Project DRIVE: A Compendium of Cancer Dependencies and Synthetic Lethal Relationships Uncovered by Large-Scale, Deep RNAi Screening

Identifying actionable synthetically lethal cancer gene pairs using mutual exclusivity

Mutually exclusive loss-of-function alterations in gene pairs are those that occur together less frequently than may be expected and may denote a synthetically lethal relationship (SSL) between the genes. SSLs can be exploited therapeutically to selectively kill cancer cells. Here, we analysed mutation, copy number variation, and methylation levels in samples from The Cancer Genome Atlas, using the hypergeometric and the Poisson binomial tests to identify mutually exclusive inactivated genes. We focused on gene pairs where one is an inactivated tumour suppressor and the other a gene whose protein product can be inhibited by known drugs. This provided an abundance of potential targeted therapeutics and repositioning opportunities for several cancers. These data are available on the MexDrugs website, https://bioinformaticslab.sussex.ac.uk/mexdrugs.

An Improved PDE6D Inhibitor Combines with Sildenafil To Inhibit KRAS Mutant Cancer Cell Growth

The trafficking chaperone PDE6D (or PDEδ) was proposed as a surrogate target for K-Ras, leading to the development of a series of inhibitors that block its prenyl binding pocket. These inhibitors suffered from low solubility and suspected off-target effects, preventing their clinical development. Here, we developed a highly soluble, low nanomolar PDE6D inhibitor (PDE6Di), Deltaflexin3, which has the lowest off-target activity as compared to three prominent reference compounds. Deltaflexin3 reduces Ras signaling and selectively decreases the growth of KRAS mutant and PDE6D-dependent cancer cells. We further show that PKG2-mediated phosphorylation of Ser181 lowers K-Ras binding to PDE6D. Thus, Deltaflexin3 combines with the approved PKG2 activator Sildenafil to more potently inhibit PDE6D/K-Ras binding, cancer cell proliferation, and microtumor growth. As observed previously, inhibition of Ras trafficking, signaling, and cancer cell proliferation remained overall modest. Our results suggest reevaluating PDE6D as a K-Ras surrogate target in cancer.

Triple Blockade of Oncogenic RAS Signaling Using KRAS and MEK Inhibitors in Combination with Irradiation in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest of human malignancies and carries an exceptionally poor prognosis. It is mostly driven by multiple oncogenic alterations, with the highest mutation frequency being observed in the KRAS gene, which is a key oncogenic driver of tumorogenesis and malignant progression in PDAC. However, KRAS remained undruggable for decades until the emergence of G12C mutation specific KRAS inhibitors. Despite this development, this therapeutic approach to target KRAS directly is not routinely used for PDAC patients, with the reasons being the rare presence of G12C mutation in PDAC with only 1-2% of occurring cases, modest therapeutic efficacy, activation of compensatory pathways leading to cell resistance, and absence of effective KRASG12D or pan-KRAS inhibitors. Additionally, indirect approaches to targeting KRAS through upstream and downstream regulators or effectors were also found to be either ineffective or known to cause major toxicities. For this reason, new and more effective treatment strategies that combine different therapeutic modalities aiming at achieving synergism and minimizing intrinsic or adaptive resistance mechanisms are required. In the current work presented here, pancreatic cancer cell lines with oncogenic KRAS G12C, G12D, or wild-type KRAS were treated with specific KRAS or SOS1/2 inhibitors, and therapeutic synergisms with concomitant MEK inhibition and irradiation were systematically evaluated by means of cell viability, 2D-clonogenic, 3D-anchorage independent soft agar, and bioluminescent ATP assays. Underlying pathophysiological mechanisms were examined by using Western blot analyses, apoptosis assay, and RAS activation assay.

RAS isoform specific activities are disrupted by disease associated mutations during cell differentiation

The RAS-MAPK-pathway is aberrantly regulated in cancer and developmental diseases called RASopathies. While typically the impact of Ras on the proliferation of various cancer cell lines is assessed, it is poorly established how Ras affects cellular differentiation. Here we implement the C2C12 myoblast cell line to systematically study the effect of Ras mutants and Ras-pathway drugs on differentiation. We first provide evidence that a minor pool of Pax7+ progenitors replenishes a major pool of transit amplifying cells that are ready to differentiate. Our data indicate that Ras isoforms have distinct roles in the differentiating culture, where K-Ras depletion increases and H-Ras depletion decreases terminal differentiation. This assay could therefore provide significant new insights into Ras biology and Ras-driven diseases. In line with this, we found that all oncogenic Ras mutants block terminal differentiation of transit amplifying cells. By contrast, RASopathy associated K-Ras variants were less able to block differentiation. Profiling of eight targeted Ras-pathway drugs on seven oncogenic Ras mutants revealed their allele-specific activities and distinct abilities to restore normal differentiation as compared to triggering cell death. In particular, the MEK-inhibitor trametinib could broadly restore differentiation, while the mTOR-inhibitor rapamycin broadly suppressed differentiation. We expect that this quantitative assessment of the impact of Ras-pathway mutants and drugs on cellular differentiation has great potential to complement cancer cell proliferation data.

Protein tyrosine phosphatases: emerging role in cancer therapy resistance

BACKGROUND

Tyrosine phosphorylation of intracellular proteins is a post-translational modification that plays a regulatory role in signal transduction during cellular events. Dephosphorylation of signal transduction proteins caused by protein tyrosine phosphatases (PTPs) contributed their role as a convergent node to mediate cross-talk between signaling pathways. In the context of cancer, PTP-mediated pathways have been identified as signaling hubs that enabled cancer cells to mitigate stress induced by clinical therapy. This is achieved by the promotion of constitutive activation of growth-stimulatory signaling pathways or modulation of the immune-suppressive tumor microenvironment. Preclinical evidences suggested that anticancer drugs will release their greatest therapeutic potency when combined with PTP inhibitors, reversing drug resistance that was responsible for clinical failures during cancer therapy.

AREAS COVERED

This review aimed to elaborate recent insights that supported the involvement of PTP-mediated pathways in the development of resistance to targeted therapy and immune-checkpoint therapy.

EXPERT OPINION

This review proposed the notion of PTP inhibition in anticancer combination therapy as a potential strategy in clinic to achieve long-term tumor regression. Ongoing clinical trials are currently underway to assess the safety and efficacy of combination therapy in advanced-stage tumors.

Genetic dependencies associated with transcription factor activities in human cancer cell lines

Transcription factors (TFs) are important mediators of aberrant transcriptional programs in cancer cells. In this study, we focus on TF activity (TFa) as a biomarker for cell-line-selective anti-proliferative effects, in that high TFa predicts sensitivity to loss of function of a given gene (i.e., genetic dependencies [GDs]). Our linear-regression-based framework identifies 3,047 pan-cancer and 3,952 cancer-type-specific candidate TFa-GD associations from cell line data, which are then cross-examined for impact on survival in patient cohorts. One of the most prominent biomarkers is TEAD1 activity, whose associations with its predicted GDs are validated through experimental evidence as proof of concept. Overall, these TFa-GD associations represent an attractive resource for identifying innovative, biomarker-driven hypotheses for drug discovery programs in oncology.

