---
title: "Predicting Cancer Immunotherapy Response Using Dynamic CD8+ T Cell Changes — Pan‑Cancer Retrospect"
id: "frontiers-in-immunology-13-a-predictive-model-for-immunotherapy-efficacy-in-cancer-based-on-dynamic"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-13-a-predictive-model-for-immunotherapy-efficacy-in-cancer-based-on-dynamic"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1870840"
published_at: "2026-08-10T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Predicting Cancer Immunotherapy Response Using Dynamic CD8+ T Cell Changes — Pan‑Cancer Retrospect
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/frontiers-in-immunology-13-a-predictive-model-for-immunotherapy-efficacy-in-cancer-based-on-dynamic
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1870840)
- **Published At:** 2026-08-10T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The source is an article published in Frontiers in Immunology titled about a **predictive model** for **immunotherapy** efficacy using dynamic changes of **CD8+ T cells** in a **pan-cancer retrospective study**. - The title indicates the study developed or evaluated a model linking longitudinal CD8+ T‑cell changes to immunotherapy outcomes across multiple cancer types. - The host journal is Frontiers in Immunology; the supplied source material contains site navigation and journal information but does not include the article text, data, methods, or results. - Key methodological details (cohort size, cancer types included, immunotherapy regimens, timepoints for CD8+ T‑cell measurement, modeling approach, validation strategy) were not reported in the provided source content. - Outcomes, performance metrics for the predictive model (e.g., accuracy, AUC, hazard ratios, calibration), and conclusions about clinical utility were not reported in the provided source content. - No information on ethical approvals, data sources (clinical cohorts vs public databases), or availability of code/data was included in the provided material. - Because the full article text and results were not present in the provided source, any interpretation of findings or clinical recommendations cannot be drawn from this material and should be verified from the full published article. - Readers should consult the full article at the journal site for complete methods, statistical analyses, results, limitations, and implications for practice or future research.
## Clinical Analysis & Structured Key Points
Frontiers | A predictive model for immunotherapy efficacy in cancer based on dynamic changes of CD8+ T cells: a pan-cancer retrospective study ORIGINAL RESEARCH article Front. Immunol. , 10 August 2026 Sec. Cancer Immunity and Immunotherapy Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1870840 Published in Frontiers in Immunology Cancer Immunity and Immunotherapy 7 impact factor 11.3 citescore Part of a Research Topic The Insights of Multi-Omics into the Microenvironment After Tumor Metastasis: A Paradigm Shift in Molecular Targeting Modeling and Immunotherapy for Advanced Cancer Patients - Vol II 30k views 15 articles Editor & Reviewers Edited by R K Rakesh K Singh Reviewed by G R Giandomenico Roviello C C Chao Chen Outline Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Figure 4 View in article Figure 5 View in article Figure 6 View in article Table 1 Cancer type classification and coding. View in article Table 2 Variable coding for multivariate cox regression. View in article Table 3 Baseline clinical characteristics of patients. View in article Table 4 Risk stratification based on Risk Score and PFS outcomes. View in article Table 5 Simplified clinical scoring system. View in article Table 6 Risk stratification based on simplified clinical scoring system and PFS outcomes. View in article ORIGINAL RESEARCH article Front. Immunol. , 10 August 2026 Sec. Cancer Immunity and Immunotherapy Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1870840 A predictive model for immunotherapy efficacy in cancer based on dynamic changes of CD8+ T cells: a pan-cancer retrospective study M X Mengyan Xie 1,2 † C W Chao Wang 1 † J Z Jun Zhang 1,3 X J Xinming Jing 4 P M Pei Ma 2 Y S Yongqian Shu 2 * 1. Department of Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China 2. Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China 3. Shanghai Key Laboratory of Gastric Neoplasms, Shanghai, China 4. Cancer Center, Daping Hospital & Army Medical Center of PLA, Third Military Medical University, Chongqing, China See more Article metrics View details Abstract Objective: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, but reliable peripheral blood biomarkers for monitoring treatment response and predicting prognosis remain limited. This study aimed to analyze the dynamic changes of lymphocyte subsets in patients receiving ICI therapy, evaluate their role in treatment response monitoring and prognosis assessment, and develop a practical clinical risk stratification tool. Methods: A total of 121 patients with malignancies who received ICI therapy and had available lymphocyte subset data were retrospectively enrolled. Peripheral blood lymphocyte subsets and routine blood test data were collected before and after treatment. The associations between changes in these parameters and treatment response as well as progression-free survival (PFS) were analyzed using univariate and multivariate Cox regression models. A risk score model and a simplified clinical scoring system were constructed and validated using time-dependent receiver operating characteristic (ROC) curves and Kaplan-Meier analysis. Results: Pan-cancer analysis showed that a decrease in CD8+ T cell count after treatment was significantly associated with progressive disease (PD) and inversely correlated with PFS (HR = 0.2308, 95% CI: 0.0875-0.5636). A non-immunotherapy validation cohort further confirmed the immunotherapy-specific nature of CD8+ T cell dynamics. Multivariate Cox analysis identified decreased CD8+ T cell count, elevated neutrophil-to-lymphocyte ratio (NLR), multiple lines of therapy, and specific cancer types (hepatopancreatobiliary malignancies) as independent unfavorable prognostic factors. Time-dependent area under the curve (AUC) values at 2.5, 3.5, and 5.7 months were 0.727, 0.827, and 0.853, respectively, indicating good predictive performance. The risk score based on these variables stratified patients into low-, medium-, and high-risk groups (median PFS: not reached, not reached, and 4.5 months, respectively; p<0.001). A simplified clinical scoring system also effectively distinguished different prognostic groups (median PFS: not reached, 6.2 months, and 4.3 months, respectively; p<0.001). Conclusions: The dynamic change in CD8+ T cell count before and after treatment is an independent predictor of PFS in patients receiving ICI therapy and exhibits immunotherapy specificity. The proposed risk stratification tool, incorporating CD8+ T cell dynamics, NLR change, and key clinical variables, provides a simple and effective approach for prognostic assessment and may facilitate individualized treatment decision-making in clinical practice. 1 Introduction The human immune system acts as an exogenous tumor suppressor that disrupts developing tumors or inhibits their growth, thereby preventing tumor initiation and progression. However, cancer can still occur in immunocompetent individuals, partly because cancers can induce immune suppression. This immunosuppression is primarily mediated by cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) and programmed cell death protein-1 (PD-1). Consequently, the advent of immune checkpoint inhibitors (ICIs) represents a major breakthrough in oncology, offering long-term survival benefits for patients with various malignancies ( 1 , 2 ). ICIs targeting PD-1/programmed death-ligand 1 (PD-L1) and CTLA-4 have established standard treatment positions in multiple cancer types, including lung cancer, melanoma, head and neck squamous cell carcinoma, and renal cell carcinoma ( 3 – 7 ), and more clinical trials are underway to further expand their indications. Currently, commonly used predictive biomarkers for ICI efficacy include PD-L1 expression level (assessed by tumor proportion score (TPS) or combined positive score (CPS)), tumor mutational burden (TMB), and microsatellite instability (MSI) ( 8 – 10 ). Nevertheless, tumor heterogeneity and the dynamic evolution during treatment limit the ability of a single tissue biopsy to fully capture the immune status. Moreover, due to the potential occurrence of “pseudoprogression” induced by immunotherapy ( 11 , 12 ), the imaging-based RECIST 1.1 criteria are insufficient to accurately determine the true disease status in patients receiving immunotherapy. Although iRECIST criteria have been recommended in immunotherapy clinical trials to address this limitation ( 13 ), simple and effective predictive factors for treatment response and prognosis remain lacking in clinical practice to assist in disease assessment and treatment guidance. Therefore, the development of novel, non-invasive, dynamically monitorable, and immunotherapy-specific biomarkers is of significant clinical importance. Peripheral blood, as a key source for”liquid biopsy”, offers distinct advantages including easy accessibility, repeatable sampling, and real-time reflection of systemic immune status. Lymphocyte subset analysis, a well-established flow cytometry-based assay, is widely used in clinical practice. Among these subsets, CD8+ cytotoxic T lymphocytes are the core effector cells mediating anti-tumor immune responses. Studies have shown that functional exhaustion of CD8+ T cells in the tumor microenvironment is a crucial mechanism underlying immunotherapy resistance ( 14 , 15 ). ICI therapy can enhance the cytotoxic effect of natural killer (NK) cells and prolong their survival ( 16 ). Regulatory T cells (Tregs) suppress anti-tumor immune responses, and abundant Treg infiltration in tumor tissues is generally associated with poor prognosis, whereas anti-CTLA-4 antibodies may attenuate their suppressive effects by depleting effector Tregs ( 17 ). However, whether dynamic changes in peripheral blood lymphocytes reflect the status of anti-tumor immune responses and can serve as biomarkers for monitoring ICI efficacy and assessing prognosis currently lacks sufficient evidence-based medical support. In recent years, several studies have investigated the relationship between dynamic changes in peripheral blood immune cells and immunotherapy outcomes. Fiala et al. ( 18 ) found that early changes in neutrophil-to-lymphocyte ratio (NLR) (ΔNLR≥2) after one month of nivolumab treatment were independently associated with poorer progression-free survival (PFS) and overall survival (OS) in patients with metastatic renal cell carcinoma. In head and neck squamous cell carcinoma, pre-treatment NLR, platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) were significantly associated with response to immunotherapy, and an elevated post-treatment NLR indicated poor prognosis ( 19 ). Another study, utilizing single-cell technology, demonstrated that dynamic changes in the polyfunctionality of peripheral blood CD8+ T cells could predict immunotherapy response and clinical outcomes in lung cancer patients ( 20 ). These findings suggest that dynamic changes in peripheral blood immune cells hold potential as biomarkers for immunotherapy. However, previous studies have mostly been confined to single cancer types, and comprehensive comparative analyses across different lymphocyte subsets (such as CD4+ T cells, CD8+ T cells, Tregs, B cells, etc.) are still lacking. Based on the above background, this study aimed to systematically evaluate the dynamic changes in peripheral blood lymphocyte subsets (including cells with fluorescently-labeled antibody of: CD3+, CD3+CD4+, CD3+CD8+, CD19+, CD16+CD56+, CD4+CD25+FoxP3+) before and after immunotherapy, and to analyze their associations with treatment efficacy and prognosis. We first explored potential predictive indicators in a lung cancer subgroup, subsequently validate them in a pan-cancer cohort, and confirm their specificity through a non-immunotherapy cohort. On this basis, we integrated clinical variables to establish a simple and feasible risk stratification tool, thereby providing a reference for individualized management of patients receiving ICI therapy. 2 Materials and methods 2.1 Study population This study enrolled patients (n=186) with malignant solid tumors who received ICI therapy at Jiangsu Province Hospital (The First Affiliated Hospital of Nanjing Medical University) from November 1, 2020, to October 31, 2021. The main inclusion criteria were: (1) pathologically confirmed malignancy; (2) treatment with PD-1/PD-L1 or CTLA-4 inhibitors as monotherapy or combination therapy; (3) locally advanced or distant metastasis; (4) presence of at least one measurable lesion; (5) lymphocyte subset testing performed during treatment; (6) imaging evaluation of disease status before and after treatment. Exclusion criteria included: (1) concurrent autoimmune diseases or long-term use of immunosuppressants; (2) blood transfusion or colony-stimulating factor (CSF) therapy within 4 weeks before treatment; (3) incomplete clinical data (patients’ basic