Abstract 846: Improving T-cell receptor clonotyping of T-cell lymphomas using hybrid-Capture and next-generation sequencing
Bibliographic record
Abstract
Abstract Background T-cell clonality assays (TCAs) support a variety of clinical and research interests including T-cell malignancy clone identification, minimal-residual disease (MRD) testing and T-cell receptor (TCR) repertoire characterization for the purposes of immunotherapy. Unfortunately, many TCAs are unable to interrogate alpha, beta, gamma, and delta TCR loci simultaneously. Aiming to overcome these limitations, we designed a T-cell clonality assay (the “NTRA”), capable of identifying T-cell gene rearrangements from all four TCR loci, using hybrid-capture followed by deep next-generation sequencing (NGS). A novel informatics package exploiting the Burrows Wheeler Alignment algorithm and finding of CDR3 seed sequences was also deployed. We then validated the assay using orthogonal methods and in a series of clinical T-cell malignancy specimens. Methods We used DNA probes for hybrid-capture of all V and J segments in the TCR loci, followed by NGS on the Illumina NextSeq 500 platform. Analytical validation used a series of 10 specimens, including 6 flow-cytometry characterized T-cell specimens of variable degrees of immunophenotypic uniformity and 4 cell-lines with known TCR rearrangements. PCR/gel electrophoresis and Sanger sequencing were used for orthogonal validation, using primer sets designed to test each specimen for all 90th-centile V-J configurations. Subsequently, 61 clinical T-cell lymphoma specimens were tested, each previously assessed for T-cell clonality using the clinical gold standard (i.e. PCR/ electrophoresis using the BIOMED-2 consensus primers for the TCR-beta (TRB) and TCR-gamma (TRG) loci). Results PCR confirmed the NTRA-identified V-J configurations with an area-under-the-curve (AUC) by receiver-operator characteristic (ROC) analysis of 0.91. Relative to single-strand Sanger sequencing, the NTRA CDR3 sequence results showed a ROC AUC of 0.83. In a DNA dilution series employing the results of a “clonal” specimen (a Jurkat cell line) spiked into a “polyclonal” specimen (a mononuclear peripheral blood specimen), we could identify “clonal” specimen-specific V-J combinations and error-corrected CDR3 sequences down to less than 10⁁-5. In the final clinical validation, the NTRA performed with a ROC AUC of 0.82 relative to the current TRB & TRG BIOMED-2 clinical assay. Conclusions Our novel assay can overcome the current TCR locus data-yield limitations resulting from primer-based TCAs in a single-tube. The NTRA shows comparable sensitivity and specificity relative to current standard assays and excellent performance relative to current MRD techniques when applied to clinical T-cell lymphoma specimens. Citation Format: Etienne Mahe, David Mulder, Mark Dowar, Mahadeo Sukhai, Linh Nguyen, Pamela Ohashi, Jan Delabie, Tracy L. Stockley, Trevor Pugh, Suzanne Kamel-Reid. Improving T-cell receptor clonotyping of T-cell lymphomas using hybrid-Capture and next-generation sequencing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 846.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".