Although oncogene-targeted therapy often elicits profound initial tumor responses in patients, responses are generally incomplete because some tumor cells survive initial therapy as residual disease that enables eventual acquired resistance. The mechanisms underlying tumor cell adaptation and survival during initial therapy are incompletely understood. Here, through the study of EGFR mutant lung adenocarcinoma, we show that NF-κB signaling is rapidly engaged upon initial EGFR inhibitor treatment to promote tumor cell survival and residual disease. EGFR oncogene inhibition induced an EGFR-TRAF2-RIP1-IKK complex that stimulated an NF-κB-mediated transcriptional survival program. The direct NF-κB inhibitor PBS-1086 suppressed this adaptive survival program and increased the magnitude and duration of initial EGFR inhibitor response in multiple NSCLC models, including a patient-derived xenograft. These findings unveil NF-κB activation as a critical adaptive survival mechanism engaged by EGFR oncogene inhibition and provide rationale for EGFR and NF-κB co-inhibition to eliminate residual disease and enhance patient responses.

Mitogen-activated protein kinase (MAPK) pathways form the backbone of signal transduction in the mammalian cell. Here we applied a systematic experimental and computational approach to map 2,269 interactions between human MAPK-related proteins and other cellular machinery and to assemble these data into functional modules. Multiple lines of evidence including conservation with yeast supported a core network of 641 interactions. Using small interfering RNA knockdowns, we observed that approximately one-third of MAPK-interacting proteins modulated MAPK-mediated signaling. We uncovered the Na-H exchanger NHE1 as a potential MAPK scaffold, found links between HSP90 chaperones and MAPK pathways and identified MUC12 as the human analog to the yeast signaling mucin Msb2. This study makes available a large resource of MAPK interactions and clone libraries, and it illustrates a methodology for probing signaling networks based on functional refinement of experimentally derived protein-interaction maps.

Influenza A virus is an RNA virus that encodes up to 11 proteins and this small coding capacity demands that the virus use the host cellular machinery for many aspects of its life cycle. Knowledge of these host cell requirements not only informs us of the molecular pathways exploited by the virus but also provides further targets that could be pursued for antiviral drug development. Here we use an integrative systems approach, based on genome-wide RNA interference screening, to identify 295 cellular cofactors required for early-stage influenza virus replication. Within this group, those involved in kinase-regulated signalling, ubiquitination and phosphatase activity are the most highly enriched, and 181 factors assemble into a highly significant host-pathogen interaction network. Moreover, 219 of the 295 factors were confirmed to be required for efficient wild-type influenza virus growth, and further analysis of a subset of genes showed 23 factors necessary for viral entry, including members of the vacuolar ATPase (vATPase) and COPI-protein families, fibroblast growth factor receptor (FGFR) proteins, and glycogen synthase kinase 3 (GSK3)-beta. Furthermore, 10 proteins were confirmed to be involved in post-entry steps of influenza virus replication. These include nuclear import components, proteases, and the calcium/calmodulin-dependent protein kinase (CaM kinase) IIbeta (CAMK2B). Notably, growth of swine-origin H1N1 influenza virus is also dependent on the identified host factors, and we show that small molecule inhibitors of several factors, including vATPase and CAMK2B, antagonize influenza virus replication.

Cancer cells utilize multiple nutrient scavenging mechanisms to support growth and survival in nutrient-poor, hypoxic tumor microenvironments. Among these mechanisms, macropinocytosis has emerged as an important pathway of extracellular nutrient acquisition in cancer cells, particularly in tumors with activated RAS signaling, such as pancreatic cancer. However, the absence of a clinically available inhibitor, as well as the gap of knowledge in macropinocytosis regulation, remain a hurdle for its use for cancer therapy. Here, we use the Informer set library to identify novel regulators of macropinocytosis-dependent growth in pancreatic cancer cells. Understanding how these regulators function will allow us to provide novel opportunities for therapeutic intervention.

Activation of the PI3K-mTOR pathway is central to breast cancer pathogenesis including resistance to many targeted therapies. The mTOR kinase forms two distinct complexes, mTORC1 and mTORC2, and understanding which is required for the survival of malignant cells has been limited by tools to selectively and completely impair either subcomplex. To address this, we used RMC-6272, a bi-steric molecule with a rapamycin-like moiety linked to an mTOR active-site inhibitor that displays >25-fold selectivity for mTORC1 over mTORC2 substrates. Complete suppression of mTORC1 by RMC-6272 causes apoptosis in ER+/HER2- breast cancer cell lines, particularly in those that harbor mutations in PIK3CA or PTEN, due to inhibition of the rapamycin resistant, mTORC1 substrate 4EBP1 and reduction of the pro-survival protein MCL1. RMC-6272 reduced translation of ribosomal mRNAs, MYC target genes, and components of the CDK4/6 pathway, suggesting enhanced impairment of oncogenic pathways compared to the partial mTORC1 inhibitor everolimus. RMC-6272 maintained efficacy in hormone therapy-resistant acquired cell lines and patient-derived xenografts (PDX), showed increased efficacy in CDK4/6 inhibitor treated acquired resistant cell lines versus their parental counterparts, and was efficacious in a PDX from a patient experiencing resistance to CDK4/6 inhibition. Bi-steric mTORC1-selective inhibition may be effective in overcoming multiple forms of therapy-resistance in ER+ breast cancers.