Discovery of WRN inhibitor HRO761 with synthetic lethality in MSI cancers

The Werner syndrome RecQ helicase WRN was identified as a synthetic lethal target in cancer cells with microsatellite instability (MSI) by several genetic screens1-6. Despite advances in treatment with immune checkpoint inhibitors7-10, there is an unmet need in the treatment of MSI cancers11-14. Here we report the structural, biochemical, cellular and pharmacological characterization of the clinical-stage WRN helicase inhibitor HRO761, which was identified through an innovative hit-finding and lead-optimization strategy. HRO761 is a potent, selective, allosteric WRN inhibitor that binds at the interface of the D1 and D2 helicase domains, locking WRN in an inactive conformation. Pharmacological inhibition by HRO761 recapitulated the phenotype observed by WRN genetic suppression, leading to DNA damage and inhibition of tumour cell growth selectively in MSI cells in a p53-independent manner. Moreover, HRO761 led to WRN degradation in MSI cells but not in microsatellite-stable cells. Oral treatment with HRO761 resulted in dose-dependent in vivo DNA damage induction and tumour growth inhibition in MSI cell- and patient-derived xenograft models. These findings represent preclinical pharmacological validation of WRN as a therapeutic target in MSI cancers. A clinical trial with HRO761 (NCT05838768) is ongoing to assess the safety, tolerability and preliminary anti-tumour activity in patients with MSI colorectal cancer and other MSI solid tumours.

MEMO1 binds iron and modulates iron homeostasis in cancer cells

Mediator of ERBB2-driven cell motility 1 (MEMO1) is an evolutionary conserved protein implicated in many biological processes; however, its primary molecular function remains unknown. Importantly, MEMO1 is overexpressed in many types of cancer and was shown to modulate breast cancer metastasis through altered cell motility. To better understand the function of MEMO1 in cancer cells, we analyzed genetic interactions of MEMO1 using gene essentiality data from 1028 cancer cell lines and found multiple iron-related genes exhibiting genetic relationships with MEMO1. We experimentally confirmed several interactions between MEMO1 and iron-related proteins in living cells, most notably, transferrin receptor 2 (TFR2), mitoferrin-2 (SLC25A28), and the global iron response regulator IRP1 (ACO1). These interactions indicate that cells with high-MEMO1 expression levels are hypersensitive to the disruptions in iron distribution. Our data also indicate that MEMO1 is involved in ferroptosis and is linked to iron supply to mitochondria. We have found that purified MEMO1 binds iron with high affinity under redox conditions mimicking intracellular environment and solved MEMO1 structures in complex with iron and copper. Our work reveals that the iron coordination mode in MEMO1 is very similar to that of iron-containing extradiol dioxygenases, which also display a similar structural fold. We conclude that MEMO1 is an iron-binding protein that modulates iron homeostasis in cancer cells.

Challenges for the Discovery of Non-Covalent WRN Helicase Inhibitors

The Werner Syndrome RecQ helicase (WRN) is a synthetic lethal target of interest for the treatment of cancers with microsatellite instability (MSI). Different hit finding approaches were initially tested. The identification of WRN inhibitors proved challenging due to a high propensity for artefacts via protein interference, i. e., hits inhibiting WRN enzymatic activities through multiple, unspecific mechanisms. Previously published WRN Helicase inhibitors (ML216, NSC19630 or NSC617145) were characterized in an extensive set of biochemical and biophysical assays and could be ruled out as specific WRN helicase probes. More innovative screening strategies need to be developed for successful drug discovery of non-covalent WRN helicase inhibitors.

Oncotherapy resistance explained by Darwinian and Lamarckian models

Cell and antibody therapies directed against surface molecules on B cells, e.g., CD19-targeting chimeric antigen receptor T cells (CD19 CAR-T), are now standard for patients with chemorefractory B cell acute lymphoblastic leukemias and other B cell malignancies. However, early relapse rates remain high. In this issue of the JCI, Aminov, Giricz, and colleagues revealed that leukemia cells resisting CD19-targeted therapy had reduced CD19 but also low CD22 expression and were sensitive to Bruton's tyrosine kinase and/or MEK inhibition. Overall, their observations support the evolution of resistance following a Lamarckian model: the oncotherapy directly elicits adaptive, resistance-conferring reconfigurations, which are then inherited by daughter cells as epigenetic changes. The findings prompt reflection also on the broader role of epigenetics in decoupling of replication from lineage differentiation activation by the B cell lineage master transcription factor hub. Such oncogenesis and resistance mechanisms, being predictable and epigenetic, offer practical opportunities for intervention, potentially non-cross-resistant and safe vis-à-vis present cytotoxic and CAR-T treatments.

Direct and selective pharmacological disruption of the YAP-TEAD interface by IAG933 inhibits Hippo-dependent and RAS-MAPK-altered cancers

The YAP-TEAD protein-protein interaction mediates YAP oncogenic functions downstream of the Hippo pathway. To date, available YAP-TEAD pharmacologic agents bind into the lipid pocket of TEAD, targeting the interaction indirectly via allosteric changes. However, the consequences of a direct pharmacological disruption of the interface between YAP and TEADs remain largely unexplored. Here, we present IAG933 and its analogs as potent first-in-class and selective disruptors of the YAP-TEAD protein-protein interaction with suitable properties to enter clinical trials. Pharmacologic abrogation of the interaction with all four TEAD paralogs resulted in YAP eviction from chromatin and reduced Hippo-mediated transcription and induction of cell death. In vivo, deep tumor regression was observed in Hippo-driven mesothelioma xenografts at tolerated doses in animal models as well as in Hippo-altered cancer models outside mesothelioma. Importantly this also extended to larger tumor indications, such as lung, pancreatic and colorectal cancer, in combination with RTK, KRAS-mutant selective and MAPK inhibitors, leading to more efficacious and durable responses. Clinical evaluation of IAG933 is underway.

RBM39 degrader invigorates natural killer cells to eradicate neuroblastoma despite cancer cell plasticity

The cellular plasticity of neuroblastoma is defined by a mixture of two major cell states, adrenergic (ADRN) and mesenchymal (MES), which may contribute to therapy resistance. However, how neuroblastoma cells switch cellular states during therapy remains largely unknown and how to eradicate neuroblastoma regardless of their cell states is a clinical challenge. To better understand the lineage switch of neuroblastoma in chemoresistance, we comprehensively defined the transcriptomic and epigenetic map of ADRN and MES types of neuroblastomas using human and murine models treated with indisulam, a selective RBM39 degrader. We showed that cancer cells not only undergo a bidirectional switch between ADRN and MES states, but also acquire additional cellular states, reminiscent of the developmental pliancy of neural crest cells. The lineage alterations are coupled with epigenetic reprogramming and dependency switch of lineage-specific transcription factors, epigenetic modifiers and targetable kinases. Through targeting RNA splicing, indisulam induces an inflammatory tumor microenvironment and enhances anticancer activity of natural killer cells. The combination of indisulam with anti-GD2 immunotherapy results in a durable, complete response in high-risk transgenic neuroblastoma models, providing an innovative, rational therapeutic approach to eradicate tumor cells regardless of their potential to switch cell states.

