Abstract PO-024: Analysis of the NOTCH pathway in HNSCC: A target with a wide prognostic and therapeutic potential
Bibliographic record
Abstract
Abstract Background: NOTCH1 is the second most frequently mutated gene in HNSCC with mutations present in 10-15% of tumors. NOTCH mutations can have an oncogenic or a tumor suppressor effect and activates and overexpresses downstream genes such as HEY1, HES1, MYC and CCND1, representing a specific NOTCH signature (NOTCHsig). Better understanding of the NOTCH pathway could be of clinical importance, with prognostic and therapeutic implications for HNSCC. Methods: HNSCC (N = 1576) patient tumors underwent DNA (592-gene or whole exome; N = 1576) and RNA (whole transcriptome; N = 799) sequencing at Caris Life Sciences (Phoenix, AZ). PD-L1+ expression was tested by IHC (22c3). NOTCHsig-High samples were defined as > 2-fold median expression of HEY1, HES1, MYC, or CCND1, while NOTCHsig-Low samples had < 2-fold median expression of each gene. Overall survival (OS) was calculated from insurance claim-based data using Kaplan-Meier estimate. Statistical significance was determined using Chi-square/Mann-Whitney U tests. Results: NOTCH1 mutations were identified in 16.4% (N = 258) of HNSCC patients, with increased prevalence in patients ≥60 years (19 vs 11% < 60 years, p < 0.001), primary tumors (19 vs 11% in metastases, p < 0.001), and p16- patients (23 vs 11% p16+, p < 0.001). Further stratification by PDL1 expression showed lower NOTCH1 mutations rates (12%) in PDL1- samples, while PDL1+ samples with low (Combined Positive Score, CPS 1-20) and high (CPS > 20) expression had similar rates (21 and 25%, respectively). Overall, NOTCH1 mutations were associated with worse OS (1.2 HR, 95% CI 1.0-1.4, p = 0.03), and while no difference was observed in p16+ and p16- subgroups, p16- patients harboring NOTCH1 mutations were more frequently co-mutated with FAT1 (43 vs 19% in NOTCH1 WT, p < 0.01), PIK3CA (17 vs 9%, p < 0.05), and other rare variants. Among NOTCH1-WT patients, NOTCHsig-High was more common in p16- patients (56 vs 45% p16+, p < 0.01), metastases (59 vs 51% in primary, p < 0.05), and PDL1- patients (67 vs 53% PDL1+ low vs 47% PDL1+ high, p < 0.05). Overall, NOTCHsig-High was associated with worse OS (1.2 HR, 95% CI 1.0-1.4, p = 0.03), with no difference in OS observed in p16- subgroups, while p16+ NOTCHsig-High was associated with worse OS (1.6 HR, 95% CI 1.1-2.2, p < 0.01). Among the NOTCHsig genes, high expression of MYC was the strongest predictor of worse OS (1.8 HR 95% CI 1.2-2.7, p < 0.01). Conclusions: NOTCH1 mutations and high expression of NOTCH-regulated genes in NOTCH1-WT patients were associated with p16- status and poor clinical outcomes. These data support a wider involvement of NOTCH signaling in HNSCC, highlighting its prognostic value and potential targetability in both p16- and p16+ patients. Further evaluation is warranted to determine whether NOTCH signaling influences treatment response and investigate its potential use in biomarker-driver clinical trials. Citation Format: Iole Ribizzi-Akhtar, Andrew Elliott, Erica Golemis, Trisha Michel Wise-Draper, Farah Rukshana Abdulla, Wafik S. El-Deiry, Ariel E. Birnbaum. Analysis of the NOTCH pathway in HNSCC: A target with a wide prognostic and therapeutic potential [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-024.
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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 | 0.000 | 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) | 0.005 | 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".