PICKLES v3: the updated database of pooled in vitro CRISPR knockout library essentiality screens.

PICKLES (https://pickles.hart-lab.org) is an updated web interface to a freely available database of genome-scale CRISPR knockout fitness screens in human cell lines. Using a completely rewritten interface, researchers can explore gene knockout fitness phenotypes across cell lines and tissue types and compare fitness profiles with fitness, expression, or mutation profiles of other genes. The database has been updated to include data from three CRISPR libraries (Avana, Score, and TKOv3), and includes information from 1162 whole-genome screens probing the knockout fitness phenotype of 18 959 genes. Source code for the interface and the integrated database are available for download.

PRKCI promotes immune suppression in ovarian cancer.

A key feature of high-grade serous ovarian carcinoma (HGSOC) is frequent amplification of the 3q26 locus harboring PRKC-ι (PRKCI). Here, we show that PRKCI is also expressed in early fallopian tube lesions, called serous tubal intraepithelial carcinoma. Transgenic mouse studies establish PRKCI as an ovarian cancer-specific oncogene. Mechanistically, we show that the oncogenic activity of PRKCI relates in part to the up-regulation of TNFα to promote an immune-suppressive tumor microenvironment characterized by an abundance of myeloid-derived suppressor cells and inhibition of cytotoxic T-cell infiltration. Furthermore, system-level and functional analyses identify YAP1 as a downstream effector in tumor progression. In human ovarian cancers, high PRKCI expression also correlates with high expression of TNFα and YAP1 and low infiltration of cytotoxic T cells. The PRKCI-YAP1 regulation of the tumor immunity provides a therapeutic strategy for highly lethal ovarian cancer.

Easy NanoString nCounter data analysis with the NanoTube.

SUMMARY: The NanoTube is an open-source pipeline that simplifies the processing, quality control, normalization and analysis of NanoString nCounter gene expression data. It is implemented in an extensible R library, which performs a variety of gene expression analysis techniques and contains additional functions for integration with other R libraries performing advanced NanoString analysis techniques. Additionally, the NanoTube web application is available as a simple tool for researchers without programming expertise. AVAILABILITY AND IMPLEMENTATION: The NanoTube R package is available on Bioconductor under the GPL-3 license (https://www.bioconductor.org/packages/NanoTube/). The R-Shiny application can be downloaded at https://github.com/calebclass/Shiny-NanoTube, or a simplified version of this application can be run on all major browsers, at https://research.butler.edu/nanotube/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sequential Administration of XPO1 and ATR Inhibitors Enhances Therapeutic Response in TP53-mutated Colorectal Cancer.

BACKGROUND & AIMS: Understanding the mechanisms by which tumors adapt to therapy is critical for developing effective combination therapeutic approaches to improve clinical outcomes for patients with cancer. METHODS: To identify promising and clinically actionable targets for managing colorectal cancer (CRC), we conducted a patient-centered functional genomics platform that includes approximately 200 genes and paired this with a high-throughput drug screen that includes 262 compounds in four patient-derived xenografts (PDXs) from patients with CRC. RESULTS: Both screening methods identified exportin 1 (XPO1) inhibitors as drivers of DNA damage-induced lethality in CRC. Molecular characterization of the cellular response to XPO1 inhibition uncovered an adaptive mechanism that limited the duration of response in TP53-mutated, but not in TP53-wild-type CRC models. Comprehensive proteomic and transcriptomic characterization revealed that the ATM/ATR-CHK1/2 axes were selectively engaged in TP53-mutant CRC cells upon XPO1 inhibitor treatment and that this response was required for adapting to therapy and escaping cell death. Administration of KPT-8602, an XPO1 inhibitor, followed by AZD-6738, an ATR inhibitor, resulted in dramatic antitumor effects and prolonged survival in TP53-mutant models of CRC. CONCLUSIONS: Our findings anticipate tremendous therapeutic benefit and support the further evaluation of XPO1 inhibitors, especially in combination with DNA damage checkpoint inhibitors, to elicit an enduring clinical response in patients with CRC harboring TP53 mutations.

