scholarly journals 1786P Distribution of KRAS G12C somatic mutations in 41,913 Chinese cancer patients

2021 ◽  
Vol 32 ◽  
pp. S1220-S1221
Author(s):  
Y. Zhang ◽  
X. Li ◽  
C. Liu ◽  
R. Liu ◽  
T. Ma
Keyword(s):  
Cancer Patients ◽  
Somatic Mutations ◽  
Chinese Cancer Patients

A DNA-mutational panel for liquid biopsy-based pan-cancer early detection.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e13687-e13687
Author(s):  
Zhao Yi ◽  
Weizhi Chen ◽  
Ji He
Keyword(s):  
Early Detection ◽  
Cancer Patients ◽  
Liquid Biopsy ◽  
Somatic Mutations ◽  
Asian Population ◽  
Plasma Samples ◽  
Sequencing Data ◽  
Cancer Early Detection ◽  
Chinese Cancer Patients ◽  
Pan Cancer

e13687 Background: Early detection through liquid biopsy can significantly increase the successful chances of treatment. The sensitivity and specificity of early detection are limited by lower signal-to-noise ratio. Thus, well design of liquid biopsy panel is important. Methods: We adopted comprehensive data sources for designing the liquid biopsy panel, including databases of TCGA, ICGC, COSMIC, MSK Cancer Hotspots, while the panel of HCCscreen and CancerSeek. We also considered the database of somatic mutations in Chinese cancer patients generated by us (Genecast (Beijing) Biotechnology Co., Ltd.). We first calculated pan-cancer carrier ratio of somatic mutations in each database and constructed the distribution of mutation counts on step-wise increased carrier ratio. Then, we selected the set of somatic mutations with “elbow point” carrier ratio from each database as part of the panel. Finally, we integrated collected hotspots with the panel of HCCscreen and CancerSeek. Results: We selected 408, 521, 214, 146 and 330 hotspots from databases of TCGA, ICGC, COSMIC, MSK Cancer Hotspots and somatic mutations in Chinese cancer patients, respectively. After integration with the panel of HCCscreen and CancerSeek, this designed panel contains 3,334 bases distributed on 23 chromosomes and 915 gene models. Comparison of hotspots collected from different databases showed that few of them shared the same genomic location except for ones from the database of MSK Cancer Hotspots, indicating the well complementarity between them. Especially, there are 186 unique hotspots collected from the database of somatic mutations in Chinese cancer patients, which can improve the sensitivity of early detection for Chinese and Asian population. Next, we evaluated detected sensitivity of the designed panel based on sequencing data of plasma samples from more than 12,000 Chinese cancer patients collected in Genecast. The result showed that the maximum sensitivity is 66.67% for small cell lung cancer and the overall sensitivity is 45.71% for 26 cancer types, in which hotspots uniquely collected from the database of somatic mutations in Chinese cancer patients accounted for 5.11%. Conclusions: We designed a liquid biopsy panel for pan-cancer early detection and evaluated its sensitivity based on sequencing data of plasma samples from more than 12,000 Chinese cancer patients. Satisfactory performance of our designed panel shows its potential application in cancer screening for healthy and highly risky individuals.

Exploration of the MSI landscape in Chinese pan-cancer patient by Next-Generation Sequencing.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e14576-e14576
Author(s):  
Xinlu Liu ◽  
Jiasheng Xu ◽  
Jian Sun ◽  
Deng Wei ◽  
Xinsheng Zhang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Correlation Analysis ◽  
Cancer Patients ◽  
Cancer Type ◽  
Multiple Tumor ◽  
Treatment Plans ◽  
Cancer Types ◽  
Chinese Cancer Patients ◽  
Colorectal Cancer Patients ◽  
Pan Cancer

e14576 Background: Clinically, MSI had been used as an important molecular marker for the prognosis of colorectal cancer and other solid tumors and the formulation of adjuvant treatment plans, and it had been used to assist in the screening of Lynch syndrome. However, there were currently few reports on the incidence of MSI-H in Chinese pan-cancer patients. This study described the occurrence of MSI in a large multi-center pan-cancer cohort in China, and explored the correlation between MSI and patients' TMB, age, PD-L1 expression and other indicators. Methods: The study included 8361 patients with 8 cancer types from multiple tumor centers. Use immunohistochemistry to detect the expression of MMR protein (MLH1, MSH2, MSH6 and PMS2) in patients with various cancer types to determine the MSI status and detect the expression of PD-L1 in patients. Through NGS technology, 831 genes of 8361 Chinese cancer patients were sequenced and the tumor mutation load of the patients was calculated. The MSI mutations of patients in 8 cancer types were analyzed and the correlation between MSI mutations of patients and the patient's age, TMB and PD-L1 expression was analyzed. Results: The test results showed that MSI patients accounted for 1.66% of pan-cancers. Among them, MSI-H patients accounted for the highest proportion in intestinal cancer, reaching 7.2%. The correlation analysis between MSI and TMB was performed on patients of various cancer types. The results showed that: in each cancer type, MSI-H patients had TMB greater than 10, and 26.83% of MSI-H patients had TMB greater than 100 in colorectal cancer patients. The result of correlation analysis showed that there was no significant correlation between the patient's age and the risk of MSI mutation ( P> 0.05). In addition to PAAD and LUAD, the expression of PD-L1 in MSI-H patients was higher than that in MSS patients in other cancer types( P< 0.05). The correlation analysis between PD-L1 expression and TMB in patients found that in colorectal cancer, the higher the expression of PD-L1, the higher the patient's TMB ( P< 0.05). Conclusions: In this study, we explored the incidence of MSI-H in pan-cancer patients in China and found that the TMB was greater than 10 in patients with MSI-H. Compared with MSS patients, MSI-H patients have higher PD-L1 expression, and the higher the PD-L1 expression in colorectal cancer, the higher the TMB value of patients.

