Integrated RNA and DNA sequencing improves mutation detection in low purity tumors

Wilkerson, Matthew D.; Cabanski, Christopher R.; Sun, Wei; Hoadley, Katherine A.; Walter, Vonn; Mose, Lisle E.; Troester, Melissa A.; Hammerman, Peter S.; Parker, Joel S.; Perou, Charles M.; Hayes, D. Neil

doi:10.1093/nar/gku489

Cited by 74 publications

(81 citation statements)

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“…17 The numbers of mutated genes and CAN segments per patient were compared between blacks and whites using Wilcoxon rank sum tests. Recurrently mutated genes and recurrent CNAs were identified by MutSigCV2 and GISTIC, 18,19 respectively.…”

Section: Methodsmentioning

confidence: 99%

Comparison of Breast Cancer Molecular Features and Survival by African and European Ancestry in The Cancer Genome Atlas

et al. 2017

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IMPORTANCE African Americans have the highest breast cancer mortality rate. Although racial difference in the distribution of intrinsic subtypes of breast cancer is known, it is unclear if there are other inherent genomic differences that contribute to the survival disparities. OBJECTIVES To investigate racial differences in breast cancer molecular features and survival and to estimate the heritability of breast cancer subtypes. DESIGN, SETTING, AND PARTICIPANTS Among a convenience cohort of patients with invasive breast cancer, breast tumor and matched normal tissue sample data (as of September 18, 2015) were obtained from The Cancer Genome Atlas. MAIN OUTCOMES AND MEASURES Breast cancer-free interval, tumor molecular features, and genetic variants. RESULTS Participants were 930 patients with breast cancer, including 154 black patients of African ancestry (mean [SD] age at diagnosis, 55.66 [13.01] years; 98.1% [n = 151] female) and 776 white patients of European ancestry (mean [SD] age at diagnosis, 59.51 [13.11] years; 99.0% [n = 768] female). Compared with white patients, black patients had a worse breast cancer-free interval (hazard ratio, HR=1.67, 95 CI: 1.02–2.74; P = .043). They had a higher likelihood of basal-like (odds ratio, 3.80; 95% CI, 2.46–5.87; P < .001) and human epidermal growth factor receptor 2 (ERBB2 [formerly HER2])-enriched (odds ratio, 2.22; 95% CI, 1.10–4.47; P= .027) breast cancer subtypes, with the Luminal A subtype as the reference. Blacks had more TP53 mutations and fewer PIK3CA mutations than whites. While most molecular differences were eliminated after adjusting for intrinsic subtype, the study found 16 DNA methylation probes, 4 DNA copy number segments, 1 protein, and 142 genes that were differentially expressed, with the gene-based signature having an excellent capacity for distinguishing breast tumors from black vs white patients (cross-validation C index, 0.878). Using germline genotypes, the heritability of breast cancer subtypes (basal vs nonbasal) was estimated to be 0.436 (P = 1.5 × 10−14). The estrogen receptor-positive polygenic risk score built from 89 known susceptibility variants was higher in blacks than in whites (difference, 0.24; P = 2.3 × 10−5), while the estrogen receptor-negative polygenic risk score was much higher in blacks than in whites (difference, 0.48; P = 2.8 × 10−11). CONCLUSIONS AND RELEVANCE On the molecular level, after adjusting for intrinsic subtype frequency differences, this study found a modest number of genomic differences but a significant clinical survival outcome difference between blacks and whites in The Cancer Genome Atlas data set. Moreover, more than 40% of breast cancer subtype frequency differences could be explained by genetic variants. These data could form the basis for the development of molecular targeted therapies to improve clinical outcomes for the specific subtypes of breast cancers that disproportionately affect black women. Findings also indicate that personalized risk assessment and optimal treatment could redu...

