Summary
Breast cancer is a global public health problem since it is the most frequently diagnosed
cancer in women in Western countries. Clinical guidelines for breast cancer
prognosis/diagnosis are currently based on tumour size, histological type and grade, lymph
node status as well as the expression of various cellular receptors. Yet, current predictions
remain unsatisfactory to identify the best treatment for the individual patient. The search for
identifying new predictive and prognostic factors is ongoing. Furthermore, compelling
evidences have solidified the notion that the evolving epithelial cells, founders of the breast
disease, are helped in their malignant course by the tumour microenvironment. Better
characterizing the dual effect of the immune regulation but also the epithelial-stromal crosstalk on both tumour-promotion and -suppression is essential for understanding patient
uniqueness and their implication in disease outcome. Because of its potential to probe tissues
and cells at the molecular level without requirement for extrinsic contrast agents, infrared
spectroscopy was seen as an attractive tool for clinical and diagnostic analysis in order to
complement the existing methods.
In a first step, recording and processing methodology had to be defined in order to
optimally compare IR spectra. The methodology developed and the analysis tools tested on
carcinoma cell lines, demonstrated that spectra could be distinguished based on the cell line
phenotypic nature.
The potential of IR imaging for breast tissular structure differentiation was highlighted in
this thesis, demonstrating that spectral signature can be correlated with the major histological
cell types observed in breast disease tissues. In order to develop a robust algorithm translating
spectral data into helpful histopathological information, a spectral database of histologically
well-defined breast tissues was built and used for the development of a cell type classifier.
This latter one was extensively validated on independent clinical cases. Firstly, the IR-based
histopathological classifier correctly assigned spectra acquired on eleven breast disease
samples based on their histological nature. Secondly, lymphocyte and Collagen & Fibroblasts
spectral signatures were demonstrated to be independent from tissue type and organ since,
although trained on reference spectra recorded into breast disease samples, the cell type
classifier correctly assigned spectra acquired on lymph nodes/tonsils and scar tissues
respectively. Thirdly, we concluded that spectroscopically, breast carcinoma cell lines in
culture are well-suited tumour models since spectra acquired on these carcinoma cell lines
were correctly recognized as epithelium by the IR-based histological classifier.
By spectral characterizing lymphocytes from lymph nodes and tonsils, we demonstrated
that the spectra acquired contained enough information to statistically discriminate them
according to their lymphocyte activation states. Although considered as activated, the breast
disease lymphoid infiltrates were found to present distinct spectral signature from
lymphocytes acquired on activated lymph nodes and tonsils. Furthermore, tumour
microenvironment, characterized by IR-imaging was demonstrated to exhibit a distinct
spectral signature from wound healing tissues. These studies proved the uniqueness of the
signature of both lymphoid infiltrate and tumour microenvironment in breast disease context.
Correlating these specific spectral signatures to patient outcome and therapeutics response
could help better consider the uniqueness of the patient. In a last step, considering the
epithelial signature of carcinomas of both low and high grades, we demonstrated that the
biochemical information reflected in the IR micro-spectra was clinically relevant for grading
purpose.