Prognostic B-cell Signatures Using mRNA-Seq in Patients with Subtype-Specific Breast and Ovarian Cancer
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: Lymphocytic infiltration of tumors predicts improved survival in patients with breast cancer. Previous studies have suggested that this survival benefit is confined predominantly to the basal-like subtype. Immune infiltration in ovarian tumors is also associated with improved prognosis. Currently, it is unclear what aspects of the immune response mediate this improved outcome. EXPERIMENTAL DESIGN: Using The Cancer Genome Atlas mRNA-seq data and a large microarray dataset, we evaluated adaptive immune gene expression by genomic subtype in breast and ovarian cancer. To investigate B-cells observed to be prognostic within specific subtypes, we developed methods to analyze B-cell population diversity and degree of somatic hypermutation (SHM) from B-cell receptor (BCR) sequences in mRNA-seq data. RESULTS: Improved metastasis-free/progression-free survival was correlated with B-cell gene expression signatures, which were restricted mainly to the basal-like and HER2-enriched breast cancer subtypes and the immunoreactive ovarian cancer subtype. Consistent with a restricted epitope-driven response, a subset of basal-like and HER2-enriched breast tumors and immunoreactive ovarian tumors showed high expression of a low-diversity population of BCR gene segments. More BCR segments showed improved prognosis with increased expression in basal-like breast tumors and immunoreactive ovarian tumors compared with other subtypes. Basal-like and HER2-enriched tumors exhibited more BCR sequence variants in regions consistent with SHM. CONCLUSION: Taken together, these data suggest the presence of a productive and potentially restricted antitumor B-cell response in basal-like breast and immunoreactive ovarian cancers. Immunomodulatory therapies that support B-cell responses may be a promising therapeutic approach to targeting these B-cell infiltrated tumors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it