Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms

Most human Transcription factors (TFs) genes encode multiple protein isoforms differing in DNA binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators", both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data

Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. Here we present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single-cell/nuclei RNA-sequencing data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperformed 12 existing gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPION's scalability to population-level analyses using a single-cell RNA-sequencing atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences between tumor regions detected by SCORPION are consistent across multiple cohorts as well as with our understanding of disease progression, and elucidate phenotypic regulators that may impact patient survival.

Integrated multi-omics analyses identify anti-viral host factors and pathways controlling SARS-CoV-2 infection

Host anti-viral factors are essential for controlling SARS-CoV-2 infection but remain largely unknown due to the biases of previous large-scale studies toward pro-viral host factors. To fill in this knowledge gap, we perform a genome-wide CRISPR dropout screen and integrate analyses of the multi-omics data of the CRISPR screen, genome-wide association studies, single-cell RNA-Seq, and host-virus proteins or protein/RNA interactome. This study uncovers many host factors that are currently underappreciated, including the components of V-ATPases, ESCRT, and N-glycosylation pathways that modulate viral entry and/or replication. The cohesin complex is also identified as an anti-viral pathway, suggesting an important role of three-dimensional chromatin organization in mediating host-viral interaction. Furthermore, we discover another anti-viral regulator KLF5, a transcriptional factor involved in sphingolipid metabolism, which is up-regulated, and harbors genetic variations linked to COVID-19 patients with severe symptoms. Anti-viral effects of three identified candidates (DAZAP2/VTA1/KLF5) are confirmed individually. Molecular characterization of DAZAP2/VTA1/KLF5-knockout cells highlights the involvement of genes related to the coagulation system in determining the severity of COVID-19. Together, our results provide further resources for understanding the host anti-viral network during SARS-CoV-2 infection and may help develop new countermeasure strategies.

PRMT blockade induces defective DNA replication stress response and synergizes with PARP inhibition

Multiple cancers exhibit aberrant protein arginine methylation by both type I arginine methyltransferases, predominately protein arginine methyltransferase 1 (PRMT1) and to a lesser extent PRMT4, and by type II PRMTs, predominately PRMT5. Here, we perform targeted proteomics following inhibition of PRMT1, PRMT4, and PRMT5 across 12 cancer cell lines. We find that inhibition of type I and II PRMTs suppresses phosphorylated and total ATR in cancer cells. Loss of ATR from PRMT inhibition results in defective DNA replication stress response activation, including from PARP inhibitors. Inhibition of type I and II PRMTs is synergistic with PARP inhibition regardless of homologous recombination function, but type I PRMT inhibition is more toxic to non-malignant cells. Finally, we demonstrate that the combination of PARP and PRMT5 inhibition improves survival in both BRCA-mutant and wild-type patient-derived xenografts without toxicity. Taken together, these results demonstrate that PRMT5 inhibition may be a well-tolerated approach to sensitize tumors to PARP inhibition.

Widespread BRCA1/2-independent homologous recombination defects are caused by alterations in RNA-binding proteins

Defects in homologous recombination DNA repair (HRD) both predispose to cancer development and produce therapeutic vulnerabilities, making it critical to define the spectrum of genetic events that cause HRD. However, we found that mutations in BRCA1/2 and other canonical HR genes only identified 10%-20% of tumors that display genomic evidence of HRD. Using a networks-based approach, we discovered that over half of putative genes causing HRD originated outside of canonical DNA damage response genes, with a particular enrichment for RNA-binding protein (RBP)-encoding genes. These putative drivers of HRD were experimentally validated, cross-validated in an independent cohort, and enriched in cancer-associated genome-wide association study loci. Mechanistic studies indicate that some RBPs are recruited to sites of DNA damage to facilitate repair, whereas others control the expression of canonical HR genes. Overall, this study greatly expands the repertoire of known drivers of HRD, with implications for basic biology, genetic screening, and therapy stratification.

Defining the condensate landscape of fusion oncoproteins

Fusion oncoproteins (FOs) arise from chromosomal translocations in ~17% of cancers and are often oncogenic drivers. Although some FOs can promote oncogenesis by undergoing liquid-liquid phase separation (LLPS) to form aberrant biomolecular condensates, the generality of this phenomenon is unknown. We explored this question by testing 166 FOs in HeLa cells and found that 58% formed condensates. The condensate-forming FOs displayed physicochemical features distinct from those of condensate-negative FOs and segregated into distinct feature-based groups that aligned with their sub-cellular localization and biological function. Using Machine Learning, we developed a predictor of FO condensation behavior, and discovered that 67% of ~3000 additional FOs likely form condensates, with 35% of those predicted to function by altering gene expression. 47% of the predicted condensate-negative FOs were associated with cell signaling functions, suggesting a functional dichotomy between condensate-positive and -negative FOs. Our Datasets and reagents are rich resources to interrogate FO condensation in the future.

Pervasive mislocalization of pathogenic coding variants underlying human disorders

Widespread sequencing has yielded thousands of missense variants predicted or confirmed as disease-causing. This creates a new bottleneck: determining the functional impact of each variant - largely a painstaking, customized process undertaken one or a few genes or variants at a time. Here, we established a high-throughput imaging platform to assay the impact of coding variation on protein localization, evaluating 3,547 missense variants of over 1,000 genes and phenotypes. We discovered that mislocalization is a common consequence of coding variation, affecting about one-sixth of all pathogenic missense variants, all cellular compartments, and recessive and dominant disorders alike. Mislocalization is primarily driven by effects on protein stability and membrane insertion rather than disruptions of trafficking signals or specific interactions. Furthermore, mislocalization patterns help explain pleiotropy and disease severity and provide insights on variants of unknown significance. Our publicly available resource will likely accelerate the understanding of coding variation in human diseases.

