Integrated Center for Oncology

Breast Cancer Gene-Expression Miner v4.3
(bc-GenExMiner v4.3)

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Glossary


[ Published annotated data ][ Published transcriptomic data ][ Data pre-processing ][ Molecular subtype classification ]
[ Statistical analyses ][ Survival statistical tests ][ Gene expression ][ Correlation map ]


Published annotated data:

bc-GenExMiner version v4.3 (current: - archives:)
Data type shown: RNAseq (available data:)

#ReferenceNo. patientsNodal
status
ER status1PR status1HER2 status1SBR
status
Age at diagnosisNPI
status
AOL
status
SSPs
status
SCMs
status
Event status   
MR   AE   
1TCGA et al., 20121 035   27   102   
2Brueffer et al., 2018405         
3Saal et al., 20183 273      336   
Total4 713   233322103327   438   

1 ER, PR and HER2 status determined by immunohistochemistry
2 NPI score could be computed only for node negative patients

Legend  Open

 No.: number of
 ER: oestrogen receptor by IHC
 PR: progesterone receptor by IHC
 HER2: HER2 receptor by IHC
 IHC: ImmunoHistoChemistry
 SBR: Scarff Bloom and Richardson grade
 NPI: Nottingham prognostic index
 AOL: Adjuvant! Online
 SSPs: Single Sample Predictors (Sorlie, Hu and PAM50)
 SCMs: Subtype Clustering Models (SCMOD1, SCMOD2, SCMGENE)
 MR: metastatic relapse
 AE: any event (any pejorative event: local relapse, metastatic relapse or death.)
 : available information
 : unavailable information

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Published transcriptomic data:

The following inclusion criteria for selection of transcriptomic data were used:
- macrodissection only (no microdissection, no biopsy),
- no neoadjuvant therapy before tumour collection,
- minimum number of patients: 40,
- no duplicate sample inside and between datasets, filtering:
. by sample ID and,
. by a threshold of Pearson correlation ‹ 0.99 which is used to avoid duplicate data,
- only female breast cancer.


bc-GenExMiner version v4.3 (current: - archives:)
Data type shown: RNAseq (available data:)

#ReferenceNo. patientsStudy codePlatform originPlatform codeDNA chipNo. unique genes (2018)Processing *bc-GenExMiner version
1TCGA et al., 20121 035   TCGARNAseqRNAseq53 700   FPKM and log24.3
2Brueffer et al., 2018405   GSE81538IlluminaGPL11154HiSeq 200018 631   FPKM and log24.3
3Saal et al., 20183 273   GSE96058IlluminaGPL11154HiSeq 200028 297   FPKM and log24.3
Total  4 713   

* Data have been converted to a common scale (median equal to 0 and standard deviation equal to 1).

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Data pre-processing:


1 DNA microarrays data

1.1 Affymetrix pre-processing:

Before being log2-transformed, Affymetrix raw CEL data were MAS5.0-normalised using the Affymetrix Expression Console.

1.2 Non-Affymetrix pre-processing:

Data have been downloaded as they were deposited in the public databases. When patient to reference ratio and its log2-transformation were not already calculated, we performed the complete process.

1.3 All DNA microarrays data:

Finally, in order to merge all studies data and create pooled cohorts, we converted studies data to a common scale (median equal to 0 and standard deviation equal to 1 a).



2 RNA-seq data

2.1 TCGA pre-processing:

RNA-Seq dataset were downloaded from the TCGA database (Genomic Data Commons Data Portal). We used the RNA-seq expression level read counts data produced by HTSeq and normalized using the FPKM normalization method b . FPKM values was log2-transformed using an offset of 0.1 in order to avoid undefined values.

2.2 GSE81540 pre-processing:

We used the Sweden Cancerome Analysis Network Breast (SCAN-B) c database. RNA-seq reads were mapped to the hg19 human genome with tophat2 and normalized in FPKM with cufflinks2 pipeline. Then Log2-transformed with an offset of 0.1.

2.3 All RNA-seq data:

Finally, in order to merge all studies data and create pooled cohorts, we converted studies data to a common scale (median equal to 0 and standard deviation equal to 1 a).

a Shabalin et al. Bioinformatics. 2008; 24,1154-1160
b Expression mRNA pipeline
c Saal et al. Genome Medicine 2015 7:20.