PAF promotes stemness and radioresistance of glioma stem cells.

An integrated genomic and functional analysis to elucidate DNA damage signaling factors promoting self-renewal of glioma stem cells (GSCs) identified proliferating cell nuclear antigen (PCNA)-associated factor (PAF) up-regulation in glioblastoma. PAF is preferentially overexpressed in GSCs. Its depletion impairs maintenance of self-renewal without promoting differentiation and reduces tumor-initiating cell frequency. Combined transcriptomic and metabolomic analyses revealed that PAF supports GSC maintenance, in part, by influencing DNA replication and pyrimidine metabolism pathways. PAF interacts with PCNA and regulates PCNA-associated DNA translesion synthesis (TLS); consequently, PAF depletion in combination with radiation generated fewer tumorspheres compared with radiation alone. Correspondingly, pharmacological impairment of DNA replication and TLS phenocopied the effect of PAF depletion in compromising GSC self-renewal and radioresistance, providing preclinical proof of principle that combined TLS inhibition and radiation therapy may be a viable therapeutic option in the treatment of glioblastoma multiforme (GBM).

Analyzing Somatic Genome Rearrangements in Human Cancers by Using Whole-Exome Sequencing.

Although exome sequencing data are generated primarily to detect single-nucleotide variants and indels, they can also be used to identify a subset of genomic rearrangements whose breakpoints are located in or near exons. Using >4,600 tumor and normal pairs across 15 cancer types, we identified over 9,000 high confidence somatic rearrangements, including a large number of gene fusions. We find that the 5' fusion partners of functional fusions are often housekeeping genes, whereas the 3' fusion partners are enriched in tyrosine kinases. We establish the oncogenic potential of ROR1-DNAJC6 and CEP85L-ROS1 fusions by showing that they can promote cell proliferation in vitro and tumor formation in vivo. Furthermore, we found that ∼4% of the samples have massively rearranged chromosomes, many of which are associated with upregulation of oncogenes such as ERBB2 and TERT. Although the sensitivity of detecting structural alterations from exomes is considerably lower than that from whole genomes, this approach will be fruitful for the multitude of exomes that have been and will be generated, both in cancer and in other diseases.

A cis-regulatory map of the Drosophila genome.

Systematic annotation of gene regulatory elements is a major challenge in genome science. Direct mapping of chromatin modification marks and transcriptional factor binding sites genome-wide has successfully identified specific subtypes of regulatory elements. In Drosophila several pioneering studies have provided genome-wide identification of Polycomb response elements, chromatin states, transcription factor binding sites, RNA polymerase II regulation and insulator elements; however, comprehensive annotation of the regulatory genome remains a significant challenge. Here we describe results from the modENCODE cis-regulatory annotation project. We produced a map of the Drosophila melanogaster regulatory genome on the basis of more than 300 chromatin immunoprecipitation data sets for eight chromatin features, five histone deacetylases and thirty-eight site-specific transcription factors at different stages of development. Using these data we inferred more than 20,000 candidate regulatory elements and validated a subset of predictions for promoters, enhancers and insulators in vivo. We identified also nearly 2,000 genomic regions of dense transcription factor binding associated with chromatin activity and accessibility. We discovered hundreds of new transcription factor co-binding relationships and defined a transcription factor network with over 800 potential regulatory relationships.

Characterization of HPV and host genome interactions in primary head and neck cancers.

