Predictive Biomarkers for PD-1 Inhibitor Response in Squamous Cell Carcinoma

Overview

This study is a multicenter cohort investigation integrating both retrospective and prospective components, designed to evaluate the predictive performance of combined blood ELISA testing and tissue multiplex immunofluorescence (mIF) staining for assessing treatment response to PD-1 inhibitors in patients with squamous cell carcinoma. The retrospective cohort will analyze clinical data and pretreatment specimens (tissue biopsies and blood samples) from SCC patients who received PD-1 inhibitor therapy between April 2022 and June 2025 across participating centers. Using the combined blood ELISA and tissue mIF approach, the investigators will develop a predictive model to stratify patients into different response groups, then evaluate clinical outcomes including objective response rate (ORR), duration of response (DoR), and progression-free survival (PFS) to preliminarily validate the model's feasibility. The prospective cohort will enroll 800 participants in a multicenter setting, stratified according to the predictive model developed from the retrospective analysis. Key stratification factors include: 1) high risk of treatment resistance based on combined blood ELISA and tissue mIF scoring; and 2) low risk of treatment resistance based on the same evaluation system. The study consists of baseline and follow-up phases. During the baseline phase, eligible subjects who provide informed consent will undergo comprehensive clinical data collection. Tumor specimens (archived formalin-fixed paraffin-embedded blocks or fresh biopsy slides obtained within the preceding 6 months) and blood samples (collected within 28 days) will be processed for blood ELISA and tissue mIF analysis to categorize patients into high-risk and low-risk groups. The follow-up phase involves longitudinal monitoring of treatment response, including documentation of therapeutic regimens (drugs, dosages, cycles), scheduled clinical evaluations at weeks 4, 8, and 12 post-treatment (capturing medical history updates, radiographic findings, and physician-assessed ORR), and quarterly survival status updates via telephone until disease progression, death, loss to follow-up, consent withdrawal, initiation of alternative therapies, or study termination.

