Background: Next-generation sequencing (NGS) is playing a transformational role in cancer discovery research; providing a powerful way to study DNA and/or RNA from clinical specimens. Unfortunately, the cost and complexity of whole genome sequencing approaches represent major barriers to use of these methodologies in routine molecular diagnostic testing. Nonetheless, a comprehensive catalog of all types of mutations in cancer opens unique opportunities for understanding the mechanism of cancer onset or progression and facilitates a more personalized approach to clinical care, including improved risk stratification and treatment selection.

Methodology: Recently developed targeted approaches reduce NGS data complexity and generate qualitative sequencing information by measurement of a subset of targets per technical replicate with minimal sample usage. To this end, we have developed a novel, multiplex PCR target enrichment-based NGS pipeline (Driver-Map) designed to comprehensively assess the genomic landscape of both solid tumors and hematologic malignancies from clinical samples. Importantly, CancerCore analyzes the genomic alterations for 569 pan-cancer driver genes. Simultaneous quantitative target gene expression analysis using multiplex quantitative RNA-Seq (Q-RNA-Seq) for ∼2,000 additional genes (CancerNet) and computational network modeling facilitates the rapid identification of cancer gene and/or pathway models to identify commonalities across tumor type(s) and supporting pharmacogenetics information.

Results: To determine the broad utility of this methodology and determine whether Driver-Map captures biologically relevant information, we apply it to clinical samples from triple-negative breast cancer (TNBC) patients and demonstrate that our approach identifies novel therapeutically tractable genes.

Conclusion: In summary, Driver-Map provides both strand-specific sequencing at single-base resolution and ‘digital’ gene expression profiling, resulting in better detection of both rare genetic variants and low abundance mRNA transcripts. Moreover, Driver-Map results in unparalleled specificity and sensitivity while increasing the cost-effectiveness for high-throughput clinical Next-Gen applications.

Citation Format: Alex Chenchik, Paul Diehl, Leonid Iakoubov, Costa Frangou. CancerNet biomarker profiling panel. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr A23.

©2015 American Association for Cancer Research.
2015