Cancer stem cells (CSCs) correlate with recurrence, metastasis and poor survival in clinical studies. Encouraging results from clinical trials of CSC inhibitors have further validated CSCs as therapeutic targets. ONC201 is a first-in-class small molecule imipridone in Phase I/II clinical trials for advanced cancer. We have previously shown that ONC201 targets self-renewing, chemotherapy-resistant colorectal CSCs via Akt/ERK inhibition and DR5/TRAIL induction. In this study, we demonstrate that the anti-CSC effects of ONC201 involve early changes in stem cell-related gene expression prior to tumor cell death induction. A targeted network analysis of gene expression profiles in colorectal cancer cells revealed that ONC201 downregulates stem cell pathways such as Wnt signaling and modulates genes (ID1, ID2, ID3 and ALDH7A1) known to regulate self-renewal in colorectal, prostate cancer and glioblastoma. ONC201-mediated changes in CSC-related gene expression were validated at the RNA and protein level for each tumor type. Accordingly, we observed inhibition of self-renewal and CSC markers in prostate cancer cell lines and patient-derived glioblastoma cells upon ONC201 treatment. Interestingly, ONC201-mediated CSC depletion does not occur in colorectal cancer cells with acquired resistance to ONC201. Finally, we observed that basal expression of CSC-related genes (ID1, CD44, HES7 and TCF3) significantly correlate with ONC201 efficacy in >1000 cancer cell lines and combining the expression of multiple genes leads to a stronger overall prediction. These proof-of-concept studies provide a rationale for testing CSC expression at the RNA and protein level as a predictive and pharmacodynamic biomarker of ONC201 response in ongoing clinical studies.

Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC₅₀ values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC₅₀ values in a 8-fold cross-validation and an independent blind test with coefficient of determination R² of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R² of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC₅₀ values rather than experimentally measuring them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity.

Evolved resistance to tyrosine kinase inhibitor (TKI)-targeted therapies remains a major clinical challenge. In epidermal growth factor receptor (EGFR) mutant non-small-cell lung cancer (NSCLC), failure of EGFR TKIs can result from both genetic and epigenetic mechanisms of acquired drug resistance. Widespread reports of histologic and gene expression changes consistent with an epithelial-to-mesenchymal transition (EMT) have been associated with initially surviving drug-tolerant persister cells, which can seed bona fide genetic mechanisms of resistance to EGFR TKIs. While therapeutic approaches targeting fully resistant cells, such as those harboring an EGFRT790M mutation, have been developed, a clinical strategy for preventing the emergence of persister cells remains elusive. Using mesenchymal cell lines derived from biopsies of patients who progressed on EGFR TKI as surrogates for persister populations, we performed whole-genome CRISPR screening and identified fibroblast growth factor receptor 1 (FGFR1) as the top target promoting survival of mesenchymal EGFR mutant cancers. Although numerous previous reports of FGFR signaling contributing to EGFR TKI resistance in vitro exist, the data have not yet been sufficiently compelling to instigate a clinical trial testing this hypothesis, nor has the role of FGFR in promoting the survival of persister cells been elucidated. In this study, we find that combining EGFR and FGFR inhibitors inhibited the survival and expansion of EGFR mutant drug-tolerant cells over long time periods, preventing the development of fully resistant cancers in multiple vitro models and in vivo. These results suggest that dual EGFR and FGFR blockade may be a promising clinical strategy for both preventing and overcoming EMT-associated acquired drug resistance and provide motivation for the clinical study of combined EGFR and FGFR inhibition in EGFR-mutated NSCLCs.

