Microenvironment drives cell state, plasticity, and drug response in pancreatic cancer.

Prognostically relevant RNA expression states exist in pancreatic ductal adenocarcinoma (PDAC), but our understanding of their drivers, stability, and relationship to therapeutic response is limited. To examine these attributes systematically, we profiled metastatic biopsies and matched organoid models at single-cell resolution. In vivo, we identify a new intermediate PDAC transcriptional cell state and uncover distinct site- and state-specific tumor microenvironments (TMEs). Benchmarking models against this reference map, we reveal strong culture-specific biases in cancer cell transcriptional state representation driven by altered TME signals. We restore expression state heterogeneity by adding back in vivo-relevant factors and show plasticity in culture models. Further, we prove that non-genetic modulation of cell state can strongly influence drug responses, uncovering state-specific vulnerabilities. This work provides a broadly applicable framework for aligning cell states across in vivo and ex vivo settings, identifying drivers of transcriptional plasticity and manipulating cell state to target associated vulnerabilities.

Deciphering the immunopeptidome in vivo reveals new tumour antigens.

Immunosurveillance of cancer requires the presentation of peptide antigens on major histocompatibility complex class I (MHC-I) molecules1-5. Current approaches to profiling of MHC-I-associated peptides, collectively known as the immunopeptidome, are limited to in vitro investigation or bulk tumour lysates, which limits our understanding of cancer-specific patterns of antigen presentation in vivo6. To overcome these limitations, we engineered an inducible affinity tag into the mouse MHC-I gene (H2-K1) and targeted this allele to the KrasLSL-G12D/+Trp53fl/fl mouse model (KP/KbStrep)7. This approach enabled us to precisely isolate MHC-I peptides from autochthonous pancreatic ductal adenocarcinoma and from lung adenocarcinoma (LUAD) in vivo. In addition, we profiled the LUAD immunopeptidome from the alveolar type 2 cell of origin up to late-stage disease. Differential peptide presentation in LUAD was not predictable by mRNA expression or translation efficiency and is probably driven by post-translational mechanisms. Vaccination with peptides presented by LUAD in vivo induced CD8+ T cell responses in naive mice and tumour-bearing mice. Many peptides specific to LUAD, including immunogenic peptides, exhibited minimal expression of the cognate mRNA, which prompts the reconsideration of antigen prediction pipelines that triage peptides according to transcript abundance8. Beyond cancer, the KbStrep allele is compatible with other Cre-driver lines to explore antigen presentation in vivo in the pursuit of understanding basic immunology, infectious disease and autoimmunity.

ABL kinases regulate the stabilization of HIF-1α and MYC through CPSF1.

The hypoxia-inducible factor 1-α (HIF-1α) enables cells to adapt and respond to hypoxia (Hx), and the activity of this transcription factor is regulated by several oncogenic signals and cellular stressors. While the pathways controlling normoxic degradation of HIF-1α are well understood, the mechanisms supporting the sustained stabilization and activity of HIF-1α under Hx are less clear. We report that ABL kinase activity protects HIF-1α from proteasomal degradation during Hx. Using a fluorescence-activated cell sorting (FACS)-based CRISPR/Cas9 screen, we identified HIF-1α as a substrate of the cleavage and polyadenylation specificity factor-1 (CPSF1), an E3-ligase which targets HIF-1α for degradation in the presence of an ABL kinase inhibitor in Hx. We show that ABL kinases phosphorylate and interact with CUL4A, a cullin ring ligase adaptor, and compete with CPSF1 for CUL4A binding, leading to increased HIF-1α protein levels. Further, we identified the MYC proto-oncogene protein as a second CPSF1 substrate and show that active ABL kinase protects MYC from CPSF1-mediated degradation. These studies uncover a role for CPSF1 in cancer pathobiology as an E3-ligase antagonizing the expression of the oncogenic transcription factors, HIF-1α and MYC.

Optofluidic real-time cell sorter for longitudinal CTC studies in mouse models of cancer.

