Membrane-Localized Mutations Predict the Efficacy of Cancer Immunotherapy
Bibliographic record
Abstract
ABSTRACT Due to their genetic instability, tumor cells bear mutations that can effectively be recognized by the immune system. In the clinic, immune checkpoint immunotherapy (ICI) can re-activate immune reactions against mutated proteins, known as neoantigens, leading to remarkable remission in cancer patients. Nevertheless, only a minority of patients are responsive to ICI, and approaches for prediction of responsiveness remain elusive yet are needed to improve the success of cancer treatments. While the tumor mutational burden (TMB) correlates positively with responsiveness and survival of patients undergoing ICI therapy, the influence of the subcellular localizations of the mutated proteins within the tumor cell has not been elucidated. Here, we hypothesized that the immune reactions are modulated by the localization of the mutated proteins and, therefore, that some subcellular localizations could favor responsiveness to ICI. We show in both a mouse melanoma model and human clinical datasets of 1722 ICI-treated patients that high membrane-localized tumor mutational burden (mTMB), particularly at the plasma membrane, correlate with responsiveness to ICI therapy and improved overall survival across multiple cancer types. We further highlight that mutations in the genes encoding for the membrane proteins NOTCH3, RNF43, NTRK3 and NOTCH1 , among others, may serve as potent biomarkers to predict extended survival upon ICI in certain cancer types. We anticipate that our results will improve the predictability of cancer patient response to ICI and therefore may have important implications to establish future clinical guidelines to direct the choice of treatment toward ICI.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".