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Recent studies have reported that genome editing by CRISPR–Cas9 induces a DNA damage response mediated by p53 in primary cells hampering their growth. This could lead to a selection of cells with pre-existing p53 mutations. In this study, employing an integrated computational and experimental framework, we systematically investigated the possibility of selection of additional cancer driver mutations during CRISPR-Cas9 gene editing. We first confirm the previous findings of the selection for pre-existing p53 mutations by CRISPR-Cas9. We next demonstrate that similar to p53, wildtype KRAS may also hamper the growth of Cas9-edited cells, potentially conferring a selective advantage to pre-existing KRAS-mutant cells. These selective effects are widespread, extending across cell-types and methods of CRISPR-Cas9 delivery and the strength of selection depends on the sgRNA sequence and the gene being edited. The selection for pre-existing p53 or KRAS mutations may confound CRISPR-Cas9 screens in cancer cells and more importantly, calls for monitoring patients undergoing CRISPR-Cas9-based editing for clinical therapeutics for pre-existing p53 and KRAS mutations., CRISPR-Cas9 gene editing can induce a p53 mediated damage response. Here the authors investigate the possibility of selection of pre-existing cancer driver mutations during CRISPR-Cas9 knockout based gene editing and identify KRAS mutants that may confer a selected advantage to edited cells.

Cancer is a predominant disease across animals. We applied a comparative genomics approach to systematically characterize genes whose conservation levels correlate positively (PC) or negatively (NC) with cancer resistance estimates across 193 vertebrates. Pathway analysis reveals that NC genes are enriched for metabolic functions and PC genes in cell cycle regulation, DNA repair, and immune response, pointing to their corresponding roles in mediating cancer risk. We find that PC genes are less tolerant to loss-of-function (LoF) mutations, are enriched in cancer driver genes, and are associated with germline mutations that increase human cancer risk. Their relevance to cancer risk is further supported via the analysis of mouse functional genomics and cancer mortality of zoo mammals’ data. In sum, our study describes a cross-species genomic analysis pointing to candidate genes that may mediate human cancer risk.

Summary The emergence of the novel SARS coronavirus 2 (SARS-CoV-2) in 2019 has triggered an ongoing global pandemic of severe pneumonia-like disease designated as coronavirus disease 2019 (COVID-19)1. The development of a vaccine is likely to require at least 12-18 months, and the typical timeline for approval of a novel antiviral therapeutic can exceed 10 years. Thus, repurposing of known drugs could significantly accelerate the deployment of novel therapies for COVID-19. Towards this end, we profiled a library of known drugs encompassing approximately 12,000 clinical-stage or FDA-approved small molecules. We report the identification of 100 molecules that inhibit viral replication, including 21 known drugs that exhibit dose response relationships. Of these, thirteen were found to harbor effective concentrations likely commensurate with achievable therapeutic doses in patients, including the PIKfyve kinase inhibitor apilimod2–4, and the cysteine protease inhibitors MDL-28170, Z LVG CHN2, VBY-825, and ONO 5334. Notably, MDL-28170, ONO 5334, and apilimod were found to antagonize viral replication in human iPSC-derived pneumocyte-like cells, and the PIKfyve inhibitor also demonstrated antiviral efficacy in a primary human lung explant model. Since most of the molecules identified in this study have already advanced into the clinic, the known pharmacological and human safety profiles of these compounds will enable accelerated preclinical and clinical evaluation of these drugs for the treatment of COVID-19.

Background:Until coronavirus disease 2019 (COVID-19) drugs specifically developed to treat COVID-19 become more widely accessible, it is crucial to identify whether existing medications have a protective effect against severe disease. Toward this objective, we conducted a large population study in Clalit Health Services (CHS), the largest healthcare provider in Israel, insuring over 4.7 million members.Methods:Two case-control matched cohorts were assembled to assess which medications, acquired in the last month, decreased the risk of COVID-19 hospitalization. Case patients were adults aged 18 to 95 hospitalized for COVID-19. In the first cohort, five control patients, from the general population, were matched to each case (n=6202); in the second cohort, two non-hospitalized SARS-CoV-2 positive control patients were matched to each case (n=6919). The outcome measures for a medication were: odds ratio (OR) for hospitalization, 95% confidence interval (CI), and the p-value, using Fisher’s exact test. False discovery rate was used to adjust for multiple testing.Results:Medications associated with most significantly reduced odds for COVID-19 hospitalization include: ubiquinone (OR=0.185, 95% CI [0.058 to 0.458], pConclusions:Ubiquinone, ezetimibe, and rosuvastatin, all related to the cholesterol synthesis pathway were associated with reduced hospitalization risk. These findings point to a promising protective effect which should be further investigated in controlled, prospective studies.Funding:This research was supported in part by the Intramural Research Program of the National Institutes of Health, NCI.

