Abstract 3447: FSTL5 improves prognostic subclassification of medulloblastoma
Bibliographic record
Abstract
Abstract Current integrated genomic approaches indicate distinct biological variants in medulloblastoma. Comprehensive molecular classification strategies utilize cytogenetic or immunohistochemical biomarkers to refine risk stratification. Novel complementary markers may ameliorate outcome prediction particularly in intermediate or high-risk medulloblastomas. We combined transcriptome and DNA copy-number analysis for 64 primary tumors. Bioinformatic tools were applied to investigate marker genes of molecular variants. Differentially expressed transcripts were evaluated for prognostic value in the entire screening cohort. Immunohistochemical markers were used to determine molecular subtypes in adult and pediatric medulloblastoma samples (n=235). Immunopositivity of FSTL5 was correlated with molecular and prognostic subgroups for 235 non-overlapping medulloblastoma samples on two independent tissue microarrays (TMA). Unsupervised cluster analyses of transcriptome profiles revealed four distinct molecular variants: WNT, SHH, Group C, and Group D. Stable subgroup separation was achieved using only 300 most varying transcripts. Specific distribution of clinical and molecular characteristics was noted for each cluster. Notably, Group C tumors were exclusively present in pediatric medulloblastomas as determined by immunohistochemistry. Delimited expression patterns of FSTL5 in each molecular subgroup were confirmed by quantitative real-time PCR. FSTL5 transcripts were most up-regulated in Group C and Group D tumors with unfavorable prognosis, whereas WNT medulloblastomas showed marked down-regulation. Immunopositivity of FSTL5 identified a large proportion of patients (84 of 235 patients; 36%) at high risk for relapse and death in particular in patients with WNT/SHH-independent tumors. Multivariate analysis revealed that FSTL5 immunopositivity constitutes an independent prognostic marker in pediatric and adult patient cohorts (p<0.0001). Importantly, adding this biomarker to comprehensive outcome prediction schemes substantially reduced the prediction error of the model. Comprehensive analyses of transcriptome and genetic alterations unravel four distinct disease variants. By addition of FSTL5 immunohistochemistry, existing molecular stratification schemes can effectively be complemented and sub-classification of WNT/SHH-independent tumors substantially optimized. This approach may ultimately define clear risk groups to individualize treatment intensities in future clinical trials. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3447. doi:10.1158/1538-7445.AM2011-3447
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".