Reliability of Whole-Exome Sequencing for Assessing Intratumor Genetic Heterogeneity

Shi, Weiwei; Ng, Charlotte K.Y.; Lim, Raymond S.; Jiang, Tingting; Kumar, Sushant; Li, Xiaotong; Wali, Vikram B.; Piscuoglio, Salvatore; Gerstein, Mark; Chagpar, Anees B.; Weigelt, Britta; Pusztai, Lajos; Reis‐Filho, Jorge S.; Hatzis, Christos

doi:10.1016/j.celrep.2018.10.046

Cited by 75 publications

(72 citation statements)

References 45 publications

(54 reference statements)

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“…2). We also tested BATCAVE using two real cancer data sets, a whole-genome Acute Myeloid Leukemia (AML) [40] and a whole-exome multi-region breast cancer [4]. In both, deep sequencing and variant validation were performed with the specific purpose of evaluating tumor variant calling pipelines.…”

Section: Resultsmentioning

confidence: 99%

“…We analyzed two real data sets, one from an acute myeloid leukemia (AML) [40] and one from a multi-region sequencing experiment in breast cancer [4]. We downloaded the normal and primary whole-genome AML tumor bam files from dbGaP accession number phs000159.v8.p4.…”

Section: Real Datamentioning

confidence: 99%

“…We downloaded the normal and tumor whole-exome breast cancer bam files from NCBI Sequence Read Archive accession SRP070662. Shi et al generated a gold set of variant calls for each tumor region sequenced [4], which we used for our true positive dataset. For these multi-region data, we ran BATCAVE separately on each sequenced region and combined results to generate precision-recall curves.…”

Section: Real Datamentioning

confidence: 99%

“…Cancer develops through the accumulation of somatic mutations and clonal selection of cells with mutations that confer an advantage. Understanding the evolutionary history of a tumor, including the mutations that drive its growth, the genetic diversity within it, and the accumulation of new mutations, requires accurate variant identification, particularly at low variant allele frequency [1,2,3,4]. Accurate variant calling is also critical for optimizing the treatment of individual patients' disease [5,6,7,8,9].…”

Section: Introductionmentioning

confidence: 99%

“…(A) The observed mutation profile of an acute myeloid leukemia used in our real data analysis [40]. (B) The observed mutation profile of a breast tumor used in our real data analysis [4]. (C)&(D) The observed mutation profiles of two additional breast tumors [41].…”

Section: Introductionmentioning

confidence: 99%

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BATCAVE: Calling somatic mutations with a tumor- and site-specific prior

Mannakee

Gutenkunst

2019

Preprint

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Detecting somatic mutations withins tumors is key to understanding treatment resistance, patient prognosis, and tumor evolution. Mutations at low allelic frequency, those present in only a small portion of tumor cells, are particularly difficult to detect. Many algorithms have been developed to detect such mutations, but none models a key aspect of tumor biology. Namely, every tumor has its own profile of mutation types that it tends to generate. We present BATCAVE (Bayesian Analysis Tools for Context-Aware Variant Evaluation), an algorithm that first learns the individual tumor mutational profile and mutation rate then uses them in a prior for evaluating potential mutations. We also present an R implementation of the algorithm, built on the popular caller MuTect. Using simulations, we show that adding the BATCAVE algorithm to MuTect improves variant detection. It also improves the calibration of posterior probabilities, enabling more principled tradeoff between precision and recall. We also show that BATCAVE performs well on real data. Our implementation is computationally inexpensive and straightforward to incorporate into existing MuTect pipelines. More broadly, the algorithm can be added to other variant callers, and it can be extended to include additional biological features that affect mutation generation.

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Section: Resultsmentioning

confidence: 99%

Section: Real Datamentioning

confidence: 99%

Section: Real Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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BATCAVE: Calling somatic mutations with a tumor- and site-specific prior

Mannakee

Gutenkunst

2019

Preprint

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The genetic landscape of metaplastic breast cancers and uterine carcinosarcomas

et al. 2021

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Metaplastic breast carcinoma (MBC) and uterine carcinosarcoma (UCS) are rare aggressive cancers, characterized by an admixture of adenocarcinoma and areas displaying mesenchymal/ sarcomatoid differentiation. We sought to define whether MBCs and UCSs harbor similar patterns of genetic alterations, and whether the different histologic components of MBCs and UCSs are clonally related. Whole-exome sequencing (WES) data from MBCs (n=35) and UCSs (n=57, The Cancer Genome Atlas) were re-analyzed to define somatic genetic alterations, altered signaling pathways, mutational signatures and genomic features of homologous recombination DNA repair deficiency (HRD). In addition, the carcinomatous and sarcomatous components of an additional cohort of MBCs (n=11) and UCSs (n=6) were microdissected separately and subjected to WES, and their clonal relatedness assessed. MBCs and UCSs harbored recurrent genetic alterations affecting TP53, PIK3CA and PTEN, similar patterns of gene copy number alterations, and an enrichment in alterations affecting the epithelial-to-mesenchymal transition (EMT)-related Wnt and Notch signaling pathways. Differences were observed, however, including FAT3 and FAT1 somatic mutations, which were significantly more common in MBCs than UCSs, and conversely, UCSs significantly more frequently harbored somatic mutations affecting FBXW7 and PPP2R1A as well as HER2 amplification than MBCs. Genomic features of HRD and bi-allelic alterations affecting bona fide HRD-related genes were found to be more prevalent in MBCs than in UCSs. The distinct histologic components of MBCs and UCSs were clonally related in all cases, with the sarcoma component likely stemming from a minor subclone of the carcinoma component in the samples with interpretable chronology of clonal evolution. Despite the similar histologic features and pathways affected by genetic alterations, UCSs differ from MBCs on the basis of FBXW7 and PPP2R1A mutations, HER2 amplification and lack of HRD, supporting the notion that these entities are more than mere phenocopies of the same tumor type in different anatomical sites.

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Artificial intelligence in cancer research: learning at different levels of data granularity

2021

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From genome‐scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and the advances in Artificial Intelligence (AI) is paving the way to develop a systems view of cancer. Nevertheless, this biomedical area is largely characterized by the co‐existence of big data and small data resources, highlighting the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of AI in the heterogeneous landscape of data granularity in cancer research. Such a variety of cancer molecular and clinical data calls for advancing the interoperability among AI approaches, with particular emphasis on the synergy between discriminative and generative models that we discuss in this work with several examples of techniques and applications.

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Reliability of Whole-Exome Sequencing for Assessing Intratumor Genetic Heterogeneity

Cited by 75 publications

References 45 publications

BATCAVE: Calling somatic mutations with a tumor- and site-specific prior

BATCAVE: Calling somatic mutations with a tumor- and site-specific prior

The genetic landscape of metaplastic breast cancers and uterine carcinosarcomas

Artificial intelligence in cancer research: learning at different levels of data granularity

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