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

Metabolic impairment of non-small cell lung cancers by mitochondrial HSPD1 targeting

Background

The identification of novel targets is of paramount importance to develop more effective drugs and improve the treatment of non-small cell lung cancer (NSCLC), the leading cause of cancer-related deaths worldwide. Since cells alter their metabolic rewiring during tumorigenesis and along cancer progression, targeting key metabolic players and metabolism-associated proteins represents a valuable approach with a high therapeutic potential. Metabolic fitness relies on the functionality of heat shock proteins (HSPs), molecular chaperones that facilitate the correct folding of metabolism enzymes and their assembly in macromolecular structures.

Methods

Gene fitness was determined by bioinformatics analysis from available datasets from genetic screenings. HSPD1 expression was evaluated by immunohistochemistry from formalin-fixed paraffin-embedded tissues from NSCLC patients. Real-time proliferation assays with and without cytotoxicity reagents, colony formation assays and cell cycle analyses were used to monitor growth and drug sensitivity of different NSCLC cells in vitro. In vivo growth was monitored with subcutaneous injections in immune-deficient mice. Cell metabolic activity was analyzed through extracellular metabolic flux analysis. Specific knockouts were introduced by CRISPR/Cas9.

Results

We show heat shock protein family D member 1 (HSPD1 or HSP60) as a survival gene ubiquitously expressed in NSCLC and associated with poor patients’ prognosis. HSPD1 knockdown or its chemical disruption by the small molecule KHS101 induces a drastic breakdown of oxidative phosphorylation, and suppresses cell proliferation both in vitro and in vivo. By combining drug profiling with transcriptomics and through a whole-genome CRISPR/Cas9 screen, we demonstrate that HSPD1-targeted anti-cancer effects are dependent on oxidative phosphorylation and validated molecular determinants of KHS101 sensitivity, in particular, the creatine-transporter SLC6A8 and the subunit of the cytochrome c oxidase complex COX5B.

Conclusions

These results highlight mitochondrial metabolism as an attractive target and HSPD1 as a potential theranostic marker for developing therapies to combat NSCLC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-021-02049-8.

Discovery of a First-in-Class Inhibitor of the PRMT5-Substrate Adaptor Interaction

PRMT5 and its substrate adaptor proteins (SAPs), pICln and Riok1, are synthetic lethal dependencies in MTAP-deleted cancer cells. SAPs share a conserved PRMT5 binding motif (PBM) which mediates binding to a surface of PRMT5 distal to the catalytic site. This interaction is required for methylation of several PRMT5 substrates, including histone and spliceosome complexes. We screened for small molecule inhibitors of the PRMT5-PBM interaction and validated a compound series which binds to the PRMT5-PBM interface and directly inhibits binding of SAPs. Mode of action studies revealed the formation of a covalent bond between a halogenated pyridazinone group and cysteine 278 of PRMT5. Optimization of the starting hit produced a lead compound, BRD0639, which engages the target in cells, disrupts PRMT5-RIOK1 complexes, and reduces substrate methylation. BRD0639 is a first-in-class PBM-competitive inhibitor that can support studies of PBM-dependent PRMT5 activities and the development of novel PRMT5 inhibitors that selectively target these functions.

Predicting and characterizing a cancer dependency map of tumors with deep learning

Genome-wide loss-of-function screens have revealed genes essential for cancer cell proliferation, called cancer dependencies. It remains challenging to link cancer dependencies to the molecular compositions of cancer cells or to unscreened cell lines and further to tumors. Here, we present DeepDEP, a deep learning model that predicts cancer dependencies using integrative genomic profiles. It uses a unique unsupervised pretraining that captures unlabeled tumor genomic representations to improve the learning of cancer dependencies. We demonstrated DeepDEP's improvement over conventional machine learning methods and validated the performance with three independent datasets. By systematic model interpretations, we extended the current dependency maps with functional characterizations of dependencies and a proof-of-concept in silico assay of synthetic essentiality. We applied DeepDEP to pan-cancer tumor genomics and built the first pan-cancer synthetic dependency map of 8000 tumors with clinical relevance. In summary, DeepDEP is a novel tool for investigating cancer dependency with rapidly growing genomic resources.

BANP opens chromatin and activates CpG-island-regulated genes

The majority of gene transcripts generated by RNA polymerase II in mammalian genomes initiate at CpG island (CGI) promoters^1,2, yet our understanding of their regulation remains limited. This is in part due to the incomplete information that we have on transcription factors, their DNA-binding motifs and which genomic binding sites are functional in any given cell type^3-5. In addition, there are orphan motifs without known binders, such as the CGCG element, which is associated with highly expressed genes across human tissues and enriched near the transcription start site of a subset of CGI promoters^6-8. Here we combine single-molecule footprinting with interaction proteomics to identify BTG3-associated nuclear protein (BANP) as the transcription factor that binds this element in the mouse and human genome. We show that BANP is a strong CGI activator that controls essential metabolic genes in pluripotent stem and terminally differentiated neuronal cells. BANP binding is repelled by DNA methylation of its motif in vitro and in vivo, which epigenetically restricts most binding to CGIs and accounts for differential binding at aberrantly methylated CGI promoters in cancer cells. Upon binding to an unmethylated motif, BANP opens chromatin and phases nucleosomes. These findings establish BANP as a critical activator of a set of essential genes and suggest a model in which the activity of CGI promoters relies on methylation-sensitive transcription factors that are capable of chromatin opening.

Molecular basis for substrate recruitment to the PRMT5 methylosome

PRMT5 is an essential arginine methyltransferase and a therapeutic target in MTAP-null cancers. PRMT5 uses adaptor proteins for substrate recruitment through a previously undefined mechanism. Here, we identify an evolutionarily conserved peptide sequence shared among the three known substrate adaptors (CLNS1A, RIOK1, and COPR5) and show that it is necessary and sufficient for interaction with PRMT5. We demonstrate that PRMT5 uses modular adaptor proteins containing a common binding motif for substrate recruitment, comparable with other enzyme classes such as kinases and E3 ligases. We structurally resolve the interface with PRMT5 and show via genetic perturbation that it is required for methylation of adaptor-recruited substrates including the spliceosome, histones, and ribosomal complexes. Furthermore, disruption of this site affects Sm spliceosome activity, leading to intron retention. Genetic disruption of the PRMT5-substrate adaptor interface impairs growth of MTAP-null tumor cells and is thus a site for development of therapeutic inhibitors of PRMT5.

