Etiologic Heterogeneity Among Non-Hodgkin Lymphoma Subtypes: The InterLymph Non-Hodgkin Lymphoma Subtypes Project
Bibliographic record
Abstract
BACKGROUND: Non-Hodgkin lymphoma (NHL) comprises biologically and clinically heterogeneous subtypes. Previously, study size has limited the ability to compare and contrast the risk factor profiles among these heterogeneous subtypes. METHODS: We pooled individual-level data from 17 471 NHL cases and 23 096 controls in 20 case-control studies from the International Lymphoma Epidemiology Consortium (InterLymph). We estimated the associations, measured as odds ratios, between each of 11 NHL subtypes and self-reported medical history, family history of hematologic malignancy, lifestyle factors, and occupation. We then assessed the heterogeneity of associations by evaluating the variability (Q value) of the estimated odds ratios for a given exposure among subtypes. Finally, we organized the subtypes into a hierarchical tree to identify groups that had similar risk factor profiles. Statistical significance of tree partitions was estimated by permutation-based P values (P NODE). RESULTS: Risks differed statistically significantly among NHL subtypes for medical history factors (autoimmune diseases, hepatitis C virus seropositivity, eczema, and blood transfusion), family history of leukemia and multiple myeloma, alcohol consumption, cigarette smoking, and certain occupations, whereas generally homogeneous risks among subtypes were observed for family history of NHL, recreational sun exposure, hay fever, allergy, and socioeconomic status. Overall, the greatest difference in risk factors occurred between T-cell and B-cell lymphomas (P NODE < 1.0×10(-4)), with increased risks generally restricted to T-cell lymphomas for eczema, T-cell-activating autoimmune diseases, family history of multiple myeloma, and occupation as a painter. We further observed substantial heterogeneity among B-cell lymphomas (P NODE < 1.0×10(-4)). Increased risks for B-cell-activating autoimmune disease and hepatitis C virus seropositivity and decreased risks for alcohol consumption and occupation as a teacher generally were restricted to marginal zone lymphoma, Burkitt/Burkitt-like lymphoma/leukemia, diffuse large B-cell lymphoma, and/or lymphoplasmacytic lymphoma/Waldenström macroglobulinemia. CONCLUSIONS: Using a novel approach to investigate etiologic heterogeneity among NHL subtypes, we identified risk factors that were common among subtypes as well as risk factors that appeared to be distinct among individual or a few subtypes, suggesting both subtype-specific and shared underlying mechanisms. Further research is needed to test putative mechanisms, investigate other risk factors (eg, other infections, environmental exposures, and diet), and evaluate potential joint effects with genetic susceptibility.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".