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Section: Methodsmentioning

confidence: 99%

Comparison of Breast Cancer Molecular Features and Survival by African and European Ancestry in The Cancer Genome Atlas

et al. 2017

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“…The median overall coverage of DNA exome sequencing was 108× (75×-250×; Supplemental Table 2). An established DNA variant pipeline (25), an RNAseq variant (26), and an RNA-seq gene expression pipeline (27) were used to call variants in the DNA and the RNA, as well as to determine gene expression levels in all 83 samples (see Methods and Supplemental Figure 2). Of the DNA single base mutations with at least 5 reads of coverage at that position in the RNA, 83% were also identified in the RNA-seq data.…”

Section: 7mentioning

confidence: 99%

“…Low read coverage or low tumor cell purity can cause the rigorous somatic mutation caller to miss mutations (26,53). Thus, all of the high-confidence somatic mutations from every tumor taken from 1 patient were reinterrogated within the same tumors from that patient.…”

Section: Author Contributionsmentioning

confidence: 99%

Integrated RNA and DNA sequencing reveals early drivers of metastatic breast cancer

Siegel¹,

He²,

Hoadley³

et al. 2018

Journal of Clinical Investigation

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133

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“…A combined analysis using genomics and transcriptomics enables derivation of highly detailed molecular information on somatic and germline variants, structural variants, and expression deregulation, providing orders of magnitude more information than other methods available. The combination of DNA and RNA-based analyses complement each other, serving as mutual controls for verifying potential findings [17] and increasing the sensitivity of variant detection, a feature especially important for analysis of low-purity tumours [25] . In particular, identification of variants affecting RNA processing, eventually leading to oncogenic isoforms (e.g., EGFR viii/ERBB2 ΔEx16), is close to impossible using genome-based analyses in isolation [26] , since the effect is only visible when considering the gene structure, as determined by RNAseq.…”

Section: Is More More?mentioning

confidence: 99%

Cancer Precision Medicine: Why More Is More and DNA Is Not Enough

Schütte

Ogilvie

Rieke

et al. 2017

Public Health Genomics

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Every tumour is different. They arise in patients with different genomes, from cells with different epigenetic modifications, and by random processes affecting the genome and/or epigenome of a somatic cell, allowing it to escape the usual controls on its growth. Tumours and patients therefore often respond very differently to the drugs they receive. Cancer precision medicine aims to characterise the tumour (and often also the patient) to be able to predict, with high accuracy, its response to different treatments, with options ranging from the selective characterisation of a few genomic variants considered particularly important to predict the response of the tumour to specific drugs, to deep genome analysis of both tumour and patient, combined with deep transcriptome analysis of the tumour. Here, we compare the expected results of carrying out such analyses at different levels, from different size panels to a comprehensive analysis incorporating both patient and tumour at the DNA and RNA levels. In doing so, we illustrate the additional power gained by this unusually deep analysis strategy, a potential basis for a future precision medicine first strategy in cancer drug therapy. However, this is only a step along the way of increasingly detailed molecular characterisation, which in our view will, in the future, introduce additional molecular characterisation techniques, including systematic analysis of proteins and protein modification states and different types of metabolites in the tumour, systematic analysis of circulating tumour cells and nucleic acids, the use of spatially resolved analysis techniques to address the problem of tumour heterogeneity as well as the deep analyses of the immune system of the patient to, e.g., predict the response of the patient to different types of immunotherapy. Such analyses will generate data sets of even greater complexity, requiring mechanistic modelling approaches to capture enough of the complex situation in the real patient to be able to accurately predict his/her responses to all available therapies.

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Integrated RNA and DNA sequencing improves mutation detection in low purity tumors

Cited by 74 publications

References 50 publications

Comparison of Breast Cancer Molecular Features and Survival by African and European Ancestry in The Cancer Genome Atlas

Comparison of Breast Cancer Molecular Features and Survival by African and European Ancestry in The Cancer Genome Atlas

Integrated RNA and DNA sequencing reveals early drivers of metastatic breast cancer

Cancer Precision Medicine: Why More Is More and DNA Is Not Enough

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