Use of the expression of specific genes for the prognosis of patients with triple negative breast cancer

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Recent advances in medical treatments have dramatically improved the outcome of triple negative breast cancers. As illustration, after a median follow-up of 36 months, only 12% of the patients included in the adjuvant bevacizumab-containing therapy in triple-negative breast cancer (BEATRICE) trial had presented a metastatic relapse. This data emphasizes the need to develop predictors of outcome in a patient with triple negative breast cancer (TNBC) who have received optimal adjuvant therapy, in order to identify those who are eligible to adjuvant trials, and need new investigational drugs.

It has been previously shown that the presence of tumor infiltration by lymphocytes after neoadjuvant chemotherapy is associated with an excellent outcome. In this study that included 304 patients, the presence of TILs>60% after neoadjuvant chemotherapy was observed in 10% of the patients and was associated with a 91% overall survival rate at 5 years. Interestingly, 85% of the samples with post-chemotherapy TIL+ were TIL− at baseline before chemotherapy (Dieci M V, Criscitiello C, Goubar A, et al. (2014) Prognostic value of tumorinfiltrating lymphocytes on residual disease after primary chemotherapy for triplenegative breast cancer: a retrospective multicenter study. Ann Oncol Off J Eur Soc Med Oncol ESMO 25:611-618. doi: 10.1093/annonc/mdt556).

1. Purpose

The Study Purpose is to Develop a Genomic Predictor of TIL after Chemotherapy and to Test its Prognostic Value in TNBC.

The strategy consists in developing a genomic predictor of TIL after neoadjuvant chemotherapy using only information obtained before the start of the neoadjuvant treatment (biopsies), and then to test whether this predictor could identify a subset of TNBC patients who do not have a systemic relapse.

One of the Aims is to Develop a Genomic Predictor of TIL after Neoadjuvant Chemotherapy in TNBC Using Only Information Before the Start of Chemotherapy.

In order to address this question, we will quantify post-chemotherapy TIL in series of TNBC treated with neoadjuvant chemotherapy and for which a genomic profile has already been generated. TIL will be assessed in post-chemotherapy samples from MDACC neoadjuvant series and TOP (Trial of Principle) trial.

The histopathologic evaluation of the percentage of intratumoral (It) and stromal (Str) TILs will be performed on Hematoxilyn and eosin-stained (HES) slides from surgical specimens and will be done according to criteria previously described and published by Denkert and colleagues. For each case, all the slides containing residual invasive breast disease will be evaluated.

The goal will be to collect information on post-chemotherapy TIL in a large series patients with TNBC treated with neoadjuvant chemotherapy that did not achieved pCR after surgery. There is a lot of discussion on the most appropriate cut-off and in the absence of a reliable gold standard; we modeled the continuous level of stromal TILS in the post chemotherapy sample as a function of gene expression. This model is more powerful than logistic models and will allow us to predict which patients would have stromal TILS superior to currently discussed cutoffs (40%, 50% or 60%). A RT-PCR based assay will then be developed on FFPE samples matched to their frozen counterparts.

The predictive value of the RT-PCR based assay for TIL-infiltration will be then validated on FFPE samples from IEO and GBG neoadjuvant studies.

Another Aim is to Validate the Prognostic Value of the Genomic Predictor in TNBC Treated with Neoadjuvant Chemotherapy

Once the genomic predictor has been generated, we will test its prognostic value in patients with TNBC treated with adjuvant chemotherapy. Several series of samples will be used. First, the ACIS validation dataset will be used where both outcome and gene expression arrays are available. Second, we will perform gene expression profilings in the IBCSG study 22 and PACS08 in order to test the prognostic value of TIL-predictor in >300 TNBC treated with adjuvant therapy.

The Primary Analysis was Performed on TNBC Patients (ER-/HER2-). Description of all the Studies Included in the Present Analysis is Shown in Table 31.

Tumors were identified as ER-/HER2—based on ER assessment by IHC and HER2 assessment by IHC and fluorescent in situ hybridization, as originally reported. When unavailable, ER and HER2 status was assigned according to ESR1 and ERBB2 gene expression.

2. Invention

The present invention relates to the use of the value of the expression of at least one gene selected from the group comprising: GBP1 gene, HLF gene, CXCL13 gene and SULT1E1 gene, for the estimation of prognosis of distant relapse-free survival or overall survival of a patient with triple negative breast cancer (TNBC) having received a neoadjuvant chemotherapy (NACT).

In a particular embodiment, the present invention relates to said use of the value of the expression of the four genes: GBP1 gene, HLF gene, CXCL13 gene and SULT1E1 gene, for the estimation of prognosis of distant relapse-free survival or overall survival of a patient with triple negative breast cancer (TNBC) having received a neoadjuvant chemotherapy (NACT).

In a particular embodiment, the present invention relates to said use of the value of the expression of the four genes: GBP1 gene, HLF gene, CXCL13 gene and SULT1E1 gene, wherein a low value of the expression of the genes SULT1E1 and HLF, and a high value of the expression of the genes GBP1 and CXCL13, measured in a biopsy taken from a patient tumor before neoadjuvant chemotherapy corresponds to an high stromal tumor-infiltrating lymphocytes (Str-TIL) after neoadjuvant chemotherapy, corresponding to a good distant relapse free-survival or overall survival of said patient.

In a particular embodiment, the present invention relates to said use of the value of the expression of the four genes: GBP1 gene, HLF gene, CXCL13 gene and SULT1E1 gene, wherein a high value of the expression of the genes SULT1E1 and HLF, and a low value of the expression of the genes GBP1 and CXCL13, measured in a biopsy taken from a patient tumor before neoadjuvant chemotherapy corresponds to an low stromal tumor-infiltrating lymphocytes (Str-TIL) after neoadjuvant chemotherapy, corresponding to a short distant relapse free-survival or overall survival of said patient.

In a particular embodiment, the present invention relates to said use of the value of the expression of the four genes: GBP1 gene, HLF gene, CXCL13 gene and SULT1E1 gene for determining a genomic predictor of formula:

Genomic predictor=0.288*GBP1 expression+0.392*CXCL13 expression−1.027*HLF expression−1.726*SULT1E1 expression,

and wherein the expression of the four genes corresponds respectively to the value of the mRNA of each one, for the estimation of prognosis of distant relapse-free survival or overall survival of a patient with triple negative breast cancer (TNBC) having received a neoadjuvant chemotherapy (NACT).

In a particular embodiment, the present invention relates to said use of the value of the expression of the four genes: GBP1 gene, HLF gene, CXCL13 gene and SULT1E1 gene wherein when the genomic predictor for a patient is more than or equal to 0.51, the patient has a good prognosis corresponding to a good distant relapse free-survival or overall survival of said patient.

In a particular embodiment, the present invention relates to sais use of the value of the expression of the four genes: GBP1 gene, HLF gene, CXCL13 gene and SULT1E1 gene wherein when the genomic predictor for a patient is strictly less than 0.51, the patient has a poor prognosis corresponding to a short distant relapse free-survival or overall survival of said patient.

The present invention also relates to an in vitro prognostic method of the distant relapse-free survival or overall survival in a patient with triple negative breast cancer (TNBC) having received a neoadjuvant chemotherapy (NACT) comprising the determination of the value of the expression of at least one gene selected from the group comprising: GBP1 gene, HLF gene, CXCL13 gene and SULT1E1 gene.

In a particular embodiment, the present invention relates to said in vitro prognostic method of the distant relapse-free survival or overall survival in a patient with triple negative breast cancer (TNBC) having received a neoadjuvant chemotherapy (NACT), comprising the determination of the value of the expression of the four following genes: GBP1 gene, HLF gene, CXCL13 gene and SULT1E1 gene.

In a particular embodiment, the present invention relates to said in vitro prognostic method of the distant relapse survival or overall survival of with triple negative breast cancer (TNBC) having received a neoadjuvant chemotherapy (NACT), wherein said value of the expression of the four following genes GBP1 gene, HLF gene, CXCL13 gene and SULT1E1 gene, is determined in a sample from a biopsy taken from a patient tumor before neoadjuvant chemotherapy.

In a particular embodiment, the present invention relates to said in vitro prognostic method of the distant relapse survival or overall survival of a patient with triple negative breast cancer (TNBC) having received a neoadjuvant chemotherapy (NACT), wherein said value of the expression of the four genes: GBP1 gene, HLF gene, CXCL13 gene and SULT1E1 gene, corresponds to a low value of the expression of the genes SULT1E1 and HLF, and a high value of the expression of the genes GBP1 and CXCL13, measured in a biopsy taken from a patient tumor before neoadjuvant chemotherapy corresponds to an high stromal tumor-infiltrating lymphocytes (Str-TIL) after neoadjuvant chemotherapy, corresponding to a good distant relapse free-survival or overall survival of said patient.

In a particular embodiment, the present invention relates to said in vitro prognostic method of the distant relapse survival or overall survival of a patient with triple negative breast cancer (TNBC) having received a neoadjuvant chemotherapy (NACT), wherein said value of the expression of the four genes: GBP1 gene, HLF gene, CXCL13 gene and SULT1E1 gene, corresponds to a high value of the expression of the genes SULT1E1 and HLF, and a low value of the expression of the genes GBP1 and CXCL13, measured in a biopsy taken from a patient tumor before neoadjuvant chemotherapy corresponds to an low stromal tumor-infiltrating lymphocytes (Str-TIL) after neoadjuvant chemotherapy, corresponding to a short distant relapse free-survival or overall survival of said patient.

In a particular embodiment, the present invention relates to said in vitro prognostic method of the distant relapse survival or overall survival of a patient with triple negative breast cancer (TNBC) having received a neoadjuvant chemotherapy (NACT), comprising the determination of a genomic predictor according to formula:

Genomic predictor=0.288*GBP1 expression+0.392*CXCL13 expression−1.027*HLF expression−1.726*SULT1E1 expression,

for the estimation of prognosis of distant relapse-free survival or overall survival of a patient with triple negative breast cancer (TNBC) having received a neoadjuvant chemotherapy (NACT).

In a particular embodiment, the present invention relates to an in vitro prognostic method of the distant relapse survival or overall survival of a patient with triple negative breast cancer (TNBC) having received a neoadjuvant chemotherapy (NACT), wherein when the genomic predictor for a patient is strictly less than 0.51, the patient has a poor prognosis.

The present invention also relates to a kit for the in vitro prognostic method of the distant relapse survival or overall survival of a patient with triple negative breast cancer (TNBC) having received a neoadjuvant chemotherapy (NACT) according to claim, comprising:

- 4 pairs of primers corresponding to the 4 genes GBP1, HLF, CXCL13 and SULT1E1,
- at least one pair of primers corresponding to a housekeeping gene selected from the group comprising 18S rRNA, ACTB, HPRT1, HSPCB, PPIA, PUM1, RPS13, SDHA and TBP,
- a reverse transcriptase,
- oligonucleotides,
- a polymerase
- and suitable buffer solutions.

The present invention also relates to an use of the value of the expression of the four genes: GBP1 gene, HLF gene, CXCL13 gene and SULT1E1 gene measured in a biopsy taken before a neoadjuvant chemotherapy (NACT), for predicting the level of stromal tumor-infiltrating lymphocytes (Str-TIL) in a patient with triple negative breast cancer (TNBC) after a NACT.

3. Training Phase

3.1 Materiel

3.1.1 Description of the Training Population

The participants' flow chart of the training dataset is shown in FIGS. 1a and 1b.

The baseline characteristics of the 99 eligible patients (ER-/HER2-) in the training dataset are presented in Table 1. The baseline characteristics of patients included in the training dataset are shown in Table 32 (n=113).

TABLE 1

Baseline characteristics of eligible patients in the training dataset

TOP
MDACC
All trials

n = 30
n = 69
n = 99

Age, years

Mean (SD)
47
(11.7)
50
(11.1)
49
(11.3)

Median (Q1-Q3)
44
(38-56)
50
(40-59)
47
(40-59)

Min-Max
27-67
31-75
27-75

cT

T1
3
(10%)
2
(3%)
5
(5%)

T2
22
(73%)
38
(55%)
60
(61%)

T3
3
(10%)
16
(23%)
19
(19%)

T4
2
(7%)
13
(19%)
15
(15%)

cN

N0
16
(53%)
19
(28%)
35
(35%)

N+
14
(47%)
50
(72%)
64
(65%)

ER status

Negative
30
(100%)
69
(100%)
99
(100%)

Positive
0
(0%)
0
(0%)
0
(0%)

PR status

Negative
18
(100%)
69
(100%)
87
(100%)

Positive
0
(0%)
0
(0%)
0
(0%)

Missing
12
0
12

Histologic grade

1-2
5
(17%)
12
(18%)
17
(17%)

3
25
(83%)
56
(82%)
81
(83%)

Missing
0
1
1

Post-chemo Stromal TILs

Mean (SD)
24
(21.0)
20
(21.6)
21
(21.4)

Median (Q1-Q3)
20
(10-29)
10
(5-30)
10
(5-30)

Min-Max
0-80
0-90
0-90

No. of relapses
9
(31%)
36
(52%)
45
(46%)

No. of deaths
7
(24%)
36
(52%)
43
(44%)

Median follow-up in years (Q1-Q3)
3.15
(2.12-3.85)
8.13
(7.46-9.61)
7.59
(3.74-8.82)

GEO
GSE16446
GSE25066

GSE20271

References
Desmedt et al⁸
Hatzis et al⁷

Data are mean (SD), median (Q1-Q3), min-max, or n (%).

Patients of the training set were from MDACC neoadjuvant series and TOP study.

^7,8SD, standard deviation;

Q1, 25th percentile;

Q3, 75th percentile;

Min, Minimum;

Max, Maximum;

cT, clinical tumor size;

cN, clinical nodal status;

ER, estrogen receptor;

PR, progesterone receptor;

HER2, human epidermal growth factor receptor 2;

TILs, tumor-infiltrating lymphocytes;

GEO, gene expression omnibus;

TOP, Trial of Principle;

MDACC, MD Anderson Cancer Center.

3.1.2 Genomic Data

The complete genomic data are publically available on the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) in the series GSE16446 for TOP samples; in the series GSE25066 and GSE20271 for MDACC samples. We performed data processing on the 113 patients with stromal TIL data available (99 patients were TNBC and 14 were HER2+, see FIGS. 1a and 1b, GEO accessions of the 14 HER2+ patients are shown in Table 32).

3.1.2.1 Quality checks before normalization.

For quality checks before normalization, we used boxplots and plots of the density estimates of the raw probe level data comparing all arrays. Plots are shown in FIG. 2 and FIG. 3.

3.1.2.2 Separate Data Normalization Using fRMA

We applied frozen robust multiarray analysis (fRMA) preprocessing algorithm to normalize the two datasets separately. This method is implemented in the R package ‘frma’. For quality checks after fRMA, we used boxplots and plots of the density estimates of the normalized data comparing all arrays. Plots are shown in FIG. 4 and FIG. 5.

3.1.2.3 Cross-Platform Normalization

We merged the two datasets using Cross-platform normalization (XPN) methods for batch effect removal [3]. This method is implemented in the R package ‘inSilicoMerging’. For quality checks after cross-platform normalization, we used boxplots and plots of the density estimates of the normalized data comparing all arrays. Plots are shown in FIG. 6 and FIG. 7.

3.1.2.4 Unspecified Filtering

Unspecified filtering consists in including only the 10 000 most variable genes (standard deviation) for further analysis. It was performed once and for all, using gene expressions from 113 samples: the 10 000 genes selected will be used for all the further analysis.

3.2 Methods and Results

3.2.1 Difference in Stromal TIL after Chemotherapy Between MDACC Samples and TOP Samples

TILs were quantified on RD after NACT in H&E slides from surgical samples from MDACC neoadjuvant series and TOP trial (training set). All mononuclear cells (i.e., lymphocytes and plasma cells) in the stromal compartment within the borders of the invasive tumor were evaluated and reported as a percentage (TILs score). TILs outside of the tumor border, around DCIS and normal breast tissue, as well as in areas of necrosis, if any, were not included in the scoring. TILs were assessed as a continuous measure (score). For each surgical specimen, all the slides containing invasive RD have been evaluated. The reproducibility of this method has been described 12. H&E slides from TOP samples have been sent to IEO, where they have been independently read for TIL-infiltration by two investigators (CC and GP). MDACC H&E slides have been read on-site by two investigators (CC and BS).

Difference in stromal TIL after chemotherapy between MDACC samples and TOP samples was assessed on the 113 patients in the training dataset. Stromal TIL significantly deviates from normality (Shapiro-Wilk normality test p-value=9.771e-11). There is a statistically significant difference in stromal TIL between MDACC samples and TOP samples (Wilcoxon rank sum test with continuity correction p-value=0.005027). Summary statistics of stromal TIL in TOP samples, MDACC samples and overall are given in Table 2. Histograms of stromal TIL in TOP samples, MDACC samples and overall are shown in FIG. 8.

TABLE 2

Summary statistics of stromal TIL in TOP samples,

MDACC samples and overall

TOP
MDACC
Overall

N = 44
N = 69
N = 113

Mean
32
20
25

SD
28.5
21.6
25.1

Median
20
10
15

Q1-Q3
10-40
5-30
5-40

Min-Max
0-95
0-90
0-95

3.2.2 Box-Cox Transformation

The Box-Cox transformation is a useful data transformation technique used to stabilize variance and make the data more normal distribution-like. Box-Cox transformation applies only to positive variables, so we applied it on (Stromal TII+1).

The univariate generalized linear model on which the Box-Cox transformation was applied included one at a time of the 10 000 most varying genes (see 3.1.2.4), center (Bordet vs. MDACC) and HER2 status (− vs. +). The model was applied on data from the 113 patients of the training dataset.

The multivariate generalized linear model on which the Box-Cox transformation was applied on 113 patients from the training dataset and included one at a time of the 10 000 most varying genes and center (Bordet vs. MDACC), age (continuous), cT (0-1-2 vs. 3-4), cN (0 vs. +), grade (1-2 vs. 3) and HER2 status (− vs. +). The model was applied on data from the 113 patients of the training dataset.

The Box-Cox transformation formula is given below:

$y^{(α)} = {\begin{matrix} \frac{y^{α} - 1}{α} & if α \neq 0 \\ \ln (y) & if α = 0 \end{matrix}$

Summary statistics of α values derived from 10 000 Box-Cox transformations are given in Table 3. We chose to set α at the median value for all the genes (10 000) in the multivariate analysis; consequently α=0.2000 for all the following models.

TABLE 3

Summary statistics of α values derived from

10 000 Box-Cox transformations

Univariate
Multivariate

Mean
0.1932
0.1987

SD
0.00641
0.00586

Median
0.1900
0.2000

Q1-Q3
0.1900-0.2000
0.2000-0.2000

Min-Max
0.1400-0.2200
0.1400-0.2300

3.2.3 Procedure 1: Univariate Selection with Adjustment

Procedure 1 steps:

- 1. To fit a general linear model to model the continuous level of stromal TIL in the post chemotherapy samples using complete cases. Stromal TIL is transformed using Box-Cox transformation.
- 2. To correct for multiple comparisons using False Discovery Rate (FDR) method [Benjamini Y, Hochberg Y (1995) Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Ser B Methodol 57:289-300.] (Bonferroni p-values are reported for information purposes only).
- 3. To report genes that achieved the selection criterion of a corrected p-value <0.05.

3.2.3.1 Univariate Analysis

3.2.3.1.1 Triple Negative Patients

There were 99 patients identified as triple negative. We fitted a general linear model to model the continuous level of stromal TIL in the post chemotherapy sample as a function of gene expression while controlling for the effect of a potential confounder that is the center (Bordet vs. MDACC). Summary of the 79 genes achieving selection criterion (corrected p-value <0.05) are shown in Table 33.

3.2.3.1.2 all Patients Stratified on HER2 Status

There were 113 patients used to build the model. We fitted a general linear model to model the continuous level of stromal TIL in the post chemotherapy sample as a function of gene expression while controlling for the effect of potential confounders that are center (Bordet vs. MDACC), and HER2 status (− vs. +). Summary of the 114 genes achieving selection criterion (corrected p-value <0.05) are shown in Table 34.

3.2.3.2 Multivariate Analysis

3.2.3.2.1 Triple Negative Patients

There were 99 patients identified as triple negative. We fitted a general linear model to model the continuous level of stromal TIL in the post chemotherapy sample as a function of gene expression while controlling for the effect of potential confounders that are center (Bordet vs. MDACC), age (continuous), cT (0-1-2 vs. 3-4), cN (0 vs. +) and grade (1-2 vs. 3). Summary of the 41 genes achieving selection criterion (corrected p-value <0.05) are shown in Table 35.

3.2.3.2.2 all Patients Stratified on HER2 Status

There were 113 patients used to build the model. We fitted a general linear model to model the continuous level of stromal TIL in the post chemotherapy sample as a function of gene expression while controlling for the effect of a potential confounder that are center (Bordet vs. MDACC), age (continuous), cT (0-1-2 vs. 3-4), cN (0 vs. +) and grade (1-2 vs. 3) and HER2 status (− vs. +). Summary of the 60 genes achieving selection criterion (corrected p-value <0.05) are shown in Table 36.

3.2.4 Procedure 2: Model Selection Using Penalization

The purpose of the shrinkage is to prevent overfit arising due to either collinearity of the covariates or high-dimensionality.

We chose to apply L1 absolute value (“lasso”) penalty as described by Tibshirani et al. [Tibshirani R (1996) Regression Shrinkage and Selection via the Lasso. J R Stat Soc Ser B Methodol 58:267-288] [Tibshirani R, others (1997) The lasso method for variable selection in the Cox model. Stat Med 16:385-395.].

Appling an L1 penalty tends to results in many regression coefficients shrunk to zero and few other regression coefficients with comparatively little shrinkage hence this method allows selection of the most significant genes.

The amount of shrinkage is determined by the tuning parameter λ. A value of zero means no shrinkage, in this case, the method is identical to maximum likelihood estimation. A value of infinity means infinite shrinkage, in this case, all regression coefficients are set to zero. It is important to note that shrinkage methods are generally not invariant to the relative scaling of the covariates. We standardized the covariates before fitting the model. This standardization makes sure that each covariate is affected more or less equally by the penalization. Note that the regression coefficients reported here have been scaled back and correspond to the original scale of the covariates.

We included only the 10 000 most variable genes (standard deviation) in this analysis (see 3.1.2.4).

The appropriate generalized linear model for the response variable stromal TIL is linear. We penalized all the gene expressions covariates. Additional clinical covariates included are center (Bordet vs. MDACC), age (continuous), cT (0-1-2 vs. 3-4), cN (0 vs. +) and grade (1-2 vs. 3). Those variables were not penalized. The penalization procedure was performed on 98 patients among the 99 eligible patients in the training dataset (one missing grade).

