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Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer.

ABSTRACT: BACKGROUND: Molecular heterogeneity of tumors suggests the presence of multiple different subclones that may limit response to targeted therapies and contribute to acquisition of drug resistance, but its quantification has remained challenging. RESULTS: We performed simulations to evaluate statistical measures that best capture the molecular diversity within a group of tumors for either continuous (gene expression) or discrete (mutations, copy number alterations) molecular data. Dispersion based metrics in the principal component space best captured the underlying heterogeneity. To demonstrate utility of these measures, we characterized the diversity in transcriptional and genomic profiles of different breast tumor subtypes, and showed that basal-like or triple-negative breast cancers (TNBC) are significantly more heterogeneous molecularly than other subtypes. Our analysis also suggests that transcriptional diversity is a global characteristic of the tumors observed across the majority of molecular pathways. Among basal-like tumors, those that were resistant to multi-agent chemotherapy showed greater transcriptional diversity compared to chemotherapy-sensitive tumors, suggesting that potentially multiple mechanisms may be contributing to chemotherapy resistance. CONCLUSIONS: We proposed and validated measures of transcriptional and genomic diversity that can quantify the molecular diversity of tumors. We applied the new measures to genomic data from breast tumors and demonstrated that basal-like breast cancers are significantly more diverse than other breast cancers. The observation that chemo-resistant tumors are significantly more diverse molecularly than chemosensitive tumors implies that multiple resistance mechanisms may be active, thus limiting the sensitivity and accuracy of predictive markers of chemotherapy response.

SUBMITTER: Jiang T

PROVIDER: S-EPMC4197225 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer.

Jiang Tingting T Shi Weiwei W Natowicz René R Ononye Sophia N SN Wali Vikram B VB Kluger Yuval Y Pusztai Lajos L Hatzis Christos C

BMC genomics 20141008

<h4>Background</h4>Molecular heterogeneity of tumors suggests the presence of multiple different subclones that may limit response to targeted therapies and contribute to acquisition of drug resistance, but its quantification has remained challenging.<h4>Results</h4>We performed simulations to evaluate statistical measures that best capture the molecular diversity within a group of tumors for either continuous (gene expression) or discrete (mutations, copy number alterations) molecular data. Dis ...[more]

PMID: 25294321

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Project description:BackgroundEndometrial cancer (EC) is a major gynecological cancer with increasing incidence. It comprises four molecular subtypes with differing etiology, prognoses, and responses to chemotherapy. In the future, clinical trials testing new single agents or combination therapies will be targeted to the molecular subtype most likely to respond. As pre-clinical models that faithfully represent the molecular subtypes of EC are urgently needed, we sought to develop and characterize a panel of novel EC patient-derived xenograft (PDX) models.MethodsHere, we report whole exome or whole genome sequencing of 11 PDX models and their matched primary tumor. Analysis of multiple PDX lineages and passages was performed to study tumor heterogeneity across lineages and/or passages. Based on recent reports of frequent defects in the homologous recombination (HR) pathway in EC, we assessed mutational signatures and HR deficiency scores and correlated these with in vivo responses to the PARP inhibitor (PARPi) talazoparib in six PDXs representing the copy number high/p53-mutant and mismatch-repair deficient molecular subtypes of EC.ResultsPDX models were successfully generated from grade 2/3 tumors, including three uterine carcinosarcomas. The models showed similar histomorphology to the primary tumors and represented all four molecular subtypes of EC, including five mismatch-repair deficient models. The different PDX lineages showed a wide range of inter-tumor and intra-tumor heterogeneity. However, for most PDX models, one arm recapitulated the molecular landscape of the primary tumor without major genomic drift. An in vivo response to talazoparib was detected in four copy number high models. Two models (carcinosarcomas) showed a response consistent with stable disease and two models (one copy number high serous EC and another carcinosarcoma) showed significant tumor growth inhibition, albeit one consistent with progressive disease; however, all lacked the HR deficiency genomic signature.ConclusionsEC PDX models represent the four molecular subtypes of disease and can capture intra-tumor heterogeneity of the original primary tumor. PDXs of the copy number high molecular subtype showed sensitivity to PARPi; however, deeper and more durable responses will likely require combination of PARPi with other agents.

Project description:BackgroundHepatocellular carcinoma (HCC) often presents with multiple nodules within the liver, with limited effective interventions. The high genetic heterogeneity of HCC might be the major cause of treatment failure. We aimed to characterize genomic heterogeneity, infer clonal evolution, investigate RNA expression pattern and explore tumour immune microenvironment profile of multifocal HCC.Patients and methodsWhole-exome sequencing and RNA sequencing were carried out in 34 tumours and 6 adjacent normal liver tissue samples from 6 multifocal HCC patients. Protein expression of Ki67, AFP, P53, Survivin and CD8 was detected by immunohistochemistry. Fluorescence in situ hybridization was carried out to validate the amplification status of sorafenib-targeted genes.ResultsWe deciphered genomic and transcriptional heterogeneity among tumours in each multifocal HCC patient including mutational profiles, copy number alterations, tumour evolutionary trajectory and tumour immune microenvironment profiles. Of note, sorafenib-targeted alterations were identified in the trunk of phylogenetic tree in only one out of the six patients, which may explain the relative low treatment response rate to sorafenib in clinical practice. Moreover, we demonstrated RNA expression patterns and tumour immune microenvironment profiles of all nodules. We found that RNA expression pattern was associated with Edmondson-Steiner grading. Based on the differential expression of 66 reported immune markers, unsupervised hierarchical clustering analysis of 34 nodules identified immune subsets: one low expression cluster with seven nodules and one high expression cluster with 11 nodules. CD8+ T cells were more enriched in nodules of the high expression cluster.ConclusionsOur study provided a detailed view of genomic and transcriptional heterogeneity, clonal evolution and immune infiltration of multifocal HCC. The heterogeneity of druggable targets and immune landscape might help interpret the clinical responsiveness to targeted drugs and immunotherapy for multifocal HCC patients.

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Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer.

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Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer.

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