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Record W2123540640 · doi:10.1158/1078-0432.ccr-14-2424

Higher Risk of Infections with PI3K–AKT–mTOR Pathway Inhibitors in Patients with Advanced Solid Tumors on Phase I Clinical Trials

2015· article· en· W2123540640 on OpenAlexaff
Saeed Rafii, Desamparados Roda, Elena Geuna, Begoña Jiménez, Karim Rihawi, Marta Capelán, Timothy A. Yap, L. Rhoda Molife, Stanley B. Kaye, Johann S. de Bono, Udai Banerji

Bibliographic record

VenueClinical Cancer Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsInstitute of Cancer Research
FundersNational Institute for Health and Care ResearchCancer Research UK
KeywordsPI3K/AKT/mTOR pathwayProtein kinase BMedicineClinical trialPharmacologyInternal medicineIncidence (geometry)OncologyBiologySignal transductionBiochemistry

Abstract

fetched live from OpenAlex

PURPOSE: Novel antitumor therapies against the PI3K-AKT-mTOR pathway are increasingly used to treat cancer, either as single agents or in combination with chemotherapy or other targeted therapies. Although these agents are not known to be myelosuppressive, an increased risk of infection has been reported with rapamycin analogues. However, the risk of infection with new inhibitors of this pathway such as PI3K, AKT, mTORC 1/2, or multikinase inhibitors is unknown. EXPERIMENTAL DESIGN: In this retrospective case-control study, we determined the incidence of infection in a group of 432 patients who were treated on 15 phase I clinical trials involving PI3K-AKT-mTOR pathway inhibitors (cases) versus a group of 100 patients on 10 phase I clinical trials of single agent non-PI3K-AKT-mTOR pathway inhibitors (controls) which did not involve conventional cytotoxic agents. We also collected data from 42 patients who were treated with phase I trials of combinations of PI3K-AKT-mTOR inhibitors and MEK inhibitors and 24 patients with combinations of PI3K-AKT-mTOR inhibitors and cytotoxic chemotherapies. RESULTS: The incidence of all grade infection was significantly higher with all single-agent PI3K-AKT-mTOR inhibitors compared with the control group [27% vs. 8%, respectively, OR, 4.26; 95% confidence intervals (CI), 1.9-9.1, P = 0.0001]. The incidence of grade 3 and 4 infection was also significantly higher with PI3K-AKT-mTOR inhibitors compared with the control group (10.3% vs. 3%, OR, 3.74; 95% CI, 1.1-12.4; P = 0.02). Also, the combination of PI3K-AKT-mTOR inhibitors and chemotherapy was associated with a significantly higher incidence of all grade (OR, 4.79; 95% CI, 2.0-11.2; P = 0.0001) and high-grade (OR, 2.87; 95% CI, 1.0-7.6; P = 0.03) infection when compared with single-agent PI3K-AKT-mTOR inhibitors. CONCLUSIONS: Inhibitors of the PI3K-AKT-mTOR pathway can be associated with a higher risk of infection. Combinations of PI3K-AKT-mTOR inhibitors and cytotoxic chemotherapy significantly increase the risk of infection. This should be taken into consideration during the design and conduct of trials involving PI3K-AKT-mTOR pathway inhibitors, particularly when combined with chemotherapy or myelosuppressive agents.

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)0.010
metaresearch head score (Gemma)0.013
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.246
GPT teacher head0.554
Teacher spread0.309 · 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

Citations36
Published2015
Admission routes1
Has abstractyes

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