Recent advances in computational biology suggest that any perturbation to the transcriptional programme of the cell can be summarised by a proper 'signature': a set of genes combined with a pattern of expression. Therefore, it should be possible to generate proxies of clinicopathological phenotypes and drug effects through signatures acquired via DNA microarray technology. Gene expression signatures have recently been assembled and compared through genome-wide metrics, unveiling unexpected drug-disease and drug-drug 'connections' by matching corresponding signatures. Consequently, novel applications for existing drugs have been predicted and experimentally validated. Here, we describe related methods, case studies and resources while discussing challenges and benefits of exploiting existing repositories of microarray data that could serve as a search space for systematic drug repositioning.

BRAF (v-raf murine sarcoma viral oncogene homolog B) inhibitors elicit a transient anti-tumor response in ∼ 80% of BRAF(V600)-mutant melanoma patients that almost uniformly precedes the emergence of resistance. Here we used a mouse model of melanoma in which melanocyte-specific expression of Braf(V618E) (analogous to the human BRAF(V600E) mutation) led to the development of skin hyperpigmentation and nevi, as well as melanoma formation with incomplete penetrance. Sleeping Beauty insertional mutagenesis in this model led to accelerated and fully penetrant melanomagenesis and synchronous tumor formation. Treatment of Braf(V618E) transposon mice with the BRAF inhibitor PLX4720 resulted in tumor regression followed by relapse. Analysis of transposon insertions identified eight genes including Braf, Mitf, and ERas (ES-cell expressed Ras) as candidate resistance genes. Expression of ERAS in human melanoma cell lines conferred resistance to PLX4720 and induced hyperphosphorylation of AKT (v-akt murine thymoma viral oncogene homolog 1), a phenotype reverted by combinatorial treatment with PLX4720 and the AKT inhibitor MK2206. We show that ERAS expression elicits a prosurvival signal associated with phosphorylation/inactivation of BAD, and that the resistance of hepatocyte growth factor-treated human melanoma cells to PLX4720 can be reverted by treatment with the BAD-like BH3 mimetic ABT-737. Thus, we define a role for the AKT/BAD pathway in resistance to BRAF inhibition and illustrate an in vivo approach for finding drug resistance genes.

CRISPR-Cas9 genome editing is widely used to study gene function, from basic biology to biomedical research. Structural rearrangements are a ubiquitous feature of cancer cells and their impact on the functional consequences of CRISPR-Cas9 gene-editing has not yet been assessed.

Patients with seemingly the same tumour can respond very differently to treatment. There are strong, well-established effects of somatic mutations on drug efficacy, but there is at-most anecdotal evidence of a germline component to drug response. Here, we report a systematic survey of how inherited germline variants affect drug susceptibility in cancer cell lines. We develop a joint analysis approach that leverages both germline and somatic variants, before applying it to screening data from 993 cell lines and 265 drugs. Surprisingly, we find that the germline contribution to variation in drug susceptibility can be as large or larger than effects due to somatic mutations. Several of the associations identified have a direct relationship to the drug target. Finally, using 17-AAG response as an example, we show how germline effects in combination with transcriptomic data can be leveraged for improved patient stratification and to identify new markers for drug sensitivity.

Drug versus Disease (DvD) provides a pipeline, available through R or Cytoscape, for the comparison of drug and disease gene expression profiles from public microarray repositories. Negatively correlated profiles can be used to generate hypotheses of drug-repurposing, whereas positively correlated profiles may be used to infer side effects of drugs. DvD allows users to compare drug and disease signatures with dynamic access to databases Array Express, Gene Expression Omnibus and data from the Connectivity Map.

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.

Targeting the MAPK signaling pathway has transformed the treatment of metastatic melanoma. CRISPR-Cas9 genetic screens provide a genome-wide approach to uncover novel genetic dependencies that might serve as therapeutic targets. Here, we analyzed recently reported CRISPR-Cas9 screens comparing data from 28 melanoma cell lines and 313 cell lines of other tumor types in order to identify fitness genes related to melanoma. We found an average of 1,494 fitness genes in each melanoma cell line. We identified 33 genes, inactivation of which specifically reduced the fitness of melanoma. This set of tumor type-specific genes includes established melanoma fitness genes as well as many genes that have not previously been associated with melanoma growth. Several genes encode proteins that can be targeted using available inhibitors. We verified that genetic inactivation of DUSP4 and PPP2R2A reduces the proliferation of melanoma cells. DUSP4 encodes an inhibitor of ERK, suggesting that further activation of MAPK signaling activity through its loss is selectively deleterious to melanoma cells. Collectively, these data present a resource of genetic dependencies in melanoma that may be explored as potential therapeutic targets.