Although targeted therapies often elicit profound initial patient responses, these effects are transient due to residual disease leading to acquired resistance. How tumors transition between drug responsiveness, tolerance and resistance, especially in the absence of preexisting subclones, remains unclear. In epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma cells, we demonstrate that residual disease and acquired resistance in response to EGFR inhibitors requires Aurora kinase A (AURKA) activity. Nongenetic resistance through the activation of AURKA by its coactivator TPX2 emerges in response to chronic EGFR inhibition where it mitigates drug-induced apoptosis. Aurora kinase inhibitors suppress this adaptive survival program, increasing the magnitude and duration of EGFR inhibitor response in preclinical models. Treatment-induced activation of AURKA is associated with resistance to EGFR inhibitors in vitro, in vivo and in most individuals with EGFR-mutant lung adenocarcinoma. These findings delineate a molecular path whereby drug resistance emerges from drug-tolerant cells and unveils a synthetic lethal strategy for enhancing responses to EGFR inhibitors by suppressing AURKA-driven residual disease and acquired resistance.

We used a quantitative, high-density genetic interaction map, or E-MAP (Epistatic MiniArray Profile), to interrogate the relationships within and between RNA-processing pathways. Due to their complexity and the essential roles of many of the components, these pathways have been difficult to functionally dissect. Here, we report the results for 107,155 individual interactions involving 552 mutations, 166 of which are hypomorphic alleles of essential genes. Our data enabled the discovery of links between components of the mRNA export and splicing machineries and Sem1/Dss1, a component of the 19S proteasome. In particular, we demonstrate that Sem1 has a proteasome-independent role in mRNA export as a functional component of the Sac3-Thp1 complex. Sem1 also interacts with Csn12, a component of the COP9 signalosome. Finally, we show that Csn12 plays a role in pre-mRNA splicing, which is independent of other signalosome components. Thus, Sem1 is involved in three separate and functionally distinct complexes.

Inferences about protein function are often made based on sequence homology to other gene products of known activities. This approach is valuable for small families of conserved proteins but can be difficult to apply to large superfamilies of proteins with diverse function. In this study we looked at sequence homology between members of the DJ-1/ThiJ/PfpI superfamily, which includes a human protein of unclear function, DJ-1, associated with inherited Parkinson's disease.

Herpesviruses constitute a family of large DNA viruses widely spread in vertebrates and causing a variety of different diseases. They possess dsDNA genomes ranging from 120 to 240 kbp encoding between 70 to 170 open reading frames. We previously reported the protein interaction networks of two herpesviruses, varicella-zoster virus (VZV) and Kaposi's sarcoma-associated herpesvirus (KSHV). In this study, we systematically tested three additional herpesvirus species, herpes simplex virus 1 (HSV-1), murine cytomegalovirus and Epstein-Barr virus, for protein interactions in order to be able to perform a comparative analysis of all three herpesvirus subfamilies. We identified 735 interactions by genome-wide yeast-two-hybrid screens (Y2H), and, together with the interactomes of VZV and KSHV, included a total of 1,007 intraviral protein interactions in the analysis. Whereas a large number of interactions have not been reported previously, we were able to identify a core set of highly conserved protein interactions, like the interaction between HSV-1 UL33 with the nuclear egress proteins UL31/UL34. Interactions were conserved between orthologous proteins despite generally low sequence similarity, suggesting that function may be more conserved than sequence. By combining interactomes of different species we were able to systematically address the low coverage of the Y2H system and to extract biologically relevant interactions which were not evident from single species.

