these functional screening platforms, and I will illustrate strategies of data integration that have enabled rapid discovery and translation of new targeted therapy strategies. This will include discussion of novel biomarkers of drug response in AML, including mutational drivers of drug response and strong correlation of AML tumor cell maturation state with response to a diversity of drugs and drug families. Finally, the lecture will provide examples of the way in which these data can be leveraged to identify novel drug combinations with broad activity and to examine the mechanistic underpinnings of improved combination activity relative to single agents. Collectively, this dataset and integrative approach provide a functional genomic landscape of AML with new biomarkers of drug response. Citation Format: Jeffrey W Tyner. Functional omics for novel therapeutic combinations in myeloid malignancies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr IA002.

Purpose of the study: YAP1 is the most frequently amplified oncogene in cervical cancer. The Cancer Genome Atlas Research Network study showed that the YAP1 (11q22) oncogene is amplified in 11% of cervical cancer cases. Our lab recently identified that YAP1 oncogene amplification is associated with a 12-year earlier age of diagnosis, poorer survival, and defies a novel aggressive cervical cancer subtype that is more frequent in minority populations. The present study aims to characterize 19 YAP1 amplified cervical cancers from Guatemala and Venezuela via Oxford Nanopore technology (ONT) whole genome long-read sequencing. Methods: In this study, 380 cervical cancer patients were assessed for YAP1 amplification using a TaqMan assay, with 45 samples identified as positive for YAP1 amplification. These 45 samples were then subjected to whole genome sequencing with 5x coverage using Oxford Nanopore Technologies (ONT) to confirm the amplification status. A subset of 19 YAP1-amplified samples underwent deeper sequencing via ONT-LSK114 library preparation. The HPV integration status in these samples was determined by uploading the sequencing data (fastq_pass files) to the Cancer Genomics Cloud, where breakpoints were manually examined using the Integrated Genome Viewer (IGV). HPV and human DNA segments were further analyzed and identified using bioinformatics tools such as BLAT and BLAST. Results: In our study, we confirmed YAP1 amplification in 24 out of 45 tumor samples using barcode sequencing, and due to limited DNA availability (1 µg), only 19 of these samples underwent deep sequencing with 25-30x coverage. Notably, we observed a higher frequency of HPV integration (11/19) in YAP1-amplified tumors compared to those with episomal HPV status, suggesting a potential link between HPV integration and YAP1 amplification. We noticed that YAP1 amplification is driven by Breakage-Fusion-Bridge (BFB) events, which also lead to the co-amplification of the nearby BIRC2/3 genes, contributing to genomic instability and immune escape. This mechanism was further supported by a similar pattern observed in the TCGA whole genome sequence data, where 27 out of 31 YAP1-amplified cervical tumors also exhibited BFB events. Finally, treatment with the PIK3CA inhibitor Alpelisib resulted in a decrease in YAP1 protein levels in CaSki cells, a cervical cancer cell line with mutated PIK3CA and amplified YAP1 expression. Conclusions: These findings highlight a possible interplay between HPV integration and BFB-driven amplification of YAP1 and its neighboring genes, offering insights into potential therapeutic targets for this novel aggressive subtype of cervical cancer. Citation Format: Sonam Tulsyan, Hong Lou, Yi Xie, Emma Robinson, Danielle Kayembe, Eduardo Gharzouzi, Roberto Orozco, Enrique Alvirez, Michael Dean. Identifying a novel aggressive subtype of cervical cancer predominantly affecting minority populations through long-read whole genome sequencing [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B018.

Heterogeneity of immune checkpoint blockade (ICB) response amongst cancer patients has a profound impact on cancer-related mortality. It is now well understood that immunotherapy efficacy and resistance relate to a wide spectrum of cellular and non-cellular components in the tumor microenvironment (TME). Although the only widely used ICB biomarker, PD-L1, treats TME cells as individual components, recent work on possible biomarkers predictive of ICB response have had success by considering interactions within the TME, such as tumor cell to CD8+ T cell distances. We hypothesized that application of generalized statistical interaction approaches to tumor spatial transcriptomic datasets, which contain genome-wide information at nearly single-cell resolution, might yield novel predictive biomarkers of ICB response through leveraging TME spatial biology. Our approach was (1) to integrate spatial transcriptomics datasets from 96 tumors across twelve tumor types and (2) to apply statistical covariation approaches to identify organizing principles of spatial biology that would facilitate comparison of these datasets. Our work has identified ‘Spatial Groups’ as a conserved unit of pan-tumor spatial organization. Spatial Groups are multi-cellular units demonstrating coordinated transcriptional programs contained within spatial domains ranging in size from tens of microns (dozens of cells) to mm-scale (large regions of a tumor). These domains are hierarchically organized such that large-scale domains exert contextual field effects on nested smaller-scale domains. We found that Spatial Groups revealed immune-related spatial biology that we hypothesized might serve as a genome-wide spatial biomarker of ICB response. To test this idea, we performed a retrospective study of 16 patients with metastatic non-small cell lung cancer treated with ICB or chemotherapy-ICB in the frontline setting. We found that our Spatial Group-derived biomarker, unlike PD-L1 status, was highly significant in predicting progression-free survival after therapy initiation. Additionally, analysis of Spatial Groups in non-responder patients provided insights into patient-specific spatial biology architectures limiting ICB efficacy. In summary, our discovery of Spatial Groups provides a general framework for using spatial biology to identify pan-tumor ICB response. Citation Format: Vivek Behera, Hannah Giba, Ue-Yu Pen, Anna Di Lello, Benjamin A. Doran, Alessandra Esposito, Apameh Pezeshk, Christine M. Bestvina, Justin Kline, Marina C. Garassino, Arjun S. Raman. Spatial groups provide a pan-tumor spatial transcriptomic biomarker of immunotherapy response [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr A038.