information, follow-up data, lymphocyte subset data, NLR, imaging evaluation data); (4) active infection or immune-related adverse events (irAEs) at the time of lymphocyte subset testing ( Figure 1 ). Figure 1 Study profile of patients’ enrollment and analysis. After applying the inclusion and exclusion criteria, 121 patients were finally included. Among them, there were 50 patients with lung cancer (non-small cell lung cancer and small cell lung cancer), 27 with upper gastrointestinal malignancies (esophageal cancer, gastric cancer), 11 with genitourinary malignancies (renal cancer, bladder cancer, prostate cancer, cervical cancer, ovarian cancer, urachal carcinoma, testicular cancer), 8 with hepatopancreatobiliary malignancies (liver cancer, pancreatic cancer, cholangiocarcinoma), 6 with malignant melanoma, and 19 with other malignancies (colorectal cancer, breast cancer, cutaneous squamous cell carcinoma, malignant mesothelioma, sarcoma, neuroendocrine carcinoma, nasopharyngeal carcinoma, tonsillar carcinoma). To verify the immunotherapy-specific association of CD8+ T cell changes, an additional cohort of patients with malignancies receiving non-immunotherapy (chemotherapy and/or targeted therapy, n=65) was included as a validation cohort. This study was conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board (IRB) of Jiangsu Province Hospital (The First Affiliated Hospital of Nanjing Medical University) (Approval No.: 2020-SR-150). Due to the retrospective nature of the study, all data were derived from routine clinical practice without additional intervention or risk to patients, and the IRB granted a waiver of informed consent. All patient information was anonymized during the study to protect personal privacy. 2.2 Laboratory tests 2.2.1 Peripheral blood lymphocyte subset analysis Peripheral venous blood (2–3 mL) was collected from patients before the start of immunotherapy and during treatment (before each immunotherapy cycle) into EDTA anticoagulant tubes. The absolute counts and percentages of the following lymphocyte subsets were measured by flow cytometry: CD3+ T cells, CD3+CD4+ T cells, CD3+CD8+ T cells, CD4/CD8 ratio, CD19+ B cells, CD16+CD56+ NK cells, and CD4+CD25+FoxP3+ Treg cells. The assay was performed using monoclonal antibody direct immunofluorescence labeling with the MultiTEST IMK kit (BD Biosciences) for absolute counting. All samples were tested within 4 hours of collection. The following dynamic change indicators were calculated: baseline value, last value, and change (last minus baseline). 2.2.2 Routine blood tests and inflammatory markers Peripheral blood was collected simultaneously with lymphocyte subset testing, and neutrophil, lymphocyte counts were measured using an automated hematology analyzer. NLR were calculated: NLR = neutrophil count/lymphocyte count. Changes (increase or decrease) of NLR before and after treatment were also calculated. 2.3 Efficacy assessment 2.3.1 Imaging evaluation All patients underwent baseline imaging (contrast-enhanced CT or MRI of the chest, abdomen, and pelvis) within 4 weeks before immunotherapy, and then imaging was repeated every 8–12 weeks (3–4 treatment cycles) until disease progression, death, loss to follow-up, or study end. If clinical symptoms or other parameters strongly suggested disease changes, additional imaging was performed for disease assessment after confirmation by the attending physician. The best treatment response was evaluated according to RECIST version 1.1: Complete response (CR): disappearance of all target lesions Partial response (PR): ≥30% decrease in the sum of diameters of target lesions Stable disease (SD): neither sufficient shrinkage to qualify for PR nor sufficient increase to qualify for PD Progressive disease (PD): ≥20% increase in the sum of diameters of target lesions or appearance of new lesions Patients were divided into three groups: CR/PR, SD, and PD. 2.3.2 Follow-up and definition of PFS Follow-up was conducted through outpatient visits, hospital records, and telephone interviews. The starting point of follow-up was the date of first ICI treatment, and the endpoints were PD, death, or the last follow-up date. PFS was defined as the time from first treatment to the first imaging-confirmed disease progression, death from any cause, or loss to follow-up. For patients who had not experienced disease progression, death, or loss to follow-up by the cutoff date (February 28, 2023), the last follow-up date was used for censoring. 