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abundant presence of immune cells: T cells, myeloid cells, monocytes, and the second little or no presence
[19]
of immune cells, especially T cells . Fibroblast cell types are part of TEM, and more precisely cancer-
[20]
associated fibroblasts (CAFs) have pro-tumor functions in breast cancer as they can enhance metastasis .
The presence of tumor-infiltrating lymphocytes (TILs) in TME is associated with an overall patient good
[19]
prognosis, better survival and the success of checkpoint immunotherapy . Studies performed a multi-
omic analysis of Tumor Cancer Genome Atlas (TCGA) datasets have allowed identification at least
[21]
six immune subtypes across cancer types . Finally, stromal cells and immune cells can preserve the
properties of cancer stem cells (CSCs), or cancer initiating cells, which are cells that exert multicellular
functions in tumor tissue-specific networks and immune resistance [22,23] . More important, CSCs display
differentiation-state plasticity that allow cancer cells to undergo epithelial to mesenchymal transition
[24]
(EMT), a process in which cancer cells acquire migratory and invasive properties . These results
underline the importance of immunophenotyping as a new modality to sub-classify cancers based on their
TME [19,20] .

The effectiveness of the targeted therapy strongly depends on both the cancer type and molecular
features of the individual tumors [25,26] . The context-specific impact of molecular features such as somatic
alterations and/or copy number events can be measured using diverse high-throughput techniques such as
transcriptomics (the number of counts of mRNA molecules) and (phospho) proteomic and transcription
factor (TF) activities [27,28] . The reverse phase protein array (RPPA) is a high-throughput antibody-based
technique, similar to Western blot, to evaluate protein activities in signaling networks [27,28] . This functional
proteomic analysis can be done in either flash-frozen or formalin-fixed, paraffin-embedded (FFPE) tissue
samples. The use of RPPA data for evaluation of functional signatures linking perturbations in down- and
[18]
up-stream signal transduction pathways might be crucial for personalizing cancer therapies in future .
Computational integrative methods that combine genomic and functional cancer phenotypes may better
predict those patients who will benefit of the combination therapies [27,28] . This system biology approach
generally uses statistical/mathematical modeling and supervised machine learning for learning and predict
disease similarities from basic and clinical data. Personalized disease subnetworks may be necessary to
uncover cancer-related associations, including genotype-phenotype relationships and spatial heterogeneity
in the tumor microenvironmental interactions [4,27,28] . However, although powerful, the use of these
methodologies still requires additional strategies to reveal functionally important biomarkers, which often
remains the rate-limiting step in the diagnostic challenge. Here we will discuss these issues using as model
the breast cancer tumors.

BREAST CANCER SUBTYPES AND THERAPY OUTCOMES
Breast cancer has the highest incidence in women worldwide and is the fifth leading cause of mortality in
the globe. Many breast cancer classifications have been proposed according to the invasive characteristics,
occurrence, histology and molecular profiling of tumor samples [29,30] . Based on their site of occurrence,
tumors can be classified as lobular (located at breast lobules) or ductal (at breast ducts). Carcinomas
may also arise from invasive epithelial cells (medullary carcinoma), mucus-producing cells (mucinous
carcinoma, also called colloid carcinoma), or a subtype of ductal carcinoma in situ (DCIS) or invasive
ductal carcinoma (tubular carcinoma). The in situ to invasive breast carcinoma progression is often caused
by interactions among epithelial, myoepithelial, and stromal cells. The progression occurs due to the loss of
normal myoepithelial cell function .
[31]

Cancers derived from luminal cells are the most common types of breast cancer expressing hormone
receptors for estrogen receptor (ER), progesterone receptor (PR), or the amplified human epidermal
growth factor receptor (EGFR) 2/erythroblastic leukemia viral oncogene homolog 2 receptor (HER2/