Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs

Signatures of cell death and proliferation in perturbation transcriptomics data-from confounding factor to effective prediction

Transcriptional perturbation signatures are valuable data sources for functional genomics. Linking perturbation signatures to screenings opens the possibility to model cellular phenotypes from expression data and to identify efficacious drugs. We linked perturbation transcriptomics data from the LINCS-L1000 project with cell viability information upon genetic (Achilles project) and chemical (CTRP screen) perturbations yielding more than 90 000 signature–viability pairs. An integrated analysis showed that the cell viability signature is a major factor underlying perturbation signatures. The signature is linked to transcription factors regulating cell death, proliferation and division time. We used the cell viability–signature relationship to predict viability from transcriptomics signatures, and identified and validated compounds that induce cell death in tumor cell lines. We showed that cellular toxicity can lead to unexpected similarity of signatures, confounding mechanism of action discovery. Consensus compound signatures predicted cell-specific drug sensitivity, even if the signature is not measured in the same cell line, and outperformed conventional drug-specific features. Our results can help in understanding mechanisms behind cell death and removing confounding factors of transcriptomic perturbation screens. To interactively browse our results and predict cell viability in new gene expression samples, we developed CEVIChE (CEll VIability Calculator from gene Expression; https://saezlab.shinyapps.io/ceviche/).

Torin2 Exploits Replication and Checkpoint Vulnerabilities to Cause Death of PI3K-Activated Triple-Negative Breast Cancer Cells

Frequent mutation of PI3K/AKT/mTOR signaling pathway genes in human cancers has stimulated large investments in targeted drugs but clinical successes are rare. As a result, many cancers with high PI3K pathway activity, such as triple-negative breast cancer (TNBC), are treated primarily with chemotherapy. By systematically analyzing responses of TNBC cells to a diverse collection of PI3K pathway inhibitors, we find that one drug, Torin2, is unusually effective because it inhibits both mTOR and other PI3K-like kinases (PIKKs). In contrast to mTOR-selective inhibitors, Torin2 exploits dependencies on several kinases for S-phase progression and cell-cycle checkpoints, thereby causing accumulation of single-stranded DNA and death by replication catastrophe or mitotic failure. Thus, Torin2 and its chemical analogs represent a mechanistically distinct class of PI3K pathway inhibitors that are uniquely cytotoxic to TNBC cells. This insight could be translated therapeutically by further developing Torin2 analogs or combinations of existing mTOR and PIKK inhibitors.

Inhibition of PARP Sensitizes Chondrosarcoma Cell Lines to Chemo- and Radiotherapy Irrespective of the IDH1 or IDH2 Mutation Status

Chondrosarcomas are chemo- and radiotherapy resistant and frequently harbor mutations in isocitrate dehydrogenase (IDH1 or IDH2), causing increased levels of D-2-hydroxyglutarate (D-2-HG). DNA repair defects and synthetic lethality with poly(ADP-ribose) polymerase (PARP) inhibition occur in IDH mutant glioma and leukemia models. Here we evaluated DNA repair and PARP inhibition, alone or combined with chemo- or radiotherapy, in chondrosarcoma cell lines with or without endogenous IDH mutations. Chondrosarcoma cell lines treated with the PARP inhibitor talazoparib were examined for dose–response relationships, as well as underlying cell death mechanisms and DNA repair functionality. Talazoparib was combined with chemo- or radiotherapy to evaluate potential synergy. Cell lines treated long term with an inhibitor normalizing D-2-HG levels were investigated for synthetic lethality with talazoparib. We report that talazoparib sensitivity was variable and irrespective of IDH mutation status. All cell lines expressed Ataxia Telangiectasia Mutated (ATM), but a subset was impaired in poly(ADP-ribosyl)ation (PARylation) capacity, homologous recombination, and O-6-methylguanine-DNA methyltransferase (MGMT) expression. Talazoparib synergized with temozolomide or radiation, independent of IDH1 mutant inhibition. This study suggests that talazoparib combined with temozolomide or radiation are promising therapeutic strategies for chondrosarcoma, irrespective of IDH mutation status. A subset of chondrosarcomas may be deficient in nonclassical DNA repair pathways, suggesting that PARP inhibitor sensitivity is multifactorial in chondrosarcoma.

Tumor diversity and the trade-off between universal cancer tasks

Recent advances have enabled powerful methods to sort tumors into prognosis and treatment groups. We are still missing, however, a general theoretical framework to understand the vast diversity of tumor gene expression and mutations. Here we present a framework based on multi-task evolution theory, using the fact that tumors need to perform multiple tasks that contribute to their fitness. We find that trade-offs between tasks constrain tumor gene-expression to a continuum bounded by a polyhedron whose vertices are gene-expression profiles, each specializing in one task. We find five universal cancer tasks across tissue-types: cell-division, biomass and energy, lipogenesis, immune-interaction and invasion and tissue-remodeling. Tumors that specialize in a task are sensitive to drugs that interfere with this task. Driver, but not passenger, mutations tune gene-expression towards specialization in specific tasks. This approach can integrate additional types of molecular data into a framework of tumor diversity grounded in evolutionary theory.

A curative combination cancer therapy achieves high fractional cell killing through low cross-resistance and drug additivity

Curative cancer therapies are uncommon and nearly always involve multi-drug combinations developed by experimentation in humans; unfortunately, the mechanistic basis for the success of such combinations has rarely been investigated in detail, obscuring lessons learned. Here, we use isobologram analysis to score pharmacological interaction, and clone tracing and CRISPR screening to measure cross-resistance among the five drugs comprising R-CHOP, a combination therapy that frequently cures Diffuse Large B-Cell Lymphomas. We find that drugs in R-CHOP exhibit very low cross-resistance but not synergistic interaction: together they achieve a greater fractional kill according to the null hypothesis for both the Loewe dose-additivity model and the Bliss effect-independence model. These data provide direct evidence for the 50 year old hypothesis that a curative cancer therapy can be constructed on the basis of independently effective drugs having non-overlapping mechanisms of resistance, without synergistic interaction, which has immediate significance for the design of new drug combinations.

Why not add some SPARKL to your life (and death)!?

Quantifying cytostatic and cytotoxic outcomes of cells responding to perturbagens is an essential component of mechanism-based studies and pharmacological screening approaches. We recently described an easy and versatile method for single-cell and population-level analyses using real-time kinetic labeling (SPARKL). This technology enables zero-handling, non-disruptive protocols for integrating proliferation profiles with cell death mechanisms, along with advanced mathematics for robust analyses.

Deoxycytidine Release from Pancreatic Stellate Cells Promotes Gemcitabine Resistance

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer deaths in the United States. The deoxynucleoside analogue gemcitabine is among the most effective therapies to treat PDAC, however, nearly all patients treated with gemcitabine either fail to respond or rapidly develop resistance. One hallmark of PDAC is a striking accumulation of stromal tissue surrounding the tumor, and this accumulation of stroma can contribute to therapy resistance. To better understand how stroma limits response to therapy, we investigated cell-extrinsic mechanisms of resistance to gemcitabine. Conditioned media from pancreatic stellate cells (PSC), as well as from other fibroblasts, protected PDAC cells from gemcitabine toxicity. The protective effect of PSC-conditioned media was mediated by secretion of deoxycytidine, but not other deoxynucleosides, through equilibrative nucleoside transporters. Deoxycytidine inhibited the processing of gemcitabine in PDAC cells, thus reducing the effect of gemcitabine and other nucleoside analogues on cancer cells. These results suggest that reducing deoxycytidine production in PSCs may increase the efficacy of nucleoside analog therapies. SIGNIFICANCE: This study provides important new insight into mechanisms that contribute to gemcitabine resistance in PDAC and suggests new avenues for improving gemcitabine efficacy.