PAX8 lineage-driven T cell engaging antibody for the treatment of high-grade serous ovarian cancer

High-grade serous ovarian cancers (HGSOC) represent the most common subtype of ovarian malignancies. Due to the frequency of late-stage diagnosis and high rates of recurrence following standard of care treatments, novel therapies are needed to promote durable responses. We investigated the anti-tumor activity of CD3 T cell engaging bispecific antibodies (TCBs) directed against the PAX8 lineage-driven HGSOC tumor antigen LYPD1 and demonstrated that anti-LYPD1 TCBs induce T cell activation and promote in vivo tumor growth inhibition in LYPD1-expressing HGSOC. To selectively target LYPD1-expressing tumor cells with high expression while sparing cells with low expression, we coupled bivalent low-affinity anti-LYPD1 antigen-binding fragments (Fabs) with the anti-CD3 scFv. In contrast to the monovalent anti-LYPD1 high-affinity TCB (VHP354), the bivalent low-affinity anti-LYPD1 TCB (QZC131) demonstrated antigen density-dependent selectivity and showed tolerability in cynomolgus monkeys at the maximum dose tested of 3 mg/kg. Collectively, these data demonstrate that bivalent TCBs directed against LYPD1 have compelling efficacy and safety profiles to support its use as a treatment for high-grade serous ovarian cancers.

Learning epistatic gene interactions from perturbation screens

The treatment of complex diseases often relies on combinatorial therapy, a strategy where drugs are used to target multiple genes simultaneously. Promising candidate genes for combinatorial perturbation often constitute epistatic genes, i.e., genes which contribute to a phenotype in a non-linear fashion. Experimental identification of the full landscape of genetic interactions by perturbing all gene combinations is prohibitive due to the exponential growth of testable hypotheses. Here we present a model for the inference of pairwise epistatic, including synthetic lethal, gene interactions from siRNA-based perturbation screens. The model exploits the combinatorial nature of siRNA-based screens resulting from the high numbers of sequence-dependent off-target effects, where each siRNA apart from its intended target knocks down hundreds of additional genes. We show that conditional and marginal epistasis can be estimated as interaction coefficients of regression models on perturbation data. We compare two methods, namely glinternet and xyz, for selecting non-zero effects in high dimensions as components of the model, and make recommendations for the appropriate use of each. For data simulated from real RNAi screening libraries, we show that glinternet successfully identifies epistatic gene pairs with high accuracy across a wide range of relevant parameters for the signal-to-noise ratio of observed phenotypes, the effect size of epistasis and the number of observations per double knockdown. xyz is also able to identify interactions from lower dimensional data sets (fewer genes), but is less accurate for many dimensions. Higher accuracy of glinternet, however, comes at the cost of longer running time compared to xyz. The general model is widely applicable and allows mining the wealth of publicly available RNAi screening data for the estimation of epistatic interactions between genes. As a proof of concept, we apply the model to search for interactions, and potential targets for treatment, among previously published sets of siRNA perturbation screens on various pathogens. The identified interactions include both known epistatic interactions as well as novel findings.

A Covalent Calmodulin Inhibitor as a Tool to Study Cellular Mechanisms of K-Ras-Driven Stemness

Recently, the highly mutated oncoprotein K-Ras4B (hereafter K-Ras) was shown to drive cancer cell stemness in conjunction with calmodulin (CaM). We previously showed that the covalent CaM inhibitor ophiobolin A (OphA) can potently inhibit K-Ras stemness activity. However, OphA, a fungus-derived natural product, exhibits an unspecific, broad toxicity across all phyla. Here we identified a less toxic, functional analog of OphA that can efficiently inactivate CaM by covalent inhibition. We analyzed a small series of benzazulenones, which bear some structural similarity to OphA and can be synthesized in only six steps. We identified the formyl aminobenzazulenone 1, here named Calmirasone1, as a novel and potent covalent CaM inhibitor. Calmirasone1 has a 4-fold increased affinity for CaM as compared to OphA and was active against K-Ras in cells within minutes, as compared to hours required by OphA. Calmirasone1 displayed a 2.5–4.5-fold higher selectivity for KRAS over BRAF mutant 3D spheroid growth than OphA, suggesting improved relative on-target activity. Importantly, Calmirasone1 has a 40–260-fold lower unspecific toxic effect on HRAS mutant cells, while it reaches almost 50% of the activity of novel K-RasG12C specific inhibitors in 3D spheroid assays. Our results suggest that Calmirasone1 can serve as a new tool compound to further investigate the cancer cell biology of the K-Ras and CaM associated stemness activities.

Synthetic Lethality in Cancer Therapeutics: The Next Generation

Synthetic lethality (SL) provides a conceptual framework for tackling targets that are not classically "druggable," including loss-of-function mutations in tumor suppressor genes required for carcinogenesis. Recent technological advances have led to an inflection point in our understanding of genetic interaction networks and ability to identify a wide array of novel SL drug targets. Here, we review concepts and lessons emerging from first-generation trials aimed at testing SL drugs, discuss how the nature of the targeted lesion can influence therapeutic outcomes, and highlight the need to develop clinical biomarkers distinct from those based on the paradigms developed to target activated oncogenes. SIGNIFICANCE: SL offers an approach for the targeting of loss of function of tumor suppressor and DNA repair genes, as well as of amplification and/or overexpression of genes that cannot be targeted directly. A next generation of tumor-specific alterations targetable through SL has emerged from high-throughput CRISPR technology, heralding not only new opportunities for drug development, but also important challenges in the development of optimal predictive biomarkers.

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

BACKGROUND & AIMS

Understanding the mechanisms by which tumors adapt to therapy is critical for developing effective combination therapeutic approaches to improve clinical outcomes for patients with cancer.

METHODS

To identify promising and clinically actionable targets for managing colorectal cancer (CRC), we conducted a patient-centered functional genomics platform that includes approximately 200 genes and paired this with a high-throughput drug screen that includes 262 compounds in four patient-derived xenografts (PDXs) from patients with CRC.

RESULTS

Both screening methods identified exportin 1 (XPO1) inhibitors as drivers of DNA damage-induced lethality in CRC. Molecular characterization of the cellular response to XPO1 inhibition uncovered an adaptive mechanism that limited the duration of response in TP53-mutated, but not in TP53-wild-type CRC models. Comprehensive proteomic and transcriptomic characterization revealed that the ATM/ATR-CHK1/2 axes were selectively engaged in TP53-mutant CRC cells upon XPO1 inhibitor treatment and that this response was required for adapting to therapy and escaping cell death. Administration of KPT-8602, an XPO1 inhibitor, followed by AZD-6738, an ATR inhibitor, resulted in dramatic antitumor effects and prolonged survival in TP53-mutant models of CRC.

CONCLUSIONS

Our findings anticipate tremendous therapeutic benefit and support the further evaluation of XPO1 inhibitors, especially in combination with DNA damage checkpoint inhibitors, to elicit an enduring clinical response in patients with CRC harboring TP53 mutations.