Circulating tumor cells (CTCs) play a fundamental role in cancer progression. However, in mice, limited blood volume and the rarity of CTCs in the bloodstream preclude longitudinal, in-depth studies of these cells using existing liquid biopsy techniques. Here, we present an optofluidic system that continuously collects fluorescently labeled CTCs from a genetically engineered mouse model (GEMM) for several hours per day over multiple days or weeks. The system is based on a microfluidic cell sorting chip connected serially to an unanesthetized mouse via an implanted arteriovenous shunt. Pneumatically controlled microfluidic valves capture CTCs as they flow through the device, and CTC-depleted blood is returned back to the mouse via the shunt. To demonstrate the utility of our system, we profile CTCs isolated longitudinally from animals over 4 days of treatment with the BET inhibitor JQ1 using single-cell RNA sequencing (scRNA-Seq) and show that our approach eliminates potential biases driven by intermouse heterogeneity that can occur when CTCs are collected across different mice. The CTC isolation and sorting technology presented here provides a research tool to help reveal details of how CTCs evolve over time, allowing studies to credential changes in CTCs as biomarkers of drug response and facilitating future studies to understand the role of CTCs in metastasis.

Mapping nucleosome positions using DNase-seq.

Although deoxyribonuclease I (DNase I) was used to probe the structure of the nucleosome in the 1960s and 1970s, in the current high-throughput sequencing era, DNase I has mainly been used to study genomic regions devoid of nucleosomes. Here, we reveal for the first time that DNase I can be used to precisely map the (translational) positions of in vivo nucleosomes genome-wide. Specifically, exploiting a distinctive DNase I cleavage profile within nucleosome-associated DNA--including a signature 10.3 base pair oscillation that corresponds to accessibility of the minor groove as DNA winds around the nucleosome--we develop a Bayes-factor-based method that can be used to map nucleosome positions along the genome. Compared to methods that require genetically modified histones, our DNase-based approach is easily applied in any organism, which we demonstrate by producing maps in yeast and human. Compared to micrococcal nuclease (MNase)-based methods that map nucleosomes based on cuts in linker regions, we utilize DNase I cuts both outside and within nucleosomal DNA; the oscillatory nature of the DNase I cleavage profile within nucleosomal DNA enables us to identify translational positioning details not apparent in MNase digestion of linker DNA. Because the oscillatory pattern corresponds to nucleosome rotational positioning, it also reveals the rotational context of transcription factor (TF) binding sites. We show that potential binding sites within nucleosome-associated DNA are often centered preferentially on an exposed major or minor groove. This preferential localization may modulate TF interaction with nucleosome-associated DNA as TFs search for binding sites.

Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.

Clustering is commonly used in single-cell RNA-sequencing (scRNA-seq) pipelines to characterize cellular heterogeneity. However, current methods face two main limitations. First, they require user-specified heuristics which add time and complexity to bioinformatic workflows; second, they rely on post-selective differential expression analyses to identify marker genes driving cluster differences, which has been shown to be subject to inflated false discovery rates. We address these challenges by introducing nonparametric clustering of single-cell populations (NCLUSION): an infinite mixture model that leverages Bayesian sparse priors to identify marker genes while simultaneously performing clustering on single-cell expression data. NCLUSION uses a scalable variational inference algorithm to perform these analyses on datasets with up to millions of cells. By analyzing publicly available scRNA-seq studies, we demonstrate that NCLUSION (i) matches the performance of other state-of-the-art clustering techniques with significantly reduced runtime and (ii) provides statistically robust and biologically relevant transcriptomic signatures for each of the clusters it identifies. Overall, NCLUSION represents a reliable hypothesis-generating tool for understanding patterns of expression variation present in single-cell populations.

Scalable, compressed phenotypic screening using pooled perturbations.

High-throughput phenotypic screens using biochemical perturbations and high-content readouts are constrained by limitations of scale. To address this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to reduce required sample size, labor and cost. We demonstrate the increased efficiency of compressed experimental designs compared to conventional approaches through benchmarking with a bioactive small-molecule library and a high-content imaging readout. We then apply compressed screening in two biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to a library of recombinant tumor microenvironment protein ligands, uncovering reproducible phenotypic shifts induced by specific ligands distinct from canonical reference signatures and correlated with clinical outcome. In the second, we identify the pleotropic modulatory effects of a chemical compound library with known mechanisms of action on primary human peripheral blood mononuclear cell immune responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts and basic biological inquiry.

