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Differential Genetic Diagnosis between Leiomyoma and Leiomyosarcoma
Published in Carlos Simón, Carmen Rubio, Handbook of Genetic Diagnostic Technologies in Reproductive Medicine, 2022
Alba Machado-Lopez, Aymara Mas
Next-generation sequencing (NGS) technologies have been applied across various scientific disciplines, from basic research to translational medicine. Specifically, The Cancer Genome Atlas (TCGA) is one of the most relevant projects, which aims to characterize the molecular signatures of human cancers. Accordingly, genetic risk assessment, prognostic information, identification of molecular subtypes, or development of therapeutic strategies are some of the possible approaches currently being applied to breast, ovarian, endometrial vulvar, and cervical cancers.
Cancer Informatics
Published in Trevor F. Cox, Medical Statistics for Cancer Studies, 2022
We are not going to give an example of reading and processing FASTAQ and other sequence files, but use sequence data that has already been processed. The example is from the National Cancer Institute's Cancer Genome Atlas Program (TCGA), which collects genomic, epigenomic, transcriptomic and proteomic data that can be used by the public for research purposes (https://portal.gdc.cancer.gov). The data are placed under “projects”, which cover 33 different cancers. Some of the data need permission to be downloaded and analysed, especially sequence files. We are going to use the project labelled “TCGA-KIRC” which contains 16,225 files relating to kidney renal clear cell carcinoma data on 537 cases. The data cover: sequencing reads, transcriptome profiling, simple nucleotide variation, copy number variation, DNA methylation, clinical and biospecimen. We are interested in the transcriptome profiling and simple nucleotide variation and its relationship to the clinical data.
Predictive Modeling with Supervised Machine Learning
Published in Altuna Akalin, Computational Genomics with R, 2020
We will start our illustration of machine learning using a real dataset from tumor biopsies. We will use the gene expression data of glioblastoma tumor samples from The Cancer Genome Atlas project. We will try to predict the subtype of this disease using molecular markers. This subtype is characterized by large-scale epigenetic alterations called the “CpG island methylator phenotype” or “CIMP” (Noushmehr et al., 2010); half of the patients in our data set have this subtype and the rest do not, and we will try to predict which ones have the CIMP subtype. There two data objects we need for this exercise, one for gene expression values per tumor sample and the other one is subtype annotation per patient. In the expression data set, every row is a patient and every column is a gene expression value. There are 184 tumor samples. This data set might be a bit small for real-world applications, however it is very relevant for the genomics focus of this book and the small datasets take less time to train, which is useful for reproducibility purposes. We will read these data sets from the compGenomRData package now with the readRDS() function.
Assessing the clinical utility of multi-omics data for predicting serous ovarian cancer prognosis
Published in Journal of Obstetrics and Gynaecology, 2023
Zhe Zhang, Zhiyao Wei, Luyang Zhao, Chenglei Gu, Yuanguang Meng
Here, we conduct our research using the Genomic Data Commons (GDC) database (previously hosted by the Cancer Genome Atlas (TCGA)) as the training set and the International Cancer Genome Consortium (ICGC) database as an independent validation set. The goal of our study is to improve the utility of the molecular data used in clinical practice. Thus, we train GDC data using different computing algorithms to evaluate the prognostic utility and the treatment outcome utility. The features that determine the prognosis and treatment outcomes are represented by gene expression, somatic DNA mutation, miRNA expression, and DNA methylation levels, which show the potential ability to differentiate cancer patients into different subtypes (Zhang et al. 2011). We then assess the predictive power of each algorithm on each type of genomic feature. In particular, we evaluate the deep learning approach for binary treatment outcome classification, which offers a set of algorithms that can use multiple processing layers for unsupervised or semi-supervised feature learning and hierarchical feature extraction (Schmidhuber 2015). We then identify multiple genetic markers and pathways that are significantly related to patient survival and treatment outcomes. Our work may provide a new perspective for classifying SOC patients into different risk groups to achieve better treatment and illuminate the molecular mechanisms of SOC for future studies.
Iris melanoma outcomes based on the Cancer Genome Atlas (TCGA) classification in 78 consecutive patients
Published in Ophthalmic Genetics, 2022
Elliot Cherkas, Guy S. Negretti, Jennifer S. Zeiger, Carol L. Shields
The Cancer Genome Atlas project is a cancer sequencing program that was developed to better understand the genomic, epigenomic, transcriptomic, and proteomic basis of several different types of cancer (22). Results of the TCGA regarding uveal melanoma were first published in 2017 and identified four molecularly distinct groups, with two groups displaying disomy 3 and demonstrating favorable prognosis and two groups with monosomy 3 showing poor prognosis (14). It was later shown that these four groups (A, B, C and D) could be distinguished based upon their chromosome 3 and 8 status with group A (disomy 3 and 8) having the lowest risk of metastasis and subsequent groups (group B (disomy 3 and 8q gain), group C (monosomy 3 and 8q gain) and group D (monosomy 3 and multiple 8q gains)) having increasing risk (15). The TCGA classification for uveal melanoma prognostication has been validated in multiple studies (8–13,16). A study of 658 patients found that more advanced TCGA group was associated with increased risk of metastasis (3% vs. 10% vs. 25% vs. 41%, respectively; p < 0.001) and death (1% vs. 0% vs. 3% vs. 9%, respectively; p < 0.001) (8). A recent study of 1001 patients found, the Kaplan—Meier rate of metastasis within 5 and 10 years, respectively, for Group A was 4%/6%, 12%/20% for Group B, 33%/49% for Group C, and 60%/greater but not available for Group D (11). The peak incidence of metastasis for Groups A/B and C occurs later (during years 5–6 and years 4–6, respectively) than Group D (during years 1–2) (13). Combining TCGA and AJCC data increases prognostic accuracy in uveal melanoma (16).
Predictive biomarkers for systemic therapy of hepatocellular carcinoma
Published in Expert Review of Molecular Diagnostics, 2021
Nurbubu T. Moldogazieva, Sergey P. Zavadskiy, Susanna S. Sologova, Innokenty M. Mokhosoev, Alexander A. Terentiev
The ongoing efforts in this field are greatly enhanced by the integrated online resources and repositories, which enable the collection and processing of data obtained by whole-genome sequencing. The most prominent resource is The Cancer Genome Atlas (TCGA) (https://www.genome.gov/) of the National Cancer Institute (NCI) and the National Human Genome Research Institute. Another resource is Catalog Catalogue of Somatic Mutations in In Cancer (COSMIC) at Sanger Institute, UK (https://cancer.sanger.ac.uk/), curable to manage cancer somatic mutations data including coding and non-coding mutations, gene fusions, CNVs, and drug-resistance mutations underlying cancer promotion. TCGA project is integrated with Cancer Genomics Hub (CGHub), the online repository of sequencing programs including the Cancer Cell Line Encyclopedia (CCLE) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) projects (https://cghub.ucsc.edu). More recently, the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium (http://docs.icgc.org/pcawg) of the International Cancer Genome Consortium (ISGC) (ICGC) and TCGA was established.