Systematic dissection of transcriptional regulatory networks by genome-scale and single-cell CRISPR screens

Millions of putative transcriptional regulatory elements (TREs) have been cataloged in the human genome, yet their functional relevance in specific pathophysiological settings remains to be determined. This is critical to understand how oncogenic transcription factors (TFs) engage specific TREs to impose transcriptional programs underlying malignant phenotypes. Here, we combine cutting edge CRISPR screens and epigenomic profiling to functionally survey ≈15,000 TREs engaged by estrogen receptor (ER). We show that ER exerts its oncogenic role in breast cancer by engaging TREs enriched in GATA3, TFAP2C, and H3K27Ac signal. These TREs control critical downstream TFs, among which TFAP2C plays an essential role in ER-driven cell proliferation. Together, our work reveals novel insights into a critical oncogenic transcription program and provides a framework to map regulatory networks, enabling to dissect the function of the noncoding genome of cancer cells.

VRK1 Depletion Facilitates the Synthetic Lethality of Temozolomide and Olaparib in Glioblastoma Cells

Background

Glioblastomas treated with temozolomide frequently develop resistance to pharmacological treatments. Therefore, there is a need to find alternative drug targets to reduce treatment resistance based on tumor dependencies. A possibility is to target simultaneously two proteins from different DNA-damage repair pathways to facilitate tumor cell death. Therefore, we tested whether targeting the human chromatin kinase VRK1 by RNA interference can identify this protein as a novel molecular target to reduce the dependence on temozolomide in combination with olaparib, based on synthetic lethality.

Materials and Methods

Depletion of VRK1, an enzyme that regulates chromatin dynamic reorganization and facilitates resistance to DNA damage, was performed in glioblastoma cells treated with temozolomide, an alkylating agent used for GBM treatment; and olaparib, an inhibitor of PARP-1, used as sensitizer. Two genetically different human glioblastoma cell lines, LN-18 and LN-229, were used for these experiments. The effect on the DNA-damage response was followed by determination of sequential steps in this process: H4K16ac, γH2AX, H4K20me2, and 53BP1.

Results

The combination of temozolomide and olaparib increased DNA damage detected by labeling free DNA ends, and chromatin relaxation detected by H4K16ac. The combination of both drugs, at lower doses, resulted in an increase in the DNA damage response detected by the formation of γH2AX and 53BP1 foci. VRK1 depletion did not prevent the generation of DNA damage in TUNEL assays, but significantly impaired the DNA damage response induced by temozolomide and olaparib, and mediated by γH2AX, H4K20me2, and 53BP1. The combination of these drugs in VRK1 depleted cells resulted in an increase of glioblastoma cell death detected by annexin V and the processing of PARP-1 and caspase-3.

Conclusion

Depletion of the chromatin kinase VRK1 promotes tumor cell death at lower doses of a combination of temozolomide and olaparib treatments, and can be a novel alternative target for therapies based on synthetic lethality.

CRISPR screens in physiologic medium reveal conditionally essential genes in human cells

Forward genetic screens across hundreds of cancer cell lines have started to define the genetic dependencies of proliferating human cells and how these vary by genotype and lineage. Most screens, however, have been carried out in culture media that poorly reflect metabolite availability in human blood. Here, we performed CRISPR-based screens in traditional versus human plasma-like medium (HPLM). Sets of conditionally essential genes in human cancer cell lines span several cellular processes and vary with both natural cell-intrinsic diversity and the combination of basal and serum components that comprise typical media. Notably, we traced the causes for each of three conditional CRISPR phenotypes to the availability of metabolites uniquely defined in HPLM versus conventional media. Our findings reveal the profound impact of medium composition on gene essentiality in human cells, and also suggest general strategies for using genetic screens in HPLM to uncover new cancer vulnerabilities and gene-nutrient interactions.

A genome-wide atlas of co-essential modules assigns function to uncharacterized genes

A central question in the post-genomic era is how genes interact to form biological pathways. Measurements of gene dependency across hundreds of cell lines have been used to cluster genes into 'co-essential' pathways, but this approach has been limited by ubiquitous false positives. In the present study, we develop a statistical method that enables robust identification of gene co-essentiality and yields a genome-wide set of functional modules. This atlas recapitulates diverse pathways and protein complexes, and predicts the functions of 108 uncharacterized genes. Validating top predictions, we show that TMEM189 encodes plasmanylethanolamine desaturase, a key enzyme for plasmalogen synthesis. We also show that C15orf57 encodes a protein that binds the AP2 complex, localizes to clathrin-coated pits and enables efficient transferrin uptake. Finally, we provide an interactive webtool for the community to explore our results, which establish co-essentiality profiling as a powerful resource for biological pathway identification and discovery of new gene functions.

Alternative glycosylation controls endoplasmic reticulum dynamics and tubular extension in mammalian cells

The endoplasmic reticulum (ER) is a central eukaryotic organelle with a tubular network made of hairpin proteins linked by hydrolysis of guanosine triphosphate nucleotides. Among posttranslational modifications initiated at the ER level, glycosylation is the most common reaction. However, our understanding of the impact of glycosylation on the ER structure remains unclear. Here, we show that exostosin-1 (EXT1) glycosyltransferase, an enzyme involved in N-glycosylation, is a key regulator of ER morphology and dynamics. We have integrated multiomics and superresolution imaging to characterize the broad effect of EXT1 inactivation, including the ER shape-dynamics-function relationships in mammalian cells. We have observed that inactivating EXT1 induces cell enlargement and enhances metabolic switches such as protein secretion. In particular, suppressing EXT1 in mouse thymocytes causes developmental dysfunctions associated with the ER network extension. Last, our data illuminate the physical and functional aspects of the ER proteome-glycome-lipidome structure axis, with implications in biotechnology and medicine.

Fragment-Based Design of a Potent MAT2a Inhibitor and in Vivo Evaluation in an MTAP Null Xenograft Model

MAT2a is a methionine adenosyltransferase that synthesizes the essential metabolite S-adenosylmethionine (SAM) from methionine and ATP. Tumors bearing the co-deletion of p16 and MTAP genes have been shown to be sensitive to MAT2a inhibition, making it an attractive target for treatment of MTAP-deleted cancers. A fragment-based lead generation campaign identified weak but efficient hits binding in a known allosteric site. By use of structure-guided design and systematic SAR exploration, the hits were elaborated through a merging and growing strategy into an arylquinazolinone series of potent MAT2a inhibitors. The selected in vivo tool compound 28 reduced SAM-dependent methylation events in cells and inhibited proliferation of MTAP-null cells in vitro. In vivo studies showed that 28 was able to induce antitumor response in an MTAP knockout HCT116 xenograft model.

