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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The ATR-Chk1 pathway is critical for DNA damage responses and cell-cycle progression. Chk1 inhibition is more deleterious to cycling cells than ATR inhibition, raising questions about ATR and Chk1 ...functions in the absence of extrinsic replication stress. Here we show that a key role of ATR in S phase is to coordinate RRM2 accumulation and origin firing. ATR inhibitor (ATRi) induces massive ssDNA accumulation and replication catastrophe in a fraction of early S-phase cells. In other S-phase cells, however, ATRi induces moderate ssDNA and triggers a DNA-PK and Chk1-mediated backup pathway to suppress origin firing. The backup pathway creates a threshold such that ATRi selectively kills cells under high replication stress, whereas Chk1 inhibitor induces cell death at a lower threshold. The levels of ATRi-induced ssDNA correlate with ATRi sensitivity in a panel of cell lines, suggesting that ATRi-induced ssDNA could be predictive of ATRi sensitivity in cancer cells.
Display omitted
•ATR protects a fraction of early S-phase cells from replication catastrophe•ATR suppresses DNA damage by coordinating RRM2 accumulation and origin firing•A DNA-PK-Chk1 pathway creates a threshold of tolerable ssDNA in ATRi-treated cells•ATRi-induced ssDNA may predict ATRi sensitivity in cancer cells
Buisson et al. show that, during S phase, ATR protects cells from a threshold of ssDNA by coordinating RRM2 accumulation and origin firing. ATR is critical in early S phase especially in cells under high replication stress. Surprisingly, ATR can be circumvented by DNA-PK and Chk1 under moderate replication stress.
Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the ...clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.
Display omitted
•We integrate heterogeneous molecular data of 11,289 tumors and 1,001 cell lines•We measure the response of 1,001 cancer cell lines to 265 anti-cancer drugs•We uncover numerous oncogenic aberrations that sensitize to an anti-cancer drug•Our study forms a resource to identify therapeutic options for cancer sub-populations
A look at the pharmacogenomic landscape of 1,001 human cancer cell lines points to new treatment applications for hundreds of known anti-cancer drugs.
Cancer drivers require statistical modeling to distinguish them from passenger events, which accumulate during tumorigenesis but provide no fitness advantage to cancer cells. The discovery of driver ...genes and mutations relies on the assumption that exact positional recurrence is unlikely by chance; thus, the precise sharing of mutations across patients identifies drivers. Examining the mutation landscape in cancer genomes, we found that many recurrent cancer mutations previously designated as drivers are likely passengers. Our integrated bioinformatic and biochemical analyses revealed that these passenger hotspot mutations arise from the preference of APOBEC3A, a cytidine deaminase, for DNA stem-loops. Conversely, recurrent APOBEC-signature mutations not in stem-loops are enriched in well-characterized driver genes and may predict new drivers. This demonstrates that mesoscale genomic features need to be integrated into computational models aimed at identifying mutations linked to diseases.
Combinations of anti-cancer drugs can overcome resistance and provide new treatments
. 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.
The apolipoprotein B mRNA editing enzyme catalytic polypeptide-like APOBEC3A and APOBEC3B have emerged as key mutation drivers in cancer. Here, we show that APOBEC3A and APOBEC3B activities impose a ...unique type of replication stress by inducing abasic sites at replication forks. In contrast to cells under other types of replication stress, APOBEC3A-expressing cells were selectively sensitive to ATR inhibitors (ATRi), but not to a variety of DNA replication inhibitors and DNA-damaging drugs. In proliferating cells, APOBEC3A modestly elicited ATR but not ATM. ATR inhibition in APOBEC3A-expressing cells resulted in a surge of abasic sites at replication forks, revealing an ATR-mediated negative feedback loop during replication. The surge of abasic sites upon ATR inhibition associated with increased accumulation of single-stranded DNA, a substrate of APOBEC3A, triggering an APOBEC3A-driven feed-forward loop that ultimately drove cells into replication catastrophe. In a panel of cancer cell lines, ATRi selectively induced replication catastrophe in those harboring high APOBEC3A and/or APOBEC3B activities, showing that APOBEC3A and APOBEC3B activities conferred susceptibility to ATRi. Our results define an APOBEC-driven replication stress in cancer cells that may offer an opportunity for ATR-targeted therapy.
.
