Predictive Factors for Outcomes of Fruquintinib Plus Immunotherapy in Colorectal Cancer
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ABSTRACT:
DISEASE(S): Adenocarcinoma,Colorectal Adenocarcinoma,Colorectal Neoplasms
PROVIDER: 1194 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
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Project description:Immunotherapy has emerged as an exciting strategy for cancer treatment. Therapeutic blockade of immune checkpoint regulators favors the ability of T cell responses to increase anti-tumor immunity. The cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death-1 (PD-1) are two T cell-inhibitory receptors with independent mechanisms of action. Immune checkpoint inhibitors targeting either CTLA-4, PD-1 or its ligand PD-L1 are currently yielding promising results in terms of efficacy in several clinical studies with melanoma patients and are being developed and tested as immunotherapy agents for multiple cancer types. To date, no reliable predictors of activity and efficacy of immunotherapy have yet been identified or validated. Even so, determining which patients derive clinical benefit from immune checkpoint agents remains an important clinical question and efforts to identify predictive markers of response are ongoing. This article reviews the current potential predictive factors for CTLA-4 and PD-1/PD-L1 immune checkpoints inhibitors in melanoma.
| S-EPMC4583591 | biostudies-literature
Project description:Background: A pressing challenge in treating non-small cell lung cancer (NSCLC) lies in the significant variability of patient responses to immunotherapy. This issue underscores the critical need for innovative predictive models that can navigate beyond conventional biomarkers to enhance patient outcomes, particularly by addressing immunotherapy resistance or responsiveness. Hypothesis: We hypothesize that an integrated multi-omics approach will uncover interactions within the NSCLC tumor immune microenvironment (TIME) and identify novel biomarkers that are predictive of individual immunotherapy responses, thus aiding in the development of a robust personalized treatment planning model. Objective: To develop a predictive model for NSCLC immunotherapy response/resistance by identifying new biomarkers using co-detection by indexing (CODEX) and Digital Spatial Profiling of whole transcriptome atlas (DSP-WTA). Methods: We utilized a multi-omics approach, combining CODEX for spatial mapping of protein expression at the single-cell level and DSP-WTA for comprehensive transcriptomic insights from the cell types identified by CODEX. This methodology facilitated a detailed examination of the TIME in NSCLC samples from patients undergoing first-line immunotherapy. Results: Our analysis identified three cell types, proliferating tumor cells, granulocytes, and vessels, that are associated with resistance to immunotherapy. The high proportion of these cell types demonstrated a hazard ratio (HR) of 3.8 (p = 0.004) in the training cohort (N = 33) and 1.8 (p = 0.05) in the validation cohort (N = 35). In the response cell type model, higher levels of M1 macrophages, M2 macrophages, and CD4 T cells returned a HR of 0.4 (p = 0.019) in the training set and 0.49 (p = 0.036) in the validation set. Gene signatures related to these cell types also predicted outcomes with high accuracy. The resistant gene model, which included 8 genes associated with epithelial-mesenchymal transition (EMT) and cell migration showed a HR of 5.3 (p < 0.001) in the training set and 2.2 (p = 0.036) in the validation set. The response gene model, consisting of 8 genes associated with immunomodulation, had an HR of 0.22 (p = 0.005) in the training set and 0.38 (p = 0.034) in the validation set. Conclusion: This research highlights the potential of a multi-omics strategy in advancing NSCLC treatment toward precision oncology. By offering insights into the TIME and unveiling novel biomarkers, our model seeks to define resistance and to improve prediction of response to treatment.
2024-08-22 | GSE271689 | GEO
Project description:Full title: Predictive Gene Signatures as Strong Prognostic Indicators of the Effectiveness of Bacillus Calmetteâ??Guérin (BCG) Immunotherapy in Primary pT1 Bladder Cancers Intravesical BCG immunotherapy is effective in prevention of recurrence and progression in many cases of non-muscle invasive bladder cancer, but many patients fail to respond. This study identified predictive gene signatures in primary pT1 bladder cancer with BCG immunotherapy. Fourty-Eight patients with primary pT1 bladder cancer treated with BCG immunotherapy were used. Microarray gene expression analysis of the 48 primary bladder cancers was carried out. Predictive gene signatures were individually selected based on the recurrence and progression status. Among 43,148 unique genes, 424 and 287 candidate predictive genes that could predict recurrence and progression, respectively, were selected. Time to recurrence or progression was shorter for patients with poor-predictive gene signatures than good-predictive gene signatures (log-rank test, p <0.001, respectively). Validation of predictive gene signatures with RT-PCR was nearly identical to those of microarray (log-rank test, p <0.05, respectively). In multivariate regression analysis, predictive gene signatures were the only independent predictors of recurrence (HR 3.38, p = 0.048) or progression (HR 10.49, p = 0.048) in validation cohorts. Predictive gene signatures have strong diagnostic value for determining the response to intravesical BCG immunotherapy in primary pT1 bladder cancer. Keywords: Gene expression, Bladder cancer, BCG Total RNA, extracted from the fresh frozen tissues of 48 pT1 bladder cancers, was isolated by TRIzol reagent (Life Technologies, NY, USA), according to the manufacturer's protocol. The quality and integrity of the RNA were confirmed by agarose gel electrophoresis and ethidium bromide staining, followed by visual examination under ultraviolet light. Five-hundred nanograms of total RNA were used for labeling hybridization according to the manufacturerâ??s protocol (Illumina HumanWG-6 BeadChip, version 2, Illumina, Inc., San Diego, CA, USA). Arrays were scanned with an Illumina Bead Array Reader confocal scanner (BeadStation 500GXDW; Illumina, Inc.) according to the manufacturer's instructions. Initial microarray gene expression data were obtained using Bead Studio software with gene expression analysis module (version 3.1.3, Illumina, Inc.).