PAX8 and MECOM are interaction partners driving ovarian cancer

The transcription factor PAX8 is critical for the development of the thyroid and urogenital system. Comprehensive genomic screens furthermore indicate an additional oncogenic role for PAX8 in renal and ovarian cancers. While a plethora of PAX8-regulated genes in different contexts have been proposed, we still lack a mechanistic understanding of how PAX8 engages molecular complexes to drive disease-relevant oncogenic transcriptional programs. Here we show that protein isoforms originating from the MECOM locus form a complex with PAX8. These include MDS1-EVI1 (also called PRDM3) for which we map its interaction with PAX8 in vitro and in vivo. We show that PAX8 binds a large number of genomic sites and forms transcriptional hubs. At a subset of these, PAX8 together with PRDM3 regulates a specific gene expression module involved in adhesion and extracellular matrix. This gene module correlates with PAX8 and MECOM expression in large scale profiling of cell lines, patient-derived xenografts (PDXs) and clinical cases and stratifies gynecological cancer cases with worse prognosis. PRDM3 is amplified in ovarian cancers and we show that the MECOM locus and PAX8 sustain in vivo tumor growth, further supporting that the identified function of the MECOM locus underlies PAX8-driven oncogenic functions in ovarian cancer.

Targeting pan-essential genes in cancer: Challenges and opportunities

Despite remarkable successes in the clinic, cancer targeted therapy development remains challenging and the failure rate is disappointingly high. This problem is partly due to the misapplication of the targeted therapy paradigm to therapeutics targeting pan-essential genes, which can result in therapeutics whereby efficacy is attenuated by dose-limiting toxicity. Here we summarize the key features of successful chemotherapy and targeted therapy agents, and use case studies to outline recurrent challenges to drug development efforts targeting pan-essential genes. Finally, we suggest strategies to avoid previous pitfalls for ongoing and future development of pan-essential therapeutics.

Structure-Based Design of Selective LONP1 Inhibitors for Probing In Vitro Biology

LONP1 is an AAA+ protease that maintains mitochondrial homeostasis by removing damaged or misfolded proteins. Elevated activity and expression of LONP1 promotes cancer cell proliferation and resistance to apoptosis-inducing reagents. Despite the importance of LONP1 in human biology and disease, very few LONP1 inhibitors have been described in the literature. Herein, we report the development of selective boronic acid-based LONP1 inhibitors using structure-based drug design as well as the first structures of human LONP1 bound to various inhibitors. Our efforts led to several nanomolar LONP1 inhibitors with little to no activity against the 20S proteasome that serve as tool compounds to investigate LONP1 biology.

siGCD: a web server to explore survival interaction of genes, cells and drugs in human cancers

It is pivotal and remains challenge for cancer precision treatment to identify the survival outcome interactions between genes, cells and drugs. Here, we present siGCD, a web-based tool for analysis and visualization of the survival interaction of Genes, Cells and Drugs in human cancers. siGCD utilizes the cancer heterogeneity to simulate the manipulated gene expression, cell infiltration and drug treatment, which overcomes the data and experimental limitations. To illustrate the performance of siGCD, we identified the survival interaction partners of EGFR (gene level), T cells (cell level) and sorafenib (drug level), and our prediction was consistent with previous reports. Moreover, we validate the synergistic effect of regorafenib and glyburide, and found that glyburide could significantly improve the regorafenib response. These results demonstrate that siGCD could benefit cancer precision medicine in a wide range of advantageous application scenarios including gene regulatory network construction, immune cell regulatory gene identification, drug (especially multiple target drugs) response biomarker screening and combination therapeutic design.

Distinct CDK6 complexes determine tumor cell response to CDK4/6 inhibitors and degraders

CDK4/6 inhibitors (CDK4/6i) are effective in metastatic breast cancer, but they have been only modestly effective in most other tumor types. Here we show that tumors expressing low CDK6 rely on CDK4 function, and are exquisitely sensitive to CDK4/6i. In contrast, tumor cells expressing both CDK4 and CDK6 have increased reliance on CDK6 to ensure cell cycle progression. We discovered that CDK4/6i and CDK4/6 degraders potently bind and inhibit CDK6 selectively in tumors in which CDK6 is highly thermo-unstable and strongly associated with the HSP90/CDC37 complex. In contrast, CDK4/6i and CDK4/6 degraders are ineffective in antagonizing tumor cells expressing thermostable CDK6, due to their weaker binding to CDK6 in these cells. Thus, we uncover a general mechanism of intrinsic resistance to CDK4/6i and CDK4/6i-derived degraders and the need for novel inhibitors targeting the CDK4/6i-resistant, thermostable form of CDK6 for application as cancer therapeutics.

A first-generation pediatric cancer dependency map

Exciting therapeutic targets are emerging from CRISPR-based screens of high mutational-burden adult cancers. A key question, however, is whether functional genomic approaches will yield new targets in pediatric cancers, known for remarkably few mutations, which often encode proteins considered challenging drug targets. To address this, we created a first-generation pediatric cancer dependency map representing 13 pediatric solid and brain tumor types. Eighty-two pediatric cancer cell lines were subjected to genome-scale CRISPR-Cas9 loss-of-function screening to identify genes required for cell survival. In contrast to the finding that pediatric cancers harbor fewer somatic mutations, we found a similar complexity of genetic dependencies in pediatric cancer cell lines compared to that in adult models. Findings from the pediatric cancer dependency map provide preclinical support for ongoing precision medicine clinical trials. The vulnerabilities observed in pediatric cancers were often distinct from those in adult cancer, indicating that repurposing adult oncology drugs will be insufficient to address childhood cancers.

A chemical genetics approach to examine the functions of AAA proteins

The structural conservation across the AAA (ATPases associated with diverse cellular activities) protein family makes designing selective chemical inhibitors challenging. Here, we identify a triazolopyridine-based fragment that binds the AAA domain of human katanin, a microtubule-severing protein. We have developed a model for compound binding and designed ASPIR-1 (allele-specific, proximity-induced reactivity-based inhibitor-1), a cell-permeable compound that selectively inhibits katanin with an engineered cysteine mutation. Only in cells expressing mutant katanin does ASPIR-1 treatment increase the accumulation of CAMSAP2 at microtubule minus ends, confirming specific on-target cellular activity. Importantly, ASPIR-1 also selectively inhibits engineered cysteine mutants of human VPS4B and FIGL1-AAA proteins, involved in organelle dynamics and genome stability, respectively. Structural studies confirm our model for compound binding at the AAA ATPase site and the proximity-induced reactivity-based inhibition. Together, our findings suggest a chemical genetics approach to decipher AAA protein functions across essential cellular processes and to test hypotheses for developing therapeutics.

