AT-22ALISERTIB MONOTHERAPY IN THE TREATMENT OF RELAPSED ATYPICAL TERATOID RHABDOID TUMOR (ATRT)
Bibliographic record
Abstract
Atypical teratoid rhabdoid tumors (ATRTs) account for up to 20% of brain tumors in children less than 3yrs of age. The overall prognosis of ATRTs is extremely poor with median survival in the range of 10-18mths from the time of diagnosis. Aurora A Kinase (AURKA) encodes a protein that regulates the formation and stability of the mitotic spindle and is highly active in ATRT through loss of the INI1 tumor suppressor gene. Alisertib (MLN8237) is a selective small molecule inhibitor of AURKA. We report a case of recurrent ATRT treated with alisertib monotherapy producing sustained and durable disease remission. Our patient underwent gross total resection (GTR) of the Posterior fossa tumor at diagnosis and was initially treated as per ACNS0333 protocol. She remained in remission for 15 months after completion of chemotherapy when she relapsed in the right frontal lobe. She underwent sub-total resection (STR) followed by focal IMRT (54Gy/30 fractions) followed by chemotherapy (DFCI-IRS-III, modified protocol) with Doxorubicin / Etoposide alternating with Actinomycin-D and triple intra-thecal chemotherapy. After 7 months of treatment, she relapsed again in the right frontal region, presenting with focal seizures. She received 10 cycles of alisertib (60mg/m2 by mouth once daily for 7 days of a 21 day treatment cycle) monotherapy and is clinically stable with her imaging showing sustained regression of disease. Somnolescence and neutropenia were the most common side effects seen in our patient.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".