Dataset Information

Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis.

ABSTRACT:

Background

The major clinical challenge in the treatment of high-grade serous ovarian cancer (HGSOC) is the development of progressive resistance to platinum-based chemotherapy. The objective of this study was to determine whether intra-tumour genetic heterogeneity resulting from clonal evolution and the emergence of subclonal tumour populations in HGSOC was associated with the development of resistant disease.

Methods and findings

Evolutionary inference and phylogenetic quantification of heterogeneity was performed using the MEDICC algorithm on high-resolution whole genome copy number profiles and selected genome-wide sequencing of 135 spatially and temporally separated samples from 14 patients with HGSOC who received platinum-based chemotherapy. Samples were obtained from the clinical CTCR-OV03/04 studies, and patients were enrolled between 20 July 2007 and 22 October 2009. Median follow-up of the cohort was 31 mo (interquartile range 22-46 mo), censored after 26 October 2013. Outcome measures were overall survival (OS) and progression-free survival (PFS). There were marked differences in the degree of clonal expansion (CE) between patients (median 0.74, interquartile range 0.66-1.15), and dichotimization by median CE showed worse survival in CE-high cases (PFS 12.7 versus 10.1 mo, p = 0.009; OS 42.6 versus 23.5 mo, p = 0.003). Bootstrap analysis with resampling showed that the 95% confidence intervals for the hazard ratios for PFS and OS in the CE-high group were greater than 1.0. These data support a relationship between heterogeneity and survival but do not precisely determine its effect size. Relapsed tissue was available for two patients in the CE-high group, and phylogenetic analysis showed that the prevalent clonal population at clinical recurrence arose from early divergence events. A subclonal population marked by a NF1 deletion showed a progressive increase in tumour allele fraction during chemotherapy.

Conclusions

This study demonstrates that quantitative measures of intra-tumour heterogeneity may have predictive value for survival after chemotherapy treatment in HGSOC. Subclonal tumour populations are present in pre-treatment biopsies in HGSOC and can undergo expansion during chemotherapy, causing clinical relapse.

SUBMITTER: Schwarz RF

PROVIDER: S-EPMC4339382 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Publications

Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis.

Schwarz Roland F RF Ng Charlotte K Y CK Cooke Susanna L SL Newman Scott S Temple Jillian J Piskorz Anna M AM Gale Davina D Sayal Karen K Murtaza Muhammed M Baldwin Peter J PJ Rosenfeld Nitzan N Earl Helena M HM Sala Evis E Jimenez-Linan Mercedes M Parkinson Christine A CA Markowetz Florian F Brenton James D JD

PLoS medicine 20150224 2

<h4>Background</h4>The major clinical challenge in the treatment of high-grade serous ovarian cancer (HGSOC) is the development of progressive resistance to platinum-based chemotherapy. The objective of this study was to determine whether intra-tumour genetic heterogeneity resulting from clonal evolution and the emergence of subclonal tumour populations in HGSOC was associated with the development of resistant disease.<h4>Methods and findings</h4>Evolutionary inference and phylogenetic quantific ...[more]

PMID: 25710373

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Project description:Multiple studies have identified transcriptome subtypes of high-grade serous ovarian carcinoma (HGSOC), but their interpretation and translation are complicated by tumor evolution and polyclonality accompanied by extensive accumulation of somatic aberrations, varying cell type admixtures, and different tissues of origin. In this study, we examined the chronology of HGSOC subtype evolution in the context of these factors using a novel integrative analysis of absolute copy-number analysis and gene expression in The Cancer Genome Atlas complemented by single-cell analysis of six independent tumors. Tumor purity, ploidy, and subclonality were reliably inferred from different genomic platforms, and these characteristics displayed marked differences between subtypes. Genomic lesions associated with HGSOC subtypes tended to be subclonal, implying subtype divergence at later stages of tumor evolution. Subclonality of recurrent HGSOC alterations was evident for proliferative tumors, characterized by extreme genomic instability, absence of immune infiltration, and greater patient age. In contrast, differentiated tumors were characterized by largely intact genome integrity, high immune infiltration, and younger patient age. Single-cell sequencing of 42,000 tumor cells revealed widespread heterogeneity in tumor cell type composition that drove bulk subtypes but demonstrated a lack of intrinsic subtypes among tumor epithelial cells. Our findings prompt the dismissal of discrete transcriptome subtypes for HGSOC and replacement by a more realistic model of continuous tumor development that includes mixtures of subclones, accumulation of somatic aberrations, infiltration of immune and stromal cells in proportions correlated with tumor stage and tissue of origin, and evolution between properties previously associated with discrete subtypes. SIGNIFICANCE: This study infers whether transcriptome-based groupings of tumors differentiate early in carcinogenesis and are, therefore, appropriate targets for therapy and demonstrates that this is not the case for HGSOC.

Project description:BackgroundHigh grade serous ovarian cancer is characterised by high initial response to chemotherapy but poor outcome in the long term due to acquired resistance. One of the main genetic features of this disease is TP53 mutation. The majority of TP53 mutated tumors harbor missense mutations in this gene, correlated with p53 accumulation. TP53 null tumors constitute a specific subgroup characterised by nonsense, frameshift or splice-site mutations associated to complete absence of p53 expression. Different studies show that this kind of tumors may have a worse prognosis than other TP53 mutated HGSC.MethodsIn this study, we sought to characterise the intra-tumor heterogeneity of a TP53 null HGSC consisting of six primary tumor samples, two intra-pelvic and four extra-pelvic recurrences using exome sequencing and comparative genome hybridisation.ResultsSignificant heterogeneity was found among the different tumor samples, both at the mutational and copy number levels. Exome sequencing identified 102 variants, of which only 42 were common to all three samples; whereas 7 of the 18 copy number changes found by CGH analysis were presented in all samples. Sanger validation of 20 variants found by exome sequencing in additional regions of the primary tumor and the recurrence allowed us to establish a sequence of the tumor clonal evolution, identifying those populations that most likely gave rise to recurrences and genes potentially involved in this process, like GPNMB and TFDP1. Using functional annotation and network analysis, we identified those biological functions most significantly altered in this tumor. Remarkably, unexpected functions such as microtubule-based movement and lipid metabolism emerged as important for tumor development and progression, suggesting its potential interest as therapeutic targets.ConclusionsAltogether, our results shed light on the clonal evolution of the distinct tumor regions identifying the most aggressive subpopulations and at least some of the genes that may be implicated in its progression and recurrence, and highlights the importance of considering intra-tumor heterogeneity when carrying out genetic and genomic studies, especially when these are aimed to diagnostic procedures or to uncover possible therapeutic strategies.

Dataset Information

Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis.

Background

Methods and findings

Conclusions

Publications

Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis.

Similar Datasets

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