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   Ruxolitinib    Crenolanib     Alectinib    Dabrafenib 
##        761910        763371        763526        764040        764134 
##      Brivanib    Gandotinib     Alectinib    Varlitinib     Bosutinib 
##        764481        764820        764821        764823        765694 
##   Refametinib   Dacomitinib   Momelotinib    Fedratinib  Lestaurtinib 
##        765866        765888        767598        767600        772196 
##     Vatalanib      Apatinib  Fostamatinib     Bafetinib    Rebastinib 
##        772499        772886        772992        773263        774831 
##     Telatinib   Encorafenib    Defactinib   Osimertinib   spebrutinib 
##        776017        778304        778364        779217        780020 
##     Volitinib    Defactinib    Poziotinib   Altiratinib    brigatinib 
##        782121        782549        783296        784590        787457 
##  gilteritinib     Bafetinib  sitravatinib Acalabrutinib     olmutinib 
##        787846        788186        788203        791164        792848 
##    ensartinib   ulixertinib    Sulfatinib  zanubrutinib      Afatinib 
##        793150        797771        797937        799318        799327 
##     Alectinib      Axitinib     Bafetinib   Binimetinib   Quizartinib 
##        799328        799341        799354        799361        799659 
##  Cabozantinib     Ceritinib   Cobimetinib    Crenolanib    Crizotinib 
##        800066        800072        800075        800079        800080 
##   Dacomitinib     Dasatinib    Defactinib     Dovitinib   Entrectinib 
##        800084        800087        800089        800092        800095 
##    Fedratinib     Foretinib  Fostamatinib     Gefitinib  Gilteritinib 
##        800099        800101        800102        800105        800106 
##    Golvatinib     Ibrutinib     Lapatinib  Lestaurtinib    Linsitinib 
##        800107        800769        800780        800782        800784 
##     Masitinib   Momelotinib     Neratinib     Nilotinib   Osimertinib 
##        800789        800800        800803        800804        800812 
##    Pacritinib     Pazopanib     Pelitinib    Pexmetinib     Ponatinib 
##        800814        800839        800841        800844        800855 
##    Poziotinib   Quizartinib    Rebastinib   Refametinib   Rociletinib 
##        800856        800857        800863        800864        800872 
##     Sapitinib   Saracatinib   Selumetinib     Sunitinib   Tesevatinib 
##        800876        800878        800882        800937        800946 
##    Tivantinib     Tivozanib    Trametinib   Ulixertinib    Vandetanib 
##        800951        800952        800956        800959        800961 
##    Varlitinib   Vemurafenib    Canertinib   futibutinib  Belvarafenib 
##        800962        800964        801011        813488        817040 
##  Zanubrutinib  Mobocertinib  Cerdulatinib  Fruquintinib     Anlotinib 
##        823807        825519        825827        829498        832523

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)