Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: While immune checkpoint blockade has greatly improved clinical outcomes in diseases such as melanoma, there remains a need for predictive biomarkers to determine who will likely benefit most from which therapy. To date, most biomarkers of response have been identified in the tumors themselves. Biomarkers that could be assessed from peripheral blood would be even more desirable, because of ease of access and reproducibility of sampling. METHODS: We used mass cytometry (CyTOF) to comprehensively profile peripheral blood of melanoma patients, in order to find predictive biomarkers of response to anti-CTLA-4 or anti-PD-1 therapy. Using a panel of ~ 40 surface and intracellular markers, we performed in-depth phenotypic and functional immune profiling to identify potential predictive biomarker candidates. RESULTS: memory/non-memory cells and other memory subsets was different between responders and non-responders to anti-CTLA-4 therapy. In anti-PD-1 (but not anti-CTLA-4) treated patients, we discovered differences in CD69 and MIP-1β expressing NK cells between responders and non-responders. Finally, multivariate analysis was used to develop a model for the prediction of response. CONCLUSIONS: memory T cell subsets play an important role in response to anti-CTLA-4, and are potential biomarker candidates. For anti-PD-1 therapy, NK cell subsets (but not memory T cell subsets) correlated with clinical response to therapy. These functionally active NK cell subsets likely play a critical role in the anti-tumor response triggered by anti-PD-1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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