Multiomics analyses reveal DARS1-AS1/YBX1-controlled posttranscriptional circuits promoting glioblastoma tumorigenesis/radioresistance

The glioblastoma (GBM) stem cell-like cells (GSCs) are critical for tumorigenesis/therapeutic resistance of GBM. Mounting evidence supports tumor-promoting function of long noncoding RNAs (lncRNAs), but their role in GSCs remains poorly understood. By combining CRISPRi screen with orthogonal multiomics approaches, we identified a lncRNA DARS1-AS1-controlled posttranscriptional circuitry that promoted the malignant properties of GBM cells/GSCs. Depleting DARS1-AS1 inhibited the proliferation of GBM cells/GSCs and self-renewal of GSCs, prolonging survival in orthotopic GBM models. DARS1-AS1 depletion also impaired the homologous recombination (HR)-mediated double-strand break (DSB) repair and enhanced the radiosensitivity of GBM cells/GSCs. Mechanistically, DARS1-AS1 interacted with YBX1 to promote target mRNA binding and stabilization, forming a mixed transcriptional/posttranscriptional feed-forward loop to up-regulate expression of the key regulators of G1-S transition, including E2F1 and CCND1. DARS1-AS1/YBX1 also stabilized the mRNA of FOXM1, a master transcription factor regulating GSC self-renewal and DSB repair. Our findings suggest DARS1-AS1/YBX1 axis as a potential therapeutic target for sensitizing GBM to radiation/HR deficiency-targeted therapy.

Alternative splicing of GSDMB modulates killer lymphocyte-triggered pyroptosis

Granzyme A from killer lymphocytes cleaves gasdermin B (GSDMB) and triggers pyroptosis in targeted human tumor cells, eliciting antitumor immunity. However, GSDMB has a controversial role in pyroptosis and has been linked to both anti- and protumor functions. Here, we found that GSDMB splicing variants are functionally distinct. Cleaved N-terminal (NT) fragments of GSDMB isoforms 3 and 4 caused pyroptosis, but isoforms 1, 2, and 5 did not. The nonfunctional isoforms have a deleted or modified exon 6 and therefore lack a stable belt motif. The belt likely contributes to the insertion of oligomeric GSDMB-NTs into the membrane. Consistently, noncytotoxic GSDMB-NTs blocked pyroptosis caused by cytotoxic GSDMB-NTs in a dominant-negative manner. Upon natural killer (NK) cell attack, GSDMB3-expressing cells died by pyroptosis, whereas GSDMB4-expressing cells died by mixed pyroptosis and apoptosis, and GSDMB1/2-expressing cells died only by apoptosis. GSDMB4 partially resisted NK cell-triggered cleavage, suggesting that only GSDMB3 is fully functional. GSDMB1-3 were the most abundant isoforms in the tested tumor cell lines and were similarly induced by interferon-γ and the chemotherapy drug methotrexate. Expression of cytotoxic GSDMB3/4 isoforms, but not GSDMB1/2 isoforms that are frequently up-regulated in tumors, was associated with better outcomes in bladder and cervical cancers, suggesting that GSDMB3/4-mediated pyroptosis was protective in those tumors. Our study indicates that tumors may block and evade killer cell-triggered pyroptosis by generating noncytotoxic GSDMB isoforms. Therefore, therapeutics that favor the production of cytotoxic GSDMB isoforms by alternative splicing may improve antitumor immunity.

AD-Syn-Net: systematic identification of Alzheimer's disease-associated mutation and co-mutation vulnerabilities via deep learning

Alzheimer's disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. To address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework ('AD-Syn-Net'), and propose deep learning models named Deep-SMCI and Deep-CMCI configured with fully connected layers that are capable of predicting cognitive impairment of subjects effectively based on genetic mutation and co-mutation profiles. Next, we apply the customized frameworks to data sets to evaluate the importance scores of the mutations and identified mutation effectors and co-mutation combination vulnerabilities contributing to cognitive impairment. Furthermore, we evaluate the influence of mutation pairs on the network architecture to dissect the genetic organization of AD and identify novel co-mutations that could be responsible for dementia, laying a solid foundation for proposing future targeted therapy for AD precision medicine. Our deep learning model codes are available open access here: https://github.com/Pan-Bio/AD-mutation-effectors.

AI-DrugNet: A network-based deep learning model for drug repurposing and combination therapy in neurological disorders

Discovering effective therapies is difficult for neurological and developmental disorders in that disease progression is often associated with a complex and interactive mechanism. Over the past few decades, few drugs have been identified for treating Alzheimer's disease (AD), especially for impacting the causes of cell death in AD. Although drug repurposing is gaining more success in developing therapeutic efficacy for complex diseases such as common cancer, the complications behind AD require further study. Here, we developed a novel prediction framework based on deep learning to identify potential repurposed drug therapies for AD, and more importantly, our framework is broadly applicable and may generalize to identifying potential drug combinations in other diseases. Our prediction framework is as follows: we first built a drug-target pair (DTP) network based on multiple drug features and target features, as well as the associations between DTP nodes where drug-target pairs are the DTP nodes and the associations between DTP nodes are represented as the edges in the AD disease network; furthermore, we incorporated the drug-target feature from the DTP network and the relationship information between drug-drug, target-target, drug-target within and outside of drug-target pairs, representing each drug-combination as a quartet to generate corresponding integrated features; finally, we developed an AI-based Drug discovery Network (AI-DrugNet), which exhibits robust predictive performance. The implementation of our network model help identify potential repurposed and combination drug options that may serve to treat AD and other diseases.

The HAPSTR2 retrogene buffers stress signaling and resilience in mammals

We recently identified HAPSTR1 (C16orf72) as a key component in a novel pathway which regulates the cellular response to molecular stressors, such as DNA damage, nutrient scarcity, and protein misfolding. Here, we identify a functional paralog to HAPSTR1: HAPSTR2. HAPSTR2 formed early in mammalian evolution, via genomic integration of a reverse transcribed HAPSTR1 transcript, and has since been preserved under purifying selection. HAPSTR2, expressed primarily in neural and germline tissues and a subset of cancers, retains established biochemical features of HAPSTR1 to achieve two functions. In normal physiology, HAPSTR2 directly interacts with HAPSTR1, markedly augmenting HAPSTR1 protein stability in a manner independent from HAPSTR1's canonical E3 ligase, HUWE1. Alternatively, in the context of HAPSTR1 loss, HAPSTR2 expression is sufficient to buffer stress signaling and resilience. Thus, we discover a mammalian retrogene which safeguards fitness.

