Identification of four immune subtypes in locally advanced rectal cancer treated with neoadjuvant chemotherapy for predicting the efficacy of subsequent immune checkpoint blockade

Development and validation of a cancer-associated fibroblast gene signature-based model for predicting immunotherapy response in colon cancer

The efficacy of immune checkpoint inhibitors in colon cancer has been established, and there is an urgent need to identify new molecular markers for colon cancer immunotherapy to guide clinical decisions. Using the "EPIC" and "MCPcounter" R packages to conduct cancer-associated fibroblast (CAF) infiltration scoring on colon cancer samples from the TCGA database and the GEO database, the WGCNA analysis was performed on the two databases' samples based on the CAF infiltration scores to screen for CAF-related genes. LASSO regression analysis was used to construct a risk model with these genes. Comprehensive bioinformatics analysis was conducted on the constructed model to evaluate the stability of its prediction of CAF infiltration abundance and the stability of its prediction of immunotherapy efficacy. The newly constructed risk model could well reflect the abundance of CAF infiltration in colon cancer, with a correlation coefficient of 0.91 in the training cohort TCGA-COAD and 0.88 in the validation cohort GSE39582. GSEA analysis revealed that CAF is closely related to functions associated with extracellular matrix remodeling. The constructed risk model can predict the efficacy of immunotherapy in colon cancer well, with the high-risk group showing significantly poorer immunotherapy response than the low-risk group, with an expected effective rate of immunotherapy of 68 vs. 24% in the training group (P < 0.001) and 64 vs. 26% in the validation group (P < 0.001). The AUC value for predicting immunotherapy response by the risk model in the training group was 0.780 (95% CI 0.736-0.820), and in the validation group, the AUC value was 0.774 (95% CI 0.735-0.810). Drug sensitivity analysis showed that the expected chemotherapeutic effect in the low-risk group was superior to that in the high-risk group. CAF is associated with immunosuppression and drug resistance. Predicting the efficacy of immunotherapy in colon cancer based on the abundance of CAF infiltration is a feasible approach. For the high-risk population identified by our model, clinical consideration should be given to prioritizing non-immunotherapy approaches to avoid potential risks associated with immunotherapy.

Correlation analysis of tertiary lymphoid structure parameters with the prognosis of patients with locally advanced rectal cancer after neoadjuvant chemotherapy: a retrospective study

BACKGROUND

The tertiary lymphoid structures (TLSs) are positively correlated with the prognosis of many solid tumors, including colorectal cancer. However, their prognostic significance in patients with locally advanced rectal cancer (LARC) after neoadjuvant chemotherapy remains unclear. This study aimed to explore the correlation between TLS parameters and the prognosis of LARC patients receiving neoadjuvant chemotherapy.

METHODS

This retrospective study included patients with LARC treated at the Harbin Medical University Cancer Hospital from 2012 to 2021. The quantity, area, and density of TLSs in the tumor, normal, and total tissues from surgical specimens were determined. Overall survival (OS) was calculated from surgery to death from any cause. The correlation between TLS parameters and prognosis was assessed using Kaplan-Meier survival analysis and Cox regression analysis. Multiplex immunofluorescence (mIF) staining was used to analyze TLS maturity and immune composition.

RESULTS

This study included 114 patients, of whom 46.5% were over 60 years old, and 70.2% were male. TLS parameters in tumor region were smaller than those in normal and total regions (P < 0.001). A larger TLS area and higher density in the total region (HR = 0.371, P = 0.023 for area; HR = 0.250, P = 0.005 for density) were significantly associated with better OS. Moreover, a higher total-region TLS density was correlated with low carcinoembryonic antigen (CEA) levels (P = 0.028), positive responses to neoadjuvant therapy (P < 0.001), and tumor regression (P < 0.001). Subgroup analysis revealed that combining total-region TLS density with clinicopathologic features such as sex, age, cTNM stage, CEA levels, and extramural vascular invasion further stratified prognosis. Additionally, mIF analysis showed that a high TLS density was associated with a higher TLS maturity (P = 0.014); mature TLSs exhibited greater infiltration of CD20⁺ B cells and CD21⁺ follicular dendritic cells compared to non-mature TLSs.

CONCLUSIONS

TLS parameters, particularly TLS density, are promising prognostic biomarkers for LARC patients undergoing neoadjuvant chemotherapy.

TRIAL REGISTRATION

not applicable.

A novel computational model ITHCS for enhanced prognostic risk stratification in ESCC by correcting for intratumor heterogeneity

Intratumor heterogeneity significantly challenges the accuracy of existing prognostic models for esophageal squamous cell carcinoma (ESCC) by introducing biases related to the varied genetic and molecular landscapes within tumors. Traditional models, relying on single-sample, single-region bulk RNA sequencing, fall short of capturing the complexity of intratumor heterogeneity. To fill this gap, we developed a computational model for intratumor heterogeneity corrected signature (ITHCS) by employing both multiregion bulk and single-cell RNA sequencing to pinpoint genes that exhibit consistent expression patterns across different tumor regions but vary significantly among patients. Utilizing these genes, we applied multiple machine-learning algorithms for sophisticated feature selection and model construction. The ITHCS model significantly outperforms existing prognostic indicators in accuracy and generalizability, markedly reducing sampling biases caused by intratumor heterogeneity. This improvement is especially notable in the prognostic assessment of early-stage ESCC patients, where the model exhibits exceptional predictive power. Additionally, we found that the risk score based on ITHCS may be associated with epithelial-mesenchymal transition characteristics, indicating that high-risk patients may exhibit a diminished efficacy to immunotherapy.

