 Breast Cancer GeneExpression Miner v4.0 (bcGenExMiner v4.0)   
Glossary
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Published annotated data ][
Published genomic data ][
Data preprocessing ][
Molecular subtype classification ]
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Statistical analyses ][
Survival statistical tests ][
Gene expression ][
Correlation map ][
Biological validation ]
Published annotated data:
#  Reference  No. patients  Nodal status  ER status  PR status  HER2 status  SBR status  Age at diagnosis  NPI status  AOL status  SSPs status  SCMs status  Event status  MR  AE  1  Van de Vijver et al., 2002  295            101  122  2  Sotiriou et al., 2003  99            30  53  3  Ma et al., 2004  59             27  4  Minn et al., 2005  82            27  27  5  Pawitan et al., 2005  159   ^{1}          40  50  6  Wang et al., 2005  286            107  107  7  Weigelt et al., 2005  50        ^{2}     13  13  8  Bild et al., 2006  158   ^{1}           50  9  Chin et al., 2006  112        ^{2}     21  42  10  Ivshina et al., 2006  249        ^{2}      89  11  Chin et al., 2007  171            38  56  12  Desmedt et al., 2007  198            62  91  13  Loi et al., 2007  401        ^{2}     101  139  14  Minn et al., 2007  58            11  11  15  Naderi et al., 2007  135             65  16  Zhou et al., 2007  54            9  9  17  Anders et al., 2008  75            14  14  18  Chanrion et al., 2008  155            48  57  19  Loi et al., 2008  77        ^{2}     10  13  20  Schmidt et al., 2008  200   ^{1}          46  46  21  Calabrò et al., 2009  139             96  22  Desmedt et al., 2009  55             55  23  Jézéquel et al., 2009  252            65  68  24  Zhang et al., 2009  136            20  20  25  Jönsson et al., 2010  346             151  26  Li et al., 2010  115        ^{2}     14  14  27  Sircoulomb et al., 2010  55            17  17  28  Buffa et al., 2011  216            82  82  29  Dedeurwaerder et al., 2011  85             36  30  Filipits et al., 2011  277            58  58  31  Hatzis et al., 2011  309            65  65  32  Kao et al., 2011  296   ^{1}          63  73  33  Sabatier et al., 2011  266             83  34  Wang et al., 2011  149             10  35  Kuo et al., 2012  51            12  12  36  Nagalla et al., 2013  41            14  14  Total  5 861  29  36  19  15  26  26  17  8  33  32  1 088  1 935 
^{1} ER status was determined based on 205225_at Affymetrix probe (HGU133) or on the median value of Affymetrix probes representing ESR1 (HGU95A v2) using a 2component Gaussian mixture distribution model. Lehmann et al. J Clin Invest. 2011 Jul 1;121(7):275067 ^{2} NPI score could be computed only for node negative patients 
Legend  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 genomic data:
#  Reference  No. patients  Study code  Platform origin  Platform code  DNA chip  No. unique genes (2015)  Processing *  bcGenExMiner version  1  Van de Vijver et al., 2002  295  Rosetta2002  Agilent   25k oligo custom  15 031  log2 ratio  1.0  2  Sotiriou et al., 2003  99  PNAS1732912100  NCI   8k cDNA custom  4 368  log2 ratio  1.0  3  Ma et al., 2004  59  GSE1378  Arcturus  GPL1223  22k oligo custom  15 558  log2 ratio  1.0  4  Minn et al., 2005  82  GSE2603  Affymetrix  GPL96  HGU133A  13 226  MAS5 and log2  1.0  5  Pawitan et al., 2005  159  GSE1456  Affymetrix  GPL96  GPL97  HGU133A + B  19 894  MAS5 and log2  1.0  6  Wang et al., 2005  286  GSE2034  Affymetrix  GPL96  HGU133A  13 226  MAS5 and log2  1.0  7  Weigelt et al., 