Showing papers by "William C. Hahn published in 2020"

Discovery of a selective inhibitor of doublecortin like kinase 1

Abstract: Doublecortin like kinase 1 (DCLK1) is an understudied kinase that is upregulated in a wide range of cancers, including pancreatic ductal adenocarcinoma (PDAC). However, little is known about its potential as a therapeutic target. We used chemoproteomic profiling and structure-based design to develop a selective, in vivo-compatible chemical probe of the DCLK1 kinase domain, DCLK1-IN-1. We demonstrate activity of DCLK1-IN-1 against clinically relevant patient-derived PDAC organoid models and use a combination of RNA-sequencing, proteomics and phosphoproteomics analysis to reveal that DCLK1 inhibition modulates proteins and pathways associated with cell motility in this context. DCLK1-IN-1 will serve as a versatile tool to investigate DCLK1 biology and establish its role in cancer. A highly selective inhibitor of the DCLK1/2 kinases is used to uncover the consequences of DCLK1 inhibition on viability, phosphosignaling and the transcriptome in patient-derived organoid models of pancreatic ductal adenocarcinoma.

Selective USP7 inhibition elicits cancer cell killing through a p53-dependent mechanism.

Abstract: Ubiquitin specific peptidase 7 (USP7) is a deubiquitinating enzyme (DUB) that removes ubiquitin tags from specific protein substrates in order to alter their degradation rate and sub-cellular localization. USP7 has been proposed as a therapeutic target in several cancers because it has many reported substrates with a role in cancer progression, including FOXO4, MDM2, N-Myc, and PTEN. The multi-substrate nature of USP7, combined with the modest potency and selectivity of early generation USP7 inhibitors, has presented a challenge in defining predictors of response to USP7 and potential patient populations that would benefit most from USP7-targeted drugs. Here, we describe the structure-guided development of XL177A, which irreversibly inhibits USP7 with sub-nM potency and selectivity across the human proteome. Evaluation of the cellular effects of XL177A reveals that selective USP7 inhibition suppresses cancer cell growth predominantly through a p53-dependent mechanism: XL177A specifically upregulates p53 transcriptional targets transcriptome-wide, hotspot mutations in TP53 but not any other genes predict response to XL177A across a panel of ~500 cancer cell lines, and TP53 knockout rescues XL177A-mediated growth suppression of TP53 wild-type (WT) cells. Together, these findings suggest TP53 mutational status as a biomarker for response to USP7 inhibition. We find that Ewing sarcoma and malignant rhabdoid tumor (MRT), two pediatric cancers that are sensitive to other p53-dependent cytotoxic drugs, also display increased sensitivity to XL177A.

ATM Loss Confers Greater Sensitivity to ATR Inhibition than PARP Inhibition in Prostate Cancer

Abstract: Alterations in DNA damage response (DDR) genes are common in advanced prostate tumors and are associated with unique genomic and clinical features. ATM is a DDR kinase that has a central role in coordinating DNA repair and cell-cycle response following DNA damage, and ATM alterations are present in approximately 5% of advanced prostate tumors. Recently, inhibitors of PARP have demonstrated activity in advanced prostate tumors harboring DDR gene alterations, particularly in tumors with BRCA1/2 alterations. However, the role of alterations in DDR genes beyond BRCA1/2 in mediating PARP inhibitor sensitivity is poorly understood. To define the role of ATM loss in prostate tumor DDR function and sensitivity to DDR-directed agents, we created a series of ATM-deficient preclinical prostate cancer models and tested the impact of ATM loss on DNA repair function and therapeutic sensitivities. ATM loss altered DDR signaling, but did not directly impact homologous recombination function. Furthermore, ATM loss did not significantly impact sensitivity to PARP inhibition but robustly sensitized to inhibitors of the related DDR kinase ATR. These results have important implications for planned and ongoing prostate cancer clinical trials and suggest that patients with tumor ATM alterations may be more likely to benefit from ATR inhibitor than PARP inhibitor therapy. SIGNIFICANCE: ATM loss occurs in a subset of prostate tumors. This study shows that deleting ATM in prostate cancer models does not significantly increase sensitivity to PARP inhibition but does sensitize to ATR inhibition.See related commentary by Setton and Powell, p. 2085.

