Multiomics Profiling of T-cell Leukemia and Lymphoma Enables Targeted Therapeutic Discovery
Bibliographic record
Abstract
T-cell leukemias and lymphomas (TCL) form a heterogeneous group of rare and often aggressive malignancies. Because of the rarity and heterogeneity of TCL subtypes, clinical trials are challenging to conduct, making pharmacogenomic studies in cell line panels critical for the discovery of targeted therapeutics. The scarcity of data repositories with integrated multiomics and drug screening data hinders the preclinical evaluation of drug vulnerabilities and the identification of molecular markers predictive of responses to monotherapies and combinations. To address this gap, we conducted comprehensive pharmacogenomic profiling on a panel of 38 TCL cell lines, representing major clinical TCL subtypes to capture the molecular and phenotypic diversity. The TCL-38 multiomics data resource includes harmonized genetic, molecular, and epigenetic profiles, with comprehensive annotations and standardized drug response assessment of each cell line. This resource, together with machine learning predictions, was leveraged to identify TCL subtype-specific therapeutic vulnerabilities, including single-agent sensitivities and synergistic drug combinations, which were linked to genetic or epigenetic features as potential predictive biomarkers. This integrated and openly available resource (https://aittokallio.group/tcl38) could help advance the currently limited treatment options for patients with TCL. SIGNIFICANCE: Integrated and harmonized multiomics analyses and drug screening across a heterogeneous panel of T-cell leukemias and lymphomas provide a resource to uncover drug targets and predictive biomarkers to improve patient outcomes.
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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 | 0.001 |
| 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".