Stromal TIL is Transformed Using Box-Cox Transformation.

3.2.4.1 the Choice of Tuning Parameter λ

Model Selection Using Penalization

The purpose of the shrinkage is to prevent overfit arising due to either collinearity of the covariates or high-dimensionality. We chose to apply L1 absolute value (“lasso”) penalty as described by Tibshirani et al. Appling an L1 penalty tends to results in many regression coefficients shrunk to zero and few other regression coefficients with comparatively little shrinkage hence this method allows selection of the most significant genes. The amount of shrinkage is determined by the tuning parameter λ. A value of zero means no shrinkage, in this case, the method is identical to maximum likelihood estimation. A value of infinity means infinite shrinkage; in this case, all regression coefficients are set to zero (FIG. 44). It is important to note that shrinkage methods are generally not invariant to the relative scaling of the covariates. We standardized the covariates before fitting the model. This standardization makes sure that each covariate is affected more or less equally by the penalization. Note that the regression coefficients reported have been scaled back and correspond to the original scale of the covariates. We included only the 10 000 most variable genes (standard deviation) in this analysis. Stromal TILs was transformed using Box-Cox transformation. We penalized all the gene expressions covariates. Additional clinicopathologic covariates included are series (TOP vs. MDACC), age (continuous), cT (0-1-2 vs. 3-4), cN (0 vs. +) and grade (1-2 vs. 3). Those variables were not penalized. The penalization procedure was performed on 98 patients among the 99 eligible patients in the training dataset (one missing grade).

Cross-validation was used to assess the predictive ability of the model described above with different values of the tuning parameter. 10-fold cross-validation was chosen to determine the optimal value of the tuning parameter λ. The allocation of the subjects to the folds is random. When using L1 optimization, the cross validated likelihood as a function of λ very often has several maxima hence it is important to cover a wide range of values (see FIG. 9). The optimal value of λ was found equal to 91.5 (see FIG. 10).

3.2.4.2 Genes Selection

Penalization was performed with the optimal value of the tuning parameter λ. The clinical covariates: center (Bordet vs. MDACC), age (continuous), cT (0-1-2 vs. 3-4), cN (0 vs. +) and grade (1-2 vs. 3) were included in the model but they were not penalized. The 4 selected genes are shown in Table 4.

TABLE 4

Genes selected using penalization

PROBEID
ENTREZID
Gene name
Symbol
Sign

202269_x_at
2633
guanylate binding
GBP1
+1

protein 1, interferon-

inducible

204753_s_at
3131
hepatic leukemia
HLF
−1

factor

205242_at
10563
chemokine (C—X—C
CXCL13
+1

motif) ligand 13

219934_s_at
6783
sulfotransferase family
SULT1E1
−1

1E, estrogen-

preferring, member 1

+1 indicates that an increasing gene expression increases the stromal TIL value.

−1 indicates that an increasing gene expression decreases the stromal TIL value.

3.2.5 Genomic Predictor of Post-Chemo TIL

3.2.5.1 Building the Genomic Predictor

After model selection and in order to determine the coefficients of the 4 selected genes in the construction of the genomic predictor, we applied a generalized linear model for the response variable stromal TIL on the 4 selected genes and the clinical covariates center (Bordet vs. MDACC), age (continuous), cT (0-1-2 vs. 3-4), cN (0 vs. +) and grade (1-2 vs. 3). The genomic predictor is the linear combination of the genes expressions weighted by the regression coefficients shown in Table 5.

Stromal TIL is Transformed Using Box-Cox Transformation.

TABLE 5

Genes associated with stromal TIL after chemotherapy

PROBEID
Gene
Description
Coefficient

202269_x_at
GBP1
guanylate binding protein 1,
0.288

interferon-inducible

204753_s_at
HLF
hepatic leukemia factor
−1.027

205242_at
CXCL13
chemokine (C—X—C motif)
0.392

ligand 13

219934_s_at
SULT1E1
sulfotransferase family 1E,
−1.726

estrogen-preferring, member 1

A positive coefficient indicates that an increasing gene expression increases the stromal TIL value.

A negative coefficient indicates that an increasing gene expression decreases the stromal TIL value.

3.2.5.2 Description of the Genomic Predictor

The genomic predictor significantly deviates from normality (Shapiro-Wilk normality test pvalue=1.518e-06). There was no statistically significant difference in the genomic predictor between MDACC samples and TOP samples (Wilcoxon rank sum test with continuity correction p-value=0.888). Summary statistics of the genomic predictor for the 99 TNBC patients in the training dataset are given in Table 6. Histograms of the genomic predictor are shown in FIG. 11.

TABLE 6

Summary statistics of the genomic predictor in TOP samples,

MDACC samples and overall

TOP
MDACC
Overall

N = 30
N = 69
N = 99

Mean
−9.06
−8.92
−8.96

SD
1.898
1.718
1.766

Median
−8.85
−8.88
−8.88

Q1-Q3
−9.50-−7.78
−9.78-−7.76
−9.77-−7.73

IQR
1.71
2.02
2.03

Min-Max
−15.19-−6.54
−16.75-−5.82
−16.75-−5.82

To facilitate interpretation of the values of the genomic predictor, we used a transformation to make the genomic predictor lie approximately between 0 (low value) and 1 (high value). The transformation has no effect on the prognostic value of the genomic predictor and is shown in the formula below, where i is the patient's index, Q_0.05is the 5% quantile of the genomic predictor in the training samples (99 patients, Q_0.05=−11.35669) and Q_0.95is 95% quantile of the genomic predictor in the training samples (99 patients, Q_0.95=−6.511546):

$transformed genomic {predictor}_{i} = \frac{genomic {predictor}_{i} - Q_{0.05}}{Q_{0.95} - Q_{0.05}}$

Summary statistics of the transformed genomic predictor in the training dataset are given in Table 7. Histograms of the transformed genomic predictor are shown in FIG. 12.

TABLE 7

Summary statistics of the transformed genomic predictor in TOP samples,

MDACC samples and Overall

TOP
MDACC
Overall

N = 30
N = 69
N = 99

Mean
0.47
0.50
0.49

SD
0.392
0.355
0.364

Median
0.52
0.51
0.51

Q1-Q3
0.38-0.74
0.33-0.74
0.33-0.75

IQR
0.35
0.42
0.42

Min-Max
−0.79-0.99
−1.11-1.14
−1.11-1.14

We Used the Transformed Value of the Genomic Predictor within the Rest of the Training Phase, Referring to it as Genomic Predictor.

3.2.5.3 Assessing the Prognostic Value of the Genomic Predictor on Survival

The median follow-up (years) in the training dataset was computed using inverse Kaplan-Meier method applied on distant relapse-free survival (Table 8). There is a statistically significant difference in follow-up between the two cohorts (Logrank p-value=1.68e-13).

TABLE 8

Follow-up
TOP
MDACC
Overall

in years
N = 26
N = 69
N = 95

Median
3.15
8.13
7.59

Q1-Q3
2.12-3.85
7.46-9.61
3.74-8.82

3.2.5.3.1 Distant Relapse-Free Survival

We assessed the prognostic value of the predictor on distant relapse-free survival (DRFS). In the training dataset, 94 patients had available data. We observed 43 events. Results of the Cox model are shown in Table 9. The Cox model is stratified on center.

TABLE 9

Multivariate cox model - Distant relapse-free survival

HR
95% IC
P

Age
1.01
0.98-1.03
0.6954

cT

0.3098

T0-1-2
1

T3-4
1.39
0.74-2.62

cN

0.5585

N0
1

N+
1.23
0.61-2.47

Grade

0.9996

1-2
1

3
1.00
0.48-2.10

Genomic predictor
0.28
0.13-0.63
0.0018

We used restricted cubic splines with 2 degrees of freedom to investigate the non-linear association between distant relapse-free survival and the genomic predictor. There was no significant non-linear effect (p=0.2874). Log-relative hazard profiles are shown in FIG. 13.

3.2.5.3.2 Overall Survival

We assessed the prognostic value of the predictor on overall survival. In the training dataset, 94 patients had available data. We observed 41 events. Results of the Cox model are shown in Table 10. The Cox model is stratified on center.

TABLE 10

Multivariate cox model - Overall survival

HR
95% IC
p

Age
1.02
0.99-1.05
0.2806

cT

0.2025

T0-1-2
1

T3-4
1.54
0.79-2.97

cN

0.5544

N0
1

N+
1.24
0.61-2.54

Grade

0.5033

1-2
1

3
0.78
0.38-1.61

Genomic predictor
0.35
0.16-0.75
0.0072

We used restricted cubic splines with 2 degrees of freedom to investigate the non-linear association between overall survival and the genomic predictor. There was no significant nonlinear effect (p=0.3057). Log-relative hazard profiles are shown in FIG. 14.

3.2.5.4 Building Risk Groups

3.2.5.4.1 Cut-Offs

We build risk groups based on:

- 1. Tertiles (33.33%, 66.66%), referred to hereafter as TER

${\begin{matrix} Genomic predictor < 0.40 & poor prognosis \\ 0.40 \leq Genomic predictor < 0.67 & intermediate prognosis \\ Genomic predictor \geq 0.67 & good prognosis \end{matrix}$

- 2. Median (50%), referred to hereafter as MED

${\begin{matrix} Genomic predictor < 0.51 & poor prognosis \\ Genomic predictor \geq 0.51 & good prognosis \end{matrix}$

- 3. Quantiles (27%, 73%) [Cox DR (1957) Note on Grouping. J Am Stat Assoc 52:543-547. doi:10.2307/2281704], referred to hereafter as COX

${\begin{matrix} Genomic predictor < 0.35 & very poor prognosis \\ 0.35 \leq Genomic predictor < 0.74 & intermediate prognosis \\ Genomic predictor \geq 0.74 & very good prognosis \end{matrix}$

The Cut-Offs Defined Above are Frozen for all the Study.

3.2.5.4.2 Distant Relapse-Free Survival

Kaplan-Meier distant relapse-free survival curves of the three risk groups according to the different cut-offs are shown in FIG. 15, FIG. 16 and FIG. 17.

3.2.5.4.3 Overall Survival

Kaplan-Meier overall survival curves of the three risk groups according to the different cutoffs are shown in FIG. 18, FIG. 19 and FIG. 20.

3.2.5.5 Testing for Correlations

3.2.5.5.1 Gene—Gene Correlation

We performed pairwise correlation between the different genes included in the predictor using Spearman correlation. The correlation was assessed on 99 patients. Correlation coefficients values and 95% confidence intervals obtained using 1000 bootstrap repetitions are given in Table 11. Heat map shown in FIG. 21 reflects hierarchic clustering of pairwise correlation between the 4 genes. The cells are colored according to Spearman's correlation coefficient values with red indicating positive correlations and green indicating negative correlations.

TABLE 11

Correlation coefficients and p-values of Spearman correlation

SULT1E1
HLF
CXCL13
GBP1

SULT1E1
1
0.19 [−0.02-0.38]
−0.28 [−0.46-−0.09]
−0.34 [−0.52-−0.12]

HLF

1
−0.27 [−0.47-−0.06]
−0.20 [−0.42-−0.01]

CXCL13

1
0.62 [0.47-0.74]

GBP1

1

3.2.5.5.2 Correlation Between the Genomic Predictor and Validated Gene Modules (Immune1 and Immune2)

Among 99 patients in the training dataset, only 85 had all genes expression to generate the genomic predictor and available immune1 and immune2 gene modules expressions [9]. We performed pairwise correlation using Spearman correlation. Correlation coefficients values and 95% confidence intervals obtained using 1000 bootstrap repetitions are given in Table 12.

TABLE 12

Correlation between the genomic predictor and gene modules

Predictor
Immune1
Immune2

Predictor
1
0.47 [0.25-0.63]
0.64 [0.50-0.76]

Immune1

1
0.43 [0.17-0.63]

Immune2

1

3.2.5.5.3 Change in Stromal TIL after Chemotherapy as Compared to Before Chemotherapy

From TOP samples, 36 patients had a GEO accession and available value of stromal TIL before chemotherapy (34 from the training dataset +2 from the validation dataset). 29 of the 34 patients in the training dataset had both information about stromal TIL before chemotherapy and stromal TIL after chemotherapy. Spearman correlation coefficient value between stromal TIL before chemotherapy and stromal TIL after chemotherapy was 0.17 (p-value=0.384). There is a significant absolute increase in stromal TIL after chemotherapy as compared to before chemotherapy (18.28, [CI95% 6.21−30.34], paired t-test p-value=0.004). Individual profiles (Grey lines) and the mean profile (Dark grey line) are shown in FIG. 22.

3.2.5.5.4 Correlation Between the Genomic Predictor and Stromal TIL Before Chemotherapy

From TOP samples, 22 had a GEO accession and available value of stromal TIL before chemotherapy. Spearman correlation coefficient value between stromal TIL before chemotherapy and the genomic predictor was 0.41 [−0.06−0.77]. 95% confidence intervals were obtained using 1000 bootstrap repetitions.

3.2.6 Prognostic Value of Stromal TIL after Chemotherapy on Survival

The Cox models are stratified on center. For illustrative purposes only, we show Kaplan-Meier survival curves, considering a cut-off value of 50% for stromal TIL.

3.2.6.1 Distant Relapse-Free Survival

3.2.6.1.1 Univariate Analysis

In the training dataset, 95 patients had available data. We observed 44 events. (Table 13).

TABLE 13

HR
95% IC
P

Stromal TIL after chemotherapy
0.98
0.96-1.00
0.023

3.2.6.1.2 Multivariate Analysis

In the training dataset, 94 patients had available data. We observed 43 events. Results of the Cox model are shown in Table 14.

TABLE 14

Multivariate Cox model - Stromal TIL on distant relapse-free survival

HR
95% IC
P

Age
1.01
0.98-1.04
0.664

cT

0.312

T0-1-2
1

T3-4
1.39
0.74-2.61

cN

0.816

N0
1

N+
1.09
0.54-2.17

Grade

0.816

1-2
1

3
1.09
0.52-2.32

Stromal TIL after chemotherapy
0.98
0.96-1.00
0.043

We used restricted cubic splines with 2 degrees of freedom to investigate the non-linear association between distant relapse-free survival and the stromal TIL after chemotherapy.

There was no significant non-linear effect (p=0.501). Log-relative hazard profiles are shown in FIG. 23.

3.2.6.2 Overall Survival

3.2.6.2.1 Univariate Analysis

In the training dataset, 95 patients had available data. We observed 42 events. (Table 15).

TABLE 15

HR
95% IC
P

Stromal TIL after chemotherapy
0.98
0.96-1.00
0.027

3.2.6.2.2 Multivariate Analysis

In the training dataset, 94 patients had available data. We observed 41 events. Results of the Cox model are shown in Table 16.

TABLE 16

Multivariate Cox model - Stromal TIL on overall survival

HR
95% IC
P

Age
1.02
0.99-1.05
0.317

cT

0.179

T0-1-2
1

T3-4
1.57
0.81-3.02

cN

0.880

N0
1

N+
1.06
0.52-2.15

Grade

0.859

1-2
1

3
0.93
0.44-1.97

Stromal TIL
0.98
0.96-1.00
0.063

after chemotherapy

We used restricted cubic splines with 2 degrees of freedom to investigate the non-linear association between overall survival and stromal TIL after chemotherapy. There was no significant non-linear effect (p=0.594). Log-relative hazard profiles are shown in FIG. 25.

4 Validation Phase

4.1 Materiel

4.1.1 Description of the Validation Population

The participants' flow chart of the validation dataset is shown in FIG. 27.

In the validation dataset, 373 patients were TNBC (ER-, HER-). Among them, 185 had available survival data. The baseline characteristics of the patients in the validation dataset are presented Table 17.

TABLE 17

Baseline characteristics of patients in the validation dataset

LBJ/INEN/

I-SPY-1
GEICAM
MAQCII/MDACC
TOP
USO-02103
All trials

n = 36
n = 21
n = 55
n = 48
n = 25
n = 185

Age, years

Mean (SD)
46
(8·2)
51
(10·2)
50
(10·9)
47
(10·3)
48
(10·5)
48
(10·1)

Median (Q1-Q3)
44
(40-53)
46
(44-58)
50
(42-57)
48
(38-56)
48
(40-55)
48
(40-57)

Min-Max
34-63
35-71
28-75
27-67
26-66
26-75

cT

T1
0
(0%)
0
(0%)
9
(16%)
8
(17%)
1
(4%)
18
(10%)

T2
17
(47%)
6
(29%)
26
(47%)
31
(65%)
10
(40%)
90
(49%)

T3
16
(44%)
8
(38%)
7
(13%)
1
(2%)
14
(56%)
46
(25%)

T4
3
(8%)
7
(33%)
13
(24%)
8
(17%)
0
(0%)
31
(17%)

cN

N0
8
(22%)
5
(24%)
10
(18%)
20
(42%)
8
(32%)
51
(28%)

N+
28
(78%)
16
(76%)
45
(82%)
28
(58%)
17
(68%)
134
(72%)

ER status

Negative
36
(100%)
21
(100%)
55
(100%)
48
(100%)
25
(100%)
185
(100%)

Positive
0
(0%)
0
(0%)
0
(0%)
0
(0%)
0
(0%)
0
(0%)

PR status

Negative
30
(91%)
20
(95%)
46
(84%)
0
(0%)
21
(84%)
117
(87%)

Positive
3
(9%)
1
(5%)
9
(16%)
0
(0%)
4
(16%)
17
(13%)

Missing
3
0
0
48
0
51

HER2 status

Negative
36
(100%)
21
(100%)
55
(100%)
48
(100%)
25
(100%)
185
(100%)

Positive
0
(0%)
0
(0%)
0
(0%)
0
(0%)
0
(0%)
0
(0%)

Histologic grade

1
0
(0%)
2
(12%)
0
(0%)
2
(4%)
0
(0%)
4
(2%)

2
3
(9%)
3
(19%)
5
(9%)
6
(13%)
3
(14%)
20
(12%)

3
21
(62%)
11
(69%)
50
(91%)
37
(82%)
19
(86%)
138
(80%)

Unknown
10
(29%)
0
(0%)
0
(0%)
0
(0%)
0
(0%)
10
(6%)

Missing
2
5
0
3
3
13

Response

pCR
10
(29%)
5
(24%)
35
(64%)
7
(15%)
11
(44%)
68
(37%)

RD
24
(71%)
16
(76%)
20
(36%)
41
(85%)
14
(56%)
115
(63%)

Missing
2
0
0
0
0
2

No. of relapses
12
(33%)
10
(48%)
15
(27%)
12
(25%)
8
(32%)
57
(31%)

Median follow-up in
2·53
(2·03-3·84)
3·20
(3·13-3·70)
2·60
(1·86-4·62)
3·59
(2·57-4·73)
4·12
(3·70-4·46)
3·24
(2·26-4·46)

years (Q1-Q3)

GEO
GSE25066
GSE25066
GSE20194
GSE16446
GSE23988

GSE25066

GSE25066

References
Hatzis et al⁷
Hatzis et al⁷
Shi et al¹⁰
Desmedt et
Hatzis et al⁷

Hatzis et al⁷
al⁸
Iwamoto et al¹¹

Data are mean (SD), median (Q1-Q3), min-max, or n (%).

Patients of the validation set were from five different cohorts.

Patients included in the training set from MDACC neoadjuvant series and TOP study were excluded from the validation set.

SD, standard deviation;

Q1, 25th percentile;

Q3, 75th percentile;

Min, Minimum;

Max, Maximum;

cT, clinical tumor size;

cN, clinical nodal status;

ER, estrogen receptor;

PR, progesterone receptor;

HER2, human epidermal growth factor receptor 2;

pCR, pathological complete response;

RD, recurrent disease;

GEO, gene expression omnibus;

I-SPY-1, Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging and Molecular Analysis;

LBJ, Lyndon B. Johnson hospital;

INEN, Instituto Nacional de Enfermedades Neoplasicas;

GEICAM, Grupo Espanol de Investigacion en Cancer de Mama;

MAQCII, MicroArray Quality Control Consortium II;

MDACC, MD Anderson Cancer Center;

TOP, Trial of Principle;

USO, US Oncology.

4.1.2 Genomic Data

The complete genomic data are available at the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/). We applied frozen robust multiarray analysis (fRMA) [McCall M N, Bolstad B M, Irizarry R A (2010) Frozen robust multiarray analysis (fRMA). Biostat Oxf Engl 11:242-253. doi: 10.1093/biostatistics/kxp059] preprocessing algorithm to normalize data separately on each series.

4.2 Methods and Results

4.2.1 Description of the Genomic Predictor

The genomic Predictor significantly deviates from normality (Shapiro-Wilk normality test pvalue=1.444e-08). There is a statistically significant difference in the genomic Predictor between the five cohorts' samples (Kruskal-Wallis rank sum test p-value <2.2e-16). Summary statistics of the genomic predictor in the validation dataset are given in Table 18. Histograms of the genomic predictor are shown in FIG. 28. TOP samples are different Affymetrix platform from all other samples (see Table 37).

TABLE 18

Summary statistics of the genomic predictor in the validation dataset

I-SPY-1
LBJ/INEN/GEICAM
MAQCII/MDACC
TOP
USO-02103
Overall

N = 36
N = 21
N = 55
N = 48
N = 25
N = 185

Mean
−10.38
−10.64
−10.20
−5.72
−10.98
−9.23

SD
1.183
1.361
1.260
2.271
1.099
2.608

Median
−10.48
−10.74
−10.24
−5.07
−11.23
−9.95

Q1-Q3
−11.33-−9.49
−11.54-−9.58
−10.91-−9.59
−6.55-−4.28
−11.64-−10.05
−10.98-−8.03

IQR
1.85
1.95
1.33
2.27
1.60
2.96

Min-Max
−13.02-−8.03
−12.90-−8.35
−13.77-−7.74
−14.30-−2.81
−12.72-−8.96
−14.30-−2.81

We performed the same transformation on the genomic predictor of the validation dataset as in the training dataset (see 3.2.5.2) using the 5% quantile of the genomic predictor in the training samples (99 patients, Q_0.05=−11.35669) and the 95% quantile of the genomic predictor in the training samples (99 patients, Q_0.95=−6.511546). Summary statistics of the transformed genomic predictor in the training dataset are given in Table 19. Histograms of the transformed genomic predictor are shown in FIG. 29.