Comprehensive unmet needs and correlations with quality of life in Chinese cancer patients

2018 ◽  
Vol 27 (2) ◽  
pp. e12813 ◽  
Author(s):  
Y.P. Zhang ◽  
Y. Zhang ◽  
W.H. Liu ◽  
Y.T. Yan ◽  
H.H. Wei
Keyword(s):  
Quality Of Life ◽  
Cancer Patients ◽  
Unmet Needs ◽  
Chinese Cancer Patients

The Prevalence of Frailty in Cancer Patients and Mortality Prediction With a Novel Frailty Index Based Clinical Algorithm-A Multicenter, Prospective, Observational Study

2020 ◽  
Author(s):  
Xi Jin ◽  
Yue Ren ◽  
Li Shao ◽  
Zengqing Guo ◽  
Chang Wang ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Frailty Index ◽  
National Level ◽  
Laboratory Data ◽  
Mortality Prediction ◽  
Prediction Algorithm ◽  
Tumor Type ◽  
Risk Of Death ◽  
Increased Risk ◽  
Chinese Cancer Patients

Abstract Purpose To investigate the prediction capacity and status of frailty in Chinese cancer patients in national level, through establishing a novel prediction algorithm. Methods The percentage of frailty in different ages, provinces and tumor type groups of Chinese cancer patients were revealed. The predictioncapacity of frailty on mortality of Chinese cancer patients was analyzed by FI-LAB that is composed of routine laboratory data from accessible blood test and calculated as the ratio of abnormal factors in 22 variables. Establishment of a novel algorithm MCP(mortality of cancer patients)to predict the five-year mortality in Chinese cancer patients was accomplished and its prediction capacity was tested in the training and validation sets using ROC analysis. ResultsWe found that the increased risk of death in cancer patients can be successfully identified through FI-LAB. The univariable and multivariable Cox regression were used to evaluate the effect of frailty on death. In the 5-year follow-up, 20.6% of the 2959 participants (age = 55.8 ± 11.7 years; 43.5% female) were dead while the mean FI-LAB score in baseline was 0.23 (standard deviation = 0.13; range = 0 to 0.73).Frailty (after adjusting for gender, age, and other confounders) could be directly correlated with increased risk of death, with a hazard ratio of 12.67 (95% confidence interval CI: 7.19, 22.31) in comparison with those without frailty. In addition, MCP algorithm presented an area under the ROC (AUC) of 0.691 (95% CI: 0.659-0.684) and 0.648 (95% CI: 0.613-0.684) in the training and validation set, respectively. Conclusion Frailty is common in cancer patients and FI-LAB has high prediction capacity on mortality. The MCP algorithm is a good supplement for frailty evaluation and mortality prediction in cancer patients.

scholarly journals Identification of Prion Disease-Related Somatic Mutations in the Prion Protein Gene (PRNP) in Cancer Patients

Cells ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1480 ◽  
Author(s):  
Yong-Chan Kim ◽  
Sae-Young Won ◽  
Byung-Hoon Jeong
Keyword(s):  
Cancer Patient ◽  
Cancer Patients ◽  
Prion Protein ◽  
Patient Population ◽  
Somatic Mutations ◽  
Protein Gene ◽  
Prion Diseases ◽  
Prnp Gene ◽  
Prion Protein Gene ◽  
Total Cancer

Prion diseases are caused by misfolded prion protein (PrPSc) and are accompanied by spongiform vacuolation of brain lesions. Approximately three centuries have passed since prion diseases were first discovered around the world; however, the exact role of certain factors affecting the causative agent of prion diseases is still debatable. In recent studies, somatic mutations were assumed to be cause of several diseases. Thus, we postulated that genetically unstable cancer tissue may cause somatic mutations in the prion protein gene (PRNP), which could trigger the onset of prion diseases. To identify somatic mutations in the PRNP gene in cancer tissues, we analyzed somatic mutations in the PRNP gene in cancer patients using the Cancer Genome Atlas (TCGA) database. In addition, to evaluate whether the somatic mutations in the PRNP gene in cancer patients had a damaging effect, we performed in silico analysis using PolyPhen-2, PANTHER, PROVEAN, and AMYCO. We identified a total of 48 somatic mutations in the PRNP gene, including 8 somatic mutations that are known pathogenic mutations of prion diseases. We identified significantly different distributions among the types of cancer, the mutation counts, and the ages of diagnosis between the total cancer patient population and cancer patients carrying somatic mutations in the PRNP gene. Strikingly, although invasive breast carcinoma and glioblastoma accounted for a high percentage of the total cancer patient population (9.9% and 5.4%, respectively), somatic mutations in the PRNP gene have not been identified in these two cancer types. We suggested the possibility that somatic mutations of the PRNP gene in glioblastoma can be masked by a diagnosis of prion disease. In addition, we found four aggregation-prone somatic mutations, these being L125F, E146Q, R151C, and K204N. To the best of our knowledge, this is the first specific analysis of the somatic mutations in the PRNP gene in cancer patients.

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