Human Solid Tumor Xenografts in Immunodeficient Mice Are Vulnerable to Lymphomagenesis Associated with Epstein-Barr Virus
Bibliographic record
Abstract
Xenografting primary human solid tumor tissue into immunodeficient mice is a widely used tool in studies of human cancer biology; however, care must be taken to prove that the tumors obtained recapitulate parent tissue. We xenografted primary human hepatocellular carcinoma (HCC) tumor fragments or bulk tumor cell suspensions into immunodeficient mice. We unexpectedly observed that 11 of 21 xenografts generated from 16 independent patient samples resembled lymphoid neoplasms rather than HCC. Immunohistochemistry and flow cytometry analyses revealed that the lymphoid neoplasms were comprised of cells expressing human CD45 and CD19/20, consistent with human B lymphocytes. In situ hybridization was strongly positive for Epstein-Barr virus (EBV) encoded RNA. Genomic analysis revealed unique monoclonal or oligoclonal immunoglobulin heavy chain gene rearrangements in each B-cell neoplasm. These data demonstrate that the lymphoid neoplasms were EBV-associated human B-cell lymphomas. Analogous to EBV-associated lymphoproliferative disorders in immunocompromised humans, the human lymphomas in these HCC xenografts likely developed from reactivation of latent EBV in intratumoral passenger B lymphocytes following their xenotransplantation into immunodeficient recipient mice. Given the high prevalence of latent EBV infection in humans and the universal presence of B lymphocytes in solid tumors, this potentially confounding process represents an important pitfall of human solid tumor xenografting. This phenomenon can be recognized and avoided by routine phenotyping of primary tumors and xenografts with human leukocyte markers, and provides a compelling biological rationale for exclusion of these cells from human solid tumor xenotransplantation assays.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".