Development and Characterization of a Wee1 Kinase Degrader

The G1/S cell cycle checkpoint is frequently dysregulated in cancer, leaving cancer cells reliant on a functional G2/M checkpoint to prevent excessive DNA damage. Wee1 regulates the G2/M checkpoint by phosphorylating CDK1 at Tyr15 to prevent mitotic entry. Previous drug development efforts targeting Wee1 resulted in the clinical-grade inhibitor, AZD1775. However, AZD1775 is burdened by dose-limiting adverse events, and has off-target PLK1 activity. In an attempt to overcome these limitations, we developed Wee1 degraders by conjugating AZD1775 to the cereblon (CRBN)-binding ligand, pomalidomide. The resulting lead compound, ZNL-02-096, degrades Wee1 while sparing PLK1, induces G2/M accumulation at 10-fold lower doses than AZD1775, and synergizes with Olaparib in ovarian cancer cells. We demonstrate that ZNL-02-096 has CRBN-dependent pharmacology that is distinct from AZD1775, which justifies further evaluation of selective Wee1 degraders.

Metabolic Modifier Screen Reveals Secondary Targets of Protein Kinase Inhibitors within Nucleotide Metabolism

Biosynthesis of the pyrimidine nucleotide uridine monophosphate (UMP) is essential for cell proliferation and is achieved by the activity of convergent de novo and salvage metabolic pathways. Here we report the development and application of a cell-based metabolic modifier screening platform that leverages the redundancy in pyrimidine metabolism for the discovery of selective UMP biosynthesis modulators. In evaluating a library of protein kinase inhibitors, we identified multiple compounds that possess nucleotide metabolism modifying activity. The JNK inhibitor JNK-IN-8 was found to potently inhibit nucleoside transport and engage ENT1. The PDK1 inhibitor OSU-03012 (also known as AR-12) and the RAF inhibitor TAK-632 were shown to inhibit the therapeutically relevant de novo pathway enzyme DHODH and their affinities were unambiguously confirmed through in vitro assays and co-crystallization with human DHODH.

Computational Drug Repurposing: Current Trends

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Biomedical discovery has been reshaped upon the exploding digitization of data which can be retrieved from a number of sources, ranging from clinical pharmacology to cheminformatics-driven databases. Now, supercomputing platforms and publicly available resources such as biological, physicochemical, and clinical data, can all be integrated to construct a detailed map of signaling pathways and drug mechanisms of action in relation to drug candidates. Recent advancements in computer-aided data mining have facilitated analyses of ‘big data’ approaches and the discovery of new indications for pre-existing drugs has been accelerated. Linking gene-phenotype associations to predict novel drug-disease signatures or incorporating molecular structure information of drugs and protein targets with other kinds of data derived from systems biology provide great potential to accelerate drug discovery and improve the success of drug repurposing attempts. In this review, we highlight commonly used computational drug repurposing strategies, including bioinformatics and cheminformatics tools, to integrate large-scale data emerging from the systems biology, and consider both the challenges and opportunities of using this approach. Moreover, we provide successful examples and case studies that combined various in silico drug-repurposing strategies to predict potential novel uses for known therapeutics.

Single-Cell and Population-Level Analyses Using Real-Time Kinetic Labeling Couples Proliferation and Cell Death Mechanisms

Quantifying cytostatic and cytotoxic outcomes are integral components of characterizing perturbagens used as research tools and in drug discovery pipelines. Furthermore, data-rich acquisition, coupled with robust methods for analysis, is required to properly assess the function and impact of these perturbagens. Here, we present a detailed and versatile method for single-cell and population-level analyses using real-time kinetic labeling (SPARKL). SPARKL integrates high-content live-cell imaging with automated detection and analysis of fluorescent reporters of cell death. We outline several examples of zero-handling, non-disruptive protocols for detailing cell death mechanisms and proliferation profiles. Additionally, we suggest several methods for mathematically analyzing these data to best utilize the collected kinetic data. Compared to traditional methods of detection and analysis, SPARKL is more sensitive, accurate, and high throughput while substantially eliminating sample processing and providing richer data.

PTEN Loss Mediates Clinical Cross-Resistance to CDK4/6 and PI3Kα Inhibitors in Breast Cancer

The combination of CDK4/6 inhibitors with antiestrogen therapies significantly improves clinical outcomes in ER-positive advanced breast cancer. To identify mechanisms of acquired resistance, we analyzed serial biopsies and rapid autopsies from patients treated with the combination of the CDK4/6 inhibitor ribociclib with letrozole. This study revealed that some resistant tumors acquired RB loss, whereas other tumors lost PTEN expression at the time of progression. In breast cancer cells, ablation of PTEN, through increased AKT activation, was sufficient to promote resistance to CDK4/6 inhibition in vitro and in vivo. Mechanistically, PTEN loss resulted in exclusion of p27 from the nucleus, leading to increased activation of both CDK4 and CDK2. Because PTEN loss also causes resistance to PI3Kα inhibitors, currently approved in the post-CDK4/6 setting, these findings provide critical insight into how this single genetic event may cause clinical cross-resistance to multiple targeted therapies in the same patient, with implications for optimal treatment-sequencing strategies. SIGNIFICANCE: Our analysis of serial biopsies uncovered RB and PTEN loss as mechanisms of acquired resistance to CDK4/6 inhibitors, utilized as first-line treatment for ER-positive advanced breast cancer. Importantly, these findings have near-term clinical relevance because PTEN loss also limits the efficacy of PI3Kα inhibitors currently approved in the post-CDK4/6 setting.This article is highlighted in the In This Issue feature, p. 1.

Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers

Chromatin accessibility data can elucidate the developmental origin of cancer cells and reveal the enhancer landscape of key oncogenic transcriptional regulators. We develop a computational strategy called PSIONIC (patient-specific inference of networks informed by chromatin) to combine chromatin accessibility data with large tumor expression data and model the effect of enhancers on transcriptional programs in multiple cancers. We generate a new ATAC-seq data profiling chromatin accessibility in gynecologic and basal breast cancer cell lines and apply PSIONIC to 723 patient and 96 cell line RNA-seq profiles from ovarian, uterine, and basal breast cancers. Our computational framework enables us to share information across tumors to learn patient-specific TF activities, revealing regulatory differences between and within tumor types. PSIONIC-predicted activity for MTF1 in cell line models correlates with sensitivity to MTF1 inhibition, showing the potential of our approach for personalized therapy. Many identified TFs are significantly associated with survival outcome. To validate PSIONIC-derived prognostic TFs, we perform immunohistochemical analyses in 31 uterine serous tumors for ETV6 and 45 basal breast tumors for MITF and confirm that the corresponding protein expression patterns are also significantly associated with prognosis.

