BipotentR identifies targets for bipotent drugs – single drugs that can kill cancer by multiple mechanisms. BipotentR was developed to address the low response rates to existing therapies targeting single mechanisms that also suffer from high relapse rates due to evolved resistance to single mechanisms. It allows researchers to systematically identify bipotent targets whose inhibition act against advanced and resistant cancers by inducing immunity and suppressing an input oncogenic pathway. BipotentR identified bipotent immunotherapeutic targets that also regulate other hallmark cancer pathways, including energy metabolism (38 immune-metabolic regulators), angiogenesis (14 regulators), and evasion of growth suppressor (14 regulators).

The website provides an interface to explore bipotent gene regulators that modulate antitumor immunity and input pathways (among KEGG pathways).

Find bipotent targets of :

For identifying bipotent regulator of any other input pathway, the website also contains detailed instructions for downloading and using a R package for more advanced applications.

Machine learning based estimation of bipotent targets

Please use the [BTAS website] to access machine learning and deep learning models which estimate tumor activity of bipotent target that are predictive of immunotherapy response for melanoma patients.

Standalone (R and Python based) software of BTAS is available [here.]

Download data and code of BipotentR manuscript

For reprodicibility, data and code from the manuscript are shared publicly at Zenodo

Bulk and single RNA-seq data generated from the current study are available at [RNA-seq] and will be submitted to a public repository upon publication.

How do I cite BipotentR?

Sahu AD, Wang X, Munson P, Wang X, Gu S, Nicol P, Qian G, Zeng Z, Brown M, Liu JS, Juric D, Meyer C, Liu XS, Fisher DE, Flaherty KT. Data-driven discovery of targets for bipotent anticancer drugs identifies Estrogen Related Receptor Alpha [Bioarxiv]

Contact:

Avinash D. Sahu: asahu@ds.dfci.harvard.edu
David E. Fisher: dfisher3@mgh.harvard.edu
Keith T. Flaherty: KFLAHERTY@mgh.harvard.edu

BipotentR | © Dana Farber Cancer Institute

Bipotent immune-metabolic regulators that modulate the energy metabolism pathways and antitumor immunity concurrently
Energy metabolism pathways included :

Glycolysis
Oxidative phosphorylation
Tricarboxylic acid cycle (TCA cycle)
Fatty acid metabolism

Bipotent regulators that modulate the angiogenesis and antitumor immunity concurrently

Bipotent regulators that modulate the evasion of growth supressor and antitumor immunity concurrently

Select/Type input pathway

Identify bipotent regulators that modulate the selected input pathway and antitumor immunity concurrently

Introduction to BipotentR package

Below, we describe instruction to setup R package of BipotentR. The following tutorial is designed to BipotentR pacakge overview of the kinds of comparative analyses on complex cell types that are possible using the Seurat integration procedure. Here, we address a few key goals:

Install BipotentR R package
Download reference data
Identify bipotent targets of CITRATE CYCLE and immune response using BipotentR

Install

library(devtools)
install_github("vinash85/TRIM")

Download reference dataset

The hdf5 file for cistrome dataset is downloaded by download.data.

library(TRIM)
download.data()

Run BipotentR

Here we describe running the package for CITRATE CYCLE as a input pathway. The genes within TCA cycle is stored in the variable c2.kegg. BipotentR is main function of the package. The outputs will be stored in bipotent.targets

tca.pathway =  c2.kegg[c2.kegg$term=="KEGG_CITRATE_CYCLE_TCA_CYCLE",]$gene  
## default location download.data store file
cistrome.hdf5.path =  sprintf("%s/data//human_100kRP.hd5", system.file(package = "TRIM")) 
bipotent.targets = BipotentR(tca.pathway, cistrome.hdf5.path)

Github

The R package is available in github at BipotentR package.

Machine learning based estimation of bipotent targets

Please use the [BTAS website https://rconnect.dfci.harvard.edu/BTAS/] to access machine learning and deep learning models which estimate tumor activity of bipotent target that are predictive of immunotherapy response for melanoma patients.

Standalone (R and Python based) software of BTAS is available [here.]

Download data and code of BipotentR manuscript

For reprodicibility, data and code from the manuscript are shared publicly at Zenodo https://zenodo.org/record/7043369

Bulk and single RNA-seq data generated from the current study are available at [RNA-seq] and will be submitted to a public repository upon publication.