Abstract SY25-02: Recent advances in the genomics and genetics of the medulloblastomas: The impact of intertumoral and intratumoral heterogeneity
Bibliographic record
Abstract
Abstract ‘Medulloblastoma’ is the most common malignant pediatric brain tumor, and a major source of morbidity and mortality in the pediatric oncology population. Current treatment regimens that include maximal safe surgery, craniospinal radiation, and high dose chemotherapy result in five-year progression free survivals in excess of 70%. Unfortunately, survivors are often left with considerable physical, neurological, and neuropsychological deficits secondary to both the disease and its therapy. Recent publications have suggested that the histological entity is in fact comprised of at least four distinct variants, each with their own separate epidemiology, transcriptomics, somatic genetic events, and clinical outcomes. Methods for subgroup assignment in the setting of clinical trials are under development around the world. Similarly, laboratories around the globe are working on developing more accurate mouse models of medulloblastoma that correspond to the various subgroups of the disease. To further highlight the extent of intertumoral heterogeneity, several of these ‘subgroups’ of medulloblastoma can likely be further subdivided into clinically and genetically distinct ‘subtypes’, the diagnoses of which have critical clinical implications. We will review the results of recent large-scale efforts to study the genetics and genomics of the various medulloblastoma subgroups and their subtypes. Intratumoral heterogeneity through space and time also likely pose a significant challenge to understanding, and eventually curing medulloblastoma. Recent evidence from mouse models and human medulloblastoma specimens demonstrates that primary medulloblastoma has very different somatic genetic events and transcriptomics as compared to leptomeningeal metastases from the same mouse/child. This supports a bicompartmental model of medulloblastoma, and suggests that in some circumstances the primary tumor and its metastases will have to be targeted separately for therapy. Similarly, it has been assumed that recurrent medulloblastoma is highly similar to its untreated forerunner. We now present evidence demonstrating that recurrent primary is highly divergent from the untreated primary tumor, and that targets for rationale therapy discovered in the untreated tumor may not be present in the recurrence. This underlines a potential fatal flaw in current method of novel drug development and therapy in which targets are identified from untreated primary tumor, and then taken to clinical trial to be tested on children with highly treated recurrent disease. The discovery, quantification, and classification of intertumoral and intratumoral heterogeneity among the medulloblastomas will allow us to design better experiments, and to select more homogeneous populations for inclusion in clinical trials. The extent of inter/intratumoral heterogeneity also highlights the need for large cohorts of tumors and children that will require effective international collaborations. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr SY25-02. doi:1538-7445.AM2012-SY25-02
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".