ABSTRACT
Tyrosine kinase inhibitor therapy revolutionized chronic myeloid leukemia treatment and showed how targeted therapy and molecular monitoring could be used to substantially improve survival outcomes. We used chronic myeloid leukemia as a model to understand a critical question: why do some patients have an excellent response to therapy, while others have a poor response? We studied gene expression in whole blood samples from 112 patients from a large phase III randomized trial (clinicaltrials gov. Identifier: NCT00471497), dichotomizing cases into good responders (BCR::ABL1 ≤10% on the International Scale by 3 and 6 months and ≤0.1% by 12 months) and poor responders (failure to meet these criteria). Predictive models based on gene expression demonstrated the best performance (area under the curve =0.76, standard deviation =0.07). All of the top 20 pathways overexpressed in good responders involved immune regulation, a finding validated in an independent data set. This study emphasizes the importance of pretreatment adaptive immune response in treatment efficacy and suggests biological pathways that can be targeted to improve response.
Subject(s)
Antineoplastic Agents , Leukemia, Myelogenous, Chronic, BCR-ABL Positive , Leukemia, Myeloid, Chronic-Phase , Humans , Antineoplastic Agents/pharmacology , Fusion Proteins, bcr-abl/genetics , Protein Kinase Inhibitors/adverse effects , Leukemia, Myelogenous, Chronic, BCR-ABL Positive/diagnosis , Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy , Leukemia, Myelogenous, Chronic, BCR-ABL Positive/genetics , Treatment OutcomeABSTRACT
Mutation detection is increasingly used for the management of hematological malignancies. Prior whole transcriptome and whole exome sequencing studies using total RNA and DNA identified diverse mutation types in cancer-related genes associated with treatment failure in patients with chronic myeloid leukemia. Variants included single-nucleotide variants and small insertions/deletions, plus fusion transcripts and partial or whole gene deletions. The hypothesis that all of these mutation types could be detected by a single cost-effective hybridization capture next-generation sequencing method using total RNA was assessed. A method was developed that targeted 130 genes relevant for myeloid and lymphoid leukemia. Retrospective samples with 121 precharacterized variants were tested using total RNA and/or DNA. Concordance of detection of precharacterized variants using RNA or DNA was 96%, whereas the enhanced sensitivity identified additional variants. Comparison between 24 matched DNA and RNA samples demonstrated 95.3% of 170 variants detectable using DNA were detected using RNA, including all but one variant predicted to activate nonsense-mediated decay. RNA identified an additional 10 variants, including fusion transcripts. Furthermore, the true effect of splice variants on RNA splicing was only evident using RNA. In conclusion, capture sequencing using total RNA alone is suitable for detecting a range of variants relevant in chronic myeloid leukemia and may be more broadly applied to other hematological malignancies where diverse variant types define risk groups.
Subject(s)
Hematologic Neoplasms , Leukemia, Myelogenous, Chronic, BCR-ABL Positive , High-Throughput Nucleotide Sequencing/methods , Humans , Leukemia, Myelogenous, Chronic, BCR-ABL Positive/diagnosis , Leukemia, Myelogenous, Chronic, BCR-ABL Positive/genetics , Mutation , RNA , Retrospective StudiesABSTRACT
The ability to search for genetic variants that may be related to human disease is one of the most exciting consequences of the availability of the sequence of the human genome. Large cohorts of individuals exhibiting certain phenotypes can be studied and candidate genes resequenced. However, the challenge of analyzing sequence data from many individuals with accuracy, speed, and economy is great. This unit describes one set of software tools: Phred, Phrap, PolyPhred, and Consed. Coverage includes the advantages and disadvantages of these analysis tools, details for obtaining and using the software, and the results one may expect. The software is being continually updated to permit further automation of mutation analysis. Currently, however, at least some manual review is required if one wishes to identify 100% of the variants in a sample set.