Mutation and cell state compatibility is required and targetable in Ph+ acute lymphoblastic leukemia minimal residual disease.

Efforts to cure BCR::ABL1 B cell acute lymphoblastic leukemia (Ph+ ALL) solely through inhibition of ABL1 kinase activity have thus far been insufficient despite the availability of tyrosine kinase inhibitors (TKIs) with broad activity against resistance mutants. The mechanisms that drive persistence within minimal residual disease (MRD) remain poorly understood and therefore untargeted. Utilizing 13 patient-derived xenograft (PDX) models and clinical trial specimens of Ph+ ALL, we examined how genetic and transcriptional features co-evolve to drive progression during prolonged TKI response. Our work reveals a landscape of cooperative mutational and transcriptional escape mechanisms that differ from those causing resistance to first generation TKIs. By analyzing MRD during remission, we show that the same resistance mutation can either increase or decrease cellular fitness depending on transcriptional state. We further demonstrate that directly targeting transcriptional state-associated vulnerabilities at MRD can overcome BCR::ABL1 independence, suggesting a new paradigm for rationally eradicating MRD prior to relapse. Finally, we illustrate how cell mass measurements of leukemia cells can be used to rapidly monitor dominant transcriptional features of Ph+ ALL to help rationally guide therapeutic selection from low-input samples.

Cancer tissue of origin constrains the growth and metabolism of metastases.

Metastases arise from subsets of cancer cells that disseminate from the primary tumour1,2. The ability of cancer cells to thrive in a new tissue site is influenced by genetic and epigenetic changes that are important for disease initiation and progression, but these factors alone do not predict if and where cancers metastasize3,4. Specific cancer types metastasize to consistent subsets of tissues, suggesting that primary tumour-associated factors influence where cancers can grow. We find primary and metastatic pancreatic tumours have metabolic similarities and that the tumour-initiating capacity and proliferation of both primary-derived and metastasis-derived cells is favoured in the primary site relative to the metastatic site. Moreover, propagating cells as tumours in the lung or the liver does not enhance their relative ability to form large tumours in those sites, change their preference to grow in the primary site, nor stably alter aspects of their metabolism relative to primary tumours. Primary liver and lung cancer cells also exhibit a preference to grow in their primary site relative to metastatic sites. These data suggest cancer tissue of origin influences both primary and metastatic tumour metabolism and may impact where cancer cells can metastasize.

Compressed phenotypic screens for complex multicellular models and high-content assays.

High-throughput phenotypic screens leveraging biochemical perturbations, high-content readouts, and complex multicellular models could advance therapeutic discovery yet remain constrained by limitations of scale. To address this, we establish a method for compressing screens by pooling perturbations followed by computational deconvolution. Conducting controlled benchmarks with a highly bioactive small molecule library and a high-content imaging readout, we demonstrate increased efficiency for compressed experimental designs compared to conventional approaches. To prove generalizability, we apply compressed screening to examine transcriptional responses of patient-derived pancreatic cancer organoids to a library of tumor-microenvironment (TME)-nominated recombinant protein ligands. Using single-cell RNA-seq as a readout, we uncover reproducible phenotypic shifts induced by ligands that correlate with clinical features in larger datasets and are distinct from reference signatures available in public databases. In sum, our approach enables phenotypic screens that interrogate complex multicellular models with rich phenotypic readouts to advance translatable drug discovery as well as basic biology.

MCB-613 exploits a collateral sensitivity in drug resistant EGFR-mutant non-small cell lung cancer through covalent inhibition of KEAP1.