Combinations of anti-cancer drugs can overcome resistance and provide new treatments1,2. The number of possible drug combinations vastly exceeds what could be tested clinically. Efforts to systematically identify active combinations and the tissues and molecular contexts in which they are most effective could accelerate the development of combination treatments. Here we evaluate the potency and efficacy of 2,025 clinically relevant two-drug combinations, generating a dataset encompassing 125 molecularly characterized breast, colorectal and pancreatic cancer cell lines. We show that synergy between drugs is rare and highly context-dependent, and that combinations of targeted agents are most likely to be synergistic. We incorporate multi-omic molecular features to identify combination biomarkers and specify synergistic drug combinations and their active contexts, including in basal-like breast cancer, and microsatellite-stable or KRAS-mutant colon cancer. Our results show that irinotecan and CHEK1 inhibition have synergistic effects in microsatellite-stable or KRAS-TP53 double-mutant colon cancer cells, leading to apoptosis and suppression of tumour xenograft growth. This study identifies clinically relevant effective drug combinations in distinct molecular subpopulations and is a resource to guide rational efforts to develop combinatorial drug treatments.

Many gene fusions are reported in tumours and for most their role remains unknown. As fusions are used for diagnostic and prognostic purposes, and are targets for treatment, it is crucial to assess their function in cancer. To systematically investigate the role of fusions in tumour cell fitness, we utilized RNA-sequencing data from 1011 human cancer cell lines to functionally link 8354 fusion events with genomic data, sensitivity to >350 anti-cancer drugs and CRISPR-Cas9 loss-of-fitness effects. Established clinically-relevant fusions were identified. Overall, detection of functional fusions was rare, including those involving cancer driver genes, suggesting that many fusions are dispensable for tumour fitness. Therapeutically actionable fusions involving RAF1, BRD4 and ROS1 were verified in new histologies. In addition, recurrent YAP1-MAML2 fusions were identified as activators of Hippo-pathway signaling in multiple cancer types. Our approach discriminates functional fusions, identifying new drivers of carcinogenesis and fusions that could have clinical implications.

Single-cell technologies have described heterogeneity across tissues, but the spatial distribution and forces that drive single-cell phenotypes have not been well defined. Combining single-cell RNA and protein analytics in studying the role of stromal cancer-associated fibroblasts (CAFs) in modulating heterogeneity in pancreatic cancer (pancreatic ductal adenocarcinoma [PDAC]) model systems, we have identified significant single-cell population shifts toward invasive epithelial-to-mesenchymal transition (EMT) and proliferative (PRO) phenotypes linked with mitogen-activated protein kinase (MAPK) and signal transducer and activator of transcription 3 (STAT3) signaling. Using high-content digital imaging of RNA in situ hybridization in 195 PDAC tumors, we quantified these EMT and PRO subpopulations in 319,626 individual cancer cells that can be classified within the context of distinct tumor gland "units." Tumor gland typing provided an additional layer of intratumoral heterogeneity that was associated with differences in stromal abundance and clinical outcomes. This demonstrates the impact of the stroma in shaping tumor architecture by altering inherent patterns of tumor glands in human PDAC.

Cancer-associated fibroblasts (CAFs) are highly heterogeneous. With the lack of a comprehensive understanding of CAFs' functional distinctions, it remains unclear how cancer treatments could be personalized based on CAFs in a patient's tumor. We have established a living biobank of CAFs derived from biopsies of patients' non-small lung cancer (NSCLC) that encompasses a broad molecular spectrum of CAFs in clinical NSCLC. By functionally interrogating CAF heterogeneity using the same therapeutics received by patients, we identify three functional subtypes: (1) robustly protective of cancers and highly expressing HGF and FGF7; (2) moderately protective of cancers and highly expressing FGF7; and (3) those providing minimal protection. These functional differences among CAFs are governed by their intrinsic TGF-β signaling, which suppresses HGF and FGF7 expression. This CAF functional classification correlates with patients' clinical response to targeted therapies and also associates with the tumor immune microenvironment, therefore providing an avenue to guide personalized treatment.