Development of a Series of Pyrrolopyridone MAT2A Inhibitors

The optimization of an allosteric fragment, discovered by differential scanning fluorimetry, to an in vivo MAT2a tool inhibitor is discussed. The structure-based drug discovery approach, aided by relative binding free energy calculations, resulted in AZ'9567 (21), a potent inhibitor in vitro with excellent preclinical pharmacokinetic properties. This tool showed a selective antiproliferative effect on methylthioadenosine phosphorylase (MTAP) KO cells, both in vitro and in vivo, providing further evidence to support the utility of MAT2a inhibitors as potential anticancer therapies for MTAP-deficient tumors.

The prevalence of ADSL (rs3788579) and CYP1A2 (rs17861162) polymorphisms in female breast cancer patients in North-West Iran

INTRODUCTION

Breast cancer is a prevalent and significant contributor to cancer-related mortality among women worldwide. Its increasing incidence, especially in regions like North-West Iran, necessitates a deeper understanding of genetic factors contributing to its development. Genetic alterations, particularly single nucleotide polymorphisms (SNPs), are implicated in breast cancer susceptibility, making investigation in this context crucial. This study explores the role of CYP1A2-rs17861162 and ADSL-rs3788579 SNPs in breast cancer risk among Iranian women.

METHODS

This study involved 200 female breast cancer patients and 200 healthy controls in North-West Iran. DNA was extracted from blood samples, and PCR-RFLP was used for genotyping the CYP1A2 and ADSL genes.

RESULTS

The CYP1A2-rs17861162 SNP exhibited a shift from the C allele to the G allele in breast cancer patients, resulting in a 21.7% decrease in CC genotype frequency and a 21.6% and 77.8% increase in CG and GG genotypes, respectively, compared to controls. In ADSL-rs3788579 SNP, breast cancer patients had a significantly higher prevalence of the T allele, with a 28.5% increase compared to controls. In healthy participants, CC was most common, while in the breast cancer group, TT was most common.

CONCLUSION

This study highlights significant genetic alterations in CYP1A2-rs17861162 and ADSL-rs3788579 SNPs among breast cancer patients in North-West Iran, suggesting their potential as diagnostic and prognostic biomarkers. Further research is warranted to elucidate the precise mechanisms underlying their contributions to breast cancer susceptibility in this population.

CSSLdb: Discovery of cancer-specific synthetic lethal interactions based on machine learning and statistic inference

Synthetic lethality (SL) occurs when the inactivation of two genes results in cell death while the inactivation of either gene alone is non-lethal. SL-based therapy has become a promising anti-cancer treatment option with the increasing researches and applications in clinical practice, while the specific therapeutic opportunities for various cancers have not yet been comprehensively investigated. Herein, we described a computational approach based on machine learning and statistical inference to discover the cancer-specific synthetic lethal interactions. First, Random Forest and One-Class SVM were used to perform cancer unbiased prediction of synthetic lethality. Then, two strategies, including mutual exclusivity and differential expression, were used to screen cancer-specific synthetic lethal interactions, resulting in 14,582 SL gene pairs in 33 cancer types. Finally, we developed a freely available database of CSSLdb (Cancer Specific Synthetic Lethality Database, http://www.tmliang.cn/CSSL/) to present cancer-specific synthetic lethal genetic interactions, which would enrich the relevant research and contribute to underlying therapy strategies based on synthetic lethality.

The power and the promise of synthetic lethality for clinical application in cancer treatment

Synthetic lethality is a phenomenon wherein the simultaneous deficiency of two or more genes results in cell death, while the deficiency of any individual gene does not lead to cell death. In recent years, synthetic lethality has emerged as a significant topic in the field of targeted cancer therapy, with certain drugs based on this concept exhibiting promising outcomes in clinical trials. Nevertheless, the presence of tumor heterogeneity and the intricate DNA repair mechanisms pose challenges to the effective implementation of synthetic lethality. This review aims to explore the concepts, development, and ethical quandaries surrounding synthetic lethality. Additionally, it will provide an in-depth analysis of the clinical application and underlying mechanism of synthetic lethality.

INPP5A phosphatase is a synthetic lethal target in GNAQ and GNA11-mutant melanomas

Activating mutations in GNAQ/GNA11 occur in over 90% of uveal melanomas (UMs), the most lethal melanoma subtype; however, targeting these oncogenes has proven challenging and inhibiting their downstream effectors show limited clinical efficacy. Here, we performed genome-scale CRISPR screens along with computational analyses of cancer dependency and gene expression datasets to identify the inositol-metabolizing phosphatase INPP5A as a selective dependency in GNAQ/11-mutant UM cells in vitro and in vivo. Mutant cells intrinsically produce high levels of the second messenger inositol 1,4,5 trisphosphate (IP3) that accumulate upon suppression of INPP5A, resulting in hyperactivation of IP3-receptor signaling, increased cytosolic calcium and p53-dependent apoptosis. Finally, we show that GNAQ/11-mutant UM cells and patients' tumors exhibit elevated levels of IP4, a biomarker of enhanced IP3 production; these high levels are abolished by GNAQ/11 inhibition and correlate with sensitivity to INPP5A depletion. Our findings uncover INPP5A as a synthetic lethal vulnerability and a potential therapeutic target for GNAQ/11-mutant-driven cancers.

A distinct class of pan-cancer susceptibility genes revealed by an alternative polyadenylation transcriptome-wide association study

Alternative polyadenylation plays an important role in cancer initiation and progression; however, current transcriptome-wide association studies mostly ignore alternative polyadenylation when identifying putative cancer susceptibility genes. Here, we perform a pan-cancer 3' untranslated region alternative polyadenylation transcriptome-wide association analysis by integrating 55 well-powered (n > 50,000) genome-wide association studies datasets across 22 major cancer types with alternative polyadenylation quantification from 23,955 RNA sequencing samples across 7,574 individuals. We find that genetic variants associated with alternative polyadenylation are co-localized with 28.57% of cancer loci and contribute a significant portion of cancer heritability. We further identify 642 significant cancer susceptibility genes predicted to modulate cancer risk via alternative polyadenylation, 62.46% of which have been overlooked by traditional expression- and splicing- studies. As proof of principle validation, we show that alternative alleles facilitate 3' untranslated region lengthening of CRLS1 gene leading to increased protein abundance and promoted proliferation of breast cancer cells. Together, our study highlights the significant role of alternative polyadenylation in discovering new cancer susceptibility genes and provides a strong foundational framework for enhancing our understanding of the etiology underlying human cancers.

