Original ArticlesSomatic gene mutation signatures predict cancer type and prognosis in multiple cancers with pan-cancer 1000 gene panel
Introduction
Pan-cancer spans multiple tumor types. Studies of pan-cancer have examined the similarities and differences across diverse tumor types. Pan-cancer analysis can identify common mutations or markers, which are extremely important in understanding the cancer mechanism as well as for screening and diagnosis [1,2]. Moreover, this analysis can reveal more universal targets or predictive markers for targeted therapy or immunotherapy [3,4]. Based on human anatomy, cancers affecting the same system may have similar environmental and genetic characteristics, influencing development and progression [[5], [6], [7]]. Most cancers are caused by somatic mutations and are mosaics of clones with varying population sizes, genetic makeups, and phenotypic characteristics. Therefore, cancers in the same system may have some coincident or similar somatic mutations. However, few studies have evaluated the mutation characteristics of pan-cancer systematically according to the human anatomy.
Different cancer types have different lineages, and some common mutations in the same cancer type can form a “signature” to distinguish it from other cancers or specifically predict prognosis [8,9]. The Cancer Genome Atlas (TCGA) database, a powerful tool for exploring commonalities, has been widely used in pan-cancer studies [[10], [11], [12]]. The next-generation sequencing (NGS) technology has been widely used to explorer key genes and reveal the mechanism of tumor development [13], in which targeted next-generation sequencing (targeted NGS) technology is expected to detect mutation and promote the diagnosis of cancer because of its higher accuracy and lower wrong sequencing rate [[14], [15], [16]].
In this study, we identified somatic mutation characteristics in different cancer systems using a training group and then established digestive, intestinal, and liver predictive signatures, these predictive signatures make further efforts to provide some combined indicators for diagnosis and prediction.
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Patients and samples
A total of 142 Chinese patients with more than 20 tumor types were collected and considered as the training group. Tumor tissues or whole blood samples were sequenced by targeted NGS of the 1000 pan-cancer gene panel, while paracancerous tissues or leukocytes were used as controls. The validation group included 50 Chinese patients covering more than 10 cancer types. All participants provided written informed consent.
Tissue mutation data and clinical data from 209 Asians and 1,332 Caucasian
Patients and mutation characteristics in all cases
The training group included stomach, intestinal, esophageal, liver, cardia, gallbladder, and pancreatic cancers (n = 14, 9, 9, 14, 8, 5, and 4, and recorded as digestive system cancers); renal cell carcinoma, prostate, ovarian, bladder, cervical, and uterine cancers (n = 4, 3, 2, 1, 1, and 1, recorded as urogenital system cancers); lung cancer 51 cases (recorded as lung cancers); and 16 other cancers (cancers of unknown origin, mesothelioma, melanoma, peritoneal cancer, fibrous histiocytoma,
Discussion
Studies of targeted genes facilitate the discovery and verification of candidate genes or loci related to diseases, which has great potential in clinical diagnosis and drug development [28,29]. In our study, the pan-cancer 1,000 gene panel was used to study pan-cancer. Compared to the single cancer gene panel, this panel contains more data and more than 30 important mutation regions in major cancers [[30], [31], [32]]. Additionally, the overall mutation characteristics of the training group
Formatting of funding sources
Funding: This work was supported by the National Natural Science Foundation of China (grant numbers 81772633, 81702098); and the Taishan Scholars Program (Shandong, China).
Declaration of competing interest
The authors have no conflicts of interest to disclosure.
Acknowledgements
The authors thank the patients who volunteered to participate in this study and the staff members at the study sites who cared for these patients; the members of the data and safety monitoring committee; representatives of the sponsors who were involved in the data collection and analyses; and those responsible for technology support.
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These authors contributed equally and should be listed as co-first authors.