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Updated predictive analysis of the WHO-defined molecular subgroups of low-grade gliomas within the high-risk treatment arms of NRG Oncology/RTOG 9802.

2019· article· en· W2947694391 on OpenAlexaff
Erica H. Bell, Minhee Won, Jessica L. Fleming, Aline Paixão Becker, Joseph P. McElroy, Edward G. Shaw, Minesh P. Mehta, David Brachman, Stanley Z. Gertler, Albert Murtha, Christopher J. Schultz, David Johnson, Nadia N. Laack, G.K. Hunter, Ian Crocker, Arnab Chakravarti

Bibliographic record

VenueJournal of Clinical Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineOncologyInternal medicineHazard ratioUnivariate analysisProportional hazards modelGliomaIDH1Subgroup analysisMultivariate analysisConfidence intervalGeneCancer researchBiologyMutationGenetics

Abstract

fetched live from OpenAlex

2002 Background: This study sought to update the predictive significance of the three WHO-defined molecular glioma subgroups ( IDHwt, IDHmt/noncodel, and IDHmt/codel) in the subset of specimens available for analysis in NRG Oncology/RTOG 9802, a phase III trial of high-risk low-grade gliomas (LGGs) treated with radiation (RT) with and without PCV after biopsy/surgical resection. Notably, this is the first phase III study to evaluate the predictive value of the WHO subgroups in LGGs using prospectively-collected, well-annotated long-term overall survival data, in a post-hoc analysis. Methods: IDH1/2 mutation status was determined by immunohistochemistry and/or next-generation sequencing. 1p/19q status was determined by Oncoscan and/or 450K methylation data. Treatment effects on overall survival (OS) and progression-free survival (PFS) by marker status were determined by the Cox proportional hazard model and tested using the log-rank test in a secondary and exploratory analysis. Results: Of all the randomized eligible high-risk G2 patients (N = 251) in NRG Oncology/RTOG 9802, 106(42%) patients had tissue available with sufficient quality DNA for profiling. Of these, 80(75%) were IDHmut; 43(41%) were IDHmut/non-co-deleted, 37(35%) were IDHmut/co-deleted, and 26(24%) were IDHwt. Upon univariate analyses, no significant difference in either PFS or OS was observed with the addition of PCV in the IDHwt subgroup. Both the IDHmut/non-co-deleted and IDHmut/co-deleted subgroups were significantly correlated with longer PFS (HR = 0.32; p = 0.003; HR = 0.13; p < 0.001) and OS (HR = 0.38; p = 0.013; HR = 0.21; p = 0.029) in the RT plus PCV arm, respectively. Conclusions: Our analyses suggest that both IDHmut/non-co-deleted and IDHmut/co-deleted subgroups received benefit from treatment with PCV although sample size is limited and analyses are post-hoc. Our results also support the notion that IDHwt high-risk LGG patients do not benefit from the addition of PCV to RT. Funding: U10CA180868, U10CA180822, and U24CA196067. Also, R01CA108633, R01CA169368, RC2CA148190, U10CA180850-01, BTFC, OSU-CCC (all to AC). Clinical trial information: NCT00003375.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.035
GPT teacher head0.377
Teacher spread0.343 · 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".

Quick stats

Citations9
Published2019
Admission routes1
Has abstractyes

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