Modeling Precision Treatment of Breast Cancer

Daemen A, Griffith OL, Heiser LM, et al. Modeling precision treatment of breast cancer. Genome Biol.

2013;14(10):R110. doi:10.1186/gb-2013-14-10-r110.

Article Background and Summary• Breast Cancer has a total of six subtypes previously identified and documented. These subtypes respond

differently to chemotherapy and hormone treatments. Currently doctors only test for a handful of molecular signatures and over 40% of those patients cancer does not fit in those categories. Cell lines are often used in research for pre-clinical models, as they mirror many of the molecular characteristics in tumors.

• This study focuses on over 70 different Breast Cancer cell lines on over 90 different therapeutic agents. This includes SNP Array, RNA-seq, exome-seq (exome capture), genome-wide methylation, and RPPA protein abundance studies as well as integrating a number of algorithmic methods to identify molecular features including: least squares-support vector machine and random forest algorithms.

• This work was able to develop predictive drug response signatures and this research can be built upon with future clinical models. One issue with this study is a cell panel does not capture features such as tumor microenvironment that is critical to understanding tumors.

• Types of Cell Lines Used :– Luminal- often chemotherapy responsive and endocrine responsive– Basal- often chemotherapy responsive and endocrine nonresponsive– claudin-low- intermediate response to chemotherapy – normal and normal like cell- breast cancer cell lines arose after chemical exposure (such as 184A1) and

primary cell lines – Unknown- includes cell lines that are contaminated (such as MT3) and lymphoblastoid cell lines (such as

HCC1007)

Data Information• Exome-Seq -GSE48215

– 75 breast cancer cell lines underwent exome-seq to identify mutations

– Samples by Type: Basal 17, Claudin-low 8, Luminal 29, Non-Malignant 5, Unknown 16

• RNA-Seq -GSE48213– 56 Cell lines were profiled in their baseline,

unperturbed state. – Samples by Type: Basal 15, Claudin-low 7, Luminal

32, Non-malignant 6, Unknown 4– Agilent Bioanalyzer High Sensitivity chip

• Methylation by Array –GSE42944– DNA methylation in 55 Breast Cancer

samples – Extraction protocol - DNA was extracted with

TNES/PK (Tris/NaCl/EDTA/SDS/proteinase K) lysis buffer prior to sodium bisulfite conversion using the Zymo Research EZ DNA Methylation Kit.

– Hybridization protocol - Bisulphite-converted DNA was amplified, fragmented and hybridized to Illumina Infinium Human Methylation27 BeadChips using the standard Illumina protocol.

This study developed candidate response signatures by analyzing associations between biological responses to therapy and pretreatment omics signatures.

Pretreatment Measurements: 1. mRNA expression (Affymetrix) (56 cell lines)2. Genome copy number (SNP6) (74 cell lines)3. Protein expression (RPPA) (49 cell lines) 4. Gene mutation (exome-seq) (75 cell lines) 5. Transcriptome sequencing (RNA-seq) (56 cell

lines) 6. Methylation assay (47 cell lines)

The software developed in the publication applies signatures of response developed in vitro to measurements of expression, copy number, and/or methylation for individual samples and produces a list of recommended treatments ranked according to predicted probability of response and in vitro GI50 dynamic range

T-Bioinfo Analysis Steps 1.RNA-seq of 56 Breast Cancer Cell Line samples

2. Junk RNA on Non-mapped Reads from Previous RNA-seq

3. Exome-seq on 75 Breast Cancer Cell Line Samples

Gene, Isoform, and exon expression profiles of Breast cancer cell lines.

Repetitive Elements and Kchain abundances

List of prospective mutations (chromosome position)

4. Machine Learning Steps: Unsupervised BiAssociation and Clustering, as well as Principal Component Analysis

Preliminary Conclusions • Principal Component Analysis: has identified genes and

isoforms and find that separation of genomic elements expressed is related to cancer progresses.

• Unsupervised Analysis: investigating BiAssociation for a number of expression datasets (gene, isoform, REs).

• Future Step: common cancer marker identification between SNPs for pancreas and lung cancers. Additionally, we are investigating “hotspots” with an algorithm (BS) developed in the Tauber Research Center