TABLE 19

Summary statistics of the transformed genomic predictor in the validation dataset

I-SPY-1
LBJ/INEN/GEICAM
MAQCII/MDACC
TOP
USO-02103
Overall

N = 36
N = 21
N = 55
N = 48
N = 25
N = 185

Mean
0.20
0.15
0.24
1.16
0.08
0.44

SD
0.244
0.281
0.260
0.469
0.227
0.538

Median
0.18
0.13
0.23
1.30
0.03
0.29

Q1-Q3
0.00-0.39
−0.04-0.37
0.09-0.36
0.99-1.46
−0.06-0.27
0.08-0.69

IQR
0.38
0.40
0.27
0.47
0.33
0.61

Min-Max
−0.34-0.69
−0.32-0.62
−0.50-0.75
−0.61-1.76
−0.28-0.49
−0.61-176

We Used the Transformed Value of the Genomic Predictor within the Rest of the Validation Phase, Referring to it as Genomic Predictor.

4.2.2 Validation of the Prognostic Value of the Genomic Predictor on Distant Relapse-Free Survival

The median follow-up (years) in the validation dataset was computed using inverse Kaplan-Meier method applied on distant relapse-free survival. There is no statistically significant difference in follow-up between the five cohorts (Logrank p-value=0.556). (Table 20).

TABLE 20

Follow-up in
I-SPY-1
LBJ/INEN/GEICAM
MAQCII/MDACC
TOP
USO-02103
Overall

years
N = 36
N = 21
N = 55
N = 48
N = 25
N = 185

Median
2.53
3.20
2.60
3.59
4.12
3.24

Q1-Q3
2.03-3.84
3.13-3.70
1.86-4.62
2.57-4.73
3.70-4.46
2.26-4.46

In the validation dataset, data were available only on distant relapse-free survival. 185 patients had available data. We observed 57 events. The Cox model is stratified on center.

4.2.2.1 Patients with No pCR (RD)

4.2.2.1.1 Univariate Analysis

115 patients were not in pCR. We observed 49 events among them. (Table 21).

TABLE 21

HR
95% IC
P

Genomic predictor
0.36
0.18-0.75
0.0057

4.2.2.1.2 Multivariate Analysis

98 patients were not in pCR and had complete data. We observed 39 events among them. Results of the Cox model are shown in Table 22.

TABLE 22

Multivariate Cox model - Genomic Predictor on distant relapse-free survival - Validation

dataset - Prognostic value of the four-gene signature on survival in a multivariate Cox model

Training set - DRFS
Validation set - DRFS
Training set - OS

(n = 94)
(n = 160)
(n = 94)

HR
95% CI
p
HR
95% CI
p
HR
95% CI
p

Age
1.01
0.98-1.03
0.695
1.00
0.97-1.03
0.880
1.02
0.99-1.05
0.281

cT

0.310

0.001

0.203

T0-1-2
1

1

1

T3-4
1.39
0.74-2.62

2.96
1.54-6.67

1.54
0.79-2.97

cN

0.559

0.011

0.554

N0
1

1

1

N+
1.23
0.61-2.47

3.19
1.30-7.83

1.24
0.61-2.54

Grade

0.100

0.981

0.503

1-2
1

1

1

3
1.00
0.48-2.10

1.01
0.43-2.37

0.78
0.38-1.61

1-unit increase in
0.28
0.13-0.63
0.002
0.29
0.13-0.67
0.004
0.35
0.16-0.75
0.007

the four-gene

signature

DRFS, distant relapse-free survival;

OS, overall survival;

cT, clinical tumor size;

cN, clinical nodal status;

HR, Hazard ratio;

CI, confidence interval;

P, p-value

We used restricted cubic splines with 2 degrees of freedom to investigate the non-linear association between distant relapse-free survival and the genomic predictor in the validation dataset for patients achieving pCR. There was no significant non-linear effect (p=0.5240). Log-relative hazard profiles are shown in FIG. 30.

4.2.2.2 All patients (pCR and RD)

4.2.2.2.1 Univariate Analysis

185 patients had available data. We observed 57 events among them. (Table 23).

TABLE 23

HR
95% IC
P

Predictor
0.36
0.18-0.74
0.0055

4.2.2.2.2 Multivariate Analysis

160 patients had complete data. We observed 45 events among them. Results of the Cox model are shown in Table 24.

TABLE 24

Multivariate Cox model - Genomic Predictor on distant relapse-free survival - Validation

Dataset - Prognostic value of the four-gene signature on survival in a multivariate Cox model

Training set - DRFS
Validation set - DRFS
Training set - OS

(n = 94)
(n = 160)
(n = 94)

HR
95% CI
p
HR
95% CI
p
HR
95% CI
p

Age
1.01
0.98-1.03
0.695
1.00
0.97-1.03
0.880
1.02
0.99-1.05
0.281

cT

0.310

0.001

0.203

T0-1-2
1

1

1

T3-4
1.39
0.74-2.62

2.96
1.54-6.67

1.54
0.79-2.97

cN

0.559

0.011

0.554

N0
1

1

1

N+
1.23
0.61-2.47

3.19
1.30-7.83

1.24
0.61-2.54

Grade

0.100

0.981

0.503

1-2
1

1

1

3
1.00
0.48-2.10

1.01
0.43-2.37

0.78
0.38-1.61

1-unit increase in
0.28
0.13-0.63
0.002
0.29
0.13-0.67
0.004
0.35
0.16-0.75
0.007

the four-gene

signature

DRFS, distant relapse-free survival;

OS, overall survival;

cT, clinical tumor size;

cN, clinical nodal status;

HR, Hazard ratio;

CI, confidence interval;

P, p-value

We used restricted cubic splines with 2 degrees of freedom to investigate the non-linear association between distant relapse-free survival and the genomic predictor in the validation dataset. There was no significant non-linear effect (p=0.4504). Log-relative hazard profiles are shown in FIG. 31.

4.2.3 Validation of Risk Groups

We used cut-off points assessed on the training dataset for building risk groups in the validation dataset (TER, MED, COX).

4.2.3.1 Patients with No pCR (RD)

Kaplan-Meier distant relapse-free survival curves of the three risk groups according to the different cut-offs and for patients that did not achieved pCR are shown in FIG. 32, FIG. 33 and FIG. 34.

4.2.3.2 All patients (pCR and RD)

Kaplan-Meier distant relapse-free survival curves of the three risk groups according to the different cut-offs and for all patients are shown in FIG. 35, FIG. 36 and FIG. 37.

4.2.4 Testing for Correlation

4.2.4.1 Gene—Gene Correlation

We performed pairwise correlation between the different genes included in the predictor using Spearman correlation. The correlation was assessed on 185 patients. Correlation coefficients values and 95% confidence intervals obtained using 1000 bootstrap repetitions are given in Table 25. Heat map shown in FIG. 38 reflects hierarchic clustering of pairwise correlation between the 4 genes. The cells are colored according to Spearman's correlation coefficient values with red indicating positive correlations and green indicating negative correlations.

TABLE 25

Correlation coefficients and p-values of Spearman correlation -

Validation dataset

SULT1E1
HLF
CXCL13
GBP1

SULT1E1
1
0.47 [0.32-0.60]
−0.30 [−0.44-−0.17]
−0.37 [−0.49-−0.23]

HLF

1
−0.16 [−0.30-−0.02]
−0.16 [−0.30-−0.02]

CXCL13

1
0.62 [0.50-0.71]

GBP1

1

4.2.4.2 Correlation Between Our Predictor and Validated Gene Modules (Immune1 and Immune2)

All patients (n=185) have expressions of the genomic predictor and available immune1 and immune2 gene modules expressions. We performed pairwise correlation using Spearman correlation. Correlation coefficients values and 95% confidence intervals obtained using 1000 bootstrap repetitions are given in Table 26.

TABLE 26

Correlation between the genomic predictor and gene modules -

Validation dataset

Predictor
Immune1
Immune2

Predictor
1
0.52 [0.39-0.63]
0.46 [0.34-0.59]

Immune1

1
0.62 [0.52-0.71]

Immune2

1

4.2.6 Validation of the Prognostic Value in the Training and in the Validation Set at Diagnosis

4.2.6.1 Study Population

Study flowchart for the training set is described in FIG. 1b. Overall, 99 patients with ER-/HER2- BC were selected to generate the signature. Patients' characteristics in the training set are given in Table 1. Flowchart for the validation set is described in supplementary material. Overall, 185 patients with ER-/HER2- BC were selected to validate the prognostic value of the signature on DRFS. Patients' characteristics in the validation set are given in Table 17.

4.2.6.2 Prognostic Value of the Four-Gene Signature in the Training Set

The prognostic value of the four-gene signature was assessed in 94 patients from the training set, for whom survival data were available. All patients had RD after NACT. Median (Q1-Q3) follow-up was 7.6 years (3.7-8.8). In a multivariate analysis (Table 42), the four-gene signature was significantly associated with better DRFS (HR for a one-unit increase in the value of the 4-gene signature: 0.28, 95% CI: 0.13-0.63, p=0-002). Kaplan-Meier DRFS curves of the risk groups (low four-gene signature vs. high four-gene signature) constructed using the median value of the 4-gene signature (median=0.51) are shown in FIG. 16. There was no evidence of a non-linear association between the 4-gene signature and DRFS. The 4-gene signature added significant prognostic information to the clinicopathological characteristics at diagnosis, as shown by the likelihood ratio test (p=0.004). The discrimination was also improved; at five years, the C-index increased from 0.617 to 0.673 (Table 42). Similar results were obtained for OS (HR for a one-unit increase in the value of the 4-gene signature: 0.35, 95% CI: 0.16-0.75, p=0-007; likelihood ratio test, p=0-012; the C-index increased from 0.631 to 0.668).

4.2.6.3 Prognostic Value of the Four-Gene Signature in the Validation Set

In the validation set, 68 (37%) patients achieved pCR and 115 (63%) relapsed (2 missing information on pCR). The prognostic value of the four-gene signature was assessed in 162 patients (23 missing information on grade). Median (Q1-Q3) follow-up was 3.2 years (2.3-4.5). In a multivariate analysis (Table 42), the four-gene signature was significantly associated with better DRFS (HR for a one-unit increase in the value of the 4-gene signature: 0.29, 95% CI: 0.13-0.67, p=0.004). Kaplan-Meier DRFS curves of the risk groups constructed using the same cutoff (0.51) as in the training set are shown in FIG. 36. There was no strong evidence of a non-linear association between the 4-gene signature and DRFS. The 4-gene signature added prognostic information to the clinicopathologic model at diagnosis as shown by the likelihood ratio test (p=0.008). Discrimination was also improved; at five years, the C-index increased from 0.686 to 0.700 in the validation set.

Results of the conditional logistic model showed no statistically significant association between the four-gene signature and the probability to achieve pCR in the validation set (OR for a one-unit increase in the four-gene signature: 0.96, 95% CI: 0.30-3.08, p=0-947, detailed results are provided in the supplementary material.

TABLE 42

Prognostic value of the four-gene signature on survival in a multivariate Cox model

Training set - DRFS
Validation set - DRFS
Training set - OS

(n = 94)
(n = 160)
(n = 94)

HR
95% CI
p
HR
95% CI
p
HR
95% CI
p

Age
1.01
0.98-1.03
0.695
1.00
0.97-1.03
0.880
1.02
0.99-1.05
0.281

cT

0.310

0.001

0.203

T0-1-2
1

1

1

T3-4
1.39
0.74-2.62

2.96
1.54-6.67

1.54
0.79-2.97

cN

0.559

0.011

0.554

N0
1

1

1

N+
1.23
0.61-2.47

3.19
1.30-7.83

1.24
0.61-2.54

Grade

0.100

0.981

0.503

1-2
1

1

1

3
1.00
0.48-2.10

1.01
0.43-2.37

0.78
0.38-1.61

1-unit increase in the
0.28
0.13-0.63
0.002
0.29
0.13-0.67
0.004
0.35
0.16-0.75
0.007

four-gene signature

DRFS, distant relapse-free survival;

OS, overall survival;

cT, clinical tumor size;

cN, clinical nodal status;

HR, Hazard ratio;

CI, confidence interval;

P, p-value

5. Distribution of the Genomic Predictor: Training Vs. Validation

Samples included 99 patients from the training dataset and 185 patients from the validation dataset. There was a statistically significant difference in the genomic predictor between the training dataset and the validation dataset (Wilcoxon rank sum test with continuity correction p-value=0.001349). Summary statistics of the genomic predictor are given in Table 27. Histograms of the genomic predictor are shown in FIG. 39.

TABLE 27

Summary statistics of the genomic predictor -

Training vs. validation

Training
validation

N = 99
N = 185

Mean
−8.96
−9.23

SD
1.766
2.608

Median
−8.88
−9.95

Q1-Q3
−9.77-−7.73
−10.98-−8.03

IQR
2.03
2.96

Min-Max
−16.75-−5.82
−14.30-−2.81

Summary statistics of the standardized genomic predictor are given in Table 28. Histograms of the genomic predictor are shown in FIG. 40.

TABLE 28

Summary statistics of the transformed genomic predictor -

Training vs. validation

Training
validation

N = 99
N = 185

Mean
0.49
0.44

SD
0.364
0.538

Median
0.51
0.29

Q1-Q3
0.33-0.75
0.08-0.69

IQR
0.42
0.61

Min-Max
−1.11-1.14
−0.61-1.76

6. Evaluating the Added Value of the Genomic Predictor to a Clinical Model

We used Uno's C-statistic to quantify the capacity of the prediction models in discriminating among subjects with different event times [10]. We considered two truncation times: 3 years and 5 years. The resulting Cs tell how well the given prediction models work in predicting events that occur in the time range from 0 to 3 years and 0 to 5 years, respectively. The clinical models (CM) included data in Table 29.

TABLE 29

Age
T
N
Grade
pCR

(continuous)
(0-1-2 vs. 3-4)
(0 vs. +)
(1-2 vs. 3)
Yes vs. RD

Training OS (n = 94)

custom character

Training DRFS (n = 94)

custom character

Validation DRFS no pCR (n = 98)

custom character

Validation DRFS (n = 160)

custom character

We used the likelihood ratio statistics in Cox regression models stratified on center to estimate the added value of the genomic predictor to the previously defined clinical models. We gave p-values of the likelihood ratio test. Results of the assessment of added value of the genomic predictor are shown in Table 30a and b. 95% confidence intervals were obtained using 1000 bootstrap repetitions.

TABLE 30a

Assessment of added value of the genomic predictor

Difference

Clinical model (CM)
CM + genomic predictor
3-year C-index
5-year C-index

3-year C-index
5-year C-index
3-year C-index
5-year C-index
increase
increase

[95% CI]
[95% CI]
[95% CI]
[95% CI]
[95% CI]
[95% CI]
χ²increase
p

Training OS (n = 94)
0.643
0.631
0.663
0.668
0.020
0.036
6.25
0.012

[0.504-0.783]
[0.449-0.764]
[0.544-0.782]
[0.554-0.781]
[−0.069-0.108]
[−0.051-0.123]

Training DRFS (n = 94)
0.657
0.617
0.681
0.673
0.024
0.056
8.23
0.004

[0.507-0.807]
[0.488-0.745]
[0.558-0.804]
[0.566-0.779]
[−0.082-0.130]
[−0.051-0.163]

Validation DRFS no pCR
0.699
0.712
0.725
0.737
0.027
0.025
9.66
0.002

(n = 98)
[0.588-0.809]
[0.601-0.823]
[0.626-0.824]
[0.637-0.838]
[−0.025-0.078]
[−0.023-0.073]

Validation DRFS (n = 160)
0.754
0.764
0.772
0.782
0.018
0.017
9.01
0.003

[0.668-0.839]
[0.680-0.849]
[0.692-0.851]
[0.702-0.861]
[−0.012-0.048]
[−0.011-0.045]

TABLE 30b

Assessing the added prognostic value of the four-gene signature to a clinical model

CM
CM + 4-gene signature
Difference

3-year C-index
5-year C-index
3-year C-index
5-year C-index
3-year C-index
5-year C-index
χ²

[95% CI]
[95% CI]
[95% CI]
[95% CI]
increase [95% CI]
increase [95% CI]
increase
p

Training DRFS (n = 94)
0.657
0.617
0.681
0.673
0.024
0.056
8.23
0.004

[0.507-0.807]
[0.488-0.745]
[0.558-0.804]
[0.566-0.779]
[−0.082-0.130]
[−0.051-0.163]

Validation DRFS (n = 160)
0.681
0.686
0.693
0.700
0.012
0.014
7.1
0.008

[0.584-0.779]
[0.592-0.780]
[0.598-0.788]
[0.606-0.795]
[−0.033-0.058]
[−0.028-0.056]

Training OS (n = 94)
0.643
0.631
0.663
0.668
0.020
0.036
6.25
0.012

[0.504-0.783]
[0.449-0.764]
[0.544-0.782]
[0.554-0.781]
[−0.069-0.108]
[−0.051-0.123]

Uno's concordance indices were computed to quantify the capacity of the prediction models in discriminating among subjects with different event times. Two truncation times were considered: 3 years and 5 years. The concordance indices indicate how well the given prediction models work in predicting events that occur in the time range from 0 to 3 years and 0 to 5 years, respectively. The likelihood ratio statistics was used in Cox regression models stratified on series to estimate the added value of the 4-gene signature to the clinical models. 95% confidence intervals were obtained using 1000 bootstrap repetitions. CM, clinical model; C-index, Concordance index; p, p-value; DRFS, distant relapse-free survival; OS, overall survival

TABLE 31

Summary information about the neoadjuvant studies included in the present analysis.

EORTC10994
I-SPY-1
LBJ/INEN/GEICAM
MDACC trial

Study design
Intergroup randomized multicentre
Investigation of Serial
Prospective Multicenter
Prospective randomized

phase 3 trial.
Studies to Predict
Trial.
multicenter trial.

Your Therapeutic

Response with Imaging

And moLecular Analysis

or I-SPY 1: Multicenter

trial.

Inclusion criteria
http://clinicaltrials.gov/ct2/show/NCT00017095?term=

⁵and

⁵

⁶

EORTC10994&rank=1
http://clinicaltrials.gov/ct2/show/NCT00033397

Objective
To assess whether the benefit of
To identify predictors of
NA
To interrogate whether patients

adding taxanes to anthracyclines is
pCR and survival in

with DLDA-30 - positive

mainly restricted to the TP53-
women with locally

tumors (DLDA 30 is a

mutated breast tumors.
advanced breast cancers

genomic predictor of pCR) are

treated with

significantly more likely to

chemotherapy.

experience pCR to T/FAC.

Primary endpoint
Progression Free Survival
pCR
NA
pCR

Patients enrolled
1856
NA
NA
273

Patients with publicly
160
79
57
178

available gene

expression data

Chemotherapy Regimen
Randomly assigned to
Doxorubicin and
Docetaxel with
Randomly assigned to

A. Fluorouracil 500 mg/m²,
cyclophosphamide (AC)
capecitabine (TxX) X4
A. Paclitaxel 80 mg/m2 q week

epirubicin 100 mg/m², and
X4 followed by
followed by fluorouracil.
x 12 followed by fluorouracil

cyclophosphamide 500 mg/m²q 3
paclitaxel X4 (N = 60) or
epirubicin and
500 mg/m2, doxorubicin 50 mg/m2

weeks (FEC) X6 or fluorouracil
docetaxel X4 (N = 18) or
cyclophosphamide (FEC)
and cyclophosphamide

600 mg/m², epirubicin 75 mg/m²,
taxane not specified
X4
500 mg/m2 q 3 weeks X4

cyclophosphamide 900 mg/m²q 3
(N = 5).

(T/FAC) and

weeks (tailored FEC) X6

B FAC X6

B. Docetaxel 100 mg/m²q 3

(Epirubicin 100 mg/m2 could

weeks X3 followed by epirubicin

be substituted for doxorubicin

90 mg/m²plus docetaxel 70 mg/m²

at the discretion of

q 3 weeks X3 (T-ET).

investigators).

Pre-treatment biopsies
Core
Core
Core/FNA
FNA

Invasive Tumor Cell
≥20%
NA
NA
70-90% pure neoplastic cells

Content per biopsy

Relapse Free Survival
Time from randomisation to
Time from initial
Time from initial
NA

locoregional progression/relapse,
diagnosis to distant
diagnosis to distant

distant metastasis, death from any
relapse or death.
relapse or death.

cause, or invasive contralateral

breast cancer (Progression Free

Survival).

Microarray experiment
RNA was purified with Qiagen.
RNA was extracted using
RNA was extracted using
RNA was extracted using

RNeasy kit, RNA amplification &
Qiagen Rneasy Kit, RNA
Qiagen Rneasy Kit, RNA
Qiagen Rneasy Kit, RNA

hybridization was performed
amplification &
amplification &
amplification & hybridization

according to standard Affymetrix
hybridization was
hybridization was
was performed according to

protocols.
performed according to
performed according to
standard Affymetrix protocols.

Affymetrix U133_X3P.
standard Affymetrix
standard Affymetrix
Human Genome U133A Array.

protocols.
protocols.

Human Genome U133A
Human Genome U133A

Array.
Array.

Ref

^7,8

^5,9,10

⁵

⁶

TOP
MAQCII/MDACC
MAQCIII
USO-02103

Study design
Prospective, multicenter study.
Prospective, multicenter
Prospective,
Phase II trial.

study.
multicenter study.

Inclusion criteria
http://clinicaltrial.gov/ct2/show/NCT00162812?term=top+

^5,11,12

^5,12

^5,13

bordet&rank=1

Objective
To evaluate the predictive value of
To assess the capabilities
To assess the
NA

topoisomerase II-(TOP2A) and
and limitations of various
technical

develop a gene expression signature
data analysis methods in
performance of next-

to identify those patients who do not
developing and validating
generation

benefit from anthracyclines.
microarray-based
sequencing platforms

predictive models.
by generating

To reach consensus on the
benchmark datasets

“best practices” for
with reference

development and
samples and

validation of predictive
evaluating

models based on
advantages and

microarray gene expression
limitations of various

and genotyping data for
bioinformatics

personalized medicine.
strategies in RNA

and DNA analyses.

Primary endpoint
pCR
NA
NA
NA

Patients enrolled
149
NA
NA
NA

Patients with publicly
114
265
82
61

available gene expression

data

Neoadjuvant Chemotherapy
Early Breast (N = 65): Epirubicin
Paclitaxel 80 mg/m2 q
Fluorouracil,
Fluorouracil 500 mg/m2,

Regimen
100 mg/m2 q 3 weeks X4
week X12 followed by
epirubicin, and
epirubicin 100 mg/m2,

Locally advanced/inflammatory
fluorouracil 500 mg/m2,
cyclophosphamide
and cyclophosphamide

(N = 49): Epirubicin 100 mg/m2
doxorubicin 50 mg/m2 and
(FEC) q 3 weeks X4
500 mg/m2, q 3 weeks,

weeks X6.
cyclophosphamide 500 mg/m2
or fluorouracil,
followed by docetaxel 35 mg/m2

q 3 weeks X4
doxorubicin and
q week X12.

