Whole-genome sequencing identifies novel predictors for hematopoietic cell transplant outcomes for patients with myelodysplastic syndrome: a CIBMTR study
Bibliographic record
Abstract
Recurrent mutations in TP53, RAS pathway and JAK2 genes were shown to be highly prognostic of allogeneic hematopoietic cell transplant (alloHCT) outcomes in myelodysplastic syndromes (MDS). However, a significant proportion of MDS patients has no such mutations. Whole-genome sequencing (WGS) empowers the discovery of novel prognostic genetic alterations. We conducted WGS on pre-alloHCT whole-blood samples from 494 MDS patients. To nominate genomic candidates and subgroups that are associated with overall survival, we ran genome-wide association tests via gene-based, sliding window and cluster-based multivariate proportional hazard models. We used a random survival forest (RSF) model with build-in cross-validation to develop a prognostic model from identified genomic candidates and subgroups, patient-, disease- and HCT-related clinical factors. Twelve novel regions and three molecular signatures were identified with significant associations to overall survival. Mutations in two novel genes, CHD1 and DDX11, demonstrated a negative impact on survival in AML/MDS and lymphoid cancer data from the Cancer Genome Atlas (TCGA). From unsupervised clustering of recurrent genomic alterations, genomic subgroup with TP53/del5q is characterized with the significant association to inferior overall survival and replicated by an independent dataset. From supervised clustering of all genomic variants, more molecular signatures related to myeloid malignancies are characterized from supervised clustering, including Fc-receptor FCGRs, catenin complex CDHs and B-cell receptor regulators MTUS2/RFTN1. The RSF model with genomic candidates and subgroups, and clinical variables achieved superior performance compared to models that included only clinical variables.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".