bioRxiv ScienceSearch

Biology subjects

Pereira, I.

Publications and source records attributed to Pereira, I..

2 recordsLinked to original sources

Determination of HLA-DR level in cytotoxic T lymphocytes: a new validated tool to predict breast cancer response to treatment

BackgroundNeoadjuvant chemotherapy (NACT) is the usual treatment for locally-advanced breast cancer (BC). However, more than half of the patients lack an effective response to this treatment. Thus, its urgent to find predictive biomarkers. Recently, we proposed the HLA-DR expression level in cytotoxic T lymphocytes (CTLs) as a robust biomarker to select, in advance, patients that will actually benefit from NACT. Patients and MethodsA total of 202 BC patients, 102 of which submitted to NACT, were enrolled in this study. 61 biopsies and 41 blood samples collected pre-NACT and 100 non-NACT tumor samples were immunophenotyped by flow cytometry. Both NACT and non-NACT patients were followed up for 34 months. Blood-isolated immune cells were cultured with BC cell lines in a 3D system. ResultsHere we confirmed that HLA-DR level in CTLs is a highly sensitive and specific biomarker to predict BC response to NACT, reflected in circulation and independent of the patients age, BC subtype and other tumor-immunological features. Therefore, we developed a predictive probability model, based on the determination of HLA-DR level in tumor-infiltrating CTLs, that could be used to guide therapeutic decisions. Interestingly, this biomarker was also associated with progression-free survival, regardless the treatment. Contrary to HLA-DRnegative CTLs, HLA-DR+ CTLs were able to reduce the viability of tumor cells, in culture, in agreement with their higher expression of activation, proliferation and cytotoxicity-related molecules. Tissue-residency and memory markers were also increased in HLA-DR+ CTLs. These anti-tumor features of HLA-DR+ CTLs may justify the clinical observations. ConclusionHLA-DR level in CTLs is a validated and independent biomarker to predict response to NACT which allow the establishment of a clinical meaningful tool to select in advance patients that will truly benefit from this treatment. Intriguingly, it may be further used as a biomarker of BC patients general prognosis. Highlights- HLA-DR level in cytotoxic T cells is an independent predictive factor of breast cancer response to NACT - A predictive probability model based on this biomarker was developed as a new tool to improve treatment decisions - HLA-DR level in cytotoxic T cells is also reflected systemically - HLA-DR level in cytotoxic T cells is also a prognostic factor, associated with progression-free survival - HLA-DR+ cytotoxic T cells exhibit several phenotypic and functional anti-tumor characteristics

immunology

TAPAS: an open-source software package for Translational Neuromodeling and Computational Psychiatry

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

neuroscience