Abstract PR11: Multi-omic disparities in head and neck squamous cell carcinomas in patients of different racio-ethnic backgrounds
Bibliographic record
Abstract
Abstract Background: Numerous studies have demonstrated poorer outcomes by race/ethnicity in head and neck squamous cell carcinoma (HNSCC). Although some studies have identified differences in socioeconomic status and access to care as important factors affecting outcomes, differences in the genomic and extracellular composition of tumors from patients of different races/ethnicities have yet to be explored. Methods: We downloaded the clinical information, single nucleotide variation (SNV), copy number aberration (CNA), mRNA sequencing, and reverse phase protein assay (RPPA) data from The Cancer Genome Atlas (TCGA) and The Cancer Proteome Altas HNSCC cohorts. Survival data and hypoxia scores were downloaded from published studies. We stratified the cohort into combined racio-ethnic groups (REG) as follows: White/Non-Hispanic (White), Hispanic/Latino (Hispanic), Black/African American (Black), Asian, American Indian/Non-Hispanic (Indigenous American). Cases positive for human papillomavirus (HPV) occurred almost exclusively among White patients (68/71) and thus they were excluded. Results: The HPV-negative cohort contained 354 White, 43 Black, 22 Hispanic and 11 Asian and 1 Indigenous American patient. Black patients had poorer overall and progression-free survival than White patients on univariate and multivariate analysis, respectively (p<0.05). There were no significant SNV differences between any REGs after false discovery rate (FDR) correction. However, there was a large number of CNAs with higher frequency in Black patients compared to White patients (2294 shallow deletions, 96 gains, FDR<0.1). In particular, loss of the 3p chromosome arm was markedly more frequent in tumors from Black patients (p<0.01), but was not associated with poorer prognosis. From the RPPA data we found 31 cancer-associated proteins and phosphoproteins differentially expressed between Black and White patients (FDR<0.1). These included proteins in the PI3K/Akt/mTOR pathway in White patients, and N-cadherin and Hsp70 in Black patients. Deconvolution of the mRNA sequencing counts revealed differences in lymphocyte infiltration of the tumor microenvironment between Black, Hispanic, and White patients (FDR<0.1). Black patients also had more hypoxic tumors than White patients (FDR<0.1). Conclusions: In summary, we have identified important biological differences between tumors of different REGs that may partially account for differences in survival and inform targeted treatment decisions towards equitable outcomes. Citation Format: Hugh A.J. Kim, Peter Y.F. Zeng, Alana Sorgini, Mushfiq H. Shaikh, Neil Mundi, Halema Khan, Danielle MacNeil, Mohammed I. Khan, Krupal Patel, Adrian Mendez, John Yoo, Kevin Fung, Pencilla Lang, David A. Palma, Joe S. Mymryk, John W. Barrett, Paul C. Boutros, Anthony C. Nichols. Multi-omic disparities in head and neck squamous cell carcinomas in patients of different racio-ethnic backgrounds [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PR11.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".