CapTCR-seq: hybrid capture for T-cell receptor repertoire profiling
Bibliographic record
Abstract
Abstract Mature T-cell lymphomas consisting of an expanded clonal population of T cells that possess common rearrangements of the T-cell receptor (TCR) encoding genes can be identified and monitored using molecular methods of T-cell repertoire analysis. We have developed a hybrid-capture method that enriches DNA sequencing libraries for fragments encoding rearranged TCR genes from all 4 loci in a single reaction. We use this method to describe the TCR repertoires of 63 putative lymphoma clinical isolates, 7 peripheral blood mononuclear cell (PBMC) populations, and a collection of tumor infiltrating lymphocytes. Dominant Variable (V) and Joining (J) gene pair rearrangements in cancer cells were confirmed by polymerase chain reaction (PCR) amplification and Sanger sequencing; clonality assessment of clinical isolates using BIOMED-2 methods showed agreement for 73% and 77% of samples at the β and γ loci, respectively, whereas β locus V and J allele prevalence in PBMCs were well correlated with results from commercial PCR-based DNA sequencing assays (r2 = 0.94 with Adaptive ImmunoSEQ, 0.77-0.83 with Invivoscribe LymphoTrack TRB Assay). CapTCR-seq allows for rapid, high-throughput and flexible characterization of dominant clones within TCR repertoire that will facilitate quantitative analysis of patient samples and enhance sensitivity of tumor surveillance over time.
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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) | 0.001 | 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".