rna seq - pdx models
Post on 09-Jan-2017
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Whole Transcriptome Profiling of Cancer Tumors in Mouse PDX Models
http://www.impactjournals.com/oncotarget/index.php?journal=oncotarget&page=article&op=view&path%5B%5D=8014
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)
Introduction
• Dataset: 21 samples from 3 subtypes of breast cancer in 3 different mouse models.• Goals: identify a clear signal showing transcriptional differences between cancer subtypes
1) Identify differences in expression between cancer subtypes and between mouse models 2) Select representative genes that could be considered as biomarker candidates
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 TN: Survival rates are lower for this cancer than ER+ cancer types.
Breast ER+: Treatment often includes Hormone Therapy and has a more positive outlook in the short term.
Breast HER2+: Tends to be a more aggressive cancer type than ER+.
Breast Cancer Subtypes
Sample Summary
For More information: http://www.cancer.org/cancer/breastcancer/detailedguide/breast-cancer-classifying
Biological Data Repositories
Project Accession Number
What is a FastQ file?What is a FastQ File?
FASTA Format:Text Based File without the Quality Score
Step 1: RNA-seq pipeline prepares all annotated and non-annotated genomic element estimation of
expression levels
Removing genomic elements that did not have any expression (all zeros) in the RSEM table. This includes both the isoform and gene tables.
Quantile NormalizationPrincipal Component
Analysis
Step 2: RSEM output tables of genes and isoforms are prepared for Machine Learning
Analysis
1. Mapping by Bowtie2 using the original GTF (Mouse and Human Genome Combined)
2. RSEM Expression Table: Quantification of Gene and Isoform Level Abundance
3. Outputs include Genes Table and Isoform Table
Factor Regression Analysis
Visualization of T-Bioinfo Bioinformatics Functions
Lets First Build Our RNA-seq Pipeline!
When your RNA-seq pipeline is complete….
Quantile Normalization
Before Normalization
After Normalization
Gene Name Sample Names
Multi-Sample Normalization is considered a standard and necessary part of RNA-seq Analysis. - Unwanted Technical Variation
Biological Databases- Great for Annotation!
https://david.ncifcrf.gov/http://www.ensembl.org
Now back to the T-BioInfo Platform! 1. Start a PCA Pipeline2. Analyze our PCA Visualization 3. Create a Scatter Plot Image from our Results4. Utilize DAVID and ENSEMBL to investigate Biological Meaning 5. Learn about other Machine Learning Methods6. Understand a “real” RNA-seq project timeline
T-Bio.Info Platform: http://tbioinfopb1.pine-biotech.com:3000PCA Visualization:
Triple_NEG ER+ HER2+
PC1:22.16%, PC2:9.22%
Estrogen Receptor
Triple Negative
-8 -6 -4 -2 0 2 4 6
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-2
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Breast Cancer Genes
PCA of Human Tumor By Samples and By Genes
Visualization Link:
• Extracellular Matrix Remodeling
• Cell Migration • Tumor Growth• Angiogenesis
ERR1084798_Triple_NEG
ERR1084799__Triple_NEG
ERR1084800__Triple_NEG
ERR1084801__Triple_NEG
ERR1084802__Triple_NEG
ERR1084803__Triple_NEG
ERR1084804__Triple_NEG
ERR1084807__Triple_NEG
ERR1084808__Triple_NEG
ERR1084809__Triple_NEG
ERR1084810__Triple_NEG
ERR1084768__Triple_NEG
ERR1084767_HER2
ERR1084811_ER+
ERR1084811_ER+
ERR1084806_ER+
ERR1084763_ER+
ERR1084764_ER+
ERR1084765_ER+02468
1012
Matrix Metalloprotease 14 Expression in Breast Cancer Samples
Breast Cancer Samples
Leve
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Upregulated in Triple Negative Cancer Samples
Defining the Breast Cancer Subtypes
• Estrogen Regulated Proteins
• Oncogenic• Bone
Metastasis
TFF3 is a promoter of angiogenesis in Breast Cancer . This protein is secreted from mammary carcinoma cells to promote angiogenesis.
TFF3 also promotes angiogenesis by direct functional effects on endothelial cellular processes promoting angiogenesis.
