Treatment With Antitumor Agents Recommended by Cancer Genome Panel for Uterine Leiomyosarcoma
Bibliographic record
Abstract
To date, cancer genomic medicine, using cancer gene panel covered by health insurance from June 2019, has been performed for advanced malignant tumors under public medical insurance. In gynecology, the first-line treatment for uterine leiomyosarcomas, which is a mesenchymal uterine tumor, is surgery. In uterine leiomyosarcoma cases, recurrence is observed within 2 years postoperatively; however, to date, clinical trials have not shown efficacy with existing antitumor agents. We noted efficacy in two cases with advanced/recurrent uterine leiomyosarcoma using an antitumor agent selected on the basis of cancer gene panel testing results. Following uterine leiomyosarcoma diagnosis, they underwent total abdominal hysterectomy and bilateral salpingo-oophorectomy as standard surgical treatment. After the surgical treatment, the imaging test revealed recurrent tumors; subsequently, they were treated with doxorubicin alone or doxorubicin combined with Gemzar. However, cancer genome gene panel test was performed because the malignant tumor worsened. Based on the cancer genome gene panel test results, the two cases with advanced uterine leiomyosarcoma were associated with increased tumor mutational burden (TMB) or pathogenic variants (PVs) of AKT serine/threonine kinase 1 ( AKT1 ). Therefore, treatment with pembrolizumab, which is a drug covered by insurance for patients with TMB-high, or treatment with kinase inhibitors for patients with PVs in AKT, was considered. Cancer genomic medicine using cancer gene panel provides a new treatment strategy for intractable malignant tumors. This study aimed to discuss the usefulness of cancer genomic medicine by cancer gene panel testing using the cases of advanced and recurrence uterine leiomyosarcoma and the latest findings. J Clin Med Res. 2023;15(10-11):461-468 doi: https://doi.org/10.14740/jocmr5052
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".