1. Study Title Biomarkers for Predicting Response to PD-1 Inhibitor Therapy in Squamous Cell Carcinoma: A Retrospective-Prospective Cohort Study 2. Background and Rationale Squamous Cell Carcinoma (SCC) is a malignancy arising from squamous epithelium, affecting multiple organs including the skin, oral cavity, esophagus, lungs, and cervix. It is characterized by a high incidence rate and significant mortality. • Epidemiology of SCC Subtypes: o Esophageal Squamous Cell Carcinoma (ESCC): According to 2022 GLOBOCAN data, esophageal cancer is the 11th most common cancer globally, with ESCC accounting for approximately 90% of cases. China is a high-incidence region for this disease. Despite a decreasing incidence, the five-year survival rate remains low at around 20%. o Head and Neck Squamous Cell Carcinoma (HNSC): HNSC represents about 90% of all head and neck cancers, with approximately 890,000 new cases and 325,000 deaths globally in 2022. In China, the mortality rate from head and neck cancer has been rising, primarily linked to risk factors like smoking, alcohol consumption, and betel nut use. o Cervical Squamous Cell Carcinoma (CESC): In 2022, China recorded the highest number of new cervical cancer cases globally, with 150,659 new diagnoses (23% of the global total) and 55,694 deaths (16% of the global total). Disparities in screening and HPV vaccination access contribute to a heavy burden, particularly in lower-income areas. o Lung Squamous Cell Carcinoma (LUSC): In 2022, China accounted for 42.8% of new global lung cancer cases and 40.3% of deaths. LUSC is a major subtype, strongly associated with smoking, and is characterized by rapid cell proliferation and a high potential for invasion and metastasis. • Therapeutic Landscape and Unmet Needs: The advent of PD-1 inhibitors over the past decade has offered new hope for patients with SCC. However, clinical trials have shown that the benefits are limited to a subset of patients, and the high cost of treatment places a significant financial burden on healthcare systems. Therefore, there is a critical need to develop accurate and reliable predictive biomarkers to identify patients most likely to respond to immunotherapy. • Current Biomarkers and Rationale for a Pan-SCC Approach: PD-L1 expression is the most widely studied biomarker, assessed via systems like Tumor Proportion Score (TPS) and Combined Positive Score (CPS). However, these methods have limitations. This study is founded on the principle that SCCs originating from different anatomical sites (esophagus, cervix, head and neck, lung) share significant molecular and immunological similarities. This "pan-cancer" approach allows for the integration of data across tumor types to identify common biological characteristics and universal mechanisms of drug resistance. By expanding from a single cancer type to a pan-SCC analysis, this study aims to accelerate the optimization of immunotherapy strategies and develop a more broadly applicable predictive model. This innovative retrospective-prospective cohort study will integrate real-world clinical data with biological samples to build and validate a predictive model specifically for Chinese patients with SCC, aiming to guide clinical decision-making and optimize resource allocation. 3. Study Objectives * Primary Objective: To evaluate the performance of a predictive model, combining blood-based ELISA and tissue-based multiplex immunofluorescence assays, in forecasting the efficacy of PD-1 inhibitor therapy in patients with squamous cell carcinoma. * Secondary Objectives: <!-- --> 1. To develop a predictive model using data from blood ELISA and tissue multiplex immunofluorescence. 2. To stratify patients into different risk groups based on the model's predictions. 3. To analyze and compare survival outcomes (e.g., survival curves, duration of response) among the different patient risk groups. 4. Study Design This is a multi-center, cohort study with both retrospective and prospective components. * 1\) Retrospective Cohort (January 2022 - June 2025): This phase involves the collection of baseline information, clinical data, and pre-treatment tumor biopsy and blood samples from SCC patients who received PD-1 inhibitor therapy at the participating centers. Data will be used to develop an initial predictive model by correlating biomarker data with clinical outcomes such as Objective Response Rate (ORR), Duration of Response (DoR), and Progression-Free Survival (PFS) to assess the model's feasibility. * 2\) Prospective Cohort (Starting July 2025): This phase aims to enroll 800 participants to validate the predictive model developed from the retrospective data. Patients will be stratified according to the model's risk score, based on: * High risk of treatment resistance as indicated by the combined ELISA and multiplex immunofluorescence scoring system. * Low risk of treatment resistance as indicated by the scoring system. 5. Study Population • Inclusion Criteria: 1\. Pathologically confirmed diagnosis of Esophageal (ESCC), Head and Neck (HNSC), Cervical (CESC), or Lung (LUSC) squamous cell carcinoma. 2\. Patients scheduled to receive PD-1 inhibitor therapy ± chemotherapy, either as neoadjuvant treatment (for operable cases) or first-line systemic therapy (for inoperable cases). 3\. Availability of pre-treatment tumor biopsy tissue and baseline blood samples. 4\. Age ≥ 18 years. 5. Capacity to provide written informed consent. • Exclusion Criteria: 1\. Presence of other concurrent malignancies. 2. History of prior anti-cancer treatments for the current diagnosis. 6. Study Endpoints • Primary Endpoint: The Area Under the Receiver Operating Characteristic (ROC) curve (AUC) of the predictive model for assessing treatment efficacy after two cycles of PD-1 inhibitor ± chemotherapy, based on RECIST 1.1 criteria. • Secondary Endpoints: Additional efficacy metrics, including model sensitivity and specificity, Objective Response Rate (ORR), Duration of Response (DoR), Progression-Free Survival (PFS), and Overall Survival (OS). 7\. Sample Size Calculation The sample size was calculated using a one-sample ROC curve analysis. Based on an expected AUC of 0.85 versus a null hypothesis threshold of AUC \> 0.8, with a one-sided alpha of 0.05 and 80% power, the required sample size is 165 participants. Accounting for a 20% potential dropout rate, the study plans to enroll 200 participants for each of the four SCC subtypes, for a total of 800 participants. 8\. Study Procedures • Baseline Period: Eligible patients who have signed the informed consent form will undergo baseline assessments, including the collection of demographic and clinical data. Pre-treatment tumor tissue samples (archival paraffin-embedded blocks or slides from within the last 6 months) and peripheral blood samples (collected within 28 days prior to treatment) will be collected. • Follow-up Period: Patients will be followed up before treatment initiation and at weeks 4, 8, and 12 post-treatment. Follow-up visits will include collection of treatment details, updated medical history, imaging scans, and physician-assessed tumor response (ORR). • Survival Follow-up: After treatment discontinuation, all participants will enter a survival follow-up phase. Survival status will be collected via phone calls every 3 months until disease progression, death, loss to follow-up, withdrawal of consent, or study termination. 9\. Sample Collection and Management * Tissue Samples: Residual pre-treatment biopsy tissue samples, left over from routine clinical diagnosis, will be collected. Samples will be formalin-fixed and paraffin-embedded (FFPE), sectioned, and stored at 4°C for subsequent H\&E and multiplex immunofluorescence staining. * Blood Samples: Residual blood samples from routine clinical draws will be collected. Samples will be centrifuged to separate serum, which will then be stored at -80°C for future ELISA analysis. 10\. Statistical Analysis Plan * Endpoint Analysis: Categorical variables will be analyzed using Chi-square or Fisher's exact tests. Survival data (PFS, OS) will be analyzed using the Kaplan-Meier method, with group comparisons performed using the Log-rank test. Multivariable analysis will be conducted using Cox proportional hazards models. * Missing Data: Multiple imputation will be used for randomly missing continuous variables. Missing key outcome variables (e.g., RECIST response) will be conservatively treated as treatment failure. * Subgroup Analyses: Pre-specified subgroup analyses will be performed based on factors such as PD-L1 expression level, smoking status, and treatment regimen. * Sensitivity Analyses: Analyses will be conducted to test the robustness of the findings, including using different adjustment sets in multivariable models and analyzing the prospective cohort data exclusively for validation. 11\. Ethical Considerations This study will be conducted in full compliance with the principles of the Declaration of Helsinki and Good Clinical Practice (GCP) guidelines. The study protocol will be submitted to and approved by the Institutional Review Board (IRB)/Ethics Committee (EC) at each participating site before patient enrollment. All participants will undergo a thorough informed consent process, conducted by trained research staff in a private setting, and will be required to provide written informed consent before any study-related procedures are performed. Patient confidentiality will be strictly maintained.