Dataset Information

Molecular Profiling of Hard-to-Treat Childhood and Adolescent Cancers.

ABSTRACT:

Importance

Little progress in pediatric cancer treatment has been noted in the past decade, urging the development of novel therapeutic strategies for adolescents and children with hard-to-treat cancers. Use of comprehensive molecular profiling in the clinical management of children and adolescents with cancer appears a suitable approach to improve patient care and outcomes, particularly for hard-to-treat cases.

Objective

To assess the feasibility of identifying potentially actionable mutations using next-generation sequencing-based assays in a clinically relevant time frame.

Design, setting, and participants

This diagnostic study reports the results of the TRICEPS study, a prospective genome sequencing study conducted in Québec, Canada. Participants, aged 18 years or younger at diagnosis, with refractory or relapsed childhood and adolescent cancers were enrolled from April 2014 through January 2018. Whole-exome sequencing (WES) of matched tumor normal samples and RNA sequencing of tumor were performed to identify single-nucleotide variants, fusion transcripts, differential gene expression, and copy number alterations. Results reviewed by a team of experts were further annotated, synthesized into a report, and subsequently discussed in a multidisciplinary molecular tumor board.

Main outcomes and measures

Molecular profiling of pediatric patients with hard-to-treat cancer, identification of actionable and targetable alteration needed for the management of these patients, and proposition of targeted and personalized novel therapeutic strategies.

Results

A total of 84 patients with hard-to-treat cancers were included in the analysis. These patients had a mean (range) age of 10.1 (1-21) years and a similar proportion of male (45 [54%]) and female (39 [46%]). Sixty-two patients (74%) had suitable tissues for multimodal molecular profiling (WES and RNA sequencing). The process from DNA or RNA isolation to genomic sequencing and data analysis steps took a median (range) of 24 (4-41) days. Potentially actionable alterations were identified in 54 of 62 patients (87%). Actions were taken in 22 of 54 patients (41%), and 18 (33%) either were on a second or third line of treatment, were in remission, or had stable disease and thus no actions were taken.

Conclusions and relevance

Incorporating genomic sequencing into the management of hard-to-treat childhood and adolescent cancers appeared feasible; molecular profiling may enable the identification of potentially actionable alterations with clinical implications for most patients, including targeted therapy and clinically relevant information of diagnostic, prognostic, and monitoring significance.

SUBMITTER: Khater F

PROVIDER: S-EPMC6487576 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

Molecular Profiling of Hard-to-Treat Childhood and Adolescent Cancers.

Khater Fida F Vairy Stephanie S Langlois Sylvie S Dumoucel Sophie S Sontag Thomas T St-Onge Pascal P Bittencourt Henrique H Dal Soglio Dorothée D Champagne Josette J Duval Michel M Leclerc Jean-Marie JM Laverdiere Caroline C Tran Thai Hoa TH Patey Natalie N Ellezam Benjamin B Perreault Sébastien S Piché Nelson N Samson Yvan Y Teira Pierre P Jabado Nada N Michon Bruno B Brossard Josée J Marzouki Monia M Cellot Sonia S Sinnett Daniel D

JAMA network open 20190405 4

<h4>Importance</h4>Little progress in pediatric cancer treatment has been noted in the past decade, urging the development of novel therapeutic strategies for adolescents and children with hard-to-treat cancers. Use of comprehensive molecular profiling in the clinical management of children and adolescents with cancer appears a suitable approach to improve patient care and outcomes, particularly for hard-to-treat cases.<h4>Objective</h4>To assess the feasibility of identifying potentially action ...[more]

PMID: 31026031

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Project description:Lung cancers are a heterogeneous group of diseases with respect to biology and clinical behavior. So far, diagnosis and classification are based on histological morphology and immunohistological methods for discrimination between two main histologic groups: small cell lung cancer (SCLC) and non-small cell lung cancer which account for 20% and 80% of lung carcinomas, respectively. While SCLCs express properties of neuroendocrine cells, NSCLCs, which are divided into the three major subtypes adenocarcinoma, squamous cell carcinoma and dedifferentiated large cell carcinoma, show different characteristics such as the expression of certain keratins or production of mucin and lack neuroedocrine differentiation. The molecular pathogenesis of lung cancer involves the accumulation of genetic und epigenetic alterations including the activation of proto-oncogenes and inactivation of tumor suppressor genes which are different for lung cancer subgroups. The development of microarray technologies opened up the possibility to quantify the expression of a large number of genes simultaneously in a given sample. There are several recent reports on expression profiling on lung cancers but the analysis interpretation of the results might be difficult because of the heterogeneity of cellular components. A contamination of the tumor sample with normal epithelia, blood vessels, stromal cells, leucocytes and tumor necrosis may confound the true expression profile of the tumor. The use of laser capture microdissection (LCM) greatly improves the sample preparation for microarray expression analysis. Consequently, we used advanced technology including LCM and microarray analysis. In detail, we examined gene expression profiles of tumor cells from 29 previously untreated patients with lung cancer (10 adenocarcinomas (AC), 10 squamous cell carcinomas (SCC), 9 small cell lung cancer (SCLC)) in comparison to normal lung tissue (LT) of 5 control patients without tumor. Bronchoscopical biopsies from the primary lung tumor were taken before treatment. Biopsies were cut into 8µm sections and from each section cancer cells were isolated using laser capture microdissection in order to obtain pure samples of tumor cells. Total RNA was extracted, reversely transcribed, in-vitro transcribed, labelled and hybridized to the array. For expression analysis, microarrays covering 8793 defined genes (Human HG Focus Array, Affymetrix) were used. Following quality control, array data were normalized and analysed for significant differences using variance stabilizing transformation (VSN) and significance analysis of microarrays (SAM), respectively. Based on differentially expressed genes cancer samples could be clearly separated from non cancer samples using hierarchical clustering. Comparing AC, SCC and SCLC with normal lung tissue, we found 205, 335 and 404 genes, respectively, that were at least 2-fold differentially expressed with an estimated false discovery rate < 2.6%. Each histological subtype showed a distinct expression profile. Further, using a genetic programming approach we constructed a classificator to discriminate AC, SCC, NT and SCLC. To this end, the 50 genes with the greatest signal-to-noise ratio were selected to train the classificator. By leave-one-out cross validation all 34 samples were correctly classified in this training set. In order to validate the 50-gene-classificator on a test set, further 13 microdissected lung cancer samples were used and correctly classified in concordance to pathologic finding. In conclusion, the different lung cancer subtypes have distinct molecular phenotypes which reflect biological characteristics of the tumor cells and which might be the basis for development of targeted therapy. Moreover, gene expression profiling and genetic programming is a suitable tool for classification and discrimination of different histological subtypes in lung cancer in comparison to normal lung tissue. Experiment Overall Design: Comparison of gene expression profiles of normal lung tissues, adenocarcinomas, squamous cell carcinomas and small cell lung cancers.

Dataset Information

Molecular Profiling of Hard-to-Treat Childhood and Adolescent Cancers.

Importance

Objective

Design, setting, and participants

Main outcomes and measures

Results

Conclusions and relevance

Publications

Molecular Profiling of Hard-to-Treat Childhood and Adolescent Cancers.

Similar Datasets

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