High and low mutational burden tumors versus immunologically hot and cold tumors and response to immune checkpoint inhibitors
Bibliographic record
Abstract
Tumors responding to immune checkpoint inhibitors (ICIs) have a higher level of immune infiltrates and/or an Interferon (IFN) signature indicative of a T-cell-inflamed phenotype. Melanoma and lung cancer demonstrate high response rates to ICIs and are commonly referred to as "hot tumors". These are in sharp contrast to tumors with low immune infiltrates called "cold tumors" or non-T-cell-inflamed cancers, such as those from the prostate and pancreas. Classification of tumors based on their immune phenotype can partially explain clinical response to ICIs. However, this model alone cannot fully explain the lack of response among many patients treated with ICIs.Dichotomizing tumors based on their mutation profile into high tumor mutation burden (TMB) or low TMB, such as many childhood malignancies, can also, to some extent, explain the clinical response to immunotherapy. This model mainly focuses on a tumor's genotype rather than its immune phenotype. High TMB tumors often have higher levels of neoantigens that can be recognized by the immune system. In the current era of immunotherapy, with the lack of definitive biomarkers, we need to evaluate tumors based on both their immune phenotype and genomic mutation profile to determine which patients have a higher likelihood of responding to treatment with ICIs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".