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Molecular subtype classification:


Table 1: Molecular subtyping methods

Molecular subtype predictor (MSP) No. genes in MSP Reference Platform correspondence R script reference Statistics Subtypes
Single sample predictor (SSP) Sorlie's SSP 500   Sorlie et al, 2003 Gene symbols; probes median (if multiple probes for a same gene) Weigelt et al, 2010 Nearest centroid classifier;
highest correlation coefficient between patient profile and the 5 centroids
Basal-like,
HER2-E,
Luminal A,
Luminal B,
Normal breast-like
Hu's SSP 306   Hu et al, 2006
PAM50 SSP 50   Parker et al, 2009
Subtype clustering model (SCM) SCMOD1 726   Desmedt et al, 2008
Wirapati et al, 2008
subtype.cluster function, R package genefu Mixture of three gaussians;
use of ESR1, ERBB2 and AURKA modules
ER-/HER2-,
HER2-E,
ER+/HER2- low proliferation,
ER+/HER2- high proliferation
SCMOD2 663  
SCMGENE 3  




Table 2: Molecular subtyping of 14 725 breast cancer patients included in bc-GenExMiner v4.3 according to 6 molecular subtype predictors. (A. DNA microarrays [n = 10 012], B. RNA-seq [n = 4 713])

A.
MSPBasal-likeHER2-ELuminal ALuminal BNormal breast-likeunclassified
No%No%No%No%No%No%
Sorlie's SSP1 457 14.6 1 182 11.8 2 937 29.3 1 144 11.4 1 315 13.1 1 977 19.7 
Hu's SSP2 282 22.8 879 8.8 2 421 24.2 1 844 18.4 1 501 15.0 1 085 10.8 
PAM50 SSP1 958 19.6 1 478 14.8 2 814 28.1 1 947 19.4 1 257 12.6 558 5.6 
RSSPC1 310 389 1 435 367 651 
MSPER-/HER2-HER2-EER+/HER2-
low proliferation
ER+/HER2-
high proliferation
-unclassified
No%No%No%No%--No%
SCMOD11 870 18.7 1 158 11.6 3 109 31.1 2 810 28.1 1 065 10.6 
SCMOD21 972 19.7 1 119 11.2 2 965 29.6 2 685 26.8 1 271 12.7 
SCMGENE2 803 28.0 1 403 14.0 2 583 25.8 2 200 22.0 1 023 10.2 
RSCMC1 294 692 1 827 1 490 
RMSPC1 060 231 827 243 
B.
MSPBasal-likeHER2-ELuminal ALuminal BNormal breast-likeunclassified
No%No%No%No%No%No%
Sorlie's SSP624 13.2 642 13.6 1 596 33.9 663 14.1 841 17.8 347 7.4 
Hu's SSP1 023 21.7 421 8.9 1 198 25.4 1 001 21.2 926 19.6 144 3.1 
PAM50 SSP832 17.7 734 15.6 1 432 30.4 1 031 21.9 640 13.6 44 0.9 
RSSPC583 208 747 224 437 
MSPER-/HER2-HER2-EER+/HER2-
low proliferation
ER+/HER2-
high proliferation
-unclassified
No%No%No%No%--No%
SCMOD1630 13.4 366 7.8 1 986 42.1 1 731 36.7 0.0 
SCMOD2666 14.1 416 8.8 1 915 40.6 1 716 36.4 0.0 
SCMGENE781 16.6 2 365 50.2 836 17.7 731 15.5 0.0 
RSCMC551 152 660 465 
RMSPC513 71 191 64 


Legend  Open

 MSP: molecular subtype predictor (SSPs + SCMs)
 No: number of patients
 SSP: single sample predictor
 RSSPC: robust SSP classification based on patients classified in the same subtype with the three SSPs
 SCM: subtype clustering model
 RSCMC: robust SCM classification based on patients classified in the same subtype with the three SCMs
 RMSPC: robust molecular subtype predictors classification



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Statistical analyses:


Several types of analyses are available: prognostic analyses, correlation analyses and expression analyses, all of which have different subtypes.

EXPRESSION ANALYSES

Targeted expression analysis:

Once the analysis criteria have been chosen (gene(s) to be tested, clinical criterion (criteria) to test the gene against), the distribution of the gene in the available population (all cohorts with availability of required information pooled together) according to the clinical criterion (criteria) is illustrated by box and whiskers plots. To assess the significance of the difference in gene distributions in between the different groups, a Welch's test is performed, as well as Dunnett-Tukey-Kramer's tests when appropriate.

Exhaustive expression analysis:

box and whiskers plots are displayed, along with Welch's (and Dunett-Tukey-Kramer's) tests for every possible clinical criteria for a unique gene.

