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Record W4296663392 · doi:10.1016/j.ccell.2022.08.015

MPS1 inhibition primes immunogenicity of KRAS-LKB1 mutant lung cancer

2022· article· en· W4296663392 on OpenAlexfundno aff
Shunsuke Kitajima, Tetsuo Tani, Benjamin F. Springer, Marco Campisi, Tatsuya Osaki, Koji Haratani, Minyue Chen, Erik H. Knelson, Navin R. Mahadevan, Jessica Ritter, Ryohei Yoshida, Jens Köhler, Atsuko Ogino, Ryu‐Suke Nozawa, Shriram K. Sundararaman, Tran C. Thai, Mizuki Homme, Brandon Piel, Sophie Kivlehan, Bonje Obua, Connor Purcell, Mamiko Yajima, Thanh U. Barbie, Patrick H. Lizotte, Pasi A. Jänne, Cloud P. Paweletz, Prafulla C. Gokhale, David A. Barbie

Bibliographic record

VenueCancer Cell · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsnot available
FundersJanssen PharmaceuticalsNational Institute of General Medical SciencesJapan Society for the Promotion of ScienceLudwig Center at HarvardTakeda OncologyEMD SeronoNational Institutes of HealthEli Lilly and CompanyAssociazione Italiana per la Ricerca sul CancroPrincess Takamatsu Cancer Research FundRevolution MedicinesArray BioPharmaRobert A. and Renee E. Belfer Family FoundationLilly OncologyBoehringer IngelheimTakeda Pharmaceuticals U.S.A.Takeda Science FoundationNational Cancer InstituteFoghorn TherapeuticsGilead SciencesDana-Farber Cancer InstituteJapan Agency for Medical Research and DevelopmentUehara Memorial FoundationNovartisAbbVieNortheastern UniversityMerckDana-Farber/Harvard Cancer CenterAstraZenecaCanadian Asian Studies AssociationExpect Miracles Foundation
KeywordsCancer researchIntracellularApoptosisProgrammed cell deathBiologyIn vivoCell biologySting

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.255
Teacher spread0.247 · 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 designBench or experimental
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

Citations101
Published2022
Admission routes1
Has abstractno

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