It is widely accepted that chronic inflammation plays an active role in cancer. Inflammatory immunocytes and related cytokines in the tumor microenvironment are supposed to be a double-edged sword in colorectal cancer (CRC) initiation and progression. Interleukin-17 (IL-17), a pleiotropic proinflammatory cytokine, can promote cancer-elicited inflammation and prevent cancer cells from immune surveillance. Despite controversy, IL-17 is generally considered to be a promoter in CRC progression. In this review, we devote to summarize the current progress regarding the role of IL-17 in tumor initiation and progression, as well as the prognostic value in CRC.

Long non-coding RNAs have been shown to have critical regulatory roles in cancer biology. However, the contributions of lncRNAs to gastric cancer remain largely unknown.

T lymphocytes are the key protective contributors in chronic infection and tumor, but experience exhaustion by persistent antigen stimulation. As an unconventional lineage of T cells, γδ T cells can rapidly response to varied infectious and tumor challenges in a non-MHC-restricted manner and play key roles in immune surveillance via pleiotropic effector functions, showing promising as candidates for cellular tumor immunotherapy. Activated γδ T cells can also acquire exhaustion signature with elevated expression of immune checkpoints, such as PD-1, decreased cytokine production, and functional impairment. However, the exhaustion features of γδ T cells are distinct from conventional αβ T cells. Here, we review the researches regarding the characteristics, heterogeneity, and mechanisms of γδ T cell exhaustion. These studies provide insights into the combined strategies to overcome the exhaustion of γδ T cells and enhance antitumor immunity.Summary sentence: Review of the characteristics, heterogeneity, and mechanisms of γδ T cell exhaustion provides insights into the combined strategies to enhance γδ T cell-based antitumor immunotherapy.

The objective of this study was to evaluate the discriminative capabilities of radiomics signatures derived from three distinct machine learning algorithms and to identify a robust radiomics signature capable of predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy in patients diagnosed with locally advanced rectal cancer (LARC). In a retrospective study, 211 LARC patients were consecutively enrolled and divided into a training cohort (n = 148) and a validation cohort (n = 63). From pretreatment contrast-enhanced planning CT images, a total of 851 radiomics features were extracted. Feature selection and radiomics score (Radscore) construction were performed using three different machine learning methods: least absolute shrinkage and selection operator (LASSO), random forest (RF) and support vector machine (SVM). The SVM-derived Radscore demonstrated a strong correlation with the pCR status, yielding area under the receiver operating characteristic curves (AUCs) of 0.880 and 0.830 in the training and validation cohorts, respectively, outperforming the RF and LASSO methods. Based on this, a nomogram was developed by combining the SVM-based Radscore with clinical indicators to predict pCR after neoadjuvant chemoradiotherapy. The nomogram exhibited superior predictive power, achieving AUCs of 0.910 and 0.866 in the training and validation cohorts, respectively. Calibration curves and decision curve analyses confirmed its appropriateness. The SVM-based Radscore demonstrated promising performance in predicting pCR for LARC patients. The machine learning-driven nomogram, which integrates the Radscore and clinical indicators, represents a valuable tool for predicting pCR in LARC patients.

Background: Therapeutic resistance is a main obstacle to achieve long-term benefits from immune checkpoint inhibitors. The underlying mechanism of neoadjuvant anti-PD-1 resistance remains unclear. Methods: Multi-omics analysis, including mass cytometry, single-cell RNA-seq, bulk RNA-seq, and polychromatic flow cytometry, was conducted using the resected tumor samples in a cohort of non-small cell lung cancer (NSCLC) patients received neoadjuvant anti-PD-1 therapy. Tumor and paired lung samples acquired from treatment-naïve patients were used as a control. In vitro experiments were conducted using primary cells isolated from fresh tissues and lung cancer cell lines. A Lewis-bearing mouse model was used in the in vivo experiment. Results: The quantity, differentiation status, and clonal expansion of tissue-resident memory CD8+ T cells (CD8+ TRMs) are positively correlated with therapeutic efficacy of neoadjuvant anti-PD-1 therapy in human NSCLC. In contrast, the quantity of immature CD1c+ classical type 2 dendritic cells (imcDC2) and galectin-9+ cancer cells is negatively correlated with therapeutic efficacy. An epithelium/imDC2 suppressive axis that restrains the antitumor response of CD8+ TRMs via galectin-9/TIM-3 was uncovered. The expression level of CD8+ TRMs and galectin-9+ cancer cell-related genes predict the clinical outcome of anti-PD-1 neoadjuvant therapy in human NSCLC patients. Finally, blockade of TIM-3 and PD-1 could improve the survival of tumor-bearing mouse by promoting the antigen presentation of imcDC2 and CD8+ TRMs-mediated tumor-killing. Conclusion: Galectin-9 expressing tumor cells sustained the primary resistance of neoadjuvant anti-PD-1 therapy in NSCLC through galectin-9/TIM-3-mediated suppression of imcDC2 and CD8+ TRMs. Supplement of anti-TIM-3 could break the epithelium/imcDC2/CD8+ TRMs suppressive loop to overcome anti-PD-1 resistance. Trial registration number: NCT03732664.