In the liver, ductal cells rarely proliferate during homeostasis but do so transiently after tissue injury. These cells can be expanded as organoids that recapitulate several of the cell-autonomous mechanisms of regeneration but lack the stromal interactions of the native tissue. Here, using organoid co-cultures that recapitulate the ductal-to-mesenchymal cell architecture of the portal tract, we demonstrate that a subpopulation of mouse periportal mesenchymal cells exerts dual control on proliferation of the epithelium. Ductal cell proliferation is either induced and sustained or, conversely, completely abolished, depending on the number of direct mesenchymal cell contacts, through a mechanism mediated, at least in part, by Notch signaling. Our findings expand the concept of the cellular niche in epithelial tissues, whereby not only soluble factors but also cell-cell contacts are the key regulatory cues involved in the control of cellular behaviors, suggesting a critical role for cell-cell contacts during regeneration.

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

In Rspondin-based 3D cultures, Lgr5 stem cells from multiple organs form ever-expanding epithelial organoids that retain their tissue identity. We report the establishment of tumor organoid cultures from 20 consecutive colorectal carcinoma (CRC) patients. For most, organoids were also generated from adjacent normal tissue. Organoids closely recapitulate several properties of the original tumor. The spectrum of genetic changes within the "living biobank" agrees well with previous large-scale mutational analyses of CRC. Gene expression analysis indicates that the major CRC molecular subtypes are represented. Tumor organoids are amenable to high-throughput drug screens allowing detection of gene-drug associations. As an example, a single organoid culture was exquisitely sensitive to Wnt secretion (porcupine) inhibitors and carried a mutation in the negative Wnt feedback regulator RNF43, rather than in APC. Organoid technology may fill the gap between cancer genetics and patient trials, complement cell-line- and xenograft-based drug studies, and allow personalized therapy design. PAPERCLIP.

We present a novel strategy to identify drug-repositioning opportunities. The starting point of our method is the generation of a signature summarising the consensual transcriptional response of multiple human cell lines to a compound of interest (namely the seed compound). This signature can be derived from data in existing databases, such as the connectivity-map, and it is used at first instance to query a network interlinking all the connectivity-map compounds, based on the similarity of their transcriptional responses. This provides a drug neighbourhood, composed of compounds predicted to share some effects with the seed one. The original signature is then refined by systematically reducing its overlap with the transcriptional responses induced by drugs in this neighbourhood that are known to share a secondary effect with the seed compound. Finally, the drug network is queried again with the resulting refined signatures and the whole process is carried on for a number of iterations. Drugs in the final refined neighbourhood are then predicted to exert the principal mode of action of the seed compound. We illustrate our approach using paclitaxel (a microtubule stabilising agent) as seed compound. Our method predicts that glipizide and splitomicin perturb microtubule function in human cells: a result that could not be obtained through standard signature matching methods. In agreement, we find that glipizide and splitomicin reduce interphase microtubule growth rates and transiently increase the percentage of mitotic cells-consistent with our prediction. Finally, we validated the refined signatures of paclitaxel response by mining a large drug screening dataset, showing that human cancer cell lines whose basal transcriptional profile is anti-correlated to them are significantly more sensitive to paclitaxel and docetaxel.

Drug resistance is an almost inevitable consequence of cancer therapy and ultimately proves fatal for the majority of patients. In many cases, this is the consequence of specific gene mutations that have the potential to be targeted to resensitize the tumor. The ability to uniformly saturate the genome with point mutations without chromosome or nucleotide sequence context bias would open the door to identify all putative drug resistance mutations in cancer models. Here, we describe such a method for elucidating drug resistance mechanisms using genome-wide chemical mutagenesis allied to next-generation sequencing. We show that chemically mutagenizing the genome of cancer cells dramatically increases the number of drug-resistant clones and allows the detection of both known and novel drug resistance mutations. We used an efficient computational process that allows for the rapid identification of involved pathways and druggable targets. Such a priori knowledge would greatly empower serial monitoring strategies for drug resistance in the clinic as well as the development of trials for drug-resistant patients.

Low success rates during drug development are due, in part, to the difficulty of defining drug mechanism-of-action and molecular markers of therapeutic activity. Here, we integrated 199,219 drug sensitivity measurements for 397 unique anti-cancer drugs with genome-wide CRISPR loss-of-function screens in 484 cell lines to systematically investigate cellular drug mechanism-of-action. We observed an enrichment for positive associations between the profile of drug sensitivity and knockout of a drug's nominal target, and by leveraging protein-protein networks, we identified pathways underpinning drug sensitivity. This revealed an unappreciated positive association between mitochondrial E3 ubiquitin-protein ligase MARCH5 dependency and sensitivity to MCL1 inhibitors in breast cancer cell lines. We also estimated drug on-target and off-target activity, informing on specificity, potency and toxicity. Linking drug and gene dependency together with genomic data sets uncovered contexts in which molecular networks when perturbed mediate cancer cell loss-of-fitness and thereby provide independent and orthogonal evidence of biomarkers for drug development. This study illustrates how integrating cell line drug sensitivity with CRISPR loss-of-function screens can elucidate mechanism-of-action to advance drug development.

