Expression and Methylation Patterns Partition Luminal-A Breast Tumors Into Distinct

Prognostic Subgroups

Dvir Netanely , Ayelet Avraham , Adit Ben-Baruch , Ella Evron and Ron Shamir

Breast Cancer Res. 2016 Jul 7;18(1):74.

Presentation by Nimrod Rappoport, “Towards the Precision Medicine Era: Computational challenges”

seminar, headed by Prof. Ron Shamir November 15, 2016

Introduction

• Improved breast cancer clustering using new expression and methylation datasets

• Use of clustering and several classic statistic tests

Expression Methylation

Cancer Subtypes

Background

• Cancer: group of diseases involving abnormal cell growth • Breast Cancer: cancer that develops from breast tissue • Most common invasive cancer in women (affects 1 in 8) • Almost half a million deaths a year

Background

• Heterogeneous disease • Therapeutic decisions based on pathologic parameters and

biomarkers

Estrogen and Progesteron receptors HER2 overexpression

Gene Expession

• RNA-Seq and microarrays

Background

• Microarray sequencing found clusters corresponding to biomarkers

• PAM50 • Attempts to cluster using different data

DNA Methylation

• Methyl group added to Cytosine (typically CpG) • Can be measured – frequency of dinucleotide methylation in tissue • Methylation of promotors typically represses gene transcription • Both hyper methylation and hypo methylation observed in cancer

Background

• Dataset by The Cancer Genome Atlas (TCGA) • Gene expression (RNA-Seq) - 1148 samples of ~20500 genes • Methylation - 679 samples of 107639 probes • Methylation samples contained in gene expression samples • PAM50 classification

Background

• Improved breast cancer clustering using new expression and methylation datasets

• Use of clustering and several classic statistic tests

Expression Methylation

Cancer Subtypes

Clustering

• Grouping objects such that objects in same group are similar • Applications in medicine, economics, politics • Overlapping vs. strict partitioning

Clustering

K Means

• Choose k initial centroids • Assign each vector to the

cluster of its closest centroid • Calculate new centroids as the

mean of vectors in that cluster • Repeat until convergence •

K Means

Clustering Results

• Clusters fit PAM50 except Luminal-A which has a mixed cluster with Luminal-B.

Luminal Clustering

• Only Luminal data is clustered with K=2.

• How can we compare which clustering better predicts prognosis?

Introduction to Survival Analysis

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Kaplan Meier

Luminal Clustering

P value

• Hypothesis testing • Null hypothesis • Probability that the sample would be the same as or more extreme

than the observed data using the null hypothesis • p = 0.05 or 0.01 are common cutoffs for statistical significance

Log Rank Test

• Nonparametric test • Compares survival distributions • Supports censoring • Confusing name

Log Rank Test

Log Rank Test

• K tables for different event times

• Symmetry • Best when hazards have constant ratio • Weighted logrank, stratified logrank and multiple variable

Luminal Clustering

• Clustering has better prognostic value than PAM50!

Luminal-A Clustering

• Reduced 5-year recurrence for R2!

Chi Square

• Tests the null hypothesis that observations are statistically independent

• (Log rank?) • Microarray data fits RNA Seq

Wilcoxon Rank Sum Test

Wilcoxon Rank Sum Test

False Discovery Rate

False Discovery Rate

Luminal A Clustering

• LumA-R1 associated with higher proliferation score, older age, decrease in normal cell percent, increase in tumor nuclei percent

• R1 and R2 correlate with ductal and lobular histological types

• Fit between Luminal-A and Luminal clusters

Gene Enrichment for Luminal A

• All 1000 most differentially expressed genes are overexpressed in R2 • LumA-R2 overexpresses genes related to the immune system. • Overexpression of both chemokines and their receptors may indicate

increased infiltration of immune system cells. • It is unclear whether this is related to reduced

tumor recurrence.

Methylation Clustering

Luminal Methylation Clustering

• LumA-M1 associated with poor 5-year prognosis! • Correlation with expression partitioning (chi square)

Methylation Cluster Analysis

• Comparing the LumA-M1 and LumA-M3 clusters • Finding differentially methylated CpGs

• Top 1000 hypermethylated CpGs are in LumA-M1

• Finding differentially methylated CpGs with over-expressed genes • Finding differentially methylated CpGs with under-expressed genes • Enrichment for differentially methylated CpGs and their genes

Spearman Correlation

Methylation and Expression Correlation

Positive correlation with expression (212 CpGs, 125 genes)

Negative correlation with expression (586 CpGs, 340 genes)

Hyper methylated CpGs (1000 CpGs, 483 genes)

Development, homeobox Tumor suppressing genes Development, homeobox, tumor suppressing genes

Gene enrichment

Gene body, under representation of TSS

TSS and 1st exon, under representation of gene body, promoter associated cell type specific

Enhancer, tissue-specific promoter, cancer DMR

CpG enrichment

Cox Survival Analysis

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Cox Survival Analysis Results

Summary

K means Clustering gene expression data

Kaplan Meier plots, log rank test Comparing prognostic value to PAM50

Chi-square Comparing RNA-Seq clustering with microarray clustering

Rank sum test, false discovery rate Finding differentially expressed genes Hypergeometric test, false discovery rate LumA-R1 and LumA-R2 enrichment

K means Clustering methylation data Kaplan Meier plots, log rank test Comparing prognosis of different clusters Rank sum test, false discovery rate, Spearman test

Finding differentially methylated CpGs and correlation with gene expression

Hypergeometric test, false discovery rate LumA-M1 and LumA-M3 enrichment

Cox Survival Analysis Adjusting prognostic value for other variables

Summary

• Clustering shows better 5-years prognostic value than PAM50 clustering

• Expression data reveals subgroup with lower 5-years recurrence rates and higher expression of immune-related genes

• Methylation data reveals subgroup of hyper methylation of developmental genes and poor survival

• Prognostic significance of partitioning confirmed by Cox analysis

Discussion

• Use of new clustering to make therapeutic decisions: • Luminal-A Luminal-B partition • hypermethylation in Luminal-A

• Overexpression of chemokines and their receptors may indicate infiltration of immune system cells

• Understanding this mechanism may lead to a specific treatment

Discussion

• Use this approach for additional subtype clustering • Take DNA Sequencing into account – expression correlation with

mutation • Use data taken over long periods of time