The U.S. Food and Drug Administration (FDA) recently approved the treatment with pembrolizumab, an immune checkpoint inhibitor (ICI) targeting PD1 (anti-PD1), for patients with advanced solid tumors with a high tumor mutational burden (TMB) (defined as TMB ≥10 mutations/Mb). However, following recent studies suggest that TMB levels and response to ICI treatment may differ between male and female melanoma patients, we investigated whether using this high-TMB threshold for selecting patients for anti-PD1 treatment may induce a sex-dependent bias. We analyzed a large ICI cohort of 1,286 patients across nine cancer types treated with anti-PD1/PDL1. We find that using this threshold would indeed result in an unwarranted sex bias in melanoma, successfully stratifying female but not male patients. While this threshold is currently not a regulatory prerequisite for ICI treatment in melanoma, it is important to raise awareness to this bias. Notably, no sex-dependent significant differences were observed in the response of melanoma patients to anti-CTLA4 therapies, different chemotherapies or combination therapies. Beyond melanoma, the high-TMB threshold additionally introduces a sex bias of considerable magnitude in glioblastoma and in patients with cancers of unknown origin, however, these results are not statistically significant. A power analysis shows that these biases may become significant with larger sample size, warranting further careful testing in larger cohorts.

Cancer is an evolutionarily conserved disease that occurs in a wide variety of species. We applied a comparative genomics approach to systematically characterize the genes whose conservation levels significantly correlates positively (PC) or negatively (NC) with a broad spectrum of cancer-resistance estimates, computed across almost 200 vertebrate species. PC genes are enriched in pathways relevant to tumor suppression including cell cycle, DNA repair, and immune response, while NC genes are enriched with a host of metabolic pathways. The conservation levels of the PC and NC genes in a species serve to build the first genomics-based predictor of its cancer resistance score. We find that PC genes are less tolerant to loss of function (LoF) mutations, are enriched in cancer driver genes and are associated with germline mutations that increase human cancer risk. Furthermore, their expression levels are associated with lifetime cancer risk across human tissues. Finally, their knockout in mice results in increased cancer incidence. In sum, we find that many genes associated with cancer resistance across species are implicated in human cancers, pointing to several additional candidate genes that may have a functional role in human cancer.

Introduction: Despite the development of two mRNA vaccines, there is an urgent unmet need of finding new antiviral strategies. One such potential antiviral strategy is to target the synthetic lethal (SL) partners of transcriptionally altered genes in infected host cells, thereby selectively killing them to halt the infection at its heels (Mast FD, JCB, 2020). Methods: Here we conduct a first proof-of-concept SL inference approach to predict anti-SARS-CoV-2 targets in a systematic genome-wide manner. This effort capitalizes on our recently published pipeline for inferring clinically relevant SL interactions in cancer (Lee et al, Cell, 2021). Based on the latter, we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict candidate antiviral targets that are SL with altered host genes. Importantly, as our predictions are fine-tuned based on the analysis of patients’ data, they are more likely to be of translational value. Results: Our key results are twofold:1) The predicted SL-based targets are highly enriched for genes that are reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens to inhibit growth of infected cells. 2) A subset of top predicted 26 genes were experimentally tested in a targeted siRNA screen conducted in both infected and non-infected human Caco-2 cells. Remarkably, as expected given that these targets were predicted to be SL specific with genes upregulated in infected cells, indeed, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming non-infected cells. Conclusion: In summary, this study is the first to demonstrate the potential of a synthetic lethality approach to identify viral (specifically anti-SARS-CoV-2) targets. Importantly, as both single cell and bulk transcriptomics patients’ data is considered from both infected people and controls, they are more likely to be of clinical relevance. Targeting host genes identified via an SL-based approach is probably more suitable when the infection is at the early stage and host can still tolerate the loss of infected host cells. Citation Format: Lipika R. Pal, Kuoyuan Cheng, Nishanth Ulhas Nair, Laura Martin-Sancho, Sanju Sinha, Yuan Pu, Laura Riva, Xin Yin, Fiorella Schischlik, Joo Sang Lee, Sumit Chanda, Eytan Ruppin. Identifying and testing cancer-derived synthetic-lethal anti-SARS-CoV-2 targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3583.

