Modeling precision treatment of Breast Cancer

1. 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.

2. 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)

3. 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

4. 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

5. 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