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Combinations of anti-cancer drugs can overcome resistance and provide new treatments 1 , 2 . The number of possible drug combinations vastly exceeds what could be tested clinically. Efforts to systematically identify active combinations and the tissues and molecular contexts in which they are most effective could accelerate the development of combination treatments. Here we evaluate the potency and efficacy of 2,025 clinically relevant two-drug combinations, generating a dataset encompassing 125 molecularly characterized breast, colorectal and pancreatic cancer cell lines. We show that synergy between drugs is rare and highly context-dependent, and that combinations of targeted agents are most likely to be synergistic. We incorporate multi-omic molecular features to identify combination biomarkers and specify synergistic drug combinations and their active contexts, including in basal-like breast cancer, and microsatellite-stable or KRAS -mutant colon cancer. Our results show that irinotecan and CHEK1 inhibition have synergistic effects in microsatellite-stable or KRAS – TP53 double-mutant colon cancer cells, leading to apoptosis and suppression of tumour xenograft growth. This study identifies clinically relevant effective drug combinations in distinct molecular subpopulations and is a resource to guide rational efforts to develop combinatorial drug treatments. A survey of potency and efficacy of 2,025 clinically relevant two-drug combinations against 125 molecularly characterized breast, colorectal and pancreatic cancer cell lines identifies rare synergistic effects of anticancer drugs, informing rational combination treatments for specific cancer subtypes.

Background Schlafen 11 (SLFN11) has been linked with response to DNA-damaging agents (DDA) and PARP inhibitors. An in-depth understanding of several aspects of its role as a biomarker in cancer is missing, as is a comprehensive analysis of the clinical significance of SLFN11 as a predictive biomarker to DDA and/or DNA damage-response inhibitor (DDRi) therapies. Methods We used a multidisciplinary effort combining specific immunohistochemistry, pharmacology tests, anticancer combination therapies and mechanistic studies to assess SLFN11 as a potential biomarker for stratification of patients treated with several DDA and/or DDRi in the preclinical and clinical setting. Results SLFN11 protein associated with both preclinical and patient treatment response to DDA, but not to non-DDA or DDRi therapies, such as WEE1 inhibitor or olaparib in breast cancer. SLFN11-low/absent cancers were identified across different tumour types tested. Combinations of DDA with DDRi targeting the replication-stress response (ATR, CHK1 and WEE1) could re-sensitise SLFN11-absent/low cancer models to the DDA treatment and were effective in upper gastrointestinal and genitourinary malignancies. Conclusion SLFN11 informs on the standard of care chemotherapy based on DDA and the effect of selected combinations with ATR, WEE1 or CHK1 inhibitor in a wide range of cancer types and models.

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. Synopsis This study integrates pharmacological and CRISPR screens in 484 cancer cell lines to systematically investigate anticancer drug mechanism of action, yielding insights into the genetic contexts and cellular networks underpinning drug response. CRISPR screens reveal important aspects of drug mechanism‐of‐action, specifically in the context of cellular activity, isoform specificity, off‐target and polypharmacological effects. By leveraging protein interaction networks that underlie drug‐responses, novel drug‐target interactions involving anti‐apoptotic MCL1 inhibitors are identified. Improved pharmacogenomic biomarker discovery using two independent and orthogonal cell viability screens. Graphical Abstract This study integrates pharmacological and CRISPR screens in 484 cancer cell lines to systematically investigate anticancer drug mechanism of action, yielding insights into the genetic contexts and cellular networks underpinning drug response.

Network models are widely used to describe complex signaling systems. Cellular wiring varies in different cellular contexts and numerous inference techniques have been developed to infer the structure of a network from experimental data of the network's behavior. To objectively identify which inference strategy is best suited to a specific network, a gold standard network and dataset are required. However, suitable datasets for benchmarking are difficult to find. Numerous tools exist that can simulate data for transcriptional networks, but these are of limited use for the study of signaling networks. Here, we describe SiGNet (Signal Generator for Networks): a Cytoscape app that simulates experimental data for a signaling network of known structure. SiGNet has been developed and tested against published experimental data, incorporating information on network architecture, and the directionality and strength of interactions to create biological data in silico. SiGNet is the first tool to simulate biological signaling data, enabling an accurate and systematic assessment of inference strategies. SiGNet can also be used to produce preliminary models of key biological pathways following perturbation.

The PI3K pathway is commonly activated in breast cancer, with PI3K-AKT pathway inhibitors used clinically. However, mechanisms that limit or enhance the therapeutic effects of PI3K-AKT inhibitors are poorly understood at a genome-wide level. Parallel CRISPR screens in 3 PTEN-null breast cancer cell lines identified genes mediating resistance to capivasertib (AKT inhibitor) and AZD8186 (PI3Kβ inhibitor). The dominant mechanism causing resistance is reactivated PI3K-AKT-mTOR signalling, but not other canonical signalling pathways. Deletion of TSC1/2 conferred resistance to PI3Kβi and AKTi through mTORC1. However, deletion of PIK3R2 and INPPL1 drove specific PI3Kβi resistance through AKT. Conversely deletion of PIK3CA , ERBB2 , ERBB 3 increased PI3Kβi sensitivity while modulation of RRAGC , LAMTOR1 , LAMTOR4 increased AKTi sensitivity. Significantly, we found that Mcl-1 loss enhanced response through rapid apoptosis induction with AKTi and PI3Kβi in both sensitive and drug resistant TSC1/2 null cells. The combination effect was BAK but not BAX dependent. The Mcl-1i + PI3Kβ/AKTi combination was effective across a panel of breast cancer cell lines with PIK3CA and PTEN mutations, and delivered increased anti-tumor benefit in vivo. This study demonstrates that different resistance drivers to PI3Kβi and AKTi converge to reactivate PI3K-AKT or mTOR signalling and combined inhibition of Mcl-1 and PI3K-AKT has potential as a treatment strategy for PI3Kβi/AKTi sensitive and resistant breast tumours.