Previous studies have established that a subset of head and neck tumors contains human papillomavirus (HPV) sequences and that HPV-driven head and neck cancers display distinct biological and clinical features. HPV is known to drive cancer by the actions of the E6 and E7 oncoproteins, but the molecular architecture of HPV infection and its interaction with the host genome in head and neck cancers have not been comprehensively described. We profiled a cohort of 279 head and neck cancers with next generation RNA and DNA sequencing and show that 35 (12.5%) tumors displayed evidence of high-risk HPV types 16, 33, or 35. Twenty-five cases had integration of the viral genome into one or more locations in the human genome with statistical enrichment for genic regions. Integrations had a marked impact on the human genome and were associated with alterations in DNA copy number, mRNA transcript abundance and splicing, and both inter- and intrachromosomal rearrangements. Many of these events involved genes with documented roles in cancer. Cancers with integrated vs. nonintegrated HPV displayed different patterns of DNA methylation and both human and viral gene expressions. Together, these data provide insight into the mechanisms by which HPV interacts with the human genome beyond expression of viral oncoproteins and suggest that specific integration events are an integral component of viral oncogenesis.

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks.

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein-protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level.

Mechanisms of resistance to oncogenic KRAS inhibition in pancreatic cancer.

KRAS inhibitors demonstrate clinical efficacy in pancreatic ductal adenocarcinoma (PDAC); however, resistance is common. Among patients with KRASG12C-mutant PDAC treated with adagrasib or sotorasib, mutations in PIK3CA and KRAS, and amplifications of KRASG12C, MYC, MET, EGFR, and CDK6 emerged at acquired resistance. In PDAC cell lines and organoid models treated with the KRASG12D inhibitor MRTX1133, epithelial-to-mesenchymal transition and PI3K-AKT-mTOR signaling associate with resistance to therapy. MRTX1133 treatment of the KrasLSL-G12D/+;Trp53LSL-R172H/+;p48-Cre (KPC) mouse model yielded deep tumor regressions, but drug resistance ultimately emerged, accompanied by amplifications of Kras, Yap1, Myc, and Cdk6/Abcb1a/b, and co-evolution of drug-resistant transcriptional programs. Moreover, in KPC and PDX models, mesenchymal and basal-like cell states displayed increased response to KRAS inhibition compared to the classical state. Combination treatment with KRASG12D inhibition and chemotherapy significantly improved tumor control in PDAC mouse models. Collectively, these data elucidate co-evolving resistance mechanisms to KRAS inhibition and support multiple combination therapy strategies.

Ataxia-Telangiectasia Mutated Loss-of-Function Displays Variant and Tissue-Specific Differences across Tumor Types.

PURPOSE: Mutations in the ATM gene are common in multiple cancers, but clinical studies of therapies targeting ATM-aberrant cancers have yielded mixed results. Refinement of ATM loss of function (LOF) as a predictive biomarker of response is urgently needed. EXPERIMENTAL DESIGN: We present the first disclosure and preclinical development of a novel, selective ATR inhibitor, ART0380, and test its antitumor activity in multiple preclinical cancer models. To refine ATM LOF as a predictive biomarker, we performed a comprehensive pan-cancer analysis of ATM variants in patient tumors and then assessed the ATM variant-to-protein relationship. Finally, we assessed a novel ATM LOF biomarker approach in retrospective clinical data sets of patients treated with platinum-based chemotherapy or ATR inhibition. RESULTS: ART0380 had potent, selective antitumor activity in a range of preclinical cancer models with differing degrees of ATM LOF. Pan-cancer analysis identified 10,609 ATM variants in 8,587 patient tumors. Cancer lineage-specific differences were seen in the prevalence of deleterious (Tier 1) versus unknown/benign (Tier 2) variants, selective pressure for loss of heterozygosity, and concordance between a deleterious variant and ATM loss of protein (LOP). A novel ATM LOF biomarker approach that accounts for variant classification, relationship to ATM LOP, and tissue-specific penetrance significantly enriched for patients who benefited from platinum-based chemotherapy or ATR inhibition. CONCLUSIONS: These data help to better define ATM LOF across tumor types in order to optimize patient selection and improve molecularly targeted therapeutic approaches for patients with ATM LOF cancers.

