We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states. Deconvolution methods infer levels of immune infiltration from bulk expression of tumour samples. Here, authors assess 6 published and 22 community-contributed methods via a DREAM Challenge using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells.

Supplementary data from PBRM1 Deficiency Confers Synthetic Lethality to DNA Repair Inhibitors in Cancer

Background Contamination of sterile pharmaceutical products can have serious consequences, in worst case scenario resulting in patient death. Cleanroom operators are the primary source of microbial contamination, where the surface of their specialist sterile clothing garments is subject to such contamination during wear. In turn these garments become a transmission vector for microorganisms within the cleanroom environment. Insight into identification of predominant bacterial isolates from garment surfaces would help to establish their original source and probable contamination route. This should assist possible intervention strategies to mitigate against this contamination. Aim The research aimed to determine identity of representative bacterial isolates recovered from the surface of cleanroom operators’ garments following wear within a cleanroom. Methods Following isolation and purification of bacterial isolates, 16S rRNA gene sequencing was used to establish species identity for isolates recovered from the surface of male and female operators’ garments following wear within the cleanroom environment. Results Of the 47 isolates recovered from the surface of garments, 16S rRNA gene sequencing successfully identified 94 % to genus level and 77 % to species level. Most were confirmed as Gram - positive bacteria; predominantly species of Staphylococcus, Micrococcus and Bacillus. The isolates recovered from the surface of female operatives’ garments were more diverse than those retrieved from male counterparts. Conclusion Most isolates recovered from garments were found to be skin commensals, with nearly 70% attributed to the operators within the environment. The remainder were credited to contamination of garments with species of environmental origin. Whilst most bacteria identified present minimal threat to healthy individuals, certain of these are opportunistic pathogens, presenting a hazard for immunocompromised and/or those with underlying health conditions.

Deconvolution methods infer levels of immune and stromal infiltration from bulk expression of tumor samples. These methods allow projection of characteristics of the tumor microenvironment, known to affect patient outcome and therapeutic response, onto the millions of bulk transcriptional profiles in public databases, many focused on uniquely valuable and clinically-annotated cohorts. Despite the wide development of such methods, a standardized dataset with ground truth to evaluate their performance has been lacking. We generated and sequenced in vitro and in silico admixtures of tumor, immune, and stromal cells and used them as ground truth in a community-wide DREAM Challenge that provided an objective, unbiased 3 . CC-BY-NC-ND 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted June 5, 2022. ; https://doi.org/10.1101/2022.06.03.494221 doi: bioRxiv preprint