A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization

Genetic screens in cancer cell lines inform gene function and drug discovery. More comprehensive screen datasets with multi-omics data are needed to enhance opportunities to functionally map genetic vulnerabilities. Here, we construct a second-generation map of cancer dependencies by annotating 930 cancer cell lines with multi-omic data and analyze relationships between molecular markers and cancer dependencies derived from CRISPR-Cas9 screens. We identify dependency-associated gene expression markers beyond driver genes, and observe many gene addiction relationships driven by gain of function rather than synthetic lethal effects. By combining clinically informed dependency-marker associations with protein-protein interaction networks, we identify 370 anti-cancer priority targets for 27 cancer types, many of which have network-based evidence of a functional link with a marker in a cancer type. Mapping these targets to sequenced tumor cohorts identifies tractable targets in different cancer types. This target prioritization map enhances understanding of gene dependencies and identifies candidate anti-cancer targets for drug development.

Evolutionary proteogenomic landscape from pre-invasive to invasive lung adenocarcinoma

Lung adenocarcinoma follows a stepwise progression from pre-invasive to invasive. However, there remains a knowledge gap regarding molecular events from pre-invasive to invasive. Here, we conduct a comprehensive proteogenomic analysis comprising whole-exon sequencing, RNA sequencing, and proteomic and phosphoproteomic profiling on 98 pre-invasive and 99 invasive lung adenocarcinomas. The deletion of chr4q12 contributes to the progression from pre-invasive to invasive adenocarcinoma by downregulating SPATA18, thus suppressing mitophagy and promoting cell invasion. Proteomics reveals diverse enriched pathways in normal lung tissues and pre-invasive and invasive adenocarcinoma. Proteomic analyses identify three proteomic subtypes, which represent different stages of tumor progression. We also illustrate the molecular characterization of four immune clusters, including endothelial cells, B cells, DCs, and immune depression subtype. In conclusion, this comprehensive proteogenomic study characterizes the molecular architecture and hallmarks from pre-invasive to invasive lung adenocarcinoma, guiding the way to a deeper understanding of the tumorigenesis and progression of this disease.

The Chromatin Remodeler CHD4 Sustains Ewing Sarcoma Cell Survival by Controlling Global Chromatin Architecture

Ewing sarcoma is an aggressive cancer with a defective response to DNA damage leading to an enhanced sensitivity to genotoxic agents. Mechanistically, Ewing sarcoma is driven by the fusion transcription factor EWS-FLI1, which reprograms the tumor cell epigenome. The nucleosome remodeling and deacetylase (NuRD) complex is an important regulator of chromatin function, controlling both gene expression and DNA damage repair, and has been associated with EWS-FLI1 activity. Here, a NuRD-focused CRISPR/Cas9 inactivation screen identified the helicase CHD4 as essential for Ewing sarcoma cell proliferation. CHD4 silencing induced tumor cell death by apoptosis and abolished colony formation. Although CHD4 and NuRD colocalized with EWS-FLI1 at enhancers and super-enhancers, CHD4 promoted Ewing sarcoma cell survival not by modulating EWS-FLI1 activity and its oncogenic gene expression program but by regulating chromatin structure. CHD4 depletion led to a global increase in DNA accessibility and induction of spontaneous DNA damage, resulting in an increased susceptibility to DNA-damaging agents. CHD4 loss delayed tumor growth in vivo, increased overall survival, and combination with PARP inhibition by olaparib treatment further suppressed tumor growth. Collectively, these findings highlight the NuRD subunit CHD4 as a therapeutic target in Ewing sarcoma that can potentiate the antitumor activity of genotoxic agents.

SIGNIFICANCE

CRISPR/Cas9 screening in Ewing sarcoma identifies a dependency on CHD4, which is crucial for the maintenance of chromatin architecture to suppress DNA damage and a promising therapeutic target for DNA damage repair-deficient malignancies.

Uncovering hidden cancer self-dependencies through analysis of shRNA-level dependency scores

Large-scale short hairpin RNA (shRNA) screens on well-characterized human cancer cell lines have been widely used to identify novel cancer dependencies. However, the off-target effects of shRNA reagents pose a significant challenge in the analysis of these screens. To mitigate these off-target effects, various approaches have been proposed that aggregate different shRNA viability scores targeting a gene into a single gene-level viability score. Most computational methods for discovering cancer dependencies rely on these gene-level scores. In this paper, we propose a computational method, named NBDep, to find cancer self-dependencies by directly analyzing shRNA-level dependency scores instead of gene-level scores. The NBDep algorithm begins by removing known batch effects of the shRNAs and selecting a subset of concordant shRNAs for each gene. It then uses negative binomial random effects models to statistically assess the dependency between genetic alterations and the viabilities of cell lines by incorporating all shRNA dependency scores of each gene into the model. We applied NBDep to the shRNA dependency scores available at Project DRIVE, which covers 26 different types of cancer. The proposed method identified more well-known and putative cancer genes compared to alternative gene-level approaches in pan-cancer and cancer-specific analyses. Additionally, we demonstrated that NBDep controls type-I error and outperforms statistical tests based on gene-level scores in simulation studies.

DCAF1-based PROTACs with activity against clinically validated targets overcoming intrinsic- and acquired-degrader resistance

Targeted protein degradation (TPD) mediates protein level through small molecule induced redirection of E3 ligases to ubiquitinate neo-substrates and mark them for proteasomal degradation. TPD has recently emerged as a key modality in drug discovery. So far only a few ligases have been utilized for TPD. Interestingly, the workhorse ligase CRBN has been observed to be downregulated in settings of resistance to immunomodulatory inhibitory drugs (IMiDs). Here we show that the essential E3 ligase receptor DCAF1 can be harnessed for TPD utilizing a selective, non-covalent DCAF1 binder. We confirm that this binder can be functionalized into an efficient DCAF1-BRD9 PROTAC. Chemical and genetic rescue experiments validate specific degradation via the CRL4DCAF1 E3 ligase. Additionally, a dasatinib-based DCAF1 PROTAC successfully degrades cytosolic and membrane-bound tyrosine kinases. A potent and selective DCAF1-BTK-PROTAC (DBt-10) degrades BTK in cells with acquired resistance to CRBN-BTK-PROTACs while the DCAF1-BRD9 PROTAC (DBr-1) provides an alternative strategy to tackle intrinsic resistance to VHL-degrader, highlighting DCAF1-PROTACS as a promising strategy to overcome ligase mediated resistance in clinical settings.

Illuminating phenotypic drug responses of sarcoma cells to kinase inhibitors by phosphoproteomics

Kinase inhibitors (KIs) are important cancer drugs but often feature polypharmacology that is molecularly not understood. This disconnect is particularly apparent in cancer entities such as sarcomas for which the oncogenic drivers are often not clear. To investigate more systematically how the cellular proteotypes of sarcoma cells shape their response to molecularly targeted drugs, we profiled the proteomes and phosphoproteomes of 17 sarcoma cell lines and screened the same against 150 cancer drugs. The resulting 2550 phenotypic profiles revealed distinct drug responses and the cellular activity landscapes derived from deep (phospho)proteomes (9-10,000 proteins and 10-27,000 phosphorylation sites per cell line) enabled several lines of analysis. For instance, connecting the (phospho)proteomic data with drug responses revealed known and novel mechanisms of action (MoAs) of KIs and identified markers of drug sensitivity or resistance. All data is publicly accessible via an interactive web application that enables exploration of this rich molecular resource for a better understanding of active signalling pathways in sarcoma cells, identifying treatment response predictors and revealing novel MoA of clinical KIs.