2010-04-07 | E-GEOD-19423 | biostudies-arrayexpress
Project description:Full title: Predictive Gene Signatures as Strong Prognostic Indicators of the Effectiveness of Bacillus Calmette–Guérin (BCG) Immunotherapy in Primary pT1 Bladder Cancers Intravesical BCG immunotherapy is effective in prevention of recurrence and progression in many cases of non-muscle invasive bladder cancer, but many patients fail to respond. This study identified predictive gene signatures in primary pT1 bladder cancer with BCG immunotherapy. Fourty-Eight patients with primary pT1 bladder cancer treated with BCG immunotherapy were used. Microarray gene expression analysis of the 48 primary bladder cancers was carried out. Predictive gene signatures were individually selected based on the recurrence and progression status. Among 43,148 unique genes, 424 and 287 candidate predictive genes that could predict recurrence and progression, respectively, were selected. Time to recurrence or progression was shorter for patients with poor-predictive gene signatures than good-predictive gene signatures (log-rank test, p <0.001, respectively). Validation of predictive gene signatures with RT-PCR was nearly identical to those of microarray (log-rank test, p <0.05, respectively). In multivariate regression analysis, predictive gene signatures were the only independent predictors of recurrence (HR 3.38, p = 0.048) or progression (HR 10.49, p = 0.048) in validation cohorts. Predictive gene signatures have strong diagnostic value for determining the response to intravesical BCG immunotherapy in primary pT1 bladder cancer. Keywords: Gene expression, Bladder cancer, BCG
2010-03-26 | GSE19423 | GEO
Project description:PurposeGlioma is the most prevalent malignant form of brain tumors, with a dismal prognosis. Currently, cancer immunotherapy has emerged as a revolutionary treatment for patients with advanced highly aggressive therapy-resistant tumors. However, there is no effective biomarker to reflect the response to immunotherapy in glioma patient so far. So we aim to assess the clinical predictive value of FCER1G in patients with glioma.MethodsThe expression level and correlation between clinical prognosis and FER1G levels were analyzed with the data from CGGA, TCGA, and GEO database. Univariate and multivariate cox regression model was built to predict the prognosis of glioma patients with multiple factors. Then the correlation between FCER1G with immune cell infiltration and activation was analyzed. At last, we predict the immunotherapeutic response in both high and low FCER1G expression subgroups.ResultsFCER1G was significantly higher in glioma with greater malignancy and predicted poor prognosis. In multivariate analysis, the hazard ratio of FCER1G expression (Low versus High) was 0.66 and 95 % CI is 0.54 to 0.79 (P < 0.001), whereas age (HR = 1.26, 95 % CI 1.04-1.52), grade (HR = 2.75, 95 % CI 2.06-3.68), tumor recurrence (HR = 2.17, 95 % CI 1.81-2.62), IDH mutant (HR = 2.46, 95 % CI 1.97-3.01) and chemotherapeutic status (HR = 1.4, 95 % CI 1.20-1.80) are also included. Furthermore, we illustrated that gene FCER1G stratified glioma cases into high and low FCER1G expression subgroups that demonstrated with distinct clinical outcomes and T cell activation. At last, we demonstrated that high FCER1G levels presented great immunotherapeutic response in glioma patients.ConclusionsThis study demonstrated FCER1G as a novel predictor for clinical diagnosis, prognosis, and response to immunotherapy in glioma patient. Assess expression of FCER1G is a promising method to discover patients that may benefit from immunotherapy.
| S-EPMC7881595 | biostudies-literature
Project description:This is a single-arm, multicentre real-world observational study of TAS-102 in combination with regorafenib or fruquintinib for third-line and above advanced colorectal cancer.
