scholarly journals The lncRNA Signatures of Genome Instability to Predict Survival in Patients with Renal Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Liang Huang ◽  
Yu Xie ◽  
Shusuan Jiang ◽  
Weiqing Han ◽  
Fanchang Zeng ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Somatic Mutation ◽  
Renal Cancer ◽  
Somatic Mutations ◽  
Genome Instability ◽  
Noncoding Rnas ◽  
Cancer Biomarkers ◽  
Computational Framework ◽  
Mutation Profile ◽  
Prognosis Model

Long noncoding RNAs (lncRNAs) exert an increasingly important effect on genome instability and the prognosis of cancer patients. The present research established a computational framework originating from the mutation assumption combining lncRNA expression profile and somatic mutation profile in the genome of renal cancer to assess the effect of lncRNAs on the gene instability of renal cancer. A total of 45 differentially expressed lncRNAs were evaluated to be genome-instability-associated from the high and low cumulative somatic mutations groups. Then we established a prognosis model based on three genome-instability-associated lncRNAs (AC156455.1, AC016405.3, and LINC01234)-GlncScore. The GlncScore was then verified in testing cohort and the total TCGA renal cancer cohort. The GlncScore was evaluated to have an accurate prediction for the survival of patients. Furthermore, GlncScore was associated with somatic mutation patterns, indicating its capacity of reflecting genome instability in renal cancer. In conclusion, this study evaluated the effect of lncRNAs on genome instability of renal cancer and provided new hidden cancer biomarkers related to genome instability in renal cancer.

scholarly journals Identification of a Four-lncRNA Prognostic Signature for Colon Cancer Based on Genome Instability

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Duo Yun ◽  
Zhirong Yang
Keyword(s):  
Colon Cancer ◽  
Somatic Mutation ◽  
Genome Instability ◽  
Noncoding Rnas ◽  
Cancer Biomarkers ◽  
Risk Groups ◽  
Mutation Rates ◽  
Risk Scores ◽  
Prognostic Signature ◽  
A Genome

LncRNAs (long noncoding RNAs) are closely associated with genome instability. However, the identification of lncRNAs related to the genome instability and their relationship with the prognosis and clinical signature of cancer remains to be explored. In this paper, we analyzed differential lncRNA expression based on the somatic mutation profiles of colon cancer patients from TCGA database and finally identified 153 lncRNAs that are associated with genome instability in colon cancer. Taking four lncRNAs from these 153, we established a genome-instability-related prognostic signature (GIRlncPSig). By applying the GIRlncPSig, we calculated a risk score for each patient, and using their risk scores, we divided them into low- and high-risk groups. We found that the prognosis between the two risk groups was significantly different, and the results were further verified in different independent patient cohorts. Moreover, we observed that the GIRlncPSig was related to somatic mutation rates in colon cancer, indicating that it may be a potential means of measuring genome instability levels in colon cancer. We also revealed that the GIRlncPSig was correlated with BRAF and DPYD mutation rates and that it may be a potential mutation marker for the BRAF and DPYD gene. In summary, we constructed a genome-instability-related lncRNA prognostic signature (GIRlncPSig), which has a significant effect on prognosis prediction and may allow for the discovery of new colon cancer biomarkers.

Germline and somatic mutation profile in Cancer patients revealed by a medium-sized pan-Cancer panel

Genomics ◽  
2021 ◽  
Author(s):  
Zhaopei Li ◽  
Hailong Wang ◽  
Zhen Zhang ◽  
Xiangwen Meng ◽  
Dujuan Liu ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Somatic Mutation ◽  
Mutation Profile ◽  
Pan Cancer ◽  
Cancer Panel

scholarly journals Heterogeneity of genomic profile in patients with HER2-positive breast cancer

2020 ◽  
Vol 27 (3) ◽  
pp. 153-162 ◽  
Author(s):  
Bo Chen ◽  
Guochun Zhang ◽  
Guangnan Wei ◽  
Yulei Wang ◽  
Liping Guo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Somatic Mutation ◽  
Breast Tumors ◽  
Molecular Heterogeneity ◽  
Breast Cancer Patients ◽  
Her2 Positive ◽  
Her2 Positive Breast Cancer ◽  
Mutation Profile ◽  
Positive Breast Cancer