Pathway-driven analysis of synthetic lethal interactions in cancer using perturbation screens

Synthetic lethality offers a promising approach for developing effective therapeutic interventions in cancer when direct targeting of driver genes is impractical. In this study, we comprehensively analyzed large-scale CRISPR, shRNA, and PRISM screens to identify potential synthetic lethal (SL) interactions in pan-cancer and 12 individual cancer types, using a new computational framework that leverages the biological function and signaling pathway information of key driver genes to mitigate the confounding effects of background genetic alterations in different cancer cell lines. This approach has successfully identified several putative SL interactions, including KRAS-MAP3K2 and APC-TCF7L2 in pan cancer, and CCND1-METTL1, TP53-FRS3, SMO-MDM2, and CCNE1-MTOR in liver, blood, skin, and gastric cancers, respectively. In addition, we proposed several FDA-approved cancer-targeted drugs for various cancer types through PRISM drug screens, such as cabazitaxel for VHL-mutated kidney cancer and alectinib for lung cancer with NRAS or KRAS mutations. Leveraging pathway information can enhance the concordance of shRNA and CRISPR screens and provide clinically relevant findings such as the potential efficacy of dasatinib, an inhibitor of SRC, for colorectal cancer patients with mutations in the WNT signaling pathway. These analyses revealed that taking signaling pathway information into account results in the identification of more promising SL interactions.

Current advances in comprehensive omics data mining for oncology and cancer research

The availability of a large amount of multiomics data enables data-driven discovery studies on cancers. High-throughput data on mutations, gene/protein expression, immune scores (tumor-infiltrating cells), drug screening, and RNAi (shRNAs and CRISPRs) screening are major integrated components of patient samples and cell line datasets. Improvements in data access and user interfaces make it easy for general scientists to carry out their data mining practices on integrated multiomics data platforms without computational expertise. Here, we summarize the extent of data integration and functionality of several portals and software that provide integrated multiomics data mining platforms for all cancer studies. Recent progress includes programming interfaces (APIs) for customized data mining. Precalculated datasets assist noncomputational users in quickly browsing data associations. Furthermore, stand-alone software provides fast calculations and smart functions, guiding optimal sampling and filtering options for the easy discovery of significant data associations. These efforts improve the utility of cancer omics big data for noncomputational users at all levels of cancer research. In the present review, we aim to provide analytical information guiding general scientists to find and utilize data mining tools for their research.

A proteomic landscape of pharmacologic perturbations for functional relevance

Pharmacological perturbation studies based on protein-level signatures are fundamental for drug discovery. In the present study, we used a mass spectrometry (MS)-based proteomic platform to profile the whole proteome of the breast cancer MCF7 cell line under stress induced by 78 bioactive compounds. The integrated analysis of perturbed signal abundance revealed the connectivity between phenotypic behaviors and molecular features in cancer cells. Our data showed functional relevance in exploring the novel pharmacological activity of phenolic xanthohumol, as well as the noncanonical targets of clinically approved tamoxifen, lovastatin, and their derivatives. Furthermore, the rational design of synergistic inhibition using a combination of histone methyltransferase and topoisomerase was identified based on their complementary drug fingerprints. This study provides rich resources for the proteomic landscape of drug responses for precision therapeutic medicine.

Structure-Based Discovery of Potent, Orally Bioavailable Benzoxazepinone-Based WD Repeat Domain 5 Inhibitors

The chromatin-associated protein WDR5 (WD repeat domain 5) is an essential cofactor for MYC and a conserved regulator of ribosome protein gene transcription. It is also a high-profile target for anti-cancer drug discovery, with proposed utility against both solid and hematological malignancies. We have previously discovered potent dihydroisoquinolinone-based WDR5 WIN-site inhibitors with demonstrated efficacy and safety in animal models. In this study, we sought to optimize the bicyclic core to discover a novel series of WDR5 WIN-site inhibitors with improved potency and physicochemical properties. We identified the 3,4-dihydrobenzo[f][1,4]oxazepin-5(2H)-one core as an alternative scaffold for potent WDR5 inhibitors. Additionally, we used X-ray structural analysis to design partially saturated bicyclic P7 units. These benzoxazepinone-based inhibitors exhibited increased cellular potency and selectivity and favorable physicochemical properties compared to our best-in-class dihydroisoquinolinone-based counterparts. This study opens avenues to discover more advanced WDR5 WIN-site inhibitors and supports their development as novel anti-cancer therapeutics.

Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience

Gene essentiality is a genetic concept crucial for a comprehensive understanding of life and evolution. In the last decade, many essential genes (EGs) have been determined using different experimental and computational approaches, and this information has been used to reduce the genomes of model organisms. A growing amount of evidence highlights that essentiality is a property that depends on the context. Because of their importance in vital biological processes, recognising context-specific EGs (csEGs) could help for identifying new potential pharmacological targets and to improve precision therapeutics. Since most of the computational procedures proposed to identify and predict EGs neglect their context-specificity, we focused on this aspect, providing a theoretical and experimental overview of the literature, data and computational methods dedicated to recognising csEGs. To this end, we adapted existing computational methods to exploit a specific context (the kidney tissue) and experimented with four different prediction methods using the labels provided by four different identification approaches. The considerations derived from the analysis of the obtained results, confirmed and validated also by further experiments for a different tissue context, provide the reader with guidance on exploiting existing tools for achieving csEGs identification and prediction.

RPL22 is a tumor suppressor in MSI-high cancers and a key splicing regulator of MDM4

Microsatellite instability high (MSI-H) tumors are malignant tumors that, despite harboring a high mutational burden, often have intact TP53. One of the most frequent mutations in MSI-H tumors is a frameshift mutation in RPL22, a ribosomal protein. Here, we identified RPL22 as a modulator of MDM4 splicing through an alternative splicing switch in exon 6. RPL22 loss increases MDM4 exon 6 inclusion, cell proliferation, and augments resistance to the MDM inhibitor Nutlin-3a. RPL22 represses expression of its paralog, RPL22L1, by mediating the splicing of a cryptic exon corresponding to a truncated transcript. Therefore, damaging mutations in RPL22 drive oncogenic MDM4 induction and reveal a common splicing circuit in MSI-H tumors that may inform therapeutic targeting of the MDM4-p53 axis and oncogenic RPL22L1 induction.

Complex synthetic lethality in cancer

The concept of synthetic lethality has been widely applied to identify therapeutic targets in cancer, with varying degrees of success. The standard approach normally involves identifying genetic interactions between two genes, a driver and a target. In reality, however, most cancer synthetic lethal effects are likely complex and also polygenic, being influenced by the environment in addition to involving contributions from multiple genes. By acknowledging and delineating this complexity, we describe in this article how the success rate in cancer drug discovery and development could be improved.