Targeted therapies have revolutionized cancer chemotherapy. Unfortunately, most patients develop multifocal resistance to these drugs within a matter of months. Here, we used a high-throughput phenotypic small molecule screen to identify MCB-613 as a compound that selectively targets EGFR-mutant, EGFR inhibitor-resistant non-small cell lung cancer (NSCLC) cells harboring diverse resistance mechanisms. Subsequent proteomic and functional genomic screens involving MCB-613 identified its target in this context to be KEAP1, revealing that this gene is selectively essential in the setting of EGFR inhibitor resistance. In-depth molecular characterization demonstrated that (1) MCB-613 binds KEAP1 covalently; (2) a single molecule of MCB-613 is capable of bridging two KEAP1 monomers together; and, (3) this modification interferes with the degradation of canonical KEAP1 substrates such as NRF2. Surprisingly, NRF2 knockout sensitizes cells to MCB-613, suggesting that the drug functions through modulation of an alternative KEAP1 substrate. Together, these findings advance MCB-613 as a new tool for exploiting the selective essentiality of KEAP1 in drug-resistant, EGFR-mutant NSCLC cells.

Spatially Resolved Single-Cell Assessment of Pancreatic Cancer Expression Subtypes Reveals Co-expressor Phenotypes and Extensive Intratumoral Heterogeneity.

Pancreatic ductal adenocarcinoma (PDAC) has been classified into classical and basal-like transcriptional subtypes by bulk RNA measurements. However, recent work has uncovered greater complexity to transcriptional subtypes than was initially appreciated using bulk RNA expression profiling. To provide a deeper understanding of PDAC subtypes, we developed a multiplex immunofluorescence (mIF) pipeline that quantifies protein expression of six PDAC subtype markers (CLDN18.2, TFF1, GATA6, KRT17, KRT5, and S100A2) and permits spatially resolved, single-cell interrogation of pancreatic tumors from resection specimens and core needle biopsies. Both primary and metastatic tumors displayed striking intratumoral subtype heterogeneity that was associated with patient outcomes, existed at the scale of individual glands, and was significantly reduced in patient-derived organoid cultures. Tumor cells co-expressing classical and basal markers were present in > 90% of tumors, existed on a basal-classical polarization continuum, and were enriched in tumors containing a greater admixture of basal and classical cell populations. Cell-cell neighbor analyses within tumor glands further suggested that co-expressor cells may represent an intermediate state between expression subtype poles. The extensive intratumoral heterogeneity identified through this clinically applicable mIF pipeline may inform prognosis and treatment selection for patients with PDAC. SIGNIFICANCE: A high-throughput pipeline using multiplex immunofluorescence in pancreatic cancer reveals striking expression subtype intratumoral heterogeneity with implications for therapy selection and identifies co-expressor cells that may serve as intermediates during subtype switching.

Using antagonistic pleiotropy to design a chemotherapy-induced evolutionary trap to target drug resistance in cancer.

Local adaptation directs populations towards environment-specific fitness maxima through acquisition of positively selected traits. However, rapid environmental changes can identify hidden fitness trade-offs that turn adaptation into maladaptation, resulting in evolutionary traps. Cancer, a disease that is prone to drug resistance, is in principle susceptible to such traps. We therefore performed pooled CRISPR-Cas9 knockout screens in acute myeloid leukemia (AML) cells treated with various chemotherapies to map the drug-dependent genetic basis of fitness trade-offs, a concept known as antagonistic pleiotropy (AP). We identified a PRC2-NSD2/3-mediated MYC regulatory axis as a drug-induced AP pathway whose ability to confer resistance to bromodomain inhibition and sensitivity to BCL-2 inhibition templates an evolutionary trap. Across diverse AML cell-line and patient-derived xenograft models, we find that acquisition of resistance to bromodomain inhibition through this pathway exposes coincident hypersensitivity to BCL-2 inhibition. Thus, drug-induced AP can be leveraged to design evolutionary traps that selectively target drug resistance in cancer.

Mapping Effector-Phenotype Landscapes in KRAS-Driven Cancers.

Oncogenic KRAS can activate numerous effector pathways to drive malignant progression. However, the relationships between specific effectors and oncogenic phenotypes, and the extent to which these relationships vary across heterogeneous tumors, are incompletely understood. Recently in Cell Reports, a team of scientists described an innovative, combinatorial siRNA-based approach to functionally link KRAS effectors and phenotypes in a large panel of cancer cell lines. Central to this work was the identification of two major subtypes of KRAS-mutant cancers with distinct effector landscapes and tractable therapeutic vulnerabilities.