Novel strategies are needed to identify drug targets and treatments for the COVID-19 pandemic. The altered gene expression of virus-infected host cells provides an opportunity to specifically inhibit viral propagation via targeting the synthetic lethal (SL) partners of such altered host genes. Pursuing this antiviral strategy, here we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict clinically relevant candidate antiviral targets that are SL with altered host genes. The predicted SL-based targets are highly enriched for infected cell inhibiting genes reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens. Integrating our predictions with the results of these screens, we further selected a focused subset of 26 genes that we experimentally tested in a targeted siRNA screen using human Caco-2 cells. Notably, as predicted, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming non-infected cells. Our results are made publicly available, to facilitate their in vivo testing and further validation.

A variety of species of bacteria are known to colonize human tumours1–11, proliferate within them and modulate immune function, which ultimately affects the survival of patients with cancer and their responses to treatment12–14. However, it is not known whether antigens derived from intracellular bacteria are presented by the human leukocyte antigen class I and II (HLA-I and HLA-II, respectively) molecules of tumour cells, or whether such antigens elicit a tumour-infiltrating T cell immune response. Here we used 16S rRNA gene sequencing and HLA peptidomics to identify a peptide repertoire derived from intracellular bacteria that was presented on HLA-I and HLA-II molecules in melanoma tumours. Our analysis of 17 melanoma metastases (derived from 9 patients) revealed 248 and 35 unique HLA-I and HLA-II peptides, respectively, that were derived from 41 species of bacteria. We identified recurrent bacterial peptides in tumours from different patients, as well as in different tumours from the same patient. Our study reveals that peptides derived from intracellular bacteria can be presented by tumour cells and elicit immune reactivity, and thus provides insight into a mechanism by which bacteria influence activation of the immune system and responses to therapy. HLA peptidomic analysis identifies recurrent intracellular bacteria-derived peptides presented on HLA-I and HLA-II molecules in melanoma tumours, revealing how bacteria can modulate immune functions and responses to cancer therapies.

Tremendous progress has been made to control the COVID‐19 pandemic caused by the SARS‐CoV‐2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS‐CoV‐2 infection using genome‐scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS‐CoV‐2 infection. We next applied the GEM‐based metabolic transformation algorithm to predict anti‐SARS‐CoV‐2 targets that counteract the virus‐induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco‐2 cells. Further generating and analyzing RNA‐sequencing data of remdesivir‐treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti‐SARS‐CoV‐2 drug. Our study provides clinical data‐supported candidate anti‐SARS‐CoV‐2 targets for future evaluation, demonstrating host metabolism targeting as a promising antiviral strategy., Metabolic modeling of 12 SARS‐CoV‐2 datasets identifies novel single or combinatory antiviral targets by reverting the virus‐induced host metabolic reprogramming.

Expression pattern of synthetic lethality genes is a previously unknown factor associated with cancer risk across human tissues., Various characteristics of cancers exhibit tissue specificity, including lifetime cancer risk, onset age, and cancer driver genes. Previously, the large variation in cancer risk across human tissues was found to strongly correlate with the number of stem cell divisions and abnormal DNA methylation levels. Here, we study the role of synthetic lethality in cancer risk. Analyzing normal tissue transcriptomics data in the Genotype-Tissue Expression project, we quantify the extent of co-inactivation of cancer synthetic lethal (cSL) gene pairs and find that normal tissues with more down-regulated cSL gene pairs have lower and delayed cancer risk. Consistently, more cSL gene pairs become up-regulated in cells treated by carcinogens and throughout premalignant stages in vivo. We also show that the tissue specificity of numerous tumor suppressor genes is associated with the expression of their cSL partner genes across normal tissues. Overall, our findings support the possible role of synthetic lethality in tumorigenesis.

The COVID‐19 pandemic caused by SARS‐CoV‐2 has is a global health challenge. Angiotensin‐converting enzyme 2 (ACE2) is the host receptor for SARS‐CoV‐2 entry. Recent studies have suggested that patients with hypertension and diabetes treated with ACE inhibitors (ACEIs) or angiotensin receptor blockers have a higher risk of COVID‐19 infection as these drugs could upregulate ACE2, motivating the study of ACE2 modulation by drugs in current clinical use. Here, we mined published datasets to determine the effects of hundreds of clinically approved drugs on ACE2 expression. We find that ACEIs are enriched for ACE2‐upregulating drugs, while antineoplastic agents are enriched for ACE2‐downregulating drugs. Vorinostat and isotretinoin are the top ACE2 up/downregulators, respectively, in cell lines. Dexamethasone, a corticosteroid used in treating severe acute respiratory syndrome and COVID‐19, significantly upregulates ACE2 both in vitro and in vivo. Further top ACE2 regulators in vivo or in primary cells include erlotinib and bleomycin in the lung and vancomycin, cisplatin, and probenecid in the kidney. Our study provides leads for future work studying ACE2 expression modulators., Analyzing large‐scale in vitro and in vivo publicly available transcriptomic data of drug treatments, we identify the effects of hundreds of clinically approved drugs on expression of ACE2, the host receptor of SARS‐CoV‐2.

