GSDME-mediated pyroptosis promotes anti-tumor immunity of neoadjuvant chemotherapy in breast cancer.

Paclitaxel and anthracycline-based chemotherapy is one of the standard treatment options for breast cancer. However, only about 6-30% of breast cancer patients achieved a pathological complete response (pCR), and the mechanism responsible for the difference is still unclear. In this study, random forest algorithm was used to screen feature genes, and artificial neural network (ANN) algorithm was used to construct an ANN model for predicting the efficacy of neoadjuvant chemotherapy for breast cancer. Furthermore, digital pathology, cytology, and molecular biology experiments were used to verify the relationship between the efficacy of neoadjuvant chemotherapy and immune ecology. It was found that paclitaxel and doxorubicin, an anthracycline, could induce typical pyroptosis and bubbling in breast cancer cells, accompanied by gasdermin E (GSDME) cleavage. Paclitaxel with LDH release and Annexin V/PI doubule positive cell populations, and accompanied by the increased release of damage-associated molecular patterns, HMGB1 and ATP. Cell coculture experiments also demonstrated enhanced phagocytosis of macrophages and increased the levels of IFN-γ and IL-2 secretion after paclitaxel treatment. Mechanistically, GSDME may mediate paclitaxel and doxorubicin-induced pyroptosis in breast cancer cells through the caspase-9/caspase-3 pathway, activate anti-tumor immunity, and promote the efficacy of paclitaxel and anthracycline-based neoadjuvant chemotherapy. This study has practical guiding significance for the precision treatment of breast cancer, and can also provide ideas for understanding molecular mechanisms related to the chemotherapy sensitivity.

The Tumorigenic Effect of the High Expression of Ladinin-1 in Lung Adenocarcinoma and Its Potential as a Therapeutic Target.

The oncogenic role of Ladinin-1 (LAD1), an anchoring filament protein, is largely unknown. In this study, we conducted a series of studies on the oncogenic role of LAD1 in lung adenocarcinoma (LUAD). Firstly, we analyzed the aberrant expression of LAD1 in LUAD and its correlation with patient survival, tumor immune infiltration, and the activation of cancer signaling pathways. Furthermore, the relationship between LAD1 expression and K-Ras and EGF signaling activation, tumor cell proliferation, migration, and colony formation was studied by gene knockout/knockout methods. We found that LAD1 was frequently overexpressed in LUAD, and high LAD1 expression predicts a poor prognosis. LAD1 exhibits promoter hypomethylation in LUAD, which may contribute to its mRNA upregulation. Single-sample gene set enrichment analysis (ssGSEA) showed that acquired immunity was negatively correlated with LAD1 expression, which was verified by the downregulated GO terms of "Immunoglobulin receptor binding" and "Immunoglobulin complex circulating" in the LAD1 high-expression group through Gene Set Variation Analysis (GSVA). Notably, the Ras-dependent signature was the most activated signaling in the LAD1 high-expression group, and the phosphorylation of downstream effectors, such as ERK and c-jun, was strongly inhibited by LAD1 deficiency. Moreover, we demonstrated that LAD1 depletion significantly inhibited the proliferation, migration, and cell-cycle progression of LUAD cells and promoted sensitivity to Gefitinib, K-Ras inhibitor, and paclitaxel treatments. We also confirmed that LAD1 deficiency remarkably retarded tumor growth in the xenograft model. Conclusively, LAD1 is a critical prognostic biomarker for LUAD and has potential as an intervention target.

Subtyping of gastric cancer based on basement membrane genes that stratifies the prognosis, immune infiltration and therapeutic response.

Gastric cancer (GC) is highly heterogeneous and prone to metastasis, which are obstacles to the effectiveness of treatment. The basement membrane (BM) acts as a barrier to tumor cell invasion and metastasis. It is critical to investigate the relationship between BM status, metastasis, and patient prognosis. In several large cohorts, we investigated BM gene expression-based molecular classification and risk-prognosis models for GC, examined tumor microenvironment (TME) differences among different molecular subtypes, and developed risk models in predicting prognosis, immunotherapy effectiveness, and chemotherapy resistance. Three GC subtypes (BMclusterA/B/C) based on BM gene expression status were discovered. Each of the three GC subtypes has unique immune infiltration and activated oncogenic signals. Moreover, a 6-gene score (BMscore) predictive model was developed. The low BMscore group had a high tumor mutation burden, high immunogenicity, and low RHOJ expression levels, implying that individuals with GC in this category may be more susceptible to immunotherapy and treatment. The EMT subtype showed a considerably higher BMscore than the other subtypes in the Asian Organization for Research on Cancer (ACRG) molecular classification. Endothelial cells, smooth muscle cells, and fibroblasts may be engaged in regulating BM reorganization in GC progression, according to single-cell transcriptome analyses. In conclusion, we defined a novel molecular classification of GC based on BM genes, developed a prognostic risk model, and elucidated the cell subpopulations involved in BM remodeling at the single-cell level. This study has deepened the understanding of the relationship between GC metastasis and BM alterations, achieved prognostic stratification, and guided therapy.

