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Record W4390461456 · doi:10.1101/2023.12.29.23300541

Investigative needle core biopsies for multi-omics in Glioblastoma

2023· preprint· en· W4390461456 on OpenAlexfundno aff
Kenny K.H. Yu, Sreyashi Basu, Gerard Baquer, Ryuhjin Ahn, Jennifer Gantchev, Sonali Jindal, Michael S. Regan, Zaki Abou-Mrad, Michael C. Prabhu, Marc Williams, Alicia D. D’Souza, Seth Malinowski, Kelsey Hopland, Yuval Elhanati, Sylwia A. Stopka, Alexei Stortchevoi, Zhong He, Jingjing Sun, Yulong Chen, Alexsandra Espejo, Kin Hoe Chow, Smitha Yerrum, Pei-Lun Kao, Brittany C. Parker Kerrigan, Lisa Norberg, Douglas Nielsen, Vinay K. Puduvalli, Jason T. Huse, Rameen Beroukhim, Sangeeta Goswami, Adrienne Boire, Sarah Frisken, Michael J. Cima, Matthias Holdhoff, Calixto‐Hope G. Lucas, Chetan Bettegowda, Stuart S. Levine, Tejus Bale, Cameron Brennan, David A. Reardon, Frederick F. Lang, E. Antonio Chiocca, Keith L. Ligon, Forest M. White, Padmanee Sharma, Viviane Tabar, Nathalie Y.R. Agar

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchUniversity of Texas MD Anderson Cancer CenterBreak Through CancerKoch Institute for Integrative Cancer Research, Massachusetts Institute of TechnologyNational Cancer InstituteLudwig Center at HarvardMassachusetts Life Sciences CenterNational Institutes of HealthMemorial Sloan-Kettering Cancer Center
KeywordsGlioblastomaCore (optical fiber)Computer scienceOmicsComputational biologyData sciencePathologyMedicineBiologyCancer researchBioinformaticsTelecommunications

Abstract

fetched live from OpenAlex

Glioblastoma (GBM) is a primary brain cancer with an abysmal prognosis and few effective therapies. The ability to investigate the tumor microenvironment before and during treatment would greatly enhance both understanding of disease response and progression, as well as the delivery and impact of therapeutics. Stereotactic biopsies are a routine surgical procedure performed primarily for diagnostic histopathologic purposes. The role of investigative biopsies - tissue sampling for the purpose of understanding tumor microenvironmental responses to treatment using integrated multi-modal molecular analyses ('Multi-omics") has yet to be defined. Secondly, it is unknown whether comparatively small tissue samples from brain biopsies can yield sufficient information with such methods. Here we adapt stereotactic needle core biopsy tissue in two separate patients. In the first patient with recurrent GBM we performed highly resolved multi-omics analysis methods including single cell RNA sequencing, spatial-transcriptomics, metabolomics, proteomics, phosphoproteomics, T-cell clonotype analysis, and MHC Class I immunopeptidomics from biopsy tissue that was obtained from a single procedure. In a second patient we analyzed multi-regional core biopsies to decipher spatial and genomic variance. We also investigated the utility of stereotactic biopsies as a method for generating patient derived xenograft models in a separate patient cohort. Dataset integration across modalities showed good correspondence between spatial modalities, highlighted immune cell associated metabolic pathways and revealed poor correlation between RNA expression and the tumor MHC Class I immunopeptidome. In conclusion, stereotactic needle biopsy cores are of sufficient quality to generate multi-omics data, provide data rich insight into a patient's disease process and tumor immune microenvironment and can be of value in evaluating treatment responses. One sentence summary: Integrative multi-omics analysis of stereotactic needle core biopsies in glioblastoma.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.150
GPT teacher head0.350
Teacher spread0.200 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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