Customised expression analysis:

Similarly to targeted analysis, distribution of a chosen gene is compared in between groups, but here, the groups are defined based on another gene: the population (all cohorts with both gene values available pooled together) is split according to the median of the latter gene, resulting in 2 groups.



PROGNOSTIC ANALYSES

Targeted prognostic analysis:

Once the analysis criteria have been chosen (gene / Probe Set to be tested, nodal and oestrogen receptor status of the cohorts to be explored, event, on which survival analysis will be based, and splitting criterion for the gene), the prognostic impact of the gene is evaluated on all cohorts pooled by means of univariate Cox proportional hazards model, stratified by cohort, and illustrated with a Kaplan-Meier curve.
Cox results are displayed on the curve. In case of more than 2 groups, detailed Cox results (pairwise comparisons) are given in a separate table.
In order to minimize unreliability at the end of the curve, the 15% of patients with the longest follow-up are not plotteda.
To evaluate independent prognostic impact of gene(s) relative to the well-established clinical markers NPIb and AOLc (10-year overall survival) and to proliferation scored, adjusted Cox proportional hazards models are performed on pool's patients with available data.

Exhaustive prognostic analysis:

Univariate Cox proportional hazards model and Kaplan-Meier curves are performed on each of the 18 possible pools corresponding to every combination of population (nodal and oestrogen receptor status) and event criteria (metatastic relapse [MR], any event [AE]) to assess the prognostic impact of the chosen gene / Probe Set, discretised according to the splitting criterion selected. Results are displayed by population and event criteria and are ordered by p-value (smallest to largest).

Molecular subtype prognostic analysis:

Patients are pooled according to their molecular subtypes, based on three single sample predictors (SSPs) and three subtype clustering models (SCMs), and on three supplementary robust molecular subtype classifications consisting on the intersections of the 3 SSPs and/or of the 3 SCMs classifications: only patients with concordant molecular subtype assignment for the 3 SSPs (RSSPC), for the 3 SCMs (RSCMC), or for all predictors (RMSPC), are kept. Univariate Cox proportional analysis and Kaplan-Meier curves are performed for the chosen gene / Probe Set, discretised according to the splitting criterion selected, for each of the different molecular subtypes populations.

Basal-like/TNBC prognostic analysis:

Univariate Cox proportional hazards analyses and Kaplan-Meier curves are performed, for the chosen gene / Probe Set, discretised according to the splitting criterion selected, on Basal-like (BL) patients (as defined by PAM50), on Triple-Negative breast cancer (TNBC) patients (as defined by immunohistochemistry [IHC]) and on patients both BL and TNBC.


CORRELATION ANALYSES

Gene correlation targeted analysis:

Pearson's correlation coefficient is computed with associated p-value for each pair of genes based on ten different populations: all patients pooled together, patients with positive oestrogen receptor status, patients with negative oestrogen receptor status, Basal-like patients, HER2-E patients, Luminal A patients and Luminal B patients (the last 4 subgroups being determined by the RMSPC), Basal-like (PAM50) patients, Triple-Negative (IHC) patients and the intersection of the 2 latter populations.
Results are displayed in a correlation map, where each cell corresponds to a pairwise correlation and is coloured according to the correlation coefficient value, from dark blue (coefficient = -1) to dark red (coefficient = 1).
Pearson's pairwise correlation plots are also computed to illustrate each pairwise correlation.

Gene correlation exhaustive analysis:

Pearson's correlation coefficient is computed, with associated p-value, between the chosen gene and all other genes that are present in the database, based on different populations: all patients pooled together, Basal-like patients, HER2-E patients, Luminal A patients and Luminal B patients, the last 4 subgroups being determined by the RMSPC.
Genes with correlation above 0.40 in absolute value and with associated p-value less than 0.05 are retained and the genes with best correlation coefficients are displayed in two different tables: one for the first 50 (or less) positive correlations, one for the first 50 (or less) negative ones.
The lists with all genes fulfilling criteria of correlation coefficient above 0.40 in absolute value and associated p-value less than 0.05 can be downloaded from the results page.

Gene Ontology analysis:

As a complement to this "screening" analysis, an analysis is performed to find Gene Ontology enrichment terms. This analysis focuses on significantly under- or over-represented terms present in the list of genes most positively correlated with the chosen gene, including itself, in the list of genes most negatively correlated with the chosen gene and in the union of these two lists.
For each term of each of the Gene Ontology trees (biological process, molecular function and cellular component), comparison is done between the number of occurrences of this term in the "target list", i.e. the number of times this term is directly linked to a gene, and the number of occurrences of this term in the "gene universe" (all of the genes that are expressed in the database) by means of Fisher's exact test. Terms with associated p-values less than 0.01 are kept.