2005  50  GSE2741  Agilent  GPL1390  Human 1A oligo UNC custom  13 927  log2 ratio  1.0  8  Bild et al., 2006  158  GSE3143  Affymetrix  GPL91  HGU95A v2  9 076  MAS5 and log2  1.0  9  Chin et al., 2006  112  E_TABM_158  Affymetrix  AAFFY76  HGU133A v2  13 226  MAS5 and log2  1.0  10  Ivshina et al., 2006  249  GSE4922  Affymetrix  GPL96  GPL97  HGU133A + B  19 894  MAS5 and log2  1.0  11  Chin et al., 2007  171  GSE8757  VUMC Microarray  GPL5737  Human 30K 60mer oligo array  18 363  log2 ratio  3.1  12  Desmedt et al., 2007  198  GSE7390  Affymetrix  GPL96  HGU133A  13 226  MAS5 and log2  1.0  13  Loi et al., 2007  401  GSE6532  Affymetrix  GPL96  GPL97  GPL570  HG U133A + B + P2  22 847  MAS5 and log2  1.0  14  Minn et al., 2007  58  GSE5327  Affymetrix  GPL96  HGU133A  13 226  MAS5 and log2  1.0  15  Naderi et al., 2007  135  E_UCON_1  Agilent  AAGIL14  Human 1A oligo G4110A  14 268  log2 ratio  1.0  16  Zhou et al., 2007  54  GSE7378  Affymetrix  GPL96  HGU133A  13 226  MAS5 and log2  3.1  17  Anders et al., 2008  75  GSE7849  Affymetrix  GPL91  HGU95A v2  9 076  MAS5 and log2  1.0  18  Chanrion et al., 2008  155  GSE9893  MLRG  GPL5049  Human 21k v12.0  15 184  MAS5 and log2  1.0  19  Loi et al., 2008  77  GSE9195  Affymetrix  GPL570  HGU133P2  22 847  MAS5 and log2  1.0  20  Schmidt et al., 2008  200  GSE11121  Affymetrix  GPL96  HGU133A  13 226  MAS5 and log2  1.1  21  Calabrò et al., 2009  139  GSE10510  DKFZ  GPL6486  35k oligo  16 536  log2 ratio  1.0  22  Desmedt et al., 2009  55  GSE16391  Affymetrix  GPL570  HGU133P2  22 847  MAS5 and log2  3.1  23  Jézéquel et al., 2009  252  GSE11264  UMGCIRCNA  GPL4819  9k cDNA custom  1 814  log2 ratio  1.0  24  Zhang et al., 2009  136  GSE12093  Affymetrix  GPL96  HGU133A  13 226  MAS5 and log2  1.1  25  Jönsson et al., 2010  346  GSE22133  SweGene  GPL5345  H_v2.1.1 55K  9 281  log2 ratio  3.1  26  Li et al., 2010  115  GSE19615  Affymetrix  GPL570  HGU133P2  22 847  MAS5 and log2  3.1  27  Sircoulomb et al., 2010  55  GSE17907  Affymetrix  GPL570  HGU133P2  22 847  MAS5 and log2  3.1  28  Buffa et al., 2011  216  GSE22219  Illumina  GPL6098  HumanRef8 v1.0 exprbc  15 623  log2 ratio  3.1  29  Dedeurwaerder et al., 2011  85  GSE20711  Affymetrix  GPL570  HGU133P2  22 847  MAS5 and log2  3.1  30  Filipits et al., 2011  277  GSE26971  Affymetrix  GPL96  HGU133A  13 226  MAS5 and log2  3.1  31  Hatzis et al., 2011  309  GSE25055  Affymetrix  GPL96  HGU133A  13 226  MAS5 and log2  3.1  32  Kao et al., 2011  296  GSE20685  Affymetrix  GPL570  HGU133P2  22 847  MAS5 and log2  3.1  33  Sabatier et al., 2011  266  GSE21653  Affymetrix  GPL570  HGU133P2  22 847  MAS5 and log2  3.1  34  Wang et al., 2011  149  GSE16987  Illumina  GPL6104  HumanRef8 v2.0 exprbc  16 976  log2 ratio  3.1  35  Kuo et al., 2012  51  GSE33926  Agilent  GPL7264  Human 1A Microarray (V2) G4110B  16 754  log2 ratio  3.1  36  Nagalla et al., 2013  41  GSE45255  Affymetrix  GPL96  HGU133A  13 226  MAS5 and log2  3.1  Total  5861  
* Data have been converted to a common scale (median equal to 0 and standard deviation equal to 1). 
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Data preprocessing:
1.1 Affymetrix preprocessing:
Before being log2transformed, Affymetrix raw CEL data were MAS5.0normalised
using the Affymetrix Expression Console.
1.2 NonAffymetrix preprocessing:
Data have been downloaded as they were deposited in the public databases.
When patient to reference ratio and its log2transformation were not already calculated,
we performed the complete process.


