Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties

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

LIM kinase inhibitors disrupt mitotic microtubule organization and impair tumor cell proliferation

The actin and microtubule cytoskeletons are critically important for cancer cell proliferation, and drugs that target microtubules are widely-used cancer therapies. However, their utility is compromised by toxicities due to dose and exposure. To overcome these issues, we characterized how inhibition of the actin and microtubule cytoskeleton regulatory LIM kinases could be used in drug combinations to increase efficacy. A previously-described LIMK inhibitor (LIMKi) induced dose-dependent microtubule alterations that resulted in significant mitotic defects, and increased the cytotoxic potency of microtubule polymerization inhibitors. By combining LIMKi with 366 compounds from the GSK Published Kinase Inhibitor Set, effective combinations were identified with kinase inhibitors including EGFR, p38 and Raf. These findings encouraged a drug discovery effort that led to development of CRT0105446 and CRT0105950, which potently block LIMK1 and LIMK2 activity in vitro, and inhibit cofilin phosphorylation and increase αTubulin acetylation in cells. CRT0105446 and CRT0105950 were screened against 656 cancer cell lines, and rhabdomyosarcoma, neuroblastoma and kidney cancer cells were identified as significantly sensitive to both LIMK inhibitors. These large-scale screens have identified effective LIMK inhibitor drug combinations and sensitive cancer types. In addition, the LIMK inhibitory compounds CRT0105446 and CRT0105950 will enable further development of LIMK-targeted cancer therapy

Targeting Acid Ceramidase to Improve the Radiosensitivity of Rectal Cancer.

Previous work utilizing proteomic and immunohistochemical analyses has identified that high levels of acid ceramidase (AC) expression confers a poorer response to neoadjuvant treatment in locally advanced rectal cancer. We aimed to assess the radiosensitising effect of biological and pharmacological manipulation of AC and elucidate the underlying mechanism. AC manipulation in three colorectal cancer cell lines (HT29, HCT116 and LIM1215) was achieved using siRNA and plasmid overexpression. Carmofur and a novel small molecular inhibitor (LCL521) were used as pharmacological AC inhibitors. Using clonogenic assays, we demonstrate that an siRNA knockdown of AC enhanced X-ray radiosensitivity across all colorectal cancer cell lines compared to a non-targeting control siRNA, and conversely, AC protein overexpression increased radioresistance. Using CRISPR gene editing, we also generated AC knockout HCT116 cells that were significantly more radiosensitive compared to AC-expressing cells. Similarly, two patient-derived organoid models containing relatively low AC expression were found to be comparatively more radiosensitive than three other models containing higher levels of AC. Additionally, AC inhibition using carmofur and LCL521 in three colorectal cancer cell lines increased cellular radiosensitivity. Decreased AC protein led to significant poly-ADP ribose polymerase-1 (PARP-1) cleavage and apoptosis post-irradiation, which was shown to be executed through a p53-dependent process. Our study demonstrates that expression of AC within colorectal cancer cell lines modulates the cellular response to radiation, and particularly that AC inhibition leads to significantly enhanced radiosensitivity through an elevation in apoptosis. This work further solidifies AC as a target for improving radiotherapy treatment of locally advanced rectal cancer

Combinations of PARP Inhibitors with Temozolomide Drive PARP1 Trapping and Apoptosis in Ewing's Sarcoma.

