High throughput sequencing-based transcriptiome analysis of diffuse large-B-cell lymphoma patients with samples taken at diagnosis and after therapy relapse: a feasibility study toward developing personalized therapies

Abstract

Traditionally, prognostic information and selection of therapies for individuals with cancer is based on the results of studies that evaluate large groups of patients, in which there has been demonstrated a statistical benefit for the group. The success of this approach is directly related to the biological homogeneity of the chosen study group. Therefore, this approach is inherently sub-optimal for many individual patients, particularly in very heterogeneous disease entities such as diffuse large B-cell lymphoma (DLBL). However, with the recent introduction of high-throughput sequencing, it has become possible to extensively evaluate the biology of individual patient samples, which may eventually be used to transition to an era of true individualized management for cancer patients. Challenges to this approach include the complexity of the technology for clinical laboratories, high cost, and the immaturity of the databases analysis technology that are required to evaluate the results. Fortunately, rapid improvements continue to be made in all of these areas. The primary goal of this project was to explore the feasibility of creating “clinical-grade” evaluation methods toward developing personalized therapies in the near future. Clinical samples from patients with DLBL were used to examine the potential of two platforms, Oxford Nanopore and Illumina company products, for the analysis of complete mRNA transcriptomes since they can be representatives of intracellular biology. I found that the Illumina platform technique is feasible for the goal, while the Oxford Nanopore technology is not. Feasibility was further shown by successful use of the Illumina technology and the analysis method developed, to verify prediction of DLBL aggressiveness as previously determined by an alternate method, considered the “gold standard” in published literature. Finally, mRNA transcriptome data generated from pre-therapy diagnostic and post-therapy recurrence samples of DLBLs was used to demonstrate that it is feasible to use opensource databases and programs to generate a list of therapeutic candidate proteins and pathways in each individual case. Although this feasibility study was carried out with only small number of patients, it shows that the components may finally be available to consider moving forward. However, further work is required to successful transition individualized tumor evaluation approaches into routine clinical practice.