Abstract 4237: Differences in the frequencies of tumor <i>VHL</i> mutation and HIF-2α expression between black and white patients with clear cell renal carcinoma
Bibliographic record
Abstract
Abstract Background: Black Americans have a poorer prognosis for clear cell renal cell carcinoma (ccRCC) than white Americans, potentially due in part to differences in tumor biology. In a recent analysis of The Cancer Genome Atlas (TCGA), tumors from black ccRCC patients had a lower rate of mutation in the VHL tumor suppressor gene and lower expression of hypoxia inducible factors (HIF) than tumors from white ccRCC patients. However, as this study was based on a small number of black patients (N=19) and had limited information on patients' medical histories and risk factors profiles, further investigation is needed. Objective: We evaluated differences in the frequencies of somatic VHL gene mutations and HIF-1α and -2α protein expression between tumors from black and white ccRCC patients. Methods: The investigation utilized formalin-fixed tissue and data collected from patients participating in a case-control study conducted in Chicago and Detroit. We sequenced VHL using the Ion Torrent platform for tumors from 69 black and 98 white patients, and measured tumor HIF-1α and -2α protein expression for 88 black and 240 white patients using immunohistochemistry. Results: Black patients' tumors had a lower frequency of VHL mutation than those of white patients (32% vs. 49%; P = 0.03) as well as a lower frequency of above-median HIF-2α expression (33% vs. 56%; P=0.002). HIF-1α expression did not differ by race (P=0.14). These racial differences persisted after multivariable model adjustment for age, sex, hypertension, chronic kidney disease, body mass index, smoking status, stage, grade, and tumor size [VHL mutation: odds ratio (OR) = 0.44, 95% confidence interval (CI) = 0.19, 0.98; HIF-2α expression: OR = 0.33, 95% CI = 0.18, 0.61]. Conclusions: Our observation that VHL mutation and high HIF-2α expression are less frequent in ccRCC tumors of black vs. white patients confirms the earlier TCGA finding. These findings suggest that ccRCC in black patients is a fundamentally different disease than ccRCC in white patients. Citation Format: Catherine L. Callahan, Lee E. Moore, Petra Lenz, Kendra Schwartz, Julie Ruterbusch, Faith Davis, Wong-Ho Chow, W. Marston Linehan, Maria J. Merino, Stephen M. Hewitt, Nathaniel Rothman, Jonathan N. Hofmann, Michael L. Nickerson, Mark P. Purdue. Differences in the frequencies of tumor VHL mutation and HIF-2α expression between black and white patients with clear cell renal carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4237.
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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 | 0.000 |
| 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) | 0.003 | 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".