Abstract PD14-03: Molecular mechanism of ipatasertib (IPAT) and its combination with atezolizumab (atezo) in patients (pts) with locally advanced/metastatic triple-negative breast cancer (aTNBC)
Bibliographic record
Abstract
Abstract Background: Phase 3 trials (IMpassion130, KEYNOTE-355) have shown improved efficacy with the addition of immune checkpoint modulators to chemotherapy in PD-L1 +ve aTNBC. However, unmet need remains in the ~60% of pts with aTNBC who have PD-L1 -ve tumors. Preliminary data from a multicenter phase 1b study (NCT03800836) evaluating the safety and efficacy of the oral AKT inhibitor IPAT + atezo + paclitaxel/nab-paclitaxel showed promising antitumor activity (73% confirmed objective response rate) irrespective of PD-L1 status [Schmid, AACR 2019], suggesting a potential role for the triplet independent of PD-L1 status. We report on-treatment changes in the tumor microenvironment in Cohort 2. Methods: In Cohort 2, pts with aTNBC and ≤2 prior lines of chemotherapy for aTNBC received oral IPAT 400 mg on d1-28 of cycle 1 (35-d cycle) and on d1-21 of subsequent cycles (28-d cycles). IV atezo 840 mg was given on d8 & 22 of cycle 1 and on d1 & 15 of subsequent cycles. Biopsies were collected before treatment administration during cycle 1 on d1 (C1D1) & d8 (C1D8), and on d15 of cycle 2 (C2D15). PD-L1 (VENTANA SP142 immune cell ≥1%) and CD8 expression was assessed by immunohistochemistry (IHC); % immune infiltrate was measured by H&E staining. Changes in gene expression were assessed by RNA-Seq. Gene set enrichment analysis was used to study molecular pathway activation (enrichment score [ES] >0 to 1) or inhibition (ES -1 to <0). Results: In Cohort 2, 11 pts had PD-L1 -ve tumors at baseline and are included in the analyses below, 2 had PD-L1 +ve tumors and 3 were unevaluable for PD-L1. IHC analysis of serial biopsies from pts with PD-L1 -ve tumors showed a statistically significant increase in immune infiltrates (mean % infiltrates C1D8/C1D1=1.44; p=0.042) and a trend toward increased CD8 protein expression (mean CD8 % staining C1D8/C1D1=1.75; Kruskal-Wallis p=0.16) at the tumor center during the first week of single-agent IPAT compared with the baseline biopsy. An increase in PD-L1 expression (mean PD-L1 % infiltrating immune cells C2D15/C1D1=4.33; Kruskal-Wallis p=0.044) was seen during IPAT + atezo combination treatment. No significant changes in immune infiltrates or CD8 expression were observed after initiating atezo versus IPAT alone. As expected, MTORC1 activity decreased (ES=-0.43; p<0.005) in response to IPAT. IPAT (± atezo) enhanced key immunogenic pathways, including antigen processing and presentation (ES=0.49; p<0.005), allograft rejection (ES=0.47; p<0.0001), inflammatory response (ES=0.34; p<0.05), and NK cell activation (ES=0.66; p<0.05). In addition, IPAT + atezo enhanced TCR signaling (ES=0.52; p<0.0005) and interferon gamma response (ES=0.30; p<0.05). IPAT treatment was associated with decreased expression of gene sets predicting pathway upregulation, including E2F signaling, G2M checkpoint, and MYC activity. IPAT was also associated with apoptotic pathway enrichment. Using a syngeneic melanoma mouse model, we validated clinical findings and identified additional molecular changes after treatment with IPAT and/or anti-PD-L1. To date, 3 enrolled pts achieved a partial response and 6 had stable disease. Clinical and preclinical studies are ongoing to explore associations between treatment-driven molecular changes and clinical response. Conclusion: Inhibition of AKT signaling may remodel the microenvironment of PD-L1 -ve tumors by increasing immune infiltration, priming immune pathways, promoting tumor cell apoptosis, and inhibiting oncogenic cell proliferative pathways. To our knowledge, these are the first reports evaluating molecular changes during AKT-targeted therapy for aTNBC in the context of immunotherapy irrespective of PD-L1 status. IPAT warrants further investigation combined with atezo as treatment for PD-L1 -ve aTNBC. Citation Format: Peter Schmid, Delphine Loirat, Peter Savas, Enrique Espinosa, Valentina Boni, Antoine Italiano, Shane White, Victor Laliman, Geraldine Strasser, Kui Lin, Karen Cheng, Aruna Mani, Matthew Wongchenko, Marie-Paule Sablin, Kalpit Shah. Molecular mechanism of ipatasertib (IPAT) and its combination with atezolizumab (atezo) in patients (pts) with locally advanced/metastatic triple-negative breast cancer (aTNBC) [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD14-03.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".