BH3 mimetics such as ABT-263 induce apoptosis in a subset of cancer models. However, these drugs have shown limited clinical efficacy as single agents in small-cell lung cancer (SCLC) and other solid tumor malignancies, and rational combination strategies remain underexplored. To develop a novel therapeutic approach, we examined the efficacy of ABT-263 across >500 cancer cell lines, including 311 for which we had matched expression data for select genes. We found that high expression of the proapoptotic gene Bcl2-interacting mediator of cell death (BIM) predicts sensitivity to ABT-263. In particular, SCLC cell lines possessed greater BIM transcript levels than most other solid tumors and are among the most sensitive to ABT-263. However, a subset of relatively resistant SCLC cell lines has concomitant high expression of the antiapoptotic myeloid cell leukemia 1 (MCL-1). Whereas ABT-263 released BIM from complexes with BCL-2 and BCL-XL, high expression of MCL-1 sequestered BIM released from BCL-2 and BCL-XL, thereby abrogating apoptosis. We found that SCLCs were sensitized to ABT-263 via TORC1/2 inhibition, which led to reduced MCL-1 protein levels, thereby facilitating BIM-mediated apoptosis. AZD8055 and ABT-263 together induced marked apoptosis in vitro, as well as tumor regressions in multiple SCLC xenograft models. In a Tp53; Rb1 deletion genetically engineered mouse model of SCLC, the combination of ABT-263 and AZD8055 significantly repressed tumor growth and induced tumor regressions compared with either drug alone. Furthermore, in a SCLC patient-derived xenograft model that was resistant to ABT-263 alone, the addition of AZD8055 induced potent tumor regression. Therefore, addition of a TORC1/2 inhibitor offers a therapeutic strategy to markedly improve ABT-263 activity in SCLC.

Fewer than half of children with high-risk neuroblastoma survive. Many of these tumors harbor high-level amplification of MYCN, which correlates with poor disease outcome. Using data from our large drug screen we predicted, and subsequently demonstrated, that MYCN-amplified neuroblastomas are sensitive to the BCL-2 inhibitor ABT-199. This sensitivity occurs in part through low anti-apoptotic BCL-xL expression, high pro-apoptotic NOXA expression, and paradoxical, MYCN-driven upregulation of NOXA. Screening for enhancers of ABT-199 sensitivity in MYCN-amplified neuroblastomas, we demonstrate that the Aurora Kinase A inhibitor MLN8237 combines with ABT-199 to induce widespread apoptosis. In diverse models of MYCN-amplified neuroblastoma, including a patient-derived xenograft model, this combination uniformly induced tumor shrinkage, and in multiple instances led to complete tumor regression.

In eukaryotic cells, the SH2 and PTB domains mediate protein-protein interactions by recognizing phosphotyrosine residues on target proteins. Here we make the unexpected finding that the C2 domain of PKCdelta directly binds to phosphotyrosine peptides in a sequence-specific manner. We provide evidence that this domain mediates PKCdelta interaction with a Src binding glycoprotein, CDCP1. The crystal structure of the PKCdelta C2 domain in complex with an optimal phosphopeptide reveals a new mode of phosphotyrosine binding in which the phosphotyrosine moiety forms a ring-stacking interaction with a histidine residue of the C2 domain. This is also the first example of a protein Ser/Thr kinase containing a domain that binds phosphotyrosine.

Recent clinical data have linked KRAS/TP53 comutation (mut) to resistance to radiotherapy (RT), but supporting laboratory in vivo evidence is lacking. In addition, the ability of different radiation doses, with/without epidermal growth factor receptor (EGFR)-directed treatment, to achieve local tumor control as a function of KRAS status is unknown. Here, we assessed clonogenic radiation survival of a panel of annotated lung cancer cell lines. KRASmut/TP53mut was associated with the highest radioresistance in nonisogenic and isogenic comparisons. To validate these findings, isogenic TP53mut NCI-H1703 models, KRASmut or wild-type (wt), were grown as heterotopic xenografts in nude mice. A clinical RT schedule of 30 fractions over 6 weeks was employed. The dose that controlled 50% of tumors (TCD50 ) was calculated. The TCD50 for KRASwt/TP53mut xenografts was 43.1 Gy whereas KRASmut/TP53mut tumors required a 1.9-fold higher TCD50 of 81.4 Gy. The EGFR inhibitor erlotinib radiosensitized KRASmut but not KRASwt cells and xenografts. The TCD50 associated with adding erlotinib to RT was 58.8 Gy for KRASmut, that is, a ~1.4-fold dose enhancement. However, the EGFR antibody cetuximab did not have a radiosensitizing effect. In conclusion, we demonstrate for the first time that KRASmut in a TP53mut background confers radioresistance when studying a clinical RT schedule and local control rather than tumor growth delay. Despite the known unresponsiveness of KRASmut tumors to EGFR inhibitors, erlotinib radiosensitized KRASmut tumors. Our data highlight KRAS/TP53 comutation as a candidate biomarker of radioresistance that can be at least partially reversed by dose escalation or the addition of a targeted agent.

