Abstract B092: Epstein-Barr virus prevalence in classical Hodgkin lymphoma tumors by race/ethnicity in a multiethnic U.S. population
Bibliographic record
Abstract
Abstract Background Classical Hodgkin lymphoma (cHL) is one of the most common cancers among adolescents and young adults (AYA). Incidence rates vary by demographic factors, with the highest incidence in whites of high SES. Malignant Hodgkin Reed-Sternberg (HRS) cells comprise a small percentage of the tumor (about 1%), with the remainder composed of various immune cells (tumor microenvironment, TME). The TME in cHL predicts survival and response to treatment in NHW, but has not been examined in diverse populations. We implemented a study to examine the TME and survival by race/ethnicity and SES in a multiethnic, multicenter set of 1,536 U.S. cHL cases. We conducted an initial analysis of factors associated with EBV status in tumors. Methods FFPE tumor blocks for 649 cases diagnosed from 1996-2016 were identified to date. Diagnosis and histological subtype was confirmed by pathology review for 554. Tissue microarrays were constructed with two 2 mm cores from each case with 29 cases on each array. EBER was assessed using in situ hybridization and scored as negative, positive in HRS cells or positive in the surrounding normal infiltrate. Demographic and clinical information, including the anatomic site of the biopsy, subtype, race/ethnicity (Hispanic, African American [AA], Asian, Native American, NHW), age at diagnosis and gender was collected. Multiple logistic regression was conducted to assess the relationship between age at diagnosis, race/ethnicity, gender, subtype and EBV tumor status. Results Race data was available at this time for 549 cases including 153 NHW, 235 Hispanics, 110 AAs, 36 Asians, 2 Native Americans and 13 with unknown race. 260 (47%) were female. EBV prevalence was available for 393 cases, with 316 nodular sclerosis (NS), 52 mixed cellularity (MC), 8 lymphocyte depleted (LD), 3 lymphocyte rich (LR), and 14 not otherwise specified (NOS). Age at diagnosis ranged from 5-87 years of age, with 189 (48.09%) in AYA (15-35 y). EBV+ by histology was 12.5%% for LD, 33% for LR, 47% for MC, 25% for NS, and 29% for NOS. Histologic subtype was a statically significant predictor of EBV tumor status (p=.0007), even after adjusting for age, sex, and race/ethnicity (p=.0085). Among NS cases, EBV+ was similar across racial/ethnic groups. However, there was a much higher prevalence of EBV+ in AA (73%) compared to NHW and Hispanic (36-42%) MC cases. HIV/AIDS status was available for 40% of the AA cases but none were positive. There was no correlation between year of diagnosis and the presence of EBV in tumors (r=0.05, p=0.42), thus block storage time did not affect the results. Conclusions This is the largest study of the Hodgkin lymphoma TME in a racially and ethnically diverse population. In an initial analysis, histologic subtype was the strongest predictor of positive EBV tumor status. Unlike previous reports, EBV-positive tumors were not more common among non-whites, except for AA MC cases. We expect to confirm these results in a larger sample as more cases are accrued. Citation Format: Rachel Bolanos, Amie Hwang, Jose Aparicio, Chun Chao, Naba Qurashi, Christopher Flowers, Sheeja Pullarkat, David Conti, Sophia Wang, Karen Mann, Leon Bernal-Mizrachi, Joo Song, Christian Steidl, Imran Siddiqi, Wendy Cozen. Epstein-Barr virus prevalence in classical Hodgkin lymphoma tumors by race/ethnicity in a multiethnic U.S. population [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr B092.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".