Immune Signatures and Survival of Patients With Metastatic Melanoma, Renal Cancer, and Breast Cancer
Bibliographic record
Abstract
Despite remarkable recent progress in treating solid cancers, especially the success of immunomodulatory antibody therapies for numerous different cancer types, it remains the case that many patients fail to respond to treatment. It is therefore of immense importance to identify biomarkers predicting clinical responses to treatment and patient survival, which would not only assist in targeting treatments to patients most likely to benefit, but might also provide mechanistic insights into the reasons for success or failure of the therapy. Several peripheral blood or tumor tissue diagnostic and predictive biomarkers known to be informative for cancer patient survival may be applicable for this purpose. The use of peripheral blood ("liquid biopsy") offers numerous advantages not only for predicting treatment responses at baseline but also for monitoring patients on-therapy. Assessment of the tumor microenvironment and infiltrating immune cells also delivers important information on cancer-host interactions but the requirement for tumor tissues makes this more challenging, especially for monitoring sequential changes in the individual patient. In this contribution, we will review our findings on immune signatures potentially informative for clinical outcome in melanoma, breast cancer and renal cell carcinoma, particularly the outcome of checkpoint blockade, by applying multiparametric flow cytometry and mass cytometry, routine clinical monitoring and functional testing for predicting and following individual patient responses to therapy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".