scholarly journals Pan‐cancer analyses reveal that increased Hedgehog activity correlates with tumor immunosuppression and resistance to immune checkpoint inhibitors

2021 ◽  
Author(s):  
Junjie Jiang ◽  
Yongfeng Ding ◽  
Yanyan Chen ◽  
Jun Lu ◽  
Yiran Chen ◽  
...  
Keyword(s):  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Tumor Immunosuppression ◽  
Pan Cancer

scholarly journals Pan-Cancer Analysis Identifies Liver Metastases as Negative Predictive Factor for Immune Checkpoint Inhibitors Treatment Outcome

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiao-Juan Chen ◽  
Aiqun Ren ◽  
Liang Zheng ◽  
En-Dian Zheng ◽  
Tao Jiang
Keyword(s):  
Combination Therapy ◽  
Liver Metastases ◽  
Predictive Factor ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Intrinsic Resistance ◽  
The Impact ◽  
Pan Cancer ◽  
Negative Predictive Factor

This study aimed to investigate the predictive value of liver metastases (LM) in patients with various advanced cancers received immune-checkpoint inhibitors (ICIs). First, clinical and survival data from a published cohort of 1,661 patients who received ICIs therapy were downloaded and analyzed. Second, a retrospective review of 182 patients with advanced non-small-cell lung cancer (NSCLC) who received PD-1/PD-L1 monotherapy was identified. Third, a meta-analysis of published trials was performed to explore the impact of LM on the efficacy of anti-PD-1/PD-L1 based therapy in advanced lung cancers. Pan-cancer analysis revealed that patients with LM had significantly shorter overall survival (OS) than those without LM (10 vs. 20 months; P < 0.0001). Subgroup analysis showed that the presence of LM was associated with markedly shorter OS than those without LM in ICI monotherapy group (P < 0.0001), but it did not reach the statistical significance in ICI-based combination therapy (P = 0.0815). In NSCLC, the presence of LM was associated with significantly inferior treatment outcomes in both pan-cancer and real-world cohort. Interestingly, ICI-based monotherapy and combination therapy could simultaneously prolong progression-free survival (PFS) and OS than chemotherapy in patients without LM. However, ICI-based monotherapy could not prolong PFS than chemotherapy in patients with LM while ICI-based combination therapy could dramatically prolong both PFS and OS. Together, these findings suggested that the presence of LM was the negative predictive factor in cancer patients received ICIs monotherapy, especially in NSCLC. ICI-based combination therapy might overcome the intrinsic resistance of LM to ICIs while the optimal combinatorial strategies remain under further investigation.

scholarly journals MDM2/4 amplification predicts poor response to immune checkpoint inhibitors: a pan-cancer analysis

ESMO Open ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. e000614 ◽  
Author(s):  
Wenfeng Fang ◽  
Huaqiang Zhou ◽  
Jiayi Shen ◽  
Jianwen Li ◽  
Yaxiong Zhang ◽  
...  
Keyword(s):  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Poor Response ◽  
Pan Cancer

scholarly journals Pan‐cancer analysis identifies TERT alterations as predictive biomarkers for immune checkpoint inhibitors treatment

2020 ◽  
Vol 10 (2) ◽  
Author(s):  
Tao Jiang ◽  
Qingzhu Jia ◽  
Wenfeng Fang ◽  
Shengxiang Ren ◽  
Xiaoxia Chen ◽  
...  
Keyword(s):  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Predictive Biomarkers ◽  
Pan Cancer

Association of KMT2C mutations with favorable outcomes with immune checkpoint inhibitors across multiple tumor types.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 2600-2600
Author(s):  
Chunling Liu ◽  
Qianqian Duan ◽  
Qin Zhang
Keyword(s):  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Multivariable Analysis ◽  
Cox Proportional Hazards ◽  
Multiple Cancer ◽  
Multiple Tumor ◽  
Potential Biomarker ◽  
Chinese Cohort ◽  
Pan Cancer

2600 Background: The KMT2 (lysine methyltransferase) family of histone modifying proteins play important roles in regulating developmental pathways, and mutations in the genes encoding these proteins have been strongly linked to many solid tumor cancers. Recently, there is emerging evidence that KMT2 family genes are involved in sensitivity to immune checkpoint inhibitors (ICIs) by modulating the immune environment. Here we explored the relationship between KMT2C mutation and its efficacy of immunotherapy. Methods: 1661 patients with next-generation sequencing (NGS) and immunotherapy data obtained from MSKCC clinical cohort were used to explore the association with KMT2C mutation and TMB and efficacy of ICIs. TMB was defined as the total number of somatic nonsynonymous mutations in the coding region. NGS data of 6624 pan-cancer patients who also detected MSI and PD-L1 expression from the Chinese clinical dataset were also analyzed relevance of mutation and these immune-related indicators. Results: In total, 9.81% (163/1661) patients in MSKCC cohort harbored KMT2C mutation. In the Chinese cohort, the KMT2C mutation ratio (11.19%, 741/6624) was similar to MSKCC. The TMB level of KMT2C mutation group in both MSKCC cohort and Chinese pan-cancer patient cohort was significantly higher than wild-type group (P < 0.001). A multivariable analysis across the pan-cancer cohort using Cox proportional-hazards regression demonstrated that KMT2C mutation was significantly associated with better OS (hazard ratio, 0.69; 95%CI, 0.52-0.90; P = 0.006), and association remained significant with bladder (P = 0.039), colorectal (P = 0.024), melanoma (P < 0.001) and renal (P < 0.001), adjusting for cancer age, sex, metastases or primary. In addition, in Chinese cohort, KMT2C mutation was associated with higher PD-L1 positive expression (≥1%) (P = 0.01203) and MSI-H (P < 0.001). Conclusions: KMT2C mutation shows impressive association with efficacy of ICIs. Meanwhile, KMT2C-mutant group has a higher TMB, PD-L1 expression and MSI-H. These results indicated that KMT2C mutation may serve as a good potential biomarker of ICI benefit in patients with multiple cancer types.

scholarly journals A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning

Cancers ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1562 ◽  
Author(s):  
Maurizio Polano ◽  
Marco Chierici ◽  
Michele Dal Bo ◽  
Davide Gentilini ◽  
Federica Di Cintio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Support Vector ◽  
Learning Approach ◽  
Expression Data ◽  
Machine Learning Approach ◽  
Cancer Types ◽  
Pan Cancer

Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we set up and validated a machine learning approach to predict the potential for positive response to ICI. Support vector machines (SVM) and extreme gradient boosting (XGboost) models were developed with a 10×5-fold cross-validation schema on 80% of TCGA cases to predict ICI responsiveness defined by a score combining tumor mutational burden and TGF- β signaling. On the remaining 20% validation subset, our SVM model scored 0.88 accuracy and 0.27 Matthews Correlation Coefficient. The proposed machine learning approach could be useful to predict the putative response to ICI treatment by expression data of primary tumors.

Abstract #827: Panhypopituitarism Due to Immune Checkpoint Inhibitors

2017 ◽  
Vol 23 ◽  
pp. 176-177
Author(s):  
Kaitlyn Steffensmeier ◽  
Bahar Cheema ◽  
Ankur Gupta
Keyword(s):  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors
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