De novo generation of multi-target compounds using deep generative chemistry.

Polypharmacology drugs-compounds that inhibit multiple proteins-have many applications but are difficult to design. To address this challenge we have developed POLYGON, an approach to polypharmacology based on generative reinforcement learning. POLYGON embeds chemical space and iteratively samples it to generate new molecular structures; these are rewarded by the predicted ability to inhibit each of two protein targets and by drug-likeness and ease-of-synthesis. In binding data for >100,000 compounds, POLYGON correctly recognizes polypharmacology interactions with 82.5% accuracy. We subsequently generate de-novo compounds targeting ten pairs of proteins with documented co-dependency. Docking analysis indicates that top structures bind their two targets with low free energies and similar 3D orientations to canonical single-protein inhibitors. We synthesize 32 compounds targeting MEK1 and mTOR, with most yielding >50% reduction in each protein activity and in cell viability when dosed at 1-10 µM. These results support the potential of generative modeling for polypharmacology.

A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors.

Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6is) have revolutionized breast cancer therapy. However, <50% of patients have an objective response, and nearly all patients develop resistance during therapy. To elucidate the underlying mechanisms, we constructed an interpretable deep learning model of the response to palbociclib, a CDK4/6i, based on a reference map of multiprotein assemblies in cancer. The model identifies eight core assemblies that integrate rare and common alterations across 90 genes to stratify palbociclib-sensitive versus palbociclib-resistant cell lines. Predictions translate to patients and patient-derived xenografts, whereas single-gene biomarkers do not. Most predictive assemblies can be shown by CRISPR-Cas9 genetic disruption to regulate the CDK4/6i response. Validated assemblies relate to cell-cycle control, growth factor signaling and a histone regulatory complex that we show promotes S-phase entry through the activation of the histone modifiers KAT6A and TBL1XR1 and the transcription factor RUNX1. This study enables an integrated assessment of how a tumor's genetic profile modulates CDK4/6i resistance.

Cancer Mutations Converge on a Collection of Protein Assemblies to Predict Resistance to Replication Stress.

Rapid proliferation is a hallmark of cancer associated with sensitivity to therapeutics that cause DNA replication stress (RS). Many tumors exhibit drug resistance, however, via molecular pathways that are incompletely understood. Here, we develop an ensemble of predictive models that elucidate how cancer mutations impact the response to common RS-inducing (RSi) agents. The models implement recent advances in deep learning to facilitate multidrug prediction and mechanistic interpretation. Initial studies in tumor cells identify 41 molecular assemblies that integrate alterations in hundreds of genes for accurate drug response prediction. These cover roles in transcription, repair, cell-cycle checkpoints, and growth signaling, of which 30 are shown by loss-of-function genetic screens to regulate drug sensitivity or replication restart. The model translates to cisplatin-treated cervical cancer patients, highlighting an RTK-JAK-STAT assembly governing resistance. This study defines a compendium of mechanisms by which mutations affect therapeutic responses, with implications for precision medicine. SIGNIFICANCE: Zhao and colleagues use recent advances in machine learning to study the effects of tumor mutations on the response to common therapeutics that cause RS. The resulting predictive models integrate numerous genetic alterations distributed across a constellation of molecular assemblies, facilitating a quantitative and interpretable assessment of drug response. This article is featured in Selected Articles from This Issue, p. 384.

Desmosome mutations impact the tumor microenvironment to promote melanoma proliferation.

Desmosomes are transmembrane protein complexes that contribute to cell-cell adhesion in epithelia and other tissues. Here, we report the discovery of frequent genetic alterations in the desmosome in human cancers, with the strongest signal seen in cutaneous melanoma where desmosomes are mutated in over 70% of cases. In primary but not metastatic melanoma biopsies, the burden of coding mutations on desmosome genes associates with a strong reduction in desmosome gene expression. Analysis by spatial transcriptomics suggests that these expression decreases occur in keratinocytes in the microenvironment rather than in primary melanoma tumor cells. In further support of a microenvironmental origin, we find that loss-of-function knockdowns of the desmosome in keratinocytes yield markedly increased proliferation of adjacent melanocytes in keratinocyte/melanocyte co-cultures. Thus, gradual accumulation of desmosome mutations in neighboring cells may prime melanocytes for neoplastic transformation.

GABA Regulates Electrical Activity and Tumor Initiation in Melanoma.