| 69888 | ecrin-mdr-crc
Project description:BackgroundColon cancer is one of the most frequent malignancies and causes high mortality worldwide. Exploring the tumor-immune interactions in the tumor microenvironment and identifying new prognostic and therapeutic biomarkers will assist in decoding the novel mechanism of tumor immunotherapy. BGN is a typical extracellular matrix protein that was previously validated as a signaling molecule regulating multiple processes of tumorigenesis. However, its role in tumor immunity requires further investigation.MethodsThe differentially expressed genes in three GEO datasets were analyzed, and BGN was identified as the target gene by intersection analysis of PPIs. The relevance between clinical outcomes and BGN expression levels was evaluated using data from the GEO database, TCGA and tissue microarray of colon cancer samples. Univariable and multivariable Cox regression models were conducted for identifying the risk factors correlated with clinical prognosis of colon cancer patients. Next, the association between BGN expression levels and the infiltration of immune cells as well as the process of the immune response was analyzed. Finally, we predicted the immunotherapeutic response rates in the subgroups of low and high BGN expression by TIS score, ImmuCellAI and TIDE algorithms.ResultsBGN expression demonstrated a statistically significant upregulation in colon cancer tissues than in normal tissues. Elevated BGN was associated with shorter overall survival as well as unfavorable clinicopathological features, including tumor size, serosa invasion and length of hospitalization. Mechanistically, pathway enrichment and functional analysis demonstrated that BGN was positively correlated with immune and stromal scores in the TME and primarily involved in the regulation of immune response. Further investigation revealed that BGN was strongly expressed in the immunosuppressive phenotype and tightly associated with the infiltration of multiple immune cells in colon cancer, especially M2 macrophages and induced Tregs. Finally, we demonstrated that high BGN expression presented a better immunotherapeutic response in colon cancer patients.ConclusionBGN is an encouraging predictor of diagnosis, prognosis and immunotherapeutic response in patients with colon cancer. Assessment of BGN expression represents a novel approach with great promise for identifying patients who may potentially benefit from immunotherapy.
| S-EPMC8790701 | biostudies-literature
Project description:To identify risk factors for smoking among pregnant women, and adverse perinatal outcomes among pregnant women. A case-control study of singleton full-term pregnant women who gave birth at a university hospital in Jordan in June 2020. Pregnant women were divided into three groups according to their smoking status, active, passive, and non-smokers. They were interviewed using a semi-structured questionnaire that included demographic data, current pregnancy history, and neonatal outcomes. Low-level maternal education, unemployment, secondary antenatal care, and having a smoking husband were identified as risk factors for smoke exposure among pregnant women. The risk for cesarean section was ninefold higher in nulliparous smoking women. Women with low family income, those who did not receive information about the hazards of smoking, unemployed passive smoking women, and multiparty raised the risk of neonatal intensive care unit admission among active smoking women. This risk increased in active and passive women with lower levels of education, and inactive smoking women with low family income by 25 times compared to women with a higher level of education. Smoking is associated with adverse perinatal outcomes. Appropriate preventive strategies should address modifiable risk factors for smoking during pregnancy.
| S-EPMC10858917 | biostudies-literature
Project description:Primary outcome(s): The perioperative factors which predict blood transfusion in patients undergoing colorectal surgery over 72 hours preoperatively and postoperatively.
Timepoint: 30 day follow-up period
| 2578652 | ecrin-mdr-crc
Project description:The association between pretreatment nutritional status and immunotherapy response in patients with advanced head and neck cancer is unclear. We retrospectively analyzed a cohort of 99 patients who underwent treatment with anti-PD-1 or anti-CTLA-4 antibodies (or both) for stage IV HNSCC between 2014 and 2020 at the Johns Hopkins Hospital. Patient demographics and clinical characteristics were retrieved from electronic medical records. Baseline prognostic nutritional index (PNI) scores and pretreatment body mass index (BMI) trends were calculated. Associations between PNI and BMI were correlated with overall survival (OS), progression-free survival (PFS), and immunotherapy response. In univariate analysis, there was a significant correlation between OS and PFS with baseline PNI (OS: HR: 0.464; 95% CI: 0.265-0.814; PFS: p = 0.007 and HR: 0.525; 95% CI: 0.341-0.808; p = 0.003). Poor OS was also associated with a greater decrease in pretreatment BMI trend (HR: 0.42; 95% CI: 0.229-0.77; p = 0.005). In multivariate analysis, baseline PNI but not BMI trend was significantly associated with OS and PFS (OS: log (HR) = -0.79, CI: -1.6, -0.03, p = 0.041; PFS: log (HR) = -0.78, CI: -1.4, -0.18, p = 0.011). In conclusion, poor pretreatment nutritional status is associated with negative post-immunotherapy outcomes.
| S-EPMC8616447 | biostudies-literature