α-Ketoglutarate (αKG) is a key node in many important metabolic pathways. The αKG analog N-oxalylglycine (NOG) and its cell-permeable prodrug dimethyloxalylglycine (DMOG) are extensively used to inhibit αKG-dependent dioxygenases. However, whether NOG interference with other αKG-dependent processes contributes to its mode of action remains poorly understood. Here we show that, in aqueous solutions, DMOG is rapidly hydrolyzed, yielding methyloxalylglycine (MOG). MOG elicits cytotoxicity in a manner that depends on its transport by monocarboxylate transporter 2 (MCT2) and is associated with decreased glutamine-derived tricarboxylic acid-cycle flux, suppressed mitochondrial respiration and decreased ATP production. MCT2-facilitated entry of MOG into cells leads to sufficiently high concentrations of NOG to inhibit multiple enzymes in glutamine metabolism, including glutamate dehydrogenase. These findings reveal that MCT2 dictates the mode of action of NOG by determining its intracellular concentration and have important implications for the use of (D)MOG in studying αKG-dependent signaling and metabolism.

Identifying robust, patient-specific, and predictive biomarkers presents a major obstacle in precision oncology. To optimize patient-specific therapeutic strategies, here we couple pathway knowledge with large-scale drug sensitivity, RNAi, and CRISPR-Cas9 screening data from 460 cell lines. Pathway activity levels are found to be strong predictive biomarkers for the essentiality of 15 proteins, including the essentiality of MAD2L1 in breast cancer patients with high BRCA-pathway activity. We also find strong predictive biomarkers for the sensitivity to 31 compounds, including BCL2 and microtubule inhibitors (MTIs). Lastly, we show that Bcl-xL inhibition can modulate the activity of a predictive biomarker pathway and re-sensitize lung cancer cells and tumors to MTI therapy. Overall, our results support the use of pathways in helping to achieve the goal of precision medicine by uncovering dozens of predictive biomarkers.

FGFR inhibitors are approved for the treatment of advanced cholangiocarcinoma harboring FGFR2 fusions. However, the response rate is moderate, and resistance emerges rapidly due to acquired secondary FGFR2 mutations or due to other less-defined mechanisms. Here, we conducted high-throughput combination drug screens, biochemical analysis, and therapeutic studies using patient-derived models of FGFR2 fusion-positive cholangiocarcinoma to gain insight into these clinical profiles and uncover improved treatment strategies. We found that feedback activation of EGFR signaling limits FGFR inhibitor efficacy, restricting cell death induction in sensitive models and causing resistance in insensitive models lacking secondary FGFR2 mutations. Inhibition of wild-type EGFR potentiated responses to FGFR inhibitors in both contexts, durably suppressing MEK/ERK and mTOR signaling, increasing apoptosis, and causing marked tumor regressions in vivo. Our findings reveal EGFR-dependent adaptive signaling as an important mechanism limiting FGFR inhibitor efficacy and driving resistance and support clinical testing of FGFR/EGFR inhibitor therapy for FGFR2 fusion-positive cholangiocarcinoma.