(T/FAC).
cyclophosphamide q
concomitant with

3 weeks X4
capecitabine 850 mg/m2

(FAC).
twice daily for 14 days, q

3 weeks (FEC/wTX).

Dose intensity for each drug
NA
NA
NA
NA

Pre-treatment biopsies
Core
FNA
FNA
FNA

Invasive Tumor Cell Content per
>30%
70-90% pure neoplastic
70-90% pure
70-90% pure neoplastic

biopsy

cells
neoplastic cells
cells

Relapse Free Survival
Time from diagnosis to distant
Time from initial diagnosis
NA
Time from initial

metastasis, contralateral breast
to distant relapse or death.

diagnosis to distant

tumor or death.

relapse or death.

Microarray experimental
RNA isolation was performed using
RNA was extracted using
RNA was extracted
RNA was extracted using

setting
the Trizol method and RNA
Qiagen Rneasy Kit, RNA
using Qiagen Rneasy
Qiagen Rneasy Kit, RNA

purification using RNeasy Kit, RNA.
amplification &
Kit, RNA
amplification &

amplification and hybridization were
hybridization was
amplification &
hybridization was

done according to standard
performed according to
hybridization was
performed according to

Affymetrix protocols.
standard Affymetrix
performed according
standard Affymetrix

Human Genome U133-2.0 plus
protocols
to standard
protocols.

GeneChip.
Human Genome U133A
Affymetrix protocols.
Human Genome U133A

Array.
Human Genome
Array.

U133A Array.

Ref

¹⁴

^5,11,12

^5,12

^5,13

TABLE 32

Baseline characteristics of patients in the training dataset

N = 113

Characteristics
N (%)

Center

Bordet (TOP)
44 (39)

MDACC
69 (61)

Demographics

Age

Mean
49

SD
11.2

Median
48

Q1-Q3
40-58

Min-Max
27-75

Tumor information

ER

Positive
0 (0)

Negative
113 (100)

PgR

Positive
0 (0)

Negative
95 (100)

Missing
18

HER2

Positive
14 (12)

Negative
99 (88)

cT

T1
6 (5)

T2
72 (64)

T3
19 (17)

T4
16 (14)

cT

T0-1-2
78 (69)

T3-4
35 (31)

cN

N0
39 (35)

N1
49 (43)

N2
13 (12)

N3
12 (11)

cN

N0
39 (35)

N+
74 (65)

Grade

2
20 (18)

3
92 (82)

Missing
1

Grade

1-2
20 (18)

3
92 (82)

Missing
1

Intratumoral TIL

Mean
3

SD
6.6

Median
1

Q1-Q3
0-2

Min-Max
0-30

Missing
1

Stromal TIL

Mean
25

SD
25.1

Median
15

Q1-Q3
5-40

Min-Max
0-95

TABLE 33

GEO accessions of HER2 positive patients included in

genomic data processing

GEO accession
Center
Trial
Stromal TIL
Intratumoral TIL

GSM411295
Bordet
TOP
35
1

GSM411369
Bordet
TOP
20
0

GSM411366
Bordet
TOP
90
30

GSM411351
Bordet
TOP
10
0

GSM411365
Bordet
TOP
75
10

GSM411338
Bordet
TOP
20
0

GSM411358
Bordet
TOP
5
0

GSM411362
Bordet
TOP
80
30

GSM411307
Bordet
TOP
40
0

GSM411291
Bordet
TOP
30
1

GSM411292
Bordet
TOP
95
15

GSM411393
Bordet
TOP
95
not evaluable

GSM411376
Bordet
TOP
85
20

GSM411305
Bordet
TOP
40
0

TABLE 34

Summary of genes achieving selection criterion (corrected p-value < 0.05) in

univariate analysis of triple negative patients

ENTREZ

Std.

GENENAME
PROBEID
ID
SYMBOL
Estimate
Error
LCI

chemokine (C—X—C motif) ligand 13
205242_at
10563
CXCL13
0.846
0.1521
0.548

guanylate binding protein 1,
202269_x_at
2633
GBP1
0.870
0.1675
0.541

interferon-inducible

sulfotransferase family 1E, estrogen-
219934_s_at
6783
SULT1E1
−2.844
0.5545
−3.931

preferring, member 1

immunoglobulin heavy constant
211430_s_at
3502
IGHG3
0.768
0.1516
0.471

gamma 3 (G3m marker)

immunoglobulin kappa constant
221671_x_at
3514
IGKC
1.100
0.2177
0.673

immunoglobulin kappa constant
221651_x_at
3514
IGKC
1.109
0.2196
0.679

chemokine (C—X—C motif) ligand 10
204533_at
3627
CXCL10
0.914
0.1816
0.558

immunoglobulin lambda joining 3
214677_x_at
28831
IGLJ3
0.771
0.1560
0.465

immunoglobulin lambda-like
215946_x_at
91353
IGLL3P
1.248
0.2534
0.751

polypeptide 3, pseudogene

immunoglobulin kappa constant
215176_x_at
3514
IGKC
0.708
0.1443
0.425

immunoglobulin lambda constant 1
215121_x_at
3537
IGLC1
0.889
0.1821
0.532

(Mcg marker)

chemokine (C-C motif) ligand 5
1405_i_at
6352
CCL5
1.073
0.2203
0.641

immunoglobulin lambda constant 1
209138_x_at
3537
IGLC1
0.804
0.1661
0.478

(Mcg marker)

immunoglobulin lambda variable
215379_x_at
3546
IGLV@
0.879
0.1825
0.522

cluster

immunoglobulin kappa constant
214836_x_at
3514
IGKC
1.064
0.2210
0.631

torsin family 3, member A
218459_at
64222
TOR3A
2.791
0.5812
1.652

hepatic leukemia factor
204753_s_at
3131
HLF
−1.884
0.3933
−2.654

immunoglobulin kappa constant
214669_x_at
3514
IGKC
0.894
0.1895
0.523

signal transducer and activator of
209969_s_at
6772
STAT1
1.068
0.2271
0.623

transcription 1, 91 kDa

chemokine (C-C motif) ligand 5
204655_at
6352
CCL5
1.073
0.2302
0.622

chemokine (C-C motif) ligand 8
214038_at
6355
CCL8
0.844
0.1820
0.487

NA
211645_x_at
NA
NA
0.688
0.1484
0.397

absent in melanoma 2
206513_at
9447
AIM2
1.498
0.3256
0.859

SLAM family member 8
219386_s_at
56833
SLAMF8
1.315
0.2872
0.752

bromodomain adjacent to zinc finger
217985_s_at
11177
BAZ1A
1.994
0.4409
1.130

domain, 1A

post-GPI attachment to proteins 1
213469_at
80055
PGAP1
−2.181
0.4841
−3.129

glucuronidase, beta pseudogene 11
213502_x_at
91316
GUSBP11
1.327
0.2969
0.745

immunoglobulin heavy constant mu
209374_s_at
3507
IGHM
0.670
0.1503
0.376

major histocompatibility complex,
211990_at
3113
HLA-DPA1
1.189
0.2711
0.658

class II, DP alpha 1

NA
217378_x_at
NA
NA
0.807
0.1844
0.446

guanylate binding protein 1,
202270_at
2633
GBP1
0.806
0.1848
0.444

interferon-inducible

tryptophanyl-tRNA synthetase
200629_at
7453
WARS
1.273
0.2929
0.699

hepatic leukemia factor
204754_at
3131
HLF
−2.149
0.4978
−3.125

chemokine (C—X—C motif) ligand 9
203915_at
4283
CXCL9
0.761
0.1766
0.414

DEAD (Asp-Glu-Ala-Asp) box helicase
200702_s_at
57062
DDX24
2.355
0.5542
1.269

24

immunoglobulin kappa constant
216576_x_at
3514
IGKC
0.702
0.1676
0.373

immunoglobulin kappa constant
217157_x_at
3514
IGKC
0.998
0.2400
0.528

tripartite motif containing 38
203567_s_at
10475
TRIM38
1.972
0.4834
1.025

adhesion G protein-coupled receptor
209867_s_at
23284
ADGRL3
−2.036
0.5032
−3.023

L3

SR-related CTD-associated factor 11
213850_s_at
9169
SCAF11
2.140
0.5310
1.100

NA
216401_x_at
NA
NA
0.748
0.1860
0.384

interferon-induced protein 44-like
204439_at
10964
IFI44L
0.703
0.1750
0.361

SWI/SNF related, matrix associated,
201072_s_at
6599
SMARCC1
2.522
0.6280
1.291

actin dependent regulator of

chromatin, subfamily c, member 1

low density lipoprotein receptor-
212850_s_at
4038
LRP4
−2.226
0.5552
−3.314

related protein 4

interferon-induced protein 44
214453_s_at
10561
IFI44
0.810
0.2041
0.410

hepatic leukemia factor
204755_x_at
3131
HLF
−1.900
0.4809
−2.842

immunoglobulin kappa constant
214768_x_at
3514
IGKC
0.655
0.1660
0.329

chemokine (C-C motif) ligand 4
204103_at
6351
CCL4
1.294
0.3299
0.647

chemokine (C—X—C motif) receptor 6
206974_at
10663
CXCR6
2.097
0.5355
1.048

interferon, gamma-inducible protein
206332_s_at
3428
IFI16
0.926
0.2371
0.461

interferon, gamma-inducible protein
208965_s_at
3428
IFI16
0.949
0.2437
0.471

16

syndecan 2
212157_at
6383
SDC2
−1.661
0.4273
−2.498

immunoglobulin heavy locus
217281_x_at
3492
IGH
0.778
0.2002
0.385

major histocompatibility complex,
209823_x_at
3119
HLA-DQB1
1.130
0.2916
0.559

class II, DQ beta 1

nuclear factor of kappa light
201502_s_at
4792
NFKBIA
1.612
0.4160
0.797

polypeptide gene enhancer in B-

cells inhibitor, alpha

immunoglobulin lambda joining 3
211798_x_at
28831
IGLJ3
0.737
0.1914
0.362

major histocompatibility complex,
217478_s_at
3108
HLA-DMA
1.153
0.2998
0.566

class II, DM alpha

ATP-binding cassette, sub-family G
209735_at
9429
ABCG2
−2.235
0.5817
−3.375

(WHITE), member 2 (Junior blood

group)

collagen, type XVII, alpha 1
204636_at
1308
COL17A1
−2.010
0.5235
−3.037

catenin (cadherin-associated
209617_s_at
1501
CTNND2
−1.897
0.4944
−2.866

protein), delta 2

glutamyl aminopeptidase
204845_s_at
2028
ENPEP
−1.722
0.4505
−2.605

(aminopeptidase A)

interferon, gamma-inducible protein
208966_x_at
3428
IFI16
0.869
0.2275
0.423

16

proteasome (prosome, macropain)
208805_at
5687
PSMA6
2.038
0.5334
0.992

subunit, alpha type, 6

E74-like factor 4 (ets domain
31845_at
2000
ELF4
1.897
0.4981
0.920

transcription factor)

immunoglobulin kappa variable 1D-
216207_x_at
28902
IGKV1D-13
0.797
0.2097
0.386

13

COP9 signalosome subunit 8
202143_s_at
10920
COPS8
−2.346
0.6178
−3.556

serpin peptidase inhibitor, clade G
200986_at
710
SERPING1
1.074
0.2830
0.520

(C1 inhibitor), member 1

transportin 1
209225_x_at
3842
TNPO1
2.195
0.5790
1.060

cytochrome b-245, beta polypeptide
203923_s_at
1536
CYBB
1.241
0.3278
0.598

DEAD (Asp-Glu-Ala-Asp) box
218943_s_at
23586
DDX58
1.020
0.2697
0.491

polypeptide 58

centrosomal protein 350 kDa
213956_at
9857
CEP350
1.844
0.4880
0.888

immunoglobulin heavy constant
216510_x_at
3493
IGHA1
0.590
0.1563
0.283

alpha 1

jun D proto-oncogene
203752_s_at
3727
JUND
1.729
0.4607
0.827

immunoglobulin kappa constant
211644_x_at
3514
IGKC
0.565
0.1506
0.270

immunoglobulin lambda constant 1
217148_x_at
3537
IGLC1
0.659
0.1762
0.313

(Mcg marker)

immunoglobulin heavy locus
217022_s_at
3492
IGH
0.517
0.1391
0.245

apolipoprotein B mRNA editing
204205_at
60489
APOBEC3G
1.261
0.3395
0.595

enzyme, catalytic polypeptide-like

3G

NA
217480_x_at
NA
NA
0.884
0.2391
0.415

peroxisomal biogenesis factor 2
210296_s_at
5828
PEX2
−1.766
0.4797
−2.706

t

GENENAME
PROBEID
HCI
value
Pval
FDR
bonferroni

chemokine (C—X—C motif) ligand 13
205242_at
1.144
5.563
0.0000
0.0024
0.0024

guanylate binding protein 1,
202269_x_at
1.198
5.192
0.0000
0.0033
0.0116

interferon-inducible

sulfotransferase family 1E, estrogen-
219934_s_at
−1.757
−5.129
0.0000
0.0033
0.0151

preferring, member 1

immunoglobulin heavy constant
211430_s_at
1.065
5.064
0.0000
0.0033
0.0198

gamma 3 (G3m marker)

immunoglobulin kappa constant
221671_x_at
1.527
5.054
0.0000
0.0033
0.0207

immunoglobulin kappa constant
221651_x_at
1.540
5.051
0.0000
0.0033
0.0209

chemokine (C—X—C motif) ligand 10
204533_at
1.270
5.029
0.0000
0.0033
0.0229

immunoglobulin lambda joining 3
214677_x_at
1.076
4.940
0.0000
0.0036
0.0330

immunoglobulin lambda-like
215946_x_at
1.745
4.925
0.0000
0.0036
0.0352

polypeptide 3, pseudogene

immunoglobulin kappa constant
215176_x_at
0.991
4.903
0.0000
0.0036
0.0384

immunoglobulin lambda constant 1
215121_x_at
1.246
4.879
0.0000
0.0036
0.0423

(Mcg marker)

chemokine (C-C motif) ligand 5
1405_i_at
1.505
4.872
0.0000
0.0036
0.0436

immunoglobulin lambda constant 1
209138_x_at
1.129
4.840
0.0000
0.0036
0.0495

(Mcg marker)

immunoglobulin lambda variable
215379_x_at
1.237
4.818
0.0000
0.0036
0.0541

cluster

immunoglobulin kappa constant
214836_x_at
1.497
4.814
0.0000
0.0036
0.0550

torsin family 3, member A
218459_at
3.931
4.802
0.0000
0.0036
0.0577

hepatic leukemia factor
204753_s_at
−1.113
−4.790
0.0000
0.0036
0.0607

immunoglobulin kappa constant
214669_x_at
1.266
4.719
0.0000
0.0045
0.0806

signal transducer and activator of
209969_s_at
1.513
4.705
0.0000
0.0045
0.0850

transcription 1, 91 kDa

chemokine (C-C motif) ligand 5
204655_at
1.524
4.661
0.0000
0.0051
0.1013

chemokine (C-C motif) ligand 8
214038_at
1.201
4.639
0.0000
0.0051
0.1109

NA
211645_x_at
0.979
4.636
0.0000
0.0051
0.1120

absent in melanoma 2
206513_at
2.136
4.599
0.0000
0.0056
0.1293

SLAM family member 8
219386_s_at
1.878
4.578
0.0000
0.0059
0.1408

bromodomain adjacent to zinc finger
217985_s_at
2.858
4.523
0.0000
0.0070
0.1747

domain, 1A

post-GPI attachment to proteins 1
213469_at
−1.232
−4.505
0.0000
0.0072
0.1872

glucuronidase, beta pseudogene 11
213502_x_at
1.909
4.469
0.0000
0.0079
0.2151

immunoglobulin heavy constant mu
209374_s_at
0.965
4.461
0.0000
0.0079
0.2221

major histocompatibility complex,
211990_at
1.720
4.387
0.0000
0.0101
0.2943

class II, DP alpha 1

NA
217378_x_at
1.168
4.378
0.0000
0.0102
0.3055

guanylate binding protein 1,
202270_at
1.169
4.364
0.0000
0.0104
0.3219

interferon-inducible

tryptophanyl-tRNA synthetase
200629_at
1.847
4.346
0.0000
0.0108
0.3450

hepatic leukemia factor
204754_at
−1.174
−4.318
0.0000
0.0116
0.3840

chemokine (C—X—C motif) ligand 9
203915_at
1.107
4.306
0.0000
0.0118
0.4013

DEAD (Asp-Glu-Ala-Asp) box helicase
200702_s_at
3.441
4.249
0.0000
0.0142
0.4970

24

immunoglobulin kappa constant
216576_x_at
1.030
4.188
0.0001
0.0173
0.6242

immunoglobulin kappa constant
217157_x_at
1.468
4.158
0.0001
0.0189
0.6995

tripartite motif containing 38
203567_s_at
2.919
4.079
0.0001
0.0246
0.9332

adhesion G protein-coupled receptor
209867_s_at
−1.050
−4.047
0.0001
0.0269
1.0000

L3

SR-related CTD-associated factor 11
213850_s_at
3.181
4.031
0.0001
0.0273
1.0000

NA
216401_x_at
1.113
4.023
0.0001
0.0273
1.0000

interferon-induced protein 44-like
204439_at
1.046
4.021
0.0001
0.0273
1.0000

SWI/SNF related, matrix associated,
201072_s_at
3.753
4.016
0.0001
0.0273
1.0000

actin dependent regulator of

chromatin, subfamily c, member 1

low density lipoprotein receptor-
212850_s_at
−1.138
−4.009
0.0001
0.0274
1.0000

related protein 4

interferon-induced protein 44
214453_s_at
1.210
3.969
0.0001
0.0309
1.0000

hepatic leukemia factor
204755_x_at
−0.957
−3.950
0.0001
0.0324
1.0000

immunoglobulin kappa constant
214768_x_at
0.980
3.943
0.0002
0.0326
1.0000

chemokine (C-C motif) ligand 4
204103_at
1.941
3.922
0.0002
0.0343
1.0000

chemokine (C—X—C motif) receptor 6
206974_at
3.147
3.916
0.0002
0.0343
1.0000

interferon, gamma-inducible protein
206332_s_at
1.391
3.905
0.0002
0.0351
1.0000

interferon, gamma-inducible protein
208965_s_at
1.427
3.895
0.0002
0.0354
1.0000

16

syndecan 2
212157_at
−0.823
−3.886
0.0002
0.0354
1.0000

immunoglobulin heavy locus
217281_x_at
1.170
3.885
0.0002
0.0354
1.0000

major histocompatibility complex,
209823_x_at
1.702
3.877
0.0002
0.0354
1.0000

class II, DQ beta 1

nuclear factor of kappa light
201502_s_at
2.427
3.875
0.0002
0.0354
1.0000

polypeptide gene enhancer in B-

cells inhibitor, alpha

immunoglobulin lambda joining 3
211798_x_at
1.112
3.851
0.0002
0.0371
1.0000

major histocompatibility complex,
217478_s_at
1.741
3.848
0.0002
0.0371
1.0000

class II, DM alpha

ATP-binding cassette, sub-family G
209735_at
−1.095
−3.842
0.0002
0.0371
1.0000

(WHITE), member 2 (Junior blood

group)

collagen, type XVII, alpha 1
204636_at
−0.984
−3.840
0.0002
0.0371
1.0000

catenin (cadherin-associated
209617_s_at
−0.928
−3.837
0.0002
0.0371
1.0000

protein), delta 2

glutamyl aminopeptidase
204845_s_at
−0.839
−3.822
0.0002
0.0376
1.0000

(aminopeptidase A)

interferon, gamma-inducible protein
208966_x_at
1.315
3.821
0.0002
0.0376
1.0000

16

proteasome (prosome, macropain)
208805_at
3.083
3.820
0.0002
0.0376
1.0000

subunit, alpha type, 6

E74-like factor 4 (ets domain
31845_at
2.873
3.808
0.0002
0.0384
1.0000

transcription factor)

immunoglobulin kappa variable 1D-
216207_x_at
1.208
3.799
0.0003
0.0384
1.0000

13

COP9 signalosome subunit 8
202143_s_at
−1.135
−3.796
0.0003
0.0384
1.0000

serpin peptidase inhibitor, clade G
200986_at
1.629
3.796
0.0003
0.0384
1.0000

(C1 inhibitor), member 1

transportin 1
209225_x_at
3.330
3.791
0.0003
0.0385
1.0000

cytochrome b-245, beta polypeptide
203923_s_at
1.883
3.785
0.0003
0.0385
1.0000

DEAD (Asp-Glu-Ala-Asp) box
218943_s_at
1.548
3.780
0.0003
0.0385
1.0000

polypeptide 58

centrosomal protein 350 kDa
213956_at
2.801
3.779
0.0003
0.0385
1.0000

immunoglobulin heavy constant
216510_x_at
0.896
3.771
0.0003
0.0390
1.0000

alpha 1

jun D proto-oncogene
203752_s_at
2.632
3.754
0.0003
0.0405
1.0000

immunoglobulin kappa constant
211644_x_at
0.860
3.753
0.0003
0.0405
1.0000

immunoglobulin lambda constant 1
217148_x_at
1.004
3.738
0.0003
0.0420
1.0000

(Mcg marker)

immunoglobulin heavy locus
217022_s_at
0.790
3.721
0.0003
0.0440
1.0000

apolipoprotein B mRNA editing
204205_at
1.926
3.714
0.0003
0.0445
1.0000

enzyme, catalytic polypeptide-like

3G

NA
217480_x_at
1.353
3.697
0.0004
0.0465
1.0000

peroxisomal biogenesis factor 2
210296_s_at
−0.826
−3.682
0.0004
0.0485
1.0000

TABLE 35

Summary of genes achieving selection criterion (corrected p-value < 0.05) in univariate analysis

of all patients stratified on HER status

ENTREZ

Std.

