Details of cell lines, reagents and oligonucleotides used in the study

Prostate cancer lineage plasticity, characterized by histologic transformation of prostate adenocarcinoma (PRAD) to neuroendocrine prostate cancer (NEPC), is an emerging mechanism of treatment resistance. NEPC accounts for up to 15% of treatment-resistant prostate cancers and is associated with poor prognosis, highlighting an unmet need for new therapies. NEPC lineage reprogramming is primarily driven by epigenetic dysregulation including differential activity of histone and DNA methyltransferases. EZH2, the catalytic component of the Polycomb repressive complex 2 (Polycomb), is overexpressed in most treatment-resistant prostate cancers and is implicated as a driver of disease progression. In this study, we define the differential, lineage-specific action of Polycomb in both PRAD and NEPC subtypes to better understand its role in modulating differentiation and lineage plasticity, and to identify novel targetable drivers of NEPC. Epigenetic H3K27me3 CUT&Tag profiling of treatment-resistant PRAD (n=9) and NEPC (n=9) rapid autopsy clinical samples revealed that Polycomb targets, including NE-lineage transcription factors, are de-repressed in NEPC. Mechanistically, Polycomb modulates H3K4/K27me3 bivalent promoters, leading to the upregulation of NEPC-associated transcriptional drivers (e.g., ASCL1) and neuronal gene programs which facilitate forward differentiation following EZH2 targeting in NEPC patient-derived organoid/xenograft models. Notably, we identified prospero-homeobox 1 (PROX1) as an understudied cell-fate determining transcription factor that is epigenetically de-repression by differential Polycomb activity. Integrative H3K27ac CUT&RUN and Capture Hi-C analyses revealed potential enhancers of PROX1 in NEPC that may be regulated by ASCL1. We sought to functionally characterize the role of PROX1 in NEPC. An unbiased CRISPR screen in two NEPC patient-derived organoid models demonstrated high cellular dependency for PROX1; knockout of PROX1 impeded tumor growth in NEPC models, while overexpression of PROX1 in PRAD promoted tumor growth and spontaneous metastases. Transcriptomic and cistromic analyses across models of CRPC and NEPC pointed to PROX1 regulation of neuroendocrine-lineage transcriptional programs. Immunoprecipitation followed by mass spectrometry identified three novel phosphorylated sites in the DNA-binding domain of PROX1 that are critical for its stability and function. In silico analyses of these phosphorylation sites predicted CHEK1 as a potential upstream kinase which could be exploited for therapeutic targeting of PROX1. Our findings provide insights into the potential role for bivalent promoters in Polycomb-mediated lineage reprogramming, which may facilitate forward differentiation in NEPC upon EZH2 inhibition. Further investigation of de-repressed candidates defines the role of PROX1 as a driver of NEPC and a potential therapeutic target. Varadha Balaji Venkadakrishnan, Nathaniel C. E. Voss, Nicole Traphagen, Richa Singh, James Neiswender, Keira Prenza. Sosa, Kenny Weng, Francisca Vazquez, David S. Rickman, Myles Brown, Himisha Beltran. Polycomb dysregulation shapes chromatin bivalency critical for prostate cancer lineage plasticity [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(2_Suppl):Abstract nr PR022.

Context-dependent synthetic lethality offers a promising strategy for expanding the scope of precision oncology beyond direct oncogene inhibition. We describe various genetic contexts that produce cancer-intrinsic vulnerabilities and consequent synthetic lethal opportunities. We also identify common mechanistic themes that underlie synthetic lethality, such as DNA repair defects, loss of functional redundancies, metabolic imbalances and narrow signalling tolerances. Whereas clinical translation has seen success - for example, with inhibitors of poly(ADP-ribose) polymerase (PARP), hypoxia-inducible factor 2 (HIF-2) and Smoothened (SMO), other targets require more nuanced strategies to achieve selectivity. Case studies highlight that therapeutic index, often inferable from functional genomics, is a critical determinant of success and should guide both target prioritization and therapeutic strategy. They also reveal that specific molecular mechanisms underlying the synthetic lethal phenotype can inform the discovery of the optimal therapeutic modality. Finally, we describe emerging approaches for synthetic lethal target discovery and drug development that enable diverse therapeutic strategies. Together, these insights provide a framework for translating synthetic lethality into more selective and durable cancer therapies.

