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Effective, low-cost therapeutics are needed to prevent and treat COVID-19. Severe COVID-19 disease is linked to excessive inflammation. Disulfiram is an approved oral drug used to treat alcohol use disorder that is a potent anti-inflammatory agent and an inhibitor of the viral proteases. We investigated the potential effects of disulfiram on SARS-CoV-2 infection and disease severity in an observational study using a large database of clinical records from the national US Veterans Affairs healthcare system. A multivariable Cox regression adjusted for demographic information and diagnosis of alcohol use disorder revealed a reduced risk of SARS-CoV-2 infection with disulfiram use at a hazard ratio of 0.66 (34% lower risk, 95% confidence interval 24–43%). There were no COVID-19 related deaths among the 188 SARS-CoV-2 positive patients treated with disulfiram, in contrast to 5–6 statistically expected deaths based on the untreated population (P = 0.03). Our epidemiological results suggest that disulfiram may contribute to the reduced incidence and severity of COVID-19. These results support carefully planned clinical trials to assess the potential therapeutic effects of disulfiram in COVID-19.

Background Poly (ADP)-ribose polymerase (PARP) inhibitors have entered routine clinical practice for the treatment of high-grade serous ovarian cancer (HGSOC), yet the molecular mechanisms underlying treatment response to PARP1 inhibition (PARP1i) are not fully understood. Methods Here, we used unbiased mass spectrometry based proteomics with data-driven protein network analysis to systematically characterize how HGSOC cells respond to PARP1i treatment. Results We found that PARP1i leads to pronounced proteomic changes in a diverse set of cellular processes in HGSOC cancer cells, consistent with transcript changes in an independent perturbation dataset. We interpret decreases in the levels of the pro-proliferative transcription factors SP1 and β-catenin and in growth factor signaling as reflecting the anti-proliferative effect of PARP1i; and the strong activation of pro-survival processes NF-κB signaling and lipid metabolism as PARPi-induced adaptive resistance mechanisms. Based on these observations, we nominate several protein targets for therapeutic inhibition in combination with PARP1i. When tested experimentally, the combination of PARPi with an inhibitor of fatty acid synthase (TVB-2640) has a 3-fold synergistic effect and is therefore of particular pre-clinical interest. Conclusion Our study improves the current understanding of PARP1 function, highlights the potential that the anti-tumor efficacy of PARP1i may not only rely on DNA damage repair mechanisms and informs on the rational design of PARP1i combination therapies in ovarian cancer.

Background Cytokines are critical to human disease and are attractive therapeutic targets given their widespread influence on gene regulation and transcription. Defining the downstream regulatory mechanisms influenced by cytokines is central to defining drug and disease mechanisms. One promising strategy is to use interactions between expression quantitative trait loci (eQTLs) and cytokine levels to define target genes and mechanisms. Results In a clinical trial for anti-IL-6 in patients with systemic lupus erythematosus, we measure interferon (IFN) status, anti-IL-6 drug exposure, and whole blood genome-wide gene expression at three time points. We show that repeat transcriptomic measurements increases the number of cis eQTLs identified compared to using a single time point. We observe a statistically significant enrichment of in vivo eQTL interactions with IFN status and anti-IL-6 drug exposure and find many novel interactions that have not been previously described. Finally, we find transcription factor binding motifs interrupted by eQTL interaction SNPs, which point to key regulatory mediators of these environmental stimuli and therefore potential therapeutic targets for autoimmune diseases. In particular, genes with IFN interactions are enriched for ISRE binding site motifs, while those with anti-IL-6 interactions are enriched for IRF4 motifs. Conclusions This study highlights the potential to exploit clinical trial data to discover in vivo eQTL interactions with therapeutically relevant environmental variables.