Lung cancer, the leading cause of cancer mortality, exhibits heterogeneity that enables adaptability, limits therapeutic success, and remains incompletely understood. Single-cell RNA sequencing (scRNA-seq) of metastatic lung cancer was performed using 49 clinical biopsies obtained from 30 patients before and during targeted therapy. Over 20,000 cancer and tumor microenvironment (TME) single-cell profiles exposed a rich and dynamic tumor ecosystem. scRNA-seq of cancer cells illuminated targetable oncogenes beyond those detected clinically. Cancer cells surviving therapy as residual disease (RD) expressed an alveolar-regenerative cell signature suggesting a therapy-induced primitive cell-state transition, whereas those present at on-therapy progressive disease (PD) upregulated kynurenine, plasminogen, and gap-junction pathways. Active T-lymphocytes and decreased macrophages were present at RD and immunosuppressive cell states characterized PD. Biological features revealed by scRNA-seq were biomarkers of clinical outcomes in independent cohorts. This study highlights how therapy-induced adaptation of the multi-cellular ecosystem of metastatic cancer shapes clinical outcomes.

Many mechanisms have been proposed for how heightened aerobic glycolytic metabolism fuels cancer pathogenicity, but there are still many unexplored pathways. Here, we have performed metabolomic profiling to map glucose incorporation into metabolic pathways upon transformation of mammary epithelial cells by 11 commonly mutated human oncogenes. We show that transformation of mammary epithelial cells by oncogenic stimuli commonly shunts glucose-derived carbons into synthesis of sialic acid, a hexosamine pathway metabolite that is converted to CMP-sialic acid by cytidine monophosphate N-acetylneuraminic acid synthase (CMAS) as a precursor to glycoprotein and glycolipid sialylation. We show that CMAS knockdown leads to elevations in intracellular sialic acid levels, a depletion of cellular sialylation, and alterations in the expression of many cancer-relevant genes to impair breast cancer pathogenicity. Our study reveals the heretofore unrecognized role of sialic acid metabolism and protein sialylation in regulating the expression of genes that maintain breast cancer pathogenicity.

Synthetic lethal screens have the potential to identify new vulnerabilities incurred by specific cancer mutations but have been hindered by lack of agreement between studies. In the case of KRAS, we identify that published synthetic lethal screen hits significantly overlap at the pathway rather than gene level. Analysis of pathways encoded as protein networks could identify synthetic lethal candidates that are more reproducible than those previously reported. Lack of overlap likely stems from biological rather than technical limitations as most synthetic lethal phenotypes are strongly modulated by changes in cellular conditions or genetic context, the latter determined using a pairwise genetic interaction map that identifies numerous interactions that suppress synthetic lethal effects. Accounting for pathway, cellular and genetic context nominates a DNA repair dependency in KRAS-mutant cells, mediated by a network containing BRCA1. We provide evidence for why most reported synthetic lethals are not reproducible which is addressable using a multi-faceted testing framework.

Leveraging insights from genomic studies of patient tumors is limited by the discordance between these tumors and the cell line models used for functional studies. We integrate omics datasets using functional networks to identify gene modules reflecting variation between tumors and show that the structure of these modules can be evaluated in cell lines to discover clinically relevant biomarkers of therapeutic responses. Applied to breast cancer, we identify 219 gene modules that capture recurrent alterations and subtype patients and quantitate various cell types within the tumor microenvironment. Comparison of modules between tumors and cell lines reveals that many modules composed primarily of gene expression and methylation are poorly preserved. In contrast, preserved modules are highly predictive of drug responses in a manner that is robust and clinically relevant. This work addresses a fundamental challenge in pharmacogenomics that can only be overcome by the joint analysis of patient and cell line data.

Cancer cells possess fundamentally altered metabolism that supports their pathogenic features, which includes a heightened reliance on aerobic glycolysis to provide precursors for synthesis of biomass. We show here that inositol polyphosphate phosphatase 1 (INPP1) is highly expressed in aggressive human cancer cells and primary high-grade human tumors. Inactivation of INPP1 leads to a reduction in glycolytic intermediates that feed into the synthesis of the oncogenic signaling lipid lysophosphatidic acid (LPA), which in turn impairs LPA signaling and further attenuates glycolytic metabolism in a feed-forward mechanism to impair cancer cell motility, invasiveness, and tumorigenicity. Taken together these findings reveal a novel mode of glycolytic control in cancer cells that can serve to promote key oncogenic lipid signaling pathways that drive cancer pathogenicity.

While there have been tremendous efforts to target oncogenic RAS signaling from inside the cell, little effort has focused on the cell-surface. Here, we used quantitative surface proteomics to reveal a signature of proteins that are upregulated on cells transformed with KRASG12V, and driven by MAPK pathway signaling. We next generated a toolkit of recombinant antibodies to seven of these RAS-induced proteins. We found that five of these proteins are broadly distributed on cancer cell lines harboring RAS mutations. In parallel, a cell-surface CRISPRi screen identified integrin and Wnt signaling proteins as critical to RAS-transformed cells. We show that antibodies targeting CDCP1, a protein common to our proteomics and CRISPRi datasets, can be leveraged to deliver cytotoxic and immunotherapeutic payloads to RAS-transformed cancer cells and report for RAS signaling status in vivo. Taken together, this work presents a technological platform for attacking RAS from outside the cell.

