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Advanced Breast Cancer Translational Laboratory

The mission of the Advanced Breast Cancer Translational Laboratory is to predict the incidence and reduce the mortality of metastatic breast cancer.

In Portugal, nearly 9,000 women were diagnosed with breast cancer in 2022, and approximately 2,200 died from the disease. Breast cancer is a heterogeneous condition comprising distinct subtypes, each defined by specific genetic alterations, therapeutic responses, and clinical outcomes. A major factor influencing these differences is the metastatic behavior of the tumor and consequently, treatment efficacy, disease progression, and patient survival are significantly compromised when breast cancer is diagnosed at advanced / metastatic stages.

Thus, the primary goal of the lab is to facilitate early-stage detection, at stage I of cancer development, by developing an easy-to-use liquid biopsy diagnostic test, which can be implemented in routine clinical settings within the next 10 years.

In parallel, the treatment of metastatic breast cancer continues to face major hurdles due to the development of resistance to available therapies.

The second goal of the lab is to design and implement custom-made tests to identify biomarkers and phenotypic traits associated with therapy resistance and metastatic potential.

To achieve these goals, we are investigating disease progression across both early and late stages of diagnosis. In collaboration with the oncology department of Hospital Santa Maria, we are establishing a prospective, longitudinal biobank of breast cancer specimens. This includes tumor tissue (at diagnosis and after treatment), adjacent normal tissue, blood, and feces, along with comprehensive clinical data. This collection will enable us to better monitor therapy responses throughout the course of treatment(s).

Advanced Breast Cancer

Our research is structured around three interrelated axes:

Imaging-Based Stratification and Metabolic Profiling of Tumor Microenvironment
Microbiome and Immune Modulation
Advanced In Vitro 3D Models and Microfluidic Systems

Imaging-Based Stratification and Metabolic Profiling of Tumor Microenvironment

The breast tumor microenvironment is a complex and dynamic ecosystem comprising tumor, immune, stromal (fibroblasts, endothelial), and extracellular matrix components. Its cellular composition and spatial organization play a critical role in disease progression and clinical outcomes. To dissect the complexity of this microenvironment, we apply multiplex imaging microscopy, a cutting-edge technology that enables the simultaneous identification and quantification of dozens of cell types in a single tissue section.

Our image analysis pipeline integrates machine learning and Python-based tools to facilitate deep phenotyping of the microenvironment and extract meaningful patterns from these high-dimensional datasets. Our preliminary data point towards the ability of multiplex imaging to stratify patients by identifying tumor cell profiles, immune infiltration, angiogenic index, and PD-L1 expression, thus predicting response to neoadjuvant therapy.

To complement the interpretation of the spatial architecture profiling, we apply high-resolution, mass spectrometry-based metabolomics to analyze the tumor microenvironment. This technique captures dynamic, real-time biochemical changes and functional insights, as such enhancing our understanding of tumor metabolism. Our preliminary results indicate that metabolomic signatures can distinguish classical breast cancer subtypes (luminal, HER2-positive, and triple-negative), supporting its value as a companion diagnostic approach.

We believe that combining spatial architecture with metabolomic profiling offers a powerful strategy to identify prognostic and predictive biomarkers in breast cancer.

Microbiome and Immune Modulation

Emerging evidence highlights the role of host-associated microbiota in shaping breast cancer biology, including tumor growth, immune responses, metastatic behavior, and treatment outcomes.

On one front, we are using precision microbiome profiling and machine learning to identify novel gut microbiota biomarkers for breast cancer progression risk stratification. We are also exploring tumor-resident microbiota as a potential non-host genetic factor that may influence disease initiation and progression.

In parallel, our research focuses on how circulating and tissue-local microbial metabolites shape immune responses. Our goal is to identify microbial metabolite signatures, in serum and breast cancer tissue, which associate with specific in situ cancer cell features and immune phenotypes. This research may uncover new targets for microbial-based therapies and contribute to the development of personalized probiotics or biotherapeutics to support treatment and reduce cancer risk.

Advanced In Vitro 3D Models and Microfluidic Systems

In addition to patient-based studies, we are developing advanced in vitro 3D hetero-culture systems to study therapy resistance and key steps of the metastatic cascade. Unlike conventional 2D cultures, 3D models such as spheroids more accurately mimic the structure and microenvironment of human tissues. This setting that reflects better in vivo conditions, enables a more realistic assessment of drug behavior such as diffusion, therapeutic efficacy, and resistance mechanisms.

We are also engineering 3D dynamics microfluidic systems. These include a metastasis-on-chip platform. With one of these metastasis-on-chip, we model tumor cell intravasation (entry into blood circulation), and we have recently made it perfusable as such mimicking a vessel with blood flux that allows real-time imaging of breast cancer cell transmigration. Additionally, we are creating another metastasis-on-chip model to study organ-specific metastasis and investigate specific interactions between tumor cells and distant organs (in particular brain).

These innovative 3D systems offer an unprecedented opportunity to examine tumor biology in a controlled but physiologically relevant environment, and hold promise for elucidating mechanisms of metastasis, which may reveal novel therapeutic targets and improve metastatic breast cancer outcomes.