Immune microenvironment and clinical feature analyses based on a prognostic model in lymph node-positive breast cancer.

Background: If lymph node metastasis occurs in breast cancer patients, the disease can progress rapidly. Based on the infiltrative immune cells of breast cancer patients with lymph node positivity, we constructed the LNPRS for selecting prognostic predictors. Methods: The LNPRS was established and the predictive value of the LNPRS was verified by independent testing cohorts. A nomogram was also established to confirm the therapeutic guidance significance of the LNPRS. The correlation of the LNPRS with tumor mutation burden, immune microenvironment score, immune checkpoints, the proportion of tumor-infiltrating immune cells, and GSEA and GSVA enrichment pathways were also evaluated. Results: In the training cohort, the overall survival of breast cancer patients who had high LNPRS was shorter than that of patients who had low LNPRS (7.98 years versus 20.42 years, P-value< 8.16E-11). The AUC values for 5-, 10-, and 15-years were 0.787, 0.739, and 0.800, respectively. The ability to predict prognosis for the LNPRS was also tested in 3 independent testing cohorts. Furthermore, the predictive value of the LNPRS for chemotherapy and immunotherapy was also proven. The GSEA and GSVA showed that the LNPRS was closely related to the activation of T and B lymphocytes and IFN-γ secretion. Moreover, breast cancer patients with low LNPRS had higher TME scores than those with high LNPRS. Conclusion: We can conclude that the LNPRS is a robust prognostic biomarker in breast cancer patients with positive lymph nodes and may be helpful for patients to make a clinical decision.

Vascular Thrombosis and Anti-PD-1 Therapy: A Series of Cases.

Immune checkpoint inhibitors may cause unique immune-related adverse events. Vascular thrombosis, especially arterial thrombosis, is rare but life-threatening, and little is known about its relevance to immunotherapy. Here, we reported two cases of vascular thrombosis, including venous and arterial thrombosis in cancer patients receiving anti-PD-1 antibody in combination with chemotherapy. Systemic corticosteroids and anticoagulant treatment were administered immediately in two cases. In case 1, anti-PD-1 antibody was permanently discontinued, and recurrence of vascular thrombosis was not observed during the follow-up. In case 2, the patient continued anti-PD-1 immunotherapy and unfortunately died of cerebral infarction 2 months later. This case report provides a strong evidence for the association between PD-1 blockade and vascular thrombosis and offers some general guidelines on the management of the immune-related vascular thrombosis events induced by anti-PD-1 therapy.

An Immune-Associated Genomic Signature Effectively Predicts Pathologic Complete Response to Neoadjuvant Paclitaxel and Anthracycline-Based Chemotherapy in Breast Cancer.

Breast cancer is now the leading cause of cancer morbidity and mortality among women worldwide. Paclitaxel and anthracycline-based neoadjuvant chemotherapy is widely used for the treatment of breast cancer, but its sensitivity remains difficult to predict for clinical use. In our study, a LASSO logistic regression method was applied to develop a genomic classifier for predicting pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer. The predictive accuracy of the signature classifier was further evaluated using four other independent test sets. Also, functional enrichment analysis of genes in the signature was performed, and the correlations between the prediction score of the signature classifier and immune characteristics were explored. We found a 25-gene signature classifier through the modeling, which showed a strong ability to predict pCR to neoadjuvant chemotherapy in breast cancer. For T/FAC-based training and test sets, and a T/AC-based test set, the AUC of the signature classifier is 1.0, 0.9071, 0.9683, 0.9151, and 0.7350, respectively, indicating that it has good predictive ability for both T/FAC and T/AC schemes. The multivariate model showed that 25-gene signature was far superior to other clinical parameters as independent predictor. Functional enrichment analysis indicated that genes in the signature are mainly enriched in immune-related biological processes. The prediction score of the classifier was significantly positively correlated with the immune score. There were also significant differences in immune cell types between pCR and residual disease (RD) samples. Conclusively, we developed a 25-gene signature classifier that can effectively predict pCR to paclitaxel and anthracycline-based neoadjuvant chemotherapy in breast cancer. Our study also suggests that the immune ecosystem is actively involved in modulating clinical response to neoadjuvant chemotherapy and is beneficial to patient outcomes.