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 and genome-wide CRISPR loss-of-function screens in 484 cell lines to systematically investigate in cellular drug mechanism-of-action. We observed an enrichment for positive associations between drug sensitivity and knockout of their nominal targets, and by leveraging protein-protein networks we identified pathways that mediate drug response. This revealed an unappreciated role of mitochondrial E3 ubiquitin-protein ligase MARCH5 in sensitivity to MCL1 inhibitors. We also estimated drug on-target and off-target activity, informing on specificity, potency and toxicity. Linking drug and gene dependency together with genomic datasets 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.

Intra-tumour phenotypic heterogeneity limits accuracy of clinical diagnostics and hampers the efficiency of anti-cancer therapies. Dealing with this cellular heterogeneity requires adequate understanding of its sources, which is extremely difficult, as phenotypes of tumour cells integrate hardwired (epi)mutational differences with the dynamic responses to microenvironmental cues. The later come in form of both direct physical interactions, as well as inputs from gradients of secreted signalling molecules. Furthermore, tumour cells can not only receive microenvironmental cues, but also produce them. Despite high biological and clinical importance of understanding spatial aspects of paracrine signaling, adequate research tools are largely lacking. Here, a partial differential equation (PDE) based mathematical model is developed that mimics the process of cell ablation. This model suggests how each cell might contribute to the microenvironment by either absorbing or secreting diffusible factors, and quantifies the extent to which observed intensities can be explained via diffusion mediated signalling. The model allows for the separation of phenotypic responses to signalling gradients within tumour microenvironments from the combined influence of responses mediated by direct physical contact and hardwired (epi)genetic differences. The differential equation is solved around cell membrane outlines using a finite element method (FEM). The method is applied to a multi-channel immunofluorescence in situ hybridization (iFISH) stained breast cancer histological specimen and correlations are investigated between: HER2 gene amplification; HER2 protein expression; and cell interaction with the diffusible microenvironment. This approach allows partial deconvolution of the complex inputs...

We observed the assembly of a nucleus-like structure in bacteria during viral infection. Using fluorescence microscopy and cryo-electron tomography, we showed that Pseudomonas chlororaphis phage 201φ2-1 assembled a compartment that separated viral DNA from the cytoplasm. The phage compartment was centered by a bipolar tubulin-based spindle, and it segregated phage and bacterial proteins according to function. Proteins involved in DNA replication and transcription localized inside the compartment, whereas proteins involved in translation and nucleotide synthesis localized outside. Later during infection, viral capsids assembled on the cytoplasmic membrane and moved to the surface of the compartment for DNA packaging. Ultimately, viral particles were released from the compartment and the cell lysed. These results demonstrate that phages have evolved a specialized structure to compartmentalize viral replication.

We report the first identified mutation in the gene encoding human cytochrome c ( CYCS ). Glycine 41, invariant throughout eukaryotes, is substituted by serine in a family with autosomal dominant thrombocytopenia caused by dysregulated platelet formation. The mutation yields a cytochrome c variant with enhanced apoptotic activity in vitro . Notably, the family has no other phenotypic indication of abnormal apoptosis, implying that cytochrome c activity is not a critical regulator of most physiological apoptosis.

Dracunculus medinensis (Guinea worm) is a parasitic nematode that can cause the debilitating disease dracunculiasis (Guinea worm disease) in humans. The global Guinea Worm Eradication Program has led intervention and eradication efforts since the 1980s, and Guinea worm infections in people have decreased >99.99%. With the final goal of eradication drawing nearer, reports of animal infections from some remaining endemic countries pose unique challenges. Currently, confirmation of suspected Guinea worm infection relies on conventional molecular techniques such as polymerase chain reaction (PCR), which is not specific to Guinea worm and, therefore, requires sequencing of the PCR products to confirm the identity of suspect samples, a process that often takes a few weeks. To decrease the time required for species confirmation, we developed a quantitative PCR assay targeting the mitochondrial cytochrome b ( cytb ) gene of Guinea worm. Our assay has a limit of detection of 10 copies per reaction. The mean analytical parameters (± SE) were as follows: efficiency = 93.4 ± 7.7%, y -intercept = 40.93 ± 1.11, slope = -3.4896 ± 0.12, and the R 2 = 0.999 ± 0.004. The assay did not amplify other nematodes found in Guinea worm-endemic regions and demonstrated 100% diagnostic sensitivity and specificity. Implementation of this quantitative PCR assay for Guinea worm identification could eliminate the need for DNA sequencing to confirm species. Thus, this approach can be implemented to provide more rapid confirmation of Guinea worm infections, leading to faster execution of Guinea worm interventions while increasing our understanding of infection patterns.