Computational tools for inferring transcription factor activity

Transcription factors (TFs) are essential players in orchestrating the regulatory landscape in cells. Still, their exact modes of action and dependencies on other regulatory aspects remain elusive. Since TFs act cell type-specific and each TF has its own characteristics, untangling their regulatory interactions from an experimental point of view is laborious and convoluted. Thus, there is an ongoing development of computational tools that estimate transcription factor activity (TFA) from a variety of data modalities, either based on a mapping of TFs to their putative target genes or in a genome-wide, gene-unspecific fashion. These tools can help to gain insights into TF regulation and to prioritize candidates for experimental validation. We want to give an overview of available computational tools that estimate TFA, illustrate examples of their application, debate common result validation strategies, and discuss assumptions and concomitant limitations.

RASSF1A promotes ATM signaling and RASSF1A methylation is a synthetic lethal marker for ATR inhibitors

Aim: The mechanism of RASSF1A in DNA damage repair remains to be further clarified for applying to synthetic lethal strategy. Materials & methods: Eight esophageal cancer cell lines, 181 cases of esophageal dysplasia and 1066 cases of primary esophageal squamous cell carcinoma (ESCC) were employed. Methylation-specific PCR, the CRISPR/Cas9 technique, immunoprecipitation assay and a xenograft mouse model were used. Results: RASSF1A was methylated in 2.21% of esophageal dysplasia and 11.73% of ESCC. RASSF1A was also involved in DNA damage repair through activating Hippo signaling. Loss of RASSF1A expression sensitized esophageal cancer cell lines to ataxia telangiectasia mutated and rad3-related (ATR) inhibitor (VE-822) both in vitro and in vivo. Conclusion: RASSF1A methylation is a synthetic lethal marker for ATR inhibitors.

Discovery of a highly potent, selective, orally bioavailable inhibitor of KAT6A/B histone acetyltransferases with efficacy against KAT6A-high ER+ breast cancer

KAT6A, and its paralog KAT6B, are histone lysine acetyltransferases (HAT) that acetylate histone H3K23 and exert an oncogenic role in several tumor types including breast cancer where KAT6A is frequently amplified/overexpressed. However, pharmacologic targeting of KAT6A to achieve therapeutic benefit has been a challenge. Here we describe identification of a highly potent, selective, and orally bioavailable KAT6A/KAT6B inhibitor CTx-648 (PF-9363), derived from a benzisoxazole series, which demonstrates anti-tumor activity in correlation with H3K23Ac inhibition in KAT6A over-expressing breast cancer. Transcriptional and epigenetic profiling studies show reduced RNA Pol II binding and downregulation of genes involved in estrogen signaling, cell cycle, Myc and stem cell pathways associated with CTx-648 anti-tumor activity in ER-positive (ER+) breast cancer. CTx-648 treatment leads to potent tumor growth inhibition in ER+ breast cancer in vivo models, including models refractory to endocrine therapy, highlighting the potential for targeting KAT6A in ER+ breast cancer.

ABCC1 and glutathione metabolism limit the efficacy of BCL-2 inhibitors in acute myeloid leukemia

The BCL-2 inhibitor Venetoclax is a promising agent for the treatment of acute myeloid leukemia (AML). However, many patients are refractory to Venetoclax, and resistance develops quickly. ATP-binding cassette (ABC) transporters mediate chemotherapy resistance but their role in modulating the activity of targeted small-molecule inhibitors is unclear. Using CRISPR/Cas9 screening, we find that loss of ABCC1 strongly increases the sensitivity of AML cells to Venetoclax. Genetic and pharmacologic ABCC1 inactivation potentiates the anti-leukemic effects of BCL-2 inhibitors and efficiently re-sensitizes Venetoclax-resistant leukemia cells. Conversely, ABCC1 overexpression induces resistance to BCL-2 inhibitors by reducing intracellular drug levels, and high ABCC1 levels predicts poor response to Venetoclax therapy in patients. Consistent with ABCC1-specific export of glutathionylated substrates, inhibition of glutathione metabolism increases the potency of BCL-2 inhibitors. These results identify ABCC1 and glutathione metabolism as mechanisms limiting efficacy of BCL-2 inhibitors, which may pave the way to development of more effective therapies.

CDK2 regulates collapsed replication fork repair in CCNE1-amplified ovarian cancer cells via homologous recombination

CCNE1 amplification is a common alteration in high-grade serous ovarian cancer and occurs in 15-20% of these tumors. These amplifications are mutually exclusive with homologous recombination deficiency, and, as they have intact homologous recombination, are intrinsically resistant to poly (ADP-ribose) polymerase inhibitors or chemotherapy agents. Understanding the molecular mechanisms that lead to this mutual exclusivity may reveal therapeutic vulnerabilities that could be leveraged in the clinic in this still underserved patient population. Here, we demonstrate that CCNE1-amplified high-grade serous ovarian cancer cells rely on homologous recombination to repair collapsed replication forks. Cyclin-dependent kinase 2, the canonical partner of cyclin E1, uniquely regulates homologous recombination in this genetic context, and as such cyclin-dependent kinase 2 inhibition synergizes with DNA damaging agents in vitro and in vivo. We demonstrate that combining a selective cyclin-dependent kinase 2 inhibitor with a DNA damaging agent could be a powerful tool in the clinic for high-grade serous ovarian cancer.

Effectively utilizing publicly available databases for cancer target evaluation

The majority of compounds designed against cancer drug targets do not progress to become approved drugs, mainly due to lack of efficacy and/or unmanageable toxicity. Robust target evaluation is therefore required before progressing through the drug discovery process to reduce the high attrition rate. There are a wealth of publicly available databases that can be mined to generate data as part of a target evaluation. It can, however, be challenging to learn what databases are available, how and when they should be used, and to understand the associated limitations. Here, we have compiled and present key, freely accessible and easy-to-use databases that house informative datasets from in vitro, in vivo and clinical studies. We also highlight comprehensive target review databases that aim to bring together information from multiple sources into one-stop portals. In the post-genomics era, a key objective is to exploit the extensive cell, animal and patient characterization datasets in order to deliver precision medicine on a patient-specific basis. Effective utilization of the highlighted databases will go some way towards supporting the cancer research community achieve these aims.

Genome-guided discovery of cancer therapeutic targets

The success of precision oncology-which aims to match the right therapies to the right patients based on molecular status-is predicated on a robust pipeline of molecular targets against which therapies can be developed. Recent advances in genomics and functional genetics have enabled the unbiased discovery of novel molecular targets at scale. We summarize the promise and challenges in integrating genomic and functional genetic landscapes of cancer to establish the next generation of cancer targets.

