lecture 11. topics in omic studies (cancer genomics, transcriptomics and epignomics) the chinese...

Lecture 11. Topics in Omic Studies (Cancer Genomics, Transcriptomics and Epignomics)

The Chinese University of Hong KongCSCI5050 Bioinformatics and Computational Biology

CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015 2

Lecture outline1. Special considerations in cancer omics

Last update: 13-Nov-2015

SPECIAL CONSIDERATIONS IN CANCER OMICS

Part 1


Some considerations• Large number of mutations– Structural variations– Driver vs. passenger mutations– Tumor heterogeneity

• Mixture of tumor and non-tumor cells• Emphasis of somatic changes– Choice of control samples

• Presence of cancer sub-types• Search for druggable targets



Large number of mutations• Causes:– Carcinogens (polycyclic

aromatic hydrocarbons (PAH) in cigarette smoke, UV, etc.)

– Defect of DNA repair– Disrupted apoptosis

pathway


Image credit: Brown and Attardi, Nature Reviews Cancer 5(3):231-237, (2005)


Structural variations• In many types of omic studies, SVs are

considered rare.• In cancer omic studies, the detection of SVs is

considered an indispensible step.



Driver vs. passenger mutations• Driver mutation: Causal mutation in oncogenesis– Growth advantage– Positively selected

• Passenger mutation: Not contributing to cancer development


Image credit: Stratton et al., Nature 458(7239):719-724, (2009)


Detection of driver mutations• Mutations that affect known cancer genes• Unexpected high frequency of recurrence– Same mutation in different cells in the same

sample• Detected by allele ratio• Implication of early event and positive selection

– Mutations that affect the same genes/pathways in different samples

– Statistical significance needs to carefully evaluated according to the non-uniform background [discussion paper]



Tumor heterogeneity• Due to the high mutation rate, different tumor

cells in a tumor can have different genomes– And potentially transcriptomes and epigenomes

• Standard sequencing of a tumor sample results in data that reflect the mixed population of cells rather than individual cells– Sequencing different parts of a tumor– Single-cell sequencing• Potential biases caused by whole-genome amplification



Tumor heterogeneity• Single-cell sequencing from different sectors of a

breast cancer sample:


Image credit: Navin et al., Nature 472(7341):90-94, (2011)


Mixture of tumor and non-tumor cells

• Presence of infiltrating stromal and immune cells– Micro-dissection– Estimation of tumor content– Computational removal of “contaminating” data

from non-tumor cells



Consequence of non-tumor cells• Suppose the sample contains c% of non-tumor

cells– If G=AA in normal cells and there are no

sequencing errors, expect• (100-c)% reads supporting alternative allele if G=aa in

tumor cells• (100-c)/2% reads supporting alternative allele if G=Aa

in tumor cells



Consequence of non-tumor cells• Assuming all bases have a phred score of 30 (base

error=0.001) and 60x coverage:


1E-2091E-1961E-1831E-1701E-1571E-1441E-1311E-1181E-105

1E-921E-791E-661E-531E-401E-271E-14

0.1

0 10 20 30 40 50 60 70Contamination rate, c

Tumor genotype=aa, non-tumor genotyp=AA

Pr(D|G=aa)

Pr(D|G=Aa)

Pr(D|G=AA)


Consequence of non-tumor cells• Assuming all bases have a phred score of 30 (base

error=0.001) and 60x coverage:


1E-1781E-1671E-1561E-1451E-1341E-1231E-1121E-101

1E-901E-791E-681E-571E-461E-351E-241E-13

0.01

0 10 20 30 40 50 60 70Contamination rate, c

Tumor genotype=Aa, non-tumor genotyp=AA

Pr(D|G=Aa)

Pr(D|G=AA)

Pr(D|G=aa)


Emphasis on somatic changes• Comparing tumor and non-tumor samples• Choice of non-tumor control:– Normal tissue• How to obtain? Transplant?

– Tumor-adjacent from same patient• Can be considered as normal?

– Blood from same patient• Useful given different tissue types?



Special analysis pipelines


Figure credit: Saunders et al., Bioinformatics 28(14):1811-1817, (2012); Wang et al., Genome Medicine 5(10):91, (2013)

http://genomemedicine.com/content/5/10/91/figure/F1


Cancer sub-types• Patients diagnosed to have the same type of

cancer could have very different prognosis and drug response

• Cancer sub-types can be identified by molecular signatures



Cancer sub-types• Sub-types of gastric cancer:


Figure credit: The Cancer Genome Atlas Research Network, Nature 513(7517):202-209, (2014)


Druggable targets• Cancer omics do not only aim at

understanding the molecular mechanisms, but also identifying druggable targets

• Druggable targets: Proteins of mutated/aberrantly activated genes with known inhibitors– Other members of the same families (e.g.,

kinases)



Identification of druggable targets• Computational docking• siRNA screens• CRISPR-Cas9 knock-out



Summary• Factors of abnormal allele ratios– Copy number variation– Tumor heterogeneity– Contamination of non-tumor cells

• Some major research directions– Identification of driver (somatic) mutations– Discovery of cancer sub-types– Search for druggable targets


lecture 11. topics in omic studies (cancer genomics, transcriptomics and epignomics) the chinese...

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