The COVID-19 pandemic caused by SARS-COV-2 has infected over 500,000 people causing over 25,000 deaths in the last 10 weeks. A key host cellular protein required for the virus entry is angiotensin-converting enzyme 2 (ACE2). Recent studies have reported that patients with hypertension and diabetes treated with ACE inhibitors or angiotensin receptor blockers might be at a higher risk of COVID-19 infection as these drugs have been reported to increase ACE2 expression. This has raised the need to systematically investigate the effect of different drugs including antihypertensives on modulating ACE2 expression. Here, we analyzed a publicly available CMAP dataset of pre/post transcriptomic profiles for drug treatment in cell lines for over 20,000 small molecules. We show that only one subclass of antihypertensives drugs - ACE inhibitors, are significantly enriched for drugs up-regulating ACE2 expression. Studying the effects of the 672 clinically approved drugs in CMAP, we chart the drug categories that affect ACE2 expression. Specifically, we find that panobinostat (an HDAC inhibitor) confers the highest up-regulation of ACE2 expression while isotretinoin (a vitamin A derivative) is its strongest down-regulator. Our results provide initial candidates guiding further in vitro and in vivo studies aimed at assessing drug effects on ACE2 expression.

While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and by predicting drug efficacy in mouse xenograft models. We then experimentally test a select set of predicted cSLi via new screening experiments, validating their predicted context-specific sensitivity in hypoxic vs normoxic conditions and demonstrating cSLi’s utility in predicting synergistic drug combinations. We show that cSLi can successfully predict patients’ drug treatment response and provide patient stratification signatures. ISLE thus complements existing actionable mutation-based methods for precision cancer therapy, offering an opportunity to expand its scope to the whole genome., Synthetic lethality (SL) offers a new precision oncology approach, which is based on targeting cancer-specific vulnerabilities across the whole genome, going beyond cancer drivers. The authors develop an approach termed ISLE to identify clinically relevant SL interactions and use them for patient stratification and novel target identification.

Motivation and research question: The U.S. Food and Drug Administration recently approved treatment with pembrolizumab, an immune checkpoint inhibitor (ICI) targeting PD1 (anti-PD1), for all advanced solid tumors with high tumor mutational burden (TMB), defined as ≥10 mutations/Megabase (mut/Mb). Recent studies have suggested that strength of immune selection, biomarkers of outcome, TMB levels and response to ICI treatment may differ between male and female cancer patients in some tumor types. This motivated us to examine what are the sex-specific implications, if any, of the ≥10 mut/Mb threshold on selecting patients for ICI treatment. Data & Methods: We analyzed the largest ICI cohort publicly available to date (Samstein et al. 2019), including 1,070 patients treated with anti-PD1/PD-L1 monotherapy, 99 treated with anti-CTLA4 and 255 treated with an ICI combination. We focused on the nine solid tumor types with TMB and clinical response data (median follow-up of 19 months) for at least 50 patients. Results: 1.We observed a significant difference in the median TMB across sex among melanoma patients (n=130, female vs male median TMB=8.36 vs 11.81, P Conclusions: The FDA-approved criteria of 10 mutations/Mb could serve as an informative biomarker for stratifying female melanoma patients but is inadequate for stratifying male patients for anti-PD1 treatment. Our results indicate that its usage is likely to introduce a sex bias in additional cancer types, which will be highly important to carefully test further in larger datasets. Citation Format: Neelam Sinha, Sanju Sinha, Kuoyuan Cheng, Sanna Madan, Alejandro Schaffer, Kenneth Aldape, Ayelet Erez, Brid M. Ryan, Eytan Ruppin. The recently approved high-TMB criteria may introduce a sex bias in response to PD1 inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 29.