In microscopy-based drug screens, fluorescent markers carry critical information on how compounds affect different biological processes. However, practical considerations, such as the labor and preparation formats needed to produce different image channels, hinders the use of certain fluorescent markers. Consequently, completed screens may lack biologically informative but experimentally impractical markers. Here, we present a deep learning method for overcoming these limitations. We accurately generated predicted fluorescent signals from other related markers and validated this new machine learning (ML) method on two biologically distinct datasets. We used the ML method to improve the selection of biologically active compounds for Alzheimer's disease (AD) from a completed high-content high-throughput screen (HCS) that had only contained the original markers. The ML method identified novel compounds that effectively blocked tau aggregation, which had been missed by traditional screening approaches unguided by ML. The method improved triaging efficiency of compound rankings over conventional rankings by raw image channels. We reproduced this ML pipeline on a biologically independent cancer-based dataset, demonstrating its generalizability. The approach is disease-agnostic and applicable across diverse fluorescence microscopy datasets.

UDP-glucose pyrophosphorylase 2 (UGP2), the enzyme that synthesizes uridine diphosphate (UDP)-glucose, rests at the convergence of multiple metabolic pathways, however, the role of UGP2 in tumor maintenance and cancer metabolism remains unclear. Here, we identify an important role for UGP2 in the maintenance of pancreatic ductal adenocarcinoma (PDAC) growth in both in vitro and in vivo tumor models. We found that transcription of UGP2 is directly regulated by the Yes-associated protein 1 (YAP)-TEA domain transcription factor (TEAD) complex, identifying UGP2 as a bona fide YAP target gene. Loss of UGP2 leads to decreased intracellular glycogen levels and defects in N-glycosylation targets that are important for the survival of PDACs, including the epidermal growth factor receptor (EGFR). These critical roles of UGP2 in cancer maintenance, metabolism, and protein glycosylation may offer insights into therapeutic options for otherwise intractable PDACs.

Individual cancers are composed of heterogeneous tumor cells with distinct phenotypes and genotypes, with triple negative breast cancers (TNBC) demonstrating the most heterogeneity among breast cancer types. Variability in transcriptional phenotypes could meaningfully limit the efficacy of monotherapies and fuel drug resistance, although to an unknown extent. To determine if transcriptional differences between tumor cells lead to differential drug responses we performed single cell RNA-seq on cell line and PDX models of breast cancer revealing cell subpopulations in states associated with resistance to standard-of-care therapies. We found that TNBC models contained a subpopulation in an inflamed cellular state, often also present in human breast cancer samples. Inflamed cells display evidence of heightened cGAS/STING signaling which we demonstrate is sufficient to cause tumor cell resistance to chemotherapy. Accordingly, inflamed cells were enriched in human tumors taken after neoadjuvant chemotherapy and associated with early recurrence, highlighting the potential for diverse tumor cell states to promote drug resistance.

Massively parallel sequencing has permitted an unprecedented examination of the cancer exome, leading to predictions that all genes important to cancer will soon be identified by genetic analysis of tumours. To examine this potential, here we evaluate the ability of state-of-the-art sequence analysis methods to specifically recover known cancer genes. While some cancer genes are identified by analysis of recurrence, spatial clustering or predicted impact of somatic mutations, many remain undetected due to lack of power to discriminate driver mutations from the background mutational load (13-60% recall of cancer genes impacted by somatic single-nucleotide variants, depending on the method). Cancer genes not detected by mutation recurrence also tend to be missed by all types of exome analysis. Nonetheless, these genes are implicated by other experiments such as functional genetic screens and expression profiling. These challenges are only partially addressed by increasing sample size and will likely hold even as greater numbers of tumours are analysed.

Chemotherapy is used to treat most cancer patients, yet our understanding of factors that dictate response and resistance to such drugs remains limited. We report the generation of a quantitative chemical-genetic interaction map in human mammary epithelial cells charting the impact of the knockdown of 625 genes related to cancer and DNA repair on sensitivity to 29 drugs, covering all classes of chemotherapy. This quantitative map is predictive of interactions maintained in other cell lines, identifies DNA-repair factors, predicts cancer cell line responses to therapy, and prioritizes synergistic drug combinations. We identify that ARID1A loss confers resistance to PARP inhibitors in cells and ovarian cancer patients and that loss of GPBP1 causes resistance to cisplatin and PARP inhibitors through the regulation of genes involved in homologous recombination. This map helps navigate patient genomic data and optimize chemotherapeutic regimens by delineating factors involved in the response to specific types of DNA damage.