Faculty Bibliography

BACKGROUND:edition of UICC/AJCC TNM classification system the primary tumor pT stage is determined based on presence and size of the invasive components. The aim of this study was to identify histological features in tumors with lepidic growth pattern that may be used to establish criteria for distinguishing invasive from non-invasive areas. MATERIALS AND METHODS/METHODS:A Delphi approach was used with two rounds of blinded anonymized analysis of resected non-mucinous lung adenocarcinoma cases with presumed invasive and non-invasive components, followed by one round of reviewer de-anonymized and unblinded review of cases with known outcomes. A digital pathology platform was used for measuring total tumor size and invasive tumor size. RESULTS:The mean coefficient of variation for measuring total tumor size and tumor invasive size was 6.9% (range 1.7-22.3%) and 54% (range 14.7-155%), respectively, with substantial variations in interpretation of the size and location of invasion among pathologists. Following the presentation of the results and further discussion among members at large of the IASLC Pathology Committee, extensive epithelial proliferation (EEP) in areas of collapsed lepidic growth pattern is recognized as a feature likely to be associated with invasive growth. EEP is characterized by multilayered luminal epithelial cell growth, usually with high grade cytological features in several alveolar spaces. CONCLUSION/CONCLUSIONS:Collapsed alveoli and transition zones with EEP were identified by the Delphi process as morphologic features that were a source of interobserver variability. Definition criteria for collapse and EEP are proposed to improve reproducibility of invasion measurement.

Lung squamous cell carcinoma (LUSC) represents a major subtype of lung cancer with limited treatment options. KMT2D is one of the most frequently mutated genes in LUSC (>20%), and yet its role in LUSC oncogenesis remains unknown. Here, we identify KMT2D as a key regulator of LUSC tumorigenesis wherein Kmt2d deletion transforms lung basal cell organoids to LUSC. Kmt2d loss increases activation of receptor tyrosine kinases (RTKs), EGFR and ERBB2, partly through reprogramming the chromatin landscape to repress the expression of protein tyrosine phosphatases. These events provoke a robust elevation in the oncogenic RTK-RAS signaling. Combining SHP2 inhibitor SHP099 and pan-ERBB inhibitor afatinib inhibits lung tumor growth in Kmt2d-deficient LUSC murine models and in patient-derived xenografts (PDXs) harboring KMT2D mutations. Our study identifies KMT2D as a pivotal epigenetic modulator for LUSC oncogenesis and suggests that KMT2D loss renders LUSC therapeutically vulnerable to RTK-RAS inhibition.

Intra-tumor heterogeneity (ITH) of human tumors is important for tumor progression, treatment response, and drug resistance. However, the spatial distribution of ITH remains incompletely understood. Here, we present spatial analysis of ITH in lung adenocarcinomas from 147 patients using multi-region mass spectrometry of >5,000 regions, single-cell copy number sequencing of ~2,000 single cells, and cyclic immunofluorescence of >10 million cells. We identified two distinct spatial patterns among tumors, termed clustered and random geographic diversification (GD). These patterns were observed in the same samples using both proteomic and genomic data. The random proteomic GD pattern, which is characterized by decreased cell adhesion and lower levels of tumor-interacting endothelial cells, was significantly associated with increased risk of recurrence or death in two independent patient cohorts. Our study presents comprehensive spatial mapping of ITH in lung adenocarcinoma and provides insights into the mechanisms and clinical consequences of GD.

PURPOSE/OBJECTIVE:Lung adenocarcinoma (LUAD) is a clinically heterogenous disease, which is highlighted by the unpredictable recurrence in low-stage tumors and highly variable responses observed in patients treated with immunotherapies, which cannot be explained by mutational profiles. DNA methylation-based classification and understanding of microenviromental heterogeneity may allow stratification into clinically relevant molecular subtypes of LUADs. EXPERIMENTAL DESIGN/METHODS:We characterize the genome-wide DNA methylation landscape of 88 resected LUAD tumors. Exome sequencing focusing on a panel of cancer-related genes was used to genotype these adenocarcinoma samples. Bioinformatic and statistical tools, the immune cell composition, DNA methylation age (DNAm age), and DNA methylation clustering were used to identify clinically relevant subgroups. RESULTS:Deconvolution of DNA methylation data identified immunologically hot and cold subsets of lung adenocarcinomas. Additionally, concurrent factors were analyzed that could affect the immune microenvironment, such as smoking history, ethnicity, or presence of KRAS or TP53 mutations. When the DNAm age was calculated, a lower DNAm age was correlated with the presence of a set of oncogenic drivers, poor overall survival, and specific immune cell populations. Unsupervised DNA methylation clustering identified 6 molecular subgroups of LUAD tumors with distinct clinical and microenvironmental characteristics. CONCLUSIONS:Our results demonstrate that DNA methylation signatures can stratify lung adenocarcinoma into clinically relevant subtypes, and thus such classification of LUAD at the time of resection may lead to better methods in predicting tumor recurrence and therapy responses.