ClinOmicsTrailbc 1.0
A visual analytics tool for breast cancer treatment stratification using multi-omics data
Standard-of-care drug assessment
ClinOmicsTrailbc assesses the putative feasibility of a set of 17 standard-of-care breast cancer treatments. To this end, for each drug, several markers affecting the effectiveness of the drug are assessed based on their genomic and transcriptomic status. ClinOmicsTrailbc investigates the molecular drug target(s), relevant enzymes like cytochromes and transporters, as well as relevant (target) signaling pathways.
The assessment of each drug is based on the feasibility status of five key molecular and functional categories:
- Predictive biomarkers
- Molecular drug targets
- Drug-processing enzymes
- Resistance-promoting efflux transporters
- Associated pathways
Predictive biomarkers that should be present in a given state for a drug to be effective are assessed mainly on the state of the biomarker as indicated by the user in the clinical data section of the start page. However, these indications are also compared to molecular evidence provided by gene expression and/or copy number data and in case there are inconsistencies in the data, i.e. one or both molecular data sets disagree with the biomarker state selected by the user, this is indicated.
The underlying hypothesis for the evaluation of the status of molecular drug targets is that it might be favorable for the drug if its molecular target is highly expressed in the tumor in comparsion to healthy cells and hence is likely to be involved in the disease-driving mechanisms. ClinOmicsTrailbc also investigates whether a somatic mutation is present and assesses its severity to determine if the target molecule is still functional and whether or not binding of the compound to the target might be compromised.
Many drugs require an activation by drug-metabolizing enzymes like cytochromes in the liver. Here, ClinOmicsTrailbc assesses the germline mutation status of relevant enzymes to assess whether they might be compromised in the liver.
A general resistance mechanism in tumors is the increased activity of efflux transporters that carry the compound out of the cell, hence decreasing its intracellular concentration and potential to act as intended. Here, we especially focus on gene expression scores to detect increased transporter activity, but also take somatic mutations from the tumor into account. However, not in all cases a high transporter activity is associated with poor drug effectiveness: Some drugs (e.g. tamoxifen or lapatinib) can inhibit certain transporters and hence can improve drug efficacy when administered in combination with another drug that might have been affected by high efflux transporter activity.
Here we assess the activity of a pathway based on the gene expression scores of a characteristic subset of pathway-associated genes. Here the hypothesis is that pathways affected or even directly targeted by the drug under consideration should be active. By this, we ensure that the drug actually tackles a signaling cascade driving the disease. On the other hand, pathways that are describing other disease mechanisms that are not targeted by the drug should be inactive, as otherwise chances of a successful treatment with one compound alone are low.