Combined KRASG12C and SOS1 inhibition enhances and extends the anti-tumor response in KRASG12C-driven cancers by addressing intrinsic and acquired resistance.

Efforts to improve the anti-tumor response to KRASG12C targeted therapy have benefited from leveraging combination approaches. Here, we compare the anti-tumor response induced by the SOS1-KRAS interaction inhibitor, BI-3406, combined with a KRASG12C inhibitor (KRASG12Ci) to those induced by KRASG12Ci alone or combined with SHP2 or EGFR inhibitors. In lung cancer and colorectal cancer (CRC) models, BI-3406 plus KRASG12Ci induces an anti-tumor response stronger than that observed with KRASG12Ci alone and comparable to those by the other combinations. This enhanced anti-tumor response is associated with a stronger and extended suppression of RAS-MAPK signaling. Importantly, BI-3406 plus KRASG12Ci treatment delays the emergence of acquired adagrasib resistance in both CRC and lung cancer models and is associated with re-establishment of anti-proliferative activity in KRASG12Ci-resistant CRC models. Our findings position KRASG12C plus SOS1 inhibition therapy as a promising strategy for treating both KRASG12C-mutated tumors as well as for addressing acquired resistance to KRASG12Ci.

Complex I inhibitor of oxidative phosphorylation in advanced solid tumors and acute myeloid leukemia: phase I trials.

Although targeting oxidative phosphorylation (OXPHOS) is a rational anticancer strategy, clinical benefit with OXPHOS inhibitors has yet to be achieved. Here we advanced IACS-010759, a highly potent and selective small-molecule complex I inhibitor, into two dose-escalation phase I trials in patients with relapsed/refractory acute myeloid leukemia (NCT02882321, n = 17) and advanced solid tumors (NCT03291938, n = 23). The primary endpoints were safety, tolerability, maximum tolerated dose and recommended phase 2 dose (RP2D) of IACS-010759. The PK, PD, and preliminary antitumor activities of IACS-010759 in patients were also evaluated as secondary endpoints in both clinical trials. IACS-010759 had a narrow therapeutic index with emergent dose-limiting toxicities, including elevated blood lactate and neurotoxicity, which obstructed efforts to maintain target exposure. Consequently no RP2D was established, only modest target inhibition and limited antitumor activity were observed at tolerated doses, and both trials were discontinued. Reverse translational studies in mice demonstrated that IACS-010759 induced behavioral and physiological changes indicative of peripheral neuropathy, which were minimized with the coadministration of a histone deacetylase 6 inhibitor. Additional studies are needed to elucidate the association between OXPHOS inhibition and neurotoxicity, and caution is warranted in the continued development of complex I inhibitors as antitumor agents.

Interferon signaling promotes tolerance to chromosomal instability during metastatic evolution in renal cancer.

Molecular routes to metastatic dissemination are critical determinants of aggressive cancers. Through in vivo CRISPR-Cas9 genome editing, we generated somatic mosaic genetically engineered models that faithfully recapitulate metastatic renal tumors. Disruption of 9p21 locus is an evolutionary driver to systemic disease through the rapid acquisition of complex karyotypes in cancer cells. Cross-species analysis revealed that recurrent patterns of copy number variations, including 21q loss and dysregulation of the interferon pathway, are major drivers of metastatic potential. In vitro and in vivo genomic engineering, leveraging loss-of-function studies, along with a model of partial trisomy of chromosome 21q, demonstrated a dosage-dependent effect of the interferon receptor genes cluster as an adaptive mechanism to deleterious chromosomal instability in metastatic progression. This work provides critical knowledge on drivers of renal cell carcinoma progression and defines the primary role of interferon signaling in constraining the propagation of aneuploid clones in cancer evolution.

Dynamic rewiring of biological activity across genotype and lineage revealed by context-dependent functional interactions.