Background With the increased volume of genomics data from studies involving treatment with immune checkpoint inhibition (ICI) and other immunotherapies, researchers remain unable to to make full use of results due to lack of comprehensive access to data or th ability to compare outcomes across datasets.The Cancer Research Institute (CRI) iAtlas1 (www.cri-iatlas.org) is a comprehensive web platform for interactive data exploration and discovery in immuno-oncology, originating in a study by The Cancer Genome Atlas (TCGA).1–3 iAtlas provides topic-oriented analysis modules, each generating visualizations and statistics for studying interactions between tumors and the immune microenvironment (figure 1). Methods Immunogenomic features from 15 ICI trials encompassing 1,142 samples were processed with a standardized bioinformatics workflow4 and incorporated into iAtlas, augmenting the 11,535 patient samples from TCGA1–3 and the Pan-Cancer Analysis of Whole Genomes5 consortia. A compendium of in-development immunotherapy drug targets6 and results of a study of germline genetic contribution to immune response in cancer7 were included. For efficient access, all data were incorporated into a relational database, and programmatic access was made available through an application programming interface (API) (figure 2). The set of available iAtlas modules was vastly extended, and numerous improvements were made to the codebase. Users can now define sample cohorts and sample groups based on any available categorical or numerical variable. Results iAtlas provides 17 interactive analysis modules (table 1) to explore immune-cancer interactions, immunotherapy treatment, and outcomes in 12,677 patient samples. Six modules are dedicated to ICI studies: dataset overview, immune readouts, immunomodulators, clinical outcome, regression analysis, and a machine learning module to enable identification of factors associated with response to therapy (figure 3). We added modules to explore how germline variation and copy number alterations relate to immune response, and how receptor-ligand interactions mediate interactions among tumor and immune cells (figure 4). Docker images using Common Workflow Language descriptors are provided so that researchers can run iAtlas workflows on their own data. Computational notebooks are provided to illustrate and explain iAtlas code, plots, and functionality and to facilitate integration of iAtlas data with data sourced from a researcher's own study. Conclusions iAtlas serves as a repository and resource for harmonized data on immune response in cancer and response to immunotherapy. iAtlas enables researchers to readily test hypotheses and access data through multiple modalities: an interactive web portal, data download, tools,8 and computational workflows and notebooks. Acknowledgements This work is supported by the Cancer Research Institute. We thank Allison Kudla, Institute for Systems Biology, for generating the illustration used in the Cell-Interaction Diagram module and for web design and implementation. References Eddy JA, Thorsson V, Lamb AE, Gibbs DL, Heimann C, Yu JX, et al. CRI iAtlas: an interactive portal for immuno-oncology research. F1000Research 2020;9:1028. https://doi.org/10.12688/f1000research.25141.1. Hutter C, Zenklusen JC. The cancer genome atlas: creating lasting value beyond its data. Cell 2018;173:283–5. https://doi.org/10.1016/j.cell.2018.03.042. Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang T-H, et al. The immune landscape of cancer. Immunity 2018;48:812–830.e14. https://doi.org/10.1016/j.immuni.2018.03.023. Bortone DS, Vensko SP, Dexheimer S, Thorsson V, Zappasodi R, Rudqvist N-P, Vincent, BG et al. Generalizability of predictive versus prognostic indicators from published transcriptomic associations with tumor response to immune checkpoint inhibition. SITC Annual Meeting 2022, submitted. ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 2020;578:82–93. https://doi.org/10.1038/s41586-020-1969-6. Upadhaya S, Hubbard-Lucey VM, Yu JX. Immuno-oncology drug development forges on despite COVID-19. Nat Rev Drug Discov 2020;19:751–2. https://doi.org/10.1038/d41573-020-00166-1. Sayaman RW, Saad M, Thorsson V, Hu D, Hendrickx W, Roelands J, et al. Germline genetic contribution to the immune landscape of cancer. Immunity 2021;54:367–386.e8. https://doi.org/10.1016/j.immuni.2021.01.011. Gibbs DL. Robust classification of immune subtypes in cancer. bioRxiv 2020:2020.01.17.910950. https://doi.org/10.1101/2020.01.17.910950.

The accurate identification and quantitation of RNA isoforms present in the cancer transcriptome is key for analyses ranging from the inference of the impacts of somatic variants to pathway analysis to biomarker development and subtype discovery. The ICGC-TCGA DREAM Somatic Mutation Calling in RNA (SMC-RNA) challenge was a crowd-sourced effort to benchmark methods for RNA isoform quantification and fusion detection from bulk cancer RNA sequencing (RNA-seq) data. It concluded in 2018 with a comparison of 77 fusion detection entries and 65 isoform quantification entries on 51 synthetic tumors and 32 cell lines with spiked-in fusion constructs. We report the entries used to build this benchmark, the leaderboard results, and the experimental features associated with the accurate prediction of RNA species. This challenge required submissions to be in the form of containerized workflows, meaning each of the entries described is easily reusable through CWL and Docker containers at https://github.com/SMC-RNA-challenge. A record of this paper's transparent peer review process is included in the supplemental information.

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.

