The use of G-banded karyotype and FISH have been standard diagnostic tools in monitoring response to treatment and disease progression in hematological disorders, including multiple myeloma. However, with the availability of array and NGS technologies in most clinical diagnostic laboratory settings, it is time to consider evaluating the use of more modern methods in diagnosing plasma cell dyscrasias. To this end, we have consented over 20 patients, most of which have evidence of disease progression to a preliminary study comparing data from RNA-Seq, SNP-CN arrays and a targeted deep sequencing cancer panel to conventional FISH and cytogenetics.
Patients with evidence of disease were asked to participate in an IRB approved study with full genomics consent. CD138+/- cells were isolated from bone marrow specimens and used for RNA and DNA extraction. RNA-Seq library preparation was performed using Illumina TruSeq protocols and sequenced at 50 million reads per sample using an Illumina HiSeq2000 instrument. Matching DNA samples were processed using Illumina Omni1-Quad or Affymetrix CytoScan HD SNP copy number (SNP-CN) arrays and either the Ion Torrent AmpliSeq Cancer or the Illumina TruSeq Cancer panels at a minimum read depth of 1000x. RNA-Seq data was processes using TopHat alignment and standard tools for identifying differential gene expression (Cuffdiff), mutations (ANNOVAR) and gene fusions. SNP-CN data was analyzed using the GenomeStudio, ChAS and BioDiscovery Nexus software. Ion Torrent and MiSeq data was analyzed with on-board and third party (CLC-Bio) software. All genomic data was entered into NextBio-Clinical software with relevant clinical history.
We have completed analysis of 2 patient samples and full results are pending for more than 20 additional samples. Although our results are preliminary, we will present compelling evidence that the combination of RNA-Seq, SNP-CN array and a targeted deep sequencing cancer panels provide greater detail into molecular markers of clonal waves and potential mechanisms that drive disease progression in multiple myeloma than can be achieved with standard karyotype and FISH. These data include identification of a low-level KRAS p.G13D mutation in the background of a NRAS p.Q61K mutation that was present in both the RNA-Seq and the targeted deep sequencing data. This patient had a partial response to targeted therapy and we are in the process of evaluating if mutations that we found may have been associated response. In addition to these data, we have evidence to support that these technologies can be implemented within a standard clinical diagnostic timeline of 2 weeks or less with available infrastructure present at many academic institutions. Furthermore, we outline a plan for HIPAA-compliant longitudinal tracking of data, data sharing and data storage using commercial vendors such as NextBio and public sources such as dbVAR and dbGAP.
In order to continue to improve outcomes in patients with multiple myeloma, we need to improve our understanding of disease progression and response to treatment. This is difficult with low complexity and low resolution technologies such as karyotype and FISH. Moreover, the ability to analyze and share clinical trials data, even low complexity data, is hampered by inefficient reporting infrastructures. The implementation of genomics workflows in clinical laboratories presents many challenges, but with those challenges also comes the opportunity to provide more informative and more actionable information that can ultimately improve the quality of care.
Rossi:Pfizer: Consultancy; Onyx: Consultancy. Kaufman:Onyx: Consultancy; Novartis: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Millennium Pharmaceuticals: Consultancy; Jansenn: Consultancy; Merck: Research Funding. Boise:Onyx Pharmaceuticals: Consultancy. Lonial:Millennium: Consultancy; Celgene: Consultancy; Novartis: Consultancy; BMS: Consultancy; Sanofi: Consultancy; Onyx: Consultancy.
Asterisk with author names denotes non-ASH members.