Pdx project

1. Whole Transcriptome Profiling of Cancer
Tumors in Mouse PDX Models
Based on Breast Cancer Samples taken from the publication “Whole
transcriptome profiling of patient-derived xenograft models as a tool to
identify both tumor and stromal specific biomarkers”
(James R. Bradford et. al.; DOI: 10.18632/oncotarget.8014)

2. PDX Mouse Species
XID: Characterized by the absence of the
thymus, mutant B lymphocytes, and no T-cell
function.
NOD SCID: Severe combined
immunodeficiency, with no mature T cells
and B cells.
Athymic Nude: Lacks the thymus and is
unable to produce T-cells
Breast Cancer Subtypes
1. ER+ : Positive for the estrogen receptor, treatment includes hormone therapy and drug treatments targeting the estrogen
receptor. The most common subtype of diagnosed breast cancer. Positive outlook in the short term.
1. HER2+ : Overexpress human epidermal growth factor, HER2/neu, a growth-promoting protein. This type of cancer tends to
be more aggressive than ER+ or PR+ breast cancer. Cannot be treated with hormone therapy, but there are targeted drug
treatments.
1. Triple Negative : Negative for estrogen receptor and progesterone receptor, and does not overexpress HER2/neu. Most
cancers with mutated BRCA1 genes are triple negative. This type responds to surgery/chemotherapy, but tends to recur
later. No targeted therapy, although some treatments in development. Survival rates lower than for other breast cancer
subtypes. This cancer type occurs in 15-20% of those diagnosed with breast cancer in the United States.
Biological Background

3. Article Background and Summary
Human tumor cells from patients with varying cancer types and stages were placed in four
different mouse models. Later, RNA from human tumor cells and mouse stromal cells was
extracted and analyzed using unsupervised and supervised analysis methods on the T-Bio
platform. Large, integrative project that is total RNA with biological replicates. Within this study,
there are many different subtypes of cancer that could be broken into educational sections that
should demonstrate the strength of the T-bio algorithms/approach.
This study is the first comprehensive analysis across PDX models, this focused on identifying the
specific stromal cell type, investigated the relationship between human tumor and mouse stroma
and identify specific biomarkers for both tumor and stroma.
Types of Cancer investigated:
Breast, Lung, GI, Ovarian, Endometrial and Leukemia

4. Data Information
Extracted Molecule: Total RNA
Extracted Protocol: 50mg of tissue were cut from the frozen tumors
and RNA isolated using the RNeasy Lipid Tissue Mini Kit (Qiagen)
Genome: mouse/human (human cells are placed into
immunocompromised mice to grow tumors)
Instrument Model: Illumina Hi-seq 2000
Sample #: 79
Sample Type:
Lung (37 samples)-
Lung adenocarcinoma (18)
Lung Squamous (14)
Small Cell Lung Cancer (3)
Lung (other)(2)
Breast (19 samples)-
Breast TN (13)
Breast ER+ (5)
Breast HER2+ (1)
GI (12 samples)-
CRC (8)
Pancreatic (2)
Ampullary (2)
Ovarian (7 samples)
Endometrial (3 samples)
CLL (1 sample)
Data Generation:
RNA libraries were made with the Illumina TruSeq RNA
Sample Preparation kit (un-stranded) according to the
manufacturer’s protocol. These libraries were then
submitted for 100 bp paired-end sequencing on the
Illumina HiSeq 2000 platform using one lane per three
to six PDX models. A concatenated human
(GRCh37/hg19) and mouse (GRCm38/mm9)
genome was then constructed to form a single
genome of 43 chromosomes (23 from human and
20 from mouse). This was indexed using StarAlign
(https://github.com/alexdobin/STAR/releases) and a
“gtf” formatted file combining annotations from both
human and mouse genes downloaded from Ensembl
version 75

5. T-Bioinfo Analysis Steps
1. RNA-seq of 79 cancer
samples
2. Junk RNA on Non-mapped
Reads from Previous RNA-seq
3. Machine Learning
Gene, Isoform, and exon
expression profiles of cancer
tumor/stroma samples .
Repetitive Elements and Kchain
abundances
Unsupervised BiAssociation and clustering
approach allowed identification of some
specific samples. As well as Batch Effect
Correction was used in this project.

6. Stroma-Specific Sample Identification
Preliminary
Conclusions
Unsupervised analysis of transcriptome sequencing
data allowed for identification of the following:
● Identification and Correction of Batch Effect across a
number of samples
● Samples with Stroma specificity identified by analysis of
the stromal expression
● Cancer-Specific samples were identified, this is currently
under investigation