Systematic mapping of BCL-2 gene dependencies in cancer reveals molecular determinants of BH3 mimetic sensitivity.

While inhibitors of BCL-2 family proteins (BH3 mimetics) have shown promise as anti-cancer agents, the various dependencies or co-dependencies of diverse cancers on BCL-2 genes remain poorly understood. Here we develop a drug screening approach to define the sensitivity of cancer cells from ten tissue types to all possible combinations of selective BCL-2, BCL-XL, and MCL-1 inhibitors and discover that most cell lines depend on at least one combination for survival. We demonstrate that expression levels of BCL-2 genes predict single mimetic sensitivity, whereas EMT status predicts synergistic dependence on BCL-XL+MCL-1. Lastly, we use a CRISPR/Cas9 screen to discover that BFL-1 and BCL-w promote resistance to all tested combinations of BCL-2, BCL-XL, and MCL-1 inhibitors. Together, these results provide a roadmap for rationally targeting BCL-2 family dependencies in diverse human cancers and motivate the development of selective BFL-1 and BCL-w inhibitors to overcome intrinsic resistance to BH3 mimetics.

Epstein-Barr virus ensures B cell survival by uniquely modulating apoptosis at early and late times after infection.

Latent Epstein-Barr virus (EBV) infection is causally linked to several human cancers. EBV expresses viral oncogenes that promote cell growth and inhibit the apoptotic response to uncontrolled proliferation. The EBV oncoprotein LMP1 constitutively activates NFκB and is critical for survival of EBV-immortalized B cells. However, during early infection EBV induces rapid B cell proliferation with low levels of LMP1 and little apoptosis. Therefore, we sought to define the mechanism of survival in the absence of LMP1/NFκB early after infection. We used BH3 profiling to query mitochondrial regulation of apoptosis and defined a transition from uninfected B cells (BCL-2) to early-infected (MCL-1/BCL-2) and immortalized cells (BFL-1). This dynamic change in B cell survival mechanisms is unique to virus-infected cells and relies on regulation of MCL-1 mitochondrial localization and BFL-1 transcription by the viral EBNA3A protein. This study defines a new role for EBNA3A in the suppression of apoptosis with implications for EBV lymphomagenesis.

CDK4/6 Therapeutic Intervention and Viable Alternative to Taxanes in CRPC.

Resistance to second-generation androgen receptor (AR) antagonists and CYP17 inhibitors in patients with castration-resistant prostate cancer (CRPC) develops rapidly through reactivation of the androgen signaling axis and has been attributed to AR overexpression, production of constitutively active AR splice variants, or the selection for AR mutants with altered ligand-binding specificity. It has been established that androgens induce cell-cycle progression, in part, through upregulation of cyclin D1 (CCND1) expression and subsequent activation of cyclin-dependent kinases 4 and 6 (CDK4/6). Thus, the efficacy of the newly described CDK4/6 inhibitors (G1T28 and G1T38), docetaxel and enzalutamide, was evaluated as single agents in clinically relevant in vitro and in vivo models of hormone-sensitive and treatment-resistant prostate cancer. CDK4/6 inhibition (CDK4/6i) was as effective as docetaxel in animal models of treatment-resistant CRPC but exhibited significantly less toxicity. The in vivo effects were durable and importantly were observed in prostate cancer cells expressing wild-type AR, AR mutants, and those that have lost AR expression. CDK4/6i was also effective in prostate tumor models expressing the AR-V7 variant or the AR F876L mutation, both of which are associated with treatment resistance. Furthermore, CDK4/6i was effective in prostate cancer models where AR expression was lost. It is concluded that CDK4/6 inhibitors are a viable alternative to taxanes as therapeutic interventions in endocrine therapy-refractory CRPC.Implications: The preclinical efficacy of CDK4/6 monotherapy observed here suggests the need for near-term clinical studies of these agents in advanced prostate cancer. Mol Cancer Res; 15(6); 660-9. ©2017 AACR.