STRIPAK directs PP2A activity toward MAP4K4 to promote oncogenic transformation of human cells.

Abstract: Cells maintain a fine balance of signals that promote or counter cell growth and division. Two sets of enzymes – called kinases and phosphatases – contribute to this balance. In general, kinases “switch on” other proteins by tagging them with a phosphate molecule. This process is called phosphorylation. Phosphatases, on the other hand, dephosphorylate these proteins, switching them off. Cancer cells often have mutations that activate kinases to drive cancer growth. The same cells can have mutations that inactivate the phosphatases or reduce their abundance. The roles of phosphatases in cancer are still being studied. One major hurdle in this research is that it is not always clear how they recognize the proteins they dephosphorylate. Protein phosphatase 2A (or PP2A for short) is one of the phosphatases that is often mutated or deleted in human cancers. Even just reduced levels of PP2A can promote cancer. Kim, Berrios, Kim, Schade et al. used an experimental trick to decrease the phosphatase activity of PP2A in human cells growing in a dish. Biochemical analysis of these cells showed that, as expected, many proteins were now in their phosphorylated states. Unexpectedly, however, some proteins were dephosphorylated under these conditions. One of these proteins was called MAP4K4. In the case of MAP4K4, the dephosphorylated state contributes to the growth of the cancer cell. Kim et al. carried out further genetic and biochemical experiments to show that, in these cells, PP2A and MAP4K4 stay physically connected to one another. This connection was enabled by a group of proteins called the STRIPAK complex. The STRIPAK proteins directed the remaining PP2A towards MAP4K4. Low levels or activity of PP2A could, therefore, promote cancer in a different way. Taken together, PP2A is not a single phosphatase that always turns proteins off, but rather is a dual switch that turns off some proteins while turning on others. Future experiments will explore to what extent these findings also apply in tumors. Information about how mutations in PP2A affect human cancers could suggest new targets for cancer drugs.

Reprogramming of the FOXA1 cistrome in treatment-emergent neuroendocrine prostate cancer

Abstract: Lineage plasticity, the ability of a cell to alter its identity, is an increasingly common mechanism of adaptive resistance to targeted therapy in cancer1,2. 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. To investigate the epigenomic basis of this resistance mechanism, we profiled histone modifications in NEPC and PRAD patient-derived xenografts (PDXs) using chromatin immunoprecipitation and sequencing (ChIP-seq). We identified 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 epithelium3,4, 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 novel cancer dependencies through epigenomic profiling.

Global computational alignment of tumor and cell line transcriptional profiles

Abstract: Cell lines are key tools for preclinical cancer research, but it remains unclear how well they represent patient tumor samples. Identifying cell line models that best represent the features of particular tumor samples, as well as tumor types that lack in vitro model representation, remain important challenges. Gene expression has been shown to provide rich information that can be used to identify tumor subtypes, as well as predict the genetic dependencies and chemical vulnerabilities of cell lines. However, direct comparisons of tumor and cell line transcriptional profiles are complicated by systematic differences, such as the presence of immune and stromal cells in tumor samples and differences in the cancer-type composition of cell line and tumor expression datasets. To address these challenges, we developed an unsupervised alignment method (Celligner) and applied it to integrate several large-scale cell line and tumor RNA-Seq datasets. While our method aligns the majority of cell lines with tumor samples of the same cancer type, it also reveals large differences in tumor/cell line similarity across disease types. Furthermore, Celligner identifies a distinct group of several hundred cell lines from diverse lineages that present a more mesenchymal and undifferentiated transcriptional state and which exhibit distinct chemical and genetic dependencies. This method could thus be used to guide the selection of cell lines that more closely resemble patient tumors and improve the clinical translation of insights gained from cell line models.

