Gene Mutation Analysis for Patient Stratification Strategies Using Response Biomarkers

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

The mechanism of action of your compound can be investigated with response parameters of cancer cell line profiling without the need for additional experiments. For instance, we perform analysis of variance (ANOVA) on IC50 values 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).

Selection of Patients Likely to Respond

Precision medicine is about the match between drug targeting and patients’ individual responses. Early proof of concept studies on response pathways help to progress your drug candidates towards the clinic and select patients whose genetic makeup corresponds to the drug’s target.

Ranking 102 cancer cell lines from the Oncolines™ panel on sensitivity for dabrafenib
Ranking 102 cancer cell lines from the Oncolines® panel on relative sensitivity in proliferation assays. Cell lines expressing mutant BRAF are indicated in red (all have V600E mutation, except for BxPC-3); wild type in blue

Identification of Oncogenic Mutation Markers

Genetic variations occur in many capacities and only a restricted number of DNA mutations has been implicated in cancer. With the aim to identify biomarkers that have clinical relevance, the pharmacogenomics analysis at Oncolines concerns mutations that have been observed in cancer patients. Clinically actionable mutations are analysed by zooming in on independently validated hotspot mutations and copy number variations.

Your Compound May Target a Subpopulation of Patients

We can help you to identify a patient stratification marker for your compound. Cancer cell line profiling data, generally IC50 data, is used as a source of input. In case your mechanism is better represented by another parameter (e.g. Area Under the Curve), we adapt the analysis accordingly.

  • Gene mutation analysis on Oncolines® data or data from other cancer cell line panels

  • Selected genes with reported oncogenic relevance

  • Genes of specific interest can be added at your request

  • Drug sensitivity data is related to gene mutation data

  • Biomarkers are identified by ANOVA analysis and t-testing, including corrections for multiple testing

  • Genetic associations can be validated by knockout studies, protein expression analysis and other assays

Figure 1: Ranking 102 cancer cell lines from the Oncolines™ panel on sensitivity (pIC50) in cell proliferation assays. Cell lines expressing V600E mutant of BRAF are indicated in red; wild type in blue
The volcano plot shows the relationship between cell line sensitivity and mutant status of cancer genes. Cell lines with a BRAF mutation are on average ~100X more sensitive to dabrafenib than wild type cell lines (x-axis; the value 1 represents the average IC50 on a log scale). The p-value (y-axis) represents the statistical significance.

Peer Reviewed

The identification of a novel genomic drug response biomarker and its validation (in xenograft models) is described by Zaman et al. (2017).
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Peer Reviewed

Uitdehaag et al. (2019) describe the identification of genomic drug response biomarkers and related clinical relevance.
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Case Study

The case study ‘CDK4/6 inhibitors’ illustrates genomic biomarker analysis.
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Information Needed?

For more information about gene mutation analysis please contact us.

References

Zaman et al. (2017) TTK inhibitors as a targeted therapy for CTNNB1 (β-catenin) mutant cancers. Molecular Cancer Therapeutics, 16 (11):2609-2617.

Uitdehaag et al. (2019) Combined cellular and biochemical profiling to identify predictive drug response biomarkers for kinase inhibitors approved for clinical use between 2013 and 2017. Molecular Cancer Therapeutics, 18 (2):470-481.

Kooijman et al. (2022) Comparative kinase and cancer cell panel profiling of kinase inhibitors approved for clinical use from 2018 to 2020. Frontiers in Oncology, 12:953013.

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