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Clonal deconvolution of mutational landscapes is crucial to understand the evolutionary dynamics of cancer. Two limiting factors for clonal deconvolution that have remained unresolved are variation in purity and chromosomal copy number across different samples of the same tumor. We developed a semi-supervised algorithm that tracks variant calls through multi-sample spatiotemporal tumor data. While normalizing allele frequencies based on purity, it also adjusts for copy number changes at clonal deconvolution. Absent à priori copy number data, it renders in silico copy number estimations from bulk sequences. Using published and simulated tumor sequences, we reliably segregated clonal/subclonal variants even at a low sequencing depth (~50×). Given at least one pure tumor sample (>70% purity), we could normalize and deconvolve paired samples down to a purity of 40%. This renders a reliable clonal reconstruction well adapted to multi-regionally sampled solid tumors, which are often aneuploid and contaminated by non-cancer cells. [ABSTRACT FROM AUTHOR]

Simple Summary: Glioblastoma is the most common and aggressive primary brain malignancy in adults. In addition to extensive inter-patient heterogeneity, glioblastoma shows intra-tumor extensive cellular and molecular heterogeneity, both spatially and temporally. This heterogeneity is one of the main reasons for the poor prognosis and overall survival. Moreover, it raises the important question of whether the molecular characterization of a single biopsy sample, as performed in standard diagnostics, actually represents the entire lesion. In this study, we sequenced the whole exome of nine spatially different cancer regions of three primary glioblastomas. We characterized their mutational profiles and copy number alterations, with implications for our understanding of tumor biology in relation to clonal architecture and evolutionary dynamics, as well as therapeutically relevant alterations. Glioblastoma is one of the most common and lethal primary neoplasms of the brain. Patient survival has not improved significantly over the past three decades and the patient median survival is just over one year. Tumor heterogeneity is thought to be a major determinant of therapeutic failure and a major reason for poor overall survival. This work aims to comprehensively define intra- and inter-tumor heterogeneity by mapping the genomic and mutational landscape of multiple areas of three primary IDH wild-type (IDH-WT) glioblastomas. Using whole exome sequencing, we explored how copy number variation, chromosomal and single loci amplifications/deletions, and mutational burden are spatially distributed across nine different tumor regions. The results show that all tumors exhibit a different signature despite the same diagnosis. Above all, a high inter-tumor heterogeneity emerges. The evolutionary dynamics of all identified mutations within each region underline the questionable value of a single biopsy and thus the therapeutic approach for the patient. Multiregional collection and subsequent sequencing are essential to try to address the clinical challenge of precision medicine. Especially in glioblastoma, this approach could provide powerful support to pathologists and oncologists in evaluating the diagnosis and defining the best treatment option. [ABSTRACT FROM AUTHOR]

Endometriosis is characterized by ectopic endometrial-like epithelium and stroma, of which molecular characteristics remain to be fully elucidated. We sequenced 107 ovarian endometriotic and 82 normal uterine endometrial epithelium samples isolated by laser microdissection. In both endometriotic and normal epithelium samples, numerous somatic mutations were identified within genes frequently mutated in endometriosis-associated ovarian cancers. KRAS is frequently mutated in endometriotic epithelium, with a higher mutant allele frequency (MAF) accompanied by arm-level allelic imbalances. Analyses of MAF, combined with multiregional sequencing, illuminated spatiotemporal evolution of the endometriosis and uterine endometrium genomes. We sequenced 109 single endometrial glands and found that each gland carried distinct cancer-associated mutations, demonstrating the heterogeneity of the genomic architecture of endometrial epithelium. Remarkable increases in MAF of mutations in cancer-associated genes in endometriotic epithelium suggest retrograde flow of endometrial cells already harboring cancer-associated mutations, with selective advantages at ectopic sites, leading to the development of endometriosis., (Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.)