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Lineage plasticity, the ability of a cell to alter its identity, is an increasingly common mechanism of adaptive resistance to targeted therapy in cancer. An archetypal example is the development of neuroendocrine prostate cancer (NEPC) after treatment of prostate adenocarcinoma (PRAD) with inhibitors of androgen signaling. NEPC is an aggressive variant of prostate cancer that aberrantly expresses genes characteristic of neuroendocrine (NE) tissues and no longer depends on androgens. Here, we investigate the epigenomic basis of this resistance mechanism by profiling histone modifications in NEPC and PRAD patient-derived xenografts (PDXs) using chromatin immunoprecipitation and sequencing (ChIP-seq). We identify a vast network of cis-regulatory elements (N~15,000) that are recurrently activated in NEPC. The FOXA1 transcription factor (TF), which pioneers androgen receptor (AR) chromatin binding in the prostate epithelium, is reprogrammed to NE-specific regulatory elements in NEPC. Despite loss of dependence upon AR, NEPC maintains FOXA1 expression and requires FOXA1 for proliferation and expression of NE lineage-defining genes. Ectopic expression of the NE lineage TFs ASCL1 and NKX2-1 in PRAD cells reprograms FOXA1 to bind to NE regulatory elements and induces enhancer activity as evidenced by histone modifications at these sites. Our data establish the importance of FOXA1 in NEPC and provide a principled approach to identifying cancer dependencies through epigenomic profiling.

The determination of molecular features that mediate clinically aggressive phenotypes in prostate cancer remains a major biological and clinical challenge.sup.1,2. Recent advances in interpretability of machine learning models as applied to biomedical problems may enable discovery and prediction in clinical cancer genomics.sup.3-5. Here we developed P-NET--a biologically informed deep learning model--to stratify patients with prostate cancer by treatment-resistance state and evaluate molecular drivers of treatment resistance for therapeutic targeting through complete model interpretability. We demonstrate that P-NET can predict cancer state using molecular data with a performance that is superior to other modelling approaches. Moreover, the biological interpretability within P-NET revealed established and novel molecularly altered candidates, such as MDM4 and FGFR1, which were implicated in predicting advanced disease and validated in vitro. Broadly, biologically informed fully interpretable neural networks enable preclinical discovery and clinical prediction in prostate cancer and may have general applicability across cancer types. A biologically informed, interpretable deep learning model has been developed to evaluate molecular drivers of resistance to cancer treatment, predict clinical outcomes and guide hypotheses on disease progression., Author(s): Haitham A. Elmarakeby [sup.1] [sup.2] [sup.3] , Justin Hwang [sup.4] , Rand Arafeh [sup.1] [sup.2] , Jett Crowdis [sup.1] [sup.2] , Sydney Gang [sup.1] , David Liu [sup.1] [sup.2] [...]

NRAS mutations are the most common alterations among RAS isoforms in cutaneous melanoma, with patients harboring these aggressive tumors having a poor prognosis and low survival rate. The main line of treatment for these patients is MAPK pathway‐targeted therapies, such as MEK inhibitors, but, unfortunately, the response to these inhibitors is variable due to tumor resistance. Identifying genetic modifiers involved in resistance toward MEK‐targeted therapy may assist in the development of new therapeutic strategies, enhancing treatment response and patient survival. Our whole‐genome CRISPR‐Cas9 knockout screen identified the target Kelch domain‐containing F‐Box protein 42 (FBXO42) as a factor involved in NRAS‐mutant melanoma‐acquired resistance to the MEK1/2 inhibitor trametinib. We further show that FBXO42, an E3 ubiquitin ligase, is involved in the TAK1 signaling pathway, possibly prompting an increase in active P38. In addition, we demonstrate that combining trametinib with the TAK1 inhibitor, takinib, is a far more efficient treatment than trametinib alone in NRAS‐mutant melanoma cells. Our findings thus show a new pathway involved in NRAS‐mutant melanoma resistance and provide new opportunities for novel therapeutic options. [ABSTRACT FROM AUTHOR]

While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and by predicting drug efficacy in mouse xenograft models. We then experimentally test a select set of predicted cSLi via new screening experiments, validating their predicted context-specific sensitivity in hypoxic vs normoxic conditions and demonstrating cSLi’s utility in predicting synergistic drug combinations. We show that cSLi can successfully predict patients’ drug treatment response and provide patient stratification signatures. ISLE thus complements existing actionable mutation-based methods for precision cancer therapy, offering an opportunity to expand its scope to the whole genome. [ABSTRACT FROM AUTHOR]

Chimeric antigen receptor (CAR) T-cell therapy can lead to dramatic clinical responses in B-cell malignancies. However, early clinical trials with CAR T-cell therapy in non-B-cell malignancies have been disappointing to date, suggesting that tumor-intrinsic features contribute to resistance. To investigate tumor-intrinsic modes of resistance, we performed genome scale CRISPR-Cas9 screens in mesothelin (MSLN)-expressing pancreatic cancer cells. Co-culture with MSLN-targeting CAR T cells identified both antigen-dependent and antigen-independent modes of resistance. In particular, loss of the majority of the genes involved in the pathway responsible for GPI-anchor biosynthesis and attachment abrogated the ability of CAR T cells to target pancreatic cancer cells, suggesting that disruption of this pathway may permit MSLN CAR T-cell evasion in the clinic. Antigen-independent mediators of CAR T-cell response included members of the death receptor pathway as well as genes that regulate tumor transcriptional responses, including TFAP4 and INTS12. TFAP4-mediated CAR T resistance depended on the NFκB transcription factor p65, indicating that tumor resistance to CAR T-cell therapy likely involves alterations in tumor-intrinsic states. Overall, this study uncovers multiple antigen-dependent and -independent mechanisms of CAR T-cell evasion by pancreatic cancer, paving the way for overcoming resistance in this disease that is notoriously refractory to immunotherapy., Significance: The identification and validation of key determinants of CAR T-cell response in pancreatic cancer provide insights into the landscape of tumor cell intrinsic resistance mechanisms and into approaches to improve therapeutic efficacy., (©2022 The Authors; Published by the American Association for Cancer Research.)