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Record W2954266854 · doi:10.1158/1538-7445.am2019-2872

Abstract 2872: Acquisition of drug resistance mutations during chemotherapy treatment in pediatric acute lymphoblastic leukemia

2019· article· en· W2954266854 on OpenAlexaff
Benshang Li, Samuel W. Brady, Xiaotu Ma, Shuhong Shen, Yingchi Zhang, Yongjin Li, Yu Liu, Ningling Wang, Diane A. Flasch, Matthew Myers, Heather L. Mulder, Lixia Ding, Yanling Lü, Liqing Tian, Kohei Hagiwara, Ke Xu, Edgar Sioson, Tianyi Wang, Yang Liu, Jie Zhao, Hui Zhang, Ying Shao, Hongye Sun, Lele Sun, Jiaoyang Cai, Ting-Nien Lin, Lijuan Du, Fan Yang, Michael Rusch, Michael N. Edmonson, John Easton, Xiaofan Zhu, Jingliao Zhang, Cheng Cheng, Benjamin J. Raphael, Jingyan Tang, James R. Downing, Bin‐Bing S. Zhou, Ching‐Hon Pui, Jun J. Yang, Jinghui Zhang

Bibliographic record

VenueCancer Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsDrug resistancePMS2Cancer researchMSH6BiologyAcute lymphocytic leukemiaMSH2ChemotherapyCancerGene signatureGermline mutationGeneGeneticsLeukemiaMedicineInternal medicineMutationDNA mismatch repairDNA repairLymphoblastic LeukemiaGene expression

Abstract

fetched live from OpenAlex

Abstract Acute lymphoblastic leukemia (ALL) is a leading cause of cancer-associated death in children. To study the mechanisms of drug resistance in ALL, we performed whole-genome sequencing of diagnosis-relapse-germline trios from 103 Chinese patients and ultra-deep sequencing of 208 serial bone marrow samples from 17 of them. Relapse-specific somatic alterations were enriched in 12 genes (NR3C1, NR3C2, TP53, NT5C2, FPGS, CREBBP, MSH2, MSH6, PMS2, WHSC1, PRPS1, and PRPS2), which were predominantly involved in response to thiopurines, glucocorticoids, methotrexate, and other drugs. Four lines of evidence indicate that these resistance mutations frequently developed during treatment, rather than pre-existing at diagnosis. First, two novel, relapse-specific mutational signatures (novel signatures 1 and 2), most likely caused by chemotherapeutic regimens, were detected in 15% and 14% of relapsed cases, respectively. Drug resistance mutations frequently appeared at novel signature-associated trinucleotide contexts, indicating that chemotherapy may directly cause drug resistance mutations in ALL. The signatures were validated in NCI TARGET relapsed ALL samples, 2% and 23% of which harbored novel signatures 1 and 2, respectively. The varying signature prevalence between cohorts may reflect treatment differences. The novel signatures were not detected in >2,000 adult cancers from the PCAWG study. Novel signature 1 induced C>G transversions, particularly at GCC and TCT trinucleotides, and showed transcription-strand bias indicating guanine adducts. Novel signature 2 favored C>T and C>G mutations at CCG, and correlated with relapse-specific dinucleotide variants and structural variants, indicating an agent causing multiple mutation types. The drugs inducing these novel signatures are being explored in vitro. Second, mathematical modeling using growth curves of drug-resistant ALL indicated that drug resistance mutations occur, in some cases, long after diagnosis, during active treatment. Third, some patients acquired multiple drug resistance mutations sequentially through successive relapses, a finding inconsistent with their pre-existence at diagnosis. Indeed, 20% of relapses had multiple drug resistance mutations targeting different drug classes. Fourth, most relapsed ALLs derived from a subclone detected at diagnosis, which then evolved additional mutations, including drug resistance mutations, not detectable at diagnosis using 2000X targeted sequencing. Drug resistance mutations were often subclonal at relapse, suggesting later appearance. Together these data indicate that fully drug-resistant clones may not necessarily pre-exist at diagnosis in ALL, but may be acquired later during treatment. Thus, early intensive or targeted treatment strategies in slow responders may forestall the subsequent development of drug resistance mutations. Citation Format: Benshang Li, Samuel W. Brady, Xiaotu Ma, Shuhong Shen, Yingchi Zhang, Yongjin Li, Yu Liu, Ningling Wang, Diane Flasch, Matthew Myers, Heather Mulder, Lixia Ding, Yanling Lu, Liqing Tian, Kohei Hagiwara, Ke Xu, Edgar Sioson, Tianyi Wang, Liu Yang, Jie Zhao, Hui Zhang, Ying Shao, Hongye Sun, Lele Sun, Jiaoyang Cai, Ting-Nien Lin, Lijuan Du, Fan Yang, Michael Rusch, Michael Edmonson, John Easton, Xiaofan Zhu, Jingliao Zhang, Cheng Cheng, Benjamin Raphael, Jingyan Tang, James Downing, Bin-Bing Zhou, Ching-Hon Pui, Jun Yang, Jinghui Zhang. Acquisition of drug resistance mutations during chemotherapy treatment in pediatric acute lymphoblastic leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2872.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.317
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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