Gene expression has more power for predicting in vitro cancer cell vulnerabilities than genomics

Abstract: Achieving precision oncology requires accurate identification of targetable cancer vulnerabilities in patients. Generally, genomic features are regarded as the state-of-the-art method for stratifying patients for targeted therapies. In this work, we conduct the first rigorous comparison of DNA- and expression-based predictive models for viability across five datasets encompassing chemical and genetic perturbations. We find that expression consistently outperforms DNA for predicting vulnerabilities, including many currently stratified by canonical DNA markers. Contrary to their perception in the literature, the most accurate expression-based models depend on few features and are amenable to biological interpretation. This work points to the importance of exploring more comprehensive expression profiling in clinical settings.

Phosphate dysregulation via the XPR1:KIDINS220 protein complex is a therapeutic vulnerability in ovarian cancer

Abstract: Clinical outcomes for patients with ovarian and uterine cancers have not improved greatly in the past twenty years. To identify ovarian and uterine cancer vulnerabilities, we analyzed genome-scale CRISPR/ Cas9 loss-of-function screens across 739 human cancer cell lines. We found that many ovarian cancer cell lines overexpress the phosphate importer SLC34A2, which renders them sensitive to loss of the phosphate exporter XPR1. We extensively validated the XPR1 vulnerability in cancer cell lines and found that the XPR1 dependency was retained in vivo. Overexpression of SLC34A2 is frequently observed in tumor samples and is regulated by PAX8 - a transcription factor required for ovarian cancer survival. XPR1 overexpression and copy number amplifications are also frequently observed. Mechanistically, SLC34A2 overexpression and impaired phosphate efflux leads to the accumulation of intracellular phosphate and cell death. We further show that proper localization and phosphate efflux by XPR1 requires a novel binding partner, KIDINS220. Loss of either XPR1 or KIDINS220 results in acidic vacuolar structures which precede cell death. These data point to the XPR1:KIDINS220 complex - and phosphate dysregulation more broadly - as a therapeutic vulnerability in ovarian cancer.

Predicting cell health phenotypes using image-based morphology profiling

Abstract: Genetic and chemical perturbations impact diverse cellular phenotypes, including multiple indicators of cell health. These readouts reveal toxicity and antitumorigenic effects relevant to drug discovery and personalized medicine. We developed two customized microscopy assays that use seven reagents to measure 70 specific cell health phenotypes including proliferation, apoptosis, reactive oxygen species (ROS), DNA damage, and aberrant cell cycle stage progression. We then tested an approach to predict multiple cell health phenotypes using Cell Painting, an inexpensive and scalable image-based morphology assay. In matched CRISPR perturbations of three cancer cell lines, we collected both Cell Painting and cell health data. We found that simple machine learning algorithms can predict many cell health readouts directly from Cell Painting images, at less than half the cost. We hypothesized that these trained models can be applied to accurately predict cell health assay outcomes for any future or existing Cell Painting dataset. For Cell Painting images from a set of 1,500+ compound perturbations across multiple doses, we validated predictions by orthogonal assay readouts, and by confirming mitotic arrest and ROS phenotypes via PLK and proteasome inhibition, respectively. We provide an intuitive web app to browse all predictions at http://broad.io/cell-health-app. Our approach can be used to add cell health annotations to Cell Painting perturbation datasets.

Biologically informed deep neural network for prostate cancer classification and discovery

Abstract: Determination of molecular features that mediate clinically aggressive phenotypes in prostate cancer (PrCa) remains a major biological and clinical challenge. Here, we developed a biologically informed deep learning model (P-NET) to stratify PrCa patients by treatment resistance state and evaluate molecular drivers of treatment resistance for therapeutic targeting through complete model interpretability. Using a molecular cohort of 1,238 prostate cancers, we demonstrated that P-NET can predict cancer state using molecular data that is superior to other modeling approaches. Moreover, the biological interpretability within P-NET revealed established and novel molecularly altered candidates, such as MDM4 and FGFR1, that 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.

