Abstract PO-025: The effect of tissue of origin on oncogenesis in HPV-related malignancies: Comparative analysis of molecular and immune signatures in HPV-positive head and neck cancer and cervical cancer
Bibliographic record
Abstract
Abstract Background: Despite sharing a common causal agent and viral oncogenes, HPV-positive head and neck cancer (HPV+ HNC) and cervical cancer (HPV+ CC) have markedly distinct natural histories and therapy responses. Here we sought to elucidate how the tissue of origin and oncogenic etiology drive molecular and microenvironmental differences between these two malignancies. Methods: Somatic mutation and RNA expression data for HPV+ HNC, HPV+ CC and HPV-negative head and neck cancer (HPV- HNC) were obtained from The Cancer Genome Atlas (TCGA). Mutation data were used to define repertoires of somatic mutations and mutational signatures. Single-sample gene set enrichment analysis (ssGSEA) was used to identify enriched immune signatures. Immune cell infiltration levels were estimated using xCell. Comparisons were primarily made between HPV+ HNC and HPV+ CC samples, with comparisons between HPV+ HNC vs. HPV- HNC tumors serving as a control to distinguish molecular/immune signatures driven by tissue of origin versus viral etiology. Results: Exome and RNA-sequencing data were available for 72 HPV+ HNC, 139 HPV+ CC, and 415 HPV- HNC tumors, and 70 HPV+ HNC, 139 HPV+ CC, and 413 HPV- HNC cases, respectively. Recurrently altered genes in head and neck and cervical cancer were not statistically significantly different between HPV+ HNC and HPV+ CC, although as previously reported HPV+ HNC and HPV- HNC differed by the higher frequency of TP53, CDKN2A, FAT1, and NOTCH1 mutations in the later. An evaluation of mutational signatures revealed that although both HPV+ HNC and CC had evidence of APOBEC mutagenesis (Signatures 2 and 13), Signature 2 and 13 exposures were both significantly higher in HPV+ CC relative to HPV+ HNC. APOBEC enrichment scores confirmed that HPV+ CC had higher APOBEC signal than HPV+ HNC despite both being virally driven. While both cancers were commonly affected by mutagenesis from aging and showed no differences in Signature 1, HPV+ HNC exhibited a higher correlation with Signature 5. Immune de-convolution evaluating 36 cell populations identified 13 cell types with different frequencies between two cancers (q < 0.1). The abundance of B-cell lineages, CD4+ naïve T-cells, and Tregs significantly differed in HPV+ HNC and HPV+ CC (p<0.001). These findings led us to investigate expression signatures of tertiary lymphoid structures, which we identified as markedly higher in HPV+ HNC versus HPV+ CC or HPV- HNC (p < 0.001, for both comparisons). Activated T-cells, however, did not appear to differ between HPV+ HNC or CC as determined by the previously defined cytolytic score. Conclusion: We find that while tissue of origin seems not to influence recurrently mutated oncogenes in HPV-related malignancies, it has significant effects on phenotypic consequences in these tumors including influencing both mutational signature exposure and the tumor microenvironment. These findings may inform further investigations into the unique molecular and microenvironmental characteristics of these virally driven tumors. Citation Format: Reith R. Sarkar, Xin Pei, Andrea Gazzo, Yingjie Zhu, Nancy Lee, Jorge Reis-Filho, Dmitriy Zamarin, Britta Weigelt, Nadeem Riaz. The effect of tissue of origin on oncogenesis in HPV-related malignancies: Comparative analysis of molecular and immune signatures in HPV-positive head and neck cancer and cervical cancer [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-025.
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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 | 0.000 |
| 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) | 0.003 | 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".