Bioinformatics Analysis Using Cancer Cell Line Proliferation Data as Input

Helping to Bring Improved and Novel Therapies to the Right Patient Population Faster

Drug sensitivity markers for your compound can be investigated using the response parameters of cancer cell line profiling, without the need for additional experiments. For instance, we perform analysis on IC50 values and gene mutation analysis to identify genomic drug response biomarkers for cancer. Case studies have confirmed patient stratification markers that have been used in the clinic (Kooijman et al., 2022; Uitdehaag et al., 2019).

Oncolines® bioinformatics analysis can also be done for other response parameters, such as GI50 or Area Under the Curve (AUC). The analysis is available for proliferation data from the Oncolines® cancer cell line panel as well as proliferation data that have been generated elsewhere.

There Are Four Types of Analysis:

  • Gene Mutation Analysis

  • Tissue Sensitivity Analysis

  • Gene Expression Analysis via GeneNominator™

  • Comparative Analysis via OncolinesProfiler™

Gene Mutation Analysis

The drug sensitivity of cancer cell lines is correlated to the cancer gene mutation status of the cell lines, yielding novel candidate drug sensitivity biomarkers (Uitdehaag et al., 2019). These biomarkers are used as selection markers for patient stratification (Zaman et al., 2017).

Tissue Sensitivity Analysis

This analysis indicates whether anti cancer drugs or drug candidates show preferential activity in a certain cancer tissue type. 

Gene Expression Analysis via GeneNominator™

Drug sensitivity data is coupled to gene expression data. It uses large datasets of gene expression to identify interconnections between sensitivity and resistance to your anti cancer drug and gene transcription (Uitdehaag et al., 2019).

Comparative Analysis via OncolinesProfiler™

The drug sensitivity fingerprint of compounds in Oncolines® is used for comparative analyses with other anti-cancer agents (Uitdehaag et al., 2016) and mechanism-of-action studies (Libouban et al., 2017).

We can use your internal cancer cell line proliferation data or Oncolines® proliferation results