Genome-Wide CRISPR-Cas9 Screen Reveals Selective Vulnerability of ATRX-Mutant Cancers to WEE1 Inhibition

The tumor suppressor gene ATRX is frequently mutated in a variety of tumors including gliomas and liver cancers, which are highly unresponsive to current therapies. Here, we performed a genome-wide synthetic lethal screen, using CRISPR-Cas9 genome editing, to identify potential therapeutic targets specific for ATRX-mutated cancers. In isogenic hepatocellular carcinoma (HCC) cell lines engineered for ATRX loss, we identified 58 genes, including the checkpoint kinase WEE1, uniquely required for the cell growth of ATRX null cells. Treatment with the WEE1 inhibitor AZD1775 robustly inhibited the growth of several ATRX-deficient HCC cell lines in vitro, as well as xenografts in vivo. The increased sensitivity to the WEE1 inhibitor was caused by accumulated DNA damage-induced apoptosis. AZD1775 also selectively inhibited the proliferation of patient-derived primary cell lines from gliomas with naturally occurring ATRX mutations, indicating that the synthetic lethal relationship between WEE1 and ATRX could be exploited in a broader spectrum of human tumors. As WEE1 inhibitors have been investigated in several phase II clinical trials, our discovery provides the basis for an easily clinically testable therapeutic strategy specific for cancers deficient in ATRX. SIGNIFICANCE: ATRX-mutant cancer cells depend on WEE1, which provides a basis for therapeutically targeting WEE1 in ATRX-deficient cancers.See related commentary by Cole, p. 375.

Multigene signatures of responses to chemotherapy derived by biochemically-inspired machine learning

Pharmacogenomic responses to chemotherapy drugs can be modeled by supervised machine learning of expression and copy number of relevant gene combinations. Such biochemical evidence can form the basis of derived gene signatures using cell line data, which can subsequently be examined in patients that have been treated with the same drugs. These gene signatures typically contain elements of multiple biochemical pathways which together comprise multiple origins of drug resistance or sensitivity. The signatures can capture variation in these responses to the same drug among different patients.

Modeling of Cisplatin-Induced Signaling Dynamics in Triple-Negative Breast Cancer Cells Reveals Mediators of Sensitivity

Triple-negative breast cancers (TNBCs) display great diversity in cisplatin sensitivity that cannot be explained solely by cancer-associated DNA repair defects. Differential activation of the DNA damage response (DDR) to cisplatin has been proposed to underlie the observed differential sensitivity, but it has not been investigated systematically. Systems-level analysis-using quantitative time-resolved signaling data and phenotypic responses, in combination with mathematical modeling-identifies that the activation status of cell-cycle checkpoints determines cisplatin sensitivity in TNBC cell lines. Specifically, inactivation of the cell-cycle checkpoint regulator MK2 or G3BP2 sensitizes cisplatin-resistant TNBC cell lines to cisplatin. Dynamic signaling data of five cell cycle-related signals predicts cisplatin sensitivity of TNBC cell lines. We provide a time-resolved map of cisplatin-induced signaling that uncovers determinants of chemo-sensitivity, underscores the impact of cell-cycle checkpoints on cisplatin sensitivity, and offers starting points to optimize treatment efficacy.

Applicability of drug response metrics for cancer studies using biomaterials

Bioengineers have built models of the tumour microenvironment (TME) in which to study cell-cell interactions, mechanisms of cancer growth and metastasis, and to test new therapies. These models allow researchers to culture cells in conditions that include features of the in vivo TME implicated in regulating cancer progression, such as extracellular matrix (ECM) stiffness, integrin binding to the ECM, immune and stromal cells, growth factor and cytokine depots, and a three-dimensional geometry more representative of the in vivo TME than tissue culture polystyrene (TCPS). These biomaterials could be particularly useful for drug screening applications to make better predictions of efficacy, offering better translation to preclinical models and clinical trials. However, it can be challenging to compare drug response reports across different biomaterial platforms in the current literature. This is, in part, a result of inconsistent reporting and improper use of drug response metrics, and vast differences in cell growth rates across a large variety of biomaterial designs. This study attempts to clarify the definitions of drug response measurements used in the field, and presents examples in which these measurements can and cannot be applied. We suggest as best practice to measure the growth rate of cells in the absence of drug, and follow our 'decision tree' when reporting drug response metrics. This article is part of a discussion meeting issue 'Forces in cancer: interdisciplinary approaches in tumour mechanobiology'.

A Pharmacogenomic Landscape in Human Liver Cancers

Liver cancers are highly heterogeneous with poor prognosis and drug response. A better understanding between genetic alterations and drug responses would facilitate precision treatment for liver cancers. To characterize the landscape of pharmacogenomic interactions in liver cancers, we developed a protocol to establish human liver cancer cell models at a success rate of around 50% and generated the Liver Cancer Model Repository (LIMORE) with 81 cell models. LIMORE represented genomic and transcriptomic heterogeneity of primary cancers. Interrogation of the pharmacogenomic landscape of LIMORE discovered unexplored gene-drug associations, including synthetic lethalities to prevalent alterations in liver cancers. Moreover, predictive biomarker candidates were suggested for the selection of sorafenib-responding patients. LIMORE provides a rich resource facilitating drug discovery in liver cancers.

Abemaciclib Is Effective Against Pancreatic Cancer Cells and Synergizes with HuR and YAP1 Inhibition

Mutation or promoter hypermethylation of CDKN2A is found in over 90% of pancreatic ductal adenocarcinomas (PDAC) and leads to loss of function of cell-cycle inhibitors p16 (INK4A) and p14 (ARF) resulting in unchecked proliferation. The CDK4/6 inhibitor, abemaciclib, has nanomolar IC₅₀s in PDAC cell lines and decreases growth through inhibition of phospho-Rb (pRb), G₁ cell-cycle arrest, apoptosis, and the senescent phenotype detected with β-galactosidase staining and relevant mRNA elevations. Daily abemaciclib treatments in mouse PDAC xenograft studies were safe and demonstrated a 3.2-fold decrease in tumor volume compared with no treatment (P < 0.0001) accompanying a decrease in both pRb and Ki67. We determined that inhibitors of HuR (ELAVL1), a prosurvival mRNA stability factor that regulates cyclin D1, and an inhibitor of Yes-Associated Protein 1 (YAP1), a pro-oncogenic, transcriptional coactivator important for CDK6 and cyclin D1, were both synergistic with abemaciclib. Accordingly, siRNA oligonucleotides targeted against HuR, YAP1, and their common target cyclin D1, validated the synergy studies. In addition, we have seen increased sensitivity to abemaciclib in a PDAC cell line that harbors a loss of the ELAVL1 gene via CRISP-Cas9 technology. As an in vitro model for resistance, we investigated the effects of long-term abemaciclib exposure. PDAC cells chronically cultured with abemaciclib displayed a reduction in cellular growth rates (GR) and coresistance to gemcitabine and 5-fluorouracil (5-FU), but not to HuR or YAP1 inhibitors as compared with no treatment controls. We believe that our data provide compelling preclinical evidence for an abemaciclib combination-based clinical trial in patients with PDAC. IMPLICATIONS: Our data suggest that abemaciclib may be therapeutically relevant for the treatment in PDAC, especially as part of a combination regimen inhibiting YAP1 or HuR.

PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies

Recent cancer pharmacogenomic studies profiled large panels of cell lines against hundreds of approved drugs and experimental chemical compounds. The overarching goal of these screens is to measure sensitivity of cell lines to chemical perturbations, correlate these measures to genomic features, and thereby develop novel predictors of drug response. However, leveraging these valuable data is challenging due to the lack of standards for annotating cell lines and chemical compounds, and quantifying drug response. Moreover, it has been recently shown that the complexity and complementarity of the experimental protocols used in the field result in high levels of technical and biological variation in the in vitro pharmacological profiles. There is therefore a need for new tools to facilitate rigorous comparison and integrative analysis of large-scale drug screening datasets. To address this issue, we have developed PharmacoDB (pharmacodb.pmgenomics.ca), a database integrating the largest cancer pharmacogenomic studies published to date. Here, we describe how the curation of cell line and chemical compound identifiers maximizes the overlap between datasets and how users can leverage such data to compare and extract robust drug phenotypes. PharmacoDB provides a unique resource to mine a compendium of curated cancer pharmacogenomic datasets that are otherwise disparate and difficult to integrate.

Lower Tubulin Expression in Glioblastoma Stem Cells Attenuates Efficacy of Microtubule-Targeting Agents

Sensitivity to microtubule-targeting agents (MTAs) varies among cancers and predicting the response of individual cancer patients to MTAs remains challenging. As microtubules possess vast molecular heterogeneity generated by tubulin isotypes and their post-translational modifications, we questioned whether this heterogeneity can impact MTA sensitivity. We investigated microtubule heterogeneity in 15 glioblastoma cell lines and measured sensitivity of orthogonal MTAs using a per-division growth rate inhibition method that corrects for the confounding effects of variable cell proliferation rates. We found that the tubulin profile is unique for each glioblastoma cell line and that the total α- and β-tubulin levels impact on MTA sensitivity. The baseline levels of α- and β-tubulin were up to 20% lower in cells that were not effectively killed by MTAs. We report that lower α/β-tubulin expression is associated with lack of cell differentiation and increased expression of stemness markers. The dedifferentiated stem-like cells with low α/β-tubulin levels survive MTAs treatment via reversible nonmutational dormancy. Our findings provide novel insights into the relationships between microtubules and MTAs and lay a foundation for better understanding of the sensitivity of cancer cells to MTAs.

Common Variants in the Glycerol Kinase Gene Reduce Tuberculosis Drug Efficacy

Despite the administration of multiple drugs that are highly effective in vitro, tuberculosis (TB) treatment requires prolonged drug administration and is confounded by the emergence of drug-resistant strains. To understand the mechanisms that limit antibiotic efficacy, we performed a comprehensive genetic study to identify Mycobacterium tuberculosis genes that alter the rate of bacterial clearance in drug-treated mice. Several functionally distinct bacterial genes were found to alter bacterial clearance, and prominent among these was the glpK gene that encodes the glycerol-3-kinase enzyme that is necessary for glycerol catabolism. Growth on glycerol generally increased the sensitivity of M. tuberculosis to antibiotics in vitro, and glpK-deficient bacteria persisted during antibiotic treatment in vivo, particularly during exposure to pyrazinamide-containing regimens. Frameshift mutations in a hypervariable homopolymeric region of the glpK gene were found to be a specific marker of multidrug resistance in clinical M. tuberculosis isolates, and these loss-of-function alleles were also enriched in extensively drug-resistant clones. These data indicate that frequently observed variation in the glpK coding sequence produces a drug-tolerant phenotype that can reduce antibiotic efficacy and may contribute to the evolution of resistance.IMPORTANCE TB control is limited in part by the length of antibiotic treatment needed to prevent recurrent disease. To probe mechanisms underlying survival under antibiotic pressure, we performed a genetic screen for M. tuberculosis mutants with altered susceptibility to treatment using the mouse model of TB. We identified multiple genes involved in a range of functions which alter sensitivity to antibiotics. In particular, we found glycerol catabolism mutants were less susceptible to treatment and that common variation in a homopolymeric region in the glpK gene was associated with drug resistance in clinical isolates. These studies indicate that reversible high-frequency variation in carbon metabolic pathways can produce phenotypically drug-tolerant clones and have a role in the development of resistance.

A Novel Polyhalogenated Monoterpene Induces Cell Cycle Arrest and Apoptosis in Breast Cancer Cells

Breast cancer is the most common cancer type and a primary cause of cancer mortality among females worldwide. Here, we analyzed the anticancer efficacy of a novel bromochlorinated monoterpene, PPM1, a synthetic analogue of polyhalogenated monoterpenes from Plocamium red algae and structurally similar non-brominated monoterpenes. PPM1, but not the non-brominated monoterpenes, decreased selectively the viability of several triple-negative as well as triple-positive breast cancer cells with different p53 status without significantly affecting normal breast epithelial cells. PPM1 induced accumulation of triple-negative MDA-MB-231 cells with 4N DNA content characterized by decreased histone H3-S10/T3 phosphorylation indicating cell cycle arrest in the G₂ phase. Western immunoblot analysis revealed that PPM1 treatment triggered an initial rapid activation of Aurora kinases A/B/C and p21^Waf1/Cip1 accumulation, which was followed by accumulation of polyploid >4N cells. Flow cytometric analysis showed mitochondrial potential disruption, caspase 3/7 activation, phosphatidylserine externalization, reduction of the amount polyploid cells, and DNA fragmentation consistent with induction of apoptosis. Cell viability was partially restored by the pan-caspase inhibitor Z-VAD-FMK indicating caspase contribution. In vivo, PPM1 inhibited growth, proliferation, and induced apoptosis in MDA-MB-231 xenografted onto the chick chorioallantoic membrane. Hence, Plocamium polyhalogenated monoterpenes and synthetic analogues deserve further exploration as promising anticancer lead compounds.

Dramatic response of BRAF V600E-mutant epithelioid glioblastoma to combination therapy with BRAF and MEK inhibitor: establishment and xenograft of a cell line to predict clinical efficacy

Epithelioid glioblastoma is a rare aggressive variant of glioblastoma (GBM) characterized by a dismal prognosis of about 6 months and frequent leptomeningeal dissemination. A recent study has revealed that 50% of epithelioid GBMs harbor three genetic alterations - BRAF V600E mutation, TERT promoter mutations, and homozygous deletions of CDKN2A/2B. Emerging evidence support the effectiveness of targeted therapies for brain tumors with BRAF V600E mutation. Here we describe a dramatic radiographical response to combined therapy with BRAF and MEK inhibitors in a patient with epithelioid GBM harboring BRAF V600E mutation, characterized by thick spinal dissemination. From relapsed tumor procured at autopsy, we established a cell line retaining the BRAF V600E mutation, TERT promoter mutation and CDKN2A/2B loss. Intracranial implantation of these cells into mice resulted in tumors closely resembling the original, characterized by epithelioid tumor cells and dissemination, and invasion into the perivascular spaces. We then confirmed the efficacy of treatment with BRAF and MEK inhibitor both in vitro and in vivo. Epithelioid GBM with BRAF V600E mutation can be considered a good treatment indication for precision medicine, and this patient-derived cell line should be useful for prediction of the tumor response and clarification of its biological characteristics.