Signaling Pathways in Cancer: Therapeutic Targets, Combinatorial Treatments, and New Developments

Molecular alterations in cancer genes and associated signaling pathways are used to inform new treatments for precision medicine in cancer. Small molecule inhibitors and monoclonal antibodies directed at relevant cancer-related proteins have been instrumental in delivering successful treatments of some blood malignancies (e.g., imatinib with chronic myelogenous leukemia (CML)) and solid tumors (e.g., tamoxifen with ER positive breast cancer and trastuzumab for HER2-positive breast cancer). However, inherent limitations such as drug toxicity, as well as acquisition of de novo or acquired mechanisms of resistance, still cause treatment failure. Here we provide an up-to-date review of the successes and limitations of current targeted therapies for cancer treatment and highlight how recent technological advances have provided a new level of understanding of the molecular complexity underpinning resistance to cancer therapies. We also raise three basic questions concerning cancer drug discovery based on molecular markers and alterations of selected signaling pathways, and further discuss how combination therapies may become the preferable approach over monotherapy for cancer treatments. Finally, we consider novel therapeutic developments that may complement drug delivery and significantly improve clinical response and outcomes of cancer patients.

RAF-Mutant Melanomas Differentially Depend on ERK2 Over ERK1 to Support Aberrant MAPK Pathway Activation and Cell Proliferation

Half of advanced human melanomas are driven by mutant BRAF and dependent on MAPK signaling. Interestingly, the results of three independent genetic screens highlight a dependency of BRAF-mutant melanoma cell lines on BRAF and ERK2, but not ERK1. ERK2 is expressed higher in melanoma compared with other cancer types and higher than ERK1 within melanoma. However, ERK1 and ERK2 are similarly required in primary human melanocytes transformed with mutant BRAF and are expressed at a similar, lower amount compared with established cancer cell lines. ERK1 can compensate for ERK2 loss as seen by expression of ERK1 rescuing the proliferation arrest mediated by ERK2 loss (both by shRNA or inhibition by an ERK inhibitor). ERK2 knockdown, as opposed to ERK1 knockdown, led to more robust suppression of MAPK signaling as seen by RNA-sequencing, qRT-PCR, and Western blot analysis. In addition, treatment with MAPK pathway inhibitors led to gene expression changes that closely resembled those seen upon knockdown of ERK2 but not ERK1. Together, these data demonstrate that ERK2 drives BRAF-mutant melanoma gene expression and proliferation as a function of its higher expression compared with ERK1. Selective inhibition of ERK2 for the treatment of melanomas may spare the toxicity associated with pan-ERK inhibition in normal tissues. IMPLICATIONS: BRAF-mutant melanomas overexpress and depend on ERK2 but not ERK1, suggesting that ERK2-selective inhibition may be toxicity sparing.

Integrated cross-study datasets of genetic dependencies in cancer

CRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated the two largest public independent CRISPR-Cas9 screens performed to date (at the Broad and Sanger institutes) by assessing, comparing, and selecting methods for correcting biases due to heterogeneous single-guide RNA efficiency, gene-independent responses to CRISPR-Cas9 targeting originated from copy number alterations, and experimental batch effects. Our integrated datasets recapitulate findings from the individual datasets, provide greater statistical power to cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. We provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets.

Cancer Dependencies: PRMT5 and MAT2A in MTAP/p16-Deleted Cancers

Discovery of targeted therapies that selectively exploit the genetic inactivation of specific tumor suppressors remains a major challenge. This includes the prevalent deletion of the CDKN2A/ MTAP locus, which was first reported nearly 40 years ago. The more recent advent of RNA interference and functional genomic screening technologies led to the identification of hidden collateral lethalities occurring with passenger deletions of MTAP in cancer cells. In particular, small-molecule inhibition of the type II arginine methyltransferase PRMT5 and the S-adenosylmethionine-producing enzyme MAT2A each presents a precision medicine approach for the treatment of patients whose tumors have homozygous loss of MTAP. In this review, we highlight key aspects of MTAP, PRMT5, and MAT2A biology to provide a conceptual framework for developing novel therapeutic strategies in tumors with MTAP deletion and to summarize ongoing efforts to drug PRMT5 and MAT2A.

Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor

Molecular and functional profiling of cancer cell lines is subject to laboratory-specific experimental practices and data analysis protocols. The current challenge therefore is how to make an integrated use of the omics profiles of cancer cell lines for reliable biological discoveries. Here, we carried out a systematic analysis of nine types of data modalities using meta-analysis of 53 omics studies across 12 research laboratories for 2,018 cell lines. To account for a relatively low consistency observed for certain data modalities, we developed a robust data integration approach that identifies reproducible signals shared among multiple data modalities and studies. We demonstrated the power of the integrative analyses by identifying a novel driver gene, ECHDC1, with tumor suppressive role validated both in breast cancer cells and patient tumors. The multi-modal meta-analysis approach also identified synthetic lethal partners of cancer drivers, including a co-dependency of PTEN deficient endometrial cancer cells on RNA helicases.

Combinatorial CRISPR screen identifies fitness effects of gene paralogues

Genetic redundancy has evolved as a way for human cells to survive the loss of genes that are single copy and essential in other organisms, but also allows tumours to survive despite having highly rearranged genomes. In this study we CRISPR screen 1191 gene pairs, including paralogues and known and predicted synthetic lethal interactions to identify 105 gene combinations whose co-disruption results in a loss of cellular fitness. 27 pairs influence fitness across multiple cell lines including the paralogues FAM50A/FAM50B, two genes of unknown function. Silencing of FAM50B occurs across a range of tumour types and in this context disruption of FAM50A reduces cellular fitness whilst promoting micronucleus formation and extensive perturbation of transcriptional programmes. Our studies reveal the fitness effects of FAM50A/FAM50B in cancer cells.

The tumor therapy landscape of synthetic lethality

Synthetic lethality is emerging as an important cancer therapeutic paradigm, while the comprehensive selective treatment opportunities for various tumors have not yet been explored. We develop the Synthetic Lethality Knowledge Graph (SLKG), presenting the tumor therapy landscape of synthetic lethality (SL) and synthetic dosage lethality (SDL). SLKG integrates the large-scale entity of different tumors, drugs and drug targets by exploring a comprehensive set of SL and SDL pairs. The overall therapy landscape is prioritized to identify the best repurposable drug candidates and drug combinations with literature supports, in vitro pharmacologic evidence or clinical trial records. Finally, cladribine, an FDA-approved multiple sclerosis treatment drug, is selected and identified as a repurposable drug for treating melanoma with CDKN2A mutation by in vitro validation, serving as a demonstrating SLKG utility example for novel tumor therapy discovery. Collectively, SLKG forms the computational basis to uncover cancer-specific susceptibilities and therapy strategies based on the principle of synthetic lethality.

Various methods have been proposed to identify synthetic lethality interactions, but selective treatment opportunities for various tumors have not yet been explored. Here, the authors develop the Synthetic Lethality Knowledge Graph webserver (SLKG, http://www.slkg.net) to explore the comprehensive tumor therapy landscape and uncover cancer-specific susceptibilities based on the principle of synthetic lethality.