Gene set enrichment analysis of differential protein expression between the highly-aneuploid clones (SS51, SS111) and pseudo-diploid clones (SS48, SS31)

Supplementary Figure 1: Dosage compensation in trisomic cells occurs at both mRNA and protein levels (related to Fig. 1). Supplementary Figure 2: RNA degradation does not confound the results of the differential gene expression analyses (related to Fig. 2). Supplementary Figure 3: Aneuploid clones activate the nonsense-mediated decay (NMD) pathway, and depend on it for downregulating their gene expression (related to Fig. 3). Supplementary Figure 4: Increased dependency on the nonsense-mediated decay pathway is a general feature of aneuploid cells (related to Fig. 3). Supplementary Figure 5: Trisomic clones activate the miRNA-mediated RNA degradation pathway, and depend on it for downregulating their gene expression (related to Fig. 4). Supplementary Figure 6: Aneuploid cells depend on miRNA machinery cofactors PRKRA and TARBP2 (related to Fig. 4). Supplementary Figure 7: Multiple models of aneuploid cells experience proteotoxic stress and attenuate protein translation (related to Fig. 5). Supplementary Figure 8: Multiple models of aneuploid cells activate the proteasome, and depend on its activity for downregulating their protein expression (related to Fig. 6).

PRISM screen results of human cancer cell lines treated with bortezomib, in the absence or presence of reversine

Pre-ranked gene set enrichment analysis of differential mRNA expression between the highly-aneuploid clones (SS51, SS111) and the pseudo-diploid clone (SS48).

Pre-ranked gene set enrichment analysis of differential mRNA expression between all aneuploid clones (SS6, SS119, SS51, SS111) and the pseudo-diploid clone (SS48)

Despite advances in precision oncology, effective personalized treatments are still lacking for most patients with cancer1. The Cancer Dependency Map (DepMap) accelerates this field by systematically identifying cancer vulnerabilities in diverse preclinical models. Data from over 1,300 cell lines have led to the discovery of new therapeutic strategies across multiple tumour types2. However, mapping cancer vulnerabilities using traditional cell lines has limitations, including insufficient cancer subtype representation and the impact of culture conditions on perturbation responses. Here we perform 147 genome-scale CRISPR screens and multi-omic characterizations of next-generation (NextGen) cancer models (organoids and spheroids) across 10 cancer types. This strategy enables the expansion of DepMap to cover new genomic and molecular subtypes and to identify new biomarker-associated vulnerabilities. These new models also preserve transcriptional programs that are silenced in traditional cell lines and facilitate the discovery of specific gene dependencies associated with these programs. Comparisons of traditional and NextGen cancer models enable further identification of distinct effects of growth format and culture medium on gene essentiality. The integrated dataset combines data from both model types to offer a valuable, expansive resource for exploring cancer vulnerabilities and is accessible via the DepMap portal.

Supplementary Figures associated with each Main Figure

Biliary tract cancers (BTC) are aggressive malignancies encompassing intrahepatic and extrahepatic cholangiocarcinoma, gallbladder carcinoma, and ampullary carcinoma. Here, we report integrative analysis of 63 BTC cell lines via multi-omics and genome-scale CRISPR screens. We identify widespread EGFR dependency in BTC, alongside dependencies selective to anatomic subtypes. Additionally, we delineate strategies to overcome therapeutic resistance, with combined EGFR inhibition potentiating targeting of KRAS-mutant and FGFR2 fusion-driven models and SHP2 inhibition effective in the latter context. Clustering RNA/protein expression and dependencies data revealed functional relationships transcending single-gene alterations, with biliary, squamous, or dual biliary/hepatocyte lineage signatures stratifying BTC models. These subtypes exhibit distinct dependency profiles-including cell fate transcription factors GRHL2, TP63, and HNF1B, respectively-and demonstrate prognostic significance in patient samples. Potential subtype-specific targetable vulnerabilities include integrinα3 and the detoxification enzyme UXS1. This cell line atlas reveals therapeutic targets in molecularly defined BTCs, unveils disease subtypes, and provides a resource for therapeutic development. SIGNIFICANCE:This integrative analysis of BTC cell lines defines the landscape of vulnerabilities across BTCs, stratifying distinct subtypes, and provides a key resource for studying disease heterogeneity. The findings highlight strategies for targeting BTCs with specific genomic alterations, as well as broader approaches based on shared molecular programs and essential pathways.