T cells play a central role in cancer immunosurveillance and current cancer immunotherapies, including adoptive cell transfer (ACT), T cell receptor or chimeric antigen receptor (CAR) T cell therapies and immune checkpoint blockade. Understanding the factors regulating T cell function is hence critical for improving the success of these immunotherapies. It has been recognized that metabolism can greatly affect different aspects of T cell function, including differentiation, cytokine production, longevity and exhaustion. Here we integrate genome-scale metabolic modeling (GEM) with biological experiments to discover novel metabolic determinants of T cell function. Previously we developed the metabolic transformation algorithm (MTA), a GEM method that was successfully applied to identify driving factors and targets for different diseases. Here applying MTA to data on CAR-T cell gene expression and patient response to anti-CD19 CAR-T therapy, we predicted mitochondrial metabolite transport and specifically proton transport in mitochondrial uncoupling, as key determinants of CAR-T therapy response. Mitochondrial uncoupling is also important for the in vivo persistence of adoptively transferred tumor-infiltrating lymphocytes, as further confirmed by analyzing their gene expressions from a KRAS-targeting ACT dataset. Focusing on the mitochondrial uncoupling protein 2 (Ucp2), which is abundantly expressed in T cells, we experimentally validated that it is required for T cell longevity and anti-tumor function. Specifically, the loss of Ucp2 either via knock-out (KO) or treatment by genipin (a Ucp2 inhibitor) in mice T cells results in accelerated differentiation into “terminal effector cells”, as shown by increased levels of T cell cytotoxicity and exhaustion markers, and decreased levels of central memory and stemness markers. Adoptive transfer of Ucp2-KO Pmel-1 T cells to mice bearing B16 melanomas displayed poorer anti-tumor efficacy and worse survival than the transfer of Ucp2-wildtype T cells. We find that Ucp2 modulates oxidative stress and DNA damage by regulating the levels of mitochondrial superoxide. Reducing mitochondrial reactive oxygen species was sufficient to rescue the loss of Ucp2-mediated effector T cell differentiation, senescence, cytokine production and anti-tumor activity Ucp2-KO Pmel-1 T cells. Tumor-specific CD8+ T cells could be metabolically reprogrammed by Ucp2 overexpression, which improved T cell longevity and anti-tumor function in the Pmel-1/B16 ACT mice model. In sum, our study establishes a novel role of Ucp2 in regulating T cell longevity and anti-tumor activity by repressing increased ROS levels accompanying mitochondrial dysfunction in differentiated and exhausted cells, suggesting that manipulating Ucp2 levels in T cells can be exploited to enhance T cell-based cancer immunotherapies. Citation Format: Madhusudhanan Sukumar, Kuoyuan Cheng, Arunakumar Gangaplara, Yogin Patel, Suman K. Vodnala, Rafiqul Islam, Arash Eidizadeh, Carolyn Subramaniam, Ping Lee, Rigel Kishton, Amanda N. Henning, Michael J. Kruhlak, Zhiya Yu, Ethan M. Shevach, Toren Finkel, Eytan Ruppin, Nicholas P. Restifo. Integrated computational and experimental analysis identifies the mitochondrial uncoupling protein 2 (Ucp2) as a key regulator of T cell anti-tumor function [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1527.

Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here, we perform a genome‐wide in silico prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients’ response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.

The tissue-specificity of cancer and cancer risk is a fundamental open research question. Beyond advancing our understanding of carcinogenesis, elucidating the factors underlying cancer risk may also contribute to cancer prevention. Recent studies have shown that the variation in tissue cancer risk can be explained by the number of tissue stem cell divisions occurring during the lifetime and by abnormal levels of DNA methylation. While cancer risk is likely not determined by a single factor, no other factor has been reported since to account for this fundamental variation. Here we show that cancer synthetic lethality (SL) is another strong contributor of cancer risk in human tissues. SL is a well-known type of genetic interaction where cell death occurs under the combined inactivation of two paired SL genes but not either of them alone. Targeting SLs has been recognized as a highly valuable approach for cancer treatment. We hypothesized that since down-regulated cancer SL (cSL) gene pairs reduce the viability of cancer cells, they may impede the malignant transformation of normal cells, thus modulating cancer risk. Utilizing several recently published large-scale cancer SL networks, we systematically quantified the cancer SL load (defined as the fraction of down-regulated cancer SL gene pairs) in numerous normal and cancer tissues from the TCGA and GTEx datasets. Our key findings are: 1. The cSL loads are lower in cancers vs their matched normal tissues. Furthermore, we find that the cSL load decreases progressively as cancers develop from normal tissues through multiple stages of pre-malignant lesions. These results testify that high cSL load is detrimental to cancer cells, acting as a barrier to cancer development. That is, as normal cells undergo malignant transformation, they need to reactivate at least some of the down-regulated cSL genes for the emerging cancer cells to survive. 2. In accordance with these observations, we find that cSL load in normal tissues is strongly inversely correlated with their lifetime cancer risk. 3. We find that cSL load is the first identified predictor of cancer onset time across different normal tissues - higher cSL load in the younger population is associated with later onset of cancers in that tissue. 4. cSL are important contributors of the tissue/cancer-type specificity of numerous tumor suppressor genes (like BRCA1) - that is, the activity state of cSL partners of quite a few tumor suppressor genes predicts the specific tissues in which they are known to drive cancer. Taken together, these results show that synthetic lethality load in normal tissues is a novel important biological contributor of cancer risk in humans. While synthetic lethality has been attracting tremendous attention as a way to identify cancer vulnerabilities and target them, this is the first time that its role in mediating cancer development is uncovered. Citation Format: Nishanth Ulhas Nair, Kuoyuan Cheng, Joo Sang Lee, Eytan Ruppin. Synthetic lethality across normal tissues is strongly associated with cancer risk, onset, and tumor suppressor specificity [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 36.