Landscape of MicroRNA Regulatory Network Architecture and Functional Rerouting in Cancer

Somatic mutations are a major source of cancer development, and many driver mutations have been identified in protein coding regions. However, the function of mutations located in miRNA and their target binding sites throughout the human genome remains largely unknown. Here, we built detailed cancer-specific miRNA regulatory networks across 30 cancer types to systematically analyze the effect of mutations in miRNAs and their target sites in 3' untranslated region (3' UTR), coding sequence (CDS), and 5' UTR regions. A total of 3,518,261 mutations from 9,819 samples were mapped to miRNA-gene interactions (mGI). Mutations in miRNAs showed a mutually exclusive pattern with mutations in their target genes in almost all cancer types. A linear regression method identified 148 candidate driver mutations that can significantly perturb miRNA regulatory networks. Driver mutations in 3'UTRs played their roles by altering RNA binding energy and the expression of target genes. Finally, mutated driver gene targets in 3' UTRs were significantly downregulated in cancer and functioned as tumor suppressors during cancer progression, suggesting potential miRNA candidates with significant clinical implications. A user-friendly, open-access web portal (mGI-map) was developed to facilitate further use of this data resource. Together, these results will facilitate novel noncoding biomarker identification and therapeutic drug design targeting the miRNA regulatory networks.

SIGNIFICANCE

A detailed miRNA-gene interaction map reveals extensive miRNA-mediated gene regulatory networks with mutation-induced perturbations across multiple cancers, serving as a resource for noncoding biomarker discovery and drug development.

Gain-of-Function Variomics and Multi-omics Network Biology for Precision Medicine

Traditionally, disease causal mutations were thought to disrupt gene function. However, it becomes more clear that many deleterious mutations could exhibit a "gain-of-function" (GOF) behavior. Systematic investigation of such mutations has been lacking and largely overlooked. Advances in next-generation sequencing have identified thousands of genomic variants that perturb the normal functions of proteins, further contributing to diverse phenotypic consequences in disease. Elucidating the functional pathways rewired by GOF mutations will be crucial for prioritizing disease-causing variants and their resultant therapeutic liabilities. In distinct cell types (with varying genotypes), precise signal transduction controls cell decision, including gene regulation and phenotypic output. When signal transduction goes awry due to GOF mutations, it would give rise to various disease types. Quantitative and molecular understanding of network perturbations by GOF mutations may provide explanations for 'missing heritability" in previous genome-wide association studies. We envision that it will be instrumental to push current paradigm toward a thorough functional and quantitative modeling of all GOF mutations and their mechanistic molecular events involved in disease development and progression. Many fundamental questions pertaining to genotype-phenotype relationships remain unresolved. For example, which GOF mutations are key for gene regulation and cellular decisions? What are the GOF mechanisms at various regulation levels? How do interaction networks undergo rewiring upon GOF mutations? Is it possible to leverage GOF mutations to reprogram signal transduction in cells, aiming to cure disease? To begin to address these questions, we will cover a wide range of topics regarding GOF disease mutations and their characterization by multi-omic networks. We highlight the fundamental function of GOF mutations and discuss the potential mechanistic effects in the context of signaling networks. We also discuss advances in bioinformatic and computational resources, which will dramatically help with studies on the functional and phenotypic consequences of GOF mutations.

Single-cell CRISPR immune screens reveal immunological roles of tumor intrinsic factors

Genetic screens are widely exploited to develop novel therapeutic approaches for cancer treatment. With recent advances in single-cell technology, single-cell CRISPR screen (scCRISPR) platforms provide opportunities for target validation and mechanistic studies in a high-throughput manner. Here, we aim to establish scCRISPR platforms which are suitable for immune-related screens involving multiple cell types. We integrated two scCRISPR platforms, namely Perturb-seq and CROP-seq, with both in vitro and in vivo immune screens. By leveraging previously generated resources, we optimized experimental conditions and data analysis pipelines to achieve better consistency between results from high-throughput and individual validations. Furthermore, we evaluated the performance of scCRISPR immune screens in determining underlying mechanisms of tumor intrinsic immune regulation. Our results showed that scCRISPR platforms can simultaneously characterize gene expression profiles and perturbation effects present in individual cells in different immune screen conditions. Results from scCRISPR immune screens also predict transcriptional phenotype associated with clinical responses to cancer immunotherapy. More importantly, scCRISPR screen platforms reveal the interactive relationship between targeting tumor intrinsic factors and T cell-mediated antitumor immune response which cannot be easily assessed by bulk RNA-seq. Collectively, scCRISPR immune screens provide scalable and reliable platforms to elucidate molecular determinants of tumor immune resistance.

BET inhibition induces vulnerability to MCL1 targeting through upregulation of fatty acid synthesis pathway in breast cancer

Therapeutic options for treatment of basal-like breast cancers remain limited. Here, we demonstrate that bromodomain and extra-terminal (BET) inhibition induces an adaptive response leading to MCL1 protein-driven evasion of apoptosis in breast cancer cells. Consequently, co-targeting MCL1 and BET is highly synergistic in breast cancer models. The mechanism of adaptive response to BET inhibition involves the upregulation of lipid synthesis enzymes including the rate-limiting stearoyl-coenzyme A (CoA) desaturase. Changes in lipid synthesis pathway are associated with increases in cell motility and membrane fluidity as well as re-localization and activation of HER2/EGFR. In turn, the HER2/EGFR signaling results in the accumulation of and vulnerability to the inhibition of MCL1. Drug response and genomics analyses reveal that MCL1 copy-number alterations are associated with effective BET and MCL1 co-targeting. The high frequency of MCL1 chromosomal amplifications (>30%) in basal-like breast cancers suggests that BET and MCL1 co-targeting may have therapeutic utility in this aggressive subtype of breast cancer.

Yan et al. show that pharmacological co-targeting of MCL1 and BET is highly effective in breast cancer cells. The proposed combination therapy may be effective for treatment of patients with aggressive subtypes of breast cancers whose tumors carry genetic aberrations associated with cell-death evasion.

Biomolecular Condensation: A New Phase in Cancer Research

Multicellularity was a watershed development in evolution. However, it also meant that individual cells could escape regulatory mechanisms that restrict proliferation at a severe cost to the organism: cancer. From the standpoint of cellular organization, evolutionary complexity scales to organize different molecules within the intracellular milieu. The recent realization that many biomolecules can "phase-separate" into membraneless organelles, reorganizing cellular biochemistry in space and time, has led to an explosion of research activity in this area. In this review, we explore mechanistic connections between phase separation and cancer-associated processes and emerging examples of how these become deranged in malignancy.