GENENAME
PROBEID
ID
SYMBOL
Estimate
Error
LCI

guanylate binding protein 1, interferon-inducible
202269_x_at
2633
GBP1
0.858
0.1588
0.547

immunoglobulin heavy constant gamma 3 (G3m
211430_s_at
3502
IGHG3
0.742
0.1408
0.466

marker)

chemokine (C—X—C motif) ligand 13
205242_at
10563
CXCL13
0.734
0.1400
0.460

sulfotransferase family 1E, estrogen-preferring,
219934_s_at
6783
SULT1E1
−2.679
0.5124
−3.683

member 1

chemokine (C—X—C motif) ligand 10
204533_at
3627
CXCL10
0.876
0.1715
0.540

immunoglobulin lambda joining 3
214677_x_at
28831
IGLJ3
0.732
0.1445
0.449

immunoglobulin kappa constant
221651_x_at
3514
IGKC
1.034
0.2050
0.633

immunoglobulin kappa constant
221671_x_at
3514
IGKC
1.025
0.2034
0.626

immunoglobulin lambda constant 1 (Mcg marker)
215121_x_at
3537
IGLC1
0.848
0.1689
0.517

immunoglobulin lambda-like polypeptide 3,
215946_x_at
91353
IGLL3P
1.164
0.2325
0.708

pseudogene

immunoglobulin lambda constant 1 (Mcg marker)
209138_x_at
3537
IGLC1
0.762
0.1536
0.461

immunoglobulin lambda variable cluster
215379_x_at
3546
IGLV@
0.835
0.1689
0.504

hepatic leukemia factor
204753_s_at
3131
HLF
−1.798
0.3681
−2.520

immunoglobulin kappa constant
215176_x_at
3514
IGKC
0.640
0.1315
0.382

immunoglobulin kappa constant
214836_x_at
3514
IGKC
0.994
0.2050
0.593

bromodomain adjacent to zinc finger domain, 1A
217985_s_at
11177
BAZ1A
2.039
0.4204
1.215

post-GPI attachment to proteins 1
213469_at
80055
PGAP1
−2.213
0.4568
−3.109

signal transducer and activator of transcription 1,
209969_s_at
6772
STAT1
1.027
0.2146
0.607

91 kDa

immunoglobulin kappa constant
214669_x_at
3514
IGKC
0.833
0.1745
0.491

chemokine (C-C motif) ligand 8
214038_at
6355
CCL8
0.792
0.1677
0.464

guanylate binding protein 1, interferon-inducible
202270_at
2633
GBP1
0.820
0.1744
0.478

SLAM family member 8
219386_s_at
56833
SLAMF8
1.267
0.2719
0.734

hepatic leukemia factor
204754_at
3131
HLF
−2.206
0.4752
−3.137

NA
211645_x_at
NA
NA
0.625
0.1350
0.361

absent in melanoma 2
206513_at
9447
AIM2
1.432
0.3108
0.823

tryptophanyl-tRNA synthetase
200629_at
7453
WARS
1.226
0.2680
0.700

glucuronidase, beta pseudogene 11
213502_x_at
91316
GUSBP11
1.208
0.2675
0.684

chemokine (C-C motif) ligand 5
1405_i_at
6352
CCL5
0.930
0.2075
0.524

NA
217378_x_at
NA
NA
0.732
0.1678
0.403

major histocompatibility complex, class II, DP
211990_at
3113
HLA-DPA1
1.118
0.2584
0.611

alpha 1

torsin family 3, member A
218459_at
64222
TOR3A
2.365
0.5484
1.290

chemokine (C-C motif) ligand 5
204655_at
6352
CCL5
0.935
0.2172
0.509

immunoglobulin heavy constant mu
209374_s_at
3507
IGHM
0.595
0.1391
0.322

low density lipoprotein receptor-related protein 4
212850_s_at
4038
LRP4
−2.212
0.5208
−3.232

chemokine (C—X—C motif) ligand 9
203915_at
4283
CXCL9
0.696
0.1643
0.375

ATP-binding cassette, sub-family G (WHITE),
209735_at
9429
ABCG2
−2.286
0.5416
−3.348

member 2 (Junior blood group)

immunoglobulin kappa constant
216576_x_at
3514
IGKC
0.644
0.1527
0.345

major histocompatibility complex, class II, DQ
209823_x_at
3119
HLA-DQB1
1.113
0.2670
0.590

beta 1

chemokine (C-C motif) ligand 4
204103_at
6351
CCL4
1.257
0.3031
0.663

immunoglobulin kappa constant
214768_x_at
3514
IGKC
0.611
0.1479
0.321

immunoglobulin kappa constant
217157_x_at
3514
IGKC
0.889
0.2184
0.461

NA
216401_x_at
NA
NA
0.684
0.1691
0.352

hepatic leukemia factor
204755_x_at
3131
HLF
−1.836
0.4564
−2.730

cytochrome b-245, beta polypeptide
203923_s_at
1536
CYBB
1.237
0.3078
0.633

DEAD (Asp-Glu-Ala-Asp) box helicase 24
200702_s_at
57062
DDX24
2.117
0.5282
1.082

immunoglobulin lambda joining 3
211798_x_at
28831
IGLJ3
0.682
0.1711
0.347

COP9 signalosome subunit 8
202143_s_at
10920
COPS8
−2.274
0.5721
−3.395

adhesion G protein-coupled receptor L3
209867_s_at
23284
ADGRL3
−1.927
0.4865
−2.880

collagen, type XVII, alpha 1
204636_at
1308
COL17A1
−2.000
0.5050
−2.990

immunoglobulin heavy constant alpha 1
216510_x_at
3493
IGHA1
0.554
0.1408
0.278

proteasome (prosome, macropain) subunit, alpha
208805_at
5687
PSMA6
1.947
0.4954
0.976

type, 6

major histocompatibility complex, class II, DM
217478_s_at
3108
HLA-DMA
1.078
0.2763
0.536

alpha

immunoglobulin heavy locus
217281_x_at
3492
IGH
0.699
0.1792
0.348

tripartite motif containing 38
203567_s_at
10475
TRIM38
1.787
0.4596
0.887

cathepsin C
201487_at
1075
CTSC
1.138
0.2945
0.561

syndecan 2
212157_at
6383
SDC2
−1.548
0.4007
−2.333

follistatin
207345_at
10468
FST
−2.296
0.5952
−3.463

jun D proto-oncogene
203752_s_at
3727
JUND
1.648
0.4282
0.809

chemokine (C—X—C motif) receptor 6
206974_at
10663
CXCR6
1.889
0.4943
0.920

immunoglobulin lambda constant 1 (Mcg marker)
217148_x_at
3537
IGLC1
0.604
0.1583
0.294

clusterin-like 1 (retinal)
206556_at
27098
CLUL1
−1.887
0.4949
−2.856

apolipoprotein L, 6
219716_at
80830
APOL6
1.617
0.4261
0.782

interferon-induced protein 44-like
204439_at
10964
IFI44L
0.625
0.1647
0.302

immunoglobulin kappa constant
211644_x_at
3514
IGKC
0.519
0.1368
0.251

KLF3 antisense RNA 1
219871_at
79667
KLF3-AS1
−2.204
0.5832
−3.347

immunoglobulin lambda variable 1-44
217227_x_at
28823
IGLV1-44
0.710
0.1879
0.342

transporter 1, ATP-binding cassette, sub-family B
202307_s_at
6890
TAP1
0.928
0.2457
0.446

(MDR/TAP)

ubiquitin-conjugating enzyme E2L6
201649_at
9246
UBE2L6
1.073
0.2850
0.515

interferon-induced protein 44
214453_s_at
10561
IFI44
0.715
0.1901
0.342

major histocompatibility complex, class II, DQ
211656_x_at
3119
HLA-DQB1
1.225
0.3261
0.586

beta 1

immunoglobulin kappa variable 1D-13
216207_x_at
28902
IGKV1D-13
0.715
0.1904
0.342

glutamyl aminopeptidase (aminopeptidase A)
204845_s_at
2028
ENPEP
−1.572
0.4197
−2.394

immunoglobulin heavy locus
211868_x_at
3492
IGH
0.921
0.2463
0.438

transformation/transcription domain-associated
214908_s_at
8295
TRRAP
−1.685
0.4522
−2.572

protein

cyclin D2
200951_s_at
894
CCND2
1.798
0.4841
0.849

guanylate binding protein 2, interferon-inducible
202748_at
2634
GBP2
1.005
0.2708
0.474

signal transducer and activator of transcription 1,
AFFX-HUMIS
6772
STAT1
1.004
0.2709
0.473

91 kDa

SR-related CTD-associated factor 11
213850_s_at
9169
SCAF11
1.863
0.5025
0.878

signal transducer and activator of transcription 1,
200887_s_at
6772
STAT1
0.950
0.2565
0.447

91 kDa

butyrophilin, subfamily 3, member A2
209846_s_at
11118
BTN3A2
1.063
0.2870
0.500

tryptophanyl-tRNA synthetase
200628_s_at
7453
WARS
1.203
0.3260
0.564

complement component 1, q subcomponent, A
218232_at
712
C1QA
0.898
0.2435
0.421

chain

NA
217480_x_at
NA
NA
0.795
0.2157
0.372

centrosomal protein 350 kDa
213956_at
9857
CEP350
1.712
0.4647
0.801

FAT atypical cadherin 4
219427_at
79633
FAT4
−2.095
0.5687
−3.209

transportin 1
209225_x_at
3842
TNPO1
2.024
0.5498
0.946

membrane associated guanylate kinase, WW and
209737_at
9863
MAGI2
−1.551
0.4216
−2.378

PDZ domain containing 2

ELL associated factor 2
219551_at
55840
EAF2
1.364
0.3708
0.637

hes-related family bHLH transcription factor with
44783_s_at
23462
HEY1
−1.186
0.3227
−1.818

YRPW motif 1

odontogenic, ameloblast asssociated
220133_at
54959
ODAM
−0.713
0.1940
−1.093

catenin (cadherin-associated protein), delta 2
209617_s_at
1501
CTNND2
−1.639
0.4478
−2.517

carbonic anhydrase II
209301_at
760
CA2
−0.777
0.2124
−1.193

immunoglobulin kappa locus
211650_x_at
50802
IGK
0.669
0.1832
0.310

immunoglobulin kappa locus
214916_x_at
50802
IGK
0.659
0.1809
0.305

dystonin
216918_s_at
667
DST
−1.766
0.4847
−2.716

butyrophilin, subfamily 3, member A3
204820_s_at
10384
BTN3A3
1.093
0.3005
0.504

immunoglobulin lambda joining 3
216984_x_at
28831
IGLJ3
0.637
0.1753
0.294

apolipoprotein B mRNA editing enzyme, catalytic
204205_at
60489
APOBEC3G
1.143
0.3156
0.525

polypeptide-like 3G

peroxisomal biogenesis factor 1
215023_s_at
5189
PEX1
−1.379
0.3808
−2.126

interferon, gamma-inducible protein 16
208965_s_at
3428
IFI16
0.843
0.2338
0.385

interferon, gamma-inducible protein 16
206332_s_at
3428
IFI16
0.819
0.2271
0.374

immunoglobulin heavy constant alpha 1
211641_x_at
3493
IGHA1
0.919
0.2550
0.419

butyrophilin, subfamily 3, member A3
204821_at
10384
BTN3A3
1.442
0.4005
0.657

fibroblast growth factor receptor 1
210973_s_at
2260
FGFR1
−1.315
0.3657
−2.032

staufen double-stranded RNA binding protein 2
204226_at
27067
STAU2
−1.723
0.4791
−2.662

CD38 molecule
205692_s_at
952
CD38
1.187
0.3305
0.539

interferon regulatory factor 9
203882_at
10379
IRF9
1.265
0.3530
0.573

butyrophilin, subfamily 3, member A3
38241_at
10384
BTN3A3
1.235
0.3452
0.558

interferon stimulated exonuclease gene 20 kDa
204698_at
3669
ISG20
0.941
0.2631
0.425

NA
217179_x_at
NA
NA
0.635
0.1780
0.286

tumor necrosis factor (ligand) superfamily,
202688_at
8743
TNFSF10
0.699
0.1964
0.315

member 10

major histocompatibility complex, class II, DR
208306_x_at
3126
HLA-DRB4
1.248
0.3511
0.560

beta 4

CD163 molecule
203645_s_at
9332
CD163
0.914
0.2575
0.410

hes-related family bHLH transcription factor with
218839_at
23462
HEY1
−1.456
0.4101
−2.260

YRPW motif 1

GENENAME
PROBEID
HCI
t value
Pval
FDR
bonferroni

guanylate binding protein 1, interferon-inducible
202269_x_at
1.169
5.404
0.0000
0.0021
0.0039

immunoglobulin heavy constant gamma 3 (G3m
211430_s_at
1.018
5.270
0.0000
0.0021
0.0069

marker)

chemokine (C—X—C motif) ligand 13
205242_at
1.008
5.242
0.0000
0.0021
0.0078

sulfotransferase family 1E, estrogen-preferring,
219934_s_at
−1.674
−5.227
0.0000
0.0021
0.0084

member 1

chemokine (C—X—C motif) ligand 10
204533_at
1.212
5.105
0.0000
0.0022
0.0141

immunoglobulin lambda joining 3
214677_x_at
1.015
5.068
0.0000
0.0022
0.0165

immunoglobulin kappa constant
221651_x_at
1.436
5.046
0.0000
0.0022
0.0182

immunoglobulin kappa constant
221671_x_at
1.423
5.036
0.0000
0.0022
0.0189

immunoglobulin lambda constant 1 (Mcg marker)
215121_x_at
1.180
5.022
0.0000
0.0022
0.0201

immunoglobulin lambda-like polypeptide 3,
215946_x_at
1.619
5.005
0.0000
0.0022
0.0216

pseudogene

immunoglobulin lambda constant 1 (Mcg marker)
209138_x_at
1.063
4.962
0.0000
0.0023
0.0259

immunoglobulin lambda variable cluster
215379_x_at
1.166
4.944
0.0000
0.0023
0.0279

hepatic leukemia factor
204753_s_at
−1.077
−4.886
0.0000
0.0025
0.0356

immunoglobulin kappa constant
215176_x_at
0.898
4.868
0.0000
0.0025
0.0384

immunoglobulin kappa constant
214836_x_at
1.396
4.851
0.0000
0.0025
0.0411

bromodomain adjacent to zinc finger domain, 1A
217985_s_at
2.863
4.850
0.0000
0.0025
0.0414

post-GPI attachment to proteins 1
213469_at
−1.318
−4.845
0.0000
0.0025
0.0421

signal transducer and activator of transcription 1,
209969_s_at
1.448
4.787
0.0000
0.0030
0.0536

91 kDa

immunoglobulin kappa constant
214669_x_at
1.175
4.771
0.0000
0.0030
0.0571

chemokine (C-C motif) ligand 8
214038_at
1.121
4.723
0.0000
0.0035
0.0695

guanylate binding protein 1, interferon-inducible
202270_at
1.162
4.702
0.0000
0.0036
0.0757

SLAM family member 8
219386_s_at
1.800
4.660
0.0000
0.0041
0.0899

hepatic leukemia factor
204754_at
−1.275
−4.643
0.0000
0.0042
0.0965

NA
211645_x_at
0.890
4.631
0.0000
0.0042
0.1010

absent in melanoma 2
206513_at
2.041
4.608
0.0000
0.0044
0.1109

tryptophanyl-tRNA synthetase
200629_at
1.751
4.574
0.0000
0.0049
0.1273

glucuronidase, beta pseudogene 11
213502_x_at
1.732
4.515
0.0000
0.0060
0.1608

chemokine (C-C motif) ligand 5
1405_i_at
1.337
4.483
0.0000
0.0065
0.1824

NA
217378_x_at
1.061
4.360
0.0000
0.0102
0.2964

major histocompatibility complex, class II, DP
211990_at
1.624
4.325
0.0000
0.0113
0.3389

alpha 1

torsin family 3, member A
218459_at
3.439
4.312
0.0000
0.0114
0.3568

chemokine (C-C motif) ligand 5
204655_at
1.361
4.305
0.0000
0.0114
0.3657

immunoglobulin heavy constant mu
209374_s_at
0.867
4.275
0.0000
0.0125
0.4113

low density lipoprotein receptor-related protein 4
212850_s_at
−1.191
−4.247
0.0000
0.0134
0.4578

chemokine (C—X—C motif) ligand 9
203915_at
1.018
4.240
0.0000
0.0134
0.4696

ATP-binding cassette, sub-family G (WHITE),
209735_at
−1.225
−4.221
0.0001
0.0139
0.5058

member 2 (Junior blood group)

immunoglobulin kappa constant
216576_x_at
0.943
4.217
0.0001
0.0139
0.5132

major histocompatibility complex, class II, DQ
209823_x_at
1.637
4.170
0.0001
0.0162
0.6138

beta 1

chemokine (C-C motif) ligand 4
204103_at
1.851
4.147
0.0001
0.0172
0.6692

immunoglobulin kappa constant
214768_x_at
0.901
4.131
0.0001
0.0178
0.7111

immunoglobulin kappa constant
217157_x_at
1.317
4.072
0.0001
0.0216
0.8853

NA
216401_x_at
1.015
4.042
0.0001
0.0235
0.9890

hepatic leukemia factor
204755_x_at
−0.941
−4.022
0.0001
0.0246
1.0000

cytochrome b-245, beta polypeptide
203923_s_at
1.840
4.018
0.0001
0.0246
1.0000

DEAD (Asp-Glu-Ala-Asp) box helicase 24
200702_s_at
3.153
4.009
0.0001
0.0249
1.0000

immunoglobulin lambda joining 3
211798_x_at
1.018
3.986
0.0001
0.0264
1.0000

COP9 signalosome subunit 8
202143_s_at
−1.152
−3.974
0.0001
0.0271
1.0000

adhesion G protein-coupled receptor L3
209867_s_at
−0.973
−3.961
0.0001
0.0273
1.0000

collagen, type XVII, alpha 1
204636_at
−1.010
−3.960
0.0001
0.0273
1.0000

immunoglobulin heavy constant alpha 1
216510_x_at
0.830
3.935
0.0001
0.0293
1.0000

proteasome (prosome, macropain) subunit, alpha
208805_at
2.918
3.930
0.0001
0.0293
1.0000

type, 6

major histocompatibility complex, class II, DM
217478_s_at
1.619
3.901
0.0002
0.0315
1.0000

alpha

immunoglobulin heavy locus
217281_x_at
1.050
3.899
0.0002
0.0315
1.0000

tripartite motif containing 38
203567_s_at
2.688
3.889
0.0002
0.0321
1.0000

cathepsin C
201487_at
1.716
3.865
0.0002
0.0340
1.0000

syndecan 2
212157_at
−0.762
−3.863
0.0002
0.0340
1.0000

follistatin
207345_at
−1.130
−3.858
0.0002
0.0340
1.0000

jun D proto-oncogene
203752_s_at
2.487
3.849
0.0002
0.0345
1.0000

chemokine (C—X—C motif) receptor 6
206974_at
2.858
3.822
0.0002
0.0374
1.0000

immunoglobulin lambda constant 1 (Mcg marker)
217148_x_at
0.914
3.815
0.0002
0.0375
1.0000

clusterin-like 1 (retinal)
206556_at
−0.917
−3.812
0.0002
0.0375
1.0000

apolipoprotein L, 6
219716_at
2.452
3.796
0.0002
0.0382
1.0000

interferon-induced protein 44-like
204439_at
0.948
3.795
0.0002
0.0382
1.0000

immunoglobulin kappa constant
211644_x_at
0.787
3.793
0.0002
0.0382
1.0000

KLF3 antisense RNA 1
219871_at
−1.061
−3.779
0.0003
0.0386
1.0000

immunoglobulin lambda variable 1-44
217227_x_at
1.078
3.778
0.0003
0.0386
1.0000

transporter 1, ATP-binding cassette, sub-family B
202307_s_at
1.409
3.777
0.0003
0.0386
1.0000

(MDR/TAP)

ubiquitin-conjugating enzyme E2L6
201649_at
1.632
3.766
0.0003
0.0391
1.0000

interferon-induced protein 44
214453_s_at
1.088
3.760
0.0003
0.0391
1.0000

major histocompatibility complex, class II, DQ
211656_x_at
1.865
3.758
0.0003
0.0391
1.0000

beta 1

immunoglobulin kappa variable 1D-13
216207_x_at
1.089
3.758
0.0003
0.0391
1.0000

glutamyl aminopeptidase (aminopeptidase A)
204845_s_at
−0.749
−3.745
0.0003
0.0403
1.0000

immunoglobulin heavy locus
211868_x_at
1.404
3.740
0.0003
0.0404
1.0000

transformation/transcription domain-associated
214908_s_at
−0.799
−3.727
0.0003
0.0415
1.0000

protein

cyclin D2
200951_s_at
2.747
3.714
0.0003
0.0415
1.0000

guanylate binding protein 2, interferon-inducible
202748_at
1.536
3.711
0.0003
0.0415
1.0000

signal transducer and activator of transcription 1,
AFFX-HUMIS
1.535
3.708
0.0003
0.0415
1.0000

91 kDa

SR-related CTD-associated factor 11
213850_s_at
2.848
3.707
0.0003
0.0415
1.0000

signal transducer and activator of transcription 1,
200887_s_at
1.452
3.703
0.0003
0.0415
1.0000

91 kDa

butyrophilin, subfamily 3, member A2
209846_s_at
1.625
3.702
0.0003
0.0415
1.0000

tryptophanyl-tRNA synthetase
200628_s_at
1.842
3.691
0.0004
0.0415
1.0000

complement component 1, q subcomponent, A
218232_at
1.375
3.689
0.0004
0.0415
1.0000

chain

NA
217480_x_at
1.218
3.687
0.0004
0.0415
1.0000

centrosomal protein 350 kDa
213956_at
2.623
3.683
0.0004
0.0415
1.0000

FAT atypical cadherin 4
219427_at
−0.980
−3.683
0.0004
0.0415
1.0000

transportin 1
209225_x_at
3.102
3.681
0.0004
0.0415
1.0000

membrane associated guanylate kinase, WW and
209737_at
−0.725
−3.679
0.0004
0.0415
1.0000

PDZ domain containing 2

ELL associated factor 2
219551_at
2.091
3.679
0.0004
0.0415
1.0000

hes-related family bHLH transcription factor with
44783_s_at
−0.553
−3.674
0.0004
0.0415
1.0000

YRPW motif 1

odontogenic, ameloblast asssociated
220133_at
−0.332
−3.673
0.0004
0.0415
1.0000

catenin (cadherin-associated protein), delta 2
209617_s_at
−0.761
−3.660
0.0004
0.0424
1.0000

carbonic anhydrase II
209301_at
−0.361
−3.660
0.0004
0.0424
1.0000

immunoglobulin kappa locus
211650_x_at
1.028
3.651
0.0004
0.0433
1.0000

immunoglobulin kappa locus
214916_x_at
1.014
3.645
0.0004
0.0435
1.0000

dystonin
216918_s_at
−0.816
−3.644
0.0004
0.0435
1.0000

butyrophilin, subfamily 3, member A3
204820_s_at
1.682
3.638
0.0004
0.0436
1.0000

immunoglobulin lambda joining 3
216984_x_at
0.981
3.637
0.0004
0.0436
1.0000

apolipoprotein B mRNA editing enzyme, catalytic
204205_at
1.762
3.622
0.0004
0.0451
1.0000

polypeptide-like 3G

peroxisomal biogenesis factor 1
215023_s_at
−0.633
−3.622
0.0004
0.0451
1.0000

interferon, gamma-inducible protein 16
208965_s_at
1.301
3.607
0.0005
0.0464
1.0000

interferon, gamma-inducible protein 16
206332_s_at
1.264
3.606
0.0005
0.0464
1.0000

immunoglobulin heavy constant alpha 1
211641_x_at
1.418
3.603
0.0005
0.0464
1.0000

butyrophilin, subfamily 3, member A3
204821_at
2.227
3.601
0.0005
0.0464
1.0000

fibroblast growth factor receptor 1
210973_s_at
−0.599
−3.597
0.0005
0.0464
1.0000

staufen double-stranded RNA binding protein 2
204226_at
−0.784
−3.596
0.0005
0.0464
1.0000

CD38 molecule
205692_s_at
1.835
3.590
0.0005
0.0468
1.0000

interferon regulatory factor 9
203882_at
1.956
3.583
0.0005
0.0476
1.0000

butyrophilin, subfamily 3, member A3
38241_at
1.911
3.577
0.0005
0.0479
1.0000

interferon stimulated exonuclease gene 20 kDa
204698_at
1.456
3.576
0.0005
0.0479
1.0000

NA
217179_x_at
0.984
3.570
0.0005
0.0485
1.0000

tumor necrosis factor (ligand) superfamily,
202688_at
1.084
3.561
0.0005
0.0494
1.0000

member 10

major histocompatibility complex, class II, DR
208306_x_at
1.936
3.554
0.0006
0.0499
1.0000

beta 4

CD163 molecule
203645_s_at
1.419
3.551
0.0006
0.0499
1.0000

hes-related family bHLH transcription factor with
218839_at
−0.652
−3.550
0.0006
0.0499
1.0000

YRPW motif 1

TABLE 36

Summary of genes achieving selection criterion (corrected p-value < 0.05) in multivariate analysis of triple negative patients

ENTREZ

Std.