Abstract Inactivation of Polybromo 1 (PBRM1), a specific subunit of the PBAF chromatin remodeling complex, occurs frequently in cancer, including 40% of clear cell renal cell carcinomas (ccRCC). To identify novel therapeutic approaches to targeting PBRM1-defective cancers, we used a series of orthogonal functional genomic screens that identified PARP and ATR inhibitors as being synthetic lethal with PBRM1 deficiency. The PBRM1/PARP inhibitor synthetic lethality was recapitulated using several clinical PARP inhibitors in a series of in vitro model systems and in vivo in a xenograft model of ccRCC. In the absence of exogenous DNA damage, PBRM1-defective cells exhibited elevated levels of replication stress, micronuclei, and R-loops. PARP inhibitor exposure exacerbated these phenotypes. Quantitative mass spectrometry revealed that multiple R-loop processing factors were downregulated in PBRM1-defective tumor cells. Exogenous expression of the R-loop resolution enzyme RNase H1 reversed the sensitivity of PBRM1-deficient cells to PARP inhibitors, suggesting that excessive levels of R-loops could be a cause of this synthetic lethality. PARP and ATR inhibitors also induced cyclic GMP-AMP synthase/stimulator of interferon genes (cGAS/STING) innate immune signaling in PBRM1-defective tumor cells. Overall, these findings provide the preclinical basis for using PARP inhibitors in PBRM1-defective cancers. Significance: This study demonstrates that PARP and ATR inhibitors are synthetic lethal with the loss of PBRM1, a PBAF-specific subunit, thus providing the rationale for assessing these inhibitors in patients with PBRM1-defective cancer.

BackgroundSpecialist cleanroom garments are a potential vector for transmission of microorganisms within these facilities. In order to maintain the low bioburden of such clothing it has been perceived best practice for operators to dress wearing sterile cleanroom gloves. However, the efficacy of such glove use upon the resultant bacterial contamination of the surface of cleanroom garments has not previously been evaluated. AimTo compare surface bacterial contamination of cleanroom garments following their donning by operators dressing with or without gloves.MethodsFollowing prior handwashing and systematic donning of cleanroom clothing by operators dressing wearing either no gloves, non-sterile nitrile gloves or sterile cleanroom latex gloves, a direct agar contact method was immediately undertaken to test garment surfaces at 7 specific sites. Following incubation bacterial levels were suitably quantified. FindingsComparing levels of growth displayed on plates used to test the surface of cleanroom garments worn by operators dressing with no gloves, non–sterile gloves or sterile cleanroom gloves, no significant difference was observed between the percentage of contact plates displaying growth and the levels of growth observed on plates, from any of the sites tested.ConclusionOmission of gloves in line with a systematic handwashing procedure prior to the cleanroom garment donning process, may result in modest economic and environmental gain coupled with a slightly less burdensome procedure. However, this is reliant on rigorous adherence to handwashing protocol and assessment of associated risk factors.

Objectives The role of rhizosphere microbiome in supporting plant growth under biotic stress is well documented. Rhizobacteria ward off phytopathogens through various mechanisms including antibiosis. We sought to recover novel antibiotic-producing bacterial strains from soil samples collected from the rhizosphere. Pseudomonas fragi A13BB was recovered as part of this effort, and the whole genome was sequenced to facilitate mining for potential antibiotic-encoding biosynthetic gene clusters. Data description Here, we report the complete genome sequence of P. fragi A13BB obtained from de novo assembly of Illumina MiSeq and GridION reads. The 4.94 Mb genome consists of a single chromosome with a GC content of 59.40%. Genomic features include 4410 CDSs, 102 RNAs, 3 CRISPR arrays, 3 prophage regions, and 37 predicted genomic islands. Two β-lactone biosynthetic gene clusters were identified; besides, metabolic products of these are known to show antibiotic and/or anticancer properties. A siderophore biosynthetic gene cluster was also identified even though P. fragi is considered a non-siderophore producing pseudomonad. Other gene clusters of broad interest identified include those associated with bioremediation, biocontrol, plant growth promotion, or environmental adaptation. This dataset unveils various un−/underexplored metabolic or biosynthetic potential of P. fragi and provides insight into molecular mechanisms underpinning these attributes.

Objectives The urgent need for novel antibiotics cannot be overemphasized. Hafnia alvei A23BA was isolated from plant rhizosphere as part of an effort to recover novel antibiotic-producing bacterial strains from soil samples. The genome of the isolate was sequenced to facilitate mining for potential antibiotic-encoding biosynthetic gene clusters and to gain insights into how these gene clusters could be activated. Data description Here, we report the complete genome sequence of H. alvei A23BA obtained from the hybrid assembly of Illumina HiSeq and GridION reads. The genome, consisting of a circular chromosome and a circular plasmid, is 4.77 Mb in size with a GC content of 48.77%. The assembly is 99.5% complete with genomic features including 4,217 CDSs, 125 RNAs, and 30 pseudogenes. Thiopeptide, beta-lactone, siderophore, and homoserine lactone biosynthetic gene clusters were also identified. Other gene clusters of interest include those associated with bioremediation, biocontrol, and plant growth promotion- all of which are reported for H. alvei for the first time. This dataset serves to expedite the exploration of the biosynthetic and metabolic potentials of the species. Furthermore, being the first published genome sequence of a soil isolate, this dataset enriches the comparative genomics study of H. alvei strains.