Oncogenes can initiate tumors only in certain cellular contexts, which is referred to as oncogenic competence. In melanoma, whether cells in the microenvironment can endow such competence remains unclear. Using a combination of zebrafish transgenesis coupled with human tissues, we demonstrate that GABAergic signaling between keratinocytes and melanocytes promotes melanoma initiation by BRAFV600E. GABA is synthesized in melanoma cells, which then acts on GABA-A receptors in keratinocytes. Electron microscopy demonstrates specialized cell-cell junctions between keratinocytes and melanoma cells, and multielectrode array analysis shows that GABA acts to inhibit electrical activity in melanoma/keratinocyte cocultures. Genetic and pharmacologic perturbation of GABA synthesis abrogates melanoma initiation in vivo. These data suggest that GABAergic signaling across the skin microenvironment regulates the ability of oncogenes to initiate melanoma. SIGNIFICANCE: This study shows evidence of GABA-mediated regulation of electrical activity between melanoma cells and keratinocytes, providing a new mechanism by which the microenvironment promotes tumor initiation. This provides insights into the role of the skin microenvironment in early melanomas while identifying GABA as a potential therapeutic target in melanoma. See related commentary by Ceol, p. 2128. This article is featured in Selected Articles from This Issue, p. 2109.

Interface-guided phenotyping of coding variants in the transcription factor RUNX1 with SEUSS.

Understanding the consequences of single amino acid substitutions in cancer driver genes remains an unmet need. Perturb-seq provides a tool to investigate the effects of individual mutations on cellular programs. Here we deploy SEUSS, a Perturb-seq like approach, to generate and assay mutations at physical interfaces of the RUNX1 Runt domain. We measured the impact of 115 mutations on RNA profiles in single myelogenous leukemia cells and used the profiles to categorize mutations into three functionally distinct groups: wild-type (WT)-like, loss-of-function (LOF)-like and hypomorphic. Notably, the largest concentration of functional mutations (non-WT-like) clustered at the DNA binding site and contained many of the more frequently observed mutations in human cancers. Hypomorphic variants shared characteristics with loss of function variants but had gene expression profiles indicative of response to neural growth factor and cytokine recruitment of neutrophils. Additionally, DNA accessibility changes upon perturbations were enriched for RUNX1 binding motifs, particularly near differentially expressed genes. Overall, our work demonstrates the potential of targeting protein interaction interfaces to better define the landscape of prospective phenotypes reachable by amino acid substitutions.

Multimodal perturbation analyses of cyclin-dependent kinases reveal a network of synthetic lethalities associated with cell-cycle regulation and transcriptional regulation.

Cell-cycle control is accomplished by cyclin-dependent kinases (CDKs), motivating extensive research into CDK targeting small-molecule drugs as cancer therapeutics. Here we use combinatorial CRISPR/Cas9 perturbations to uncover an extensive network of functional interdependencies among CDKs and related factors, identifying 43 synthetic-lethal and 12 synergistic interactions. We dissect CDK perturbations using single-cell RNAseq, for which we develop a novel computational framework to precisely quantify cell-cycle effects and diverse cell states orchestrated by specific CDKs. While pairwise disruption of CDK4/6 is synthetic-lethal, only CDK6 is required for normal cell-cycle progression and transcriptional activation. Multiple CDKs (CDK1/7/9/12) are synthetic-lethal in combination with PRMT5, independent of cell-cycle control. In-depth analysis of mRNA expression and splicing patterns provides multiple lines of evidence that the CDK-PRMT5 dependency is due to aberrant transcriptional regulation resulting in premature termination. These inter-dependencies translate to drug-drug synergies, with therapeutic implications in cancer and other diseases.

NDEx IQuery: a multi-method network gene set analysis leveraging the Network Data Exchange.

MOTIVATION: The investigation of sets of genes using biological pathways is a common task for researchers and is supported by a wide variety of software tools. This type of analysis generates hypotheses about the biological processes that are active or modulated in a specific experimental context. RESULTS: The Network Data Exchange Integrated Query (NDEx IQuery) is a new tool for network and pathway-based gene set interpretation that complements or extends existing resources. It combines novel sources of pathways, integration with Cytoscape, and the ability to store and share analysis results. The NDEx IQuery web application performs multiple gene set analyses based on diverse pathways and networks stored in NDEx. These include curated pathways from WikiPathways and SIGNOR, published pathway figures from the last 27 years, machine-assembled networks using the INDRA system, and the new NCI-PID v2.0, an updated version of the popular NCI Pathway Interaction Database. NDEx IQuery's integration with MSigDB and cBioPortal now provides pathway analysis in the context of these two resources. AVAILABILITY AND IMPLEMENTATION: NDEx IQuery is available at https://www.ndexbio.org/iquery and is implemented in Javascript and Java.

Epigenetic aging: Biological age prediction and informing a mechanistic theory of aging.

