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Ipatasertib in patients with <i>AKT1/2/3</i> mutation-positive (<i>AKT</i>mut) tumors: TAPISTRY study.

2024· article· en· W4399737838 on OpenAlexaff
David M. Thomas, Gennaro Daniele, Jeong Eun Kim, Shirish M. Gadgeel, Eugene R. Ahn, Luis Paz‐Ares, Hans Prenen, David Chen, Junhan Fang, Timothy R. Wilson, Brian Simmons, Fabrice Barlési

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsRoche (Canada)
FundersF. Hoffmann-La Roche
KeywordsMedicineAKT1Protein kinase BMutationCancer researchOncologyInternal medicinePathologyGeneticsPhosphorylationGeneBiology

Abstract

fetched live from OpenAlex

3092 Background: AKT1/2/3 point mutations are found in ~1% of all solid tumors, with varying prevalence across different tumor types. Ipatasertib is an inhibitor of the AKT kinase, but its antitumor activity as monotherapy in patients with AKTmut tumors is unknown. We present efficacy and safety data of ipatasertib in patients with advanced/metastatic AKTmut solid tumors from Cohort E of the TAPISTRY trial (NCT04589845). Methods: TAPISTRY is a phase II, global, open-label, multi-cohort trial evaluating the efficacy and safety of different therapies in patients with advanced/metastatic solid tumors. Patients in Cohort E were aged ≥12 years and had solid tumors harboring an AKT1/2/3mutation identified by next-generation sequencing and measurable disease by RECIST v1.1. Oral ipatasertib 400 mg was administered once daily. Tumor assessments were performed at screening, every eight weeks from Day 1/Cycle 1 for one year, and every 12 weeks after that. The primary endpoint was objective response rate (ORR) by independent review committee (IRC). Key secondary endpoints included ORR by investigator, duration of response, progression-free survival, overall survival and safety. Results: At data cut-off (16 Jul 2023), 50 patients were safety evaluable and 48 were efficacy evaluable. In the safety-evaluable population, median age was 59 years (range, 30–79); 98% of patients (n/N = 49/50) had an AKT1 mutation ( AKT1 E17K, n=47) and 2% (n/N = 1/50) had an AKT2 E17K mutation; 41/50 patients (82%) had received ≥2 prior lines of treatment. Efficacy-evaluable patients had 10 different tumor types, the most common being breast cancer (n/N = 21/48; 44%). Key outcomes are summarized in the Table. After a median follow-up of 11.3 months, ORR by IRC in efficacy-evaluable patients was 31.3% (n/N = 15/48; 95% CI 18.7–46.3), driven by responses in three tumor types: breast (n/N = 7/21; 33%), endometrial (n/N = 7/7; 100%), and head and neck (n/N = 1/2; 50%). The most frequent adverse event was diarrhea (n/N = 39/50; 78%). Safety was consistent with the known profile of ipatasertib; no new safety signals were identified. Conclusions: Treatment with ipatasertib led to a marked and durable antitumor activity in some tumor types such as endometrial cancer, but not in the overall tumor-agnostic cohort. Further studies are needed to understand the relevance of AKT inhibition in these tumor types. Clinical trial information: NCT04589845 . [Table: see text]

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)0.002
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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.027
GPT teacher head0.399
Teacher spread0.371 · 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 designNon-randomized trial
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

Citations5
Published2024
Admission routes1
Has abstractyes

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