Ewing's sarcoma is a malignant pediatric bone tumor with a poor prognosis for patients with metastatic or recurrent disease. Ewing's sarcoma cells are acutely hypersensitive to poly (ADP-ribose) polymerase (PARP) inhibition and this is being evaluated in clinical trials, although the mechanism of hypersensitivity has not been directly addressed. PARP inhibitors have efficacy in tumors with BRCA1/2 mutations, which confer deficiency in DNA double-strand break (DSB) repair by homologous recombination (HR). This drives dependence on PARP1/2 due to their function in DNA single-strand break (SSB) repair. PARP inhibitors are also cytotoxic through inhibiting PARP1/2 auto-PARylation, blocking PARP1/2 release from substrate DNA. Here, we show that PARP inhibitor sensitivity in Ewing's sarcoma cells is not through an apparent defect in DNA repair by HR, but through hypersensitivity to trapped PARP1-DNA complexes. This drives accumulation of DNA damage during replication, ultimately leading to apoptosis. We also show that the activity of PARP inhibitors is potentiated by temozolomide in Ewing's sarcoma cells and is associated with enhanced trapping of PARP1-DNA complexes. Furthermore, through mining of large-scale drug sensitivity datasets, we identify a subset of glioma, neuroblastoma and melanoma cell lines as hypersensitive to the combination of temozolomide and PARP inhibition, potentially identifying new avenues for therapeutic intervention. These data provide insights into the anti-cancer activity of PARP inhibitors with implications for the design of treatment for Ewing's sarcoma patients with PARP inhibitors.Research in the M.J.G. laboratory is supported by grants from the Wellcome Trust (086357 and 102696/Z/13/Z; http://www.wellcome.ac.uk/Funding). Research in the S.P.J. laboratory is funded by Cancer Research UK Program Grant C6/A11224 (http://www.cancerresearchuk.org/funding-for-researchers/our-funding-schemes), the European Research Council (http://erc.europa.eu/funding-and-grants)and the European Community Seventh Framework Program grant agreement no. HEALTH-F2-2010-259893 (DDResponse). Core infrastructure funding was provided by Cancer Research UK Grant C6946/A14492 and Wellcome Trust Grant WT092096. S.P.J. receives a salary from the University of Cambridge, supplemented by Cancer Research UK. J.T. was funded by the European Community Seventh Framework Program grant agreement no. HEALTH-F2-2010-259893 (DDResponse). U.M. is supported by a Cancer Research UK Clinician Scientist Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final version of the article. It first appeared from PLOS via http://dx.doi.org/10.1371/journal.pone.014098

A Chromatin-Mediated Reversible Drug-Tolerant State in Cancer Cell Subpopulations

SummaryAccumulating evidence implicates heterogeneity within cancer cell populations in the response to stressful exposures, including drug treatments. While modeling the acute response to various anticancer agents in drug-sensitive human tumor cell lines, we consistently detected a small subpopulation of reversibly “drug-tolerant” cells. These cells demonstrate >100-fold reduced drug sensitivity and maintain viability via engagement of IGF-1 receptor signaling and an altered chromatin state that requires the histone demethylase RBP2/KDM5A/Jarid1A. This drug-tolerant phenotype is transiently acquired and relinquished at low frequency by individual cells within the population, implicating the dynamic regulation of phenotypic heterogeneity in drug tolerance. The drug-tolerant subpopulation can be selectively ablated by treatment with IGF-1 receptor inhibitors or chromatin-modifying agents, potentially yielding a therapeutic opportunity. Together, these findings suggest that cancer cell populations employ a dynamic survival strategy in which individual cells transiently assume a reversibly drug-tolerant state to protect the population from eradication by potentially lethal exposures.PaperCli

Genomics of Drug Sensitivity in Cancer (GDSC): a Resource for Therapeutic Biomarker Discovery in Cancer Cells

Alterations in cancer genomes strongly influence clinical responses to treatment and in many instances are potent biomarkers for response to drugs. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) is the largest public resource for information on drug sensitivity in cancer cells and molecular markers of drug response. Data are freely available without restriction. GDSC currently contains drug sensitivity data for almost 75 000 experiments, describing response to 138 anticancer drugs across almost 700 cancer cell lines. To identify molecular markers of drug response, cell line drug sensitivity data are integrated with large genomic datasets obtained from the Catalogue of Somatic Mutations in Cancer database, including information on somatic mutations in cancer genes, gene amplification and deletion, tissue type and transcriptional data. Analysis of GDSC data is through a web portal focused on identifying molecular biomarkers of drug sensitivity based on queries of specific anticancer drugs or cancer genes. Graphical representations of the data are used throughout with links to related resources and all datasets are fully downloadable. GDSC provides a unique resource incorporating large drug sensitivity and genomic datasets to facilitate the discovery of new therapeutic biomarkers for cancer therapies