GENENAME
PROBEID
ID
SYMBOL
Estimate
Error
LCl

guanylate binding protein 1, interferon-inducible
202269_x_at
2633
GBP1
0.927
0.1748
0.584

chemokine (C—X—C motif) ligand 13
205242_at
10563
CXCL13
0.834
0.1583
0.523

sulfotransferase family 1E, estrogen-preferring,
219934_s_at
6783
SULT1E1
−2.935
0.5679
−4.048

member 1

chemokine (C—X—C motif) ligand 10
204533_at
3627
CXCL10
0.938
0.1904
0.565

immunoglobulin kappa constant
221651_x_at
3514
IGKC
1.117
0.2296
0.667

immunoglobulin kappa constant
221671_x_at
3514
IGKC
1.104
0.2272
0.659

immunoglobulin heavy constant gamma 3 (G3m
211430_s_at
3502
IGHG3
0.766
0.1588
0.455

marker)

absent in melanoma 2
206513_at
9447
AIM2
1.554
0.3285
0.910

SLAM family member 8
219386_s_at
56833
SLAMF8
1.372
0.2913
0.801

chemokine (C-C motif) ligand 8
214038_at
6355
CCL8
0.875
0.1859
0.511

immunoglobulin lambda joining 3
214677_x_at
28831
IGLJ3
0.752
0.1622
0.434

immunoglobulin kappa constant
215176_x_at
3514
IGKC
0.690
0.1493
0.397

immunoglobulin lambda constant 1 (Mcg marker)
215121_x_at
3537
IGLC1
0.868
0.1883
0.499

immunoglobulin lambda-like polypeptide 3,
215946_x_at
91353
IGLL3P
1.199
0.2608
0.688

pseudogene

immunoglobulin kappa constant
214836_x_at
3514
IGKC
1.049
0.2286
0.601

hepatic leukemia factor
204753_s_at
3131
HLF
−1.856
0.4061
−2.652

chemokine (C-C motif) ligand 5
1405_i_at
6352
CCL5
1.030
0.2259
0.587

immunoglobulin lambda constant 1 (Mcg marker)
209138_x_at
3537
IGLC1
0.785
0.1722
0.447

signal transducer and activator of transcription 1,
209969_s_at
6772
STAT1
1.060
0.2332
0.603

91 kDa

immunoglobulin lambda variable cluster
215379_x_at
3546
IGLV@
0.856
0.1883
0.486

immunoglobulin kappa constant
214669_x_at
3514
IGKC
0.893
0.1974
0.506

chemokine (C-C motif) ligand 5
204655_at
6352
CCL5
1.037
0.2357
0.575

NA
211645_x_at
NA
NA
0.672
0.1529
0.372

torsin family 3, member A
218459_at
64222
TOR3A
2.842
0.6473
1.573

guanylate binding protein 1, interferon-inducible
202270_at
2633
GBP1
0.831
0.1913
0.456

chemokine (C—X—C motif) ligand 9
203915_at
4283
CXCL9
0.770
0.1793
0.418

bromodomain adjacent to zinc finger domain, 1A
217985_s_at
11177
BAZ1A
1.934
0.4522
1.048

post-GPI attachment to proteins 1
213469_at
80055
PGAP1
−2.106
0.4973
−3.081

major histocompatibility complex, class II, DP
211990_at
3113
HLA-DPA1
1.163
0.2748
0.625

alpha 1

hepatic leukemia factor
204754_at
3131
HLF
−2.195
0.5208
−3.216

tryptophanyl-tRNA synthetase
200629_at
7453
WARS
1.280
0.3046
0.683

immunoglobulin heavy constant mu
209374_s_at
3507
IGHM
0.651
0.1552
0.347

NA
217378_x_at
NA
NA
0.786
0.1904
0.412

glucuronidase, beta pseudogene 11
213502_x_at
91316
GUSBP11
1.259
0.3083
0.655

DEAD (Asp-Glu-Ala-Asp) box helicase 24
200702_s_at
57062
DDX24
2.272
0.5679
1.159

interferon-induced protein 44-like
204439_at
10964
IFI44L
0.722
0.1830
0.364

immunoglobulin kappa constant
217157_x_at
3514
IGKC
0.964
0.2453
0.483

adhesion G protein-coupled receptor L3
209867_s_at
23284
ADGRL3
−2.017
0.5145
−3.025

immunoglobulin kappa constant
216576_x_at
3514
IGKC
0.674
0.1720
0.337

charged multivesicular body protein 2B
202537_s_at
25978
CHMP2B
1.773
0.4573
0.877

mitochondrial assembly of ribosomal large
203819_s_at
115416
MALSU1
0.907
0.2338
0.448

subunit 1

t

GENENAME
PROBEID
HCl
value
Pval
FDR
bonferroni

guanylate binding protein 1, interferon-inducible
202269_x_at
1.269
5.303
0.0000
0.0046
0.0079

chemokine (C—X—C motif) ligand 13
205242_at
1.144
5.266
0.0000
0.0046
0.0093

sulfotransferase family 1E, estrogen-preferring,
219934_s_at
−1.822
−5.168
0.0000
0.0046
0.0139

member 1

chemokine (C—X—C motif) ligand 10
204533_at
1.311
4.926
0.0000
0.0080
0.0374

immunoglobulin kappa constant
221651_x_at
1.567
4.864
0.0000
0.0080
0.0479

immunoglobulin kappa constant
221671_x_at
1.549
4.860
0.0000
0.0080
0.0487

immunoglobulin heavy constant gamma 3 (G3m
211430_s_at
1.077
4.824
0.0000
0.0080
0.0562

marker)

absent in melanoma 2
206513_at
2.198
4.730
0.0000
0.0085
0.0819

SLAM family member 8
219386_s_at
1.943
4.708
0.0000
0.0085
0.0892

chemokine (C-C motif) ligand 8
214038_at
1.240
4.708
0.0000
0.0085
0.0892

immunoglobulin lambda joining 3
214677_x_at
1.070
4.637
0.0000
0.0085
0.1177

immunoglobulin kappa constant
215176_x_at
0.982
4.618
0.0000
0.0085
0.1267

immunoglobulin lambda constant 1 (Mcg marker)
215121_x_at
1.237
4.612
0.0000
0.0085
0.1299

immunoglobulin lambda-like polypeptide 3,
215946_x_at
1.711
4.598
0.0000
0.0085
0.1370

pseudogene

immunoglobulin kappa constant
214836_x_at
1.497
4.591
0.0000
0.0085
0.1408

hepatic leukemia factor
204753_s_at
−1.060
−4.569
0.0000
0.0085
0.1537

chemokine (C-C motif) ligand 5
1405_i_at
1.473
4.559
0.0000
0.0085
0.1596

immunoglobulin lambda constant 1 (Mcg marker)
209138_x_at
1.122
4.557
0.0000
0.0085
0.1609

signal transducer and activator of transcription 1,
209969_s_at
1.517
4.543
0.0000
0.0085
0.1695

91 kDa

immunoglobulin lambda variable cluster
215379_x_at
1.225
4.543
0.0000
0.0085
0.1700

immunoglobulin kappa constant
214669_x_at
1.280
4.523
0.0000
0.0087
0.1836

chemokine (C-C motif) ligand 5
204655_at
1.499
4.399
0.0000
0.0127
0.2944

NA
211645_x_at
0.972
4.394
0.0000
0.0127
0.3006

torsin family 3, member A
218459_at
4.110
4.390
0.0000
0.0127
0.3049

guanylate binding protein 1, interferon-inducible
202270_at
1.206
4.341
0.0000
0.0147
0.3674

chemokine (C—X—C motif) ligand 9
203915_at
1.121
4.291
0.0000
0.0170
0.4427

bromodomain adjacent to zinc finger domain, 1A
217985_s_at
2.820
4.278
0.0000
0.0172
0.4654

post-GPI attachment to proteins 1
213469_at
−1.131
−4.234
0.0001
0.0190
0.5474

major histocompatibility complex, class II, DP
211990_at
1.702
4.233
0.0001
0.0190
0.5498

alpha 1

hepatic leukemia factor
204754_at
−1.174
−4.214
0.0001
0.0196
0.5897

tryptophanyl-tRNA synthetase
200629_at
1.877
4.204
0.0001
0.0196
0.6133

immunoglobulin heavy constant mu
209374_s_at
0.955
4.197
0.0001
0.0196
0.6283

NA
217378_x_at
1.159
4.127
0.0001
0.0247
0.8140

glucuronidase, beta pseudogene 11
213502_x_at
1.863
4.084
0.0001
0.0279
0.9491

DEAD (Asp-Glu-Ala-Asp) box helicase 24
200702_s_at
3.385
4.001
0.0001
0.0367
1.0000

interferon-induced protein 44-like
204439_at
1.081
3.948
0.0002
0.0431
1.0000

immunoglobulin kappa constant
217157_x_at
1.444
3.928
0.0002
0.0440
1.0000

adhesion G protein-coupled receptor L3
209867_s_at
−1.009
−3.920
0.0002
0.0440
1.0000

immunoglobulin kappa constant
216576_x_at
1.012
3.920
0.0002
0.0440
1.0000

charged multivesicular body protein 2B
202537_s_at
2.669
3.877
0.0002
0.0487
1.0000

mitochondrial assembly of ribosomal large
203819_s_at
1.365
3.877
0.0002
0.0487
1.0000

subunit 1

TABLE 37

Summary of genes achieving selection criterion (corrected p-value < 0.05) in multivariate

analysis of all patients stratified on HER status

ENTREZ

GENENAME
PROBE ID
ID
SYMBOL
Estimate
Std. Error
LCl

guanylate binding protein 1,
202269_x_at
2633
GBP1
0.900
0.1634
0.579

interferon-inducible

sulfotransferase family 1E,
219934_s_at
6783
SULT1E1
−2.747
0.5217
−3.769

estrogen-preferring, member 1

chemokine (C—X—C motif)
204533_at
3627
CXCL10
0.899
0.1783
0.550

ligand 10

immunoglobulin heavy
211430_s_at
3502
IGHG3
0.736
0.1468
0.449

constant gamma 3 (G3m

marker)

chemokine (C—X—C motif)
205242_at
10563
CXCL13
0.718
0.1439
0.436

ligand 13

chemokine (C-C motif) ligand 8
214038_at
6355
CCL8
0.838
0.1724
0.500

immunoglobulin kappa
221651_x_at
3514
IGKC
1.036
0.2134
0.618

constant

immunoglobulin kappa
221671_x_at
3514
IGKC
1.023
0.2115
0.609

constant

SLAM family member 8
219386_s_at
56833
SLAMF8
1.335
0.2759
0.794

immunoglobulin lambda
214677_x_at
28831
IGLJ3
0.714
0.1490
0.422

joining 3

immunoglobulin lambda
215121_x_at
3537
IGLC1
0.829
0.1735
0.489

constant 1 (Mcg marker)

immunoglobulin lambda-like
215946_x_at
91353
IGLL3P
1.123
0.2376
0.658

polypeptide 3, pseudogene

absent in melanoma 2
206513_at
9447
AIM2
1.483
0.3136
0.868

guanylate binding protein 1,
202270_at
2633
GBP1
0.843
0.1786
0.493

interferon-inducible

immunoglobulin lambda
209138_x_at
3537
IGLC1
0.744
0.1580
0.434

constant 1 (Mcg marker)

immunoglobulin lambda
215379_x_at
3546
IGLV@
0.813
0.1731
0.474

variable cluster

signal transducer and
209969_s_at
6772
STAT1
1.023
0.2188
0.594

activator of transcription 1,

91 kDa

bromodomain adjacent to
217985_s_at
11177
BAZ1A
2.001
0.4293
1.160

zinc finger domain, 1A

immunoglobulin kappa
214836_x_at
3514
IGKC
0.981
0.2109
0.568

constant

hepatic leukemia factor
204753_s_at
3131
HLF
−1.767
0.3800
−2.512

immunoglobulin kappa
215176_x_at
3514
IGKC
0.625
0.1351
0.360

constant

immunoglobulin kappa
214669_x_at
3514
IGKC
0.830
0.1806
0.476

constant

post-GPI attachment to
213469_at
80055
PGAP1
−2.146
0.4672
−3.061

proteins 1

hepatic leukemia factor
204754_at
3131
HLF
−2.236
0.4954
−3.207

tryptophanyl-tRNA
200629_at
7453
WARS
1.238
0.2774
0.695

synthetase

NA
211645_x_at
NA
NA
0.616
0.1385
0.345

chemokine (C—X—C motif)
203915_at
4283
CXCL9
0.708
0.1655
0.384

ligand 9

ATP-binding cassette, sub-
209735_at
9429
ABCG2
−2.461
0.5777
−3.593

family G (WHITE), member 2

(Junior blood group)

chemokine (C-C motif) ligand 5
1405_i_at
6352
CCL5
0.892
0.2115
0.478

glucuronidase, beta
213502_x_at
91316
GUSBP11
1.151
0.2746
0.612

pseudogene 11

major histocompatibility
211990_at
3113
HLA-DPA1
1.095
0.2620
0.582

complex, class II, DP alpha 1

NA
217378_x_at
NA
NA
0.717
0.1724
0.379

major histocompatibility
209823_x_at
3119
HLA-DQB1
1.117
0.2702
0.587

complex, class II, DQ beta 1

chemokine (C-C motif) ligand 5
204655_at
6352
CCL5
0.906
0.2213
0.472

low density lipoprotein
212850_s_at
4038
LRP4
−2.167
0.5297
−3.205

receptor-related protein 4

chemokine (C-C motif) ligand 4
204103_at
6351
CCL4
1.269
0.3119
0.657

immunoglobulin heavy
209374_s_at
3507
IGHM
0.576
0.1420
0.298

constant mu

cytochrome b-245, beta
203923_s_at
1536
CYBB
1.247
0.3101
0.639

polypeptide

immunoglobulin kappa
214768_x_at
3514
IGKC
0.608
0.1519
0.310

constant

immunoglobulin kappa
216576_x_at
3514
IGKC
0.624
0.1566
0.317

constant

proteasonne (prosonne,
208805_at
5687
PSMA6
2.007
0.5088
1.010

macropain) subunit, alpha

type, 6

immunoglobulin kappa
217157_x_at
3514
IGKC
0.867
0.2219
0.432

constant

charged multivesicular body
202537_s_at
25978
CHMP2B
1.659
0.4264
0.824

protein 2B

torsin family 3, member A
218459_at
64222
TOR3A
2.295
0.5920
1.135

guanylate binding protein 2,
202748_at
2634
GBP2
1.053
0.2727
0.519

interferon-inducible

NA
216401_x_at
NA
NA
0.667
0.1728
0.328

immunoglobulin lambda
211798_x_at
28831
IGLJ3
0.667
0.1732
0.327

joining 3

collagen, type XVII, alpha 1
204636_at
1308
COL17A1
−2.046
0.5370
−3.099

DEAD (Asp-Glu-Ala-Asp) box
200702_s_at
57062
DDX24
2.053
0.5392
0.996

helicase 24

hepatic leukemia factor
204755_x_at
3131
HLF
−1.812
0.4759
−2.744

perilipin 2
209122_at
123
PLIN2
1.061
0.2788
0.515

cathepsin C
201487_at
1075
CTSC
1.150
0.3023
0.558

immunoglobulin heavy
216510_x_at
3493
IGHA1
0.547
0.1437
0.265

constant alpha 1

adhesion G protein-coupled
209867_s_at
23284
ADGRL3
−1.890
0.4968
−2.863

receptor L3

mitochondrial assembly of
203819_s_at
115416
MALSU1
0.841
0.2213
0.408

ribosomal large subunit 1

FAT atypical cadherin 4
219427_at
79633
FAT4
−2.190
0.5779
−3.322

carbonic anhydrase II
209301_at
760
CA2
−0.810
0.2137
−1.228

major histocompatibility
217478_s_at
3108
HLA-DMA
1.051
0.2793
0.503

complex, class II, DM alpha

immunoglobulin heavy locus
217281_x_at
3492
IGH
0.683
0.1819
0.327

clusterin-like 1 (retinal)
206556_at
27098
CLUL1
−1.898
0.5058
−2.890

GENENAME
PROBE ID
HCl
t value
Pval
FDR
bonferroni

guanylate binding protein 1,
202269_x_at
1.220
5.507
0.0000
0.0027
0.0027

interferon-inducible

sulfotransferase family 1E,
219934_s_at
−1.724
−5.265
0.0000
0.0038
0.0076

estrogen-preferring, member 1

chemokine (C—X—C motif)
204533_at
1.248
5.043
0.0000
0.0048
0.0195

ligand 10

immunoglobulin heavy
211430_s_at
1.024
5.016
0.0000
0.0048
0.0218

constant gamma 3 (G3m

marker)

chemokine (C—X—C motif)
205242_at
1.000
4.991
0.0000
0.0048
0.0242

ligand 13

chemokine (C-C motif) ligand 8
214038_at
1.176
4.859
0.0000
0.0049
0.0419

immunoglobulin kappa
221651_x_at
1.454
4.854
0.0000
0.0049
0.0427

constant

immunoglobulin kappa
221671_x_at
1.438
4.839
0.0000
0.0049
0.0455

constant

SLAM family member 8
219386_s_at
1.875
4.837
0.0000
0.0049
0.0458

immunoglobulin lambda
214677_x_at
1.006
4.793
0.0000
0.0049
0.0549

joining 3

immunoglobulin lambda
215121_x_at
1.169
4.780
0.0000
0.0049
0.0578

constant 1 (Mcg marker)

immunoglobulin lambda-like
215946_x_at
1.589
4.729
0.0000
0.0049
0.0712

polypeptide 3, pseudogene

absent in melanoma 2
206513_at
2.098
4.729
0.0000
0.0049
0.0713

guanylate binding protein 1,
202270_at
1.193
4.720
0.0000
0.0049
0.0738

interferon-inducible

immunoglobulin lambda
209138_x_at
1.053
4.707
0.0000
0.0049
0.0777

constant 1 (Mcg marker)

immunoglobulin lambda
215379_x_at
1.153
4.699
0.0000
0.0049
0.0805

variable cluster

signal transducer and
209969_s_at
1.452
4.675
0.0000
0.0049
0.0884

activator of transcription 1,

91 kDa

bromodomain adjacent to
217985_s_at
2.843
4.662
0.0000
0.0049
0.0932

zinc finger domain, 1A

immunoglobulin kappa
214836_x_at
1.394
4.650
0.0000
0.0049
0.0977

constant

hepatic leukemia factor
204753_s_at
−1.022
−4.649
0.0000
0.0049
0.0984

immunoglobulin kappa
215176_x_at
0.890
4.627
0.0000
0.0051
0.1073

constant

immunoglobulin kappa
214669_x_at
1.184
4.594
0.0000
0.0054
0.1223

constant

post-GPI attachment to
213469_at
−1.230
−4.592
0.0000
0.0054
0.1232

proteins 1

hepatic leukemia factor
204754_at
−1.265
−4.514
0.0000
0.0070
0.1680

tryptophanyl-tRNA
200629_at
1.782
4.464
0.0000
0.0082
0.2047

synthetase

NA
211645_x_at
0.888
4.449
0.0000
0.0084
0.2172

chemokine (C—X—C motif)
203915_at
1.032
4.277
0.0000
0.0156
0.4210

ligand 9

ATP-binding cassette, sub-
209735_at
−1.329
−4.261
0.0000
0.0160
0.4489

family G (WHITE), member 2

(Junior blood group)