BackgroundExamination of histopathology slides is a crucial step in making cancer diagnoses and treatment decisions. Rapid developments in machine learning models in digital pathology have enabled quantitative high-resolution information to be extracted from whole-slide images. Meanwhile, genomic tests and molecular assays have also become powerful in assisting pathologists and oncologists in decision making, but these tests are not routinely performed to consistently provide molecular information. In this study, we developed tissue and cell classification models using Hematoxylin and Eosin-stained (H&E) slides, extracted human-interpretable features (HIFs) quantifying the tumor microenvironment, and investigated the association between abundance and distribution of tumor infiltrating lymphocytes (TILs) and molecular phenotypes.MethodsWe trained convolutional neural network-based tissue and cell classification models using H&E slides with annotations collected from US board-certified pathologists, resulting in PathExplore models specific for eight indications, including breast cancer, colorectal cancer, gastric cancer, melanoma, non-small cell lung cancer, pancreatic cancer, prostate cancer, and renal cell carcinoma. We deployed the models on the corresponding indications in TCGA data and quantified HIFs for over 5,000 slides across 13 cancer types. We then analyzed the TIL-associated HIFs with publicly available gene expression and immune signature data.ResultsTIL-associated HIFs, such as the frequency of TILs within cancer tissue (cTIL frequency), were correlated with gene expression of known lymphocyte markers, such as CD8A (median Spearman ρ = 0.539 for individual indications), CD3G (ρ = 0.536), and CD2 (ρ = 0.536). Regularized regression models using a panel of TIL-associated HIFs accurately predicted median-binarized expression of these three genes (median AUROC 0.751–0.755 for individual indications, pan-cancer AUROC 0.775–0.782) with best performance in melanoma (AUROC 0.831–0.850). We found good correlations between cTIL frequency with immune signature scores derived from gene expression, including a published lymphocyte infiltration signature score1 (ρ = 0.504) and T-cell signature score2 (ρ = 0.409). In particular, classification models using TIL-associated HIFs can predict the inflammatory subtype (C3 subtype in,1 median AUROC = 0.691, pan-cancer AUROC = 0.765 ± 0.008, 5-fold cross-validation) and the immune-enriched non-fibrotic subtype (IE subtype in,2 median AUROC = 0.755, pan-cancer AUROC = 0.737 ± 0.017).ConclusionsHistopathology image-based quantification of TILs is consistently associated with immune phenotypes derived from molecular measurements. These results suggest that quantitative HIFs extracted from tissue and cell classification models provide rich information for understanding of inflammation in the tumor microenvironment and potential discovery of immune biomarkers.AcknowledgementsThe results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.ReferencesThorsson A, et al. The Immune Landscape of Cancer. Immunity. 2018;48–4:812–830Bagaev A, et al. Conserved pan-cancer microenvironment subtypes predict response to immunotherapy. Cancer Cell. 2021;39:845–865

Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation–response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation–response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth. scPerturb is an information resource for single-cell perturbation data analysis and comparison.

Combination therapy in cancer can provide enhanced anticancer efficacy and reduce the risk of drug resistance. Focused molecular experiments to discover combinations in a large search space are inefficient while high-throughput screening approaches do not provide sufficient mechanistic insights. We present two systematic approaches for nominating combination therapies, i) computational prediction of cellular response to unseen combination perturbations based on network models of cell biology, and ii) unbiased proteomic profiling of cellular response upon drug treatment to identify resistance mechanisms.Systematic perturbation of cells followed by comprehensive measurements of molecular and phenotypic responses provides information for constructing computational models of cell biology. Using a perturbation-response dataset of a melanoma cell line after drug treatments as a testbed, we developed a hybrid approach that combines explicit mathematical models of cell dynamics with a machine-learning framework to quantitatively predict cell behavior in response to perturbation of molecular targets. When sufficient perturbation-response data is available for model training, this model can predict cellular response to a vast number of unseen combination perturbations and therefore efficiently narrow down the search space and nominate promising sets of experimentally testable combination candidates.Investigation of resistance mechanisms can be used for the rational design of combination therapy. In an alternative approach, we used unbiased quantitative protein mass spectrometry to assess the cellular response profile to a small number of anti-cancer drug perturbations in ovarian cancer cells. Data-driven protein network analysis revealed known and novel markers of resistance, which we used to propose combination drug candidates. In a first round of validation experiments, synergistic and additive effects were observed for some combination candidates across multiple ovarian cancer cell lines, suggesting potential therapeutic value for future pre-clinical and clinical studies.Both systematic approaches can be used to effectively nominate combination therapies, provided the availability of sufficiently informative perturbation-response data. With further validation, the proposed combination candidates may contribute to the development of novel effective cancer therapeutics. We believe the approaches can be generalized to other cancer types and potentially be applied to other areas of cell biology.