Recurrent mutation of IGF signalling genes and distinct patterns of genomic rearrangement in osteosarcoma

Osteosarcoma is a primary malignancy of bone that affects children and adults. Here, we present the largest sequencing study of osteosarcoma to date, comprising 112 childhood and adult tumours encompassing all major histological subtypes. A key finding of our study is the identification of mutations in insulin-like growth factor (IGF) signalling genes in 8/112 (7%) of cases. We validate this observation using fluorescence in situ hybridization (FISH) in an additional 87 osteosarcomas, with IGF1 receptor (IGF1R) amplification observed in 14% of tumours. These findings may inform patient selection in future trials of IGF1R inhibitors in osteosarcoma. Analysing patterns of mutation, we identify distinct rearrangement profiles including a process characterized by chromothripsis and amplification. This process operates recurrently at discrete genomic regions and generates driver mutations. It may represent an age-independent mutational mechanism that contributes to the development of osteosarcoma in children and adults alike

Circulating tumor DNA in patients with colorectal adenomas: assessment of detectability and genetic heterogeneity.

Improving early detection of colorectal cancer (CRC) is a key public health priority as adenomas and stage I cancer can be treated with minimally invasive procedures. Population screening strategies based on detection of occult blood in the feces have contributed to enhance detection rates of localized disease, but new approaches based on genetic analyses able to increase specificity and sensitivity could provide additional advantages compared to current screening methodologies. Recently, circulating cell-free DNA (cfDNA) has received much attention as a cancer biomarker for its ability to monitor the progression of advanced disease, predict tumor recurrence and reflect the complex genetic heterogeneity of cancers. Here, we tested whether analysis of cfDNA is a viable tool to enhance detection of colon adenomas. To address this, we assessed a cohort of patients with adenomas and healthy controls using droplet digital PCR (ddPCR) and mutation-specific assays targeted to trunk mutations. Additionally, we performed multiregional, targeted next-generation sequencing (NGS) of adenomas and unmasked extensive heterogeneity, affecting known drivers such as APC, KRAS and mismatch repair (MMR) genes. However, tumor-related mutations were undetectable in patients' plasma. Finally, we employed a preclinical mouse model of Apc-driven intestinal adenomas and confirmed the inability to identify tumor-related alterations via cfDNA, despite the enhanced disease burden displayed by this experimental cancer model. Therefore, we conclude that benign colon lesions display extensive genetic heterogeneity, that they are not prone to release DNA into the circulation and are unlikely to be reliably detected with liquid biopsies, at least with the current technologies

Rapid targeted mutational analysis of human tumours: a clinical platform to guide personalized cancer medicine

Targeted cancer therapy requires the rapid and accurate identification of genetic abnormalities predictive of therapeutic response. We sought to develop a high-throughput genotyping platform that would allow prospective patient selection to the best available therapies, and that could readily and inexpensively be adopted by most clinical laboratories. We developed a highly sensitive multiplexed clinical assay that performs very well with nucleic acid derived from formalin fixation and paraffin embedding (FFPE) tissue, and tests for 120 previously described mutations in 13 cancer genes. Genetic profiling of 250 primary tumours was consistent with the documented oncogene mutational spectrum and identified rare events in some cancer types. The assay is currently being used for clinical testing of tumour samples and contributing to cancer patient management. This work therefore establishes a platform for real-time targeted genotyping that can be widely adopted. We expect that efforts like this one will play an increasingly important role in cancer management

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

Objective Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. Design Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. Results Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. Conclusion This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows