Contents

1 Overview

The NCI-60 cancer cell line panel has been used over the course of several decades as an anti-cancer drug screen. This panel was developed as part of the Developmental Therapeutics Program (DTP, http://dtp.nci.nih.gov/) of the U.S. National Cancer Institute (NCI). Thousands of compounds have been tested on the NCI-60, which have been extensively characterized by many platforms for gene and protein expression, copy number, mutation, and others (Reinhold, et al., 2012). The purpose of the CellMiner project (http://discover.nci.nih.gov/cellminer) has been to integrate data from multiple platforms used to analyze the NCI-60, and to provide a powerful suite of tools for exploration of NCI-60 data. While CellMiner is an unmatched resource for online exploration of the NCI-60 data, consideration of more specialized scientific questions often requires custom programming. The rcellminer R package complements the functionality of CellMiner, providing programmatic data access, together with functions for data visualization and analysis. These functions are approachable for even beginning R users, as illustrated by the initial examples below. The subsequent case studies, inspired by CellMiner-related publications, show how modest amounts of code can script specialized analyses, integrating multiple types of data to yield new scientific insights. rcellminer functions also provide robust building blocks for more extensive tools, as exemplifed by the package’s interactive Shiny applications.

2 Basics

2.1 Installation

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("rcellminer")
BiocManager::install("rcellminerData")

2.2 Getting Started

Load rcellminer and rcellminerData packages:

library(rcellminer)
library(rcellminerData)

A list of all accessible vignettes and methods is available with the following command.

help.search("rcellminer")

2.3 Searching for Compounds

The NSC number is a numeric identifier for substances submitted to the National Cancer Institute (NCI) for testing and evaluation. It is a registration number for the Developmental Therapeutics Program (DTP) repository, and it is used as the unique identifier for compounds in the CellMiner database. NSC stands for National Service Center.

rcellminer allows users to quickly search for NSC IDs by compound name or partial name. For example, many kinase inhibitors end with the suffix “nib”. Users can quickly search NSCs for compound names with this suffix; queries are case insensitive and are treated as regular expressions.

searchForNscs("nib$")  
##  Fostamatinib     Semaxanib     Gefitinib     Erlotinib     Lapatinib 
##        365798        696819        715055        718781        727989 
##     Dasatinib     Pazopanib   Selumetinib      Imatinib     Lapatinib 
##        732517        737754        741078        743414        745750 
##     Nilotinib     Sunitinib      Afatinib     Pazopanib    Amuvatinib 
##        747599        750690        750691        752782        754349 
##     Bosutinib     Masitinib     Cediranib     Foretinib    Lenvatinib 
##        755389        755400        755606        755775        755980 
##    Crizotinib   Quizartinib    Linsitinib     Intedanib  Cabozantinib 
##        756645        756647        756652        756659        757436 
##     Neratinib      Axitinib     Intedanib     Sapitinib     Tivozanib 
##        757439        757441        757442        758005        758007 
##    Tivantinib     tepotinib    Trametinib     Ponatinib   Saracatinib 
##        758242        758244        758246        758487        758872 
##     Dovitinib     Gefitinib     Dasatinib    Tipifarnib    Vandetanib 
##        759661        759856        759877        760444        760766 
##    Tandutinib     Motesanib  Cabozantinib    brigatinib   Vemurafenib 
##        760841        760843        761068        761191        761431 
##     Ibrutinib    Crenolanib     Alectinib    Dabrafenib      Brivanib 
##        761910        763526        764040        764134        764481 
##    Gandotinib     Alectinib    Varlitinib     Bosutinib   Refametinib 
##        764820        764821        764823        765694        765866 
##   Dacomitinib   Momelotinib    Fedratinib  Lestaurtinib  Fostamatinib 
##        765888        767598        767600        772196        772992 
##     Bafetinib    Rebastinib     Telatinib   Encorafenib    Defactinib 
##        773263        774831        776017        778304        778364 
##   Osimertinib   spebrutinib     Volitinib    Defactinib    Poziotinib 
##        779217        780020        782121        782549        783296 
##   Altiratinib    brigatinib  gilteritinib     Bafetinib  sitravatinib 
##        784590        787457        787846        788186        788203 
## Acalabrutinib     olmutinib    ensartinib   ulixertinib    Sulfatinib 
##        791164        792848        793150        797771        797937 
##  zanubrutinib      Afatinib     Alectinib      Axitinib     Bafetinib 
##        799318        799327        799328        799341        799354 
##   Binimetinib   Quizartinib  Cabozantinib     Ceritinib   Cobimetinib 
##        799361        799659        800066        800072        800075 
##    Crenolanib    Crizotinib   Dacomitinib     Dasatinib    Defactinib 
##        800079        800080        800084        800087        800089 
##     Dovitinib   Entrectinib    Fedratinib     Foretinib  Fostamatinib 
##        800092        800095        800099        800101        800102 
##     Gefitinib  Gilteritinib    Golvatinib     Ibrutinib     Lapatinib 
##        800105        800106        800107        800769        800780 
##  Lestaurtinib    Linsitinib     Masitinib   Momelotinib     Neratinib 
##        800782        800784        800789        800800        800803 
##     Nilotinib   Osimertinib    Pacritinib     Pazopanib     Pelitinib 
##        800804        800812        800814        800839        800841 
##    Pexmetinib     Ponatinib    Poziotinib   Quizartinib    Rebastinib 
##        800844        800855        800856        800857        800863 
##   Refametinib   Rociletinib     Sapitinib   Saracatinib   Selumetinib 
##        800864        800872        800876        800878        800882 
##     Sunitinib   Tesevatinib    Tivantinib     Tivozanib    Trametinib 
##        800937        800946        800951        800952        800956 
##   Ulixertinib    Vandetanib    Varlitinib   Vemurafenib    Canertinib 
##        800959        800961        800962        800964        801011 
##   futibutinib  Belvarafenib  Zanubrutinib  Mobocertinib  Cerdulatinib 
##        813488        817040        823807        825519        825827 
##  Fruquintinib 
##        829498

2.4 Profile Visualization

Often, it is useful for researchers to plot multiple data profiles next to each other in order to visually identify patterns. Below are examples for the visualization of various profiles: single drugs and multiple drugs, as well as molecular profiles and combinations of drug and molecular profiles.

# Get Cellminer data
drugAct <- exprs(getAct(rcellminerData::drugData))
molData <- getMolDataMatrices()

# One drug
nsc <- "94600"
plots <- c("drug") 
plotCellMiner(drugAct, molData, plots, nsc, NULL)

# One expression
gene <- "TP53"
plots <- c("exp") 
plotCellMiner(drugAct, molData, plots, NULL, gene)