fehrman nat gen 2014 - journal club

Fehrman et al, Nat Gen 2014. Gene Expression analysis

identifies global gene dosage sensitivity in cancer

Giovanni JC 30 March 2015

What is a PCA?

• PCA is a technique to reduce a dataset with 3+ variables to two or few dimensions

• Examples: – a dataset of

individuals age, height, weight, etc..

– a dataset of gene expression

height

age

weight

heig

ht

age

weight

What is a PCA projection?

• In a PCA we rotate a 3+ dimensional plane, trying to find the best “projection” for observing separation between data points

• Implementation:– Find a line (PC1) that

separates the dataset in two groups, explaining most of the variance

– Find a second line (PC2) orthogonal to the first, to explain most of the remaining variance

PC1

PC2

Variance explained by each PC

PC coefficients• The PCA will produce a new set of data

“axes”, called Principal Components (PC)• Each PC is a combination of the original

variables, multiplied by a coefficient

Expression gene 1

Expression gene 2

Expression gene 3

Expression gene 4

Expression gene 5

PC1 PC2 PC3 PC4

* 5.4 * 3.2 *-0.4 * 0.0 *-0.2

Eigenvectorcoefficient

Interpreting each PC

● Depending on which variables contribute to a PC, we can give a biological interpretation– If weight and height contribute to PC1 while

age does not, then PC1 describes the “size” of the individual

● In gene expression, PCs can represent a set of genes expressed in the same transcription profile– Thus we rename PCs as Transcriptional Components

(TCs)

Gene Expression Dataset

• Expression data from Gene Expression Omnibus (Affymetrix, 4 datasets)

• Quality Control: – a PCA is applied to each dataset, obtaining a PC explaining

80-90% of data variance– This PC can be interpreted as probe- or platform- specific

variance, independently on the sample– All the samples that have a correlation <0.75 with this PC are

removed, as they are considered low quality

• Final dataset:– Human small: 17,309 samples– Human large: 32,427–Mouse: 17,081 – Rat: 6,023

Copy Number data and samples annotation

• 470 tumor samples with array CGH data (Agilent), analyzed with DNACopy

– 51 ERBB2-amplified breast cancer, 173 inflammatory breast cancer, 246 multiple myeloma

• Sample annotation: text-mining to determine cancer/cell line/normal samples

Number of probes and genes

Datasets for Gene Set Enrichment Analysis

PCA implementation

• Each of the 4 datasets was analyzed separately

• PCA done on the n-by-n correlation matrix, instead of co-variance matrix

– Reduces noise produced by samples with high variance

• Goal of the PCA is to identify Transcription Components, e.g. set of genes expressed in the same conditions

Parameters of the PCA• TC size: order of the component– How much of the gene expression variance is represented by the TC

• TC setting: score of the component in a given sample– How much the expression profile represented by a TC is active in the sample

• TC wiring: PC coefficient– For every gene and for every expression profile (TC), how much the expression is

supposed to change

How many Transcriptional Components there are?

● About 300 in Human

small, 600 in

HumanLarge

, …

● 2,206 TCs across all datasets

● The robustly estimated TCs (Cronbach's alpha > 0.7) captured 79-90% of the variance

Do the TCs have biological meaning?

● All the TCs had at least one gene set enriched (GSEA), meaning that they represent biological phenomena

Are the TCs different across the four datasets?

● Humansmall is very similar to Humanlarge,

● Mouse is similar to Rat

● Overall the most robust components are similar in all four datasets

TC3 represents genes

expressed in the brain

A TC-based gene network

● Constructed a gene regulation network with 19,997 genes– Two genes are connected if they are in the

same TC (co-expressed)

● This network can be used to predict gene function using “guilt-by-association” – A gene involved in a TC where 100 other

genes are associated to apoptosis is probably also associated to it

Guilt by association

● Used the 2,206 TCs from the 4 datasets

● Calculated a GSEA Z-score in each TC for each gene set

● A gene with unknown function is associated to a gene set if its GSEA scores are correlated with its eigenvector coefficients

Genes with similar function to BRCA1 and BRCA2

FEN1 is co-expressed with BRCA1 and BRCA2

The role of FEN1 in homologous recombination was not confirmed in mammals

Involvement of FEN1 in Homologous Recombination

1b: siRNA silencing of FEN1

2C, top: if homologous recombination occurs, GFP is expressed

FEM1 inhibition reduces homologous recombination

2d: chemical inhibition of FEM1 with MTT

2e: decrease of HR after inhibition of FEN1

Inhibition of FEM1 and PARP1 increases DNA breaks

2f: PARP1 inhibition

2g: higher number of DNA breaks if when both PARP1 and FEN1 are inhibited

2h: higher sensitivity to PARP1 inhibition

Identification of unstable samples

● A subset of human samples showed enrichment for genes mapping to the same chromosome band

● This is the effect of large SCNAs in cancer tissue or cancer cell lines

Autocorrelation between TC and chromosome position

Autocorrelation: eigenvector coefficients of a gene is correlated with its neighbors

e.g. expression of gene is correlated with neighbors

Identification of SCNAs from expression profiles

● Used 18,713 samples with no SCNAs to determine 718 non-genetic TCs, which are then applied to the other 18,714 samples

● SCNAs levels where correlated with residual expression (not explained by TCs), explaining 28% of variation

● This 28% variation is called Functional Genomic mRNA profile (FGM) and represent variation in gene expression that diverge from the physiological status status

Identification of potential SCNA events from expression profiles

Functional Genomic mRNA profile● FGM: Functional Genomic mRNA profile

– The portion of expression that can not be explained by the 718 physiological non-genetic PCs

● 20 trisomy samples clearly showed higher FGM expression

● In 470 cancer samples, FGM levels correlated with SCNA levels (aCGH), explaining 86% of variance

Most genes are dosage-sensitiveto chromosome arm duplications

● They did another PCA on the FGM profile data, for every chromosome arm

– Describing if there are changes in the expression of all the genes in a chromosome arm, not due to physiological constraints (718 TCs)

● The PC1, representing the most prominent FGM pattern, described a complete duplication or deletion of the arm

● 91% of the probes were dosage-sensitive to the complete duplication/deletion of a chromosome arm

More on dosage sensitivity

● Fig 4b: highly expressed genes are more dosage-sensitive

● Fig 4c: similar patterns are observed with an eQTL meta-analysis

FGM profiling of 16,172 tumor samples

● Data preparation:– Excluded cell lines (text mining + similar TC

profile)

– Excluded genetically identical samples and related individuals (based on similarity of eQTL expression) (234 mix-ups)

– Only samples with high genomic instability (high auto-correlation) (potentially cancer samples)

Hierarchical clustering of FGM

Most cancer types show samples with similarly altered expression

Some cancers have similar alteration patterns

Amplifications and deletions in the regions involved in

the FGM profiles

● Used DNACopy to determine whether the regions in FGM profiles in cancer are amplified or deleted, based on change of expression patterns (no aCGH data)

Distribution of genomic instability

● Genomic instability: autocorrelation between expression of a gene and its neighbors' – e.g. tendency of a sample to have a high number of regions

with altered expression, likely to be amplified/deleted

Higher genomic instability corresponds to lower survival and higher grade

Distribution of genomic instability across genome and genes

Samples where

CDKN2A and ERBB2 have

altered expression

Summary

● Used PCA to obtain 2,206 expression components

● Of these, 718 represent physiological non-genetic expression profiles

● The expression not explained by these 718 TCs (FGM profile) can be explained by SCNA alterations

● Most genes are dosage-sensitive, at least for arm-level alterations