Large pharmacogenomic screenings integrate heterogeneous cancer genomic datasets as well as anti-cancer drug responses on thousand human cancer cell lines. Mining this data to identify new therapies for cancer sub-populations would benefit from common data structures, modular computational biology tools and user-friendly interfaces.

High-throughput drug screens in cancer cell lines test compounds at low concentrations, thereby enabling the identification of drug-sensitivity biomarkers, while resistance biomarkers remain underexplored. Dissecting meaningful drug responses at high concentrations is challenging due to cytotoxicity, i.e., off-target effects, thus limiting resistance biomarker discovery to frequently mutated cancer genes. To address this, we interrogate subpopulations carrying sensitivity biomarkers and consecutively investigate unexpectedly resistant (UNRES) cell lines for unique genetic alterations that may drive resistance. By analyzing the GDSC and CTRP datasets, we find 53 and 35 UNRES cases, respectively. For 24 and 28 of them, we highlight putative resistance biomarkers. We find clinically relevant cases such as EGFRT790M mutation in NCI-H1975 or PTEN loss in NCI-H1650 cells, in lung adenocarcinoma treated with EGFR inhibitors. Interrogating the underpinnings of drug resistance with publicly available CRISPR phenotypic assays assists in prioritizing resistance drivers, offering hypotheses for drug combinations.

A limitation of pooled CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes arising from copy-number-amplified genomics regions. To solve this issue, we previously developed CRISPRcleanR: a computational method implemented as R/python package and in a dockerized version. CRISPRcleanR detects and corrects biased responses to CRISPR-Cas9 targeting in an unsupervised fashion, accurately reducing false-positive signals while maintaining sensitivity in identifying relevant genetic dependencies. Here, we present CRISPRcleanR WebApp , a web application enabling access to CRISPRcleanR through an intuitive interface. CRISPRcleanR WebApp removes the complexity of R/python language user interactions; provides user-friendly access to a complete analytical pipeline, not requiring any data pre-processing and generating gene-level summaries of essentiality with associated statistical scores; and offers a range of interactively explorable plots while supporting a more comprehensive range of CRISPR guide RNAs' libraries than the original package. CRISPRcleanR WebApp is available at https://crisprcleanr-webapp.fht.org/.

Multiple myeloma is a biologically heterogenous plasma-cell disorder. In this study, we aimed at dissecting the functional impact on transcriptome of gene mutations, copy-number abnormalities (CNA), and chromosomal rearrangements (CR). Moreover, we applied a geno-transcriptomic approach to identify specific biomarkers for personalized treatments.

Genome editing by CRISPR-Cas9 technology allows large-scale screening of gene essentiality in cancer. A confounding factor when interpreting CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes within copy number amplified regions of the genome. We have developed the computational tool CRISPRcleanR which is capable of identifying and correcting gene-independent responses to CRISPR-Cas9 targeting. CRISPRcleanR uses an unsupervised approach based on the segmentation of single-guide RNA fold change values across the genome, without making any assumption about the copy number status of the targeted genes.

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

The results of current studies are not conclusive on the efficacy of incisional negative-pressure wound therapy (NPWT) for the prevention of sternal wound infection (SWI) after adult cardiac surgery. A systematic review of the literature was performed through PubMed, Scopus and Google to identify studies which investigated the efficacy of NPWT to prevent SWI after adult cardiac surgery. Available data were pooled using RevMan and Meta-analyst with random effect models. Out of 191 studies retrieved from the literature, ten fulfilled the inclusion criteria and were included in this analysis. The quality of these studies was judged fair for three of them and poor for seven studies. Only one study was powered to address the efficacy of NPWT for the prevention of postoperative SWI. Pooled analysis of these studies showed that NPWT was associated with lower risk of any SWI (4.5% vs. 9.0%, RR 0.54, 95% CI 0.34-0.84, I2 48%), superficial SWI (3.8% vs. 4.4%, RR 0.63, 95% CI 0.29-1.36, I2 65%), and deep SWI (1.8% vs. 4.7%, RR 0.46, 95% CI 0.26-0.74, I2 0%), but such a difference was not statistically significant for superficial SWI. When only randomized and alternating allocated studies were included, NPWT was associated with a significantly lower risk of any SWI (3.3% vs. 16.5%, RR 0.22, 95% CI 0.08-0.62, I2 0%), superficial SWI (2.6% vs. 12.4%, RR 0.21, 95% CI 0.06-0.69, I2 0%), and deep SWI (1.2% vs. 4.8%, RR 0.17, 95% CI 0.03-0.95, I2 0%). This pooled analysis showed that NPWT may prevent postoperative SWI after adult cardiac surgery. NPWT is expected to be particularly useful in patients at risk for surgical site infection and may significantly reduce the burden of resources needed to treat such a complication. However, the methodology of the available studies was judged as poor for most of them. Further studies are needed to obtain conclusive results on the potential benefits of this preventative strategy.