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Simple deep sequencing-based post-remission MRD surveillance predicts clinical relapse in B-ALL.

ABSTRACT: Next-generation sequencing (NGS) of the rearranged immunoglobulin heavy-chain gene has emerged as a highly sensitive method to detect minimal residual disease (MRD) in B acute lymphoblastic leukemia/lymphoma (B-ALL). However, a sensitive and easily implemented NGS methodology for routine clinical laboratories is lacking and clinical utility of NGS-MRD surveillance in a post-remission setting to predict clinical relapse has not been determined.Here we described a simple and quantitative NGS platform and assessed its performance characteristics, quantified NGS-MRD levels in 122 B-ALL samples from 30 B-ALL patients, and explored the clinical merit of NGS-based MRD surveillance.The current NGS platform has an analytic sensitivity of 0.0001% with excellent specificity and reproducibility. Overall, it performs better than routine multi-color flow cytometry (MCF) in detecting MRD. Utilizing this assay in MRD surveillance in a post-remission setting showed that it detected conversion to positive MRD (CPMRD) in patients with NGS-based molecular remission much earlier than MCF, and that positive MRD conversion could be detected as early as 25.6 weeks prior to clinical relapse in closely surveilled patients. Post-remission CPMRD, but not NGS-based MRD positivity at end of induction, can accurately predict clinical relapse in our limited cohort of B-ALL patients.This pilot proof-of-concept study illustrates the clinical utility of a simple, sensitive, and clinically feasible MRD detection platform in post-remission NGS-based MRD surveillance and early relapse detection in B-ALL patients.

SUBMITTER: Cheng S

PROVIDER: S-EPMC6103872 | biostudies-other | 2018 Aug

REPOSITORIES: biostudies-other

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Simple deep sequencing-based post-remission MRD surveillance predicts clinical relapse in B-ALL.

Cheng Shuhua S Inghirami Giorgio G Cheng Shuo S Tam Wayne W

Journal of hematology & oncology 20180822 1

<h4>Background</h4>Next-generation sequencing (NGS) of the rearranged immunoglobulin heavy-chain gene has emerged as a highly sensitive method to detect minimal residual disease (MRD) in B acute lymphoblastic leukemia/lymphoma (B-ALL). However, a sensitive and easily implemented NGS methodology for routine clinical laboratories is lacking and clinical utility of NGS-MRD surveillance in a post-remission setting to predict clinical relapse has not been determined.<h4>Methods</h4>Here we described ...[more]

PMID: 30134947

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Project description:ObjectivePost-operative biochemical relapse (BCR) continues to occur in a significant percentage of patients with localized prostate cancer (PCa). Current stratification methods are not adequate to identify high-risk patients. The present study exploits the ability of deep learning (DL) algorithms using the H2O package to combine multi-omics data to resolve this problem.MethodsFive-omics data from 417 PCa patients from The Cancer Genome Atlas (TCGA) were used to construct the DL-based, relapse-sensitive model. Among them, 265 (63.5%) individuals experienced BCR. Five additional independent validation sets were applied to assess its predictive robustness. Bioinformatics analyses of two relapse-associated subgroups were then performed for identification of differentially expressed genes (DEGs), enriched pathway analysis, copy number analysis and immune cell infiltration analysis.ResultsThe DL-based model, with a significant difference (P = 6e-9) between two subgroups and good concordance index (C-index = 0.767), were proven to be robust by external validation. 1530 DEGs including 678 up- and 852 down-regulated genes were identified in the high-risk subgroup S2 compared with the low-risk subgroup S1. Enrichment analyses found five hallmark gene sets were up-regulated while 13 were down-regulated. Then, we found that DNA damage repair pathways were significantly enriched in the S2 subgroup. CNV analysis showed that 30.18% of genes were significantly up-regulated and gene amplification on chromosomes 7 and 8 was significantly elevated in the S2 subgroup. Moreover, enrichment analysis revealed that some DEGs and pathways were associated with immunity. Three tumor-infiltrating immune cell (TIIC) groups with a higher proportion in the S2 subgroup (p = 1e-05, p = 8.7e-06, p = 0.00014) and one TIIC group with a higher proportion in the S1 subgroup (P = 1.3e-06) were identified.ConclusionWe developed a novel, robust classification for understanding PCa relapse. This study validated the effectiveness of deep learning technique in prognosis prediction, and the method may benefit patients and prevent relapse by improving early detection and advancing early intervention.

Dataset Information

Simple deep sequencing-based post-remission MRD surveillance predicts clinical relapse in B-ALL.

Publications

Simple deep sequencing-based post-remission MRD surveillance predicts clinical relapse in B-ALL.

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