Introduction Sleep and physical activity are both important to health, but the demands of our modern schedule often require individuals to choose one over the other. In adolescents, the association between objectively measured sleep and physical activity is not well established in the literature. The aim of current study was to assess associations between free-living and physical activity and sleep among 15-year-old adolescents. Methods Free-living physical activity and sleep were assessed with wrist-worn accelerometers, sleep diary, and questionnaires during a 7-day period including school days and non-school days in 270 (161 girls) adolescents (mean age 15.8±0.3y) in Reykjavik, Iceland. Linear regression analysis was used to explore the associations between objectively measured physical activity and sleep. T-test was used to determine if there is a significant difference in objectively measured sleep between those who reported sports or exercising Results Weekly mean physical activity (2040±466 counts/min of wear/day) was negatively associated with total sleep time (6.6±0.64 h/night) (β±SE=-3.5±0.7, p Conclusion The negative association between physical activity and sleep duration suggests that in more active individuals’ physical activity may be displacing sleep. However, greater physical activity is also associated with fewer minutes of awakening and a less variable sleep schedule, indicating better sleep quality. These findings suggest that physical activity is important for good sleep quality, but students should more closely consider sleep guidelines when designing an exercise schedule. Future studies should test how change in sleep patterns might influence physical activity. Support Icelandic Centre for Research, National Institute of Diabetes and Digestive and Kidney Diseases.

Recent studies have reported that CRISPR-Cas9 gene editing induces a p53-dependent DNA damage response in primary cells, which may select for cells with oncogenic p53 mutations11,12. It is unclear whether these CRISPR-induced changes are applicable to different cell types, and whether CRISPR gene editing may select for other oncogenic mutations. Addressing these questions, we analyzed genome-wide CRISPR and RNAi screens to systematically chart the mutation selection potential of CRISPR knockouts across the whole exome. Our analysis suggests that CRISPR gene editing can select for mutants of KRAS and VHL, at a level comparable to that reported for p53. These predictions were further validated in a genome-wide manner by analyzing independent CRISPR screens and patients’ tumor data. Finally, we performed a new set of pooled and arrayed CRISPR screens to evaluate the competition between CRISPR-edited isogenic p53 WT and mutant cell lines, which further validated our predictions. In summary, our study systematically charts and points to the potential selection of specific cancer driver mutations during CRISPR-Cas9 gene editing.

Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involvesynthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (therescuer). Here we perform a genome-wide prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients’ response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.

As a widely used anti-bacterial agent and a metabolic inhibitor as well as AMP-activated protein kinase (AMPK) activator, berberine (BBR) has been shown to cross the blood-brain barrier. Its efficacy has been investigated in various disease models of the central nervous system. Neurite outgrowth is critical for nervous system development and is a highly energy-dependent process regulated by AMPK-related pathways. In the present study, we aimed to investigate the effects of BBR on AMPK activation and neurite outgrowth in neurons. The neurite outgrowth of primary rat cortical neurons at different stages of polarization was monitored after exposure of BBR. Intracellular energy level, AMPK activation and polarity-related pathways were also inspected. The results showed that BBR suppressed neurite outgrowth and affected cytoskeleton stability in the early stages of neuronal polarization, which was mediated by lowered energy status and AMPK activation. Liver kinase B1 and PI3K-Akt-GSK3β signaling pathways were also involved. In addition, mitochondrial dysfunction and endoplasmic reticulum stress contributed to the lowered energy status induced by BBR. This study highlighted the knowledge of the complex activities of BBR in neurons and corroborated the significance of energy status during the neuronal polarization.

