Abstract P6-12-02: Phase Ib dose-escalation study of an Akt inhibitor ipatasertib (Ipat) in combination with docetaxel (Doc) or paclitaxel (Pac) in patients (pts) with metastatic breast cancer (MBC)
Bibliographic record
Abstract
Abstract Background: The Akt pathway is frequently aberrantly activated in MBC (e.g. via PTEN loss, and/or alterations of PIK3CA, AKT1, or AKT3); additionally, Akt activation may occur in response to chemotherapy, leading to cell survival and chemoresistance. Ipat (GDC-0068) is a potent oral, ATP-competitive inhibitor of all Akt isoforms. In preclinical models, Ipat synergistically combined with taxanes. In the Phase I dose-escalation single agent study, Ipat was given to pts including MBC, and downregulated Akt signaling at doses ≥ 100 mg. Methods: Eligible pts with MBC, treated with up to 3 prior systemic chemotherapy regimens, received Doc 75 mg/m2 intravenously (IV) on Day 1 with escalating doses of Ipat PO QD on Days 2-15 every 21 days or received Pac 90 mg/m2 IV on Days 1, 8, and 15 with escalating doses of Ipat PO QD on Days 1-21 every 28 days. A standard 3+3 does-escalation design was used, with an expansion cohort of HER2-negative/hormone receptor positive [HER2-/HR+] MBC patents (including triple-negative MBC [TNBC]) in the Pac cohort at the recommended phase II dose. Pharmacokinetic (PK) and circulating tumor cell (CTC) samples were collected. Archival tumors were assessed for PTEN by immunohistochemistry and for pathway-relevant mutations. Results: As of 1 Apr 2014, 54 patients with multiple solid tumors, including 19 pts with MBC (TNBC: n=13, HER2-/HR+: n=4, and HER2-positive: n=2) were enrolled, and pts with MBC received Ipat with Doc (n=5) or Pac (n=14). The common Grade ≥ 2 adverse events (AEs) related to Ipat in combination with Doc (≥ 10% and > 1 patient) were diarrhea (80%), nausea (60%) and vomiting (40%), versus in combination with Pac (≥ 10% of pts) were diarrhea (43%), fatigue (29%) and hyperglycemia (14%). The PKs of Ipat, Doc, or Pac were comparable to the single agents. Partial responses by RECIST v1.1 were seen in 5 pts, including pts with HER2-/HR+ (n=2) or TNBC (n=3) who had previously progressed on Pac (n=4) or PI3K inhibitors (n=2) or had tumors with PI3K/Akt alterations [PTEN loss (n=1), PIK3CA mut (n=2), or AKT1 mut (n=1)]. Time on study > 9 months occurred in 4 pts (HER2-/HR+ and TNBC) who had progressed on prior Pac (n=3) or PI3K inhibitors (n=3), and/or had tumors with PIK3CA mut (n=3). Conclusions: Ipat in combination with Doc or Pac is well-tolerated and has a safety profile generally consistent with the single agent. Anti-tumor activity with Ipat in combination with taxanes was seen in MBC, including HER2-/HR+ or TNBC with baseline PI3K/Akt alterations. Updated safety, efficacy, and biomarker data will be presented. Citation Format: Steven J Isakoff, Johanna C Bendell, Andrés Cervantes, Jean-Charles Soria, L R Molife, Sandra M Sanabria-Bohorquez, Elizabeth A Punnoose, Shidong Jia, Premal Patel, Cristina Saura. Phase Ib dose-escalation study of an Akt inhibitor ipatasertib (Ipat) in combination with docetaxel (Doc) or paclitaxel (Pac) in patients (pts) with metastatic breast cancer (MBC) [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-12-02.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".