Abstract 1998: Predictive and prognostic role of T- and B-cell receptor repertoire in HER2-positive breast cancer: An analysis of the NeoALTTO clinical trial
Bibliographic record
Abstract
Abstract Background: Disease relapse is observed in a significant proportion of HER2-positive breast cancer (BC) patients with residual disease (RD) after neoadjuvant treatment, as well as in a subgroup of those achieving pathological complete response (pCR). As the host immune response plays a key role in modulating the activity of anti-HER2 agents, we investigated the association of T- and B-cell receptor (TCR and BCR) repertoires with pCR and event-free survival (EFS) in the NeoALTTO phase 3 trial. Methods: RNA sequencing (RNAseq) data from baseline tumor biopsies were available for 254 patients out of the 455 enrolled in the study. Among those, 166 did not achieve a pCR defined as ypT0/is. Matched RNAseq data from RD samples were available for 43 cases. TCR/BCR repertoires were extracted from RNAseq data using the MiXCR software. TCR and BCR read counts, number of clones, evenness, Shannon entropy, Gini index, length of the complementarity determining region 3, top and second top clone proportion were evaluated. Survival analysis was performed using univariate and multivariate (adjusted for tumor size, nodal status, grade, estrogen receptor [ER] status, age and treatment arm) Cox proportional hazard models, while logistic regressions were used for pCR. False discovery rate (FDR) was obtained using Benjamini & Hochberg method. Results: Baseline TCR top (odds ratio [OR]=0.63 [95% CI 0.46-0.87], FDR=0.021) and second top (OR=0.57 [0.41-0.79], FDR=0.004) clone proportion were significantly associated with a lower probability of achieving pCR in the multivariate analysis. BCR evenness (hazard ratio [HR]=1.5 [1.2-2], FDR=0.015) and Gini index (HR=0.66 [0.52-0.85], FDR=0.015) were significantly associated with EFS in the multivariate analysis. In residual disease, BCR evenness and Gini index showed a similar trend in the EFS univariate analysis, while TCR read counts, number of clones, and entropy were associated with lower HR (P<0.05), although FDR were borderline significant (0.05 Conclusions: In the NeoALTTO trial, the presence of an evenly distributed BCR repertoire was associated with worse EFS. A model integrating baseline immune-related and clinical features was able to identify patients with excellent prognosis despite RD or, conversely, with poor prognosis after pCR. We envision that our model has the potential to allow the personalization of post-operative treatment strategies after both pCR and RD in HER2-positive BC. Further validation of our findings is warranted. Citation Format: Mattia Rediti, David Venet, Françoise Rothé, Tao Qing, Marion Maetens, Ian Bradbury, Miguel A. Izquierdo, Serena Di Cosimo, Florentine Hilbers, Mohammed Bajji, Nadia Harbeck, Michael Untch, David L. Rimm, Stephen Chia, Minetta C. Liu, Cristina Saura, Jens Huober, Paolo Nuciforo, Roberto Salgado, Sherene Loi, Lajos Pusztai, Christos Sotiriou. Predictive and prognostic role of T- and B-cell receptor repertoire in HER2-positive breast cancer: An analysis of the NeoALTTO clinical trial [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1998.
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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 | 0.005 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".