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Abstract LB-341: Evolving functional heterogeneity in B-acute lymphoblastic leukemia

2016· article· en· W4250015940 on OpenAlexaff
Stephanie M. Dobson, Robert J. Vanner, Esmé Waanders, Olga I. Gan, Jessica McLeod, Ildiko Grandal, Debbie Payne-Turner, Michael N. Edmonson, Zhaohui Gu, Xioatu Ma, Yiping Fan, Pankaj Gupta, Sagi Abelson, Michael Rusch, Ying Shao, Scott R. Olsen, Geoffrey Neale, John Easton, Cynthia J. Guidos, Jayne S. Danska, Jinghui Zhang, Mark D. Minden, Charles G. Mullighan, John E. Dick

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsSomatic evolution in cancerclone (Java method)BiologyCancerSNP arrayLeukemiaCancer researchAcute lymphocytic leukemiaExome sequencingGeneticsImmunologyOncologyMutationMedicineLymphoblastic LeukemiaGeneSingle-nucleotide polymorphismGenotype

Abstract

fetched live from OpenAlex

Abstract Current cancer therapies are directed at molecular markers or dominant pathways present in the bulk of neoplastic cells. However, recent studies have identified many genetically distinct subclones co-existing within a single neoplasm. In over 50% of patients with relapsed acute lymphoblastic leukemia (ALL), the genetic clones present at relapse are not the dominant clone present at diagnosis, but have evolved from a minor or ancestral clone (Mullighan et al., Science, 2008). Previous work has shown that this subclonal diversity in B-ALL exists at the level of the leukemia-initiating cells (L-IC) capable of generating patient derived xenografts (Notta et al., Nature, 2011). In order to investigate the functional consequences of genetic clonal evolution during disease progression, we performed in-depth genomic and functional analysis of 14 paired diagnosis/relapse samples from adult and pediatric B-ALL patients of varying cytogenetics. Patient samples were subjected to whole exome sequencing (WES), SNP analysis and RNA sequencing. Diagnosis-specific, relapse-specific, and shared variants at both clonal and subclonal frequencies were identified. Limiting dilution analysis by transplantation of CD19+ leukemic blasts into 870 immune-deficient mice (xenografts) identified no significant trend in enrichment in L-IC frequency between paired patient samples with a median frequency of 1 in 2691. Despite similar frequencies of L-IC, functional differences within identically sourced patient xenografts were observed, including increased leukemic dissemination of relapse cells to distal sites such as the central nervous system (CNS), differences in engraftment levels and differences in immunophenotypes. Targeted-sequencing and copy number analysis of the xenografts, in comparison to the patient sample from which they were derived, has uncovered clonal variation and the unequivocal identification of minor subclones ancestral to the relapse in xenografts transplanted with the diagnostic sample from 8 patients. Some of these subclones are rare and were not captured through standard WES analysis of the patient samples, highlighting the value of xenografting to functionally identify and viably isolate subclones for further study. Interrogation of the therapeutic responses of the ‘relapse-like’ diagnosis subclones in secondary xenografts displayed differential resistance to standard chemotherapeutic agents (vincristine and L-asparaginase) pre-existing in the patient diagnosis samples prior to treatment. Furthermore, investigation of different sites of leukemic infiltration in the xenografts provided evidence of distinct clonal selection in the CNS, a known site of disease relapse, in comparison to the bone marrow. Using this data we can begin to draw the evolutionary paths to relapse. We have shown evidence that minor subclones at diagnosis, ancestral to the relapsing clone, possess functional advantages over other diagnostic clones. Overall, this work provides a substantial advance in connecting genetic diversity to functional consequences, thereby furthering our understanding of the heterogeneity identified in B-ALL and its contributions to therapy failure and disease recurrence. Citation Format: Stephanie M. Dobson, Robert Vanner, Esmé Waanders, Olga I. Gan, Jessica McLeod, Ildiko Grandal, Debbie Payne-Turner, Michael Edmonson, Zhaohui Gu, Xioatu Ma, Yiping Fan, Pankaj Gupta, Sagi Abelson, Michael Rusch, Ying Shao, Scott Olsen, Geoffrey Neale, John Easton, Cynthia J. Guidos, Jayne S. Danska, Jinghui Zhang, Mark D. Minden, Charles G. Mullighan, John E. Dick. Evolving functional heterogeneity in B-acute lymphoblastic leukemia. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-341.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

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)0.0020.001

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 head0.042
GPT teacher head0.343
Teacher spread0.301 · 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
Published2016
Admission routes1
Has abstractyes

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