BACKGROUND: Coessentiality networks derived from CRISPR screens in cell lines provide a powerful framework for identifying functional modules in the cell and for inferring the roles of uncharacterized genes. However, these networks integrate signal across all underlying data and can mask strong interactions that occur in only a subset of the cell lines analyzed. RESULTS: Here, we decipher dynamic functional interactions by identifying significant cellular contexts, primarily by oncogenic mutation, lineage, and tumor type, and discovering coessentiality relationships that depend on these contexts. We recapitulate well-known gene-context interactions such as oncogene-mutation, paralog buffering, and tissue-specific essential genes, show how mutation rewires known signal transduction pathways, including RAS/RAF and IGF1R-PIK3CA, and illustrate the implications for drug targeting. We further demonstrate how context-dependent functional interactions can elucidate lineage-specific gene function, as illustrated by the maturation of proreceptors IGF1R and MET by proteases FURIN and CPD. CONCLUSIONS: This approach advances our understanding of context-dependent interactions and how they can be gleaned from these data. We provide an online resource to explore these context-dependent interactions at diffnet.hart-lab.org.

A functional genomic approach to actionable gene fusions for precision oncology.

Fusion genes represent a class of attractive therapeutic targets. Thousands of fusion genes have been identified in patients with cancer, but the functional consequences and therapeutic implications of most of these remain largely unknown. Here, we develop a functional genomic approach that consists of efficient fusion reconstruction and sensitive cell viability and drug response assays. Applying this approach, we characterize ~100 fusion genes detected in patient samples of The Cancer Genome Atlas, revealing a notable fraction of low-frequency fusions with activating effects on tumor growth. Focusing on those in the RTK-RAS pathway, we identify a number of activating fusions that can markedly affect sensitivity to relevant drugs. Last, we propose an integrated, level-of-evidence classification system to prioritize gene fusions systematically. Our study reiterates the urgent clinical need to incorporate similar functional genomic approaches to characterize gene fusions, thereby maximizing the utility of gene fusions for precision oncology.

Shared Nearest Neighbors Approach and Interactive Browser for Network Analysis of a Comprehensive Non-Small-Cell Lung Cancer Data Set.

PURPOSE: Advances in biological measurement technologies are enabling large-scale studies of patient cohorts across multiple omics platforms. Holistic analysis of these data can generate actionable insights for translational research and necessitate new approaches for data integration and mining. METHODS: We present a novel approach for integrating data across platforms on the basis of the shared nearest neighbors algorithm and use it to create a network of multiplatform data from the immunogenomic profiling of non-small-cell lung cancer project. RESULTS: Benchmarking demonstrates that the shared nearest neighbors-based network approach outperforms a traditional gene-gene network in capturing established interactions while providing new ones on the basis of the interplay between measurements from different platforms. When used to examine patient characteristics of interest, our approach provided signatures associated with and new leads related to recurrence and TP53 oncogenotype. CONCLUSION: The network developed offers an unprecedented, holistic view into immunogenomic profiling of non-small-cell lung cancer, which can be explored through the accompanying interactive browser that we built.

Combined inhibition of DDR1 and CDK4/6 induces synergistic effects in ER-positive, HER2-negative breast cancer with PIK3CA/AKT1 mutations.

