Dataset Information

A comparison of isolated circulating tumor cells and tissue biopsies using whole-genome sequencing in prostate cancer.

ABSTRACT: Previous studies have demonstrated focal but limited molecular similarities between circulating tumor cells (CTCs) and biopsies using isolated genetic assays. We hypothesized that molecular similarity between CTCs and tissue exists at the single cell level when characterized by whole genome sequencing (WGS). By combining the NanoVelcro CTC Chip with laser capture microdissection (LCM), we developed a platform for single-CTC WGS. We performed this procedure on CTCs and tissue samples from a patient with advanced prostate cancer who had serial biopsies over the course of his clinical history. We achieved 30X depth and ? 95% coverage. Twenty-nine percent of the somatic single nucleotide variations (SSNVs) identified were founder mutations that were also identified in CTCs. In addition, 86% of the clonal mutations identified in CTCs could be traced back to either the primary or metastatic tumors. In this patient, we identified structural variations (SVs) including an intrachromosomal rearrangement in chr3 and an interchromosomal rearrangement between chr13 and chr15. These rearrangements were shared between tumor tissues and CTCs. At the same time, highly heterogeneous short structural variants were discovered in PTEN, RB1, and BRCA2 in all tumor and CTC samples. Using high-quality WGS on single-CTCs, we identified the shared genomic alterations between CTCs and tumor tissues. This approach yielded insight into the heterogeneity of the mutational landscape of SSNVs and SVs. It may be possible to use this approach to study heterogeneity and characterize the biological evolution of a cancer during the course of its natural history.

SUBMITTER: Jiang R

PROVIDER: S-EPMC4792591 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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A comparison of isolated circulating tumor cells and tissue biopsies using whole-genome sequencing in prostate cancer.

Jiang Runze R Lu Yi-Tsung YT Ho Hao H Li Bo B Chen Jie-Fu JF Lin Millicent M Li Fuqiang F Wu Kui K Wu Hanjie H Lichterman Jake J Wan Haolei H Lu Chia-Lun CL OuYang William W Ni Ming M Wang Linlin L Li Guibo G Lee Tom T Zhang Xiuqing X Yang Jonathan J Rettig Matthew M Chung Leland W K LW Yang Huanming H Li Ker-Chau KC Hou Yong Y Tseng Hsian-Rong HR Hou Shuang S Xu Xun X Wang Jun J Posadas Edwin M EM

Oncotarget 20151201 42

Previous studies have demonstrated focal but limited molecular similarities between circulating tumor cells (CTCs) and biopsies using isolated genetic assays. We hypothesized that molecular similarity between CTCs and tissue exists at the single cell level when characterized by whole genome sequencing (WGS). By combining the NanoVelcro CTC Chip with laser capture microdissection (LCM), we developed a platform for single-CTC WGS. We performed this procedure on CTCs and tissue samples from a patie ...[more]

PMID: 26575023

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Project description:ObjectiveDeep sequencing offers unparalleled access to rare variants in human populations. Understanding their role in disease is a priority, yet prohibitive sequencing costs mean that many cohorts lack the sample size to discover these effects on their own. Meta-analysis of individual variant scores allows the combination of rare variants across cohorts and study of their aggregated effect at the gene level, boosting discovery power. However, the methods involved have largely not been field-tested. In this study, we aim to perform the first meta-analysis of gene-based rare variant aggregation optimal tests, applied to the human cardiometabolic proteome.MethodsHere, we carry out this analysis across MANOLIS, Pomak and ORCADES, three isolated European cohorts with whole-genome sequencing (total N = 4,422). We examine the genetic architecture of 250 proteomic traits of cardiometabolic relevance. We use a containerised pipeline to harmonise variant lists across cohorts and define four sets of qualifying variants. For every gene, we interrogate protein-damaging variants, exonic variants, exonic and regulatory variants, and regulatory only variants, using the CADD and Eigen scores to weigh variants according to their predicted functional consequence. We perform single-cohort rare variant analysis and meta-analyse variant scores using the SMMAT package.ResultsWe describe 5 rare variant pQTLs (RV-pQTL) which pass our stringent significance threshold (7.45 × 10-11) and quality control procedure. These were split between four cis signals for MARCO, TEK, MMP2 and MPO, and one trans association for GDF2 in the SERPINA11 gene. We show that the cis-MPO association, which was not detectable using the single-point data alone, is driven by 5 missense and frameshift variants. These include rs140636390 and rs119468010, which are specific to MANOLIS and ORCADES, respectively. We show how this kind of signal could improve the predictive accuracy of genetic factors in common complex disease such as stroke and cardiovascular disease.ConclusionsOur proof-of-concept study demonstrates the power of gene-based meta-analyses for discovering disease-relevant associations complementing common-variant signals by incorporating population-specific rare variation.

Project description:BackgroundBetter blood tests to elucidate the behaviour of metastatic castration-resistant prostate cancer (mCRPC) are urgently needed to drive therapeutic decisions. Plasma cell-free DNA (cfDNA) comprises normal and circulating tumour DNA (ctDNA). Low-pass whole-genome sequencing (lpWGS) of ctDNA can provide information on mCRPC behaviour.ObjectiveTo validate and clinically qualify plasma lpWGS for mCRPC.Design, setting, and participantsPlasma lpWGS data were obtained for mCRPC patients consenting to optional substudies of two prospective phase 3 trials (FIRSTANA and PROSELICA). In FIRSTANA, chemotherapy-naïve patients were randomised to treatment with docetaxel (75 mg/m2) or cabazitaxel (20 or 25 mg/m2). In PROSELICA, patients previously treated with docetaxel were randomised to 20 or 25 mg/m2 cabazitaxel. lpWGS data were generated from 540 samples from 188 mCRPC patients acquired at four different time points (screening, cycle 1, cycle 4, and end of study). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: lpWGS data for ctDNA were evaluated for prognostic, response, and tumour genomic measures. Associations with response and survival data were determined for tumour fraction. Genomic biomarkers including large-scale transition (LST) scores were explored in the context of prior treatments.Results and limitationsPlasma tumour fraction was prognostic for overall survival in univariable and stratified multivariable analyses (hazard ratio 1.75, 95% confidence interval 1.08-2.85; p = 0.024) and offered added value compared to existing biomarkers (C index 0.722 vs 0.709; p = 0.021). Longitudinal changes were associated with drug response. PROSELICA samples were enriched for LSTs (p = 0.029) indicating genomic instability, and this enrichment was associated with prior abiraterone and enzalutamide treatment but not taxane or radiation therapy. Higher LSTs were correlated with losses of RB1/RNASEH2B, independent of BRCA2 loss.ConclusionsPlasma lpWGS of ctDNA describes CRPC behaviour, providing prognostic and response data of clinical relevance. The added prognostic value of the ctDNA fraction over established biomarkers should be studied further.Patient summaryWe studied tumour DNA in blood samples from patients with prostate cancer. We found that levels of tumour DNA in blood were indicative of disease prognosis, and that changes after treatment could be detected. We also observed a "genetic scar" in the results that was associated with certain previous treatments. This test allows an assessment of tumour activity that can complement existing tests, offer insights into drug response, and detect clinically relevant genetic changes.

Dataset Information

A comparison of isolated circulating tumor cells and tissue biopsies using whole-genome sequencing in prostate cancer.

Publications

A comparison of isolated circulating tumor cells and tissue biopsies using whole-genome sequencing in prostate cancer.

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