Numerous studies have shown that epigenetic age-an individual's degree of aging based on patterns of DNA methylation-can be computed and is associated with an array of factors including diet, lifestyle, genetics, and disease. One can expect that still further associations will emerge with additional aging research, but to what end? Prediction of age was an important first step, but-in our view-the focus must shift from chasing increasingly accurate age computations to understanding the links between the epigenome and the mechanisms and physiological changes of aging. Here, we outline emerging areas of epigenetic aging research that prioritize biological understanding and clinical application. First, we survey recent progress in epigenetic clocks, which are beginning to predict not only chronological age but aging outcomes such as all-cause mortality and onset of disease, or which integrate aging signals across multiple biological processes. Second, we discuss research that exemplifies how investigation of the epigenome is building a mechanistic theory of aging and informing clinical practice. Such examples include identifying methylation sites and the genes most strongly predictive of aging-a subset of which have shown strong potential as biomarkers of neurodegenerative disease and cancer; relating epigenetic clock predictions to hallmarks of aging; and using longitudinal studies of DNA methylation to characterize human disease, resulting in the discovery of epigenetic indications of type 1 diabetes and the propensity for psychotic experiences.

Uncovering Tumorigenesis Circuitry with Combinatorial CRISPR.

Oncogenesis relies on the alteration of multiple driver genes, but precisely which groups of alterations lead to cancer is not well understood. To chart these combinations, Zhao and colleagues use the CRISPR-Cas9 system to knockout all pairwise combinations among 52 tumor suppressor genes, with the goal of identifying groups of alterations that collaborate to promote cell growth. Interaction screens are performed across multiple models of tumorigenesis in cell cultures and mice, revealing clear cooperation among NF2, PTEN, and TP53 in multiple models. These and other strongly synergistic interactions are characterized further by single-cell transcriptomic profiling. This methodology presents a scalable approach to move beyond single-gene drivers to map the complex gene networks that give rise to tumorigenesis.See related article by Zhao et al., p. 6090.

A protein interaction landscape of breast cancer.

Cancers have been associated with a diverse array of genomic alterations. To help mechanistically understand such alterations in breast-invasive carcinoma, we applied affinity purification­mass spectrometry to delineate comprehensive biophysical interaction networks for 40 frequently altered breast cancer (BC) proteins, with and without relevant mutations, across three human breast cell lines. These networks identify cancer-specific protein-protein interactions (PPIs), interconnected and enriched for common and rare cancer mutations, that are substantially rewired by the introduction of key BC mutations. Our analysis identified BPIFA1 and SCGB2A1 as PIK3CA-interacting proteins, which repress PI3K-AKT signaling, and uncovered USP28 and UBE2N as functionally relevant interactors of BRCA1. We also show that the protein phosphatase 1 regulatory subunit spinophilin interacts with and regulates dephosphorylation of BRCA1 to promote DNA double-strand break repair. Thus, PPI landscapes provide a powerful framework for mechanistically interpreting disease genomic data and can identify valuable therapeutic targets.

Interpretation of cancer mutations using a multiscale map of protein systems.

A major goal of cancer research is to understand how mutations distributed across diverse genes affect common cellular systems, including multiprotein complexes and assemblies. Two challenges­how to comprehensively map such systems and how to identify which are under mutational selection­have hindered this understanding. Accordingly, we created a comprehensive map of cancer protein systems integrating both new and published multi-omic interaction data at multiple scales of analysis. We then developed a unified statistical model that pinpoints 395 specific systems under mutational selection across 13 cancer types. This map, called NeST (Nested Systems in Tumors), incorporates canonical processes and notable discoveries, including a PIK3CA-actomyosin complex that inhibits phosphatidylinositol 3-kinase signaling and recurrent mutations in collagen complexes that promote tumor proliferation. These systems can be used as clinical biomarkers and implicate a total of 548 genes in cancer evolution and progression. This work shows how disparate tumor mutations converge on protein assemblies at different scales.

A protein network map of head and neck cancer reveals PIK3CA mutant drug sensitivity.

We outline a framework for elucidating tumor genetic complexity through multidimensional protein-protein interaction maps and apply it to enhancing our understanding of head and neck squamous cell carcinoma. This network uncovers 771 interactions from cancer and noncancerous cell states, including WT and mutant protein isoforms. Prioritization of cancer-enriched interactions reveals a previously unidentified association of the fibroblast growth factor receptor tyrosine kinase 3 with Daple, a guanine-nucleotide exchange factor, resulting in activation of Gαi- and p21-activated protein kinase 1/2 to promote cancer cell migration. Additionally, we observe mutation-enriched interactions between the human epidermal growth factor receptor 3 (HER3) receptor tyrosine kinase and PIK3CA (the alpha catalytic subunit of phosphatidylinositol 3-kinase) that can inform the response to HER3 inhibition in vivo. We anticipate that the application of this framework will be valuable for translating genetic alterations into a molecular and clinical understanding of the underlying biology of many disease areas.

Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients.

