Cancer is one of the deadliest diseases known to mankind. Throughout history, doctors, healers, and researchers have been trying to find a cure for this terrible condition. According to the National Cancer Institute, there are about eight different ways of treating cancer. Cancer has been a disease that has been researched for years, often times resulting in novel technologies that are paving the way for researchers to study it deeply. As a result, a growing collection of highly detailed data on various manifestations of this disease are being collected. Specifically, sequencing of DNA and RNA using “high throughput” or “next-generation” sequencing has become a standard for studying molecular mechanisms of cancer, cancer progression, and treatment. Next-Generation Sequencing (NGS) in cancer research provides new perspectives for diagnosis, cancer classification, predictive biomarkers, and personalized treatment.
NGS is a powerful technique that can reveal the whole genome, exome and transcriptome data on samples. This data can be analyzed for new and infrequent gene alterations, identify biomarkers and create a molecular portrait for different kinds of cancer. DNA sequences offer important insights for biomedical research and are now being used for clinical diagnostics as well. With improving technologies, NGS can now be used to sequence many DNA samples at once at a lower cost. Genetic analysis of tumors using NGS helps researchers understand tumor composition and identify specific changes that have led to normal tissue to become a tumor.
As biomedical researchers gain access to more and more data, new algorithms including machine learning are being used for detecting patterns associated with cancer malignancy, stage or outcome. This type of gene expression profiling of cancer tumors and identification of subtypes provides actionable insight for diagnosis and treatment of the disease. It also helps identify predictive biomarkers that are key to successful treatment at an earlier stage and are showing promise in making Oncology diagnostics and treatment selection more precise. Currently, predictive biomarkers discovery to clinical practice for personalized medicine has been approved for only five diseases: colon, breast, lung cancer, melanoma, and chronic myeloid leukemia published recently by Shabani et. al. (Iran J Public Health. 2018).
|ERBB2 (HER2)||Receptor tyrosine kinase (ERBB2)||Breast, bladder, gastric & lung cancer||ERBB2 inhibitors
|MET||RTK (MET)||Bladder, gastric & renal cancer||MET inhibitors
|DDR2||RTK||Lung adenoid cystic carcinoma & lung large cell carcinoma||Some tyrosine kinase inhibitors|
|PIK3CA, PIK3R1||PI3K||Breast, colorectal & endometrial cancer||PI3K inhibitors|
|PTEN MTOR & TSC1||PI3K mTOR||Numerous cancers Tuberous sclerosis & bladder cancer||PI3K inhibitors
|FGFR1||FGFR1||Myeloma, sarcoma, bladder, bresast, ovarian, lung, endometrial & myeloid cancer||FGFR inhibitors
|BRCA1 & BRCA2||(DNA damage repair signaling)||Breast & ovarian cancer||PARP inhibitors|
To learn more about these topics and get hands-on experience with NGS data in oncology, we are launching a new program where you can get acquainted with the various -omics data types. In this program, we will discuss methods of analysis, annotation, and interpretation of NGS data that can be analyzed to understand the basic biology associated with cancer onset, development, and outcomes. We will also see how large-scale clinical trials and experiments provide an opportunity to improve precision oncology with machine learning.
The Precision Oncology 2020 program will begin on 9th March 2020. This 3-month hands-on program will be conducted online with bi-weekly sessions and online tutorials.
Pre-register here: https://edu.tbioinfo.com/precision-oncology-2020