Gene correlation analysis by chromosomal location:

Pearson's correlation coefficient is computed, with associated p-value, between the chosen gene and genes located around the chosen gene (up to 15 up and 15 down) on the same chromosome, based on seven different populations: all patients pooled together, patients with positive oestrogen receptor status, patients with negative oestrogen receptor status, Basal-like patients, HER2-E patients, Luminal A patients and Luminal B patients, the last 4 subgroups being determined by the RMSPC.
Detailed results are displayed in a table for each population. Pearson's pairwise correlation plots are also performed to illustrate correlation of each gene with the chosen one.

Targeted correlation analysis (TCA):

As a complement, results of gene correlation analysis for genes selected via the "TCA" column can be displayed.
Targeted correlation analysis ("TCA" button), which aims at evaluating the robustness of clusters, is proposed: correlation analyses are automatically computed between all possible pairs of genes that compose a selected cluster.



a Pocock et al. Lancet. 2002; 359(9318):1686-9
b Galea et al. Breast Cancer Res Treat. 1982; 45(3):361-6.
c Adjuvant! Online
d Dexter et al. BMC Syst Biol. 2010; 4:127.




Nota bene:
  • When working with gene symbols and in case of multiple probesets for the same gene, probeset values median is taken as unique value for the gene.
  • Kaplan-Meier curves will not be computed in populations with less than 5 patients.



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Statistical tests:


  Survival statistical tests
Optimal Discretisation

In prognostic analyses, when choosing "optimal" as the splitting criterion for discretisation, gene / Probe Set is split according to


all percentiles from the 20th to the 80th, with a step of 5, and the cutoff giving the best p-value (Cox model) is kept.




Cox model

  - Aim of the Cox model:
Cox model is a regression model to express the relation between a covariate, either continuous (e.g. G gene) or ordered discrete (e.g. SBR grade), and the risk of occurrence of a certain event (e.g. metastatic relapse).
Its simplified formula for G gene can be written as follows:
h(t,g) = h0(t)*exp(.g), where h is the hazard function of the event occurrence at time t, dependent on the value g of G and h0(t) is the positive baseline hazard function, shared by all patients.
is the regression coefficient associated with G, the parameter one wants to evaluate.

  - Interpretation of Cox model results:
There are two particularly interesting results when building a Cox model: the p-value associated with , which tells us whether the covariate (e.g. gene) has a significant impact on the event-free survival (if the p-value is less than a certain threshold, usually 5%) and the hazard ratio (HR) (equal to exp()), sometimes summed up by its way (sign of ).


The HR, which is really interesting when the p-value is significant, is actually a risk ratio of an event occurrence between patients with regards to their relative measurements for the gene under study. To be more specific, the HR corresponds to the factor by which the risk of occurrence of the event is multiplied when the risk factor increases by one unit: h(t,G+1) = h(t,G)*exp().
The "way" of this HR permits therefore to know how the gene will generally affect the patients event-free survival.
For example, saying that parameter associated with the gene G under study is negative (thus exp() < 1) means that the greater the value of G, the lower the risk of event: if A and B are two patients such as A's G value gA is greater than B's G value gB, then one can say that patient A has a lower risk of metastatic relapse than patient B:
    gA > gB, < 0
 ⇒ .gA < .gB
 ⇒ exp(.gA) < exp(.gB)
 ⇒ h0(t)*exp(.gA) < h0(t)*exp(.gB), that is, h(t, gA) < h(t, gB).



Kaplan-Meier curves

  - The Kaplan-Meier estimator:
Kaplan-Meier method, also known as the product-limit method, is a non-parametric method to estimate the survival function S(t) (= Pr(T > t): probability of having a survival time T longer than time t) of a given population. It is based on the idea that being alive at time t means being alive just before t and staying alive at t.
Suppose we have a population of n patients, among whom k patients have experienced an event (metastastic relapse or death for instance) at distinct times t1 < t2 < ... < tm (m=k if all events occurred at different times). For each time ti, let ni designs the number of patients still at risk just before ti, that is patients who have not yet experienced the event and are not censored, and let ei designs the number of events that occurred at ti. The event-free survival probability at time ti, S(ti), is then the probability S(ti-1) of not experiencing the event before time ti (at time ti-1) multiply by the probability (ni-ei)/ni of not experiencing the event at time ti (which by definition of ti corresponds to the probability of not experiencing the event during the interval between ti-1 and ti): S(ti) = S(ti-1) x (ni-ei)/ni.
The Kaplan-Meier estimator of the survival function S(t) is thus the cumulative product:

