Slide #1 image

Cancer Drug Resistance

1: Efflux pumps.
2: Decrease in drug uptake.
3: Increased drug metabolism.
4: Altered cell cycle check points.
5: Altered drug targets.
6: Impaired apoptotic pathways. .

Slide #1 image

Which Drugs to take ???

In the era of NGS, it is well known that every cancer patient is unique. Looking at the drug resistance problem to anticancerous drugs, there is a need for predictive rules which can suggest right drug to the right patient.

Slide #1 image

Drug Prioritization Prediction

Prioritization: Prediction of drug prioritization based on mutation/expression/CNV of given genes.
Drug Calculator: Interactive calculation of drug resistance based on probability.
Genome Submit: Prediction of anticancer drug based on NGS data.
Signatures: Browsing significant and correlated genes.

Drugs for available for Cancer

Slide #3 image

Slide #3

NextPrev
jQuery UI Tabs - Default functionality
##### Reference: Gupta et al. (2016) Prioritization of anticancer drugs against a cancer using genomic features of cancer cells: A step towards personalized medicine. Scientific Reports 6, 23857. #####

Major Features of CancerDP


  • Drug Prioritization:

    Drug prioritization modules allowed users to priortize drugs on the basis SVM models. These SVM models were developed on four types of genomic features viz. mutation, variation, expression and copy number variation (CNV). These genomic features wer e used as input features to model the drug response of 24 anticancer drugs.
    - Mutation based SVM models were developed on the mutation status of 388 cancer genes (extracted from CCLE). User can either give the mutation status of these 388 genes of they can also give the mutation status of lesser number of genes, but at the cost of performance.
    - Variation based SVM models were developed on the normal variations of these cancer genes reprted in 1000 Genome project.
    - Expression based SVM models were developed on the expression status of 1200 cancer genes in around 900 cancer cell lines.
    - CNV based SVM models were developed on the copy number status cancer genes in around 900 cancer cell lines.
    - Hybrid SVM models were developed using all the four genomic features together to take their cumulative advantage.
    Users can use any of these models for drug prioritization by providing their respective information.