Abstract 4023: Co-occurring mutations in recurrent/persistent head and neck squamous cell carcinoma (HNSCC) patients
Bibliographic record
Abstract
Abstract Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer and lacks effective targeted therapies. HPV(-) patients have a 50-60% recurrence rate and could benefit from adjuvant therapy. Although HPV(+) patients have a significantly better survival, 20% have persistent/recurrent disease. Therefore, biomarkers could potentially help identify patients that would benefit from adjuvant targeted agents. Our objective was to evaluate if the mutational and biomarker analysis of tumor samples from OPSCC patients predict recurrence and/ or persistence in patients undergoing definitive therapy with curative intent. 44 advanced stage OPSCC patients that underwent comprehensive genomic profiling by Caris Life Sciences were included in this retrospective study. Next Generation Sequencing (NGS) on genomic DNA from FFPE tumors was performed using the Illumina Nextseq (592-gene, n=17)/MiSeq (44-gene, n=23) platform. Tumors were analyzed for total mutational load (TML), CNV’s and microsatellite instability status. IHC for tumor protein expression of ERCC1, PD-L1, RRM1, TrkA/B/C, TS and TUBB3 was performed using automated platforms. The ASCO/CAP scoring criteria and the cutoff points from published evidence was used in IHC evaluation. Of the 44 patients, 27 were HPV(+) tumors and 11 had HPV(-) disease. Patients with lack of progression free survival data were excluded. Only 4 patients in the entire cohort harbored a pathogenic PIK3CA mutation. Among HPV(+) patients, 22 patients were TP53 WT and 2 patients were found to be TP53 mutant. Although there was no significant change in the TML in smokers compared to nonsmokers (mean 8.77 vs 6.5, respectively; p=0.253), TML was higher in HPV(-) smokers compared to HPV(+) nonsmokers (mean 10.33 vs 6.5, p=0.0661). Our study presented a higher incidence of recurrent/persistent disease in the Caucasians (60%) in comparison to African-Americans (21%). Considering the sample size in the current study, the racial difference in outcomes appears likely to be present regardless of HPV status or disease state. The co-occurrence of multiple deleterious mutations was observed in 70% of the non-responders. Particularly, mutations in CHEK2, PTCH, MUTYH were noted in 50% of the recurrent/persistent patients. CNV mutations in SMAD2, MALT1, NFKBIA in 50% of the recurrent/persistent patients were found. The co-occurrence of multiple mutations were absent in responders in spite of an appreciable sample size in the responder group (n=12). This study also affirmed an improved prognosis in patients with HPV(+), with higher expression of TUBB3, and with positive expression of PD-1 in the tumors. Co-occurrence of multiple deleterious mutations were associated with patients who did not respond to therapy. Therefore, combination rescue therapies that can target multiple pathways to abrogate the mutational effects can aid/enhance therapeutic benefits in HNSCC patients. Citation Format: Alok R. Khandelwal, Kelsey Poorman, Tara Moore-Medlin, Xiaohui Ma, Abhijit Gundale, Ronald Horswell, San Chu, Michelle Winerip, Cherie-Ann O. Nathan. Co-occurring mutations in recurrent/persistent head and neck squamous cell carcinoma (HNSCC) patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4023.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".