High-throughput detection of actionable genomic alterations in clinical tumor samples by targeted, massively parallel sequencing

Abstract

Knowledge of "actionable" somatic genomic alterations present in each tumor (e.g., point mutations, small insertions/deletions, and copy-number alterations that direct therapeutic options) should facilitate individualized approaches to cancer treatment. However, clinical implementation of systematic genomic profiling has rarely been achieved beyond limited numbers of oncogene point mutations. To address this challenge, we utilized a targeted, massively parallel sequencing approach to detect tumor genomic alterations in formalin-fixed, paraffin-embedded (FFPE) tumor samples. Nearly 400-fold mean sequence coverage was achieved, and single-nucleotide sequence variants, small insertions/deletions, and chromosomal copynumber alterations were detected simultaneously with high accuracy compared with other methods in clinical use. Putatively actionable genomic alterations, including those that predict sensitivity or resistance to established and experimental therapies, were detected in each tumor sample tested. Thus, targeted deep sequencing of clinical tumor material may enable mutation-driven clinical trials and, ultimately, "personalized" cancer treatment.

Significance: Despite the rapid proliferation of targeted therapeutic agents, systematic methods to profile clinically relevant tumor genomic alterations remain underdeveloped. We describe a sequencingbased approach to identifying genomic alterations in FFPE tumor samples. These studies affirm the feasibility and clinical utility of targeted sequencing in the oncology arena and provide a foundation for genomics-based stratification of cancer patients.

Figure 1. Genomic alterations in breast cancer cell line MDA-MB-231

(A-C) Representative genome images from the Integrated Genome Viewer (IGV) for several alterations found in the breast cancer cell line MD-MBA-231. The number of reads for the reference allele and the variant allele are shown for each alteration. (A) BRAF oncogene point mutation. (B) Point mutation in the TP53 tumor suppressor gene. (C) A 1-bp insertion in tumor suppressor NF1. (D) Sequence coverage for each target exon in breast cancer cell line MD-MBA-231 as compared to a normal diploid sample. Targets from several genes with copy number gains and losses are highlighted. (E) Comparison of gene-level copy number alterations as detected by exon capture and copy number data previously obtained using a high-density SNParray (Affymetrix SNP 6.0 platform). Several genes with copy number gains and losses are highlighted. Copy number data are highly correlated, with a correlation coefficient of 0.94.

Figure 2. Copy number alterations in an archival breast cancer sample

(A) Sequence coverage is shown for each target in the tumor sample as compared to a normal diploid sample. Exon targets from several genes with copy number gains and losses are highlighted. (B) Copy number correlation between exon capture and QPCR in sample FFPE 5. Quantitative PCR of FGFR1, CCND1, and NOTCH1 using 3 independent sets of primers was performed and average values for each gene were compared to exon capture copy number.