Partial gene suppression improves identification of cancer vulnerabilities when CRISPR-Cas9 knockout is pan-lethal

BACKGROUND

Hundreds of functional genomic screens have been performed across a diverse set of cancer contexts, as part of efforts such as the Cancer Dependency Map, to identify gene dependencies-genes whose loss of function reduces cell viability or fitness. Recently, large-scale screening efforts have shifted from RNAi to CRISPR-Cas9, due to superior efficacy and specificity. However, many effective oncology drugs only partially inhibit their protein targets, leading us to question whether partial suppression of genes using RNAi could reveal cancer vulnerabilities that are missed by complete knockout using CRISPR-Cas9. Here, we compare CRISPR-Cas9 and RNAi dependency profiles of genes across approximately 400 matched cancer cell lines.

RESULTS

We find that CRISPR screens accurately identify more gene dependencies per cell line, but the majority of each cell line's dependencies are part of a set of 1867 genes that are shared dependencies across the entire collection (pan-lethals). While RNAi knockdown of about 30% of these genes is also pan-lethal, approximately 50% have selective dependency patterns across cell lines, suggesting they could still be cancer vulnerabilities. The accuracy of the unique RNAi selectivity is supported by associations to multi-omics profiles, drug sensitivity, and other expected co-dependencies.

CONCLUSIONS

Incorporating RNAi data for genes that are pan-lethal knockouts facilitates the discovery of a wider range of gene targets than could be detected using the CRISPR dataset alone. This can aid in the interpretation of contrasting results obtained from CRISPR and RNAi screens and reinforce the importance of partial gene suppression methods in building a cancer dependency map.

The SWI/SNF complex member SMARCB1 supports lineage fidelity in kidney cancer

Lineage switching can induce therapy resistance in cancer. Yet, how lineage fidelity is maintained and how it can be lost remain poorly understood. Here, we have used CRISPR-Cas9-based genetic screening to demonstrate that loss of SMARCB1, a member of the SWI/SNF chromatin remodeling complex, can confer an advantage to clear cell renal cell carcinoma (ccRCC) cells upon inhibition of the renal lineage factor PAX8. Lineage factor inhibition-resistant ccRCC cells formed tumors with morphological features, but not molecular markers, of neuroendocrine differentiation. SMARCB1 inactivation led to large-scale loss of kidney-specific epigenetic programs and restoration of proliferative capacity through the adoption of new dependencies on factors that represent rare essential genes across different cancers. We further developed an analytical approach to systematically characterize lineage fidelity using large-scale CRISPR-Cas9 data. An understanding of the rules that govern lineage switching could aid the development of more durable lineage factor-targeted and other cancer therapies.

The mechanistic insights of the antioxidant Keap1-Nrf2 pathway in oncogenesis: a deadly scenario

The Nuclear factor erythroid 2-related factor 2 (Nrf2) protein has garnered significant interest due to its crucial function in safeguarding cells and tissues. The Nrf2 protein is crucial in preserving tissue integrity by safeguarding cells against metabolic, xenobiotic and oxidative stress. Due to its various functions, Nrf2 is a potential pharmacological target for reducing the incidence of diseases such as cancer. However, mutations in Keap1-Nrf2 are not consistently favored in all types of cancer. Instead, they seem to interact with specific driver mutations of tumors and their respective tissue origins. The Kelch-like ECH-associated protein 1 (Keap1)-Nrf2 pathway mutations are a powerful cancer adaptation that utilizes inherent cytoprotective pathways, encompassing nutrient metabolism and ROS regulation. The augmentation of Nrf2 activity elicits significant alterations in the characteristics of neoplastic cells, such as resistance to radiotherapy and chemotherapy, safeguarding against apoptosis, heightened invasiveness, hindered senescence, impaired autophagy and increased angiogenesis. The altered activity of Nrf2 can arise from diverse genetic and epigenetic modifications that instantly impact Nrf2 regulation. The present study aims to showcase the correlation between the Keap1-Nrf2 pathway and the progression of cancers, emphasizing genetic mutations, metabolic processes, immune regulation, and potential therapeutic strategies. This article delves into the intricacies of Nrf2 pathway anomalies in cancer, the potential ramifications of uncontrolled Nrf2 activity, and therapeutic interventions to modulate the Keap1-Nrf2 pathway.

Identification of 2-Sulfonyl/Sulfonamide Pyrimidines as Covalent Inhibitors of WRN Using a Multiplexed High-Throughput Screening Assay

Werner syndrome protein (WRN) is a multifunctional enzyme with helicase, ATPase, and exonuclease activities that are necessary for numerous DNA-related transactions in the human cell. Recent studies identified WRN as a synthetic lethal target in cancers characterized by genomic microsatellite instability resulting from defects in DNA mismatch repair pathways. WRN's helicase activity is essential for the viability of these high microsatellite instability (MSI-H) cancers and thus presents a therapeutic opportunity. To this end, we developed a multiplexed high-throughput screening assay that monitors exonuclease, ATPase, and helicase activities of full-length WRN. This screening campaign led to the discovery of 2-sulfonyl/sulfonamide pyrimidine derivatives as novel covalent inhibitors of WRN helicase activity. The compounds are specific for WRN versus other human RecQ family members and show competitive behavior with ATP. Examination of these novel chemical probes established the sulfonamide NH group as a key driver of compound potency. One of the leading compounds, H3B-960, showed consistent activities in a range of assays (IC50 = 22 nM, KD = 40 nM, KI = 32 nM), and the most potent compound identified, H3B-968, has inhibitory activity IC50 ∼ 10 nM. These kinetic properties trend toward other known covalent druglike molecules. Our work provides a new avenue for screening WRN for inhibitors that may be adaptable to different therapeutic modalities such as targeted protein degradation, as well as a proof of concept for the inhibition of WRN helicase activity by covalent molecules.

UHRF1 is a mediator of KRAS driven oncogenesis in lung adenocarcinoma

KRAS is a frequent driver in lung cancer. To identify KRAS-specific vulnerabilities in lung cancer, we performed RNAi screens in primary spheroids derived from a Kras mutant mouse lung cancer model and discovered an epigenetic regulator Ubiquitin-like containing PHD and RING finger domains 1 (UHRF1). In human lung cancer models UHRF1 knock-out selectively impaired growth and induced apoptosis only in KRAS mutant cells. Genome-wide methylation and gene expression analysis of UHRF1-depleted KRAS mutant cells revealed global DNA hypomethylation leading to upregulation of tumor suppressor genes (TSGs). A focused CRISPR/Cas9 screen validated several of these TSGs as mediators of UHRF1-driven tumorigenesis. In vivo, UHRF1 knock-out inhibited tumor growth of KRAS-driven mouse lung cancer models. Finally, in lung cancer patients high UHRF1 expression is anti-correlated with TSG expression and predicts worse outcomes for patients with KRAS mutant tumors. These results nominate UHRF1 as a KRAS-specific vulnerability and potential target for therapeutic intervention.

