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Record W2982152775 · doi:10.1002/jcp.29300

Single peptides and combination modalities for triple negative breast cancer

2019· review· en· W2982152775 on OpenAlexaff
Atefeh Razazan, Javad Behravan

Bibliographic record

VenueJournal of Cellular Physiology · 2019
Typereview
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTriple-negative breast cancerOncolytic virusImmunotherapyBreast cancerImmune systemAdjuvantTumor microenvironmentMedicineCancer researchCancer immunotherapyAntigenCancerImmunologyOncologyInternal medicine

Abstract

fetched live from OpenAlex

Unlike other types of breast cancers (BCs), no specific therapeutic targets have been established for triple negative breast cancer (TNBC). Therefore, chemotherapy and radiotherapy are the only available adjuvant therapeutic choices for TNBC. New emerging reports show that TNBC is associated with higher numbers of intratumoral tumor infiltrating lymphocytes. This is indicative of host anti-TNBC immune surveillance and suggesting that immunotherapy can be considered as a therapeutic approach for TNBC management. Recent progress in molecular mechanisms of tumor-immune system interaction and cancer vaccine development studies, fast discoveries and FDA approvals of immune checkpoint inhibitors, chimeric antigen receptor T-cells, and oncolytic virotherapy have significantly attracted attention and research directions toward the immunotherapeutic approach to TNBC. Here in this review different aspects of TNBC immunotherapies including the host immune system-tumor interactions, the tumor microenvironment, the relevant molecular targets for immunotherapy, and clinical trials in the field are discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.973
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.057
GPT teacher head0.335
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 teacher head, 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

Citations13
Published2019
Admission routes1
Has abstractyes

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