Massively parallel phenotyping of variant impact in cancer with Perturb-seq reveals a shift in the spectrum of cell states induced by somatic mutations

Abstract: Genome sequencing studies have identified millions of somatic variants in cancer, but their phenotypic impact remains challenging to predict. Current experimental approaches to distinguish between functionally impactful and neutral variants require customized phenotypic assays that often report on average effects, and are not easily scaled. Here, we develop a generalizable, high-dimensional, and scalable approach to functionally assess variant impact in single cells by pooled Perturb-seq. Specifically, we assessed the impact of 200 TP53 and KRAS variants in >300,000 single lung cancer cells, and used the profiles to categorize variants into phenotypic subsets to distinguish gain-of-function, loss-of-function and dominant negative variants, which we validated by comparison to orthogonal assays. Surprisingly, KRAS variants did not merely fit into discrete functional categories, but rather spanned a continuum of gain-of-function phenotypes driven by quantitative shifts in cell composition at the single cell level. We further discovered novel gain-of-function KRAS variants whose impact could not have been predicted solely by their occurrence in patient samples. Our work provides a scalable, gene-agnostic method for coding variant impact phenotyping, which can be applied in cancer and other diseases driven by somatic or germline coding mutations.

Rhabdoid Tumors Are Sensitive to the Protein-Translation Inhibitor Homoharringtonine

Abstract: Purpose: Rhabdoid tumors are devastating pediatric cancers in need of improved therapies We sought to identify small molecules that exhibit in vitro and in vivo efficacy against preclinical models of rhabdoid tumor Experimental Design: We screened eight rhabdoid tumor cell lines with 481 small molecules and compared their sensitivity with that of 879 other cancer cell lines Genome-scale CRISPR–Cas9 inactivation screens in rhabdoid tumors were analyzed to confirm target vulnerabilities Gene expression and CRISPR–Cas9 data were queried across cell lines and primary rhabdoid tumors to discover biomarkers of small-molecule sensitivity Molecular correlates were validated by manipulating gene expression Subcutaneous rhabdoid tumor xenografts were treated with the most effective drug to confirm in vitro results Results: Small-molecule screening identified the protein-translation inhibitor homoharringtonine (HHT), an FDA-approved treatment for chronic myelogenous leukemia (CML), as the sole drug to which all rhabdoid tumor cell lines were selectively sensitive Validation studies confirmed the sensitivity of rhabdoid tumor to HHT was comparable with that of CML cell lines Low expression of the antiapoptotic gene BCL2L1, which encodes Bcl-XL, was the strongest predictor of HHT sensitivity, and HHT treatment consistently depleted Mcl-1, the synthetic-lethal antiapoptotic partner of Bcl-XL Rhabdoid tumor cell lines and primary-tumor samples expressed low BCL2L1, and overexpression of BCL2L1 induced resistance to HHT in rhabdoid tumor cells Furthermore, HHT treatment inhibited rhabdoid tumor cell line and patient-derived xenograft growth in vivo Conclusions: Rhabdoid tumor cell lines and xenografts are highly sensitive to HHT, at least partially due to their low expression of BCL2L1 HHT may have therapeutic potential against rhabdoid tumors

A genome-scale CRISPR screen reveals PRMT1 as a critical regulator of androgen receptor signaling in prostate cancer