chemokine (C-C motif) ligand 5
1405_i_at
1.307
4.218
0.0001
0.0182
0.5269

glucuronidase, beta
213502_x_at
1.689
4.191
0.0001
0.0195
0.5853

pseudogene 11

major histocompatibility
211990_at
1.609
4.180
0.0001
0.0196
0.6084

complex, class II, DP alpha 1

NA
217378_x_at
1.055
4.158
0.0001
0.0207
0.6626

major histocompatibility
209823_x_at
1.646
4.132
0.0001
0.0221
0.7278

complex, class II, DQ beta 1

chemokine (C-C motif) ligand 5
204655_at
1.339
4.093
0.0001
0.0243
0.8423

low density lipoprotein
212850_s_at
−1.129
−4.091
0.0001
0.0243
0.8491

receptor-related protein 4

chemokine (C-C motif) ligand 4
204103_at
1.880
4.067
0.0001
0.0257
0.9271

immunoglobulin heavy
209374_s_at
0.855
4.060
0.0001
0.0257
0.9517

constant mu

cytochrome b-245, beta
203923_s_at
1.855
4.020
0.0001
0.0290
1.0000

polypeptide

immunoglobulin kappa
214768_x_at
0.905
3.999
0.0001
0.0306
1.0000

constant

immunoglobulin kappa
216576_x_at
0.931
3.982
0.0001
0.0317
1.0000

constant

proteasonne (prosonne,
208805_at
3.005
3.946
0.0001
0.0353
1.0000

macropain) subunit, alpha

type, 6

immunoglobulin kappa
217157_x_at
1.302
3.905
0.0002
0.0399
1.0000

constant

charged multivesicular body
202537_s_at
2.495
3.892
0.0002
0.0409
1.0000

protein 2B

torsin family 3, member A
218459_at
3.455
3.877
0.0002
0.0422
1.0000

guanylate binding protein 2,
202748_at
1.588
3.862
0.0002
0.0431
1.0000

interferon-inducible

NA
216401_x_at
1.006
3.858
0.0002
0.0431
1.0000

immunoglobulin lambda
211798_x_at
1.006
3.851
0.0002
0.0434
1.0000

joining 3

collagen, type XVII, alpha 1
204636_at
−0.994
−3.811
0.0002
0.0441
1.0000

DEAD (Asp-Glu-Ala-Asp) box
200702_s_at
3.109
3.807
0.0002
0.0441
1.0000

helicase 24

hepatic leukemia factor
204755_x_at
−0.879
−3.806
0.0002
0.0441
1.0000

perilipin 2
209122_at
1.608
3.806
0.0002
0.0441
1.0000

cathepsin C
201487_at
1.743
3.806
0.0002
0.0441
1.0000

immunoglobulin heavy
216510_x_at
0.828
3.805
0.0002
0.0441
1.0000

constant alpha 1

adhesion G protein-coupled
209867_s_at
−0.916
−3.804
0.0002
0.0441
1.0000

receptor L3

mitochondrial assembly of
203819_s_at
1.275
3.802
0.0002
0.0441
1.0000

ribosomal large subunit 1

FAT atypical cadherin 4
219427_at
−1.057
−3.789
0.0003
0.0446
1.0000

carbonic anhydrase II
209301_at
−0.391
−3.788
0.0003
0.0446
1.0000

major histocompatibility
217478_s_at
1.598
3.762
0.0003
0.0479
1.0000

complex, class II, DM alpha

immunoglobulin heavy locus
217281_x_at
1.040
3.758
0.0003
0.0479
1.0000

clusterin-like 1 (retinal)
206556_at
−0.907
−3.753
0.0003
0.0479
1.0000

TABLE 38

One-to-one mapping from gene to ‘best’ probe sets using ‘jetset’ package

Platform
Series
GBP1
HLF
CXCL13

HG-U133A
GSE25066
202270_at
204754_at
205242_at

GSE20271

GSE20194

GSE22093

GSE23988

HG-U133_Plus_2
GSE16446
202270_at
204754_at
205242_at

U133-X3P
GSE6861
g12803662_3p_a_at
Hs.250692.0.S4_3p_at
g5453576_3p_at

Platform
Series
LRRC23
SULTIEI
IGKC

HG-U133A
GSE25066
206076_at
219934_s_at
211644_x_at

GSE20271

GSE20194

GSE22093

GSE23988

HG-U133_Plus_2
GSE16446
206076_at
222940_at
211644_x_at

U133-X3P
GSE6861
g5901897_3p_at
Hs.54576.0.S2_3p_at
214669_3p_x_at

Association Between the Four-Gene Signature and Stromal TILs

To assess the prognostic value of the four-gene signature on stromal TILs (Box-cox-transformed), we applied a general linear model for the response variable stromal TIL on the four-gene signature and the clinical covariates series (TOP vs. MDACC), age (continuous), cT (0-1-2 vs. 3-4), cN (0 vs. +) and grade (1-2 vs. 3). Results of the general linear model are shown in Table 39.

TABLE 39

General linear model with nonlinear effects - (Box-cox-

transformed) Stromal TILs - 4-gene signature

Coefficient
95% IC
P

Age
0.00
−0.03-0.04
0.800

cT

0.186

T3-4 vs. T0-1-2
−0.62
−1.54-0.30

cN

0.593

N+ vs. N0
−0.25
−1.17-0.67

Grade

0.299

3 vs. 1-2
0.59
−0.53-1.71

4-gene signature
6.53
4.59-8.48
<0.001

4-gene signature - Non linear
−3.18
−6.13-−0.22
0.035

Series

0.770

MDACC vs. Bordet
0.14
−0.82-1.10

We used restricted cubic splines with 2 degrees of freedom to investigate the non-linear association between stromal TILs and the 4-gene signature. The non-linear effect was found significant. Plot of fitted stromal TILs (Box-cox-transformed) vs. observed stromal TILs (Box-cox-transformed) is shown in FIG. 45.

We computed the root mean squared prediction error (RMSE) using 1 000 repetitions of a ten-fold cross validation in the following way; the training dataset is first randomly split into ten previously obtained blocks of approximately equal size. Each of the ten data blocks is left out once to fit the model, and predictions are computed for the observations in the left-out block with the predict method of the fitted model. Thus, a prediction is obtained for each observation. The observed stromal TILs value and the obtained predictions for all observations are then passed to the prediction loss function cost (RMSE) to estimate the prediction error. This process is replicated 1 000 times and the estimated prediction errors from all replications as well as their average are estimated.

Assessing the Association Between the Four-Gene Signature and Pathological Complete Response in the Validation Set

We explored the association between the probability to achieve pathological complete response (pCR) and the four-gene signature in the validation data set, we computed odds ratios (ORs) and 95% CI using a conditional logistic model that included the four-gene signature and the clinical covariates: age (continuous), cT (0-1-2 vs. 3-4), cN (0 vs. +) and grade (1-2 vs. 3) and was stratified on series (TOP vs. MDACC). Results of the conditional logistic model are shown in Table 40.

TABLE 40

Results of the conditional logistic regression assessing

the association between the probability to achieve

pathological complete response and the four-gene signature

OR
95% IC
P

Age
0.98
0.95-1.02
0.344

cT

0.131

T0-1-2
1

T3-4
0.55
0.26-1.19

cN

0.758

N0
1

N+
0.88
0.39-1.99

Grade

0.046

1-2
1

3
3.43
1.02-11.48

One-unit increase in the
0.96
0.30-3.08
0.947

four-gene signature

cT, clinical tumor size;

cN, clinical nodal status;

OR, Odds ratio;

CI, confidence interval;

P, p-value.

Univariate Selection (Including One Gene at a Time) with Correction for Multiple Comparisons (Secondary Analysis)

The univariate selection with correction for multiple comparisons procedure includes three steps:

- 1. To fit a general linear model to model the continuous level of Stromal TILs in the post chemotherapy samples using complete cases. Stromal TILs is transformed using Box-Cox transformation.
- 2. To correct for multiple comparisons using False Discovery Rate (FDR) method (Bonferroni p-values are reported for information purposes only).
- 3. To report genes that achieved the selection criterion of a corrected p-value <0-05.

TABLE 41

Summary of univariate selection with correction for

multiple comparisons

TNBC
All patients

n = 99
n = 113

Without
With
Without
With

adjustment
adjustment
adjustment
adjustment

Variables included in

addition to the gene

expression

Series (TOP vs.
X
X
X
X

MDACC)

HER2 status (Negative

X
X

vs. Positive)

Age (continuous)

X

X

cT (0-1-2 vs. 3-4)

X

X

cN (0 vs. +)

X

X

Grade (1-2 vs. 3)

X

X

Number of genes
79
41
114
60

achieving selection

criterion†

Results table
Table A13
Table A14
Table A15
Table A16

TNBC, Triple Negative Breast Cancer.

†corrected p-value < 0.05

EXAMPLE

The starting biological material is a sample from patient having a TNBC, such as as tumor biopsy, fine needle aspiration or blood sample.

Said sample is taken before any treatment.

mRNA are extracted from said sample by well-known technics by a person skilled in the art.

These mRNA are used to quantify the expression of the 4 genes GBP1, HLF, CXCL13 and SULT1E1 by a RT-PCR technic or similar technics, using 4 pairs of primers corresponding to the 4 genes of interest.

At least one housekeeping gene selected from the group comprising 18S rRNA, ACTB, HPRT1, HSPCB, PPIA, PUM1, RPS13, SDHA and TBP, is used to performed RT-PCR.

The measured expressions of the 4 genes GBP1, HLF, CXCL13 and SULT1E1 are then incorporated in the following equation in order to obtain the genomic predictor:

Genomic predictor=0.288*GBP1 expression+0.392*CXCL13 expression −1.027*HLF expression −1.726*SULT1E1 expression

Coefficients applied to each of the gene expressions have been determined according to Table 5.

A distant relapse free and overall survival probability is calculated based on an equation that integrates the expression measurements of the 4 genes through the genomic predictor and the patient clinicopathological characteristics such as age, tumour size, tumour grade and tumour stage.

If the predicted survival probabilities are deemed high enough by the treating physician, the patient will receive a NACT.

If the predicted survival probabilities are deemed too low enough by the treating physician, the patient will receive more aggressive treatments (that can either by new experimental treatments in clinical trials or established therapy regimens for early breast cancer).

Another Aspect of the Invention is the Study of HLF (Hepatic Leukemia Factor) Gene.

As previously shown by our unit, treatment with chemotherapeutic agents induced an antitumor immune response in TNBC and this high infiltration with TILs was connected to favourable outcome (Dieci et al., 2014). By large scale study, the prognostic role of TILs in early TNBC patients was confirmed, since the ten-year overall survival rates were 89% and 68% for TNBC with high TILs and low TILs, respectively (Dieci et al., 2015). Another study, performed on primary TNBC patients of international FinHER trial, showed high TIL levels at a time of diagnosis associated with decreased distant recurrence rates (Loi et al., 2014).

In our group, in order to develop a genomic predictor of TILs after neoadjuvant ChT and to validate the possible prognostic value of this tool, post-ChT levels of TILs were quantified in series of TNBC patients that did not achieve pathological complete remission after surgery, and for which a genomic profile was already available. For the analysis, TILs have been evaluated after ChT in 113 samples from TNBC patients; 44 samples from TOP trial of Institut Jules Bordet (Brussels, Belgium) and 69 samples from MD Anderson Cancer Center (Houston, Tex., USA) series. Our biostaticians proceeded to model the continuous level of stromal TILs in the post-ChT samples as a function of gene expression. Analyses led to the selection of four genes sharing a triggered gene expression levels in connection to high stromal TILs. One of these signature genes is HLF (Hepatic Leukemia Factor) that was found in negative relation with stromal TILs presence. In other words, the increasing HLF expression levels within tumor cells decreased the presence of stromal TILs and probably the lymphocytic infiltration in tumor in general.

Gene HLF is located on chromosome 17 (17q22), encodes for proline and acidic-rich (PAR) protein family member, and represents a bZIP (basic leucine zipper) transcription factor, as DBP (Albumin D Box-Binding Protein) and TEF (Thyrotrophic Embryonic Factor). Gene HLF was originally identified in a chromosomal translocation with the gene E2A, linked to acute lymphoblastic leukemia (ALL) (Inaba et al., 1992). This led to its aberrant expression as a fusion protein (E2A-HLF), and to a form of ALL connected to poor prognosis due to the resistance to ChT (Jabbour et al., 2015).

However, high impact was given to HLF in connection to circadian rhythms and the mammalian timing system. Transcription factor HLF, as one of the PAR bZIP proteins involved in circadian behaviour, is a regulatory protein that clearly varies with high amplitudes during circadian rhythms and is accepted as an output regulator of this process. The circadian genes have been implicated in the regulation of cell cycle, stress response and drug toxicity (Waters et al., 2013).

The chronotherapy and circadian rhythms consideration in cancer and metabolism will probably play more important role in drug development and therapeutic efficacy (Ferrell and Chiang, 2015). The potential importance of HLF functional analyses in cancer is underlined by certain studies of fatigue-related safety issues and shift work impact on human body. The rotating night shift work has been associated with increased risk of breast carcinoma (Schernhammer et al., 2001). Additionally, in the example of colon cancer, the improved chronopharmacology in 5-fluorouracil night time administration reduced the therapy toxicity and improved the tumor size reduction (L6vi et al., 2001).

It has been shown that HLF regulates the expression of numerous genes involved in the metabolism of endobiotics and xenobiotics (Gachon et al., 2006). In this study, mouse models with PAR bZIP proteins triple knock-out (for Hlf, Dbp and Tef genes) were hypersensitive to xenobiotics and their early aging was detected as a consequence of the deficiency in xenobiotics detoxification properties. Recent studies with knock-out mice deficient in both alleles of mouse HLF showed that HSCs in these mice become more sensitive to 5-fluorouracil and that HLF is essential for maintaining the function of HSCs (Komorowska et al., 2015). Furthermore, the literature-based data are clearly connecting the HLF expression in cancer with reduced tumor cells apoptosis and improved cancer cell survival (Waters et al., 2013).

Given these previously published data, we decided to focus on HLF functional analysis, in order to study the role of the post-ChT lymphocytic attraction within tumor. For this objective, we decided to downregulate the expression of HLF in TNBC cell lines. Cells used for siHLF experiments were chosen according to literature-based data of HLF expression levels in various available BC cell lines (Kao et al., 2009).

Breast carcinoma cell lines SUM-52-PE, MDA-MB-468 and MDA-MB-231 were chosen for their respective high, moderate, or low, HLF expression levels (FIG. 41A). The HLF mRNA levels were also tested in our laboratory conditions (FIG. 41B) and compared to the ones obtained in literature, and immunoblot of HLF protein expression levels was performed in parallel (FIG. 41C).

According to literature-based and our conditions-based findings of HLF expression levels, we decided to consider both SUM-52-PE and MDA-MB-468 as cell lines with high HLF expression level for downregulation experiments.

For the initial experiments, the HLF gene expression in TNBC cell lines was inhibited by specific siRNA (ON-TARGETplus HLF siRNA, Dharmacon), using Lipofectamine RNAiMAX transfection agent (FIG. 42A). The CTRL siRNA (ON-TARGETplus Non-targeting siRNA, Dharmacon) was used as a negative control of transfection and further experiments were performed by comparing the HLF-knocked-down effect in siHLF cells vs siCTRL cells.

The previous genomic data of our group declared that HLF expression level was reduced in patients' samples with high post-ChT TILs presence. This supports the hypothesis of the low HLF expression levels being connected to ChT-sensitivity of cancer cells, so the first step of our experiments was to verify the possible effect of HLF knock-down on cellular viability under ChT treatment, 24 hours after transient transfection with siHLF and siCTRL. We performed a set of experiments using doxorubicin as a ChT treatment during 48 hours in various concentrations, and cell viability was determined using CellTiter Glow Luminescent Cell Viability Assay (Promega) according to the manufacturer's recommendations. As shown in FIG. 42B, no significant difference in cell viability was detected between siHLF cells, when compared to siCTRL counterparts, in any of tested doxorubicin concentrations. Two other time points (24 h, 72 h) were applied and did not show any further effect (data not shown). Furthermore, the HLF expression level decrease, performed by transient transfection on both cell lines, did not significantly affect cellular viability or morphology (data not shown).

This initial set of siRNA experiments of HLF downregulation has shown unclear results, so for the next experiments of HLF activity in ChT treated cells and to analyze various gene expression levels due to HLF downregulation, we decided to perform the HLF knock-down using CRISPR/Cas9 (Clustered Regularly Interspaced Palindromic Repeats Associated Protein 9) system. The CRISPR/Cas9 is a system of targeted genome editing that works with a principle of short guide RNA sequence that recognizes the target DNA with very limited off-target effect (Barrangou et al., 2015). Subsequently, the endonuclease Cas9 is responsible for target DNA cleavage (DNA flanked by a protospacer-adjacent motif), and the DNA repair of both cleaved parts follows by the machineries of non-homologous end joining or homology-directed repair (Hsu et al., 2014).

Cell lines SUM-52-PE and MDA-MB-468 were transfected by LIPOFECTAMINE® 2000 using plasmid pX278 with HLF-recognizing sequence developed by our collaborators from IGBMC, Strasbourg (FIG. 43; SEQ ID NOs: 5 and 6). The plasmid bears a sequence specific for human HLF gene, and another plasmid was developed for mouse Hlf editing, which is planned to use on murine models, containing the sequences represented by the nucleotide sequences SEQ ID NO: 5, and SEQ ID NO: 6 and their complements, as shown in FIG. 43.

Transfection effectivity of plasmid pX278 carrying the GFP-tag was verified by IF and transfected cells selection in puromycin-containing culture medium was performed for 48 hours, in order to select only clones bearing a knock-down of HLF together with puromycin resistance cassette. Further subcloning of resistant clones was done and the HLF expression levels were tested in each of potentially HLF-knocked-down clones. Three clones for each of SUM-52-PE and MDA-MB-468 cell line were established and can serve as a model for studies of HLF knock-down in stable manner. The analyses of HLF knock-down effect on cells treated with doxorubicin are ongoing, and the preliminary data show the decreasing tendency in cancer cell viability as a direct effect of HLF knock-down. This trend is not yet clear and needs a further confirmation, although it is in line with literature-based information.

Additionally, the microarray-based gene expression analysis of genes affected by HLF expression level decrease is programmed in parallel in those cells carrying the HLF knock-down. Microarray gene expression analysis in HLF transduced cell models suggested the upregulation of cytochrome P450 enzymes, often associated with circadian rhythms and drug metabolism, as well as the upregulation of genes influencing chemical toxicity (Waters et al., 2013). Gene expression analysis in our laboratory aims to compare the genomic profiles of TNBC cells with HLF knock-down vs control cells, and will inform us about the impact of HLF on breast carcinoma cells. The possible implication of HLF downregulation in apoptotic pathways, in drug metabolism, or in genes implicated in lymphocytic attraction will be studied intensively.

Breast mouse cell lines transfections by CRISPR/Cas9 method based plasmid to knock-out mouse HLF are ongoing in our laboratory. In this future project direction, mouse models are intended to be established, in order to be able to study the direct impact of HLF knock-out in the tumor development in vivo and to monitor the lymphocytic infiltration of these tumors. Since the carcinomas of TNBC subtype cannot be treated using ET-based agents or by anti-HER2 targeted therapy, the majority of these tumors are treated by ChT. The presence of TILs in tumor after neoadjuvant ChT is associated with good prognosis and therefore it is of major interest to find out the mechanisms of this lymphocytic infiltration. Potential therapeutic targets, involved in this mechanism, could serve for new therapies development and could improve the prognosis, when combined with standard ChT, applied on TNBC patients. Additionally, the role of potential predictive biomarkers of response to neoadjuvant ChT, such possibly HLF, could be very important, in order to avoid the over-dose of chemotherapeutic agents in potentially non-responding patients, or contrarily, to select those patients with high benefit of ChT in neoadjuvant settings.

