Defining the clonality of peripheral T cell lymphomas using RNA-seq
Bibliographic record
Abstract
Motivation: In T-cell lymphoma, malignant T cells arising from a founding clone share an identical T cell receptor (TCR) and can be identified by the over-representation of this TCR relative to TCRs from the patient's repertoire of normal T cells. Here, we demonstrate that TCR information extracted from RNA-seq data can provide a higher resolution view of peripheral T cell lymphomas (PTCLs) than that provided by conventional methods. Results: For 60 subjects with PTCL, flow cytometry/FACS was used to identify and sort aberrant T cell populations from diagnostic lymph node cell suspensions. For samples that did not appear to contain aberrant T cell populations, T helper (T H ), T follicular helper (T FH ) and cytotoxic T lymphocyte (CTL) subsets were sorted. RNA-seq was performed on sorted T cell populations, and TCR alpha and beta chain sequences were extracted and quantified directly from the RNA-seq data. 96% of the immunophenotypically aberrant samples had a dominant T cell clone readily identifiable by RNA-seq. Of the samples where no aberrant population was found by flow cytometry, 80% had a dominant clone by RNA-seq. This demonstrates the increased sensitivity and diagnostic ability of RNA-seq over flow cytometry and shows that the presence of a normal immunophenotype does not exclude clonality. Availability and Implementation: R scripts used in the processing of the data are available online at https://www.github.com/scottdbrown/RNAseq-TcellClonality. Contacts: rholt@bcgsc.ca or ksavage@bccancer.bc.ca. Supplementary information: Supplementary data are available at Bioinformatics online.
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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".