Identification and validation of a genomic mutation signature as a predictor for immunotherapy in NSCLC

Impact of PIK3CA and cell cycle pathway genetic alterations on durvalumab efficacy in patients with head and neck squamous cell carcinoma: Post hoc analysis of TRIUMPH study

OBJECTIVES

This study aimed to investigate whether genetic alterations in PI3KCA and the cell cycle pathways influence the efficacy of durvalumab, an immune checkpoint inhibitor, in patients with head and neck squamous cell carcinoma (HNSCC) who had previously failed platinum-based treatment.

MATERIALS AND METHODS

We obtained data from a phase II umbrella trial of patients with HNSCC who failed platinum-based treatment (TRIUMPH, NCT03292250). Patients receiving durvalumab treatment comprised those with PIK3CA alterations (Group A), those with cell cycle pathway alterations such as CDKN2A (Group B), and those with no druggable genetic alterations (Group C). We analyzed the overall response rate (ORR), progression-free survival (PFS), and overall survival (OS) in each group and evaluated the potential predictive factors for durvalumab.

RESULTS

We analyzed the data of 87 patients: 18, 12, and 57 in groups A, B, and C, respectively. The ORRs were 27.8 %, 8.3 %, and 15.8 % in Groups A, B, and C, respectively (P = 0.329), and the median PFS for each group was 2.3, 1.6, and 1.7 months, respectively, with no significant differences between the groups (P = 0.24). Notably, patients with lower neutrophil-lymphocyte ratio (NLR) (≤5.8) had longer PFS (median, 2.8 vs 1.6 months, P < 0.001), while those with lower platelet-lymphocyte ratio (PLR) (≤491.2) exhibited longer PFS (median, 1.8 vs 1.2 months, P < 0.001).

CONCLUSION

Durvalumab's efficacy was similar, irrespective of the presence of PIK3CA or cell cycle pathway genetic alterations in patients with platinum-resistant HNSCC. The NLR and PLR may be promising predictive biomarkers.

A gene mutation-based risk model for prognostic prediction in liver metastases

BACKGROUND

Liver metastasis is the major challenge in the treatment for malignant tumors. Genomic profiling is increasingly used in the diagnosis, treatment and prediction of prognosis in malignancies. In this study, we constructed a gene mutation-based risk model to predict the survival of liver metastases.

METHOD

We identified the gene mutations associated with survival and constructed the risk model in the training cohort including 800 patients with liver metastases from Memorial Sloan-Kettering Cancer Center (MSKCC) dataset. Other 794 patients with liver metastases were collected from 4 cohorts for validation. Furthermore, the analyses of tumor microenvironment (TME) and somatic mutations were performed on 51 patients with breast cancer liver metastases (BCLM) who had both somatic mutation data and RNA-sequencing data.

RESULTS

A gene mutation-based risk model involved 10 genes was constructed to divide patients with liver metastases into the high- and low-risk groups. Patients in the low-risk group had a longer survival time compared to those in the high-risk group, which was observed in both training and validation cohorts. The analyses of TME in BCLM showed that the low-risk group exhibited more immune infiltration than the high-risk group. Furthermore, the mutation signatures of the high-risk group were completely different from those of the low-risk group in patients with BCLM.

CONCLUSIONS

The gene mutation-based risk model constructed in our study exhibited the reliable ability of predicting the prognosis in liver metastases. The difference of TME and somatic mutations among BCLM patients with different risk score can guide the further research and treatment decisions for liver metastases.

Targeting immune cell types of tumor microenvironment to overcome resistance to PD-1/PD-L1 blockade in lung cancer

Lung cancer is the common malignant tumor with the highest mortality rate. Lung cancer patients have achieved benefits from immunotherapy, including immune checkpoint inhibitors (ICIs) therapy. Unfortunately, cancer patients acquire adaptive immune resistance, leading to poor prognosis. Tumor microenvironment (TME) has been demonstrated to play a critical role in participating in acquired adaptive immune resistance. TME is associated with molecular heterogeneity of immunotherapy efficacy in lung cancer. In this article, we discuss how immune cell types of TME are correlated with immunotherapy in lung cancer. Moreover, we describe the efficacy of immunotherapy in driven gene mutations in lung cancer, including KRAS, TP53, EGFR, ALK, ROS1, KEAP1, ZFHX3, PTCH1, PAK7, UBE3A, TNF-α, NOTCH, LRP1B, FBXW7, and STK11. We also emphasize that modulation of immune cell types of TME could be a promising strategy for improving adaptive immune resistance in lung cancer.