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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

2023· article· en· W4386784398 on OpenAlexaboutno aff
Reith Sarkar, Xin Pei, Andrea Gazzo, Yingjie Zhu, Nancy Y. Lee, Jorge S. Reis‐Filho, Dmitriy Zamarin, Britta Weigelt, Nadeem Riaz

Bibliographic record

VenueClinical Cancer Research · 2023
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCarcinogenesisCDKN2AHead and neck cancerCervical cancerCancerHPV infectionMedicineCancer researchImmune systemSomatic cellBiologyOncologyInternal medicineGeneImmunologyGenetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.113
GPT teacher head0.519
Teacher spread0.406 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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