Biomarkers of response and resistance to immune checkpoint inhibitors in breast cancer
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) have recently been approved in subsets of patients with breast cancer (BC). Currently, programmed death ligand 1 (PD-L1) immunohistochemistry is used as a biomarker of response for metastatic triple negative breast cancer (TNBC). Other tumor-agnostic indications in metastatic BC include high tumor mutational burden and mismatch repair deficiency. In early TNBC, the ICI pembrolizumab is routinely added to neoadjuvant chemotherapy, yet no biomarker is currently available to predict response or resistance. Further, while luminal BC is often thought to be immune-depleted, preliminary efficacy data in early-stage disease suggests that the addition of ICIs to neoadjuvant chemotherapy can significantly improve rates of pathological complete response. However, not all patients will benefit from ICI treatment and it also comes with significant treatment toxicities. This review will describe biomarkers of response and resistance to ICIs in BC. These currently include tumor infiltrating lymphocytes, homologous recombination deficiency, CD274 gain or amplification, estrogen receptor and/or progesterone receptor expression, more precise tumoral immune characterization, gene expression analysis, and the T-cell receptor repertoire. Although still investigational, these approaches hold the potential to advance personalized medicine by tailoring the use of ICIs to BC patients who will benefit.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".