Abstract: Androgen receptor (AR) signaling is the central driver of prostate cancer across disease states. While androgen deprivation therapy (ADT) is effective in the initial treatment of prostate cancer, resistance to ADT or to next-generation androgen pathway inhibitors invariably arises, most commonly through re-activation of the AR axis. Thus, orthogonal approaches to inhibit AR signaling in advanced prostate cancer are essential. Here, via genome-scale CRISPR/Cas9 screening, we identify protein arginine methyltransferase 1 (PRMT1) as a critical mediator of AR expression and signaling. PRMT1 regulates recruitment of AR to genomic target sites and inhibition of PRMT1 impairs AR binding at lineage-specific enhancers, leading to decreased expression of key oncogenes, including AR itself. Additionally, AR-driven prostate cancer cells are uniquely susceptible to combined AR and PRMT1 inhibition. Our findings implicate PRMT1 as a key regulator of AR output and provide a preclinical framework for co-targeting of AR and PRMT1 in advanced prostate cancer.

Transcriptional subtype-specific microenvironmental crosstalk and tumor cell plasticity in metastatic pancreatic cancer

Abstract: In pancreatic ductal adenocarcinoma (PDAC), the basal-like and classical transcriptional subtypes are associated with differential chemotherapy sensitivity and patient survival. These phenotypes have been defined using bulk transcriptional profiling, which can mask underlying cellular heterogeneity and the biologic mechanisms that distinguish these subtypes. Furthermore, few studies have interrogated metastases, which are the cause of mortality in most patients with this highly lethal disease. Using single-cell RNA-sequencing of metastatic needle biopsies and matched organoid models, we demonstrate intra-tumoral subtype heterogeneity at the single-cell level and define a continuum for the basal-like and classical phenotypes that includes hybrid cells that co-express features of both states. Basal-like tumors show enrichment of mesenchymal and stem-like programs, and demonstrate immune exclusion and tumor cell crosstalk with specific macrophage subsets. Conversely, classical tumors harbor greater immune infiltration and a relatively pro-angiogenic microenvironment. Matched organoid models exhibit a strong bias against the growth of basal-like cells in standard organoid media, but modification of culture conditions can rescue the basal-like phenotype. This study reframes the transcriptional taxonomy of PDAC, demonstrates how divergent transcriptional subtypes associate with unique tumor microenvironments, and highlights the importance of evaluating both genotype and transcriptional phenotype to establish high-fidelity patient-derived cancer models.

Genome-scale genetic screening identifies PRMT1 as a critical vulnerability in castration-resistant prostate cancer

Abstract: Androgen receptor (AR) signaling is the central driver of prostate cancer growth and progression across disease states, including in most cases of castration-resistant prostate cancer (CRPC) While next-generation AR antagonists and androgen synthesis inhibitors are effective for a time in CRPC, tumors invariably develop resistance to these agents, commonly through mechanisms resulting in the overexpression of AR or the production of constitutively active AR splice variants (eg AR-V7) Improved mechanistic understanding of the factors that modulate AR expression and signaling may reveal additional therapeutic intervention points in CRPC Here, we leverage genome-scale CRISPR/Cas9 genetic screening to systematically identify regulators of AR/AR-V7 expression We identify protein arginine methyltransferase 1 (PRMT1) as a critical mediator of AR expression and signaling that regulates recruitment of AR to genomic target sites PRMT1 suppression globally perturbs the expression and splicing of AR target genes and inhibits the proliferation and survival of AR-positive prostate cancer cells Genetic or pharmacologic inhibition of PRMT1 reduces AR binding at lineage-specific enhancers, which leads to decreased expression of key oncogenes, including AR itself CRPC cells displaying activated AR signaling due to overexpression of AR or AR-V7 are uniquely susceptible to combined AR and PRMT1 inhibition Our findings implicate PRMT1 as a critical regulator of AR output and provide a preclinical framework for co-targeting of AR and PRMT1 as a promising new therapeutic strategy in CRPC

Therapeutic targeting of extracellular FGFR2 activating deletions in intrahepatic cholangiocarcinoma.

Abstract: 567Background: Fibroblast growth factor receptor (FGFR) pathway alterations have been identified in approximately 20% of patients (pts) with intrahepatic cholangiocarcinoma (IHCC), most commonly by...