Excel file containing all data corresponding to Figure 7

As precision cancer medicine develops, new bioinformatic tools that are both more granular and interpretable become imperative. In particular, when working with cancer cell lines to identify and validate potential treatment targets in cohorts of interest it is necessary to understand whether and precisely how cancer cell lines recapitulate tumor biology. As part of the Cancer Dependency Map project we are working to develop approaches for relating cell lines to patient tumors. Here we demonstrate a simple supervised classifier inspired by order-parameter methods from spin-glass physics that uses a pre-determined set of tissue type labels to produce scores per-gene and per-type that can be evaluated on both tumors and cell lines. The model described here complements this previous work, producing scores that qualitatively agree with the unsupervised model, while also enabling a quantitative, gene-level understanding of the origin of the classification result. Further, this method allows multiple cohorts of tumors and cell lines to be compared directly, relative to a common basis. We describe both the method itself, a variation of which has been deployed in single-cell developmental biology, as well as the metadata processing necessary for its effective use in comparing models to tumors. We developed an algorithm that defines our scope of tissue types maximizing granularity while minimizing collinearity and accounting for gaps and overlaps in annotation between tumor and cell line datasets, with over 35 total disease types assessed. We show using several examples from cell lines that this simple method can identify not only which type of tumor each cell lines most closely resembles, but also extract the most important genes involved in that determination. Our model, using 75 samples per tissue type, achieves >95% top-3 accuracy on a test dataset of over 10k tumor samples. The matchup between annotated type and predicted type is weaker in cell lines and also highly type-specific, ranging from 95% in some cohorts of 20% in others, and we explore the reasons for this. In one example of lung cell line models our method identifies cell line cohorts that match up to adenoid as well as squamous tumors, and another cohort that doesn’t match with either set of tumors. Based on the genes identified by the model as important for classifying these lines we describe their phenotype as mesenchymal. Additionally, we show how this method can be augmented with normal tissue data from GTEx to foreground signals that are specific to cancer relative to related normal tissues. Lauren Golden, Naima Abdirahman, Xiaomeng Zhang, Maria Yampolskaya, Barbara DeKegel, Pankaj Mehta, Francisca Vazquez, Catarina D. Campbell. Order parameter methods for interpretable comparisons across tumors and cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2397.

Excel file containing all data corresponding to Figure 2

Liposarcomas (LPS) are mesenchymal cell malignancies that are diagnosed in more than 3500 patients in the US each year. LPS are characterized by DNA amplifications of MDM2 (100%) and CDK4 (90+%) genes on neochromosomes, generally with wild-type TP53. Management of metastatic or surgically unresectable LPS remains purely palliative. Recent clinical trials targeting MDM2 or CDK4/6 with small-molecule inhibitors have shown modest activity but have generally failed in phase 3 trials. The development of new therapeutics is greatly needed to improve outcomes for patients with LPS. To identify unique liposarcoma-specific vulnerabilities, we utilized the Dependency Map (DepMap) database to analyze gene-specific dependencies of 9 LPS cell lines as compared to non-LPS cancers and identified CSNK1A1 as one of the top essential genes preferentially for LPS. siRNA-mediated depletion of CSNK1A1 confirmed that CK1α negatively regulates p53 and is critical for the proliferation and survival of LPS cells. DepMap analyses identified CDK7 and CDK9 as two additional top LPS-essential genes. Three CDK9 inhibitors suppressed LPS cell growth and induced apoptosis by decreasing MDM2 levels while inducing expression of p53 and PUMA. The cytotoxic effects of CDK9 inhibitors were enhanced upon CK1α depletion. Furthermore, we quantitatively determined synergism between CDK7 and CDK9 inhibitors in LPS cells. These data led us to examine combined targeting of CK1α and CDK7/9 in LPS with the novel small molecule inhibitor BTX-A51, which has previously also been shown to inhibit CK1α, CDK7, and CDK9 with nanomolar potency in acute myeloid leukemia (AML) models. BTX-A51 treatment significantly reduced expression of MDM2 with marked induction of p53, resulting in increased PUMA expression and profound apoptosis of LPS cells. Through CRISPR/Cas9-mediated TP53 knockout, we established that BTX-A51-treatment-induced apoptosis was in part mediated in a p53-dependent manner, which was further validated transcriptionally using RNA-Seq based pathway analysis. BTX-A51 also reduced expression of the apoptosis inhibitor MCL1 and primed LPS cell lines and PDX-derived cells for BIM/PUMA-induced apoptosis. Importantly, in vivo experiments in two LPS PDX models demonstrated that BTX-A51 is well-tolerated with anti-tumor efficacy. These data have confirmed CK1α, CDK9, and CDK7 as essential for LPS cells and indicate that BTX-A51 has potent preclinical efficacy in LPS, through combined inhibition of CK1α, CDK9, and CDK7. Our data form the scientific foundation for our ongoing pilot clinical trial evaluating BTX-A51 in patients with advanced WDLPS or DDLPS (NCT06414434). Renyan Liu, Nicole L. Solimini, Caoibhne McSweeney, Patrick Bhola, Daryl Griffin, Tim B. Branigan, Michael J. Wagner, Audrey Turchick, Ricardo de Simoes, Joshua M. Dempster, Jie Hao, Xin Wang, Roshen Alharthi, Michael Yorsz, Shaili Soni, Cing-siang Hu, Irit Snir-Alkalay, Francisca Vazquez, Prafulla C. Gokhale, Constantine Mitsiades, Anthony Letai, Yinon Ben-Neriah, George D. Demetri, Geoffrey I. Shapiro. Therapeutic potential of combined targeting of casein kinase 1 alpha (CK1 alpha) and CDK7/9 with the inhibitor BTX-A51 in human liposarcomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2980.

Excel file containing all data corresponding to Figure 5

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Supplementary data and supplementary figure legends