Case Report: Lasting complete response to pembrolizumab in mismatch repair-deficient cardiac sarcoma: a genomic characterization
Bibliographic record
Abstract
Sarcomas are traditionally considered “cold” tumors with poor response to immunotherapy. However, evidence accumulating over the last years shows that immune checkpoint inhibitors (ICIs) may have a role in selected sarcoma patients according to predictive markers. Here, we report the case of a woman diagnosed with a primary cardiac undifferentiated sarcoma. Following failure of standard first line chemotherapy, high-throughput sequencing (HTS) revealed a high tumor mutational burden (TMB), pathogenic mutations in FAT1 and NOTCH2 and a microsatellite instability (MSI)-associated signature. Immunohistochemistry confirmed mismatch repair-deficiency (MMRd) and abundant CD8+ tumor-infiltrating lymphocytes (TILs), in the absence of tertiary lymphoid structures. The patient was, therefore, treated with the ICI pembrolizumab, reaching a complete response that continues to persist at last follow-up, more than seven years from initial diagnosis and nearly six years from initiation of ICI treatment. This case illustrates the importance of performing HTS in rare sarcomas given the availability of efficient therapies, such as those for tumors displaying high TMB or MMRd/MSI. In agreement with other reports, it supports the contention that MMRd/MSI status and high numbers of TILs are valuable predictive markers of response to immunotherapy in sarcomas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| 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".