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Record W4386531447 · doi:10.1158/2159-8290.cd-23-0383

SHP2: A Pleiotropic Target at the Interface of Cancer and Its Microenvironment

2023· review· en· W4386531447 on OpenAlexaff
Nicole M. Sodir, Gaurav Pathria, Joanne I. Adamkewicz, Elizabeth H. Kelley, Jawahar Sudhamsu, Mark Merchant, Roberto Chiarle, Danilo Maddalo

Bibliographic record

VenueCancer Discovery · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Tyrosine Phosphatases
Canadian institutionsDiscovery Centre
FundersNational Institutes of HealthNational Cancer InstituteAssociazione Italiana per la Ricerca sul CancroFoundation for the National Institutes of Health
KeywordsTumor microenvironmentCancerCancer researchPhosphataseRegulatorBiologyMaster regulatorCancer therapyCancer cellPhosphorylationCell biologyTumor cellsTranscription factorBiochemistryGeneticsGene

Abstract

fetched live from OpenAlex

The protein phosphatase SHP2/PTPN11 has been reported to be a key modulator of proliferative pathways in a wide range of malignancies. Intriguingly, SHP2 has also been described as a critical regulator of the tumor microenvironment. Based on this evidence SHP2 is considered a multifaceted target in cancer, spurring the notion that the development of direct inhibitors of SHP2 would provide the twofold benefit of tumor intrinsic and extrinsic inhibition. In this review, we will discuss the role of SHP2 in cancer and the tumor microenvironment, and the clinical strategies in which SHP2 inhibitors are leveraged as combination agents to improve therapeutic response. SIGNIFICANCE: The SHP2 phosphatase functions as a pleiotropic factor, and its inhibition not only hinders tumor growth but also reshapes the tumor microenvironment. Although their single-agent activity may be limited, SHP2 inhibitors hold the potential of being key combination agents to enhance the depth and the durability of tumor response to therapy.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.025
GPT teacher head0.339
Teacher spread0.314 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations98
Published2023
Admission routes1
Has abstractyes

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