In order to avoid immune predation, particularly in response to immunotherapy, tumors must acquire mechanisms to evade immune detection or withstand its activity. Disruption of human leukocyte antigen (HLA), which may lead to reduced neoantigen presentation, has been proposed as an immune escape strategy in many cancers, including lung cancer. A high proportion of cancer types have been found to downregulate HLA expression, potentially leading to the evasion of T cell-mediated destruction (Hicklin, 1999; Garrido, 2017) with reduced HLA expression associating with decreased overall survival and tumor progression (Mehta, 2008). Another irreversible means of HLA disruption is via loss of heterozygosity (LOH) at the HLA locus (Koopman, 2000). However, to date, the diversity of the HLA locus has impeded copy number analysis and determination of HLA loss. To investigate the prevalence, timing, and importance of HLA disruption, we developed LOHHLA (Loss Of Heterozygosity in Human Leukocyte Antigen), a computational tool to determine HLA allele-specific copy number from sequencing data. Supporting the notion that loss of antigen presentation may play an important role in immune evasion during tumor evolution, HLA LOH was identified in 40% of the NSCLC samples analyzed, frequently as a late event in tumor evolution and associated with a high subclonal neoantigen burden, increase in immune infiltration, and PD-L1 positivity. Characterizing HLA haplotype specific copy number with LOHHLA refines neoantigen prediction and may have implications for immunotherapeutic approaches targeting neoantigens. Citation Format: Nicholas McGranahan, Rachel Rosenthal, Crispin T. Hiley, Andrew J. Rowan, Thomas B.K. Watkins, Gareth A. Wilson, Nicolai J. Birkbak, Selvaraju Veeriah, Peter Van Loo, Javier Herrero, Charles Swanton. Allele-specific HLA loss and immune escape in lung cancer evolution [abstract]. In: Proceedings of the Fifth AACR-IASLC International Joint Conference: Lung Cancer Translational Science from the Bench to the Clinic; Jan 8-11, 2018; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(17_Suppl):Abstract nr IA23.

Environmental carcinogenic exposures are major contributors to global disease burden yet how they promote cancer is unclear. Over 70 years ago, the concept of tumour promoting agents driving latent clones to expand was rst proposed. In support of this model, recent evidence suggests that human tissue contains a patchwork of mutant clones, some of which harbour oncogenic mutations, and many environmental carcinogens lack a clear mutational signature. We hypothesised that the environmental carcinogen, <2.5μm particulate matter (PM2.5), might promote lung cancer promotion through nonmutagenic mechanisms by acting on pre-existing mutant clones within normal tissues in patients with lung cancer who have never smoked, a disease with a high frequency of EGFR activating mutations. We analysed PM2.5 levels and cancer incidence reported by UK Biobank, Public Health England, Taiwan Chang Gung Memorial Hospital (CGMH) and Korean Samsung Medical Centre (SMC) from a total of 463,679 individuals between 2006-2018. We report associations between PM2.5 levels and the incidence of several cancers, including EGFR mutant lung cancer. We nd that pollution on a background of EGFR mutant lung epithelium promotes a progenitor-like cell state and demonstrate that PM accelerates lung cancer progression in EGFR and Kras mutant mouse lung cancer models. Through parallel exposure studies in mouse and human participants, we nd evidence that in ammatory mediators, such as interleukin-1 , may act upon EGFR mutant clones to drive expansion of progenitor cells. Ultradeep mutational pro ling of histologically normal lung tissue from 247 individuals across 3 clinical cohorts revealed oncogenic EGFR and KRAS driver mutations in 18% and 33% of normal tissue samples, respectively. These results support a tumour-promoting role for PM acting on latent mutant clones in normal lung tissue and add to evidence providing an urgent mandate to address air pollution in urban areas.