Poly-(ADP-ribose) polymerase (PARP) inhibitors (PARPis) selectively kill BRCA1/2-deficient cells, but their efficacy in BRCA-deficient patients is limited by drug resistance. Here, we used derived ...cell lines and cells from patients to investigate how to overcome PARPi resistance. We found that the functions of BRCA1 in homologous recombination (HR) and replication fork protection are sequentially bypassed during the acquisition of PARPi resistance. Despite the lack of BRCA1, PARPi-resistant cells regain RAD51 loading to DNA double-stranded breaks (DSBs) and stalled replication forks, enabling two distinct mechanisms of PARPi resistance. Compared with BRCA1-proficient cells, PARPi-resistant BRCA1-deficient cells are increasingly dependent on ATR for survival. ATR inhibitors (ATRis) disrupt BRCA1-independent RAD51 loading to DSBs and stalled forks in PARPi-resistant BRCA1-deficient cells, overcoming both resistance mechanisms. In tumor cells derived from patients, ATRis also overcome the bypass of BRCA1/2 in fork protection. Thus, ATR inhibition is a unique strategy to overcome the PARPi resistance of BRCA-deficient cancers.
The formation of protein complexes and the co-regulation of the cellular concentrations of proteins are essential mechanisms for cellular signaling and for maintaining homeostasis. Here we use ...isobaric-labeling multiplexed proteomics to analyze protein co-regulation and show that this allows the identification of protein-protein associations with high accuracy. We apply this 'interactome mapping by high-throughput quantitative proteome analysis' (IMAHP) method to a panel of 41 breast cancer cell lines and show that deviations of the observed protein co-regulations in specific cell lines from the consensus network affects cellular fitness. Furthermore, these aberrant interactions serve as biomarkers that predict the drug sensitivity of cell lines in screens across 195 drugs. We expect that IMAHP can be broadly used to gain insight into how changing landscapes of protein-protein associations affect the phenotype of biological systems.
KRAS is the most commonly mutated oncogene, yet no effective targeted therapies exist for KRAS mutant cancers. We developed a pooled shRNA-drug screen strategy to identify genes that, when inhibited, ...cooperate with MEK inhibitors to effectively treat KRAS mutant cancer cells. The anti-apoptotic BH3 family gene BCL-XL emerged as a top hit through this approach. ABT-263 (navitoclax), a chemical inhibitor that blocks the ability of BCL-XL to bind and inhibit pro-apoptotic proteins, in combination with a MEK inhibitor led to dramatic apoptosis in many KRAS mutant cell lines from different tissue types. This combination caused marked in vivo tumor regressions in KRAS mutant xenografts and in a genetically engineered KRAS-driven lung cancer mouse model, supporting combined BCL-XL/MEK inhibition as a potential therapeutic approach for KRAS mutant cancers.
Display omitted
► A MEK inhibitor-based synthetic lethal screen in KRAS mutant cancers identified BCL-XL ► Combined inhibition of BCL-XL and MEK induces marked apoptosis in KRAS mutant cancers ► Epithelial-to-mesenchymal transition predicts insensitivity to BCL-XL/MEK inhibition ► Combined BCL-XL/MEK inhibition causes tumor regressions in KRAS mutant cancer models
Transcriptional dysregulation induced by aberrant transcription factors (TF) is a key feature of cancer, but its global influence on drug sensitivity has not been examined. Here, we infer the ...transcriptional activity of 127 TFs through analysis of RNA-seq gene expression data newly generated for 448 cancer cell lines, combined with publicly available datasets to survey a total of 1,056 cancer cell lines and 9,250 primary tumors. Predicted TF activities are supported by their agreement with independent shRNA essentiality profiles and homozygous gene deletions, and recapitulate mutant-specific mechanisms of transcriptional dysregulation in cancer. By analyzing cell line responses to 265 compounds, we uncovered numerous TFs whose activity interacts with anticancer drugs. Importantly, combining existing pharmacogenomic markers with TF activities often improves the stratification of cell lines in response to drug treatment. Our results, which can be queried freely at dorothea.opentargets.io, offer a broad foundation for discovering opportunities to refine personalized cancer therapies.
Systematic analysis of transcriptional dysregulation in cancer cell lines and patient tumor specimens offers a publicly searchable foundation to discover new opportunities to refine personalized cancer therapies.
.
PDF