SIGNIFICANCE

One of the fundamental functions of phase separation is to rapidly and dynamically respond to environmental perturbations. Importantly, these changes often lead to alterations in cancer-relevant pathways and processes. This review covers recent advances in the field, including emerging principles and mechanisms of phase separation in cancer.

i-Modern: Integrated multi-omics network model identifies potential therapeutic targets in glioma by deep learning with interpretability

Effective and precise classification of glioma patients for their disease risks is critical to improving early diagnosis and patient survival. In the recent past, a significant amount of multi-omics data derived from cancer patients has emerged. However, a robust framework for integrating multi-omics data types to efficiently and precisely subgroup glioma patients and predict survival prognosis is still lacking. In addition, effective therapeutic targets for treating glioma patients with poor prognoses are in dire need. To begin to resolve this difficulty, we developed i-Modern, an integrated Multi-omics deep learning network method, and optimized a sophisticated computational model in gliomas that can accurately stratify patients based on their prognosis. We built a survival-associated predictive framework integrating transcription profile, miRNA expression, somatic mutations, copy number variation (CNV), DNA methylation, and protein expression. This framework achieved promising performance in distinguishing high-risk glioma patients from those with good prognoses. Furthermore, we constructed multiple fully connected neural networks that are trained on prioritized multi-omics signatures or even only potential single-omics signatures, based on our customized scoring system. Together, the landmark multi-omics signatures we identified may serve as potential therapeutic targets in gliomas.

A network-based approach to identify expression modules underlying rejection in pediatric liver transplantation

Selecting the right immunosuppressant to ensure rejection-free outcomes poses unique challenges in pediatric liver transplant (LT) recipients. A molecular predictor can comprehensively address these challenges. Currently, there are no well-validated blood-based biomarkers for pediatric LT recipients before or after LT. Here, we discover and validate separate pre- and post-LT transcriptomic signatures of rejection. Using an integrative machine learning approach, we combine transcriptomics data with the reference high-quality human protein interactome to identify network module signatures, which underlie rejection. Unlike gene signatures, our approach is inherently multivariate and more robust to replication and captures the structure of the underlying network, encapsulating additive effects. We also identify, in an individual-specific manner, signatures that can be targeted by current anti-rejection drugs and other drugs that can be repurposed. Our approach can enable personalized adjustment of drug regimens for the dominant targetable pathways before and after LT in children.

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.

mi-IsoNet: systems-scale microRNA landscape reveals rampant isoform-mediated gain of target interaction diversity and signaling specificity

MicroRNA (miRNA) is not a single sequence, but a series of multiple variants (also termed isomiRs) with sequence and expression heterogeneity. Whether and how these isoforms contribute to functional variation and complexity at the systems and network levels remain largely unknown. To explore this question systematically, we comprehensively analyzed the expression of small RNAs and their target sites to interrogate functional variations between novel isomiRs and their canonical miRNA sequences. Our analyses of the pan-cancer landscape of miRNA expression indicate that multiple isomiRs generated from the same miRNA locus often exhibit remarkable variation in their sequence, expression and function. We interrogated abundant and differentially expressed 5' isomiRs with novel seed sequences via seed shifting and identified many potential novel targets of these 5' isomiRs that would expand interaction capabilities between small RNAs and mRNAs, rewiring regulatory networks and increasing signaling circuit complexity. Further analyses revealed that some miRNA loci might generate diverse dominant isomiRs that often involved isomiRs with varied seeds and arm-switching, suggesting a selective advantage of multiple isomiRs in regulating gene expression. Finally, experimental validation indicated that isomiRs with shifted seed sequences could regulate novel target mRNAs and therefore contribute to regulatory network rewiring. Our analysis uncovers a widespread expansion of isomiR and mRNA interaction networks compared with those seen in canonical small RNA analysis; this expansion suggests global gene regulation network perturbations by alternative small RNA variants or isoforms. Taken together, the variations in isomiRs that occur during miRNA processing and maturation are likely to play a far more complex and plastic role in gene regulation than previously anticipated.

An expanded universe of cancer targets

The characterization of cancer genomes has provided insight into somatically altered genes across tumors, transformed our understanding of cancer biology, and enabled tailoring of therapeutic strategies. However, the function of most cancer alleles remains mysterious, and many cancer features transcend their genomes. Consequently, tumor genomic characterization does not influence therapy for most patients. Approaches to understand the function and circuitry of cancer genes provide complementary approaches to elucidate both oncogene and non-oncogene dependencies. Emerging work indicates that the diversity of therapeutic targets engendered by non-oncogene dependencies is much larger than the list of recurrently mutated genes. Here we describe a framework for this expanded list of cancer targets, providing novel opportunities for clinical translation.

e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks

Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.

Integrated Genomic Characterization of the Human Immunome in Cancer

Alterations in immune-related pathways are common hallmarks of cancer. A comprehensive understanding of how cancer mutations rewire immune signaling networks and functional output across cancer types is instrumental to realize the full potential of immunotherapy. Here, we systematically interrogated somatic mutations involved in immune signaling that alter immune responses in patients with cancer. To do so, we developed a Network-based Integrative model to Prioritize Potential immune respondER genes (NIPPER). Identified mutations were enriched in essential protein domains and genes identified by NIPPER were associated with responsiveness to multiple immunotherapy modalities. These genes were used to devise an interactome network propagation framework integrated with drug-associated gene signatures to identify potential immunomodulatory drug candidates. Together, our systems-level analysis results help interpret the heterogeneous immune responses among patients and serve as a resource for future functional studies and targeted therapeutics. SIGNIFICANCE: This study demonstrates that integration of multi-omics data can help identify critical molecular determinants for effective targeted therapeutics.

Abstract NG14: Proteome instability is an immunogenic therapeutic vulnerability in mismatch repair deficient cancer

Deficient DNA mismatch repair (dMMR) induces a hypermutator phenotype, leaving a genomic scar known as microsatellite instability (MSI). MSI is observed in approximately 30% of endometrial cancers, 20% of gastric cancers, 15% of colorectal cancers, and in a smaller fraction of other tumor types. This hypermutator phenotype is thought to produce large numbers of immunogenic neoantigens, leading to the approval of MSI status as a clinical biomarker for immunotherapy. However, more than 60% of patients with MSI tumors fail to respond to immune checkpoint therapy. To uncover alternative therapeutic vulnerabilities for these patients, we used transcriptome signature-guided approaches to identify MLN4924 (pevonedistat), a Nedd8-activating enzyme inhibitor, as a potential therapy for dMMR/MSI cancers. We discover that destabilizing mutations from the dMMR mutation process lead to rampant proteome instability in MSI tumors, resulting in an abundance of misfolded protein aggregates. To compensate, MSI cancer cells activate a Nedd8-mediated degradation pathway to facilitate clearance of misfolded proteins, which is blocked by treatment with MLN4924. The accumulation of misfolded proteins in MSI cancer cells following MLN4924 treatment activated the unfolded protein response, promoted immune cell migration, and induced immunogenic cell death. Antitumor vaccination with MLN4924-treated cells stimulated the generation of endogenous tumor antibodies and prevented tumor incidence upon re-challenge. Based on this immunostimulation, we combined MLN4924 with PD1 blockade, finding that the combination increased recruitment of CD8+ lymphocytes and improved therapeutic efficacy beyond either treatment alone. Taken together, our results indicate that targeting proteome instability is a novel therapeutic avenue for MSI patients and may potentiate immune checkpoint blockade, potentially increasing the depth and duration of response, as well as the fraction of dMMR/MSI patients who can benefit.

Citation Format: Nidhi Sahni, Daniel J. McGrail, Jeannine Garnett, Jun Yin, Hui Dai, David J. H. Shih, Guang Peng, David Menter, Melinda S. Yates, Scott Kopetz, Karen Lu, Russell Broaddus, Gordon B. Mills, Shiaw Y. Lin. Proteome instability is an immunogenic therapeutic vulnerability in mismatch repair deficient cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr NG14.

Cellular origins and genetic landscape of cutaneous gamma delta T cell lymphomas

Primary cutaneous γδ T cell lymphomas (PCGDTLs) represent a heterogeneous group of uncommon but aggressive cancers. Herein, we perform genome-wide DNA, RNA, and T cell receptor (TCR) sequencing on 29 cutaneous γδ lymphomas. We find that PCGDTLs are not uniformly derived from Vδ2 cells. Instead, the cell-of-origin depends on the tissue compartment from which the lymphomas are derived. Lymphomas arising from the outer layer of skin are derived from Vδ1 cells, the predominant γδ cell in the epidermis and dermis. In contrast, panniculitic lymphomas arise from Vδ2 cells, the predominant γδ T cell in the fat. We also show that TCR chain usage is non-random, suggesting common antigens for Vδ1 and Vδ2 lymphomas respectively. In addition, Vδ1 and Vδ2 PCGDTLs harbor similar genomic landscapes with potentially targetable oncogenic mutations in the JAK/STAT, MAPK, MYC, and chromatin modification pathways. Collectively, these findings suggest a paradigm for classifying, staging, and treating these diseases.

Proteome Instability Is a Therapeutic Vulnerability in Mismatch Repair-Deficient Cancer

Deficient DNA mismatch repair (dMMR) induces a hypermutator phenotype that can lead to tumorigenesis; however, the functional impact of the high mutation burden resulting from this phenotype remains poorly explored. Here, we demonstrate that dMMR-induced destabilizing mutations lead to proteome instability in dMMR tumors, resulting in an abundance of misfolded protein aggregates. To compensate, dMMR cells utilize a Nedd8-mediated degradation pathway to facilitate clearance of misfolded proteins. Blockade of this Nedd8 clearance pathway with MLN4924 causes accumulation of misfolded protein aggregates, ultimately inducing immunogenic cell death in dMMR cancer cells. To leverage this immunogenic cell death, we combined MLN4924 treatment with PD1 inhibition and found the combination was synergistic, significantly improving efficacy over either treatment alone.

Pathway perturbations in signaling networks: Linking genotype to phenotype

Genes and gene products interact with each other to form signal transduction networks in the cell. The interactome networks are under intricate regulation in physiological conditions, but could go awry upon genome instability caused by genetic mutations. In the past decade with next-generation sequencing technologies, an increasing number of genomic mutations have been identified in a variety of disease patients and healthy individuals. As functional and systematic studies on these mutations leap forward, they begin to reveal insights into cellular homeostasis and disease mechanisms. In this review, we discuss recent advances in the field of network biology and signaling pathway perturbations upon genomic changes, and highlight the success of various omics datasets in unraveling genotype-to-phenotype relationships.

Anesthetic management of a patient with Sheehan's syndrome and twin pregnancy while undergoing a cesarean section

Sheehan's syndrome (SS) is caused by infarction of the pituitary gland usually precipitated by hypotension due to massive uterine hemorrhage during the peripartum period. Once SS develops, it becomes a major comorbidity for the young females and predisposes them to further medical, obstetric, and anesthetic complications. Herein, we report the perioperative anesthetic management of a 28-year-old female, already diagnosed with SS precipitated by urosepsis and septicemic shock in a previous pregnancy, now presenting with twin pregnancy for elective cesarean section. Her magnetic resonance imaging brain revealed pituitary apoplexy and she had hypothyroidism with gestational diabetes mellitus. The overall successful perioperative management of the patient is described along with an emphasis on aggressive management of hypotension due to any cause in the peripartum period to prevent infarction/necrosis of anterior pituitary gland.

Comparison of stress and burnout among anesthesia and surgical residents in a tertiary care teaching hospital in North India

Objective:

The residents undergoing training at hospitals in our country face challenges in terms of infrastructure and high workload with undefined working hours. The aim of the study was to compare the stress and burnout levels in trainee doctors doing residency in surgical fields and anesthesia at a tertiary care academic center in North India.

Materials and Methods:

A comparative, observational study was conducted in a tertiary care teaching hospital in North India. After Ethics Committee approval, 200 residents (100 each from surgical branches and anesthesia) were required to fill a questionnaire with information about age, sex, year of residency, marital status, and the Perceived Stress Scale-10, and Burnout Clinical Subtype Questionnaire-12. Burnout and perceived stress were compared between residents of anesthesia and surgical specialties.

Results:

Residents of both surgical and anesthesia branches scored high in perceived stress, namely 21 and 18, respectively. The score was significantly higher in surgical residents (P = 0.03) and increased progressively with the year of residency. The majority of residents (90% surgical, 80% anesthesia) felt that they were being overloaded with work. However, only 20%–30% of respondents felt that there was lack of development of individual skills and still fewer (<10%) reported giving up in view of difficulties.