TFF3 stimulates angiogenesis to co-coordinate with the growth promoting and metastatic actions of TFF3 in mammary carcinoma to enhance tumor progression and dissemination.
ERR1084809_Tr
iple_NEG
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ERR1084810_Tr
iple_NEG
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iple_NEG
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iple_NEG
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iple_NEG
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iple_NEG
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+
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+
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+
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ERR1084775_ER
+02468
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Trefoil Factor 3 in Breast Cancer
Breast Cancer Samples
Leve
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ssio
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Upregulated in Estrogen Receptor + Samples
Significance of Hormones to Breast Cancer- Endocrine Therapy
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ERR1084767_HER2
ERR1084766_Triple_NEG
ERR1084768_Triple_NEG
ERR1084800_Triple_NEG
ERR1084802_Triple_NEG
ERR1084803_Triple_NEG
ERR1084804_Triple_NEG
ERR1084807_Triple_NEG
ERR1084808_Triple_NEG
ERR1084809_Triple_NEG
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Estrogen Receptor Expression in Breast Cancer Samples
Breast Cancer Samples
Leve
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xpre
ssio
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Estrogen Stimulates the cell proliferation of the Breast cancer cell
Progesterone receptor testing is a standard part of testing for breast cancer diagnosis
ERR1084768_Triple_NEG
ERR1084775_Triple_NEG
ERR1084798_Triple_NEG
ERR1084799_Triple_NEG
ERR1084800_Triple_NEG
ERR1084801_Triple_NEG
ERR1084802_Triple_NEG
ERR1084804_Triple_NEG
ERR1084807_Triple_NEG
ERR1084808_Triple_NEG
ERR1084809_Triple_NEG
ERR1084810_Triple_NEG
ERR1084767_HER2
ERR1084811_ER+
ERR1084805_ER+
ERR1084806_ER+
ERR1084763_ER+
ERR1084764_ER+
ERR1084765_ER+012345678
Progesterone Receptor Expression in Breast Cancer Samples
Breast Cancer Sample
Leve
l of E
xpre
ssio
n Progesterone receptors, when activated by progesterone, actually attached themselves to the estrogen receptors, which caused the estrogen receptors to stop turning on the cancer promotion gene.
Then they actually turned on the genes that promote death of cancer cells (called apoptosis), and the growth of healthy cells!
Upregulated in Estrogen Receptor Cancer
Factor Regression Analysis
A0B0 Triple Neg/ Athymic Nude
A0B1 Triple Neg-/SCID
A1B0 ER+/ Athymic Nude
A1B1 ER+/ SCID
Factor A: Triple Negative vs. ER+
Factor Table (2 factors, 2 levels each)
Triple Negative Samples ER+ Samples
Selecting Human Genes Under the Influence of Either Triple Negative Breast Cancer or Estrogen Positive Breast Cancer
* Will have either large table for Factor analysis or visualization table
Gene Expression Key
*No Significant Mouse Genes
Tumor – Stroma Association Study
•Expression Table (alignment of reads on comprised genome)•Separate Human and Mouse genes/isoforms/exons•BiAssociation: Links between Human and Mouse
genes/isoforms/exons•P-clustering of all selected correlated/anti-correlated
genes/isoforms/exons •Results: Network of associations between stroma and
tumor genes/isoforms/exons
Tumor Samples enriched with immune processes
Batch Effect- Enrichment of mitochondrial and ribosomal genes
RNA-Seq Experiment Overview 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)
HER2 ER+TNBC
NOD SCID XID Athymic CB17 SCID
1. Ribosomal Depleted RNA2. Fragment RNA3. TruSeq RNA Sample
Preparation Kit4. Concatenated Genome
(Mouse/Human) 5. Indexed with star align
Secondary Analysis
Tertiary Analysis Gene Summary and Ontology Report
1. Mapping using TopHat2. Finding Isoforms using Cufflinks3. GTF file of isoforms using Cuffmerge4. Mapping Bowtie-2t on new transcriptome
Cancer Subtypes
Mouse Species
Thanks for Listening!
Any Questions? Contact: Info@pine-biotech.com
T-Bioinfo Platform : http://tbioinfopb1.pine-biotech.com:3000
Pine Biotech Website: http://pine-biotech.com
Pine Biotech Education Website: http://edu.t-bio.info
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