Despite the urgency for prevention and treatment of lung adenocarcinoma (LUAD), we still do not know drivers in pathogenesis of the disease. Earlier work revealed that mice with knockout of the G-protein coupled receptor Gprc5a develop late onset lung tumors including LUADs. Here, we sought to further probe the impact of Gprc5a expression on LUAD pathogenesis. We first surveyed GPRC5A expression in human tissues and found that GPRC5A was markedly elevated in human normal lung relative to other normal tissues and was consistently down-regulated in LUADs. In sharp contrast to wild type littermates, Gprc5a-/- mice treated chronically with the nicotine-specific carcinogen NNK developed LUADs by six months following NNK exposure. Immunofluorescence analysis revealed that the LUADs exhibited abundant expression of surfactant protein C and lacked the clara cell marker Ccsp, suggesting that these LUADs originated from alveolar type II cells. Next, we sought to survey genome-wide alterations in the pathogenesis of Gprc5a-/- LUADs. Using whole exome sequencing, we found that carcinogen-induced LUADs exhibited markedly higher somatic mutation burdens relative to spontaneous tumors. All LUADs were found to harbor somatic mutations in the Kras oncogene (p. G12D or p. Q61R). In contrast to spontaneous lesions, carcinogen-induced Gprc5a-/- LUADs exhibited mutations (variants and copy number gain) in additional drivers (Atm, Kmt2d, Nf1, Trp53, Met, Ezh2). Our study underscores genomic alterations that represent early events in the development of Kras mutant LUAD following Gprc5a loss and tobacco carcinogen exposure and that may constitute targets for prevention and early treatment of this disease.

Visually normal cells adjacent to, and extending from, tumors of the lung may carry molecular alterations characteristics of the tumor itself, an effect referred to as airway field of cancerization. This airway field has been postulated as a model for early events in lung cancer pathogenesis. Yet the genomic landscape of somatically acquired molecular alterations in airway epithelia of lung cancer patients has remained unknown. To begin to fill this void, we sought to comprehensively characterize the genomic architecture of chromosomal alterations inducing allelic imbalance (AI) in the airway field of the most common type of lung tumors, non–small cell lung cancer (NSCLC). To do so, we conducted a genome-wide survey of multiple spatially distributed normal-appearing airways, multiregion tumor specimens, and uninvolved normal tissues or blood from 45 patients with early-stage NSCLC. We detected alterations in airway epithelia from 22 patients, with an increased frequency in NSCLCs of squamous histology. Our data also indicated a spatial gradient of AI in samples at closer proximity to the NSCLC. Chromosome 9 displayed the highest levels of AI and comprised recurrent independent events. Furthermore, the airway field AI included oncogenic gains and tumor suppressor losses in known NSCLC drivers. Our results demonstrate that genome-wide AI is common in the airway field of cancerization, providing insights into early events in the pathogenesis of NSCLC that may comprise targets for early treatment and chemoprevention.

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Purpose: Most patients with pancreatic ductal adenocarcinoma (PDAC) present with surgically unresectable cancer. As a result, endoscopic ultrasound–guided fine-needle aspiration (EUS-FNA) is the most common biospecimen source available for diagnosis in treatment-naïve patients. Unfortunately, these limited samples are often not considered adequate for genomic analysis, precluding the opportunity for enrollment on precision medicine trials. Experimental Design: Applying an epithelial cell adhesion molecule (EpCAM)-enrichment strategy, we show the feasibility of using real-world EUS-FNA for in-depth, molecular-barcoded, whole-exome sequencing (WES) and somatic copy-number alteration (SCNA) analysis in 23 patients with PDAC. Results: Potentially actionable mutations were identified in >20% of patients. Further, an increased mutational burden and higher aneuploidy in WES data were associated with an adverse prognosis. To identify predictive biomarkers for first-line chemotherapy, we developed an SCNA-based complexity score that was associated with response to platinum-based regimens in this cohort. Conclusions: Collectively, these results emphasize the feasibility of real-world cytology samples for in-depth genomic characterization of PDAC and show the prognostic potential of SCNA for PDAC diagnosis.

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Background: Genomic investigation of atypical adenomatous hyperplasia (AAH), the only known precursor lesion to lung adenocarcinomas (LUAD), presents challenges due to the low mutant cell fractions. This necessitates sensitive methods for detection of chromosomal aberrations to better study the role of critical alterations in early lung cancer pathogenesis and the progression from AAH to LUAD. Methods: We applied a sensitive haplotype-based statistical technique to detect chromosomal alterations leading to allelic imbalance (AI) from genotype array profiling of 48 matched normal lung parenchyma, AAH and tumor tissues from 16 stage-I LUAD patients. To gain insights into shared developmental trajectories among tissues, we performed phylogenetic analyses and integrated our results with point mutation data, highlighting significantlymutated driver genes in LUAD pathogenesis. Findings: AI was detected in nine AAHs (56%). Six cases exhibited recurrent loss of 17p. AI and the enrichment of 17p events were predominantly identified in patients with smoking history. Among the nine AAH tissues with detected AI, seven exhibited evidence for shared chromosomal aberrations with matched LUAD specimens, including losses harboring tumor suppressors on 17p, 8p, 9p, 9q, 19p, and gains encompassing oncogenes on 8q, 12p and 1q. Interpretation: Chromosomal aberrations, particularly 17p loss, appear to play critical roles early in AAH pathogenesis. Genomic instability in AAH, as well as truncal chromosomal aberrations shared with LUAD, provide evidence for mutation accumulation and are suggestive of a cancerized field contributing to the clonal selection and expansion of these premalignant lesions.

From DNA analysis, our results indicate a monoclonal disease origin for this cohort. Yet expression-derived EMT statuses of the carcinomatous and sarcomatous components were often discrepant, and advanced cases displayed greater genomic heterogeneity. Therefore, separately-profiled components of UCS tumors may better inform disease progression or potential.

Somatic copy number alterations (SCNAs) serve as hallmarks of tumorigenesis and often result in deviations from one-to-one allelic ratios at heterozygous loci, leading to allelic imbalance (AI). The Cancer Genome Atlas (TCGA) reports SCNAs identified using a circular binary segmentation algorithm, providing segment mean copy number estimates from single-nucleotide polymorphism DNA microarray total intensities (log R ratio), but not allele-specific intensities (“B allele” frequencies) that inform of AI. Our approach provides more sensitive identification of SCNAs by modeling the “B allele” frequencies jointly, thereby bolstering the catalog of chromosomal alterations in this widely utilized resource. Here we present AI summaries for all 33 tumor sites in TCGA, including those induced by SCNAs and copy-neutral loss-of-heterozygosity (cnLOH). We identified AI in 94% of the tumors, higher than in previous reports. Recurrent events included deletions of 17p, 9q, 3p, amplifications of 8q, 1q, 7p, as well as mixed event types on 8p and 13q. We also observed both site-specific and pan-cancer (spanning 17p) cnLOH, patterns which have not been comprehensively characterized. The identification of such cnLOH events elucidates tumor suppressors and multi-hit pathways to carcinogenesis. We also contrast the landscapes inferred from AI- and total intensity-derived SCNAs and propose an automated procedure to improve and adjust SCNAs in TCGA for cases where high levels of aneuploidy obscured baseline intensity identification. Our findings support the exploration of additional methods for robust automated inference procedures and to aid empirical discoveries across TCGA.