Dynamic profiling of immune microenvironment during anti-PD-1 immunotherapy for head and neck squamous cell carcinoma: the IPRICE study
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint inhibitors of programmed cell death protein 1 (PD-1) represent a significant breakthrough in treating head and neck squamous cell carcinoma (HNSCC), with long-lasting responses and prolonged survival observed in first- and second-line therapy. However, this is observed in < 20% of patients and high primary/secondary resistance may occur. The primary objective of the identification of predictive factors for the response to anti-PD-1 immunotherapy in head and neck squamous cell carcinoma (IPRICE) study is to identify predictive factors of response to anti-PD-1 immunotherapy. METHODS: The IPRICE study is a single-center, prospective, non-randomized, open-label, and interventional clinical trial. Liquid and tumor biopsies will be performed in 54 patients with recurrent/metastatic (R/M) HNSCC undergoing anti-PD-1 immunotherapy alone to compare the evolution of gene expression and immunological profile between responders and non-responders. We will use a multidisciplinary approach including spatial transcriptomics, single seq-RNA analysis, clinical data, and medical images. Genes, pathways, and transcription factors potentially involved in the immune response will also be analyzed, including genes involved in the interferon-gamma (IFN-γ) pathway, immunogenic cell death and mitophagy, hypoxia, circulating miRNA-mediated immunomodulation, cytokines, and immune repertoire within the tumor microenvironment (TME). With a follow-up period of 3-years, these data will help generate effective biomarkers to define optimal therapeutic strategy and new immunomodulatory agents based on a better understanding of primary/secondary resistance mechanisms. Tumor biopsy will be performed initially before the start of immunotherapy at the first tumor assessment and is only proposed at tumor progression. Clinical data will be collected using a dedicated Case Report Form (CRF). DISCUSSION: Identifying predictive factors of the response to anti-PD-1 immunotherapy and optimizing long-term immune response require a thorough understanding of the intrinsic and acquired resistance to immunotherapy. To achieve this, dynamic profiling of TME during anti-PD-1 immunotherapy based on analysis of tumor biopsy samples is critical. This will be accomplished through the anatomical localization of HNSCC, which will allow for the analysis of multiple biopsies during treatment and the emergence of breakthrough technologies including single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics. TRIAL REGISTRATION: Clinicaltrial.gov. Registered April 14, 2022, https://www. CLINICALTRIALS: gov/study/NCT05328024 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".