Radiation-induced changes in gene expression in rectal cancer specimens

PURPOSE

The standard-of-care for locally advanced rectal cancer is radiotherapy-based neoadjuvant therapy followed by surgical resection. This article reviews the evidence of molecular changes at the transcriptome level induced through radiotherapy in rectal cancer.

METHODS

The PubMed search "(radiation OR radiotherapy) cancer (transcriptome OR "gene expression") rectal" was used. The studies taken forward utilised gene-expression data on both pre-treatment and post-treatment rectal adenocarcinoma biospecimens from patients treated with RT-based neoadjuvant strategies.

RESULTS

Twelve publications met the review criteria. There was variation in approaches in terms of design, patient population, cohort size, timing of the post-radiotherapy sampling and method of measuring gene expression. Most of the post-treatment biospecimen retrievals were at resection. The literature indicates a broad upregulation of immune activity through radiotherapy using gene-expression data.

CONCLUSION

Future studies would benefit from standardised prospective approaches to sampling to enable the inclusion of timepoints relevant to the tumour and immune response.

Developing a RiskScore Model based on Angiogenesis-related lncRNAs for Colon Adenocarcinoma Prognostic Prediction

AIM

We screened key angiogenesis-related lncRNAs based on colon adenocarcinoma (COAD) to construct a RiskScore model for predicting COAD prognosis and help reveal the pathogenesis of the COAD as well as optimize clinical treatment.

BACKGROUND

Regulatory roles of lncRNAs in tumor progression and prognosis have been confirmed, but few studies have probed into the role of angiogenesis-related lncRNAs in COAD.

OBJECTIVE

To identify key angiogenesis-related lncRNAs and build a RiskScore model to predict the survival probability of COAD patients and help optimize clinical treatment.

METHODS

Sample data were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The HALLMARK pathway score in the samples was calculated using the single sample gene set enrichment analysis (ssGSEA) method. LncRNAs associated with angiogenesis were filtered by an integrated pipeline algorithm. LncRNA-based subtypes were classified by ConsensusClusterPlus and then compared with other established subtypes. A RiskScore model was created based on univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and stepwise regression analysis. The Kaplan-Meier curve was drawn by applying R package survival. The time-dependent ROC curves were drawn by the timeROC package. Finally, immunotherapy benefits and drug sensitivity were analyzed using tumor immune dysfunction and exclusion (TIDE) software and pRRophetic package.

RESULTS

Pathway analysis showed that the angiogenesis pathway was a risk factor affecting the prognosis of COAD patients. A total of 66 lncRNAs associated with angiogenesis were screened, and three molecular subtypes (S1, S2, S3) were obtained. The prognosis of S1 and S2 was better than that of S3. Compared with the existing subtypes, the S3 subtype was significantly different from the other two subtypes. Immunoassay showed that immune cell scores of the S2 subtype were lower than those of the S1 and S3 subtypes, which also had the highest TIDE scores. We recruited 8 key lncRNAs to develop a RiskScore model. The high RiskScore group with inferior survival and higher TIDE scores was predicted to benefit limitedly from immunotherapy, but it may be more sensitive to chemotherapeutics. A nomogram designed by RiskScore signature and other clinicopathological characteristics shed light on rational predictive power for COAD treatment.

CONCLUSION

We constructed a RiskScore model based on angiogenesis-related lncRNAs, which could serve as potential prognostic predictors for COAD patients and may offer clues for the intervention of anti-angiogenic application. Our results may help evaluate the prognosis of COAD and provide better treatment strategies.

Identification of Novel Stemness-based Subtypes and Construction of a Prognostic Risk Model for Patients with Lung Squamous Cell Carcinoma

BACKGROUND

Although cancer stem cells (CSCs) contribute to tumorigenesis, progression, and drug resistance, stemness-based classification and prognostic signatures of lung squamous cell carcinoma (LUSC) remain unclarified. This study attempted to identify stemness-based subtypes and develop a prognostic risk model for LUSC.

METHODS

Based on RNA-seq data from The Cancer Genome Atlas (TCGA), Gene-Expression Omnibus (GEO) and Progenitor Cell Biology Consortium (PCBC), mRNA expression-based stemness index (mRNAsi) was calculated by one-class logistic regression (OCLR) algorithm. A weighted gene coexpression network (WGCNA) was employed to identify stemness subtypes. Differences in mutation, clinical characteristics, immune cell infiltration, and antitumor therapy responses were determined. We constructed a prognostic risk model, followed by validations in GEO cohort, pan-cancer and immunotherapy datasets.

RESULTS

LUSC patients with subtype C2 had a better prognosis, manifested by higher mRNAsi, higher tumor protein 53 (TP53) and Titin (TTN) mutation frequencies, lower immune scores and decreased immune checkpoints. Patients with subtype C2 were more sensitive to Imatinib, Pyrimethamine, and Paclitaxel therapy, whereas those with subtype C1 were more sensitive to Sunitinib, Saracatinib, and Dasatinib. Moreover, we constructed stemness-based signatures using seven genes (BMI1, CCDC51, CTNS, EIF1AX, FAM43A, THBD, and TRIM68) and found high-risk patients had a poorer prognosis in the TCGA cohort. Similar results were found in the GEO cohort. We verified the good performance of risk scores in prognosis prediction and therapy responses.

CONCLUSION

The stemness-based subtypes shed novel insights into the potential roles of LUSC-stemness in tumor heterogeneity, and our prognostic signatures offer a promising tool for prognosis prediction and guide therapeutic decisions in LUSC.