A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines

Evidence that some high-impact biomedical results cannot be repeated has stimulated interest in practices that generate findable, accessible, interoperable, and reusable (FAIR) data. Multiple papers have identified specific examples of irreproducibility, but practical ways to make data more reproducible have not been widely studied. Here, five research centers in the NIH LINCS Program Consortium investigate the reproducibility of a prototypical perturbational assay: quantifying the responsiveness of cultured cells to anti-cancer drugs. Such assays are important for drug development, studying cellular networks, and patient stratification. While many experimental and computational factors impact intra- and inter-center reproducibility, the factors most difficult to identify and control are those with a strong dependency on biological context. These factors often vary in magnitude with the drug being analyzed and with growth conditions. We provide ways to identify such context-sensitive factors, thereby improving both the theory and practice of reproducible cell-based assays.

Factors that impact the reproducibility of experimental data are poorly understood. Five NIH-LINCS centers performed the same set of drug-response measurements and compared results. Technical and biological variables that impact precision and reproducibility and are also sensitive to biological context were the most problematic.

Interplay of Darwinian Selection, Lamarckian Induction and Microvesicle Transfer on Drug Resistance in Cancer

Development of drug resistance in cancer has major implications for patients’ outcome. It is related to processes involved in the decrease of drug efficacy, which are strongly influenced by intratumor heterogeneity and changes in the microenvironment. Heterogeneity arises, to a large extent, from genetic mutations analogously to Darwinian evolution, when selection of tumor cells results from the adaptation to the microenvironment, but could also emerge as a consequence of epigenetic mutations driven by stochastic events. An important exogenous source of alterations is the action of chemotherapeutic agents, which not only affects the signalling pathways but also the interactions among cells. In this work we provide experimental evidence from in vitro assays and put forward a mathematical kinetic transport model to describe the dynamics displayed by a system of non-small-cell lung carcinoma cells (NCI-H460) which, depending on the effect of a chemotherapeutic agent (doxorubicin), exhibits a complex interplay between Darwinian selection, Lamarckian induction and the nonlocal transfer of extracellular microvesicles. The role played by all of these processes to multidrug resistance in cancer is elucidated and quantified.

Synthesis and Enhanced Cellular Uptake In Vitro of Anti-HER2 Multifunctional Gold Nanoparticles

Nanoparticle carriers offer the possibility of enhanced delivery of therapeutic payloads in tumor tissues due to tumor-selective accumulation through the enhanced permeability and retention effect (EPR). Gold nanoparticles (AuNP), in particular, possess highly appealing features for development as nanomedicines, such as biocompatibility, tunable optical properties and a remarkable ease of surface functionalization. Taking advantage of the latter, several strategies have been designed to increase treatment specificity of gold nanocarriers by attaching monoclonal antibodies on the surface, as a way to promote selective interactions with the targeted cells—an approach referred to as active-targeting. Here, we describe the synthesis of spherical gold nanoparticles surface-functionalized with an anti-HER2 antibody-drug conjugate (ADC) as an active targeting agent that carries a cytotoxic payload. In addition, we enhanced the intracellular delivery properties of the carrier by attaching a cell penetrating peptide to the active-targeted nanoparticles. We demonstrate that the antibody retains high receptor-affinity after the structural modifications performed for drug-conjugation and nanoparticle attachment. Furthermore, we show that antibody attachment increases cellular uptake in HER2 amplified cell lines selectively, and incorporation of the cell penetrating peptide leads to a further increase in cellular internalization. Nanoparticle-bound antibody-drug conjugates retain high antimitotic potency, which could contribute to a higher therapeutic index in high EPR tumors.

Multiomics Profiling Establishes the Polypharmacology of FDA-Approved CDK4/6 Inhibitors and the Potential for Differential Clinical Activity

The target profiles of many drugs are established early in their development and are not systematically revisited at the time of FDA approval. Thus, it is often unclear whether therapeutics with the same nominal targets but different chemical structures are functionally equivalent. In this paper we use five different phenotypic and biochemical assays to compare approved inhibitors of cyclin-dependent kinases 4/6-collectively regarded as breakthroughs in the treatment of hormone receptor-positive breast cancer. We find that transcriptional, proteomic, and phenotypic changes induced by palbociclib, ribociclib, and abemaciclib differ significantly; abemaciclib in particular has advantageous activities partially overlapping those of alvocidib, an older polyselective CDK inhibitor. In cells and mice, abemaciclib inhibits kinases other than CDK4/6 including CDK2/cyclin A/E-implicated in resistance to CDK4/6 inhibition-and CDK1/cyclin B. The multifaceted experimental and computational approaches described here therefore uncover underappreciated differences in CDK4/6 inhibitor activities with potential importance in treating human patients.

An omentum-inspired 3D PEG hydrogel for identifying ECM-drivers of drug resistant ovarian cancer

Ovarian cancer (OvCa) is a challenging disease to treat due to poor screening techniques and late diagnosis. There is an urgent need for additional therapy options, as patients recur in 70% of cases. The limited availability of clinical treatment options could be a result of poor predictions in early stage drug screens on standard tissue culture polystyrene (TCPS). TCPS does not capture the mechanical and biochemical cues that cells experience in vivo, which can impact how cells will respond to a drug. Therefore, an in vitro model that captures some of the microenvironment features that the cells experience in vivo could provide better insights into drug responses. In this study, we formed 3D multicellular tumor spheroids (MCTS) in microwells and encapsulated them in 3D omentum-inspired hydrogels. SKOV-3 MCTS were resistant to Paclitaxel in our 3D hydrogels compared to a monolayer on TCPS. Toward clinical application, we tested cells from patients [ovarian carcinoma ascites spheroids (OCAS)] who had been treated with Paclitaxel, and drug responses predicted by using the 3D omentum-inspired hydrogels demonstrated the lack of the Paclitaxel response of these samples. Additionally, we observed the presence of collagen production around the encapsulated SKOV-3 MCTS, but not significantly on TCPS. Our results demonstrated that our 3D omentum-inspired hydrogel is an improved in vitro drug testing platform to study the OvCa drug response for patient-derived cells and helped us identify collagen 3 as a potential driver of Paclitaxel resistance in 3D.

A promiscuous kinase inhibitor reveals secrets to cancer cell survival

Deregulated kinase signaling networks drive the growth and survival of many cancer cells. However, the genetic complexity and rapidly evolving nature of most cancer cells create challenges when identifying the most relevant kinases to inhibit to achieve optimal therapeutic benefits. A new strategy that takes advantage of a well-characterized multitargeted kinase inhibitor describes a nongenetic approach to tease out key kinases that promote proliferation of specific cancer cell types.

Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric

Treatment response assays are often summarized by sigmoidal functions comparing cell survival at a single timepoint to applied drug concentration. This approach has a limited biophysical basis, thereby reducing the biological insight gained from such analysis. In particular, drug pharmacokinetic and pharmacodynamic (PK/PD) properties are overlooked in developing treatment response assays, and the accompanying summary statistics conflate these processes. Here, we utilize mathematical modeling to decouple and quantify PK/PD pathways. We experimentally modulate specific pathways with small molecule inhibitors and filter the results with mechanistic mathematical models to obtain quantitative measures of those pathways. Specifically, we investigate the response of cells to time-varying doxorubicin treatments, modulating doxorubicin pharmacology with small molecules that inhibit doxorubicin efflux from cells and DNA repair pathways. We highlight the practical utility of this approach through proposal of the “equivalent dose metric.” This metric, derived from a mechanistic PK/PD model, provides a biophysically-based measure of drug effect. We define equivalent dose as the functional concentration of drug that is bound to the nucleus following therapy. This metric can be used to quantify drivers of treatment response and potentially guide dosing of combination therapies. We leverage the equivalent dose metric to quantify the specific intracellular effects of these small molecule inhibitors using population-scale measurements, and to compare treatment response in cell lines differing in expression of drug efflux pumps. More generally, this approach can be leveraged to quantify the effects of various pharmaceutical and biologic perturbations on treatment response.

Identification of a mechanogenetic link between substrate stiffness and chemotherapeutic response in breast cancer

Mechanical feedback from the tumor microenvironment regulates an array of processes underlying cancer biology. For example, increased stiffness of mammary extracellular matrix (ECM) drives malignancy and alters the phenotypes of breast cancer cells. Despite this link, the role of substrate stiffness in chemotherapeutic response in breast cancer remains unclear. This is complicated by routine culture and adaptation of cancer cell lines to unnaturally rigid plastic or glass substrates, leading to profound changes in their growth, metastatic potential and, as we show here, chemotherapeutic response. We demonstrate that primary breast cancer cells undergo dramatic phenotypic changes when removed from the host microenvironment and cultured on rigid surfaces, and that drug responses are profoundly altered by the mechanical feedback cells receive from the culture substrate. Conversely, primary breast cancer cells cultured on substrates mimicking the mechanics of their host tumor ECM have a similar genetic profile to the in situ cells with respect to drug activity and resistance pathways. These results suggest substrate stiffness plays a significant role in susceptibility of breast cancer to clinically-approved chemotherapeutics, and presents an opportunity to improve drug discovery efforts by integrating mechanical rigidity as a parameter in screening campaigns.

CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer

Bioinformatics techniques to analyze time course bulk and single cell omics data are advancing. The absence of a known ground truth of the dynamics of molecular changes challenges benchmarking their performance on real data. Realistic simulated time-course datasets are essential to assess the performance of time course bioinformatics algorithms. We develop an R/Bioconductor package, CancerInSilico, to simulate bulk and single cell transcriptional data from a known ground truth obtained from mathematical models of cellular systems. This package contains a general R infrastructure for running cell-based models and simulating gene expression data based on the model states. We show how to use this package to simulate a gene expression data set and consequently benchmark analysis methods on this data set with a known ground truth. The package is freely available via Bioconductor: http://bioconductor.org/packages/CancerInSilico/

BRB-seq: ultra-affordable high-throughput transcriptomics enabled by bulk RNA barcoding and sequencing

Despite its widespread use, RNA-seq is still too laborious and expensive to replace RT-qPCR as the default gene expression analysis method. We present a novel approach, BRB-seq, which uses early multiplexing to produce 3' cDNA libraries for dozens of samples, requiring just 2 hours of hands-on time. BRB-seq has a comparable performance to the standard TruSeq approach while showing greater tolerance for lower RNA quality and being up to 25 times cheaper. We anticipate that BRB-seq will transform basic laboratory practice given its capacity to generate genome-wide transcriptomic data at a similar cost as profiling four genes using RT-qPCR.

BET bromodomain inhibition blocks the function of a critical AR-independent master regulator network in lethal prostate cancer

BET bromodomain inhibitors block prostate cancer cell growth at least in part through c-Myc and androgen receptor (AR) suppression. However, little is known about other transcriptional regulators whose suppression contributes to BET bromodomain inhibitor anti-tumor activity. Moreover, the anti-tumor activity of BET bromodomain inhibition in AR-independent castration-resistant prostate cancers (CRPC), whose frequency is increasing, is also unknown. Herein, we demonstrate that BET bromodomain inhibition blocks growth of a diverse set of CRPC cell models, including those that are AR-independent or in which c-Myc is not suppressed. To identify transcriptional regulators whose suppression accounts for these effects, we treated multiple CRPC cell lines with the BET bromodomain inhibitor JQ1 and then performed RNA-sequencing followed by Master Regulator computational analysis. This approach identified several previously unappreciated transcriptional regulators that are highly expressed in CRPC and whose suppression, via both transcriptional or post-translational mechanisms, contributes to the anti-tumor activity of BET bromodomain inhibitors.

A Nonquiescent "Idling" Population State in Drug-Treated, BRAF-Mutated Melanoma

Targeted therapy is an effective standard of care in BRAF-mutated malignant melanoma. However, the duration of tumor remission varies unpredictably among patients, and relapse is almost inevitable. Here, we examine the responses of several BRAF-mutated melanoma cell lines (including isogenic subclones) to BRAF inhibitors. We observe complex response dynamics across cell lines, with short-term responses (<100 h) varying from cell line to cell line. In the long term, however, we observe equilibration of all drug-treated populations into a nonquiescent state characterized by a balanced rate of death and division, which we term the "idling" state, and to our knowledge, this state has not been previously reported. Using mathematical modeling, we propose that the observed population-level dynamics are the result of cells transitioning between basins of attraction within a drug-modified phenotypic landscape. Each basin is associated with a drug-induced proliferation rate, a recently introduced metric of an antiproliferative drug effect. The idling population state represents a new dynamic equilibrium in which cells are distributed across the landscape such that the population achieves zero net growth. By fitting our model to experimental drug-response data, we infer the phenotypic landscapes of all considered melanoma cell lines and provide a unifying view of how BRAF-mutated melanomas respond to BRAF inhibition. We hypothesize that the residual disease observed in patients after targeted therapy is composed of a significant number of idling cells. Thus, defining molecular determinants of the phenotypic landscape that idling populations occupy may lead to "targeted landscaping" therapies based on rational modification of the landscape to favor basins with greater drug susceptibility.