Kaplan-Meier formula




  - The curve:
The Kaplan-Meier survival curve, i. e. the plot of the survival function, permits to visualize the evolution of the survival function (estimate). The curve is shaped like a staircase, with a step corresponding to events at the end of each [ti-1; ti[ interval.
The illustration of the Kaplan-Meier survival estimator by the Kaplan-Meier survival curve becomes especially interesting when there are different groups of patients (e.g. according to different treatments or different values of biological markers) and one wants to compare their relative event-free survival. The different survival curves are then plotted together and can be visually compared.
The colour palette used for the curve is from R package viridisa, it permits to keep the colour difference when converted to black and white scale and is designed to be perceived by readers with the most common form of color blindness.

  - Reliability of the estimation:
Caution must be taken concerning the interpretation of the survival curve, especially at the end of the survival curve: the censored patients induce a loss of information and reduce the sample size, making the survival curve less reliable; the end of the curve is obviously particularly affected. For our analyses, in order to minimize unreliability at the end of the curve, the 15% of patients with the longest event-free survival or follow-up are not plotteda.


a R package viridis: default color maps from 'matplotlib'
b Pocock et al. Lancet. 2002; 359(9318):1686-9

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  Gene expression correlations
Pearson correlation

  - The coefficient:
Pearson correlation coefficient, also known as the Pearson's product moment correlation coefficient and denoted by r, measures the linear dependence (correlation) between two variables (e.g. genes).
It is obtained by the formula r = cov(G1,G2) / (std(G1)*std(G2)), where cov(G1,G2) is the covariance between the variables G1 and G2 and std denotes the standard deviation of each variable.
r values can vary from -1 to 1. A negative r means that when the first variable increases, the second one decreases, a postive r means that both variables increase or decrease simultaneously. The greater the r in absolute value, the stronger the linear dependence between the two variables, with the extreme values of -1 or 1 meaning a perfect linear dependence between the two variables, in which case, if the two variables are plotted, all data points lie on a line.


  - The associated p-value:
Along with the Pearson correlation coefficient, one can test if this coefficient is different from 0, knowing that the statistic
t = r*√(n-2)/√(1-r2) follows a Student distribution with (n-2) degrees of freedom, n being the number of values.
The p-value associated with the Pearson correlation coefficient permits thus to know if a linear dependence exists between the two variables.
Note that one has to be careful when interpreting p-value associated with Pearson correlation coefficient: a significant p-value means that a linear dependence exists between two variables but does not mean that this linear dependence is strong; for example, a coefficient of 0.05 with 1600 data points is associated with a significant p-value (p = 0.046) but one can certainly not conclude that there is a strong linear dependence between the two variables !



Correlation map

A correlation map illustrates pairwise correlations among a given group of genes.
A correlation map is a square table where each line and each column represent a gene. Each cell represents an "interaction" between two genes and is coloured according to the value of the Pearson correlation coefficient between these two genes, from dark blue (coefficient = -1) to dark red (coefficient = 1).
Cells from the diagonal of the correlation map represents "interaction" of a gene with itself and are coloured in black.

Pairwise correlation plot

On a correlation plot, the least-squares regression line is plotted along with the data points to illustrate the correlation between two given genes.

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  Gene expression analyses
Box and whiskers plots

Box and whiskers plots permit to graphically represent descriptive statistics of a continuous variable (e.g. gene) : the box goes from the lower quartile (Q1) to the upper quartile (Q3), with an horizontal line marking the median. At the bottom and the top of the box, whiskers indicate the distance between the Q1, respectively Q3, and 1.5 times the interquartile range, that is : Q1-1.5*(Q3-Q1) and Q3+1.5*(Q3-Q1). Finally, stars indicate outliers, if there is any, that is, patients with values below or above the end of the whiskers.



Box and whiskers plots permit to visually compare distributions of a gene among the different population groups. When there is more than one group, Welch's test is used to evaluate the difference of gene's expression in between the groups. Moreover, when there are at least three different groups and Welch's p-value is significative (indicating that gene's expression is different in between at least two subpopulations), Dunnett-Tukey-Kramer's test is used for two-by-two comparisons (this test permits to know the significativity level but does not give a precise p-value).
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