A Landscape of Therapeutic Cooperativity in KRAS Mutant Cancers Reveals Principles for Controlling Tumor Evolution.

Combinatorial inhibition of effector and feedback pathways is a promising treatment strategy for KRAS mutant cancers. However, the particular pathways that should be targeted to optimize therapeutic responses are unclear. Using CRISPR/Cas9, we systematically mapped the pathways whose inhibition cooperates with drugs targeting the KRAS effectors MEK, ERK, and PI3K. By performing 70 screens in models of KRAS mutant colorectal, lung, ovarian, and pancreas cancers, we uncovered universal and tissue-specific sensitizing combinations involving inhibitors of cell cycle, metabolism, growth signaling, chromatin regulation, and transcription. Furthermore, these screens revealed secondary genetic modifiers of sensitivity, yielding a SRC inhibitor-based combination therapy for KRAS/PIK3CA double-mutant colorectal cancers (CRCs) with clinical potential. Surprisingly, acquired resistance to combinations of growth signaling pathway inhibitors develops rapidly following treatment, but by targeting signaling feedback or apoptotic priming, it is possible to construct three-drug combinations that greatly delay its emergence.

Targeting MCL-1/BCL-XL Forestalls the Acquisition of Resistance to ABT-199 in Acute Myeloid Leukemia.

ABT-199, a potent and selective small-molecule antagonist of BCL-2, is being clinically vetted as pharmacotherapy for the treatment of acute myeloid leukemia (AML). However, given that prolonged monotherapy tends to beget resistance, we sought to investigate the means by which resistance to ABT-199 might arise in AML and the extent to which those mechanisms might be preempted. Here we used a pathway-activating genetic screen to nominate MCL-1 and BCL-XL as potential nodes of resistance. We then characterized a panel of ABT-199-resistant myeloid leukemia cell lines derived through chronic exposure to ABT-199 and found that acquired drug resistance is indeed driven by the upregulation of MCL-1 and BCL-XL. By targeting MCL-1 and BCL-XL, resistant AML cell lines could be resensitized to ABT-199. Further, preemptively targeting MCL-1 and/or BCL-XL alongside administration of ABT-199 was capable of delaying or forestalling the acquisition of drug resistance. Collectively, these data suggest that in AML, (1) the selection of initial therapy dynamically templates the landscape of acquired resistance via modulation of MCL-1/BCL-XL and (2) appropriate selection of initial therapy may delay or altogether forestall the acquisition of resistance to ABT-199.

PIK3CA mutations enable targeting of a breast tumor dependency through mTOR-mediated MCL-1 translation.

Therapies that efficiently induce apoptosis are likely to be required for durable clinical responses in patients with solid tumors. Using a pharmacological screening approach, we discovered that combined inhibition of B cell lymphoma-extra large (BCL-XL) and the mammalian target of rapamycin (mTOR)/4E-BP axis results in selective and synergistic induction of apoptosis in cellular and animal models of PIK3CA mutant breast cancers, including triple-negative tumors. Mechanistically, inhibition of mTOR/4E-BP suppresses myeloid cell leukemia-1 (MCL-1) protein translation only in PIK3CA mutant tumors, creating a synthetic dependence on BCL-XL This dual dependence on BCL-XL and MCL-1, but not on BCL-2, appears to be a fundamental property of diverse breast cancer cell lines, xenografts, and patient-derived tumors that is independent of the molecular subtype or PIK3CA mutational status. Furthermore, this dependence distinguishes breast cancers from normal breast epithelial cells, which are neither primed for apoptosis nor dependent on BCL-XL/MCL-1, suggesting a potential therapeutic window. By tilting the balance of pro- to antiapoptotic signals in the mitochondria, dual inhibition of MCL-1 and BCL-XL also sensitizes breast cancer cells to standard-of-care cytotoxic and targeted chemotherapies. Together, these results suggest that patients with PIK3CA mutant breast cancers may benefit from combined treatment with inhibitors of BCL-XL and the mTOR/4E-BP axis, whereas alternative methods of inhibiting MCL-1 and BCL-XL may be effective in tumors lacking PIK3CA mutations.