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Record W2979315058 · doi:10.1182/blood.v126.23.692.692

Genomic Landscape of Relapsed Acute Lymphoblastic Leukemia

2015· article· en· W2979315058 on OpenAlexaff
Esmé Waanders, Stephanie M. Dobson, Xiaotu Ma, Debbie Payne-Turner, Guangchun Song, Yiping Fan, Zhaohui Gu, Ying Shao, Michael Rusch, Kelly McCastlain, Ilaria Iacobucci, Kathryn G. Roberts, Shann-Ching Chen, Pankaj Gupta, Ji Wen, Gang Wu, Jing Ma, Colin Bailey, Matthew Lear, Scott R. Olsen, Deanna Naeve, Geoffrey Neale, John Easton, Sima Jeha, Ching‐Hon Pui, James R. Downing, William E. Evans, Mary V. Relling, Jun J. Yang, John E. Dick, Jinghui Zhang, Charles G. Mullighan

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineMyeloid leukemiaInternal medicineSNP arrayOncologyExome sequencingLeukemiaSingle-nucleotide polymorphismBiologyGeneticsGeneGenotypeMutation

Abstract

fetched live from OpenAlex

Abstract Introduction: Despite risk stratification according to presenting clinical and genetic features, 10-25% of children with acute lymphoblastic leukemia (ALL) relapse, which is associated with a poor prognosis. Here, we sought to provide a comprehensive overview of the genetic alterations associated with relapse in ALL. Methods: We studied 93 children (27 female, 43 male) diagnosed with ALL (62 B-progenitor, 25 T-lineage) between 1987 and 2008 and treated on total therapy studies XI-XVI who experienced relapse and/or a second tumor. Age at diagnosis ranged from 3 months to 18 years. Median time to relapse was 3 years (range 3 months to 10 years). Seventy patients had a single relapse, 15 cases had 2 relapses, and 8 cases developed a second tumor of different lineage (B-cell lymphoma, chronic myeloid leukemia (n=1 each) and acute myeloid leukemia (n=6)). Diagnosis, relapse and matched normal samples (n=299) were studied using Affymetrix SNP 6.0 microarrays and whole genome or whole exome sequencing. Results: We found 2692 copy number aberrations (CNAs) with a median of 9 (range 0-109) in the diagnosis samples (n=91) compared to a median of 10 (range 0-112) in the relapse samples (n=89) and 12 (range 0-70) in subsequent samples (n=20). The number of CNAs did not differ significantly between diagnosis, relapse or subsequent samples. We identified a 7286 non-silent single nucleotide variants (SNVs) and small insertions or deletions (indels) in 5002 genes, 1392 of which were recurrent. The median number of variants was 12 (range 0-70) at diagnosis (n=91), 21 (range 0-858) at relapse (n=91; P=0.0029 v. diagnosis) and 60 (range 10-650) in subsequent samples (n=20; P<0.0001 v. diagnosis). A subset of samples revealed very high (variant range 658-1703; 7 cases, 9 samples: all relapse or subsequent samples), or high mutation burden (variant range 104-290; 12 cases, 16 samples: 2 diagnosis, 10 relapse, and 4 subsequent samples). Genes encoding mediators of DNA repair were affected in all cases with very high mutation burden, compared to 7 of the high burden cases and 18 out of 72 other cases (most commonly genes TP53, MSH2, and MUTYH; P<0.0001). The most frequently mutated genes were NOTCH1 (n=33), NRAS (n=24), CREBBP (n=20) and KRAS (n=16). Of the recurrently altered genes, only 87 genes were known to be affected in cancer (Cancer Gene Census, COSMIC database), of which 59 were affected in leukemia and lymphoma tissues, indicating that we have identified 1306 novel recurrently affected genes, most commonly C13orf40 and MKI67. Mutations in epigenetic regulators were particularly frequent, with genes mutated in at least 3 cases altered in over 60% of the cohort (e.g. CREBBP, EP300, MLL2, MLL3, KDM6A/B, CTCF, SETD2, TET2/3, and EZH2). Clonal evolution analyses showed multiple patterns of evolution, with relapses sharing either few or many variants with the diagnosis sample in a frequency that reflects both predominant clones and minor subclones propagating relapse. Variants in NOTCH1, NRAS, and CREBBP were preserved from a major clone at diagnosis in 4, 6, and 5 cases respectively, but acquired at relapse or grown out from a minor subclone at diagnosis in 3, 5, and 8 cases respectively. In contrast, variants in USH2A (n=4), FOXA1 (n=3), and purine/pyrimidine synthesis pathway genes NT5C2 (n=3), PRPS1 (n=3) and NT5C1B (n=1) were exclusively found in relapse samples. Notably, the NT5C2 mutations, which are thought to confer resistance to thiopurines, were subclonal at relapse in the majority of cases. We identified 13 cases (10 B-lineage, 3 T-lineage) in which the diagnosis and relapse were fully discordant for all CNAs and sequence mutations, only 4 of which showed a prolonged remission time (>5 years). This suggests that these patients developed a second primary malignancy and may be predisposed to leukemia development. Indeed, one case revealed focal amplifications on chromosome 1q21.1 encompassing the neuroblastoma breakpoint family genes, which are implicated in cancer development. Comprehensive germline analyses are underway. Conclusion: This study has provided detailed insight into the genetic basis of relapse, implicating multiple new genes and pathways involved in treatment resistance, demonstrating multiple patterns of clonal evolution, and revealing an unexpectedly high frequency of genetically discordant second malignancy in relapse in ALL. Disclosures Evans: Prometheus Labs: Patents & Royalties: Royalties from licensing TPMT genotyping. Mullighan:Amgen: Honoraria, Speakers Bureau; Cancer Science Institute: Membership on an entity's Board of Directors or advisory committees; Incyte: Consultancy, Honoraria; Loxo Oncology: Research Funding.

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)0.000
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.265
Teacher spread0.247 · 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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Citations2
Published2015
Admission routes1
Has abstractyes

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