novel single-cell profiling technology for cancer cells

Novel single-cell profiling technologyfor cancer cells

Samsung Genome Institute, Samsung Medical Center

Hae-Ock Lee

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

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

Abundant population

Rare cell population

Genome Biol. 2014 Aug 30;15(8):452. PMID: 25222669

Single cell capture platforms

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

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)

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

High-throughput single cell capture & barcoded sequencing technologyenables transcriptome profiling for a large number of single cells

Application in cancer

1. Gene expression heterogeneity: Targeting Tumor & Microenvironment

2. Heterogeneity in drug response: Drug Combinations

3. Mutational heterogeneity: Genotype-Phenotype Correlations

[Study from 11 breast cancer patients and 511 individual cells]

I. Breast cancer landscape

Tumor vs. non-tumor separation using RNA-seq data

CNV inference SNV expression

Cell type specific gene expression

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

Immune cell heterogeneity

Immune cell heterogeneity

Tumor and infiltrating immune cells in breast cancer

Tumor

Macrophages

B cells

Dendritic cells

T cells

Fibroblasts

Implications in T cell-targeting therapyExpression of gene signatures on T lymphocytes extracted from 5 patients covering 4 cancer types

Cytotoxic Exhausted

Naive

II. Heterogeneity in drug response

Primary

Lung metastasis

Patient tumor Xenograft tumor Xenograft tumor cells

Genome Biology 2016

[A case Study in Metastatic renal cell carcinoma]

Evaluation of expression signatures associated with metastatic RCC

Prediction of drug sensitivity: Heterogeneity

Combinatorial drug treatment based on the prediction

H-W. Lee & KM. Joo

Nature Review Genetics, 2016 PMID26806412

-Difficulties in the single cell DNA analysis

III. Heterogeneity in genetic mutation

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

Expressed mutations in single cell RNA-seq

WES vs. Expressed mutations in RNA

-identification of candidate driver gene(s) from a single patient.

Expressed mutations for genotype-phenotype analysis

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