1. Transcriptomics Jiri Zavadil, PhD Molecular Mechanisms and Biomarkers
International Agency for Research on Cancer, Lyon

2. Transcriptomics - Definitions
Transcriptome - the complete set of RNA transcripts produced by the genome at a given time
Transcriptome is highly dynamic and complex in comparison to the relatively stable genome
Transcriptomics - the global study of gene expression at the RNA level
- can include genes for ncRNAs (microRNAs etc)

3. Biospecimens in I4C Mother-Child and Infant-Child Cohorts
Blood spots
Cord blood
Whole blood
Genetic, epigenetic, transcriptomic analyses (nucleic acids)
Proteomic analysis, serological and chemical analyses
Urine
Chemical, proteomic and nucleic acid analysis
Tumor cells, tissues

4. Case for Integrated Omics Analyses
The prospective biospecimen collection and retrospective case analysis will yield interconnected results
Epigenetics  gene regulation  RNA and protein markers
DNA methylation
Histone modification
Studied by transcriptomics

5. Transcriptomics – Applications for I4C
Specific gene expression
Genes and signatures determined by particular genetic, epigenetic regulatory factors, environmental exposures
Exploratory approaches
Not hypothesis driven, e.g global gene expression in tumors versus healthy tissues, differential responses to distinct environmental exposures
Disease etiology and classification
Patterns/signatures rather than single markers can improve knowledge about etiology and diagnosis

6. DNA Microarray Platforms
Illumina BeadArray
Affymetrix GeneChip
Workflow
Reverse transcription, IVT with labeled nucleotides, array hybridization, staining, washing scanning
Pros/Cons
Rapid and streamlined protocols, standardized analysis; biased target collection, levels but limited sequence information

7. MicroRNA - TaqMan Low Density Array Quantile Normalization
Total RNA
Sample
ABI 7900 SDS HT
miRNA TLDA Array
742 total target miRs
Quantile Normalization
Pros/Cons
Quantitative abundance analysis; biased target collection

8. Integrated Molecular Profiling By MPS Cancer Genome Sequencing
Massively Parallel Sequencing (MPS) - powerful nucleic acid analysis tool providing base-pair resolution information at the genome scale
Stratton, MR. Science 331, 1553 (2011)

9. Massively Parallel Sequencing
emPCR
ABI SOLiD 5500
Accuracy < 99.99%
Throughput/Day <10–15 Gb
Throughput/Run <90 Gb or >1.4 B reads
(paired-end or mate-paired runs)
Samples/Run
• 1 genome
• 12 exomes
• 6 transcriptomes

10. Massively Parallel Sequencing
Bridge amplification, clonal expansion
Illumina HiSeq2000/2500
6 human genomes at 30x
64 transcriptomes at 20M mapped reads/sample

11. mRNA Abundance Analysis
RPKM (Reads Per Kilobase per Million mapped reads)
FPKM (Fragments Per Kilobase per Million mapped reads)
Methods of quantifying gene expression levels from RNA-seq data by normalizing for total read length and the number of sequencing reads or fragments (PE reads).
Unnormalized data
Scaling Normalization
Quantile Normalization
log2(RPKM)
A A A3
A A A3
A A A3
Equivalent distribution
Identical distribution
(spread, range and median)

12. Differential mRNA Abundance Analysis
ACSL5 – normalized differential abundance ratio = 8.4

13. Single Nucleotide Variant Analysis
DNA
RNA
Non-syn SNV/mutation identified at both DNA/RNA levels

14. Acceptor Splice Sites Mutated in UUC
mRNA Splicing Aberrations 5’
Exon N
GU------A-----AG
Exon N+1 3’
Tumor RNA
Tumor DNA
The answer is yes, it does although the numbers vary. It is important to control these analyses for DNA contents as we can see here.
Normal DNA

15. Stage-Specific RNA Aberrations in ALL Mutant Allele Frequency
6 matched diagnosis/relapse pediatric ALL samples (n=12)
RNA-seq to discover novel mutations specific to relapse disease
Targeted amplicon resequencing at ultra-deep coverage
Patient ID
Mutation
Mutant Allele Frequency
(17,000-50,000x coverage)
Diagnosis
Relapse
719515
p.R238W
0.01%
27%
737185
18%
761159
31%
756421
p.R367Q
0.02%
25%
716996
p.K404KD
55%
763368
p.S408R
50%
769886
p. S445F
728610
p. L626F
49%
726584
p. E274Q
19%
p. S171I
43%
p. M244L
0.38%
47%
p. A53V
20%

16. Solutions for Low Yield Samples
Microarray and RNA-seq Transcriptome Profiling
Possible with >10 picograms total RNA
Degraded samples, RIN scores >2.0
Formalin-fixed, paraffin-embedded (FFPE) samples
Whole blood
Direct cell lysate from the equivalent of a single or a few cells
microRNA Profiling
megaplex amplification protocols ng total RNA
non- amplification based for 350 – 1000 ng total RNA

17. The Future of MPS-based OMICS
The Economist, 2011
MPS cost goes down, technologies become more advanced and powerful, platforms develop rapidly – a strong case for transcriptomics within integrated omics approaches applied to large cohorts such as I4C.

18. Considerations for I4C Transcriptomics
Low yield samples
(blood spots, extracellular microRNAs) might require application of amplification methods
Tissue and cell specificity of gene expression (e.g. cord blood vs leukemic clone) – need for carefully matched controls
Only genes and RNAs expressed at the time of sampling are detected
Depth of coverage needs for RNAseq affect cost-related decisions
Specific disease progression stages might mask etiology-associated aberrations
Bioinformatics – limited standards for complex data processing and analysis (RNAseq), more benchmarking studies needed using data from consortia-like efforts (FDA’s SEQC). Data storage and access solutions.

19. Thank you….