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Record W2619229835 · doi:10.1093/neuonc/nox083.044

DIPG-29. GENOMIC LANDSCAPE OF DIFFUSE INTRINSIC PONTINE GLIOMA: AN ANALYSIS OF THE DIPG-BATS COHORT

2017· article· en· W2619229835 on OpenAlexaff
Pratiti Bandopadhayay, Noah F. Greenwald, Jeremiah A. Wala, Ofer Sharpira, Adam Tracy, Mariella G. Filbin, Ryan O’Rourke, Patricia Ho, Claire Sinai, Hayley Malkin, Lianne Greenspan, Kristen Lawler, Kristine Pelton, Anu Banerjee, Oren J. Becher, K. Ayyanar, William Gump, Anne Bendel, Daniel C. Bowers, Mahmoud G. Nagib, Bradley E. Weprin, Amy‐Lee Bredlau, Sridharan Gururangan, Herbert E. Fuchs, Kenneth J. Cohen, Melanie Comito, Mark S. Dias, Jason Fangusaro, Stewart Goldman, Jennifer Elster, Paul G. Fisher, Tadanori Tomita, Tord D. Alden, Arthur J. DiPatri, Sharon L. Gardner, Matthias A. Karajannis, David H. Harter, Michael H. Handler, Karen Gauvain, David D. Limbrick, Russ Geyer, Sarah Leary, Ziab Khatib, Samuel R. Browd, John Ragheb, Sanjiv Bhatia, Tobey McDonald, Dolly Aguilera, Barun Brahma, Peter Manley, Karen Wright, Susan Chi, Sabine Mueller, Jeff Murray, Kellie J. Nazemi, Lissa Baird, Michelle Monje, Nathan Robison, Erin N. Kiehna, Mark D. Krieger, Eric Sandler, Philipp R. Aldana, Joshua B. Rubin, Matija Snuderl, Zhihong Joanne Wang, Sandeep Sood, Donna Neuberg, Mario L. Suvà, Rosalind A. Segal, Nada Jabado, Mäneka Puligandla, Michael D. Prados, Karen J. Marcus, Daphne A. Haas‐Kogan, Liliana Goumnerova, Nalin Gupta, Keith L. Ligon, Rameen Beroukhim, Mark W. Kieran

Bibliographic record

VenueNeuro-Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsPTENBiopsyMedicineCohortKRASGliomaPathologyAutopsyOncologyBiologyInternal medicineMutationCancer researchGeneGeneticsPI3K/AKT/mTOR pathway

Abstract

fetched live from OpenAlex

INTRODUCTION: Diffuse intrinsic pontine glioma (DIPG) remains a devastating and incurable disease. The DIPG-BATs clinical trial incorporates diagnostic biopsy with molecularly determined treatment stratification. Here we present the initial genomic analysis of the DIPG-BATs cohort. METHODS: Children enrolled on the DIPG-BATs clinical trial underwent upfront diagnostic biopsies prior to commencement of therapy. RNA and DNA were extracted from single core biopsies and subjected to whole-genome sequencing (WGS) and RNA-sequencing. Tumor samples were also collected at autopsy if there was parental consent. RESULTS: Fifty-three patients were enrolled on study, of whom 50 underwent biopsy. There were no biopsy-related deaths. A mean of 5ug of RNA and 10ug of DNA were extracted from single frozen cores. WGS on 41 DIPG samples (including eight autopsy samples) revealed a mean mutation rate of 0.753 mutations per Mb. We confirmed TP53, PIK3CA, H3F3A, ACVR1, PPM1D, and HIST1H3B to be recurrently mutated in DIPG. Additional mutations were found in epigenetic modifiers including ASXL1. Copy-number analysis revealed PDGFRA to meet statistical significance as a recurrent amplification peak in DIPG (q<0.25). Gene-set analysis revealed mutations in the TERT pathway, ARF pathway, AKT/PTEN pathway, TP53 pathway, cell cycle and apoptosis pathways to be statistically enriched across the cohort (q<0.10). Analysis of paired diagnosis and autopsy samples revealed evolution of tumors following treatment. Samples obtained at autopsy exhibited a significantly increased mean mutation rate compared to untreated biopsies (p<0.0001). CONCLUSIONS: Whole-genome sequencing of DIPG-BATs samples confirms driver mutations in multiple pathways implicated in cancer. Initial investigation of paired biopsy and autopsy samples allows the analysis of the genomic evolution of DIPG. These findings shed insight into both oncogenic and resistance drivers in DIPG. *equal contribution

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)0.001
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.001
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 head0.296
Teacher spread0.278 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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