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Background : In 2012, two large pharmacogenomic studies, the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE), were published, each reported gene expression data and measures of drug response for a large number of drugs and hundreds of cell lines. In 2013, we published a comparative analysis that reported gene expression profiles for the 471 cell lines profiled in both studies and dose response measurements for the 15 drugs characterized in the common cell lines by both studies. While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. Our paper was widely discussed and we received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. Here we present a new analysis using these expanded data in which we address the most significant suggestions Consistency of drug sensitivity dataWe began by computing the area between the two drug doseresponse curves (ABC) to assess consistency of cell viability data for each cell line combination screened in both GDSC and CCLE using the common concentration range. ABC measures the difference between two drugdose response curves by estimating the absolute area between these curves, which ranges from 0% (perfect consistency) to 100% (perfect inconsistency). The ABC statistic identified highly consistent ( Figure 4A,B) and highly inconsistent ( Figure 4C,D) doseresponse curves between GDSC and CCLE. The mean of the ABC estimates for all drugcell line combinations was 10% ( Supplementary Figure 2A), with PD0332991 yielding the highest discrepancies (Supplementary Figure 2B).We compared biological replicates in GDSC, which were performed independently at the Massachusetts General Hospital (MGH) and the Wellcome Trust Sanger Institute (WTSI). These experiments are comprised of 577 cell lines treated with AZD6482, a PI3Kβ inhibitor screened in GDSC ( Supplementary File 4). We computed the ABC of these biological replicates and observed both highly consistent and inconsistent cases ( Supplementary Figure 3). We then computed the median ABC values for each pair of drugs in GDSC and used these as a distance metric for complete linkage hierarchical clustering. We found that the MGH and WTSIadministered AZD6482 experiments clustered together, suggesting that the differences between doseresponse curves of biological replicates were smaller than the differences observed between different drugs (Supplementary Figure 4A). We performed the same clustering analysis by computing the ABCbased distance between all the drugs in GDSC and CCLE and observed that only three out of the fifteen common drugs clustered tightly (17AAG, lapatinib, and PHA−665752; Supplementary Figure 4B). Despite the small number of cell lines exhibiting sensitivity to PHA−665752 and lapatinib, these drugs closely clustered between GDSC and CCLE; however this was not the case for other highly ta...

Introduction Minimal residual disease (MRD) detection in solid tumors describes isolation of circulating tumor DNA (ctDNA) molecules in plasma following definitive treatment of a cancer. Detection of MRD following surgical tumor excision categorizes patients as high risk for disease recurrence. Establishing an MRD approach to treating early-stage NSCLC will facilitate escalation of standard of care (SoC) treatment only in patients destined to relapse from their cancer and overcome challenges associated with conventional adjuvant drug-trial design. Here, we present data from the lung TRACERx study where patients with early-stage NSCLC underwent phylogenetic ctDNA profiling following resection. Methods Patient specific anchored-multiplex PCR (AMP) enrichment panels were generated for 78 lung TRACERx patients who underwent surgery for stage I-III NSCLC; 608 plasma samples were analyzed. Extensive patient-specific cfDNA enrichment panels targeted a median of 196 (range 72 to 482) clonal and subclonal variants detected in primary tumor tissue by multi-region exome sequencing. A novel MRD-caller controlled and estimated background sequencing error to maximize ctDNA detection at low mutant allele frequencies (MAFs). Analytical validation experiments benchmarked assay performance. Results Analytical validation of a 50-variant AMP-MRD assay demonstrated a sensitivity of 89% for mutant DNA at a MAF of 0.008% (with 25ng of DNA input into the assay), specificity was 100% experimentally and 99.9% (95% CI: 99.67 to 99.99%) modelled in-silico. 45 patients suffered relapse of their primary NSCLC; ctDNA was detected at or before clinical relapse in 37 of 45 patients. In these 37 patients the median ctDNA lead-time (time from ctDNA detection to clinical relapse) was 151 days (range 0 to 984 days) and the median time to relapse from surgery was 413 days (range 41 to 1242 days). In 10 of 10 patients who developed second primary cancers during follow-up no ctDNA was detected, reflecting specificity of the MRD assay toward the primary tumor. In 23 patients who remained relapse-free during a median of 1184 days of study follow-up, ctDNA was detected in 1 of 199 time-points analyzed. Analysis of SoC adjuvant surveillance imaging (CT, PET-CT or MRI, 220 encounters) revealed examples of MRD positive patients where SoC radiological surveillance was negative for impending relapse. Through application of large cfDNA enrichment panels targeting up to 483 variants per patient we observed dynamic changes in clonal composition and copy-number status prior to NSCLC relapse, categorized relapse as monoclonal or polyclonal and identified distinct subclonal dynamics during systemic intervention for disease recurrence. Conclusions ctDNA is an adjuvant biomarker capable of both detecting MRD following surgery and defining the clonality of relapsing disease. These data pave the way for clinical trials predicated on escalation of adjuvant standard of care in NSCLC patients who exhibit MRD positive status following surgery. Citation Format: Chris Abbosh, Alexander Frankell, Aaron Garnett, Thomas Harrison, Morgan Weichert, Abel Licon, Selvaraju Veeriah, Bob Daber, Mike Moreau, Adrian Chesh, Kevin Litchfield, Emilia Lim, Daniel Cooke, Clare Puttick, Maise Al Bakir, Fabio Gomes, Akshay Patel, Lizi Manzano, Ariana Huebner, Nicolas Carey, Joan Riley, Paula Roberts, Todd Druley, Jacqui A. Shaw, Nicholas McGranahan, Mariam Jamal-Hanjani, Nicolai Birkbak, Josh Stahl, Charles Swanton, Lung TRACERx consortium. Phylogenetic tracking and minimal residual disease detection using ctDNA in early-stage NSCLC: A lung TRACERx study [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 CT023.