Discovery Cancer Panels

The term "Panel" originates from NGS (next-generation sequencing) technology and pertains to the concurrent identification of numerous genes and loci. The composition of a gene panel isn't fixed in terms of gene quantity. It can encompass a small handful of genes, dozens, or even extend to hundreds or thousands. The panel's dimensions are contingent upon both the quantity of genes identified and the expanse of the genomic region they cover.

Sequencing coverage indicates the fraction of sequences obtained through sequencing in relation to the total genome size of the panel. Due to complexities such as high GC content and repetitive sequences in the genome, certain regions might remain unsequenced, forming gaps. For instance, if a tumor panel has a coverage of 99%, it means that 1% of the sequence remains unobtained from sequencing, resulting in a 1% coverage gap.

Sequencing depth represents the total number of bases obtained through sequencing divided by the size of the genome being tested. This parameter assesses the amount of sequencing performed. For example, if a panel has a size of 2.0M and a sequencing depth of 500X, the total data acquired is 1.0G. (Sequencing depth = total data volume / panel size = 1.0G / 2.0M = 500X).

Distinct from sequencing depth, effective sequencing depth reflects the average depth of the target region after eliminating duplicate sequences. Consider a 2.0M panel with downstream data volume of 1.0G. If 50% of the data corresponds to the target region and 50% to duplicate sequences, the sequencing depth is 500X (1.0G / 2.0M). However, the effective depth is 125X (1.0G * 50% * 50% / 2.0M) after accounting for duplicates.

Sensitivity measures the percentage of individuals with a positive test result, indicating the ability of the test to correctly identify true positives. It is linked to the false-negative rate. Sensitivity (Sen) is calculated as Sen = a / (a + c) %, where 'a' represents true positives and 'c' signifies false negatives.

Specificity gauges the proportion of negative test results among individuals without the disease, reflecting the test's ability to correctly identify true negatives. This parameter is related to the false-positive rate. Specificity (Spe) is calculated as Spe = d / (b + d) %, where 'd' represents true negatives and 'b' signifies false positives.

The Limit of Detection refers to the lowest concentration of an analyte that the test can detect within a sample. This metric showcases the reagent assay's analytical sensitivity, demonstrating its capability to identify even minute amounts of the target substance.

Yes, elevating sequencing depth can enhance sensitivity and specificity, impacting LOD. However, there's a point of diminishing returns. Beyond a certain depth, the LOD won't improve further. Increasing depth excessively can generate data duplication, wasting resources. For example, if a ctDNA sample's LOD is 5,000 copies, deeper sequencing won't detect more. So, while depth matters, an excessively deep sequence doesn't necessarily mean a better LOD. You can contact our technology teams for more details.

Our sequencing service delivers a comprehensive array of data based on our in-house sequencing platforms, including:

When considering oncogene testing, the following sample hierarchy is recommended:

Our ctDNA NGS Panel is the recommended genetic testing panel for peripheral blood samples. This panel is tailored to effectively analyze circulating tumor DNA (ctDNA) present in peripheral blood, providing valuable insights into tumor genetics. This approach enables a non-invasive and convenient method for tumor genetic testing through blood samples.

Yes, peripheral blood, as a control sample, serves as a valuable reference point, enhancing the precision and reliability of your results. By providing peripheral blood as a control, the specificity of detected genetic variants can be confidently attributed to tumor cells. This control aids in distinguishing between somatic mutations present in the tumor tissue and germline variants inherent to an individual's genetic makeup. Ensuring the presence of tumor-specific genetic changes is paramount for accurate oncology medication decisions or determining TMB.

The optimal choice among oncogene test panels (small, medium, or large) depends on a variety of factors. Each panel size has its own advantages, and the decision should be based on economic considerations and the patient's specific situation.

In cases TMB (Tumor Mutational Burden), MSI (Microsatellite Instability), and MMR (Mismatch Repair) are needed, a larger panel can be valuable. The choice should be guided by factors like tumor type, the purpose and significance of the research, financial considerations, and your capacity for resource allocation.

Our tailored approach to customization ensures that you get the most relevant and informative results for informed decision-making.