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Record W3194598620 · doi:10.3390/curroncol28050280

Multitumor Case Series of Germline BRCA1, BRCA2 and CHEK2-Mutated Patients Responding Favorably on Immune Checkpoint Inhibitors

2021· article· en· W3194598620 on OpenAlexvenueno aff
Lisa Kinget, Oliver Bechter, Kevin Punie, Philip R. Debruyne, Hilde Brems, Paul Clement, Eduard Roussel, Yannick Van Herck, Maarten Albersen, Marcella Baldewijns, Patrick Schöffski, Benoit Beuselinck

Bibliographic record

VenueCurrent Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsnot available
FundersFonds Wetenschappelijk OnderzoekIpsenKom op tegen KankerPfizer
KeywordsGermlineCHEK2MedicineGermline mutationImmune checkpointSomatic cellCancer researchDNA damageDiseaseImmune systemMutationGeneImmunotherapyBioinformaticsImmunologyGeneticsBiologyDNAInternal medicine

Abstract

fetched live from OpenAlex

In recent years, immune checkpoint inhibitors (ICPI) have become widely used for multiple solid malignancies. Reliable predictive biomarkers for selection of patients who would benefit most are lacking. Several tumor types with somatic or germline alterations in genes involved in the DNA damage response (DDR) pathway harbor a higher tumor mutational burden, possibly associated with an increased tumoral neoantigen load. These neoantigens are thought to lead to stronger immune activation and enhanced response to ICPIs. We present a series of seven patients with different malignancies with germline disease-associated variants in DDR genes (BRCA1, BRCA2, CHEK2) responding favorably to ICPIs.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.050
GPT teacher head0.360
Teacher spread0.310 · 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 designCase report
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
Published2021
Admission routes1
Has abstractyes

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