HER2-positive breast cancer is a biologically and clinically heterogeneous disease. Based on the expression of hormone receptors (HR), breast tumors can be further categorized into HR positive and HR negative. Here, we elucidated the comprehensive somatic mutation profile of HR+ and HR− HER2-positive breast tumors to understand their molecular heterogeneity. In this study, 64 HR+/HER2+ and 43 HR-/HER2+ stage I-III breast cancer patients were included. Capture-based targeted sequencing was performed using a panel consisting of 520 cancer-related genes, spanning 1.64 megabases of the human genome. A total of 1119 mutations were detected among the 107 HER2-positive patients. TP53, CDK12 and PIK3CA were the most frequently mutated, with mutation rates of 76, 61 and 49, respectively. HR+/HER2+ tumors had more gene amplification, splice site and frameshift mutations and a smaller number of missense, nonsense and insertion-deletion mutations than HR-/HER2+ tumors. In KEGG analysis, HR+/HER2+ tumors had more mutations in genes involved in homologous recombination (P = 0.004), TGF-beta (P = 0.007) and WNT (P = 0.002) signaling pathways than HR-/HER2+ tumors. Moreover, comparative analysis of our cohort with datasets from The Cancer Genome Atlas and Molecular Taxonomy of Breast Cancer International Consortium revealed the distinct somatic mutation profile of Chinese HER2-positive breast cancer patients. Our study revealed the heterogeneity of somatic mutations between HR+/HER2+ and HR-/HER2+ in Chinese breast cancer patients. The distinct mutation profile and related pathways are potentially relevant in the development of optimal treatment strategies for this subset of patients.

scholarly journals ROKET: Associating Somatic Mutation with Clinical Outcomes through Kernel Regression and Optimal Transport

2021 ◽  
Author(s):  
Paul Little ◽  
Li Hsu ◽  
Wei Sun
Keyword(s):  
Cancer Patients ◽  
Clinical Outcomes ◽  
Somatic Mutation ◽  
Somatic Mutations ◽  
Optimal Transport ◽  
Kernel Regression ◽  
Multiple Genes ◽  
Cancer Types ◽  
Mutation Data ◽  
Gene Similarity

Somatic mutations in cancer patients are inherently sparse and potentially high dimensional. Cancer patients may share the same set of deregulated biological processes perturbed by different sets of somatically mutated genes. Therefore, when assessing the associations between somatic mutations and clinical outcomes, gene-by-gene analyses is often under-powered because it does not capture the complex disease mechanisms shared across cancer patients. Rather than testing genes one by one, an intuitive approach is to aggregate somatic mutation data of multiple genes to assess the joint association. The challenge is how to aggregate such information. Building on the optimal transport method, we propose a principled approach to estimate the similarity of somatic mutation profiles of multiple genes between tumor samples, while accounting for gene-gene similarity defined by gene annotations or empirical mutational patterns. Using such similarities, we can assess the associations between somatic mutations and clinical outcomes by kernel regression. We have applied our method to analyze somatic mutation data of 17 cancer types and identified at least three cancer types harboring associations between somatic mutations and overall survival, progression-free interval or cytolytic activity.

SO042WHOLE GENOME SEQUENCING OF HUMAN KIDNEY PROGENITORS IDENTIFIES A MUTATION-PRONE CELL TYPE IN THE PROXIMAL TUBULE

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Irene Franco ◽  
Hafdis Helgadottir ◽  
Aldo Moggio ◽  
Malin Larsson ◽  
Peter Vrtacnik ◽  
...  
Keyword(s):  
Gene Expression ◽  
Proximal Tubule ◽  
Somatic Mutation ◽  
Somatic Mutations ◽  
Human Kidney ◽  
Cell Types ◽  
Healthy Human ◽  
Mutation Profile ◽  
Different Populations