Conclusion:

There is high level of stress and overload dimension of burnout among the residents of anesthesia and surgical branches at our tertiary care academic institution and the surgical residents score marginally higher than anesthesia residents.

MERIT: Systematic Analysis and Characterization of Mutational Effect on RNA Interactome Topology

The interaction between RNA-binding proteins (RBPs) and RNA plays an important role in regulating cellular function. However, decoding genome-wide protein-RNA regulatory networks as well as how cancer-related mutations impair RNA regulatory activities in hepatocellular carcinoma (HCC) remains mostly undetermined. We explored the genetic alteration patterns of RBPs and found that deleterious mutations are likely to occur on the surface of RBPs. We then constructed protein-RNA interactome networks by integration of target binding screens and expression profiles. Network analysis highlights regulatory principles among interacting RBPs. In addition, somatic mutations selectively target functionally important genes (cancer genes, core fitness genes, or conserved genes) and perturb the RBP-gene regulatory networks in cancer. These regulatory patterns were further validated using independent data. A computational method (Mutational Effect on RNA Interactome Topology) and a web-based, user-friendly resource were further proposed to analyze the RBP-gene regulatory networks across cancer types. Pan-cancer analysis also suggests that cancer cells selectively target "vulnerability" genes to perturb protein-RNA interactome that is involved in cancer hallmark-related functions. Specifically, we experimentally validated four pairs of RBP-gene interactions perturbed by mutations in HCC, which play critical roles in cell proliferation. Based on the expression of perturbed RBP and target genes, we identified three subtypes of HCC with different survival rates. Conclusion: Our results provide a valuable resource for characterizing somatic mutation-perturbed protein-RNA regulatory networks in HCC, yielding valuable insights into the genotype-phenotype relationships underlying human cancer, and potential biomarkers for precision medicine.

LncMAP: Pan-cancer atlas of long noncoding RNA-mediated transcriptional network perturbations

Gene regulatory network perturbations contribute to the development and progression of cancer, however, molecular determinants that mediate transcriptional perturbations remain a fundamental challenge for cancer biology. We show that transcriptional perturbations are widely mediated by long noncoding RNAs (lncRNAs) via integration of genome-wide transcriptional regulation with paired lncRNA and gene expression profiles. Systematic construction of an LncRNA Modulator Atlas in Pan-cancer (LncMAP) reveals distinct types of lncRNA regulatory molecules, which are expressed in multiple tissues, exhibit higher conservation. Strikingly, cancers with similar tissue origin share lncRNA modulators which perturb the regulation of cell cycle and immune response-related functions. Furthermore, we identified a large number of pan-cancer lncRNA modulators with potential clinical significance, which are differentially expressed in cancer or are strongly correlated with drug sensitivity across cell lines. Further stratification of cancer patients based on lncRNA-mediated transcriptional perturbations identifies subtypes with distinct survival rates. Finally, we made a user-friendly web interface available for exploring lncRNA-mediated transcriptional perturbations across cancer types. Our study provides a systems-level dissection of lncRNA-mediated regulatory perturbations in cancer, and also presents a valuable tool and resource for investigating the function of lncRNAs in cancer.

Abstract 1646: Beyond BRCA: Discovery of novel drivers of homologous recombination deficiencies in cancer

Since the discovery of BRCA1 and BRCA2 mutations as cancer risk factors, we have gained much understanding of their role in maintaining genomic stability through homologous recombination (HR) DNA repair. However, mutations in BRCA1/2 and other classical HR proteins such as RAD51 and PALB2 only identify 10-20% of TCGA patients who display HR deficiencies, indicating that we do not understand the vast majority of HR defects. Here, we leveraged the abundance of molecular characterization from TCGA patients for network analysis to fill this knowledge void. We discovered that over half of HR deficiencies originate outside of canonical DNA damage response genes, with particular enrichment for RNA binding proteins (RBPs). Experimental techniques validated over 90% of our predictions in a panel of 50 genes tested by siRNA, as well as 30/31 additional engineered mutations identified in TCGA patients. We further cross-validated these findings in independent patient cohorts, finding that the identified RNA processing mutations again enriched for HR deficient patients to an equal or greater degree than mutations in DNA damage genes. Using a series of experimental approaches, including protein interactome screening, RNA sequencing, and quantitative imaging cytometry, we probed how loss of RBP function induced global DNA damage response rewiring, including changes in RNA splicing, protein-protein interactions, and recruitment of repair factors to DNA damage sites. Clinically, defects in HR are known to promote cancer initiation, but also sensitize cells to targeted therapies such as PARP inhibition. We found that depletion RBPs from PARP-resistant triple-negative breast cancer cells induced sensitivity to multiple PARP inhibitors, indicating that this could identify new cohorts of patients who may benefit from PARP inhibition, beyond the small number of BRCA1/2-mutant patients. Moreover, we find that the identified RBP genes are significantly enriched for genes associated with cancer risk identified through GWAS. In patients, HR deficiencies were not equally distributed across all demographics, so preferential screening of those most at risk could further heighten this benefit. Thus, these novel drivers may show clinical relevance both for treatment stratification and for identifying individuals at high risk for cancer development to improve patient outcomes. Taken together, this exhaustive study greatly expands our repertoire of known drivers of HR deficiencies and mechanisms of damage repair, which may impact research from the most basic biology studies to clinical screening and stratification.

Citation Format: Nidhi Sahni, Daniel McGrail, Yongsheng Li, Gordon Mills, Song (Stephen) Yi. Beyond BRCA: Discovery of novel drivers of homologous recombination deficiencies in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1646.

Abstract 3390: RNA interactome topology perturbation analysis reveals candidate driver mutations in cancer

Identification of the driver genomic variants is important for precision therapy of human cancer. Interaction between RNA-binding proteins (RBPs) and RNA plays important roles in regulating cellular function. Decoding genome-wide protein-RNA regulatory networks as well as how cancer-related mutations impair RNA regulatory activities in cancer will help prioritizing candidate driver mutations.

