Bioinformatics

Helping You to Find a Mechanistic Hypothesis before Entering the Clinic

Biomarker Analysis, Tissue Sensitivity and Targeting

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 IC50values and gene mutation data to identify genomic drug response biomarkers for cancer. Case studies have confirmed patient stratification markers that have been used in the clinic (Uitdehaag et al., 2019).

Cancer Cell Line Proliferation Data as Input

Oncolines™ bioinformatics analysis can be done for IC50 values or other response parameters, for instance 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.

Back to Home – Oncolines™

There are 4 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., 2018).

Read more

Tissue Sensitivity Analysis

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

Read more

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).

Read more

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).

Read more

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