Molecular alterations in the PI3K/AKT pathway occur frequently in hormone receptor-positive breast tumors. Patients with ER-positive, HER2-negative metastatic breast cancer are often treated with CDK4/6 inhibitors such as palbociclib in combination with endocrine therapy. Although this is an effective regimen, most patients ultimately progress. The purpose of this study was identifying synthetic lethality partners that can enhance palbociclib's antitumor efficacy in the presence of PIK3CA/AKT1 mutations. We utilized a barcoded shRNA library to determine critical targets for survival in isogenic MCF7 cells with PIK3CA/AKT1 mutations. We demonstrated that the efficacy of palbociclib is reduced in the presence of PIK3CA/AKT1 mutations. We also identified that the downregulation of discoidin domain receptor 1 (DDR1) is synthetically lethal with palbociclib. DDR1 knockdown and DDR1 pharmacological inhibitor decreased cell growth and inhibited cell cycle progression in all cell lines, while enhanced the sensitivity of PIK3CA/AKT1 mutant cells to palbociclib. Combined treatment of palbociclib and 7rh further induced cell cycle arrest in PIK3CA/AKT1 mutant cell lines. In vivo, 7rh significantly enhanced palbociclib's antitumor efficacy. Our data indicates that DDR1 inhibition can augment cell cycle suppressive effect of palbociclib and could be effective strategy for targeted therapy of ER-positive, HER2-negative breast cancers with PI3K pathway activation.

Targeting CDK4 overcomes EMT-mediated tumor heterogeneity and therapeutic resistance in KRAS-mutant lung cancer.

Lack of sustained response to therapeutic agents in patients with KRAS-mutant lung cancer poses a major challenge and arises partly due to intratumor heterogeneity that defines phenotypically distinct tumor subpopulations. To attain better therapeutic outcomes, it is important to understand the differential therapeutic sensitivities of tumor cell subsets. Epithelial-mesenchymal transition is a biological phenomenon that can alter the state of cells along a phenotypic spectrum and cause transcriptional rewiring to produce distinct tumor cell subpopulations. We utilized functional shRNA screens, in in vitro and in vivo models, to identify and validate an increased dependence of mesenchymal tumor cells on cyclin-dependent kinase 4 (CDK4) for survival, as well as a mechanism of resistance to MEK inhibitors. High zinc finger E-box binding homeobox 1 levels in mesenchymal tumor cells repressed p21, leading to perturbed CDK4 pathway activity. Increased dependence on CDK4 rendered mesenchymal cancer cells particularly vulnerable to selective CDK4 inhibitors. Coadministration of CDK4 and MEK inhibitors in heterogeneous tumors effectively targeted different tumor subpopulations, subverting the resistance to either single-agent treatment.

Targeting Glucose Metabolism Sensitizes Pancreatic Cancer to MEK Inhibition.

Pancreatic ductal adenocarcinoma (PDAC) is almost universally lethal. A critical unmet need exists to explore essential susceptibilities in PDAC and to identify druggable targets to improve PDAC treatment. KRAS mutations dominate the genetic landscape of PDAC and lead to activation of multiple downstream pathways and cellular processes. Here, we investigated the requirement of these pathways for tumor maintenance using an inducible KrasG12D -driven PDAC mouse model (iKras model), identifying that RAF-MEK-MAPK signaling is the major effector for oncogenic KRAS-mediated tumor maintenance. However, consistent with previous studies, MEK inhibition had minimal therapeutic effect as a single agent for PDAC in vitro and in vivo. Although MEK inhibition partially downregulated transcription of glycolysis genes, it failed to suppress glycolytic flux in PDAC cells, which is a major metabolic effector of oncogenic KRAS. Accordingly, an in vivo genetic screen identified multiple glycolysis genes as potential targets that may sensitize tumor cells to MEK inhibition. Inhibition of glucose metabolism with low-dose 2-deoxyglucose in combination with a MEK inhibitor induced apoptosis in KrasG12D -driven PDAC cells in vitro. The combination also inhibited xenograft PDAC tumor growth and prolonged overall survival in a genetically engineered PDAC mouse model. Molecular and metabolic analyses indicated that co-targeting glycolysis and MAPK signaling results in apoptosis via induction of lethal endoplasmic reticulum stress. Together, our work suggests that combined inhibition of glycolysis and the MAPK pathway may serve as an effective approach to target KRAS-driven PDAC. SIGNIFICANCE: This study demonstrates the critical role of glucose metabolism in resistance to MAPK inhibition in KRAS-driven pancreatic cancer, uncovering a potential therapeutic approach for treating this aggressive disease.