Anna Cichonska1,2,3*, Balaguru Ravikumar1*, Robert J Allaway4*, Sungjoon Park5, Fangping Wan6, Olexandr Isayev7, Shuya Li6, Michael Mason4, Andrew Lamb4, Ziaurrehman Tanoli1, Minji Jeon5, Sunkyu Kim5, Mariya Popova7, Stephen Capuzzi8, Jianyang Zeng6, Kristen Dang4, Gregory Koytiger9, Jaewoo Kang5, Carrow I. Wells10, Timothy M. Willson10, The IDG-DREAM Drug-Kinase Binding Prediction Challenge Consortium, Tudor I. Oprea11, Avner Schlessinger12, David H. Drewry10, Gustavo Stolovitzky13, Krister Wennerberg14**, Justin Guinney4**, Tero Aittokallio1,2,15,16,17**

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community.

Despite decades of intensive search for compounds that modulate the activity of particular targets, there are currently small-molecules available only for a small proportion of the human proteome. Effective approaches are therefore required to map the massive space of unexplored compound-target interactions for novel and potent activities. Here, we carried out a crowdsourced benchmarking of predictive models for kinase inhibitor potencies across multiple kinase families using unpublished bioactivity data. The top-performing predictions were based on kernel learning, gradient boosting and deep learning, and their ensemble resulted in predictive accuracy exceeding that of kinase activity assays. We then made new experiments based on the model predictions, which further improved the accuracy of experimental mapping efforts and identified unexpected potencies even for under-studied kinases. The open-source algorithms together with the novel bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking new prediction algorithms and for extending the druggable kinome.

Operators are the primary source of cleanroom contamination, with the majority of their detritus identified as skin squames and their associated microorganisms. To reduce contamination, operatives are required to wear a specific arrangement of specialist garments. However, bacteria can evade this clothing and tarnish outer surfaces whilst operators work, with adverse implication for cleanroom environment and product sterility. Gender plays a significant role in bacterial dispersion, with male rates being in excess of female counterparts. Currently there is a lack of published literature evaluating the effect of gender on contamination of cleanroom garments. Such information would assist cleanroom facilities to more robustly assess and mitigate operator-associated contamination risks.

