Use of immune repertoire sequencing to resolve discordant microscopic and immunochemical findings in a case of T cell-rich large B cell lymphoma in a young dog
Bibliographic record
Abstract
BACKGROUND: Lymphocytic neoplasms with frequent reactive lymphocytes are uncommonly reported in dogs, and can pose a diagnostic challenge. Different diagnostic modalities such as cytology, flow cytometry, histopathology, immunohistochemistry, and clonality testing, are sometimes required for a diagnosis. This report illustrates the value of using a multi-modal diagnostic approach to decipher a complex lymphocytic tumor, and introduces immune repertoire sequencing as a diagnostic adjunct. CASE PRESENTATION: A 10-month-old Great Dane was referred for marked ascites. Cytologic analysis of abdominal fluid and hepatic aspirates revealed a mixed lymphocyte population including numerous large lymphocytes, yielding a diagnosis of lymphoma. Flow cytometrically, abdominal fluid lymphocytes were highly positive for CD4, CD5, CD18, CD45, and MHC II, consistent with T cell lymphoma. Due to a rapidly deteriorating clinical condition, the dog was euthanized. Post mortem histologic evaluation showed effacement of the liver by aggregates of B cells surrounded by T cells, suggestive of hepatic T cell-rich large B cell lymphoma. Immune repertoire sequencing confirmed the presence of clonal B cells in the liver but not the abdominal fluid, whereas reactive T cells with shared, polyclonal immune repertoires were found in both locations. CONCLUSIONS: T cell-rich large B cell lymphoma is a rare neoplasm in dogs that may be challenging to diagnose and classify due to mixed lymphocyte populations. In this case, the results of histopathology, immunohistochemistry and immune repertoire sequencing were most consistent with a hepatic B cell neoplasm and reactive T cells exfoliating into the abdominal fluid. Immune repertoire sequencing was helpful in delineating neoplastic from reactive lymphocytes and characterizing repertoire overlap in both compartments. The potential pitfalls of equating atypical cytomorphology and monotypic marker expression in neoplasia are highlighted.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".