Two-dimensional (2D) culture of tumor cells fails to recapitulate some important aspects of cellular organization seen in in vivo experiments. In addition, cell cultures traditionally use non-physiological concentration of nutrients. Here, we describe a protocol for a facile three-dimensional (3D) culture format for cancer cells. This 3D platform helps overcome the 2D culture limitations. In addition, it allows for longitudinal modeling of responses to cancer therapeutics. For complete details on the use and execution of this protocol, please refer to Lhuissier et al. (2017), Lehmann et al. (2016), Liu et al. (2016), and Duval et al. (2011).

Drug combinations have the potential to improve efficacy while limiting toxicity. To robustly identify synergistic combinations, high-throughput screens using full dose-response surface are desirable but require an impractical number of data points. Screening of a sparse number of doses per drug allows to screen large numbers of drug pairs, but complicates statistical assessment of synergy. Furthermore, since the number of pairwise combinations grows with the square of the number of drugs, exploration of large screens necessitates advanced visualization tools.

A newer generation of anti-cancer drugs targeting underlying somatic genetic driver events have resulted in high single-agent or single-pathway response rates in selected patients, but few patients achieve complete responses and a sizeable fraction of patients relapse within a year. Thus, there is a pressing need for identification of combinations of targeted agents which induce more complete responses and prevent disease progression. We describe the results of a combination screen of an unprecedented scale in mammalian cells performed using a collection of targeted, clinically tractable agents across a large panel of melanoma cell lines. We find that even the most synergistic drug pairs are effective only in a discrete number of cell lines, underlying a strong context dependency for synergy, with strong, widespread synergies often corresponding to non-specific or off-target drug effects such as multidrug resistance protein 1 (MDR1) transporter inhibition. We identified drugs sensitizing cell lines that are BRAFV600E mutant but intrinsically resistant to BRAF inhibitor PLX4720, including the vascular endothelial growth factor receptor/kinase insert domain receptor (VEGFR/KDR) and platelet derived growth factor receptor (PDGFR) family inhibitor cediranib. The combination of cediranib and PLX4720 induced apoptosis in vitro and tumor regression in animal models. This synergistic interaction is likely due to engagement of multiple receptor tyrosine kinases (RTKs), demonstrating the potential of drug- rather than gene-specific combination discovery approaches. Patients with elevated biopsy KDR expression showed decreased progression free survival in trials of mitogen-activated protein kinase (MAPK) kinase pathway inhibitors. Thus, high-throughput unbiased screening of targeted drug combinations, with appropriate library selection and mechanistic follow-up, can yield clinically-actionable drug combinations.

Personalized cancer therapy is based on a patient's tumor lineage, histopathology, expression analyses, and/or tumor DNA or RNA analysis. Here, we aim to develop an in vitro functional assay of a patient's living cancer cells that could complement these approaches. We present methods for developing cell cultures from tumor biopsies and identify the types of samples and culture conditions associated with higher efficiency of model establishment. Toward the application of patient-derived cell cultures for personalized care, we established an immunofluorescence-based functional assay that quantifies cancer cell responses to targeted therapy in mixed cell cultures. Assaying patient-derived lung cancer cultures with this method showed promise in modeling patient response for diagnostic use. This platform should allow for the development of co-clinical trial studies to prospectively test the value of drug profiling on tumor-biopsy-derived cultures to direct patient care.

Many cancer types are driven by oncogenic transcription factors that have been difficult to drug. Transcriptional inhibitors, however, may offer inroads into targeting these cancers. Through chemical genomics screening, we identified that Ewing sarcoma is a disease with preferential sensitivity to THZ1, a covalent small-molecule CDK7/12/13 inhibitor. The selective CDK12/13 inhibitor, THZ531, impairs DNA damage repair in an EWS/FLI-dependent manner, supporting a synthetic lethal relationship between response to THZ1/THZ531 and EWS/FLI expression. The combination of these molecules with PARP inhibitors showed striking synergy in cell viability and DNA damage assays in vitro and in multiple models of Ewing sarcoma, including a PDX, in vivo without hematopoietic toxicity.

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.