Complete Response of a Colonic High-Grade Neuroendocrine Carcinoma to Platinum-Based Therapy: Insights from Comprehensive Genomic Profiling
Bibliographic record
Abstract
Abstract Background Comprehensive genomic profiling (CGP) is an essential tool in precision medicine, providing diagnostic, prognostic, and predictive (therapeutic) information that enables personalized optimal care for cancer patients. We present the case of a 54-year-old woman with stage IV large-cell neuroendocrine carcinoma (LCNEC) of the colon with liver and nodal metastases with complete response to therapy and demonstrate the value of CGP in identifying potential targets for treatment in these tumors. Results CGP performed on the tumor showed pathogenic mutations in multiple oncogenes and tumor suppressor genes including BRCA1, BAP1, and BRAF, high tumor mutation burden (TMB), and high microsatellite instability (MSI-H). Treatment with platinum-based therapy resulted in a complete radiographic response of the metastases, with no evidence of recurrence after 6.5 years. Assessment by Medical Genetics did not identify any evidence of hereditary cancer syndrome. The dramatic response to therapy is likely due to loss of BRCA1 and/or BAP1 function, as deleterious mutations in both genes predict response to platinum-based therapy through exploitation of deficient homologous recombination repair (HRR). The information provided by CGP also suggested potential tumor sensitivity to poly(ADP-Ribose) polymerase inhibitors (PARPi), immunotherapy (IT) and BRAF/MEK inhibitor therapy, should the tumor recur. Conclusion This case highlights the value of CGP in guiding diagnosis and management of rare and aggressive tumors.
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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 | 0.000 |
| 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) | 0.000 | 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 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".