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Discovery and research
Published in Peter S. Harper, The Evolution of Medical Genetics, 2019
This work, like that involving the Huntington's disease gene described above, arose from genetic linkage studies; after the BRCA1 gene had been localised by an American group, the ICR workers, also studying familial breast cancer, realised that a number of large British families were clearly not linked to the same region and in fact proved to be localised to chromosome 13. Using positional cloning techniques, including chromosomal changes involving the region, they were able to isolate the gene, opening the door not only to accurate genetic testing, but also to a series of studies, still ongoing, indicating the normal function of the gene in DNA repair and its role when mutated in the genesis of tumours. These have now continued into the genomic era and are giving important information on the somatic changes found in the much more frequent non-familial cases of breast and other cancers. The Cancer Genome Project, based at the Cambridge Sanger Institute and again led by Michael Stratton, is now taking these primarily somatic genomic studies forward on a large scale, especially in relation to identifying differing responses to therapies.
Historical Milestones
Published in Tariq I. Mughal, Precision Haematological Cancer Medicine, 2018
Progress in precision cancer medicine, following the important landmark in the study of DNA in 1953 by James Watson and Francis Crick (Cambridge) with Rosalind Franklin and Maurice Wilkins (London), was initially relatively slow, as molecular pathology studies progressed, in tandem with efforts to sequence RNA and DNA. Many of the sequencing efforts were led by Frederick Sanger (Cambridge), in 1975. The concept of sequencing tumours was considered appealing to both patients and physicians, and encouraged substantial investments by global governments and other funding bodies as well as many cancer centres. The Sanger Institute led the way for the development of the Cancer Genome Project and in the United States, the Cancer Genome Atlas (TCGA) commenced in 2005 and the Genomic Data Commons (GDC) Data Portal in June 2016. Collectively these and other global efforts, have led to our currently enhanced understanding of the molecular pathways that underlie malignancies. Since 2015, there has been additional interest with the launch of several substantive endeavours, such as the Cancer Moonshot Project conceptualized by the previous US President Barack Obama and Vice President Joseph Biden.
Applying immune-related lncRNA pairs to construct a prognostic signature and predict the immune landscape of stomach adenocarcinoma
Published in Expert Review of Anticancer Therapy, 2021
Yujiao Wang, XinXing Zhang, Xiaosong Dai, Dingxiu He
To evaluate the clinical application of the signature, we computed the half inhibitory concentration (IC50) of commonly used chemotherapy or targeted drugs in the TCGA dataset. Cancer-related chemotherapeutic drugs such as cisplatin, mitomycin C, rapamycin, gefitinib, doxorubicin, docetaxel, PAC.1, lapatinib, cytarabine, vinorelbine and methotrexate are commonly used in the treatment of STAD. The pRRophetic package selected 138 kinds of drugs from more than 700 cell lines in the Cancer Genome Project (CGP) database and developed a drug response prediction algorithm using the expression matrix of the CGP database [23]. We used this package in R software to calculate the IC50 values. The Wilcoxon test was used to compare the difference in IC50 values between the high-risk and low-risk groups, and these results are displayed as a box plot obtained with the ggplot2 package.
Unifying heterogeneous expression data to predict targets for CAR-T cell therapy
Published in OncoImmunology, 2021
Patrick Schreiner, Mireya Paulina Velasquez, Stephen Gottschalk, Jinghui Zhang, Yiping Fan
Publicly available expression data have been used to identify tumor-specific antigens in previous studies. For example, microarray data have been used to mine potential immunotherapy targets in pediatric cancers such as B-lineage ALL (B-ALL) and solid tumors.22 Similarly, RNA-sequencing (RNA-seq) and proteomics data have been used to predict antigen targets in adult AML patients.23 An integrative approach that can use data generated from microarray and RNA-seq can maximize the power of discovering immunotherapy targets by increasing the sample size, which is critical for pediatric cancer, a rare disease with limited number of patient samples. Such an approach has not yet been explored due to heterogeneities in sample acquisition and assay platforms. For example, data on the expression of non-disease tissues by transcriptome sequencing (RNA-seq) can be compiled from publicly available resources such as the Genotype-Tissue Expression (GTEx) project.24 Data on disease tissues such as cancer in children, however, have been generated by RNA-seq through other initiatives such as the Pediatric Cancer Genome Project (PCGP) or the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project.25,26 Further, more than 3,000 microarray datasets have been made publicly available in the Gene Expression Omnibus in 2019 alone, which shows that microarray data remain a valuable resource for gene expression profiling despite the emergence of RNA-seq as the main platform for gene expression quantification in recent years. Significant challenges remain in unifying and normalizing these two data resources, given that microarray data measure relative expression whereas RNA-seq data measure absolute read counts.
Profiling targetable immune checkpoints in osteosarcoma
Published in OncoImmunology, 2018
Troy A McEachron, Timothy J Triche, Laurie Sorenson, David M Parham, John D Carpten
Osteosarcoma RNA sequencing data was downloaded from the NCI TARGET Osteosarcoma database. B-acute lymphoblastic leukemia, T-acute lymphoblastic leukemia, mixed lineage leukemia, acute myeloid leukemia, low-grade glioma, ependymoma, high-grade glioma, medulloblastoma, choroid plexus carcinoma, rhabdomyosarcoma, melanoma, retinoblastoma, adrenocortical tumor, and osteosarcoma RNA sequencing data are part of the Pediatric Cancer Genome Project and was downloaded from the St. Jude Cloud PeCan Data Portal (https://pecan.stjude.cloud/home).69