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Written by Lynna Truong, PharmD 2027
February 19, 2024
Introduction
Having emerged in the 1990s to integrate the experimentation and data analysis of genomic information, the field of genomic data science focuses on using computational and statistical methods to uncover the functional information in DNA sequences1. Notably driven by the Human Genome Project, which sequenced the human genome for the first time, genomics has since grown exponentially, helping us better understand human health and disease. Oncology, the study of cancer, is one field where genomics has played a key role in driving new advancements.
Genomic Data and Oncology
Genomic data has greatly assisted in making sense of the intricate genetic landscape of cancer, a disease characterized by a range of genetic and epigenetic alterations. Virtually all cancer genomes contain nucleotide alterations compared to the germline of cancer patients. Alterations that cause or promote cancer are known as “drivers”, and early analyses have led to the identification of these “drivers” as new targets for cancer therapy.
Genomic analyses have also helped build our understanding of the relationship between specific mutations and clinical response, leading to new approaches in the diagnoses and prognoses of cancer. For example, analyzing the entire transcriptome, which includes mRNA and miRNA sequences (coding vs noncoding sequences), reveals distinctive somatic and epigenomic alterations in different types of tumor tissues that can be used for tumor classification and prognosis. Technology has been developed to detect each alteration and has led to the discovery of new oncogenes in ovarian cancer, melanoma, lung carcinoma, colon carcinoma, and tumor suppressor genes in leukemias.
Examples and Impacts
There are a handful of organizations dedicated to the collection of genomic data in oncology. The International Cancer Genome Consortium (ICGC) unites researchers from around the world to share cancer genomics data and platforms like the Genomic Data Commons (GDC) and The Cancer Genome Atlas (TCGA) also act as repositories for large-scale coordinated cancer genomic efforts. These initiatives harmonize data using uniform analytic pipelines, and provides researchers with bioinformatics tools, increasing the interpretive power of the data.
Sharing large genomic datasets globally is crucial for ensuring that this data is being analyzed to its full capacity in order to open new opportunities for discovery. When cancer-causing changes in the genome are identified, scientists can gain a deeper understanding of cancer progression, metastasis, and drug resistance on the molecular level.
Challenges
The field of genomics also faces significant challenges, such as the ethical, legal, and societal implications of genomic data sharing. For example, privacy concerns exist in the field of genomics as there are tools that can take genomic data and trace it back to the individual from which it originated. Additionally, artificial intelligence can be used to look for hidden patterns, but AI algorithms lack transparency so biases can enter undetected. Therefore, informed consent, or permission from individuals whose data is being collected, is an important and highly regulated aspect of genomic data collection.
Moreover, the sheer volume of genomic data poses a challenge for scientists and requires improved information technology infrastructure and new computational tools to render the data suitable for meaningful analyses.
Graphic from genome.gov
Future Developments
The Cancer Genome Atlas project's identification of subtypes of endometrial cancer based on genetic characteristics has already influenced clinical trials and improved the future of endometrial cancer care. Further work in genomic data in oncology involves developing a new taxonomy of disease based on molecular pathogenesis using shared cancer genomic and clinical data. Additionally, long-term challenges include uncovering all genetic alterations that promote malignant phenotypes and discovering rare alleles for cancer drivers, and will require the analysis of a vast number of cancer cases.
Conclusion
As genomics continues to evolve, its integration into oncology will help us achieve a better understanding of cancer at the molecular level. From identifying new therapeutic targets to helping in the development of personalized treatment strategies, genomic data is revolutionizing the landscape of cancer research and care. As we navigate the ethical, technological, and analytical challenges, the potential for groundbreaking discoveries in the years to come is exciting and hopeful.
References:
Genomic Data Science Fact Sheet. Genome.gov. https://www.genome.gov/about-genomics/fact-sheets/Genomic-Data-Science
Chin L, Hahn WC, Getz G, Meyerson M. Making sense of cancer genomic data. Genes & Development. 2011;25(6):534-555. doi:https://doi.org/10.1101/gad.2017311
Home | NCI Genomic Data Commons. gdc.cancer.gov. Published 2024. https://gdc.cancer.gov/
National Cancer Institute. The Cancer Genome Atlas Program (TCGA) - NCI. www.cancer.gov. Published May 13, 2022. https://www.cancer.gov/ccg/research/genome-sequencing/tcga
Grossman RL, Heath AP, Ferretti V, et al. Toward a Shared Vision for Cancer Genomic Data. New England Journal of Medicine. 2016;375(12):1109-1112. doi:https://doi.org/10.1056/nejmp1607591
Cancer genome research and precision medicine - NCI. www.cancer.gov. Published May 13, 2022. Accessed February 18, 2024. https://www.cancer.gov/ccg/research/cancer-genomics-overview#:~:text=Putting%20large%20genomic%20datasets%20together
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