Abstract Background and Aims The genome of every cell accumulates somatic mutations while aging. Somatic mutation data can be used to track a cell´s exposure to mutagens, thereby allowing the discovery of cell types that are more susceptible to mutate and become cancer and the underling mechanisms. Method To detect somatic mutations in healthy, human kidney, we set up a protocol for whole genome DNA sequencing of single non-cancer cells. The protocol requires in vitro clonal expansion prior to sequencing, a step that restricts the analysis to cells able to proliferate in vitro (progenitors), but allows a gene expression analysis in addition to genome sequencing. Cells were obtained from six living kidney donors undergoing surgery. In addition to the kidney cortex biopsy, multiple control tissues (skin, subcutaneous fata and visceral fat) were obtained from each donor, allowing a well-controlled comparison of mutation landscapes in different cell types. Donors´ age spanned from 30 to 69. Results Somatic mutation and gene expression data showed that we were able to culture two different populations of CD133/CD24 positive, tubular cells. One population showed a low amount of somatic mutations and a mutation profile similar to progenitors from other tissues (fat, skeletal muscle and blood), consistent with a lack of exposure to mutagens. Conversely, the other population showed high mutation burden and a unique mutation landscape, characterized by mutation enrichment in active chromatin, regulatory, and transcribed regions. Accumulation of potential, cancer-driver mutations was 6-fold faster in these compared to control cells. The mutation profile was similar to that of the most common kidney cancer subtypes (clear cell- and papillary cell-carcinoma) and indicated that these cells originated from the proximal tubule, in agreement with gene expression data. Conclusion Our somatic mutation data from single genomes support the existence of two different populations of proliferating tubule cells in healthy, human kidney. One is protected from mutagen exposure, similar to stem cells from other organs. The other population is derived from damaged proximal tubule cells and shows a high mutation rate between 30 and 70 years of age. Mutations are enriched in transcribed genes and regulatory regions, thus enhancing the chances of tumorigenic transformation and suggesting conditions that predispose to cancer in the kidney proximal tubule.

scholarly journals Single Cell Genetic Profiling of Tumors of Breast Cancer Patients Aged 50 Years and Older Reveals Enormous Intratumor Heterogeneity Independent of Individual Prognosis

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3366
Author(s):  
Anna-Sophie Liegmann ◽  
Kerstin Heselmeyer-Haddad ◽  
Annette Lischka ◽  
Daniela Hirsch ◽  
Wei-Dong Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Copy Number ◽  
Genome Instability ◽  
Single Cells ◽  
Gene Mutations ◽  
Intratumor Heterogeneity ◽  
Breast Cancer Patients ◽  
Luminal A ◽  
Pik3ca Mutations

Purpose: Older breast cancer patients are underrepresented in cancer research even though the majority (81.4%) of women dying of breast cancer are 55 years and older. Here we study a common phenomenon observed in breast cancer which is a large inter- and intratumor heterogeneity; this poses a tremendous clinical challenge, for example with respect to treatment stratification. To further elucidate genomic instability and tumor heterogeneity in older patients, we analyzed the genetic aberration profiles of 39 breast cancer patients aged 50 years and older (median 67 years) with either short (median 2.4 years) or long survival (median 19 years). The analysis was based on copy number enumeration of eight breast cancer-associated genes using multiplex interphase fluorescence in situ hybridization (miFISH) of single cells, and by targeted next-generation sequencing of 563 cancer-related genes. Results: We detected enormous inter- and intratumor heterogeneity, yet maintenance of common cancer gene mutations and breast cancer specific chromosomal gains and losses. The gain of COX2 was most common (72%), followed by MYC (69%); losses were most prevalent for CDH1 (74%) and TP53 (69%). The degree of intratumor heterogeneity did not correlate with disease outcome. Comparing the miFISH results of diploid with aneuploid tumor samples significant differences were found: aneuploid tumors showed significantly higher average signal numbers, copy number alterations (CNAs) and instability indices. Mutations in PIKC3A were mostly restricted to luminal A tumors. Furthermore, a significant co-occurrence of CNAs of DBC2/MYC, HER2/DBC2 and HER2/TP53 and mutual exclusivity of CNAs of HER2 and PIK3CA mutations and CNAs of CCND1 and PIK3CA mutations were revealed. Conclusion: Our results provide a comprehensive picture of genome instability profiles with a large variety of inter- and intratumor heterogeneity in breast cancer patients aged 50 years and older. In most cases, the distribution of chromosomal aneuploidies was consistent with previous results; however, striking exceptions, such as tumors driven by exclusive loss of chromosomes, were identified.

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