SEQUENCE LISTING

SEQ ID NO: 1

Homo sapiens guanylate binding protein 1 (GBP1), DNA

NCBI Reference Sequence: NM_002053.2

1
ggagtcagtg atttgaacga agtactttca gtttcatatt actctaaatc cattacaaat

61
ctgcttagct tctaaatatt tcatcaatga ggaaatccca gccctacaac ttcggaacag

121
tgaaatatta gtccagggat ccagtgagag acacagaagt gctagaagcc agtgctcgtg

181
aactaaggag aaaaagaaca gacaagggaa cagcctggac atggcatcag agatccacat

241
gacaggccca atgtgcctca ttgagaacac taatgggcga ctgatggcga atccagaagc

301
tctgaagatc ctttctgcca ttacacagcc tatggtggtg gtggcaattg tgggcctcta

361
ccgcacaggc aaatcctacc tgatgaacaa gctggctgga aagaaaaagg gcttctctct

421
gggctccacg gtgcagtctc acactaaagg aatctggatg tggtgtgtgc cccaccccaa

481
gaagccaggc cacatcctag ttctgctgga caccgagggt ctgggagatg tagagaaggg

541
tgacaaccag aatgactcct ggatcttcgc cctggccgtc ctcctgagca gcaccttcgt

601
gtacaatagc ataggaacca tcaaccagca ggctatggac caactgtact atgtgacaga

661
gctgacacat agaatccgat caaaatcctc acctgatgag aatgagaatg aggttgagga

721
ttcagctgac tttgtgagct tcttcccaga ctttgtgtgg acactgagag atttctccct

781
ggacttggaa gcagatggac aacccctcac accagatgag tacctgacat actccctgaa

841
gctgaagaaa ggtaccagtc aaaaagatga aacttttaac ctgcccagac tctgtatccg

901
gaaattcttc ccaaagaaaa aatgctttgt ctttgatcgg cccgttcacc gcaggaagct

961
tgcccagctc gagaaactac aagatgaaga gctggacccc gaatttgtgc aacaagtagc

1021
agacttctgt tcctacatct ttagtaattc caaaactaaa actctttcag gaggcatcca

1081
ggtcaacggg cctcgtctag agagcctggt gctgacctac gtcaatgcca tcagcagtgg

1141
ggatctgccg tgcatggaga acgcagtcct ggccttggcc cagatagaga actcagctgc

1201
agtgcaaaag gctattgccc actatgaaca gcagatgggc cagaaggtgc agctgcccac

1261
agaaaccctc caggagctgc tggacctgca cagggacagt gagagagagg ccattgaagt

1321
cttcatcagg agttccttca aagatgtgga ccatctattt caaaaggagt tagcggccca

1381
gctagaaaaa aagcgggatg acttttgtaa acagaatcag gaagcatcat cagatcgttg

1441
ctcagcttta cttcaggtca ttttcagtcc tctagaagaa gaagtgaagg cgggaattta

1501
ttcgaaacca gggggctatc gtctctttgt tcagaagcta caagacctga agaaaaagta

1561
ctatgaggaa ccgaggaagg ggatacaggc tgaagagatt ctgcagacat acttgaaatc

1621
caaggagtct atgactgatg caattctcca gacagaccag actctcacag aaaaagaaaa

1681
ggagattgaa gtggaacgtg tgaaagctga gtctgcacag gcttcagcaa aaatgttgca

1741
ggaaatgcaa agaaagaatg agcagatgat ggaacagaag gagaggagtt atcaggaaca

1801
cttgaaacaa ctgactgaga agatggagaa cgacagggtc cagttgctga aagagcaaga

1861
gaggaccctc gctcttaaac ttcaggaaca ggagcaacta ctaaaagagg gatttcaaaa

1921
agaaagcaga ataatgaaaa atgagataca ggatctccag acgaaaatga gacgacgaaa

1981
ggcatgtacc ataagctaaa gaccagagcc ttcctgtcac ccctaaccaa ggcataattg

2041
aaacaatttt agaatttgga acaagcgtca ctacatttga taataattag atcttgcatc

2101
ataacaccaa aagtttataa aggcatgtgg tacaatgatc aaaatcatgt tttttcttaa

2161
aaaaaaaaaa agactgtaaa ttgtgcaaca aagatgcatt tacctctgta tcaactcagg

2221
aaatctcata agctggtacc actcaggaga agtttattct tccagatgac cagcagtaga

2281
caaatggata ctgagcagag tcttaggtaa aagtcttggg aaatatttgg gcattggtct

2341
ggccaagtct acaatgtccc aatatcaagg acaaccaccc tagcttctta gtgaagacaa

2401
tgtacagtta tccgttagat caagactaca cggtctatga gcaataatgt gatttctgga

2461
cattgcccat gtataatcct cactgatgat ttcaagctaa agcaaaccac cttatacaga

2521
gatctagaat ctctttatgt tctccagagg aaggtggaag aaaccatggg caggagtagg

2581
aattgagtga taaacaattg ggctaatgaa gaaaacttct cttattgttc agttcatcca

2641
gattataact tcaatgggac actttagacc attagacaat tgacactgga ttaaacaaat

2701
tcacataatg ccaaatacac aatgtattta tagcaacgta taatttgcaa agatggactt

2761
taaaagatgc tgtgtaacta aactgaaata attcaattac ttattattta gaatgttaaa

2821
gcttatgata gtcttttcta actcttaaca ctcatacttg aaaactttct gagtttcccc

2881
agaagagaat atgggatttt ttttgacatt tttgactcat ttaataatgc tcttgtgttt

2941
acctagtata tgtagacttt gtcttatgtg tgaaaagtcc taggaaagtg gttgatgttt

3001
cttatagcaa ttaaaaatta tttttgaact gaaaatacaa tgtatttcac

SEQ ID NO: 2

Homo sapiens HLF, PAR bZIP transcription factor (HLF), DNA

NCBI Reference Sequence: NM_002126.4

1
actcttgtca gggccgcggc acatgggcgg ccggatgcgc tgagcccggc gctgcggggc

61
cgcggagcgc tggggagcag cggccgccgg cgcggggagg ggggtggggt gggacggcgc

121
accgcctccg gtgctggcac taggggctgg ggtcggcgcg gtgtcttctg cccttctgca

181
gccgtcgaca tttttttttc tttctttttt tcaattttga acattttgca aaacgagggg

241
ttcgaggcag gtgagagcat cctgcacgtc gccggggagc ccgcgggcac ttggcgcgct

301
ctcctgggac cgtctgcact ggaaacccga aagttttttt ttaatatata tttttatgca

361
gatgtattta taaagatata agtaattttt ttcttccctt ttctccaccg ccttgagagc

421
gagtactttt ggcaaaggac ggaggaaaag ctcagcaaca ttttaggggg cggttgtttc

481
tttcttattt ctttttttaa ggggaaaaaa tttgagtgca tcgcgatgga gaaaatgtcc

541
cgaccgctcc ccctgaatcc cacctttatc ccgcctccct acggcgtgct caggtccctg

601
ctggagaacc cgctgaagct cccccttcac cacgaagacg catttagtaa agataaagac

661
aaggaaaaga agctggatga tgagagtaac agcccgacgg tcccccagtc ggcattcctg

721
gggcctacct tatgggacaa aacccttccc tatgacggag atactttcca gttggaatac

781
atggacctgg aggagttttt gtcagaaaat ggcattcccc ccagcccatc tcagcatgac

841
cacagccctc accctcctgg gctgcagcca gcttcctcgg ctgccccctc ggtcatggac

901
ctcagcagcc gggcctctgc accccttcac cctggcatcc catctccgaa ctgtatgcag

961
agccccatca gaccaggtca gctgttgcca gcaaaccgca atacaccaag tcccattgat

1021
cctgacacca tccaggtccc agtgggttat gagccagacc cagcagatct tgccctttcc

1081
agcatccctg gccaggaaat gtttgaccct cgcaaacgca agttctctga ggaagaactg

1141
aagccacagc ccatgatcaa gaaagctcgc aaagtcttca tccctgatga cctgaaggat

1201
gacaagtact gggcaaggcg cagaaagaac aacatggcag ccaagcgctc ccgcgacgcc

1261
cggaggctga aagagaacca gatcgccatc cgggcctcgt tcctggagaa ggagaactcg

1321
gccctccgcc aggaggtggc tgacttgagg aaggagctgg gcaaatgcaa gaacatactt

1381
gccaagtatg aggccaggca cgggcccctg taggatggca tttttgcagg ctggctttgg

1441
aatagatgga cagtttgttt cctgtctgat agcaccacac gcaaaccaac ctttctgaca

1501
tcagcacttt accagaggca taaacacaac tgactcccat tttggtgtgc atctgtgtgt

1561
gtgtgcgtgt atatgtgctt gtgctcatgt gtgtggtcag cggtatgtgc gtgtgcgtgt

1621
tcctttgctc ttgccatttt aaggtagccc tctcatcgtc ttttagttcc aacaaagaaa

1681
ggtgccatgt ctttactaga ctgaggagcc ctctcgcggg tctcccatcc cctccctcct

1741
tcactcctgc ctcctcagct ttgcttcatg ttcgagctta cctactcttc caggactctc

1801
tgcttggatt cactaaaaag ggccctggta aaatagtgga tctcagtttt taagagtaca

1861
agctcttgtt tctgtttagt ccgtaagtta ccatgctaat gaggtgcaca caataactta

1921
gcactactcc gcagctctag tcctttataa gttgctttcc tcttactttc agttttggtg

1981
ataatcgtct tcaaattaaa gtgctgttta gatttattag atcccatatt tacttactgc

2041
tatctactaa gtttcctttt aattctacca accccagata agtaagagta ctattaatag

2101
aacacagagt gtgtttttgc actgtctgta cctaaagcaa taatcctatt gtacgctaga

2161
gcatgctgcc tgagtattac tagtggacgt aggatatttt ccctacctaa gaatttcact

2221
gtcttttaaa aaacaaaaag taaagtaatg catttgagca tggccagact attccctagg

2281
acaaggaagc agagggaaat gggaggtcta aggatgaggg gttaatttat cagtacatga

2341
gccaaaaact gcgtcttgga ttagcctttg acattgatgt gttcggtttt gttgttcccc

2401
ttccctcaca ccctgcctcg cccccacttt tctagttaac tttttccata tccctcttga

2461
cattcaaaac agttacttaa gattcagttt tcccactttt tggtaatata tatatttttg

2521
tgaattatac tttgttgttt ttaaaaagaa aatcagttga ttaagttaat aagttgatgt

2581
tttctaaggc cctttttcct agtggtgtca tttttgaatg cctcataaat taatgattct

2641
gaagcttatg tttcttattc tctgtttgct tttgaacgta tgtgctctta taaagtggac

2701
ttctgaaaaa tgaatgtaaa agacactggt gtatctcaga aggggatggt gttgtcacaa

2761
actgtggtta atccaatcaa tttaaatgtt tactatagac caaaaggaga gattattaaa

2821
tcgtttaatg tttatacaga gtaattatag gaagttcttt tttgtacagt atttttcaga

2881
tataaatact gacaatgtat tttggaagac atatattata tatagaaaag aggagaggaa

2941
aactattcca tgttttaaaa ttatatagca aagatatata ttcaccaatg ttgtacagag

3001
aagaagtgct tgggggtttt tgaagtcttt aatattttaa gccctatcac tgacacatca

3061
gcatgttttc tgctttaaat taaaatttta tgacagtatc gaggcttgtg atgacgaatc

3121
ctgctctaaa atacacaagg agctttcttg tttcttatta ggcctcagaa agaagtcagt

3181
taacgtcacc caaaagcaca aaatggattt tagtcaaata tttattggat gatacagtgt

3241
tttttaggaa aagcatctgc cacaaaaatg ttcacttcga aattctgagt tcctggaatg

3301
gcacgttgct gccagtgccc cagacagttc ttttctaccc tgcgggcccg cacgttttat

3361
gaggttgata tcggtgctat gtgtttggtt tataatttga tagatgtttg actttaaaga

3421
tgattgttct tttgtttcat taagttgtaa aatgtcaaga aattctgctg ttacgacaaa

3481
gaaacatttt acgctagatt aaaatatcct ttcatcaatg ggattttcta gtttcctgcc

3541
ttcagagtat ctaatccttt aatgatctgg tggtctcctc gtcaatccat cagcaatgct

3601
tctctcatag tgtcatagac ttgggaaacc caaccagtag gatatttcta caaggtgttc

3661
attttgtcac aagctgtaga taacagcaag agatgggggt gtattggaat tgcaatacat

3721
tgttcaggtg aataataaaa tcaaaaactt ttgcaatctt aagcagagat aaataaaaga

3781
tagcaatatg agacacaggt ggacgtagag ttggcctttt tacaggcaaa gaggcgaatt

3841
gtagaattgt tagatggcaa tagtcattaa aaacatagaa aaatgatgtc tttaagtgga

3901
gaattgtgga aggattgtaa catggaccat ccaaatttat ggccgtatca aatggtagct

3961
gaaaaaacta tatttgagca ctggtctctc ttggaattag atgtttatat caaatgagca

4021
tctcaaatgt tttctgcaga aaaaaataaa aagattctaa taaaatgtat tctcttgtgt

4081
gccaggagag gtttcagaaa cctacctcgt cttacaaatt taaacacttt ggagtctgta

4141
caggtgcctt atatgtaggt cattgtcacg atacacacac acgaacactc cctctggact

4201
ggctgcctct ccatccaggg cagttaacta gcaaacaagg cagatctgct tcatggagcg

4261
ggaggccatg gcttgactct gagtgatttg ggtcaaccgg agtcagacgc atgtctgcac

4321
gctgcagcta ttatgagagt ccctttgtca tttttcacct tttcatccta agcatctttc

4381
agagattaat tatttggcca ttaacaatga atccaaatca tatcatactg acatcatcta

4441
gacatgattt ggaaggaaca gcttaggacc tcctgatgag gtcacattgt tgtttctttt

4501
aactagactt ggcaaagaaa ggcaaaaatt gaccagccta tctttctgct ggtgctgcct

4561
taaggaggta gtttgttgag gggagggctg tagatcatta cttctttctc ttcaggaagt

4621
ggccactttg aaccattcaa ataccacatt aggcaagact gtgataggcc ttttgtcttc

4681
aaatacaaca ggcctccact gacccatccc tcaaagcaga aggacccttt gaggagagta

4741
cagatgggat tccacagtgg ggtgggtgga atggaaacct gtactagacc acccagaggt

4801
tccttctaac ccactggttt ggtggggaac tcacagtaat tccaaatgta caatcagatg

4861
tctagggtct gttttcggaa gaagcaagaa ttatcagtgg caccctcccc actgccccca

4921
gtgtaaaaca atagacattc tgtgaaatgc aaagctattc tttggttttt ctagtagttt

4981
atctcatttt accctattct tcctttaagg aaaactcaat ctttatcaca gtcaattaga

5041
gcgatcccaa ggcatgggac caggcctgct tgcctatgtg tgatggcaat tggagatctg

5101
gatttagcac tggggtctca gcaccctgca ggtgtctgag actaagtgat ctgccctcca

5161
ggtggcgatc accttctgct cctaggtacc cccactggca aggccaaggt ctcctccacg

5221
ttttttctgc aattaataat gtcatttaaa aaatgagcaa agccttatcc gaatcggata

5281
tagcaactaa agtcaataca ttttgcagga ggctaagtgt aagagtgtgt gtgtgtgtgt

5341
gtgcgtgcat gtgtgtgtgt gtgtatgtgt gtgaataagt cgacataaag tctttaattt

5401
tgagcacctt accaaacata acaataatcc attatccttt tggcaacacc acaaagatcg

5461
catctgttaa acaggtacaa gttgacatga ggttagttta attgtacacc atgatattgg

5521
tggtatttat gctgttaagt ccaaaccttt atctgtctgt tattcttaat gttgaataaa

5581
ctttgaattt tttcctttca aaaaaaa

SEQ ID NO: 3

Homo sapiens C-X-C motif chemokine ligand 13 (CXCL13), DNA

NCBI Reference Sequence: NM_006419.2

1
gagaagatgt ttgaaaaaac tgactctgct aatgagcctg gactcagagc tcaagtctga

61
actctacctc cagacagaat gaagttcatc tcgacatctc tgcttctcat gctgctggtc

121
agcagcctct ctccagtcca aggtgttctg gaggtctatt acacaagctt gaggtgtaga

181
tgtgtccaag agagctcagt ctttatccct agacgcttca ttgatcgaat tcaaatcttg

241
ccccgtggga atggttgtcc aagaaaagaa atcatagtct ggaagaagaa caagtcaatt

301
gtgtgtgtgg accctcaagc tgaatggata caaagaatga tggaagtatt gagaaaaaga

361
agttcttcaa ctctaccagt tccagtgttt aagagaaaga ttccctgatg ctgatatttc

421
cactaagaac acctgcattc ttcccttatc cctgctctgg attttagttt tgtgcttagt

481
taaatctttt ccaggaaaaa gaacttcccc atacaaataa gcatgagact atgtaaaaat

541
aaccttgcag aagctgatgg ggcaaactca agcttcttca ctcacagcac cctatataca

601
cttggagttt gcattcttat tcatcaggga ggaaagtttc tttgaaaata gttattcagt

661
tataagtaat acaggattat tttgattata tacttgttgt ttaatgttta aaatttctta

721
gaaaacaatg gaatgagaat ttaagcctca aatttgaaca tgtggcttga attaagaaga

781
aaattatggc atatattaaa agcaggcttc tatgaaagac tcaaaaagct gcctgggagg

841
cagatggaac ttgagcctgt caagaggcaa aggaatccat gtagtagata tcctctgctt

901
aaaaactcac tacggaggag aattaagtcc tacttttaaa gaatttcttt ataaaattta

961
ctgtctaaga ttaatagcat tcgaagatcc ccagacttca tagaatactc agggaaagca

1021
tttaaagggt gatgtacaca tgtatccttt cacacatttg ccttgacaaa cttctttcac

1081
tcacatcttt ttcactgact ttttttgtgg ggggcggggc cggggggact ctggtatcta

1141
attctttaat gattcctata aatctaatga cattcaataa agttgagcaa acattttact

1201
taaaaaaaaa aaaaaaaaa

SEQ ID NO: 4

Homo sapiens sulfotransferase family 1E member 1 (SULT1E1), DNA

NCBI Reference Sequence: NM_005420.2

1
caaatgcaga agtggttctc atcttttttt gcagcttaag atctgccttg gtatttgaag

61
agatataaac tagatcaatt tctttcacag gatcaactaa acagtgtacc acaatgaatt

121
ctgaacttga ctattatgaa aagtttgaag aagtccatgg gattctaatg tataaagatt

181
ttgtcaaata ttgggataat gtggaagcgt tccaggcaag accagatgat cttgtcattg

241
ccacctaccc taaatctggt acaacctggg ttagtgaaat tgtgtatatg atctataaag

301
agggtgatgt ggaaaagtgc aaagaagatg taatttttaa tcgaatacct ttcctggaat

361
gcagaaaaga aaacctcatg aatggagtaa aacaattaga tgagatgaat tctcctagaa

421
ttgtgaagac tcatttgcca cctgaacttc ttcctgcctc attttgggaa aaggattgta

481
agataatcta tctttgccgg aatgcaaagg atgtggctgt ttccttttat tatttctttc

541
taatggtggc tggtcatcca aatcctggat cctttccaga gtttgtggag aaattcatgc

601
aaggacaggt tccttatggt tcctggtata aacatgtaaa atcttggtgg gaaaagggaa

661
agagtccacg tgtactattt cttttctacg aagacctgaa agaggatatc agaaaagagg

721
tgataaaatt gatacatttc ctggaaagga agccatcaga ggagcttgtg gacaggatta

781
tacatcatac ttcgttccaa gagatgaaga acaatccatc cacaaattac acaacactgc

841
cagacgaaat tatgaaccag aaattgtcgc ccttcatgag aaagggaatt acaggagact

901
ggaaaaatca ctttacagta gccctgaatg aaaaatttga taaacattat gagcagcaaa

961
tgaaggaatc tacactgaag tttcgaactg agatctaaga aggtctttct ttacttaaca

1021
tatctgatat taaagatttc ttttcattat tctccacttt ttcttatttt agattgctag

1081
aaaagacata atcatggatt atgttgacat tttcttttta aatttttgtt taactttttt

1141
tttttttttt tgagacagag tctcactctg ttgcctaggc tggaggacag tggcacaatc

1201
atggctgatt gcagccttga cctccttgac tcaattgatc ctcccatctc agcctcccaa

1261
gtagctagga ctacagacat gtgcaaccat gtttggctaa tttttttaat gtttttttgt

1321
agagatgagg tcttattata ttgtccaggc tggtcttgaa ttcctgggct caagcttccc

1381
aagtagctgc aacaacaggc acacaccacc atgctcaact aattttattt ctattttttg

1441
tatagacagg ggcttgctat agtgtccagg ctggtctgaa acccttgagc tcaagtgatc

1501
ttcccacacc agcctcccaa aatactggga ttacaggctt gagcctccat gcctggccca

1561
ggtaacatgt ttattgagct gtacatgcat atgagaaata agaaactttt ttttcctact

1621
atcatctctt aaattttgtt ttctttttct tttgcttcct cttcttcttt tctatttttt

1681
ataaatatca tgcacaacta taacctatgg gaatgatgta gtaacacaga ttattcatct

1741
tgttagagtt gtattaaaaa taaacaagca tttcaaatta aaaaaaaaaa aaaaaaaaaa

1801
aaaaa

FIGURES

FIGS. 1a and 1b: Participants' flow chart in the training phase

FIG. 2: Box plots of raw data

FIG. 3: Density plots of raw data

FIG. 4: Box plots after separate frozen normalization

FIG. 5: Density plots after separate frozen normalization

FIG. 6: Box plots after cross-platform normalization

FIG. 7: Density plots after cross-platform normalization

FIG. 8: Histograms of stromal TIL in TOP samples, MDACC samples and overall

FIG. 9: Cross validated likelihood as a function of the tuning parameter

FIG. 10: Cross validated likelihood as a function of the tuning parameter in the neighborhood of the maxima

FIG. 11: Histograms of the genomic predictor in TOP samples, MDACC sample and overall

FIG. 12: Histograms of the transformed genomic predictor in TOP samples, MDACC sample and overall

FIG. 13: Check for non-log-linear effect of the predictor on distant relapse-free survival

FIG. 14: Check for non-log-linear effect of the predictor on overall survival

FIG. 15: Distant relapse-free survival of different risk groups—TER

FIG. 16: Distant relapse-free survival of different risk groups—MED

FIG. 17: Distant relapse-free survival of different risk groups—COX

FIG. 18: Overall survival of different risk groups—TER

FIG. 19: Overall survival of different risk groups—MED

FIG. 20: Overall survival of different risk groups—COX

FIG. 21: Spearman pairwise correlation of genes—Training

FIG. 22: Profiles of stromal TIL—Grey lines: individual profiles—Green line: mean profile

FIG. 23: Check for non-log-linear effect of stromal TIL on distant relapse-free survival

FIG. 24: Kaplan-Meier distant-relapse free survival curves according to stromal TIL cut-off (50%)

FIG. 25: Check for non-log-linear effect of stromal TIL on overall survival

FIG. 26: Kaplan-Meier overall survival curves according to stromal TIL cut-off (50%)

FIG. 27: Participants' flow chart of the validation dataset

FIG. 28: Histograms of the genomic predictor in the validation dataset

FIG. 29: Histograms of the transformed genomic predictor in the validation dataset

FIG. 30: Check for non-log-linear effect of the genomic predictor on distant relapse-free survival—Validation dataset—Patients achieving pCR

FIG. 31: Check for non-log-linear effect of the genomic predictor on distant relapse-free survival—Validation dataset

FIG. 32: Distant relapse-free survival of different risk groups—No pCR—TER

FIG. 33: Distant relapse-free survival of different risk groups—No pCR—MED

FIG. 34: Distant relapse-free survival of different risk groups—No pCR—COX

FIG. 35: Distant relapse-free survival of different risk groups—All patients—TER

FIG. 36: Distant relapse-free survival of different risk groups—All patients—MED

FIG. 37: Distant relapse-free survival of different risk groups—All patients—COX

FIG. 38: Spearman pairwise correlation of genes—Validation

FIG. 39: Histograms of the genomic predictor—Training vs. validation

FIG. 40: Histograms of the transformed genomic predictor—Training vs. validation

FIG. 41: Comparison of HLF expression levels in three breast carcinoma cell lines Literature microarray-based log 2 ratio of HLF mRNA levels, compared to “universal reference RNA” that represents a mixture of RNAs of 11 well described BC cell lines, on SUM-52-PE, MDA-MB-468 and MD-MB-231 cell lines (A) (Kao et al., 2009). HLF mRNA content showed as a ddCT with 18S expression levels as an internal control in our laboratory conditions (B). Western blot of HLF protein expression on three cell lines. Beta-tubulin was used as loading control. Used antibodies: rabbit monoclonal anti-HLF (Genetex), mouse monoclonal anti-β-tubulin (Sigma Aldrich). (C)

FIG. 42: Cell lines SUM-52-PE and MDA-MB-468 with HLF siRNA knock-down Cell lines SUM-52-PE and MDA-MB-468 were transfected with siRNA specific for HLF (or non-targeting control). The HLF mRNA expression level was tested for each experiment and was summarized in one graph. The amount of 18S mRNA was used as internal reference for normalizing qPCR. (A). Cell viability was tested between siHLF and siCTRL cells when treated with doxorubicin (B).

FIG. 43: Plasmid for CRISPR/Cas9 human HLF targeted genome editing (comprising the sequences SEQ ID NOs: 5 and 6)

The structure of plasmid pX278 (carrying the GFP-tag) for CRISPR/Cas9 human HLF targeted genome editing designed by Bernardo Reina San Martin, IGBMC, Strasbourg.

FIG. 44: Effect of changing the tuning parameter on the values of fitted regression coefficients

FIG. 45: Fitted stromal TILs (Box-cox-transformed) vs. observed stromal TILs (Box-cox-transformed)

Use of the expression of specific genes for the prognosis of patients with triple negative breast cancer

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