Here, we first explored the genetic alteration patterns of RBPs and found that candidate driver mutations are likely to occur on the surface of RBPs. This observations indicate that the driver mutations may perturb the RBP regulatory network in cancer. We then constructed protein-RNA interactome networks by integration of target binding screens and expression profiles. Regulatory network analysis highlights regulatory principles among interacting RBPs. In addition, somatic mutations selectively target functionally important genes (cancer genes, core fitness genes or conserved genes) in cancer. These regulatory patterns were further validated using independent data. A computational method (MERIT) and a web-based user friendly resource were further proposed to analyze the RBP-gene regulatory network perturbation induced by mutations across cancer types. Pan-cancer analysis also suggests that cancer cells selectively target "vulnerability" genes to perturb protein-RNA interactome that is involved in cancer hallmark-related functions. Based on the expression of perturbed RBP and target genes, we identified three subtypes of liver cancer with different survival rates. Specifically, we experimentally validated four pairs of RBP-gene interactions perturbed by mutations, which play critical roles in cell proliferation.

Taken together, our results provide a valuable resource for characterizing somatic mutation-perturbed protein-RNA regulatory networks in cancer, yielding valuable insights into the genotype-phenotype relationships underlying human cancer, and potential biomarkers for precision medicine.

Note: This abstract was not presented at the meeting.

Citation Format: Yongsheng Li, Daniel J. McGrail, Juan Xu, Junyi Li, Song Yi, Nidhi Sahni. RNA interactome topology perturbation analysis reveals candidate driver mutations in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3390.

FACER: comprehensive molecular and functional characterization of epigenetic chromatin regulators

Epigenetic alterations, a well-recognized cancer hallmark, are driven by chromatin regulators (CRs). However, little is known about the extent of CR deregulation in cancer, and less is known about their common and specialized roles across various cancers. Here, we performed genome-wide analyses and constructed molecular signatures and network profiles of functional CRs in over 10 000 tumors across 33 cancer types. By integration of DNA mutation, genome-wide methylation, transcriptional/post-transcriptional regulation, and protein interaction networks with clinical outcomes, we identified CRs associated with cancer subtypes and clinical prognosis as potential oncogenic drivers. Comparative network analysis revealed principles of CR regulatory specificity and functionality. In addition, we identified common and specific CRs by assessing their prevalence across cancer types. Common CRs tend to be histone modifiers and chromatin remodelers with fundamental roles, whereas specialized CRs are involved in context-dependent functions. Finally, we have made a user-friendly web interface-FACER (Functional Atlas of Chromatin Epigenetic Regulators) available for exploring clinically relevant CRs for the development of CR biomarkers and therapeutic targets. Our integrative analysis reveals specific determinants of CRs across cancer types and presents a resource for investigating disease-associated CRs.

Sequential Therapy with PARP and WEE1 Inhibitors Minimizes Toxicity while Maintaining Efficacy

We demonstrate that concurrent administration of poly(ADP-ribose) polymerase (PARP) and WEE1 inhibitors is effective in inhibiting tumor growth but poorly tolerated. Concurrent treatment with PARP and WEE1 inhibitors induces replication stress, DNA damage, and abrogates the G₂ DNA damage checkpoint in both normal and malignant cells. Following cessation of monotherapy with PARP or WEE1 inhibitors, effects of these inhibitors persist suggesting that sequential administration of PARP and WEE1 inhibitors could maintain efficacy while ameliorating toxicity. Strikingly, while sequential administration mirrored concurrent therapy in cancer cells that have high basal replication stress, low basal replication stress in normal cells protected them from DNA damage and toxicity, thus improving tolerability while preserving efficacy in ovarian cancer xenograft and patient-derived xenograft models.

Neomorphic PDGFRA extracellular domain driver mutations are resistant to PDGFRA targeted therapies

Activation of platelet-derived growth factor receptor alpha (PDGFRA) by genomic aberrations contributes to tumor progression in several tumor types. In this study, we characterize 16 novel PDGFRA mutations identified from different tumor types and identify three previously uncharacterized activating mutations that promote cell survival and proliferation. PDGFRA Y288C, an extracellular domain mutation, is primarily high mannose glycosylated consistent with trapping in the endoplasmic reticulum (ER). Strikingly, PDGFRA Y288C is constitutively dimerized and phosphorylated in the absence of ligand suggesting that trapping in the ER or aberrant glycosylation is sufficient for receptor activation. Importantly, PDGFRA Y288C induces constitutive phosphorylation of Akt, ERK1/2, and STAT3. PDGFRA Y288C is resistant to PDGFR inhibitors but sensitive to PI3K/mTOR and MEK inhibitors consistent with pathway activation results. Our findings further highlight the importance of characterizing functional consequences of individual mutations for precision medicine.

Abstract 3298: High-content phenotyping of somatic cancer mutations by functional variomics

Cancer genomes are highly complex with numerous somatic mutations identified across patient populations. Previous studies on genomic mutations have shed light on new means for cancer therapeutic interventions. With rapid advances in next-generation sequencing, accumulating genotypic information in the absence of efficient and systematic functional analyses of genomic aberrations will create a bottleneck in understanding genotype-phenotype relationships in cancer. To address these challenges, here we report a systems-level functional variomics approach integrating high-throughput phenotyping with robust computational analyses to investigate mutation-specific effects. This systematic functional platform consists of massively parallel mutagenesis, sensitive survival assays using growth factor-dependent cell models, and functional network perturbation profiling of mutations on signaling effects. We profile several thousands of genomic aberrations, including point mutations, gene fusions and indels, and significantly expand the repertoire of characterized actionable mutations. This study represents a valuable resource and provides insights in prioritizing cancer-causing mutations, and uncovering patient-specific disease mechanisms at a high resolution, a critical step towards personalized precision medicine.

Citation Format: Nidhi Sahni, Patrick Kwok-Shing Ng, Kang Jin Jeong, Gordon B. Mills. High-content phenotyping of somatic cancer mutations by functional variomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3298.

Abstract 397: Systematic functional annotation of somatic mutations in cancer

Recent sequencing studies have identified thousands of unique somatic mutations across patient tumors. However, the functional impact of the vast majority of these mutations remains unknown, representing a critical knowledge gap for implementing precision oncology. Here, we report the development of a moderate-throughput functional genomic platform consisting of efficient mutant open reading frame generation, sensitive viability assays using two growth factor-dependent cell models, and functional proteomic profiling of downstream signaling effects for select aberrations. We apply the platform to annotate &gt;1,000 genomic aberrations, including gene amplifications, point mutations, indels, and gene fusions, potentially doubling the number of driver mutations characterized in clinically actionable genes. We show that our platform has higher sensitivity for characterizing weak drivers than pooled screening. Our data are accessible through the user-friendly, open-access data portal we created. Our study will facilitate the discovery of novel biomarkers, improvement of existing prediction algorithms, and development of new therapeutic approaches.