Novel Small Molecule Hsp90/Cdc37 Interface Inhibitors Indirectly Target K-Ras-Signaling

The ATP-competitive inhibitors of Hsp90 have been tested predominantly in kinase addicted cancers; however, they have had limited success. A mechanistic connection between Hsp90 and oncogenic K-Ras is not known. Here, we show that K-Ras selectivity is enabled by the loss of the K-Ras membrane nanocluster modulator galectin-3 downstream of the Hsp90 client HIF-1α. This mechanism suggests a higher drug sensitivity in the context of KRAS mutant, HIF-1α-high and/or Gal3-high cancer cells, such as those found, in particular, in pancreatic adenocarcinoma. The low toxicity of conglobatin further indicates a beneficial on-target toxicity profile for Hsp90/Cdc37 interface inhibitors. We therefore computationally screened >7 M compounds, and identified four novel small molecules with activities of 4 μM-44 μM in vitro. All of the compounds were K-Ras selective, and potently decreased the Hsp90 client protein levels without inducing the heat shock response. Moreover, they all inhibited the 2D proliferation of breast, pancreatic, and lung cancer cell lines. The most active compounds from each scaffold, furthermore, significantly blocked 3D spheroids and the growth of K-Ras-dependent microtumors. We foresee new opportunities for improved Hsp90/Cdc37 interface inhibitors in cancer and other aging-associated diseases.

Towards the routine use of in silico screenings for drug discovery using metabolic modelling

Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the underlying mechanisms in cancer. Here, we discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. Furthermore, we list valuable resources that are needed for the reconstruction of cancer-specific models by integrating various available datasets with genome-scale metabolic reconstructions using model-building algorithms. We also discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects.

Adenylosuccinate lyase is oncogenic in colorectal cancer by causing mitochondrial dysfunction and independent activation of NRF2 and mTOR-MYC-axis

Rationale: Adenylosuccinate lyase (ADSL) is an essential enzyme for de novo purine biosynthesis. Here we sought to investigate the putative role of ADSL in colorectal carcinoma (CRC) carcinogenesis and response to antimetabolites.

Methods: ADSL expression levels were assessed by immunohistochemistry or retrieved from The Cancer Genome Atlas (TCGA) dataset. The effects of ADSL silencing or overexpression were evaluated on CRC cell proliferation, cell migration and cell-cycle. In vivo tumor growth was assessed by the chicken chorioallantoic membrane (CAM). Transfected cell lines or patient-derived organoids (PDO) were treated with 5-fluorouracil (5-FU) and 6-mercaptopurine (6-MP) and drug response was correlated with ADSL expression levels. Metabolomic and transcriptomic profiling were performed to identify dysregulated pathways and ADSL downstream effectors. Mitochondrial respiration and glycolytic capacity were measured using Seahorse; mitochondrial membrane potential and the accumulation of ROS were measured by FACS using MitoTracker Red and MitoSOX staining, respectively. Activation of canonical pathways was assessed by immunohistochemistry and immunoblotting.

Results: ADSL expression is significantly increased in CRC tumors compared to non-tumor tissue. ADSL-high CRCs show upregulation of genes involved in DNA synthesis, DNA repair and cell cycle. Accordingly, ADSL overexpression accelerated progression through the cell cycle and significantly increased proliferation and migration in CRC cell lines. Additionally, ADSL expression increased tumor growth in vivo and sensitized CRCs to 6-MP in vitro, ex vivo (PDOs) and in vivo (CAM model). ADSL exerts its oncogenic function by affecting mitochondrial function via alteration of the TCA cycle and impairment of mitochondrial respiration. The KEAP1-NRF2 and mTORC1-cMyc axis are independently activated upon ADSL overexpression and may favor the survival and proliferation of ROS-accumulating cells, favoring DNA damage and tumorigenesis.

Conclusions: Our results suggest that ADSL is a novel oncogene in CRC, modulating mitochondrial function, metabolism and oxidative stress, thus promoting cell cycle progression, proliferation and migration. Our results also suggest that ADSL is a predictive biomarker of response to 6-mercaptopurine in the pre-clinical setting.

Therapeutic Assessment of Targeting ASNS Combined with l-Asparaginase Treatment in Solid Tumors and Investigation of Resistance Mechanisms

Asparagine deprivation by l-asparaginase (L-ASNase) is an effective therapeutic strategy in acute lymphoblastic leukemia, with resistance occurring due to upregulation of ASNS, the only human enzyme synthetizing asparagine (Annu. Rev. Biochem. 2006, 75 (1), 629-654). l-Asparaginase efficacy in solid tumors is limited by dose-related toxicities (OncoTargets and Therapy 2017, pp 1413-1422). Large-scale loss of function genetic in vitro screens identified ASNS as a cancer dependency in several solid malignancies (Cell 2017, 170 (3), 564-576.e16. Cell 2017, 170 (3), 577-592.e10). Here we evaluate the therapeutic potential of targeting ASNS in melanoma cells. While we confirm in vitro dependency on ASNS silencing, this is largely dispensable for in vivo tumor growth, even in the face of asparagine deprivation, prompting us to characterize such a resistance mechanism to devise novel therapeutic strategies. Using ex vivo quantitative proteome and transcriptome profiling, we characterize the compensatory mechanism elicited by ASNS knockout melanoma cells allowing their survival. Mechanistically, a genome-wide CRISPR screen revealed that such a resistance mechanism is elicited by a dual axis: GCN2-ATF4 aimed at restoring amino acid levels and MAPK-BCLXL to promote survival. Importantly, pharmacological inhibition of such nodes synergizes with l-asparaginase-mediated asparagine deprivation in ASNS deficient cells suggesting novel potential therapeutic combinations in melanoma.

Targeting alternative splicing by RNAi: from the differential impact on splice variants to triggering artificial pre-mRNA splicing

Alternative splicing generates multiple transcript and protein isoforms from a single gene and controls transcript intracellular localization and stability by coupling to mRNA export and nonsense-mediated mRNA decay (NMD). RNA interference (RNAi) is a potent mechanism to modulate gene expression. However, its interactions with alternative splicing are poorly understood. We used artificial microRNAs (amiRNAs, also termed shRNAmiR) to knockdown all splice variants of selected target genes in Arabidopsis thaliana. We found that splice variants, which vary by their protein-coding capacity, subcellular localization and sensitivity to NMD, are affected differentially by an amiRNA, although all of them contain the target site. Particular transcript isoforms escape amiRNA-mediated degradation due to their nuclear localization. The nuclear and NMD-sensitive isoforms mask RNAi action in alternatively spliced genes. Interestingly, Arabidopsis SPL genes, which undergo alternative splicing and are targets of miR156, are regulated in the same manner. Moreover, similar results were obtained in mammalian cells using siRNAs, indicating cross-kingdom conservation of these interactions among RNAi and splicing isoforms. Furthermore, we report that amiRNA can trigger artificial alternative splicing, thus expanding the RNAi functional repertoire. Our findings unveil novel interactions between different post-transcriptional processes in defining transcript fates and regulating gene expression.

Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses

Tumor-specific elucidation of physical and functional oncoprotein interactions could improve tumorigenic mechanism characterization and therapeutic response prediction. Current interaction models and pathways, however, lack context specificity and are not oncoprotein specific. We introduce SigMaps as context-specific networks, comprising modulators, effectors and cognate binding-partners of a specific oncoprotein. SigMaps are reconstructed de novo by integrating diverse evidence sources-including protein structure, gene expression and mutational profiles-via the OncoSig machine learning framework. We first generated a KRAS-specific SigMap for lung adenocarcinoma, which recapitulated published KRAS biology, identified novel synthetic lethal proteins that were experimentally validated in three-dimensional spheroid models and established uncharacterized crosstalk with RAB/RHO. To show that OncoSig is generalizable, we first inferred SigMaps for the ten most mutated human oncoproteins and then for the full repertoire of 715 proteins in the COSMIC Cancer Gene Census. Taken together, these SigMaps show that the cell's regulatory and signaling architecture is highly tissue specific.

FIREWORKS: a bottom-up approach to integrative coessentiality network analysis

Genetic coessentiality analysis, a computational approach which identifies genes sharing a common effect on cell fitness across large-scale screening datasets, has emerged as a powerful tool to identify functional relationships between human genes. However, widespread implementation of coessentiality to study individual genes and pathways is limited by systematic biases in existing coessentiality approaches and accessibility barriers for investigators without computational expertise. We created FIREWORKS, a method and interactive tool for the construction and statistical analysis of coessentiality networks centered around gene(s) provided by the user. FIREWORKS incorporates a novel bias reduction approach to reduce false discoveries, enables restriction of coessentiality analyses to custom subsets of cell lines, and integrates multiomic and drug-gene interaction datasets to investigate and target contextual gene essentiality. We demonstrate the broad utility of FIREWORKS through case vignettes investigating gene function and specialization, indirect therapeutic targeting of "undruggable" proteins, and context-specific rewiring of genetic networks.

Cross-species identification of PIP5K1-, splicing- and ubiquitin-related pathways as potential targets for RB1-deficient cells

The RB1 tumor suppressor is recurrently mutated in a variety of cancers including retinoblastomas, small cell lung cancers, triple-negative breast cancers, prostate cancers, and osteosarcomas. Finding new synthetic lethal (SL) interactions with RB1 could lead to new approaches to treating cancers with inactivated RB1. We identified 95 SL partners of RB1 based on a Drosophila screen for genetic modifiers of the eye phenotype caused by defects in the RB1 ortholog, Rbf1. We validated 38 mammalian orthologs of Rbf1 modifiers as RB1 SL partners in human cancer cell lines with defective RB1 alleles. We further show that for many of the RB1 SL genes validated in human cancer cell lines, low activity of the SL gene in human tumors, when concurrent with low levels of RB1 was associated with improved patient survival. We investigated higher order combinatorial gene interactions by creating a novel Drosophila cancer model with co-occurring Rbf1, Pten and Ras mutations, and found that targeting RB1 SL genes in this background suppressed the dramatic tumor growth and rescued fly survival whilst having minimal effects on wild-type cells. Finally, we found that drugs targeting the identified RB1 interacting genes/pathways, such as UNC3230, PYR-41, TAK-243, isoginkgetin, madrasin, and celastrol also elicit SL in human cancer cell lines. In summary, we identified several high confidence, evolutionarily conserved, novel targets for RB1-deficient cells that may be further adapted for the treatment of human cancer.

Functional antagonism of chromatin modulators regulates epithelial-mesenchymal transition

Epithelial-mesenchymal transition (EMT) is a developmental process hijacked by cancer cells to modulate proliferation, migration, and stress response. Whereas kinase signaling is believed to be an EMT driver, the molecular mechanisms underlying epithelial-mesenchymal interconversion are incompletely understood. Here, we show that the impact of chromatin regulators on EMT interconversion is broader than that of kinases. By combining pharmacological modulation of EMT, synthetic genetic tracing, and CRISPR interference screens, we uncovered a minority of kinases and several chromatin remodelers, writers, and readers governing homeostatic EMT in lung cancer cells. Loss of ARID1A, DOT1L, BRD2, and ZMYND8 had nondeterministic and sometimes opposite consequences on epithelial-mesenchymal interconversion. Together with RNAPII and AP-1, these antagonistic gatekeepers control chromatin of active enhancers, including pan-cancer-EMT signature genes enabling supraclassification of anatomically diverse tumors. Thus, our data uncover general principles underlying transcriptional control of cancer cell plasticity and offer a platform to systematically explore chromatin regulators in tumor-state-specific therapy.

Aneuploidy renders cancer cells vulnerable to mitotic checkpoint inhibition

Selective targeting of aneuploid cells is an attractive strategy for cancer treatment1. However, it is unclear whether aneuploidy generates any clinically relevant vulnerabilities in cancer cells. Here we mapped the aneuploidy landscapes of about 1,000 human cancer cell lines, and analysed genetic and chemical perturbation screens2-9 to identify cellular vulnerabilities associated with aneuploidy. We found that aneuploid cancer cells show increased sensitivity to genetic perturbation of core components of the spindle assembly checkpoint (SAC), which ensures the proper segregation of chromosomes during mitosis10. Unexpectedly, we also found that aneuploid cancer cells were less sensitive than diploid cells to short-term exposure to multiple SAC inhibitors. Indeed, aneuploid cancer cells became increasingly sensitive to inhibition of SAC over time. Aneuploid cells exhibited aberrant spindle geometry and dynamics, and kept dividing when the SAC was inhibited, resulting in the accumulation of mitotic defects, and in unstable and less-fit karyotypes. Therefore, although aneuploid cancer cells could overcome inhibition of SAC more readily than diploid cells, their long-term proliferation was jeopardized. We identified a specific mitotic kinesin, KIF18A, whose activity was perturbed in aneuploid cancer cells. Aneuploid cancer cells were particularly vulnerable to depletion of KIF18A, and KIF18A overexpression restored their response to SAC inhibition. Our results identify a therapeutically relevant, synthetic lethal interaction between aneuploidy and the SAC.

Targeting glutamine dependence through GLS1 inhibition suppresses ARID1A-inactivated clear cell ovarian carcinoma

Alterations in components of the SWI/SNF chromatin-remodeling complex occur in ~20% of all human cancers. For example, ARID1A is mutated in up to 62% of clear cell ovarian carcinoma (OCCC), a disease currently lacking effective therapies. Here we show that ARID1A mutation creates a dependence on glutamine metabolism. SWI/SNF represses glutaminase (GLS1) and ARID1A inactivation upregulates GLS1. ARID1A inactivation increases glutamine utilization and metabolism through the tricarboxylic acid cycle to support aspartate synthesis. Indeed, glutaminase inhibitor CB-839 suppresses the growth of ARID1A mutant, but not wildtype, OCCCs in both orthotopic and patient-derived xenografts. In addition, glutaminase inhibitor CB-839 synergizes with immune checkpoint blockade anti-PDL1 antibody in a genetic OCCC mouse model driven by conditional Arid1a inactivation. Our data indicate that pharmacological inhibition of glutaminase alone or in combination with immune checkpoint blockade represents an effective therapeutic strategy for cancers involving alterations in the SWI/SNF complex such as ARID1A mutations.

