Decoding the Cancer Genome: Insights from Bioinformatic Studies

Abstract

The characterization of the landscape of genetic lesions that underlie cancer has been significantly advanced with the recent application of next-generation sequencing (NGS) technology. To define the genomic landscapes of 21 different pediatric cancer subtypes of brain tumors, solid tumors and leukemias, we analyzed >1,000 pediatric cancers and matched control tissue by whole-genome, whole-exome or transcriptome sequencing as part of the St Jude Children’s Research Hospital – Washington University Pediatric Cancer Genome Project (PCGP). Novel bioinformatics methods for integrative analysis of single-nucleotide variation (SNV), small insertion/deletion (indel), copy number alteration (CNA) and structural variation (SV) have been developed to ensure high sensitivity and accuracy, which is critical for the discovery of highly recurrent somatic lesions that have the potential for developing new cancer therapy. For example, our fusion transcript detection method CICERO has played a key role in identifying recurrent kinase fusions in 90% of Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL), a subgroup characterized by a gene expression profile similar to BCR-ABL1-positive ALL. To evaluate whether NGS is able to identify germline and somatic lesions reported by molecular diagnostic assay, we carried out a pilot clinical sequencing study that employed whole-genome, whole-exome and transcriptome sequencing of matched tumor/normal samples from 78 pediatric cancer patients. We implemented an analysis pipeline that integrates the genetic lesions from all three NGS platforms as well as a data portal that supports classification of somatic and germline lesions. We present a comparison of the sensitivity and accuracy of single-platform analysis with that of integrative multi-platform analysis in identifying somatic and germline SNVs/indels, translocation, gene fusion, CNAs, karyotype, and loss of heterozygosity. Our experience provides informative insight for the design of clinical sequencing of pediatric cancer. Disclosures No relevant conflicts of interest to declare.