HSPA12A was identified as a key driver in colorectal cancer GWAS loci 10q26.12 and modulated by an enhancer-promoter interaction

Although genome-wide association studies (GWASs) have identified over 100 colorectal cancer (CRC) risk loci, an understanding of causal genes or risk variants and their biological functions in these loci remain unclear. Recently, genomic loci 10q26.12 with lead SNP rs1665650 was identified as an essential CRC risk loci of Asian populations. However, the functional mechanism of this region has not been fully clarified. Here, we applied an RNA interfering-based on-chip approach to screen for the genes essential for cell proliferation in the CRC risk loci 10q26.12. Notably, HSPA12A had the most significant effect among the identified genes and functioned as a crucial oncogene facilitating cell proliferation. Moreover, we conducted an integrative fine-mapping analysis to identify putative casual variants and further explored their association with CRC risk in a large-scale Chinese population consisting of 4054 cases and 4054 controls and also independently validated in 5208 cases and 20,832 controls from the UK biobank cohort. We identified a risk SNP rs7093835 in the intron of HSPA12A that was significantly associated with an increased risk of CRC (OR 1.23, 95% CI 1.08-1.41, P = 1.92 × 10-3). Mechanistically, the risk variant could facilitate an enhancer-promoter interaction mediated by the transcriptional factor (TF) GRHL1 and ultimately upregulate HSPA12A expression, which provides functional evidence to support our population findings. Collectively, our study reveals the important role of HSPA12A in CRC development and illustrates a novel enhancer-promoter interaction module between HSPA12A and its regulatory elements rs7093835, providing new insights into the etiology of CRC.

Integration of cancer-related genetic landscape of Eph receptors and ephrins with proteomics identifies a crosstalk between EPHB6 and EGFR

Eph receptors and their ephrin ligands are viewed as promising targets for cancer treatment; however, targeting them is hindered by their context-dependent functionalities. To circumvent this, we explore molecular landscapes underlying their pro- and anti-malignant activities. Using unbiased bioinformatics approaches, we construct a cancer-related network of genetic interactions (GIs) of all Ephs and ephrins to assist in their therapeutic manipulation. We also apply genetic screening and BioID proteomics and integrate them with machine learning approaches to select the most relevant GIs of one Eph receptor, EPHB6. This identifies a crosstalk between EPHB6 and EGFR, and further experiments confirm the ability of EPHB6 to modulate EGFR signaling, enhancing the proliferation of cancer cells and tumor development. Taken together, our observations show EPHB6 involvement in EGFR action, suggesting its targeting might be beneficial in EGFR-dependent tumors, and confirm that the Eph family genetic interactome presented here can be effectively exploited in developing cancer treatment approaches.

A landscape of response to drug combinations in non-small cell lung cancer

Combination of anti-cancer drugs is broadly seen as way to overcome the often-limited efficacy of single agents. The design and testing of combinations are however very challenging. Here we present a uniquely large dataset screening over 5000 targeted agent combinations across 81 non-small cell lung cancer cell lines. Our analysis reveals a profound heterogeneity of response across the tumor models. Notably, combinations very rarely result in a strong gain in efficacy over the range of response observable with single agents. Importantly, gain of activity over single agents is more often seen when co-targeting functionally proximal genes, offering a strategy for designing more efficient combinations. Because combinatorial effect is strongly context specific, tumor specificity should be achievable. The resource provided, together with an additional validation screen sheds light on major challenges and opportunities in building efficacious combinations against cancer and provides an opportunity for training computational models for synergy prediction.

Global analysis of the yeast knockout phenome

Genome-wide phenotypic screens in the budding yeast Saccharomyces cerevisiae, enabled by its knockout collection, have produced the largest, richest, and most systematic phenotypic description of any organism. However, integrative analyses of this rich data source have been virtually impossible because of the lack of a central data repository and consistent metadata annotations. Here, we describe the aggregation, harmonization, and analysis of ~14,500 yeast knockout screens, which we call Yeast Phenome. Using this unique dataset, we characterized two unknown genes (YHR045W and YGL117W) and showed that tryptophan starvation is a by-product of many chemical treatments. Furthermore, we uncovered an exponential relationship between phenotypic similarity and intergenic distance, which suggests that gene positions in both yeast and human genomes are optimized for function.

Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms

Anticipating and understanding cancers' need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies. We found that in all examined cancer types, F1 scores were greater than 0.90, and model accuracy remained robust under multiple hyperparameter tests. We then deconstructed these models to identify tumor type-specific coordinators of gene dependency and identified that in certain cancers, such as thyroid and kidney, tumors' dependencies are highly predicted by gene connectivity. In contrast, other histologies relied on pathway-based features such as lung, where gene dependencies were highly predictive by associations with cell death pathway genes. In sum, we show that biologically informed network features can be a valuable and robust addition to predictive pharmacology models while simultaneously providing mechanistic insights.

An integrated model for predicting KRAS dependency

The clinical approvals of KRAS G12C inhibitors have been a revolutionary advance in precision oncology, but response rates are often modest. To improve patient selection, we developed an integrated model to predict KRAS dependency. By integrating molecular profiles of a large panel of cell lines from the DEMETER2 dataset, we built a binary classifier to predict a tumor's KRAS dependency. Monte Carlo cross validation via ElasticNet within the training set was used to compare model performance and to tune parameters α and λ. The final model was then applied to the validation set. We validated the model with genetic depletion assays and an external dataset of lung cancer cells treated with a G12C inhibitor. We then applied the model to several Cancer Genome Atlas (TCGA) datasets. The final "K20" model contains 20 features, including expression of 19 genes and KRAS mutation status. In the validation cohort, K20 had an AUC of 0.94 and accurately predicted KRAS dependency in both mutant and KRAS wild-type cell lines following genetic depletion. It was also highly predictive across an external dataset of lung cancer lines treated with KRAS G12C inhibition. When applied to TCGA datasets, specific subpopulations such as the invasive subtype in colorectal cancer and copy number high pancreatic adenocarcinoma were predicted to have higher KRAS dependency. The K20 model has simple yet robust predictive capabilities that may provide a useful tool to select patients with KRAS mutant tumors that are most likely to respond to direct KRAS inhibitors.

Genome-scale functional genomics identify genes preferentially essential for multiple myeloma cells compared to other neoplasias

Clinical progress in multiple myeloma (MM), an incurable plasma cell (PC) neoplasia, has been driven by therapies that have limited applications beyond MM/PC neoplasias and do not target specific oncogenic mutations in MM. Instead, these agents target pathways critical for PC biology yet largely dispensable for malignant or normal cells of most other lineages. Here we systematically characterized the lineage-preferential molecular dependencies of MM through genome-scale clustered regularly interspaced short palindromic repeats (CRISPR) studies in 19 MM versus hundreds of non-MM lines and identified 116 genes whose disruption more significantly affects MM cell fitness compared with other malignancies. These genes, some known, others not previously linked to MM, encode transcription factors, chromatin modifiers, endoplasmic reticulum components, metabolic regulators or signaling molecules. Most of these genes are not among the top amplified, overexpressed or mutated in MM. Functional genomics approaches thus define new therapeutic targets in MM not readily identifiable by standard genomic, transcriptional or epigenetic profiling analyses.