BackgroundMelanoma is the most aggressive type of skin cancer and often exhibits therapeutic resistance.1 2 Different types of mutagenesis, for example UV exposure,3 4 have been shown to result in distinct genetic signatures; however, their impact on histological features of the tumor microenvironment (TME) and response to treatment remains unknown. Alkylating agents are one of the most commonly used chemo-therapeutics for melanoma5; however, the impact of alkylating agents on the melanoma TME is also poorly understood. In this work, we quantified the TME in melanoma using machine learning and investigated TME feature associations with 1) increased UV mutagenesis, and 2) alkylating agent-induced mutations.MethodsPathExplore convolutional neural network-based models using hematoxylin and eosin (H&E)-stained whole slide images (WSI) were trained to classify histologic substances in the TME (table 1). We quantified model performance using nested pairwise comparisons with pathologist annotation.6 We deployed PathExplore Melanoma along with a separately trained stromal subtyping model7 to extract human-interpretable features (HIFs) that quantify the TME across each WSI in the TCGA (SKCM, N=363) cohort. We identified mutational signatures indicative of UV and alkylating agents using the deconstructSigs R package.8 We utilized primary (N=71) and lymph node metastasis (N=255) slides for UV exposure analysis, and only primary slides for alkylating agent analysis. We quantified associations between HIFs and mutational signatures using univariate logistic regressions. P-values were corrected using Benjamini-Hochberg. Multivariable Cox models were used for survival analysis.ResultsWe found a positive association between tumor-infiltrating lymphocyte (TIL) abundance (p=0.01), as well as the area proportion of densely inflamed stromal regions (p=0.015), with UV exposure. Features quantifying neutrophil abundance were associated with alkylating agent treatment, most notably neutrophil-to-lymphocyte ratio (NLR; p=0.013). Higher NLR was associated with worse overall survival in general, but this effect was attenuated in patients previously treated with alkylating agents.ConclusionsWe found that TIL abundance was associated with UV exposure, likely due to increased tumor mutational burden, which may have implications for immunotherapy. Additionally, NLR has previously been associated with poor prognosis in melanoma.9 10 Our results indicate that the effect of NLR on prognosis is also mediated by prior treatment, pointing to a complex causal web between TME, treatment, and patient outcomes. Broadly, these results suggest that machine learning can extract meaningful information regarding underlying mutation-driven or treatment-induced changes in the TME.ReferencesKavran, Andrew J, et al. ‘Intermittent treatment of BRAFV600E melanoma cells delays resistance by adaptive resensitization to drug rechallenge.’ Proceedings of the National Academy of Sciences 2022;119(12):e2113535119.Rossi, Alessandro, et al. ‘Drug resistance of BRAF-mutant melanoma: Review of up-to-date mechanisms of action and promising targeted agents.’ European journal of pharmacology 2019;862:172621.Autier, Philippe, Jean-François Doré. ‘Ultraviolet radiation and cutaneous melanoma: a historical perspective.’ Melanoma Research 2020;30(2):113–125.Dousset, Léa, et al. ‘Positive association between location of melanoma, ultraviolet signature, tumor mutational burden, and response to anti-PD-1 therapy.’ JCO precision oncology 2021;5:1821–1829.Arozarena, Imanol, et al. ‘Differential chemosensitivity to antifolate drugs between RAS and BRAF melanoma cells.’ Molecular Cancer 2014;13(1):1–13.Gerardin, Ylaine, et al. ‘Improved statistical benchmarking of digital pathology models using pairwise frames evaluation.’ arXiv preprint arXiv:2306.04709 (2023).Najdawi, Fedaa, et al. ‘Artificial intelligence (AI)-based classification of stromal subtypes reveals associations between stromal composition and prognosis in NSCLC.’ Cancer Research 2023;83(7_Supplement):5447–5447.Rosenthal R, McGranahan N, Herrero J, Taylor BS, Swanton C. DeconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biol. 2016 Feb 22;17:31. doi: 10.1186/s13059–016-0893–4. PMID: 26899170; PMCID: PMC4762164.Capone, Mariaelena, et al. ‘Baseline neutrophil-to-lymphocyte ratio (NLR) and derived NLR could predict overall survival in patients with advanced melanoma treated with nivolumab.’ Journal for immunotherapy of cancer 2018;6:1–7.Cohen, Joshua T, Thomas J Miner, Michael P Vezeridis. ‘Is the neutrophil-to-lymphocyte ratio a useful prognostic indicator in melanoma patients?.’ Melanoma Management 2020;7(3):MMT47.Abstract 110 Table 1Cell and tissue substances in PathExplore Melanoma.

Effective, low-cost therapeutics are needed to prevent and treat COVID-19. Severe COVID-19 disease is linked to excessive inflammation. Disulfiram is an approved oral drug used to treat alcohol use disorder that is a potent anti-inflammatory agent and an inhibitor of the viral proteases. We investigated the potential effects of disulfiram on SARS-CoV-2 infection and disease severity in an observational study using a large database of clinical records from the national US Veterans Affairs healthcare system. A multivariable Cox regression adjusted for demographic information and diagnosis of alcohol use disorder revealed a reduced risk of SARS-CoV-2 infection with disulfiram use at a hazard ratio of 0.66 (34% lower risk, 95% confidence interval 24-43%). There were no COVID-19 related deaths among the 188 SARS-CoV-2 positive patients treated with disulfiram, in contrast to 5-6 statistically expected deaths based on the untreated population (P = 0.03). Our epidemiological results suggest that disulfiram may contribute to the reduced incidence and severity of COVID-19. These results support carefully planned clinical trials to assess the potential therapeutic effects of disulfiram in COVID-19.

High-grade serous ovarian cancer (HGSOC) remains the most lethal gynecologic malignancy and novel treatment approaches are needed. Here, we used unbiased quantitative protein mass spectrometry to assess the cellular response profile to drug perturbations in ovarian cancer cells for the rational design of potential combination therapies. Analysis of the perturbation profiles revealed proteins responding across several drug perturbations (called frequently responsive below) as well as drug-specific protein responses. The frequently responsive proteins included proteins that reflected general drug resistance mechanisms such as changes in drug efflux pumps. Network analysis of drug-specific protein responses revealed known and potential novel markers of resistance, which were used to rationalize the design of anti-resistance drug pairs. We experimentally tested the anti-proliferative effects of 12 of the proposed drug combinations in 6 HGSOC cell lines. Drug combinations tested with additive or synergistic effects are plausible candidates for overcoming or preventing resistance to single agents; these include several combinations that were synergistic (with PARPi, MEKi, and SRCi). Additionally, we observed 0.05-0.11 micromolar response to GPX4 inhibitors as single agents in the OVCAR-4 cell line. We propose several drug combinations as potential therapeutic candidates in ovarian cancer, as well as GPX4 inhibitors as single agents.