Abstract PO-048: Baseline blood DNA methylation-based immune profiles are associated with survival outcomes in head and neck cancer patients on immune checkpoint therapy
Bibliographic record
Abstract
Abstract Background: Standard-of-care anti-PD-1 therapy does not provide long-lasting benefits for most patients with recurrent/metastatic (R/M) head and neck squamous cell carcinoma (HNSCC). A current biomarker of potential response, PD-ligand-1 combined proportion score, has limitations due to the heterogeneity and availability of tumor samples. The development of peripheral blood-based biomarkers offers a contemporary noninvasive sample for immunotherapy response. DNA methylation-based immune cell deconvolution provides opportunities for developing blood-based biomarkers to predict immunotherapy response outcomes in HNSCC. Methods: Our study is an ongoing prospective multi-center study aimed at identifying blood DNA methylation biomarkers of therapy response in patients with R/M HNSCC undergoing standard-of-care, FDA-approved anti-PD-1 therapy. DNA isolated from these samples underwent bisulfite conversion and methylation profiling using the Illumina EPIC microarray. Peripheral blood immune profiles were generated using cellular deconvolution. 58 consecutive R/M HNSCC patients with anti-PD-1 monotherapy were included (4/31/23). We investigated 47 immune variables for relation with progression-free survival (PFS) and overall survival (OS) using Cox proportional-hazard models adjusted for age, sex, and a marker for corticosteroid exposure (NDMI). Immune variables were treated as continuous and binary predictors in the models. A data-based statistical cutpoint (surv_cutpoint) was used to dichotomize the immune variables. Differences between the baseline immune profiles of patients who received anti-PD-1 monotherapy with durable clinical benefit for at least one year (Benefit group) and patients who experienced progression or death within 100 days (Non-benefit group) from the start of therapy were modeled using linear regression, adjusted for age, sex, and NDMI. A p-value < 0.05 was used as the cut-off for statistical significance. Results: In 58 patients with R/M HNSCC who received only anti-PD-1 therapy for this preliminary analysis, a granulocytic myeloid-derived suppressor cell (gMDSC) score was consistently associated with PFS as a continuous (HR: 1.27) and a binary variable (HR: 2.70). Several immune profiling variables were significantly associated with PFS and OS as a binary variable. Consistently, baseline neutrophil proportion (PFS HR 3.62; OS HR: 4.67), total T cell count (PFS HR 0.32; OS HR: 0.25), total lymphocyte proportion (PFS HR 0.36; OS HR: 0.27), lymphocyte to monocyte ratio (PFS HR 0.43; OS HR: 0.32), monocyte count (PFS HR 2.50; OS HR: 2.84), and total CD4T cell count (PFS HR 0.37; OS HR: 0.38) were associated with PFS and OS. A significantly lower gMDSC score was observed in the Benefit group compared to the Non-benefit group. Conclusion: DNA methylation-based immune profiling in peripheral blood at baseline identifies clinically relevant biomarkers of benefit from anti-PD-1 therapy. Our results promise the potential development of new blood-based DNA methylation biomarkers to predict immunotherapy response before treatment. Citation Format: Ze Zhang, Kartik Sehgal, Keisuke Shirai, Rondi Butler, Min Kyung Lee, Annette Molinaro, John Wiencke, Devin Koestler, Hannah Stolrow, Geat Ramush, Lucas Salas, Robert Haddad, Karl Kelsey, Brock Christensen. Baseline blood DNA methylation-based immune profiles are associated with survival outcomes in head and neck cancer patients on immune checkpoint therapy [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-048.
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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 | 0.000 | 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) | 0.002 | 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".