Cell-line screens create expansive datasets for learning predictive markers of drug response, but these models do not readily translate to the clinic with its diverse contexts and limited data. In the present study, we apply a recently developed technique, few-shot machine learning, to train a versatile neural network model in cell lines that can be tuned to new contexts using few additional samples. The model quickly adapts when switching among different tissue types and in moving from cell-line models to clinical contexts, including patient-derived tumor cells and patient-derived xenografts. It can also be interpreted to identify the molecular features most important to a drug response, highlighting critical roles for RB1 and SMAD4 in the response to CDK inhibition and RNF8 and CHD4 in the response to ATM inhibition. The few-shot learning framework provides a bridge from the many samples surveyed in high-throughput screens (n-of-many) to the distinctive contexts of individual patients (n-of-one).

Functional landscape of SARS-CoV-2 cellular restriction.

A deficient interferon (IFN) response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been implicated as a determinant of severe coronavirus disease 2019 (COVID-19). To identify the molecular effectors that govern IFN control of SARS-CoV-2 infection, we conducted a large-scale gain-of-function analysis that evaluated the impact of human IFN-stimulated genes (ISGs) on viral replication. A limited subset of ISGs were found to control viral infection, including endosomal factors inhibiting viral entry, RNA binding proteins suppressing viral RNA synthesis, and a highly enriched cluster of endoplasmic reticulum (ER)/Golgi-resident ISGs inhibiting viral assembly/egress. These included broad-acting antiviral ISGs and eight ISGs that specifically inhibited SARS-CoV-2 and SARS-CoV-1 replication. Among the broad-acting ISGs was BST2/tetherin, which impeded viral release and is antagonized by SARS-CoV-2 Orf7a protein. Overall, these data illuminate a set of ISGs that underlie innate immune control of SARS-CoV-2/SARS-CoV-1 infection, which will facilitate the understanding of host determinants that impact disease severity and offer potential therapeutic strategies for COVID-19.

Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells.

Most drugs entering clinical trials fail, often related to an incomplete understanding of the mechanisms governing drug response. Machine learning techniques hold immense promise for better drug response predictions, but most have not reached clinical practice due to their lack of interpretability and their focus on monotherapies. We address these challenges by developing DrugCell, an interpretable deep learning model of human cancer cells trained on the responses of 1,235 tumor cell lines to 684 drugs. Tumor genotypes induce states in cellular subsystems that are integrated with drug structure to predict response to therapy and, simultaneously, learn biological mechanisms underlying the drug response. DrugCell predictions are accurate in cell lines and also stratify clinical outcomes. Analysis of DrugCell mechanisms leads directly to the design of synergistic drug combinations, which we validate systematically by combinatorial CRISPR, drug-drug screening in vitro, and patient-derived xenografts. DrugCell provides a blueprint for constructing interpretable models for predictive medicine.

HPV E2, E4, E5 drive alternative carcinogenic pathways in HPV positive cancers.

The dominant paradigm for HPV carcinogenesis includes integration into the host genome followed by expression of E6 and E7 (E6/E7). We explored an alternative carcinogenic pathway characterized by episomal E2, E4, and E5 (E2/E4/E5) expression. Half of HPV positive cervical and pharyngeal cancers comprised a subtype with increase in expression of E2/E4/E5, as well as association with lack of integration into the host genome. Models of the E2/E4/E5 carcinogenesis show p53 dependent enhanced proliferation in vitro, as well as increased susceptibility to induction of cancer in vivo. Whole genomic expression analysis of the E2/E4/E5 pharyngeal cancer subtype is defined by activation of the fibroblast growth factor receptor (FGFR) pathway and this subtype is susceptible to combination FGFR and mTOR inhibition, with implications for targeted therapy.

A genetic interaction map centered on cohesin reveals auxiliary factors involved in sister chromatid cohesion in S. cerevisiae.

Eukaryotic chromosomes are replicated in interphase and the two newly duplicated sister chromatids are held together by the cohesin complex and several cohesin auxiliary factors. Sister chromatid cohesion is essential for accurate chromosome segregation during mitosis, yet has also been implicated in other processes, including DNA damage repair, transcription and DNA replication. To assess how cohesin and associated factors functionally interconnect and coordinate with other cellular processes, we systematically mapped the genetic interactions of 17 cohesin genes centered on quantitative growth measurements of >52,000 gene pairs in the budding yeast Saccharomyces cerevisiae Integration of synthetic genetic interactions unveiled a cohesin functional map that constitutes 373 genetic interactions, revealing novel functional connections with post-replication repair, microtubule organization and protein folding. Accordingly, we show that the microtubule-associated protein Irc15 and the prefoldin complex members Gim3, Gim4 and Yke2 are new factors involved in sister chromatid cohesion. Our genetic interaction map thus provides a unique resource for further identification and functional interrogation of cohesin proteins. Since mutations in cohesin proteins have been associated with cohesinopathies and cancer, it may also help in identifying cohesin interactions relevant in disease etiology.