Aim: Polybromo-1 (PBRM1), a specific subunit of the pBAF chromatin remodeling complex, is frequently inactivated in cancer. For example, 40% of clear cell Renal Cell Carcinoma (ccRCC) and 15% of cholangiocarcinoma present deleterious PBRM1 mutations. There is currently no precision medicine-based therapeutic approach that targets PBRM1 defects. To identify novel, targeted therapeutic strategies for PBRM1-defective cancers, we carried out high-throughput functional genomics and drug screenings followed by in vitro and in vivo validation studies. Methods: High-throughput siRNA-drug sensitization and drug sensitivity screens evaluating \u003e 150 cancer-relevant small molecules in dose-response were performed in Pbrm1 siRNA-transfected mouse embryonic stem cells (mES) and isogenic PBRM1-KO or -WT HAP1 cells, respectively. After identification of PBRM1-selective small molecules, revalidation was carried out in a series of in-house-generated isogenic models of PBRM1 deficiency - including 786-O (ccRCC), A498 (ccRCC), U2OS (osteosarcoma) and H1299 (non-small cell lung cancer) human cancer cell lines - and non-isogenic ccRCC models, using multiple clinical compounds. Mechanistic dissection was performed using immunofluorescence, RT-qPCR, western blotting, DNA fiber assay, transcriptomics, proteomics and DRIP-sequencing to evaluate markers of DNA damage response (DDR), replication stress and cell-autonomous innate immune signaling. Preclinical data were integrated with TCGA tumor data. Results: Parallel high-throughput drug screens independently identified PARP inhibitors (PARPi) as being synthetic lethal with PBRM1 defects - a cell type-independent effect which was exacerbated by ATR inhibitors (ATRi) and which we revalidated in vitro in isogenic and non-isogenic systems and in vivo in a xenograft model. PBRM1 defects were associated with increased replication fork stress (higher γH2AX and RPA foci levels, decreased replication fork speed and increased ATM checkpoint activation), R-loop accumulation and enhanced genomic instability in vitro; these effects were exacerbated upon PARPi exposure. In patient tumor samples, we also found that PBRM1-mutant cancers possessed a higher mutational load. Finally, we found that ATRi selectively activated the cGAS/STING cytosolic DNA sensing pathway in PBRM1-deficient cells, resulting in increased expression of type I interferon genes. Conclusion: PBRM1-defective cancer cells present increased replication fork stress, R-loop formation, genome instability and are selectively sensitive to PARPi and ATRi through a synthetic lethal mechanism that is cell type-independent. Our data provide the pre-clinical rationale for assessing PARPi as a monotherapy or in combination with ATRi or immune-modulating agents in molecularly-selected patients with PBRM1-defective cancers. Citation Format: Roman Merial Chabanon, Daphne Morel, Leo Colmet-Daage, Thomas Eychenne, Nicolas Dorvault, Ilirjana Bajrami, Marlene Garrido, Suzanna Hopkins, Cornelia Meisenberg, Andrew Lamb, Theo Roumeliotis, Samuel Jouny, Clemence Astier, Asha Konde, Genevieve Almouzni, Jyoti Choudhary, Jean-Charles Soria, Jessica Downs, Christopher J. Lord, Sophie Postel-Vinay. Targeting chromatin remodeling-associated genetic vulnerabilities in cancer: PBRM1 defects are synthetic lethal with PARP and ATR inhibitors [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 1058.

Abstract Neoantigen-based therapies hold the promise of being safe, personalized anti-cancer therapies for a broad range of cancers. A crucial step for the development of effective neoantigen therapies is the identification of putative tumor specific neoantigens typically achieved through in silico analyses. In silico-based approaches have the benefit of being fast, modular and reproducible. A wide range of tools are available for variant calling, determining peptide-HLA binding affinity, peptide foreignness, agritopicity and more, and researchers are continuously adding to the lot. However, there does not currently exist a common reference data set with which these different approaches can be compared, and the key parameters for effective neoantigen identification remain elusive. Here we introduce a consortium-based initiative, the Tumor nEoantigen SeLection Alliance (TESLA), to address these needs and describe straight-forward strategies through which the sensitivity and ranking ability of neoantigen prediction methods can be improved. TESLA participants were provided with whole exome and RNA sequencing from six tumors (3 melanoma and 3 NSCLC) and in turn submitted predictions for immunogenic peptides. Twenty-five groups from academia, pharma and biotech participated to this challenge. Peptides identification and ranking across the teams were assessed and showed little overlap. The limited overlap could not be explained simply by differences in variant calling. From these predictions, 608 peptides regrouping the teams' highly ranked epitopes plus epitopes frequently called and ranked across the teams, were assessed for T-cell recognition in patient-matched samples as well as for in-vitro MHC binding affinity. Analyses showed that peptides recognized by T-cells had significantly stronger MHC binding affinity and were derived from genes with significantly higher gene expression. Concomitantly, neoantigen pipelines which prioritized epitopes with strong binding affinity and/or which filtered out those originating from genes with low tumor variant allele fraction or low gene expression were associated with improved ability to identify and rank neoantigens. Direct interventions on participant predictions using these identified traits demonstrated substantial improvement in the performance of the neoantigen predictions. Pipeline analysis indicated that there are a range of approaches to neoantigen prediction and improving upon an existing neoantigen pipeline requires assessing that pipeline for a range of characteristics, including variant detection ability, peptide filtering ability, and peptide ranking ability, and improving those areas where the pipeline performance is sub-optimal. TESLA data will be continually available and serve as a living benchmark to improve neoantigen prediction. Citation Format: Daniel K. Wells, Kristen Dang, Vanessa M. Hubbard-Lucey, Kathleen C. Sheehan, Andrew Lamb, Jeffrey P. Ward, John Sidney, Ana B. Blazquez, Andrew J. Rech, Jesse Zaretsky, Begonya Comin-Anduix, Alphonsus H. Ng, William Chour, Thomas V. Yu, Hira Rizvi, Jia Chen, Patrice Manning, Taha Merghoub, Justin Guinney, Adam Kolom, Cheryl Selinsky, Antoni Ribas, Matthew D. Hellmann, Ton N. Schumacher, Nir Hacohen, Pia Kvistborg, Alessandro Sette, James R. Heath, Nina Bhardwaj, Fred Ramsdell, Robert D. Schreiber, Nadine A. Defranoux, TESLA Consortium. Strategies to improve the sensitivity and ranking ability of neoantigen prediction methods: Report on the results of the Tumor nEoantigen SeLection Alliance (TESLA) [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 3210.