Summary: Aims: Oxidative stress is a direct cause of injury in various neural diseases. Manganese porphyrins (MnPs), a large category of superoxide dismutase (SOD) mimics, shown universally to have effects in numerous neural disease models in vivo. Given their complex intracellular redox activities, detailed mechanisms underlying the biomedical efficacies are not fully elucidated. This study sought to investigate the regulation of endogenous antioxidant systems by a MnP (MnTM-4-PyP) and its role in the protection against neural oxidative stress. Methods: Primary cortical neurons were treated with MnTM-4-PyP prior to hydrogen peroxide-induced oxidative stress. Results: MnTM-4-PyP increased cell viability, reduced intracellular level of reactive oxygen species, inhibited mitochondrial apoptotic pathway, and ameliorated endoplasmic reticulum function. The protein levels and activities of endogenous SODs were elevated, but not those of catalase. SOD2 transcription was promoted in a transcription factor-specific manner. Additionally, we found FOXO3A and Sirt3 levels also increased. These effects were not observed with MnTM-4-PyP alone. Conclusion: Induction of various levels of endogenous antioxidant responses by MnTM-4-PyP has indispensable functions in its protection for cortical neurons against hydrogen peroxide-induced oxidative stress.

Synthetic lethality (SL) describes an interaction between a pair of genes whereby their double knockout is lethal, while their respective knockout is not. The identification of SL interactions (SLi) via large-scale genomic screens offers promising opportunities for developing selective therapies in cancer. However, our analysis of the TCGA cohort shows that many of the interactions do not carry predictive signal of patient survival or drug response. Here we present a data-driven approach termed ISLE (Identification of clinically relevant Synthetic LEthality) that mines the TCGA cohort to identify a subset of clinically relevant SL interactions (cSLi). ISLE consists of the following inference steps, analysis of tumor, cell line, and gene evolutionary data. We first create an initial pool of SL pairs identified through direct double knockout screens/isogenic cell line screens or inferred from large-scale shRNA/sgRNA single-gene knockout screens. Starting from this initial SL pool, ISLE first identifies putative SL gene pairs whose co-inactivation is under-represented in tumors, testifying that it is selected against. Second, it prioritizes candidate SL pairs whose co-inactivation is associated with improved patient’s prognosis, testifying that it may hamper tumor progression. Finally, it prioritizes SL-gene pairs with similar evolutionary phylogenetic profiles based on the notion that SL interactions are conserved across multiple species. We validate the identified SL pairs using an unseen large-scale in vitro drug response screen by showing the SL pairs marks a decent prediction accuracy (AUC~0.8). We compare ISLE’s performance to the standard supervised drug response prediction approaches in DREAM challenges, and our prediction based on generic pretreatment tumor samples (from TCGA) was within top 3 in prediction accuracy among the top predictors. ISLE-based approach also successfully distinguishes responders vs nonresponders to drug treatment (for >70% of drugs) in mouse xenografts using the activity profile of the drug target’s SL-partners. We then experimentally show the utility of SL in predicting synergistic drug combinations in patient-derived cell lines based on the notion that the two drugs whose targets have SL interactions are synergistic. Most importantly, we demonstrate for the first time that an SL network can successfully predict the treatment outcome in cancer patients in multiple large-scale patient datasets including TCGA, where cSLi are successfully predict patients’ response for more than 70% of cancer drugs. ISLE is predictive of patients’ response for the majority of current cancer drugs without any drug-specific training. Of paramount importance, the predictions of ISLE are based on SLi between (potentially) all genes in the cancer genome, thus prioritizing treatments for patients whose tumors do not bear specific actionable mutations in cancer driver genes, offering a novel approach to precision-based cancer therapy. Citation Format: Joo S. Lee, Avinash Das, Livnat Jerby-Arnon, Rand Arafeh, Matthew Davidson, Arnaud Amzallag, Seung Gu Park, Kuoyuan Cheng, Welles Robinson, Dikla Atias, Chani Stossel, Ella Buzhor, Gidi Stein, Joshua J. Waterfall, Paul S. Meltzer, Talia Golan, Sridhar Hannenhalli, Eyal Gottlieb, Cyril H. Benes, Yardena Samuels, Emma Shanks, Eytan Ruppin. Harnessing synthetic lethality to predict the response to cancer treatments [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A188.

