Abstract B21: Childhood Cancer Molecular Map (C2M2) to define medulloblastoma heterogeneity and predict treatment response
Bibliographic record
Abstract
Abstract Background: Medulloblastoma is a heterogenous group of tumors that collectively are the most common malignant brain tumor of childhood. Advances in treatment are required as one third of patients die from the disease, and those who survive suffer severe long-term side effects from therapy. The ability to sequence entire genome, methylome, and transcriptome of tumors provides the opportunity to identify underlying drivers of malignancy, predict treatment response, and develop novel therapies. A lack of reproducibility when comparing identified genetic mutations with treatment response is challenging because a single genetic change does not reflect the cellular state of a cancer cell in its entirety, which is expressing a multitude of genes. Computational methods allow for the creation of mapping systems that may more accurately describe the cellular state and thereby predict treatment response. Objective: We used Childhood Cancer Molecular Map (C2M2) to define medulloblastoma heterogeneity and predict treatment response using patient-derived xenografts (PDX). Methods: RNA transcription abundance from medulloblastoma samples published by Cho et al. were used to create C2M2. This was accomplished by analyzing the distribution of transcriptional abundance for each gene across all samples in order to select the genes that display the most asymmetric and non-Gaussian behavior. This delineated a “context score” for each gene, emphasizing those over- and underexpressed, allowing for the creation of a unique signature to model cellular states. The medulloblastoma samples from Cho et al. were then plotted onto the map based upon their RNA transcriptional abundance signatures, creating clusters of similar cellular states. Likewise, RNA transcription abundance from 20 PDX samples, for which drug response was known, was then mapped. Results: C2M2 identified ten cellular states for medulloblastoma by which to define patient samples: SHH DNA repair, SHH glutamate signaling, SHH RNA repair, WNT, Photoreceptor and MYC (in which Group 3 medulloblastoma falls), Neuronal migration, Neuronal MAPK activation and Axonal (in which Group 4 medulloblastoma falls), and Homeobox activation. PDX samples for which drug response was known clustered similarly onto the map. Conclusion: C2M2 using RNA transcriptional abundance from medulloblastoma samples could be used to predict drug response. Citation Format: Huwate Yeerna, Benjamin Briggs, Jessica Rusert, Lukas Chavez, Jill Mesirov, Robert Wechsler-Reya, Pablo Tamayo. Childhood Cancer Molecular Map (C2M2) to define medulloblastoma heterogeneity and predict treatment response [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B21.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".