Software Information

1. Use of nDx 1 Software is only available to partners who are registered separately in Novomics.

2. You must pass the certification procedure on the next screen before you can use the software.

3. Click the "Certification" button to install it automatically on your connection PC.

S/W InstallPlease enter your
partner number and certification number
Partner No. Certification No.

* Contact us : +82-2-2068-3700


Adaptive Regression Algorithm

Adaptive regression algorithm was defined by adaptive regression analysis applying ΔCq values of 4 target genes and 5 reference genes. Generally, while data is processed based on a median or average of values selected by an array, in an algorithm according to the adaptive regression technique, a point having the largest variance of separated average interval values obtained when an arbitrary point of the total data is determined as a reference point is determined as an adaptive regression value. That is, the threshold value is a reference point that distinguishes high expression and low expression of a corresponding gene, which are biologically significant, in normal and cancer tissue.

Binary Signal Based Two Tier System

The prognosis and predictive classification are performed by a binary signal based two tier system. The first tier uses two immune classifier genes (GZMB and WARS) to identify immune high patients. The second tier uses a stem-like classifier gene (SFRP4) or an epithelial classifier gene (CDX1) to classify other patients as stem-like high patients or epithelial high patients independently by using ST and EP classifiers, respectively.

First Tier: Classifying a group as a Low risk group and no-benefit group when ΔCq values of GZMB and WARS are higher than threshold values.

Second Tier-Prognosis: At least one ΔCq value of GZMB and WARS is lower than the threshold value, classifying a group as an Intermediate risk group when the ΔCq value of SFRP4 is lower than threshold value and a group as a High risk group when the ΔCq value of SFPR4 is higher than the threshold value.

Second Tier-Prediction: At least one ΔCq value of GZMB and WARS is lower than the threshold value, classifying a group as a no-benefit group when the ΔCq value of CDX1 is lower than threshold value and a group as a chemotherapy-benefit group when the ΔCq value of CDX1 is higher than the threshold value.

This binary signal based two tier system for prognosis and chemotherapy response prediction in gastric cancer is Single Patient Classifier (SPC).

Sahoo D, Dill DL, Tibshirani R, Plevritis SK. Extracting binary signals from microarray time-course data. Nucleic Acids Res. 2007;35(11):3705-12.

Distinctive Features of Single Patient Classifier (SPC)

  • 1. The first distinctive feature of nProfiler® 1 Stomach Cancer Assay is being able to assign individual patients into different parameters by applying SPC-prognosis and SPC-prediction, while other molecular classification system (The Cancer Genomes Atlas (TCGA), Asian Cancer Research Group (ACRG), and Singapore-Duke Classification) are population-directed and heavily dependent on the composition of the data-set and therefore not directly applicable to individual patients.
  • 2. The second distinctive feature is that to facilitate the implementation of SPC in clinical practice, it has been evaluated and confirmed the compatibility in various tissue types (fresh tissue vs. FFPE) using different assays (microarray or RNA sequencing vs. RT-qPCR).
  • 3. The third distinctive feature is that SPC stratifies gastric cancer (GC) patients to 5 molecular distinct subtypes of GC related biological characteristics; inflammatory, intestinal, gastric, mixed-stromal, and mesenchymal, with different clinical outcomes and selected 3 clinically actionable types; immune (IM), stem-Like (ST), and epithelial (EP) features, with specific gene modules. Instead of using a parallel IM+/−ST+/−EP+/− coordinate, IM-high patients sequentially stratified in the first tier in both the SPC-prognosis and SPC-prediction. The pre-filtration of the IM-high patients is pursued as Epstein-Barr virus (EBV) infection- or microsatellite instability (MSI)-induced GCs are assumed to be etiologically different diseases in GC. So that, the ST and EP classifiers would function in a more robust way independently and efficiently in the second tier.