Perivascular signals alter global gene expression profile of glioblastoma and response to temozolomide in a gelatin hydrogel

Glioblastoma (GBM) is the most common primary malignant brain tumor, with patients exhibiting poor survival (median survival time: 15 months). Difficulties in treating GBM include not only the inability to resect the diffusively-invading tumor cells, but also therapeutic resistance. The perivascular niche (PVN) within the GBM tumor microenvironment contributes significantly to tumor cell invasion, cancer stem cell maintenance, and has been shown to protect tumor cells from radiation and chemotherapy. In this study, we examine how the inclusion of non-tumor cells in culture with tumor cells within a hydrogel impacts the overall gene expression profile of an in vitro artificial perivascular niche (PVN) comprised of endothelial and stromal cells directly cultured with GBM tumor cells within a methacrylamide-functionalized gelatin hydrogel. Using RNA-seq, we demonstrate that genes related to angiogenesis and extracellular matrix remodeling are upregulated in the PVN model compared to hydrogels containing only tumor or perivascular niche cells, while downregulated genes are related to cell cycle and DNA damage repair. Signaling pathways and genes commonly implicated in GBM malignancy, such as MGMT, EGFR, PI3K-Akt signaling, and Ras/MAPK signaling are also upregulated in the PVN model. We describe the kinetics of gene expression within the PVN hydrogels over a course of 14 days, observing the patterns associated with tumor cell-mediated endothelial network co-option and regression. We finally examine the effect of temozolomide, a frontline chemotherapy used clinically against GBM, on the PVN culture. Notably, the PVN model is less responsive to TMZ compared to hydrogels containing only tumor cells. Overall, these results demonstrate that inclusion of cellular and matrix-associated elements of the PVN within an in vitro model of GBM allows for the development of gene expression patterns and therapeutic response relevant to GBM.

Development of a Selective CDK7 Covalent Inhibitor Reveals Predominant Cell-Cycle Phenotype

Cyclin-dependent kinase 7 (CDK7) regulates both cell cycle and transcription, but its precise role remains elusive. We previously described THZ1, a CDK7 inhibitor, which dramatically inhibits superenhancer-associated gene expression. However, potent CDK12/13 off-target activity obscured CDK7s contribution to this phenotype. Here, we describe the discovery of a highly selective covalent CDK7 inhibitor. YKL-5-124 causes arrest at the G₁/S transition and inhibition of E2F-driven gene expression; these effects are rescued by a CDK7 mutant unable to covalently engage YKL-5-124, demonstrating on-target specificity. Unlike THZ1, treatment with YKL-5-124 resulted in no change to RNA polymerase II C-terminal domain phosphorylation; however, inhibition could be reconstituted by combining YKL-5-124 and THZ531, a selective CDK12/13 inhibitor, revealing potential redundancies in CDK control of gene transcription. These findings highlight the importance of CDK7/12/13 polypharmacology for anti-cancer activity of THZ1 and posit that selective inhibition of CDK7 may be useful for treatment of cancers marked by E2F misregulation.

A multitargeted probe-based strategy to identify signaling vulnerabilities in cancers

Most cancer cells are dependent on a network of deregulated signaling pathways for survival and are insensitive, or rapidly evolve resistance, to selective inhibitors aimed at a single target. For these reasons, drugs that target more than one protein (polypharmacology) can be clinically advantageous. The discovery of useful polypharmacology remains serendipitous and is challenging to characterize and validate. In this study, we developed a non-genetic strategy for the identification of pathways that drive cancer cell proliferation and represent exploitable signaling vulnerabilities. Our approach is based on using a multitargeted kinase inhibitor, SM1-71, as a tool compound to identify combinations of targets whose simultaneous inhibition elicits a potent cytotoxic effect. As a proof of concept, we applied this approach to a KRAS-dependent non-small cell lung cancer (NSCLC) cell line, H23-KRAS^G12C Using a combination of phenotypic screens, signaling analyses, and kinase inhibitors, we found that dual inhibition of MEK1/2 and insulin-like growth factor 1 receptor (IGF1R)/insulin receptor (INSR) is critical for blocking proliferation in cells. Our work supports the value of multitargeted tool compounds with well-validated polypharmacology and target space as tools to discover kinase dependences in cancer. We propose that the strategy described here is complementary to existing genetics-based approaches, generalizable to other systems, and enabling for future mechanistic and translational studies of polypharmacology in the context of signaling vulnerabilities in cancers.

Maintenance of MYC expression promotes de novo resistance to BET bromodomain inhibition in castration-resistant prostate cancer

The BET bromodomain protein BRD4 is a chromatin reader that regulates transcription, including in cancer. In prostate cancer, specifically, the anti-tumor activity of BET bromodomain inhibition has been principally linked to suppression of androgen receptor (AR) function. MYC is a well-described BRD4 target gene in multiple cancer types, and prior work demonstrates that MYC plays an important role in promoting prostate cancer cell survival. Importantly, several BET bromodomain clinical trials are ongoing, including in prostate cancer. However, there is limited information about pharmacodynamic markers of response or mediators of de novo resistance. Using a panel of prostate cancer cell lines, we demonstrated that MYC suppression-rather than AR suppression-is a key determinant of BET bromodomain inhibitor sensitivity. Importantly, we determined that BRD4 was dispensable for MYC expression in the most resistant cell lines and that MYC RNAi + BET bromodomain inhibition led to additive anti-tumor activity in the most resistant cell lines. Our findings demonstrate that MYC suppression is an important pharmacodynamic marker of BET bromodomain inhibitor response and suggest that targeting MYC may be a promising therapeutic strategy to overcome de novo BET bromodomain inhibitor resistance in prostate cancer.

CHCHD4 confers metabolic vulnerabilities to tumour cells through its control of the mitochondrial respiratory chain

Background

Tumour cells rely on glycolysis and mitochondrial oxidative phosphorylation (OXPHOS) to survive. Thus, mitochondrial OXPHOS has become an increasingly attractive area for therapeutic exploitation in cancer. However, mitochondria are required for intracellular oxygenation and normal physiological processes, and it remains unclear which mitochondrial molecular mechanisms might provide therapeutic benefit. Previously, we discovered that coiled-coil-helix-coiled-coil-helix domain-containing protein 4 (CHCHD4) is critical for regulating intracellular oxygenation and required for the cellular response to hypoxia (low oxygenation) in tumour cells through molecular mechanisms that we do not yet fully understand. Overexpression of CHCHD4 in human cancers correlates with increased tumour progression and poor patient survival.

Results

Here, we show that elevated CHCHD4 expression provides a proliferative and metabolic advantage to tumour cells in normoxia and hypoxia. Using stable isotope labelling with amino acids in cell culture (SILAC) and analysis of the whole mitochondrial proteome, we show that CHCHD4 dynamically affects the expression of a broad range of mitochondrial respiratory chain subunits from complex I-V, including multiple subunits of complex I (CI) required for complex assembly that are essential for cell survival. We found that loss of CHCHD4 protects tumour cells from respiratory chain inhibition at CI, while elevated CHCHD4 expression in tumour cells leads to significantly increased sensitivity to CI inhibition, in part through the production of mitochondrial reactive oxygen species (ROS).

Conclusions

Our study highlights an important role for CHCHD4 in regulating tumour cell metabolism and reveals that CHCHD4 confers metabolic vulnerabilities to tumour cells through its control of the mitochondrial respiratory chain and CI biology.

Genome-scale network model of metabolism and histone acetylation reveals metabolic dependencies of histone deacetylase inhibitors

Histone acetylation plays a central role in gene regulation and is sensitive to the levels of metabolic intermediates. However, predicting the impact of metabolic alterations on acetylation in pathological conditions is a significant challenge. Here, we present a genome-scale network model that predicts the impact of nutritional environment and genetic alterations on histone acetylation. It identifies cell types that are sensitive to histone deacetylase inhibitors based on their metabolic state, and we validate metabolites that alter drug sensitivity. Our model provides a mechanistic framework for predicting how metabolic perturbations contribute to epigenetic changes and sensitivity to deacetylase inhibitors.

