Driver Gene Discovery
Driver Gene Discovery - Related Videos
06:52
Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
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Cited by 1 •
2020
Presented here is a protocol to discover the overexpressed driver genes maintaining the established cancer stem-like cells derived from colorectal HT29 cells. RNAseq with available bioinformatics was performed to investigate and screen gene expression networks for elucidating a potential mechanism involved in the survival of targeted tumor cells.
09:29
Investigating Drivers of Antireward in Addiction Behavior with Anatomically Specific Single-Cell Gene Expression Methods
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Cited by 1 •
2022
The combination of laser capture microdissection and microfluidic RT-qPCR provides anatomic and biotechnical specificity in measuring the transcriptome in single neurons and glia. Applying creative methods with a system's biology approach to psychiatric disease may lead to breakthroughs in understanding and treatment such as the neuroinflammation antireward hypothesis in addiction.
13:19
Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
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Cited by 16 •
2013
Human endogenous retroviruses (HERV), which occupy 8% of the human genome, retain scarce coding capacities but a hundred thousand long terminal repeats (LTRs). A custom Affymetrix microarray was designed to identify individual HERV locus expression and was used on prostate cancer tissues as a proof of concept for future clinical studies.
13:37
Polymer Microarrays for High Throughput Discovery of Biomaterials
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Cited by 32 •
2012
A description of the formation of a polymer microarray using an on-chip photopolymerization technique. The high throughput surface characterization using atomic force microscopy, water contact angle measurements, X-ray photoelectron spectroscopy and time of flight secondary ion mass spectrometry and a cell attachment assay is also described.
01:26
Drug Discovery: Overview
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2023
Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
FAQs
Driver gene discovery distinguishes alterations that actively influence tumor behavior from changes that simply accompany growth. Recurrent alterations can provide evidence that a gene contributes to cancer, while functional importance indicates that the change has a relevant effect on tumor biology. This distinction helps prioritize candidates for further study and prevents every detected genomic change from being treated as equally meaningful.
Tumor sequencing can reveal mutations, copy-number changes, and other genomic alterations, while computational analyses help identify patterns that are recurrent or functionally important. Considering these signals together broadens the search beyond one mutation type. The resulting candidates can then be assessed for their connection to cancer initiation, progression, or treatment resistance.
Validation moves candidate genes from computational prioritization to biological testing. Cellular or experimental models can determine whether an alteration produces effects consistent with a cancer-promoting role. Linking those effects to disrupted pathways or molecular dependencies explains why the candidate matters and can reveal features that distinguish tumor cells from healthy tissue.
A typical investigation begins with sequencing tumor samples to catalogue genomic alterations. Computational analysis then highlights recurrent or functionally important candidates. Researchers next use cellular or experimental models to test the candidates' effects. This sequence connects genomic observations with biological evidence and produces findings suitable for interpretation in cancer research.
Findings from driver gene discovery can support cancer classification, biomarker development, targeted therapy selection, and design of new treatments. Classification uses genetic features to distinguish tumor groups, whereas biomarkers can indicate relevant molecular characteristics. Targeted therapy selection connects a tumor's alterations with treatment choices, and new-treatment design uses identified dependencies as starting points.
Within cancer research, the approach is especially valuable when tumors differ in the genes they alter or when treatment resistance emerges. Identifying alterations linked to resistance can help characterize that outcome, while recognizing molecular dependencies may expose distinctions between tumor and healthy tissue. These results support treatments tailored to individual tumors rather than relying only on broad cancer categories.