Abstract PD10-05: Activity of atezolizumab (atezo) plus paclitaxel (pac) in metastatic triple-negative breast cancer (mTNBC) according to Burstein molecular subtype: Analysis of the IMpassion131 trial
Bibliographic record
Abstract
Abstract Background: In contrast to the IMpassion130 trial evaluating atezo + nab-pac [Schmid, NEJM 2018], the randomized phase 3 IMpassion131 trial (NCT03125902) did not demonstrate significantly improved progression-free survival (PFS; primary endpoint) and showed no improvement in overall survival (OS; secondary endpoint) with the addition of atezo to pac as first-line therapy for mTNBC in either the PD-L1+ or the intention-to-treat (ITT) population [Miles, Ann Oncol 2021]. In IMpassion130, enhanced PFS and OS improvement with atezo + nab-pac were seen in the basal-like immune-activated (BLIA) subtype, whereas potential resistance to atezo + nab-pac was observed in the luminal androgen receptor (LAR) subtype [Emens, ASCO 2021]. LAR may be more prevalent in Asian populations [Ding, Oncotarget 2019], which represented one-third of patients enrolled in IMpassion131 and could have influenced the overall result. PFS outcomes were numerically worse in Asian vs non-Asian subgroups in IMpassion131 [Miles, Ann Oncol 2021]. To investigate potential reasons for observing different effects in IMpassion130 and IMpassion131, we explored the prevalence and impact on clinical outcomes of TNBC molecular subtypes and race in IMpassion131. Patients and Methods: Patients with mTNBC (no prior systemic therapy or ≥12 months since [neo]adjuvant chemotherapy) were randomized 2:1 to atezolizumab 840 mg or placebo (days 1, 15), both with paclitaxel 90 mg/m2 (days 1, 8, 15), every 28 days until disease progression or unacceptable toxicity, stratified by tumor PD-L1 status, prior taxane, liver metastases, and geographic region. Molecular subtypes according to Burstein classification were determined by RNA sequencing (Illumina TruSeq RNA Access) of baseline tumor samples. Prevalence of Burstein molecular subtypes [Burstein, Clin Cancer Res 2015] was assessed in Asian (of whom 69% were enrolled in China) and non-Asian populations; clinical outcomes were assessed according to molecular subtype and race using Cox proportional hazards analysis. All analyses were performed using the final data cut-off (Sep 4, 2020; median follow-up duration: 14.4 months). Results: The biomarker-evaluable population (BEP; n=471) was representative of the ITT population (n=651) with respect to baseline characteristics and PFS hazard ratio (HR; 0.75 [95% CI 0.61-0.92] vs 0.81 [95% CI 0.68-0.96], respectively). Distribution of molecular subtypes in the BEP was: 30% BLIA, 41% basal-like immune suppressed (BLIS), 24% LAR, 5% mesenchymal (MES), similar to IMpassion130. Among the BLIA samples, 82% were PD-L1+ and 18% PD-L1-; corresponding percentages were 41% vs 59% for BLIS, 31% vs 69% for LAR, and 32% vs 68% for MES. Compared with non-Asian patients, the Asian subgroup (n=117) included more LAR (31% vs 22%) and fewer MES (1% vs 6%) samples; this was particularly pronounced in the cohort enrolled in China (n=79; 37% LAR, 0% MES). PFS was improved with atezo + pac in the BLIA subtype (HR 0.66, 95% CI 0.45-0.97). None of the Burstein subgroups derived OS benefit from atezo + pac. Findings were similar irrespective of PD-L1 status. Direction of effect for PFS and OS favored the placebo + pac arm in the LAR Asian subgroup (n=30). Conclusion: In these exploratory analyses, the distribution of molecular subtypes and enhanced effect of atezo + pac in the BLIA subtype are consistent with findings from IMpassion130. The lack of improved efficacy with the combination of atezo + pac in the IMpassion131 trial cannot be explained by overrepresentation of a Burstein subtype less sensitive to atezo in the trial population. Citation Format: Fabrice André, Regula Deurloo, Aditi Qamra, David Cameron, Joseph Gligorov, Andreas Schneeweiss, Carlos Barrios, Binghe Xu, Ching-Wei Chang, Luciana Molinero, Shilpen Patel, Andrea Liptrot, Leilani Morales, David Miles, Joyce O’Shaughnessy. Activity of atezolizumab (atezo) plus paclitaxel (pac) in metastatic triple-negative breast cancer (mTNBC) according to Burstein molecular subtype: Analysis of the IMpassion131 trial [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD10-05.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".