Genome-wide association studies (GWAS) have identified multiple associations with emphysema apicobasal distribution (EABD), but the biological functions of these variants are unknown. To characterize the functions of EABD-associated variants, we integrated GWAS results with 1) expression quantitative trait loci (eQTL) from the Genotype Tissue Expression (GTEx) project and subjects in the COPDGene (Genetic Epidemiology of COPD) study and 2) cell type epigenomic marks from the Roadmap Epigenomics project. On the basis of these analyses, we selected a variant near ACVR1B (activin A receptor type 1B) for functional validation. SNPs from 168 loci with P values less than 5 × 10-5 in the largest GWAS meta-analysis of EABD were analyzed. Eighty-four loci overlapped eQTL, with 12 of these loci showing greater than 80% likelihood of harboring a single, shared GWAS and eQTL causal variant. Seventeen cell types were enriched for overlap between EABD loci and Roadmap Epigenomics marks (permutation P < 0.05), with the strongest enrichment observed in CD4+, CD8+, and regulatory T cells. We selected a putative causal variant, rs7962469, associated with ACVR1B expression in lung tissue for additional functional investigation, and reporter assays confirmed allele-specific regulatory activity for this variant in human bronchial epithelial and Jurkat immune cell lines. ACVR1B expression levels exhibit a nominally significant association with emphysema distribution. EABD-associated loci are preferentially enriched in regulatory elements of multiple cell types, most notably T-cell subsets. Multiple EABD loci colocalize to regulatory elements that are active across multiple tissues and cell types, and functional analyses confirm the presence of an EABD-associated functional variant that regulates ACVR1B expression, indicating that transforming growth factor-β signaling plays a role in the EABD phenotype. Clinical trial registered with www.clinicaltrials.gov (NCT00608764).

The cyclic GMP-AMP synthase/stimulator of IFN genes (cGAS/STING) pathway detects cytosolic DNA to activate innate immune responses. Poly(ADP-ribose) polymerase inhibitors (PARPi) selectively target cancer cells with DNA repair deficiencies such as those caused by BRCA1 mutations or ERCC1 defects. Using isogenic cell lines and patient-derived samples, we showed that ERCC1-defective non-small cell lung cancer (NSCLC) cells exhibit an enhanced type I IFN transcriptomic signature and that low ERCC1 expression correlates with increased lymphocytic infiltration. We demonstrated that clinical PARPi, including olaparib and rucaparib, have cell-autonomous immunomodulatory properties in ERCC1-defective NSCLC and BRCA1-defective triple-negative breast cancer (TNBC) cells. Mechanistically, PARPi generated cytoplasmic chromatin fragments with characteristics of micronuclei; these were found to activate cGAS/STING, downstream type I IFN signaling, and CCL5 secretion. Importantly, these effects were suppressed in PARP1-null TNBC cells, suggesting that this phenotype resulted from an on-target effect of PARPi on PARP1. PARPi also potentiated IFN-gamma-induced PD-L1 expression in NSCLC cell lines and in fresh patient tumor cells; this effect was enhanced in ERCC1-deficient contexts. Our data provide a preclinical rationale for using PARPi as immunomodulatory agents in appropriately molecularly selected populations.