Superoxide is the primary reactive oxygen species generated in the mitochondria. Manganese superoxide dismutase (SOD2) is the major enzymatic superoxide scavenger present in the mitochondrial matrix and one of the most crucial reactive oxygen species-scavenging enzymes in the cell. SOD2 is activated by sirtuin 3 (SIRT3) through NAD(+)-dependent deacetylation. However, the exact acetylation sites of SOD2 are ambiguous and the mechanisms underlying the deacetylation-mediated SOD2 activation largely remain unknown. We are the first to characterize SOD2 mutants of the acetylation sites by investigating the relative enzymatic activity, structures, and electrostatic potential of SOD2 in this study. These SOD2 mutations affected the superoxide-scavenging activity in vitro and in HEK293T cells. The lysine 68 (K68) site is the most important acetylation site contributing to SOD2 activation and plays a role in cell survival after paraquat treatment. The molecular basis underlying the regulation of SOD2 activity by K68 was investigated in detail. Molecular dynamics simulations revealed that K68 mutations induced a conformational shift of residues located in the active center of SOD2 and altered the charge distribution on the SOD2 surface. Thus, the entry of the superoxide anion into the coordinated core of SOD2 was inhibited. Our results provide a novel mechanistic insight, whereby SOD2 acetylation affects the structure and charge distribution of SOD2, its tetramerization, and p53-SOD2 interactions of SOD2 in the mitochondria, which may play a role in nuclear-mitochondrial communication during aging.

Significance: The identification of Synthetic Lethal interactions (SLi) has long been considered a foundation for the advancement of cancer treatment. The rapidly accumulating large-scale patient data now provides a golden opportunity to infer SLi directly from patient samples. Here we present a new data-driven approach termed ISLE for identifying SLi, which is then shown to be predictive of clinical outcomes of cancer treatment in an unsupervised manner, for the first time. Methods: ISLE consists of four inference steps, analyzing tumor, cell line and gene evolutionary data: It first identifies putative SL gene pairs whose co-inactivation is underrepresented in tumors, testifying that they are selected against. Second, it further prioritizes candidate SL pairs whose co-inactivation is associated with better prognosis in patients, testifying that they may hamper tumor progression. Finally, it eliminates false positive SLi using gene essentiality screens (testifying to causal SLi relations) and prioritizing SLi paired genes with similar evolutionary phylogenetic profiles. Results: We applied ISLE to analyze the TCGA tumor collection and generated the first clinically-derived pan-cancer SL-network, composed of SLi common across many cancer types. We validated that these SLi match the known, experimentally identified SLi (AUC=0.87), and show that the SL-network is predictive of patient survival in an independent breast cancer dataset (METABRIC). Based on the predicted SLi, we predicted drug response of single agents and drug combinations in a wide variety of in vitro, mouse xenograft and patient data, altogether encompassing >700 single drugs and >5,000 drug combinations in >1,000 cell lines, 375 xenograft models and >5,000 patient samples. Of note, these predictions were performed in an unsupervised manner, reducing the known risk of over-fitting the data commonly associated with supervised prediction methods. Our prediction is based on the notion that a drug is likely to be more effective in tumors where many of its targets’ SL-partners are inactive, and drug synergism may be mediated by underlying SLi between their targets. Most importantly, we demonstrate for the first time that an SL-network can successfully predict the treatment outcome in cancer patients in multiple large-scale patient datasets including the TCGA, where SLis successfully predict patients’ response for 75% of cancer drugs. Conclusions: ISLE is predictive of the patients’ response for the majority of current cancer drugs. Of paramount importance, the predictions of ISLE are based on SLi between (potentially) all genes in the cancer genome, thus prioritizing treatments for patients whose tumors do not bear specific actionable mutations in cancer driver genes, offering a novel approach to precision-based cancer therapy. The predictive performance of ISLE is likely to further improve with the expected rapid accumulation of additional patient data. Citation Format: Joo Sang Lee, Avinash Das, Livnat Jerby-Arnon, Seung Gu Park, Matthew Davidson, Dikla Atias, Arnaud Amzallag, Chani Stossel, Ella Buzhor, Welles Robinson, Kuoyuan Cheng, Joshua J. Waterfall, Paul S. Meltzer, Sridhar Hannenhalli, Cyril H. Benes, Talia Golan, Emma Shanks, Eytan Ruppin. Harnessing synthetic lethality to predict clinical outcomes of cancer treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 543. doi:10.1158/1538-7445.AM2017-543