2 All 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; 24,11541160

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Molecular subtype classification:
RMSPC (Robust Molecular Subtype Predictors Classification): patients classified in the same molecular subtype with the six molecular subtype predictors (MSP).

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 
Basallike, HER2E, Luminal A, Luminal B, Normal breastlike 
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, HER2E, ER+/HER2 low proliferation, ER+/HER2 high proliferation 
SCMOD2 
663 
SCMGENE 
3 
Table 2: Molecular subtyping of 5 861 breast cancer patients
included in bcGenExMiner v3.1 according to 6 molecular subtype predictors

MSP 
Basallike  HER2E  Luminal A  Luminal B  Normal breastlike  unclassified 
No  %  No  %  No  %  No  %  No  %  No  % 
Sorlie's SSP  795  13.6  606  10.3  1503  25.6  637  10.9  663  11.3  1657  28.3  Hu's SSP  1268  21.6  502  8.6  1339  22.8  989  16.9  808  13.8  955  16.3  PAM50 SSP  1144  19.5  828  14.1  1581  27  1068  18.2  728  12.4  512  8.7  RSSPC  703    190    761    190    335       

MSP 
ER/HER2  HER2E  ER+/HER2 low proliferation  ER+/HER2 high proliferation    unclassified 
No  %  No  %  No  %  No  %      No  % 
SCMOD1  929  15.9  861  14.7  1653  28.2  1499  25.6      919  15.7  SCMOD2  996  17  1027  17.5  1588  27.1  1418  24.2      832  14.2  SCMGENE  2038  34.8  911  15.5  1048  17.9  945  16.1      919  15.7  RSCMC  699    373    656    524           

RMSPC 
580    124    324    80           
Legend  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 DunnettTukeyKramer's tests when appropriate.
Exhaustive expression analysis:
box and whiskers plots are displayed, along with Welch's (and DunettTukeyKramer'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(s) to be tested,
nodal and oestrogen receptor status of the cohorts to be explored and event),
several statistical tests are conducted on each cohort and on all cohorts
pooled.
The prognostic impact of each gene is evaluated by means of univariate Cox proportional hazards model.
Results are displayed by cohorts (including pool) and are illustrated in a forest plot.
KaplanMeier curves are then performed on the pool with the gene values
dichotomised according to gene median (calculated from the pool).
Cox results corresponding to dichotomised values are
displayed on the curve. In order to minimize unreliability
at the end of the curve, the 15% of patients with the longest followup are not plotted ^{a}.
To evaluate independent prognostic impact of gene(s) relative to
the wellestablished clinical markers NPI ^{b} and AOL ^{c} (10year overall survival) and to proliferation score ^{d},
adjusted Cox proportional hazards models are performed on pool's patients with available data.
Exhaustive prognostic analysis:
Univariate Cox proportional hazards model is 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 a unique gene. Results are displayed by
population and event criteria and are ordered by pvalue (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
is performed for the chosen gene for each of the different molecular subtypes populations.
KaplanMeier curves are also computed.
Basallike/TNBC prognostic analysis:
Univariate Cox proportional hazards analyses are performed, for the chosen gene,
on Basallike (BL) patients (as defined by PAM50), on TripleNegative breast cancer (TNBC) patients (as defined by immunohistochemistry [IHC])
and on patients both BL and TNBC. KaplanMeier curves are also computed.


CORRELATION ANALYSES
Gene correlation targeted analysis:
Pearson's correlation coefficient is computed with associated pvalue 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, Basallike patients,
HER2E patients, Luminal A patients and Luminal B patients (the last 4 subgroups being determined by the RMSPC),
Basallike (PAM50) patients, TripleNegative (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 pvalue, between the chosen gene and all other genes that are present in the database,
based on different populations: all patients pooled together, Basallike patients, HER2E 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 pvalue 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 pvalue 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 overrepresented 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 pvalues less than 0.01 are kept.
Gene correlation analysis by chromosomal location:
Pearson's correlation coefficient is computed, with associated pvalue, 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, Basallike patients,
HER2E 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):16869
^{b} Galea et al. Breast Cancer Res Treat. 1982; 45(3):3616.
^{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.
 Cox models performed on pool(s) are stratified by cohort.
 The value of gene median taken as a cutoff to dichotomise gene expression values and
perform KaplanMeier curves on the pool is an arbitrary value and may not be  and in most case is not 
the best cutoff for the specific gene. Hence, a gene that is significant when considering continue values
might not remain significant after dichotomisation.