2.4 Statistical methods 2.4.1 Software platform All statistical analyses were performed using GraphPad Prism (version 11.0.0) and R software (version 4.5.3) with relevant extension packages (forestplot, tidyverse, readxl, survival, timeROC, ggplot2, rms, regplot, MASS, dplyr, survminer, shiny, shinythemes, rsconnect). The detailed R code can be found in Supplementary Materials . Two-sided tests were used, and p < 0.05 was considered statistically significant. 2.4.2 Univariate analysis In the lung cancer subgroup (n=50), we first analyzed the relationships between baseline values, last values, and changes (increase/decrease) of each lymphocyte subset (percentages/counts) with treatment response and PFS. Tukey’s multiple comparisons test was used to compare differences in continuous variables among different response groups. Kaplan-Meier curves were plotted for PFS among different patient groups, and survival differences were compared using the log-rank test. The above analyses were repeated in the pan-cancer cohort (n=121) and the non-immunotherapy validation cohort (n=65) to confirm generalizability and specificity. 2.4.3 Cox proportional hazards regression model Variables included in the multivariate Cox regression model were: age, sex, ECOG score, line of therapy, cancer type, CD4/CD8 change, CD8+ T cell change, and NLR change. Coding for cancer types and model variables is shown in Tables 1 and 2 . Hazard ratios (HRs) and their 95% confidence intervals (CIs) were calculated. Model goodness-of-fit was assessed using the Akaike information criterion (AIC), and discrimination was assessed using Harrell’s C-index. Table 1 Cancer type Specific cancers Code Lung cancer Non-small cell lung cancer, Small cell lung cancer 0 Upper gastrointestinal malignancies Esophageal cancer, Gastric cancer 1 Genitourinary malignancies Renal cancer, Bladder cancer, Prostate cancer, Cervical cancer, Ovarian cancer, Urachal carcinoma, Testicular cancer 2 Hepatopancreatobiliary malignancies Liver cancer, Pancreatic cancer, Cholangiocarcinoma 3 Malignant melanoma – 4 Others Colorectal cancer, Breast cancer, Cutaneous squamous cell carcinoma, Malignant mesothelioma, Sarcoma, Neuroendocrine carcinoma, Nasopharyngeal carcinoma, Tonsillar carcinoma 5 Cancer type classification and coding. Table 2 Variable Coding Reference to Age Continuous – Sex Female = 0, Male = 1 0 ECOG 0-1 = 0, ≥2 = 1 0 Therapy Line First-line = 1, Second-line = 2, Third-line or above= 3 1 Cancer Type See Table 1 0 CD4/CD8 Change Increase = 0, Decrease = 1 0 CD8+ T cell Change Increase = 0, Decrease = 1 1 NLR Change Increase = 0, Decrease = 1 0 Variable coding for multivariate cox regression. 2.4.4 Time-dependent ROC analysis To evaluate the predictive accuracy of the model at different time points, time-dependent receiver operating characteristic (ROC) analysis was performed. The quartiles of PFS event times (2.5 months, 3.5 months, and 5.7 months) were selected as evaluation time points, and the area under the curve (AUC) at each time point was calculated. Due to the limited number of events (n=34), confidence intervals for the AUCs were not estimated. 2.4.5 Risk score construction 2.4.5.1 Risk score-based risk stratification A linear risk score formula was constructed based on the regression coefficients (β) from the multivariate Cox regression model: Risk Score = -0.008874×Age + (-0.01482)×Sex[1] + (-0.1993)×ECOG[1] + 0.6255×line[2] + 0.704×line[3] + 0.3053×cancer_type[1] + 1.257×cancer_type[2] + 2.56×cancer_type[3] + 1.312×cancer_type[4] + 1.149×cancer_type[5] + (-0.4928)×CD4/CD8_change[1] + (-1.466)×CD8_change[0] + (-0.8289)×NLR_change[1]. Patients were divided into three groups based on the tertiles of the Risk Score: low-risk group (Risk Score < -2.093), medium-risk group (-2.093 ≤ Risk Score < -0.846), and high-risk group (Risk Score ≥ -0.846). Kaplan-Meier curves and log-rank tests were used to compare PFS among the three groups, and 3-, 6-, and 12-month PFS rates were calculated. An interactive web-based calculator for the Risk Score was developed using the Shiny R package and is available at: https://xmy13181.shinyapps.io/CALCULATOR/ . Clinicians can input patient variables online to obtain the Risk Score and risk stratification in real time. 2.4.5.2 Simplified clinical scoring system To facilitate clinical applica
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