Tensor clustering with algebraic constraints gives interpretable groups of crosstalk mechanisms in breast cancer

We introduce a tensor-based clustering method to extract sparse, low-dimensional structure from high-dimensional, multi-indexed datasets. This framework is designed to enable detection of clusters of data in the presence of structural requirements which we encode as algebraic constraints in a linear program. Our clustering method is general and can be tailored to a variety of applications in science and industry. We illustrate our method on a collection of experiments measuring the response of genetically diverse breast cancer cell lines to an array of ligands. Each experiment consists of a cell line–ligand combination, and contains time-course measurements of the early signalling kinases MAPK and AKT at two different ligand dose levels. By imposing appropriate structural constraints and respecting the multi-indexed structure of the data, the analysis of clusters can be optimized for biological interpretation and therapeutic understanding. We then perform a systematic, large-scale exploration of mechanistic models of MAPK–AKT crosstalk for each cluster. This analysis allows us to quantify the heterogeneity of breast cancer cell subtypes, and leads to hypotheses about the signalling mechanisms that mediate the response of the cell lines to ligands.

Quantifying Drug Combination Synergy along Potency and Efficacy Axes

Two goals motivate treating diseases with drug combinations: reduce off-target toxicity by minimizing doses (synergistic potency) and improve outcomes by escalating effect (synergistic efficacy). Established drug synergy frameworks obscure such distinction, failing to harness the potential of modern chemical libraries. We therefore developed multi-dimensional synergy of combinations (MuSyC), a formalism based on a generalized, multi-dimensional Hill equation, which decouples synergistic potency and efficacy. In mutant-EGFR-driven lung cancer, MuSyC reveals that combining a mutant-EGFR inhibitor with inhibitors of other kinases may result only in synergistic potency, whereas synergistic efficacy can be achieved by co-targeting mutant-EGFR and epigenetic regulation or microtubule polymerization. In mutant-BRAF melanoma, MuSyC determines whether a molecular correlate of BRAFi insensitivity alters a BRAF inhibitor's potency, efficacy, or both. These findings showcase MuSyC's potential to transform the enterprise of drug-combination screens by precisely guiding translation of combinations toward dose reduction, improved efficacy, or both.

High content screening identifies monensin as an EMT-selective cytotoxic compound

Epithelial-to-mesenchymal transition (EMT) is implicated in cancer metastasis and drug resistance. Specifically targeting cancer cells in an EMT-like state may have therapeutic value. In this study, we developed a cell imaging-based high-content screening protocol to identify EMT-selective cytotoxic compounds. Among the 2,640 compounds tested, salinomycin and monensin, both monovalent cation ionophores, displayed a potent and selective cytotoxic effect against EMT-like cells. The mechanism of action of monensin was further evaluated. Monensin (10 nM) induced apoptosis, cell cycle arrest, and an increase in reactive oxygen species (ROS) production in TEM 4-18 cells. In addition, monensin rapidly induced swelling of Golgi apparatus and perturbed mitochondrial function. These are previously known effects of monensin, albeit occurring at much higher concentrations in the micromolar range. The cytotoxic effect of monensin was not blocked by inhibitors of ferroptosis. To explore the generality of our findings, we evaluated the toxicity of monensin in 24 human cancer cell lines and classified them as resistant or sensitive based on IC₅₀ cutoff of 100 nM. Gene Set Enrichment Analysis identified EMT as the top enriched gene set in the sensitive group. Importantly, increased monensin sensitivity in EMT-like cells is associated with elevated uptake of ³H-monensin compared to resistant cells.

DRAP: a toolbox for drug response analysis and visualization tailored for preclinical drug testing on patient-derived xenograft models

Background

One of the key reasons for the high failure rate of new agents and low therapeutic benefit of approved treatments is the lack of preclinical models that mirror the biology of human tumors. At present, the optimal cancer model for drug response study to date is patient-derived xenograft (PDX) models. PDX recaptures both inter- and intra-tumor heterogeneity inherent in human cancer, which represent a valuable platform for preclinical drug testing and personalized medicine applications. Building efficient drug response analysis tools is critical but far from adequate for the PDX platform.

Results

In this work, we first classified the emerging PDX preclinical trial designs into four patterns based on the number of tumors, arms, and animal repeats in every arm. Then we developed an R package, DRAP, which implements Drug Response Analyses on PDX platform separately for the four patterns, involving data visualization, data analysis and conclusion presentation. The data analysis module offers statistical analysis methods to assess difference of tumor volume between arms, tumor growth inhibition (TGI) rate calculation to quantify drug response, and drug response level analysis to label the drug response at animal level. In the end, we applied DRAP in two case studies through which the functions and usage of DRAP were illustrated.

Conclusion

DRAP is the first integrated toolbox for drug response analysis and visualization tailored for PDX platform. It would greatly promote the application of PDXs in drug development and personalized cancer treatments.

Electronic supplementary material

The online version of this article (10.1186/s12967-019-1785-7) contains supplementary material, which is available to authorized users.

Computational Analyses Connect Small-Molecule Sensitivity to Cellular Features Using Large Panels of Cancer Cell Lines

We recently pioneered several analyses of small-molecule sensitivity data collected from large-scale perturbation of hundreds of cancer cell lines with hundreds of small molecules, with cell viability measured as a readout of compound sensitivity. We performed these studies using cancer cell lines previously annotated with cellular, genomic, and basal gene-expression features. By combining small-molecule sensitivity data with these other datasets, we identified new candidate biomarkers of sensitivity, gained insights into small-molecule mechanisms of action, and proposed candidate hypotheses for cancer dependencies (including candidate combination therapies). Nevertheless, given the size of these datasets, we expect that many connections between cellular features and small-molecule sensitivity remain underexplored. In this chapter, we provide a step-by-step account of foundational data-analysis methods underlying our published studies, including working MATLAB code applied to our own public datasets. These procedures will allow others to repeat analyses of our data with new parameters, in additional contexts, and to adapt our procedures to their own datasets.

PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction

Cancer is a complex collection of diseases that are to some degree unique to each patient. Precision oncology aims to identify the best drug treatment regime using molecular data on tumor samples. While omics-level data is becoming more widely available for tumor specimens, the datasets upon which computational learning methods can be trained vary in coverage from sample to sample and from data type to data type. Methods that can 'connect the dots' to leverage more of the information provided by these studies could offer major advantages for maximizing predictive potential. We introduce a multi-view machinelearning strategy called PLATYPUS that builds 'views' from multiple data sources that are all used as features for predicting patient outcomes. We show that a learning strategy that finds agreement across the views on unlabeled data increases the performance of the learning methods over any single view. We illustrate the power of the approach by deriving signatures for drug sensitivity in a large cancer cell line database. Code and additional information are available from the PLATYPUS website https://sysbiowiki.soe.ucsc.edu/platypus.