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Statistical tests:
Survival statistical tests


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(ß.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.
ß 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 pvalue
associated with ß, which tells us whether the covariate (e.g. gene) has a significant
impact on the eventfree survival (if the pvalue is less than a certain threshold,
usually 5%) and the hazard ratio (HR) (equal to exp(ß)), sometimes summed up by its “way”
(sign of ß).


The HR, which is really interesting when the pvalue 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(ß).
The "way" of this HR permits therefore to know how the gene will generally affect
the patients eventfree survival.
For example, saying that parameter ß associated with the gene G under study is negative
(thus exp(ß) < 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, ß < 0
⇒ ß.gA < ß.gB
⇒ exp(ß.gA) < exp(ß.gB)
⇒ h0(t)*exp(ß.gA) < h0(t)*exp(ß.gB), that is, h(t, gA) < h(t, gB).

KaplanMeier curves
 The KaplanMeier estimator:
KaplanMeier method, also known as the productlimit method, is a nonparametric 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 eventfree survival probability at time ti, S(ti),
is then the probability S(ti1) of not experiencing the event before time ti
(at time ti1) multiply by the probability (niei)/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 ti1 and ti): S(ti) = S(ti1) x (niei)/ni.
The KaplanMeier estimator of the survival function S(t) is thus the cumulative product:
 The curve:
The KaplanMeier 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 [ti1; ti[ interval.


The illustration of the KaplanMeier survival estimator by the KaplanMeier 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 eventfree survival. The different survival
curves are then plotted together and can be visually compared.
 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 eventfree survival or followup are not plotted ^{a}.
Forest plot
A forest plot is a graphical means to view results, i.e. a score (odds or hazard ratio)
and a confidence interval (CI), of the same analysis applied to different populations (studies).
In particular it permits, via Cox HRs, to survey the impact of a gene
on survival in different cohorts all at once, and thus to get a better (visual) idea of
how the results vary between studies.
A forest plot is organized as follows: for each study, the score (eg. HR) is represented
by a square centred on the value of the score (HR) and whose size depends on the precision
of the score estimation (the more precise the estimation, the bigger the square).
A horizontal line passing through the square represents the (usually 95%) CI.
At the bottom of the forest plot are represented the score (HR) and CI obtained by
the pool (i.e. all cohorts pooled) in the shape of a diamond with the centre
representing the score (HR) and the right and left ends representing the CI limits.
Finally, a vertical line representing a no effect score (HR=1) is drawn.
^{a} Pocock et al. Lancet. 2002; 359(9318):16869

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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(G_{1},G_{2}) / (std(G_{1})*std(G_{2})),
where cov(G_{1},G_{2}) is the covariance between the variables G_{1} and G_{2} 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 pvalue:
Along with the Pearson correlation coefficient, one can test if this coefficient is different from 0, knowing that the statistic
t = r*√(n2)/√(1r^{2}) follows a Student distribution with (n2) degrees of freedom, n being the number of values.
The pvalue 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 pvalue associated with Pearson correlation coefficient: a significant pvalue 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 pvalue (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 leastsquares 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 : Q11.5*(Q3Q1) and Q3+1.5*(Q3Q1). 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 pvalue is significative (indicating that gene's expression
is different in between at least two subpopulations), DunnettTukeyKramer's test is used for twobytwo comparisons
(this test permits to know the significativity level but does not give a precise pvalue).

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Biological validation:
Complexity of bioinformatics process may distort genomic data, and downstream,
statistics applied on these data and metadata may conduct to erroneous results.
That is why biological validation of our tool is needed.
Since 2010, we conduct a screening of breast cancer markers (RNA and protein) referenced in
PubMed
(keywords: breast cancer marker/biomarker). Significance of these genes is then tested in our tool.


These tests proved that bcGenExMiner caught biological sense contained
in annotated genomic data and preserved it from bioinformatics biases,
even when data are merged in new cohorts, and that its results are pertinent.
Following tables display concordant conclusions about significance
of recently published candidate markers in breast cancer.

