Tumor Mutation Analysis
Tumor Mutation Analysis - Related Videos
11:15
Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
0 Views •
Cited by 8 •
2016
This manuscript describes clinical protocols for two next-generation sequencing panels. One panel interrogates hematologic malignancies while the other panel targets genes commonly mutated in solid tumors. Molecular classification of driver mutations in human malignancies offers valuable prognostic and predictive information.
13:34
A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
0 Views •
Cited by 29 •
2016
We present a three-dimensional (3D) lung cancer model based on a biological collagen scaffold to study sensitivity towards non-small-cell-lung-cancer-(NSCLC)-targeted therapies. We demonstrate different read-out techniques to determine the proliferation index, apoptosis and epithelial-mesenchymal transition (EMT) status. Collected data are integrated into an in silico model for prediction of drug sensitivity.
06:43
Robotic Duodenal Sleeve Resection for Gastrointestinal Stromal Tumor with Rare Exon 8 KIT Mutation Following Neoadjuvant Imatinib
0 Views •
2026
This video case report demonstrates the unique management of a duodenal gastrointestinal stromal tumor with a rare, understudied, exon 8 KIT mutation. Neoadjuvant therapy resulted in a 30% reduction in tumor size, making the patient a candidate for a robotic-assisted duodenal sleeve resection instead of the more morbid pancreaticoduodenectomy.
00:36
Viral Mutations
0 Views •
2019
A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material for adaptive...
07:53
Quantification of Colonic Stem Cell Mutations
0 Views •
Cited by 1 •
2015
We report significant improvements for the reproducible measurement of somatic colonic stem cell mutations after exposure of mice to potential DNA damaging agents.
FAQs
The analysis compares DNA from tumor tissue with matched normal DNA or an appropriate reference sequence. Alterations detected in tumor DNA but absent from the matched normal sample can be classified as somatic changes, whereas variants present in normal DNA may be inherited. This distinction helps separate tumor-acquired biology from a person’s underlying genetic background.
Matched normal DNA provides a patient-specific comparison for identifying alterations restricted to tumor cells. Without that comparison, inherited variants could be mistaken for cancer-specific mutations. Using both samples therefore improves interpretation of the tumor profile and supports more accurate classification of changes relevant to cancer development, diagnosis, or treatment selection.
Computational filtering narrows the large set of detected sequence changes to alterations that may be biologically or clinically relevant. It helps prioritize potentially important mutations for interpretation rather than treating every difference as meaningful. This step connects raw sequencing results with questions about tumor classification, driver mutations, and possible treatment options.
Tumor mutation analysis may examine a defined group of genes or survey broader genomic regions. A selected-gene approach concentrates interpretation on specified targets, while broader analysis can assess a wider range of alterations. The appropriate scope depends on the scientific or clinical question and influences which tumor-associated changes can be identified and prioritized.
A typical workflow uses tumor tissue as the source of DNA, sequences selected genes or broader genomic regions, and compares the resulting data with matched normal DNA or reference sequences. Computational filtering then prioritizes potentially relevant alterations. The final interpretation can support molecular diagnosis, tumor classification, treatment selection, or studies of cancer biology.
Results can help identify alterations associated with treatment selection, including mutations that indicate a tumor may respond to a targeted therapy. The analysis also contributes to molecular diagnosis and classification, giving clinicians or researchers a genetic profile to consider alongside other information. Its value lies in linking tumor alterations with precision-medicine questions.
By examining tumor mutations over time or across disease states, researchers can track changes in the tumor’s genetic composition. The appearance, persistence, or alteration of mutation patterns may reveal clonal evolution and provide insight into resistance. This application supports research into how tumors change and why treatment responses may differ as cancer progresses.