Identification of important genes and drug repurposing based on clinical-centered analysis across human cancers

Identification of the functional impact of mutated and altered genes in cancer is critical for implementing precision oncology and drug repurposing. In recent years, the emergence of multiomics data from large, well-characterized patient cohorts has provided us with an unprecedented opportunity to address this problem. In this study, we investigated survival-associated genes across 26 cancer types and found that these genes tended to be hub genes and had higher K-core values in biological networks. Moreover, the genes associated with adverse outcomes were mainly enriched in pathways related to genetic information processing and cellular processes, while the genes with favorable outcomes were enriched in metabolism and immune regulation pathways. We proposed using the number of survival-related neighbors to assess the impact of mutations. In addition, by integrating other databases including the Human Protein Atlas and the DrugBank database, we predicted novel targets and anticancer drugs using the drug repurposing strategy. Our results illustrated the significance of multidimensional analysis of clinical data in important gene identification and drug development.

KRAS mutation in pancreatic cancer

Pancreatic cancer is a recalcitrant cancer with one of the lowest 5-year survival rates. A hallmark of pancreatic cancer is the prevalence of oncogenic mutation in the KRAS gene. The KRAS oncogene plays a critical role in the initiation and maintenance of pancreatic tumors and its signaling network represents a major target for therapeutic intervention. A number of inhibitors have been developed against kinase effectors in various Ras signaling pathways. Their clinical activity, however, has been disappointing thus far. More recently, covalent inhibitors targeting the KRASG12C oncoprotein have been developed. These inhibitors showed promising activity in KRASG12C mutant pancreatic cancer in early clinical trials. This review will present an updated summary of our understanding of mutant KRAS function in pancreatic cancer and discuss therapeutic strategies that target oncogenic KRAS signaling in this disease.

CRISP-view: a database of functional genetic screens spanning multiple phenotypes

High-throughput genetic screening based on CRISPR/Cas9 or RNA-interference (RNAi) enables the exploration of genes associated with the phenotype of interest on a large scale. The rapid accumulation of public available genetic screening data provides a wealth of knowledge about genotype-to-phenotype relationships and a valuable resource for the systematic analysis of gene functions. Here we present CRISP-view, a comprehensive database of CRISPR/Cas9 and RNAi screening datasets that span multiple phenotypes, including in vitro and in vivo cell proliferation and viability, response to cancer immunotherapy, virus response, protein expression, etc. By 22 September 2020, CRISP-view has collected 10 321 human samples and 825 mouse samples from 167 papers. All the datasets have been curated, annotated, and processed by a standard MAGeCK-VISPR analysis pipeline with quality control (QC) metrics. We also developed a user-friendly webserver to visualize, explore, and search these datasets. The webserver is freely available at http://crispview.weililab.org.

Cysteine and Folate Metabolism Are Targetable Vulnerabilities of Metastatic Colorectal Cancer

Simple Summary

In this work, we studied the metabolic reprogramming of same-patient-derived cell lines with increasing metastatic potential to develop new therapeutic approaches against metastatic colorectal cancer. Using a novel systems biology approach to integrate multiple layers of omics data, we predicted and validated that cystine uptake and folate metabolism, two key pathways related to redox metabolism, are potential targets against metastatic colorectal cancer. Our findings indicate that metastatic cell lines are selectively dependent on redox homeostasis, paving the way for new targeted therapies.

Abstract

With most cancer-related deaths resulting from metastasis, the development of new therapeutic approaches against metastatic colorectal cancer (mCRC) is essential to increasing patient survival. The metabolic adaptations that support mCRC remain undefined and their elucidation is crucial to identify potential therapeutic targets. Here, we employed a strategy for the rational identification of targetable metabolic vulnerabilities. This strategy involved first a thorough metabolic characterisation of same-patient-derived cell lines from primary colon adenocarcinoma (SW480), its lymph node metastasis (SW620) and a liver metastatic derivative (SW620-LiM2), and second, using a novel multi-omics integration workflow, identification of metabolic vulnerabilities specific to the metastatic cell lines. We discovered that the metastatic cell lines are selectively vulnerable to the inhibition of cystine import and folate metabolism, two key pathways in redox homeostasis. Specifically, we identified the system xCT and MTHFD1 genes as potential therapeutic targets, both individually and combined, for combating mCRC.

Getting a Grip on the Undrugged: Targeting β-Catenin with Fragment-Based Methods

Aberrant WNT pathway activation, leading to nuclear accumulation of β‐catenin, is a key oncogenic driver event. Mutations in the tumor suppressor gene APC lead to impaired proteasomal degradation of β‐catenin and subsequent nuclear translocation. Restoring cellular degradation of β‐catenin represents a potential therapeutic strategy. Here, we report the fragment‐based discovery of a small molecule binder to β‐catenin, including the structural elucidation of the binding mode by X‐ray crystallography. The difficulty in drugging β‐catenin was confirmed as the primary screening campaigns identified only few and very weak hits. Iterative virtual and NMR screening techniques were required to discover a compound with sufficient potency to be able to obtain an X‐ray co‐crystal structure. The binding site is located between armadillo repeats two and three, adjacent to the BCL9 and TCF4 binding sites. Genetic studies show that it is unlikely to be useful for the development of protein–protein interaction inhibitors but structural information and established assays provide a solid basis for a prospective optimization towards β‐catenin proteolysis targeting chimeras (PROTACs) as alternative modality.

CellBox: Interpretable Machine Learning for Perturbation Biology with Application to the Design of Cancer Combination Therapy

Systematic perturbation of cells followed by comprehensive measurements of molecular and phenotypic responses provides informative data resources for constructing computational models of cell biology. Models that generalize well beyond training data can be used to identify combinatorial perturbations of potential therapeutic interest. Major challenges for machine learning on large biological datasets are to find global optima in a complex multidimensional space and mechanistically interpret the solutions. To address these challenges, we introduce a hybrid approach that combines explicit mathematical models of cell dynamics with a machine-learning framework, implemented in TensorFlow. We tested the modeling framework on a perturbation-response dataset of a melanoma cell line after drug treatments. The models can be efficiently trained to describe cellular behavior accurately. Even though completely data driven and independent of prior knowledge, the resulting de novo network models recapitulate some known interactions. The approach is readily applicable to various kinetic models of cell biology. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.