Novel single-cell profiling technology for cancer cells
Transcript of Novel single-cell profiling technology for cancer cells
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Novel single-cell profiling technologyfor cancer cells
Samsung Genome Institute, Samsung Medical Center
Hae-Ock Lee
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Why you need single cell genomics in cancer
Nature 501, 338-345, The causes and consequences of genetic heterogeneity in cancer evolution
Nature 501, 346-354, Influence of tumour micro-environment heterogeneity on therapeutic response
Genomics: most comprehensive tool for the characterization of cancer cells
Heterogeneity in tumor cells and the Microenvironmt
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Disease with heterogeneity in DNA & RNA level
Transformation Tumor evolution Metastasis Treatment resistance
Genome Biol. 2014 Aug 30;15(8):452. PMID: 25222669
All the important events in cancer are single cell processes
Heterogeneity in mutation and copy number profiles: targeted therapies
Heterogeneity in gene expression: information on phenotype/prognosis/treatment resistance
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Abundant population
Rare cell population
Genome Biol. 2014 Aug 30;15(8):452. PMID: 25222669
Single cell capture platforms
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Amplification of DNA
Nature Review Genetics, 2016 PMID26806412
Allele dropout
Sequence Errors
Amplification bias
Method
Commercial
Source
False-negative
False-positive
Non-uniformity
DOP-PCR,
Degenerate
oligonucleotide-
primed PCR
MDA, Multiple
Displacement
Amplification
MALBAC, Multiple
Annealing and
Looping Based
Amplification Cycles
LIANTI, Linear
Amplification via
Transposon Insertion
high low intermediate
low high low
Sigma Qiagen Yikon
high low intermediate
Nature. 2014 Aug 14;512(7513):155-60. PMID: 25079324
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Most application uses SMART-seq-Either in full-length or 3’ library preparation Cell barcode and UMI (unique molecular identifier)
Clontech/Takara
K. Kim, SGI
Amplification of RNA (cDNA)
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Single cell capture
and lysis
cDNA synthesis and
amplificationHarvest and QC
Library construction
and sequencing
• Up to 96 cells (full length cDNA sequencing; WGS applications) • Size limitation (5-10,10-17, or 17-25 micron separately)• No marker selection
Microfluidic platform, C1
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High-throughput single cell capture & barcoded sequencing technologyenables transcriptome profiling for a large number of single cells
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Application in cancer
1. Gene expression heterogeneity: Targeting Tumor & Microenvironment
2. Heterogeneity in drug response: Drug Combinations
3. Mutational heterogeneity: Genotype-Phenotype Correlations
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[Study from 11 breast cancer patients and 511 individual cells]
I. Breast cancer landscape
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Tumor vs. non-tumor separation using RNA-seq data
CNV inference SNV expression
Cell type specific gene expression
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TNBC type cells in ER or HER2 tumor tissues Treatment resistance
Tumor cell heterogeneity
Rare cells with high stemness, EMT, and/or angiogenesis signatures Tumor progression and metastasis
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Immune cell heterogeneity
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Immune cell heterogeneity
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Tumor and infiltrating immune cells in breast cancer
Tumor
Macrophages
B cells
Dendritic cells
T cells
Fibroblasts
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Implications in T cell-targeting therapyExpression of gene signatures on T lymphocytes extracted from 5 patients covering 4 cancer types
Cytotoxic Exhausted
Naive
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II. Heterogeneity in drug response
Primary
Lung metastasis
Patient tumor Xenograft tumor Xenograft tumor cells
Genome Biology 2016
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[A case Study in Metastatic renal cell carcinoma]
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Evaluation of expression signatures associated with metastatic RCC
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Prediction of drug sensitivity: Heterogeneity
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Combinatorial drug treatment based on the prediction
H-W. Lee & KM. Joo
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Nature Review Genetics, 2016 PMID26806412
-Difficulties in the single cell DNA analysis
III. Heterogeneity in genetic mutation
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Typical low sequencing depth in single cell data
-CNV estimation from DNA 0.4x (0.07x with new sequencing method)-SNV: needs targeted deep sequencing-Gene expression, 100,000 reads in general
C1 platform
10x genomics Gemcode platform
[RNA-seq]
[WGS]
Nature. 2014 Aug 14;512(7513):155-60. PMID: 25079324
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Expressed mutations in single cell RNA-seq
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WES vs. Expressed mutations in RNA
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-identification of candidate driver gene(s) from a single patient.
Expressed mutations for genotype-phenotype analysis
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Conclusions
Single cell RNA sequencing enables visualization of inter- and intra-tumoral heterogeneity
In tumor cell clones
Drug sensitivity and drug combinations
Treatment resistant mechanisms
Linking genotype and phenotype: identification of driver mutations
Better strategies for anti-cancer therapy targeting the tumor and microenvironment