Citation Format: Han Liang, Patrick Kwok-Shing Ng, Jun Li, Kang Jin Jeong, Yiling Lu, Song Yi, Nidhi Sahni, Gordon Mills. Systematic functional annotation of somatic mutations in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 397.

Gene Regulatory Network Perturbation by Genetic and Epigenetic Variation

Gene regulatory networks underlie biological function and cellular physiology. Alternative splicing (AS) is a fundamental step in gene regulatory networks and plays a key role in development and disease. In addition to the identification of aberrant AS events, an increasing number of studies are focusing on molecular determinants of AS, including genetic and epigenetic regulators. We review here recent efforts to identify various deregulated AS events as well as their molecular determinants that alter biological functions, and discuss clinical features of AS and their druggable potential.

Revealing the Determinants of Widespread Alternative Splicing Perturbation in Cancer

It is increasingly appreciated that alternative splicing plays a key role in generating functional specificity and diversity in cancer. However, the mechanisms by which cancer mutations perturb splicing remain unknown. Here, we developed a network-based strategy, DrAS-Net, to investigate more than 2.5 million variants across cancer types and link somatic mutations with cancer-specific splicing events. We identified more than 40,000 driver variant candidates and their 80,000 putative splicing targets deregulated in 33 cancer types and inferred their functional impact. Strikingly, tumors with splicing perturbations show reduced expression of immune system-related genes and increased expression of cell proliferation markers. Tumors harboring different mutations in the same gene often exhibit distinct splicing perturbations. Further stratification of 10,000 patients based on their mutation-splicing relationships identifies subtypes with distinct clinical features, including survival rates. Our work reveals how single-nucleotide changes can alter the repertoires of splicing isoforms, providing insights into oncogenic mechanisms for precision medicine.

Multi-omics analysis reveals neoantigen-independent immune cell infiltration in copy-number driven cancers

To realize the full potential of immunotherapy, it is critical to understand the drivers of tumor infiltration by immune cells. Previous studies have linked immune infiltration with tumor neoantigen levels, but the broad applicability of this concept remains unknown. Here, we find that while this observation is true across cancers characterized by recurrent mutations, it does not hold for cancers driven by recurrent copy number alterations, such as breast and pancreatic tumors. To understand immune invasion in these cancers, we developed an integrative multi-omics framework, identifying the DNA damage response protein ATM as a driver of cytokine production leading to increased immune infiltration. This prediction was validated in numerous orthogonal datasets, as well as experimentally in vitro and in vivo by cytokine release and immune cell migration. These findings demonstrate diverse drivers of immune cell infiltration across cancer lineages and may facilitate the clinical adaption of immunotherapies across diverse malignancies.

Systematic Functional Annotation of Somatic Mutations in Cancer

The functional impact of the vast majority of cancer somatic mutations remains unknown, representing a critical knowledge gap for implementing precision oncology. Here, we report the development of a moderate-throughput functional genomic platform consisting of efficient mutant generation, sensitive viability assays using two growth factor-dependent cell models, and functional proteomic profiling of signaling effects for select aberrations. We apply the platform to annotate >1,000 genomic aberrations, including gene amplifications, point mutations, indels, and gene fusions, potentially doubling the number of driver mutations characterized in clinically actionable genes. Further, the platform is sufficiently sensitive to identify weak drivers. Our data are accessible through a user-friendly, public data portal. Our study will facilitate biomarker discovery, prediction algorithm improvement, and drug development.

Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures

Comprehensive understanding of human cancer mechanisms requires the identification of a thorough list of cancer-associated genes, which could serve as biomarkers for diagnoses and therapies in various types of cancer. Although substantial progress has been made in functional studies to uncover genes involved in cancer, these efforts are often time-consuming and costly. Therefore, it remains challenging to comprehensively identify cancer candidate genes. Network-based methods have accelerated this process through the analysis of complex molecular interactions in the cell. However, the extent to which various interactome networks can contribute to prediction of candidate genes responsible for cancer is still enigmatic. In this study, we evaluated different human protein-protein interactome networks and compared their application to cancer gene prioritization. Our results indicate that network analyses can increase the power to identify novel cancer genes. In particular, such predictive power can be enhanced with the use of unbiased systematic protein interaction maps for cancer gene prioritization. Functional analysis reveals that the top ranked genes from network predictions co-occur often with cancer-related terms in literature, and further, these candidate genes are indeed frequently mutated across cancers. Finally, our study suggests that integrating interactome networks with other omics datasets could provide novel insights into cancer-associated genes and underlying molecular mechanisms.

Abstract B03: Functional Stratification of Cancer Variants via Network Perturbations

In the past decade, genome and exome sequencing projects have identified thousands of genetic variants in patients across a large number of cancer types. However, the explosion of genomic information has left many fundamental questions regarding genotype-phenotype relationships unresolved. One critical challenge is to distinguish causal disease mutations from non-pathogenic polymorphisms. Even when causal mutations are identified, the functional consequence of such mutations is often elusive. Classical one gene, one function, one disease models can not reconcile with the complexity that different mutations of the same gene often lead to different phenotypes. The extent to which network perturbations are involved in disease malfunction and how distinct interaction perturbation patterns can distinguish cancer mutations are largely unknown. Here we report a systematic approach to investigate genetic variant-specific effects on molecular interactions at large scale across diverse human cancers. Remarkably, in comparison to non-disease polymorphisms, disease mutations are more likely to associate with interaction perturbations. A large fraction of missense disease mutations are found to cause protein interaction alterations. While half result in loss of all their interactions, the other half exhibit selective elimination of specific interactions (edgetic). Different mutations of the same gene give rise to different interaction profiles, accounting for distinct disease outcomes. Edgetic mutations perturb interactions through disrupting specific interaction interfaces, and the perturbed partners are more likely expressed in relevant disease tissue. Together, our approach is insightful in prioritizing disease-causing variants, and uncovering patient mutation-specific disease mechanisms at a base-pair resolution, a critical step towards personalized precision medicine. Furthermore, our results suggest distinct interaction perturbations as a widespread mechanism underlying genetic heterogeneity, providing a fundamental link between genotype and phenotype in cancer.

Citation Format: Nidhi Sahni, Song Yi. Functional Stratification of Cancer Variants via Network Perturbations [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr B03.