Breast cancer classification | EPFL Graph Search

Breast cancer classification divides breast cancer into categories according to different schemes criteria and serving a different purpose. The major categories are the histopathological type, the grade of the tumor, the stage of the tumor, and the expression of proteins and genes. As knowledge of cancer cell biology develops these classifications are updated. The purpose of classification is to select the best treatment. The effectiveness of a specific treatment is demonstrated for a specific breast cancer (usually by randomized, controlled trials). That treatment may not be effective in a different breast cancer. Some breast cancers are aggressive and life-threatening, and must be treated with aggressive treatments that have major adverse effects. Other breast cancers are less aggressive and can be treated with less aggressive treatments, such as lumpectomy. Treatment algorithms rely on breast cancer classification to define specific subgroups that are each treated according to th

Breast texture synthesis and estimation of the role of the anatomy and tumor shape in the radiological detection process

Cyril Castella

Breast cancer is the most common, and the number one cause of death by cancer among women. However, when it is sufficiently early detected, heavy treatments can be avoided, and morbidity and mortality can be reduced. This is the reason why randomized clinical trials were started in the 60s, followed in the last decades by screening mammography national programs. The process of breast cancer detection in mammography is complex. Its understanding offers numerous challenges to radiologists and medical physicists. One way to apprehend it is to model this process step-by-step by performing psychophysical experiments with anatomical or synthetic images. In this approach, the images information content is controlled. Since the first experiments with synthetic images created with white noise and simple geometric signals, technical and computational improvements allowed to get ever closer to clinical realism for studying the mechanism of perception of a signal on a radiological image. The present work extends the list of tools that have been used until now in psychophysical experiments in mammography. It proposes a detailed statistical analysis of anatomical images, from which algorithms for breast density classification and realistic breast texture synthesis are developed. In a second phase, psychophysical experiments with simple signals and benign or malignant masses combined with anatomical or synthetic backgrounds are presented. The performance of human observers is analyzed as a function of parameters such as background type, signal type, or uncertainty about the size or the shape of the signal. These results are compared to that of existing or adapted models from the literature, and the different models are evaluated in their ability to predict the performance of human observers for the detection of lesions in such conditions. Each step of this project focused on the objective and reproducible aspect of the image evaluation or of the observer performance. Controlled yet realistic conditions ensure the robustness of the results, as well as their clinical adaptability. Among the main results, synthetic mammographic texture images have been generated and validated. They provide a virtually unlimited database of images with demonstrated visual and statistical realism. Concerning the analysis of the human observers' performance, this work shows that they are sensitive to uncertainty about the signal size but not about its shape, that they use similar detection strategies with real and synthetic images, and that they are mainly sensitive to the anatomical fluctuations in the immediate area around the signal location. These effects, as well as the performance level of human observers for the detection tasks under consideration, could be reproduced by models taking into account human visual system characteristics. The conclusions of this work will be able to guide future studies in the field of detection tasks in mammography or tomosynthesis. This 3D breast imaging technique presents, like mammography, numerous challenges in order to understand and to objectively characterize its clinical potential. Studies with model observers specifically validated for detection tasks in medical imaging provide an excellent alternative in terms of time and costs for answering these questions.

EPFL2009

Type I interferon/IRF7 axis instigates chemotherapy-induced immunological dormancy in breast cancer

Mauro Delorenzi, Yu-Ting Huang

Neoadjuvant and adjuvant chemotherapies provide survival benefits to breast cancer patients, in particular in estrogen receptor negative (ER-) cancers, by reducing rates of recurrences. It is assumed that the benefits of (neo)adjuvant chemotherapy are due to the killing of disseminated, residual cancer cells, however, there is no formal evidence for it. Here, we provide experimental evidence that ER- breast cancer cells that survived high-dose Doxorubicin and Methotrexate based chemotherapies elicit a state of immunological dormancy. Hallmark of this dormant phenotype is the sustained activation of the IRF7/IFN-beta/IFNAR axis subsisting beyond chemotherapy treatment. Upregulation of IRF7 in treated cancer cells promoted resistance to chemotherapy, reduced cell growth and induced switching of the response from a myeloid derived suppressor cell-dominated immune response to a CD4(+)/CD8(+) T cell-dependent anti-tumor response. IRF7 silencing in tumor cells or systemic blocking of IFNAR reversed the state of dormancy, while spontaneous escape from dormancy was associated with loss of IFN-beta production. Presence of IFN-beta in the circulation of ER- breast cancer patients treated with neoadjuvant Epirubicin chemotherapy correlated with a significantly longer distant metastasis-free survival. These findings establish chemotherapy-induced immunological dormancy in ER- breast cancer as a novel concept for (neo)adjuvant chemotherapy activity, and implicate sustained activation of the IRF7/IFN-beta/IFNAR pathway in this effect. Further, IFN-beta emerges as a potential predictive biomarker and therapeutic molecule to improve outcome of ER- breast cancer patients treated with (neo)adjuvant chemotherapy.

Springer2019

Guidance molecule SEMA3A restricts tumor growth by differentially regulating the proliferation of tumor-associated macrophages

Margarita Bartish, Michele De Palma, Mario Leonardo Squadrito, Tatjana Wallmann

Accumulation of tumor-associated macrophages (TAM) correlates with malignant progression, immune suppression and poor prognosis. In this study, we defined a critical role for the cell-surface guidance molecule SEMA3A in differential proliferative control of TAMs. Tumor cell-derived SEMA3A restricted the proliferation of pro-tumoral M2 macrophages but increased the proliferation of anti-tumoral M1, acting through the SEMA3A receptor neuropilin-1 (NP1). Expansion of M1 macrophages in vivo enhanced the recruitment and activation of NK cells and cytotoxic CD8+ T cells to tumors, inhibiting their growth. In human breast cancer specimens, we found that immunohistochemical levels of SEMA3A correlated with the expression of genes characteristic of M1 macrophages, CD8+ T cells and NK cells, while inversely correlating with established characters of malignancy. In summary, our results illuminate a mechanism whereby the TAM phenotype is controlled, and identify the cell surface molecule SEMA3A as a candidate for therapeutic targeting.

American Association for Cancer Research2016

Breast texture synthesis and estimation of the role of the anatomy and tumor shape in the radiological detection process

Cyril Castella

EPFL2009