Abstract 2784: A robust copy number based clonality classification for multiple pulmonary tumors in routine pathology practice by shallow next generation sequencing of formalin fixed clinical specimens

Multiple synchronous or metachronous independent tumors in one patient, particularly those with thoracic involvement, can be as high as 20%. Defining whether a patient has multiple primary cancers (MPC) or metastatic disease is an urgent clinical need due to differences in treatment strategies, with curative intend for MPCs. Discerning the clonal and non-clonal relationship of two lesions for metastases and MPCs, respectively, has been done since the 1970s by analyzing histological characteristics, which nowadays are complemented with genomic features. However, one or few mutation features are insufficient to unequivocally diagnose tumor clonality, due to intratumoral heterogeneity and common mutations. We and others have demonstrated convincing clonality classification by genome-wide copy number aberrations (CNAs) analysis, which is the recommendation from the International Association for the Study of Lung Cancer (IASLC), independent of histological differences or similarities. Notwithstanding, a gold standard clonality assessment is currently lacking such that quality evaluation and calibration of the diagnostic tests are not possible. Gold-standard data: Genetic data from non-clonal cancer samples has become virtually endless by means of Whole Exome and Whole Genome sequencing (WES and WGS) since the mutational discrepancies by WES between two MPCs from a single lung cancer patient is the same as those of two primary cancers from different patients. Therefore, publicly available WES or WGS data from tumors of different lung cancer patients can serve as a gold standard reference for non-clonality. A gold standard reference for clonal samples can be derived from WES data from multiple biopsies of one patient, i.e. from the TRACERx initiative. These datasets now allow for the first time to calibrate CNA clonality testing against a gold standard and calculate the sensitivity and specificity of multiple lung tumors in one patient. Diagnostic test: Shallow Whole Genome Sequencing (sWGS) based genome-wide CNA analysis on Formalin-Fixed Paraffin-Embedded (FFPE) specimens has been used as the diagnostic routine at the Cancer Center Amsterdam (CCA), Amsterdam UMC over the past 5 years. The pipeline was calibrated against gold standard clonal and non-clonal WES data. We present sensitivity and specificity for an adequate, fast, and more economic clonality test based on genome-wide CNAs.

Citation Format: Bárbara Andrade Barbosa, Teodora Radonic, Yongsoo Kim, Bauke Ylstra. A robust copy number based clonality classification for multiple pulmonary tumors in routine pathology practice by shallow next generation sequencing of formalin fixed clinical specimens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2784.

Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data

Deconvolution of bulk gene expression profiles into the cellular components is pivotal to portraying tissue's complex cellular make-up, such as the tumor microenvironment. However, the inherently variable nature of gene expression requires a comprehensive statistical model and reliable prior knowledge of individual cell types that can be obtained from single-cell RNA sequencing. We introduce BLADE (Bayesian Log-normAl Deconvolution), a unified Bayesian framework to estimate both cellular composition and gene expression profiles for each cell type. Unlike previous comprehensive statistical approaches, BLADE can handle > 20 types of cells due to the efficient variational inference. Throughout an intensive evaluation with > 700 simulated and real datasets, BLADE demonstrated enhanced robustness against gene expression variability and better completeness than conventional methods, in particular, to reconstruct gene expression profiles of each cell type. In summary, BLADE is a powerful tool to unravel heterogeneous cellular activity in complex biological systems from standard bulk gene expression data.