Asier Antoranz

Mechanism-based biomarker discovery

Biomarkers are cornerstones of healthcare spanning a wide spectrum of applications like disease diagnosis and stratification of patient populations. Despite significant efforts that have identified thousands of potential biomarkers, their translation into clinical practice remains poor: less than two approvals per year across all diseases. In part, this inefficiency arises from experimental limitations on the biomarker discovery pipeline. Widely used mass-spectrometry approaches suffer from sample throughput whereas targeted approaches such as immunoassays suffer from low multiplexability. On top of the technological limitations, the current single-biomarker-to-single-disease approach does not capture the multifactorial nature of complex diseases. Hence, mechanism based biomarker discovery aims to identify signatures that capture the diversity of the diseases origin and deliver more precise diagnostic and predictive information.

A functional landscape of chronic kidney disease entities from public transcriptomic data

To develop efficient therapies and identify novel early biomarkers for chronic kidney disease (CKD) an understanding of the molecular mechanisms orchestrating it is essential. We here set out to understand how differences in CKD origin are reflected in gene regulatory mechanisms. To this end, we collected and integrated publicly available human-kidney glomerular microarray gene expression data for nine kidney disease entities that account for a majority of CKD worldwide [Focal segmental glomerulosclerosis (FSGS), Minimal Change Disease (MCD), FSGS-MCD, IgA nephropathy (IgAN), Lupus nephritis (LN), Membranous glomerulonephropathy (MGN), Diabetic nephropathy (DN), Hypertensive nephropathy (HN) and Rapidly progressive glomerulonephritis (RPGN)]. We included data from five distinct studies and compared glomerular gene expression profiles to that of non-tumor part of kidney cancer nephrectomy tissues. A major challenge was the integration of the data from different sources, platforms and conditions, that we mitigated with a bespoke stringent procedure. This allowed us to perform a global transcriptome-based delineation of different kidney disease entities, obtaining a landscape of their similarities and differences based on the genes that acquire a consistent differential expression between each kidney disease entity and tumor nephrectomy. Furthermore, we derived functional insights by inferring signaling pathway and transcription factor activity from the collected gene expression data, and identified potential drug candidates based on expression signature matching. These results provide a foundation to comprehend the specific molecular mechanisms underlying different kidney disease entities, that can pave the way to identify biomarkers and potential therapeutic targets.

High-dimensional single cell-analysis of lymphocytic activation in melanoma

Abstract In melanoma, the lymphocytic infiltrate is a prognostic parameter classified morphologically in “brisk”, “non-brisk” and “absent”. The meaning of “brisk” is “active, energetic”, implying a functional connotation which has never been proved. Recently, it has been shown that not all brisk infiltrates have a good prognosis, that lymphocytic populations can be very heterogeneous, and that anti-PD-1 immunotherapy supports activated T cells. Here, we characterize the immune landscape in primary melanoma by high-dimensional single cell multiplex analysis in tissue sections (MILAN technique) followed by image analysis, RT-PCR and shotgun proteomics. We observed that the brisk and non-brisk patterns of tumor infiltrating lymphocytes (TILs) in primary melanoma are heterogeneous functional categories that can be further sub-classified according to the functional status of their lymphocytes as active, transitional or exhausted. We found that only activation of TILs correlates with spontaneous regression, and that the good prognosis of melanomas with a brisk pattern could be due to the fact that in these case the Tcy that are in contact with the melanoma cells are still active, and consequently the melanoma is still under immune control at the moment of surgical excision. The main inflammatory cell subpopulations that are present in the microenvironment associated with activation and exhaustion and their spatial relationships are described using neighbourhood analysis. Finally, we show a bioinformatic pipeline that, starting from common immunofluorescence stainings, can transform the tissue into a digitalized image, which represents the starting point for multiple deeper levels of analysis.In melanoma, the lymphocytic infiltrate is a prognostic parameter classified morphologically into “brisk”, “non-brisk” and “absent” entailing a functional association that has never been proved. Recently, it has been shown that lymphocytic populations can be very heterogeneous, and that anti-PD-1 immunotherapy supports activated T cells. Here, we characterize the immune landscape in primary melanoma by high-dimensional single cell multiplex analysis in tissue sections (MILAN technique) followed by image analysis, RT-PCR and shotgun proteomics. We observed that the brisk and non-brisk patterns are heterogeneous functional categories that can be further sub-classified into active, transitional or exhausted. The classification of primary melanomas based on the functional paradigm also shows correlation with spontaneous regression, and an improved prognostic value than that of the brisk classification. Finally, the main inflammatory cell subpopulations that are present in the microenvironment associated with activation and exhaustion and their spatial relationships are described using neighbourhood analysis.

Proteomic profile of patients with atrial fibrillation undergoing cardiac surgery

OBJECTIVES Proteomic analysis of patients with advanced cardiovascular disease was conducted to identify possible biomarkers for atrial fibrillation (AF). METHODS A total of 123 patients undergoing cardiac surgery (22 with AF and 101 without AF) and 20 healthy subjects were recruited. Demographic data, patient history and blood samples were collected. Growth/differentiation factor 15, resistin, intracellular adhesion molecule-1, galectin-3, trefoil factor 3, tissue inhibitor of metalloproteinases 1, matrix metallopeptidase 9, high-sensitive troponin T, interleukins 6, 1α, 3, 4, 8, 20 and 22, tumour necrosis factor alpha, C-X-C motif chemokines 10 and 11, S100A6 and Type III procollagen were measured in blood serum. Differential expression between any 2 groups for any of the measured proteins was identified by fitting linear models, whereas Matthews Correlation Coefficient was used to evaluate their predictive capacity. Combined markers using more than 1 protein were attained via weighted Support Vector Machines. RESULTS Although serum levels of the markers were higher in patients with cardiovascular disease than in healthy subjects, only growth/differentiation factor 15 and resistin were significantly higher in patients with AF among the subpopulation who underwent heart surgery (P = 0.029 and P = 0.007, respectively). Specific pairs of several biomarkers had mediocre predictive capacity for AF. CONCLUSIONS Growth/differentiation factor 15 and resistin are 2 markers that could be helpful in stratifying risk for AF in patients with cardiovascular disease. Yet, more research in terms of proteomics and investigation of possible molecular pathways implicated is required.

Phosphoprotein patterns predict trametinib responsiveness and optimal trametinib sensitisation strategies in melanoma

Malignant melanoma is a highly aggressive form of skin cancer responsible for the majority of skin cancer-related deaths. Recent insight into the heterogeneous nature of melanoma suggests more personalised treatments may be necessary to overcome drug resistance and improve patient care. To this end, reliable molecular signatures that can accurately predict treatment responsiveness need to be identified. In this study, we applied multiplex phosphoproteomic profiling across a panel of 24 melanoma cell lines with different disease-relevant mutations, to predict responsiveness to MEK inhibitor trametinib. Supported by multivariate statistical analysis and multidimensional pattern recognition algorithms, the responsiveness of individual cell lines to trametinib could be predicted with high accuracy (83% correct predictions), independent of mutation status. We also successfully employed this approach to case specifically predict whether individual melanoma cell lines could be sensitised to trametinib. Our predictions identified that combining MEK inhibition with selective targeting of c-JUN and/or FAK, using siRNA-based depletion or pharmacological inhibitors, sensitised resistant cell lines and significantly enhanced treatment efficacy. Our study indicates that multiplex proteomic analyses coupled with pattern recognition approaches could assist in personalising trametinib-based treatment decisions in the future.