1) Prognostic module validation
 11) Exhaustive
  Table 3: Tested genes for biological validation of prognostic module
 12) By molecular subtype
  Table 4: Tested genes for biological validation of prognostic module by molecular subtype
 2) Correlation module validation
 21) Targeted
  Table 5: Tested genes for biological validation of targeted correlation analysis #  Gene(s) symbol  Authors  Year(s)  Journal(s)  PubMed  1  ESR1; GATA3; FOXA1; XBP1  Lacroix M et al.  2004  Mol Cell Endocrinol   2  FBP1; ESR1  Dong C et al.  2013  Cancer Cell   3  MKI67; AURKA; UBE2C  Wirapati P et al. Jézéquel P et al. Loussouarn D et al.  2008 2009 2009  Breast Cancer Res Breast Cancer Res Treat Br J Cancer 
 4  PIP (GCDFP15);AR  DarbEsfahani S et al.  2014  BMC Cancer   5  TNFAIP1; POLDIP2 RAF1; MKRN2 TBCB; POLR2I  Grinchuk OV et al.  2010  BMC Genomics  
 22) Exhaustive and Gene ontology analysis
  Table 6: Tested genes for biological validation of exhaustive correlation and gene ontology analyses
 23) By chromosomal location
  Table 7: Tested genes for biological validation of correlation analysis by chromosomal location #  Gene(s) symbol  Authors  Year(s)  Journal(s)  PubMed  1  ESR1; C6orf97; C6orf211; RMND1  Dunbier AK et al.  2011  PLoS Genet   2  LSM1; BAG4; DDHD2; PPAPDC1B; WHSC1L1  BernardPierrot I et al. André F et al.  2008 2009  Cancer Res Clin Cancer Res 
 3  Numerous genes  Buness A et al. Jézéquel P et al.  2007 2013  Bioinformatics Database (Oxford) 
 4  TRAF4; MED24; GGA3  Bergamaschi A et al. Buness A et al. Hu X et al.  2006 2007 2009  Genes Chromosomes Cancer Bioinformatics Mol Cancer Res 

 3) Expression map module validation
 By molecular subtype
  Table 8: Tested genes for biological validation of expression map analysis #  Gene(s) symbol  Authors  Year(s)  Journal(s)  PubMed  1  CALB2  Taliano RJ et al.  2013  Hum Pathol   2  CDH3  Liu N et al.  2012  Med Oncol   3  CDH3  Tsang JYS et al.  2013  Hum Pathol   4  CEACAM6  Tsang JYS et al.  2013  Breast Cancer Res Treat   5  CEACAM6  BalkMøller E et al.  2014  Am J Pathol   6  CKAP2  Kim HS et al.  2014  PLoS One   7  CRYAB  Malin D et al.  2013  Clin Cancer Res   8  CRYAB  Koletsa T et al.  2014  BMC Clin Pathol   9  CXCR4  Zhang M et al.  2012  Ultrastruct Pathol   10  DACH1  Powe DG et al.  2014  PLoS One   11  ERCC1  Gerhard R et al.  2013  Pathol Res Pract   12  FBP1  Dong C et al.  2013  Cancer Cell   13  FEN1  AbdelFatah TMA et al.  2014  Mol Oncol   14  FOXC1  Ray PS et al.  2011  Ann Surg Oncol   15  FSCN1  Esnakula AK et al.  2013  J Clin Pathol   16  FZD7  Yang L et al.  2011  Oncogene   17  LDHB  McCleland ML et al.  2012  Cancer Res   18  LRP6  Yang L et al.  2011  Oncogene   19  MED1; STARD3; TCAP; PNMT; PGAP3; C17orf37; ORMDL3; PSMD3; NR1D1  Kauraniemi P et al.  2006  Endocr Relat Cancer   20  MET; ETS1; KRT6A; KRT6B; ANXA8; MMP9  CharafeJauffret E et al.  2006  Oncogene   21  MKI67; AURKA; UBE2C  Wirapati P et al. Jézéquel P et al. Loussouarn D et al.  2008 2009 2009  Breast Cancer Res Breast Cancer Res Treat Br J Cancer 
 22  PI3  LabidiGaly SI et al.  2014  Oncogene   23  PIP (GCDFP15)  DarbEsfahani S et al.  2014  BMC Cancer   24  PRLR; KRT19  CharafeJauffret E et al.  2006  Oncogene   25  SDC1  Nguyen TL et al.  2013  Am J Clin Pathol   26  SFRP1  Jeong YJ et al.  2013  Oncol Rep   27  SOX10  CiminoMathews A et al.  2012  Hum Pathol   28  SPDEF  Buchwalter G et al.  2013  Cancer Cell   29  TCF7  Yang L et al.  2011  Oncogene   30  VIM  Tsang JYS et al.  2013  Hum Pathol  

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