Mechanism-Based Biomarker Discovery

Biomarkers are cornerstones of healthcare spanning a variety of applications from disease diagnosis to stratification and prediction of likely outcome. Despite significant efforts that have identified thousands of potential biomarkers, their translation into clinical practice remains poor, averaging 1.5 per year across all diseases. This inefficiency primarily results from the lack of connection of the candidate biomarkers with the underlying pathophysiological mechanisms that they monitor which results in poor reproducibility in their developmental pipeline. On top of these limitations, the current single-biomarker-tosingle-disease approach does not capture the multifactorial nature of complex diseases like Chronic Kidney Disease (CKD). CKD is a major public health problem that affects approximately to 14% of the general population and requires asymptomatic, early-stage, and diseasespecific, biomarkers to deliver more precise diagnostic and predictive information. Here we propose, and experimentally validate, a biomarker discovery pipeline that aims to identify molecular signatures that not only allow to discern different CKD subtypes but also capture the underlying biology of the disease. To that end, first, we integrate protein-protein interaction networks with annotated gene-sets into a knowledge-base that captures plethora of information about biological entities and their interactions. This model is then fed with CKD transcriptional data to generate a disease specific model. Third, the model is analysed using different state-of-the-art methods (e.g.: network analysis, pathway analysis) which result in a molecular profile for each protein capturing different disease biology. Relevant features are then selected and optimized by training an elastic network model on plasma samples which is used to predict biomarker performance. Finally, the resulting individual candidates are integrated into a biomarker panel with increased performance and stability using linear discriminant analysis as machine learning integrative method. Results show that our holistic approach can find biomarkers that are associated with disease mechanisms while keeping competent predictive abilities

Mechanism-based biomarker discovery

Biomarkers are cornerstones of healthcare spanning a variety of applications from disease diagnosis to stratification and prediction of likely outcome. Despite significant efforts that have identified thousands of potential biomarkers, their translation into clinical practice remains poor, averaging 1.5 per year across all diseases. This inefficiency primarily results from the lack of connection of the candidate biomarkers with the underlying pathophysiological mechanisms that they monitor which results in poor reproducibility in their developmental pipeline. On top of these limitations, the current single-biomarker-tosingle-disease approach does not capture the multifactorial nature of complex diseases like Chronic Kidney Disease (CKD). CKD is a major public health problem that affects approximately to 14% of the general population and requires asymptomatic, early-stage, and diseasespecific, biomarkers to deliver more precise diagnostic and predictive information. Here we propose, and experimentally validate, a biomarker discovery pipeline that aims to identify molecular signatures that not only allow to discern different CKD subtypes but also capture the underlying biology of the disease. To that end, first, we integrate protein-protein interaction networks with annotated gene-sets into a knowledge-base that captures plethora of information about biological entities and their interactions. This model is then fed with CKD transcriptional data to generate a disease specific model. Third, the model is analysed using different state-of-the-art methods (e.g.: network analysis, pathway analysis) which result in a molecular profile for each protein capturing different disease biology. Relevant features are then selected and optimized by training an elastic network model on plasma samples which is used to predict biomarker performance. Finally, the resulting individual candidates are integrated into a biomarker panel with increased performance and stability using linear discriminant analysis as machine learning integrative method. Results show that our holistic approach can find biomarkers that are associated with disease mechanisms while keeping competent predictive abilities

Computerised scoring protocol for identification and quantification of different immune cell populations in breast tumour regions by the use of QuPath software

AIMS: As important prognostic and predictive information can be obtained from the composition, functionality and spatial arrangement of different immune cell subtypes, this study aims at characterizing the immune infiltrate in breast tumours. METHODS AND RESULTS: Tumour-infiltrating lymphocytes (TILs) in 62 patients with luminal B-like breast cancer were characterised by immunohistochemical staining with standard markers, and were subsequently classified and quantified by the use of QuPath software. In different delineated tumour regions, the proportion and density of CD3+ , CD4+ , CD5+ , CD8+ , CD20+ and FOXP3+ cells were assessed. The results of the software analysis were compared with those of manual counting for CD8 and CD20 staining. The QuPath scoring protocol slightly overestimated positive, negative and total lymphocyte counts and density, while minimally underestimating the proportion of positively stained lymphocytes. However, for density and proportion, no real differences from manual counting were observed. For all markers, the density of positively stained immune cells was higher in the invasive front than in the tumour centre, pointing to an accumulation of immune cells near the tumour boundaries. When we looked at the proportion of immunohistochemically positive immune cells, we observed enrichment of CD5 (P = 0.025) and CD20 (P < 0.001) at the periphery, and FOXP3 enrichment in the centre (P < 0.001). CONCLUSION: The QuPath scoring protocol can adequately identify positively stained immune cells in breast tumours, and allows the evaluation of differences in immune cell proportion and density within different tumour regions. The entire tumour section can be quantitatively assessed quite rapidly, which is a major advantage over manual counting.status: publishe

The Adaptive and Innate Immune Cell Landscape of Uterine Leiomyosarcomas

Reactivation of the anti-tumor response has shown substantial progress in aggressive tumors such as melanoma and lung cancer. Data on less common histotypes are scanty. Immune checkpoint inhibitor therapy has been applied to few cases of uterine leiomyosarcomas, of which the immune cell composition was not examined in detail. We analyzed the inflammatory infiltrate of 21 such cases in high-dimensional, single cell phenotyping on routinely processed tissue. T-lymphoid cells displayed a composite phenotype common to all tumors, suggestive of antigen-exposure, acute and chronic exhaustion. To the contrary, myelomonocytic cells had case-specific individual combinations of phenotypes and subsets. We identified five distinct monocyte-macrophage cell types, some not described before, bearing immunosuppressive molecules (TIM3, B7H3, VISTA, PD1, PDL1). Detailed in situ analysis of routinely processed tissue yields comprehensive information about the immune status of sarcomas. The method employed provides equivalent information to extractive single-cell technology, with spatial contexture and a modest investment.status: publishe

Mapping the immune landscape in metastatic melanoma reveals localized cell-cell interactions that predict immunotherapy response

While immune checkpoint–based immunotherapy (ICI) shows promising clinical results in patients with cancer, only a subset of patients responds favorably. Response to ICI is dictated by complex networks of cellular interactions between malignant and nonmalignant cells. Although insights into the mechanisms that modulate the pivotal antitumoral activity of cytotoxic T cells (Tcy) have recently been gained, much of what has been learned is based on single-cell analyses of dissociated tumor samples, resulting in a lack of critical information about the spatial distribution of relevant cell types. Here, we used multiplexed IHC to spatially characterize the immune landscape of metastatic melanoma from responders and nonresponders to ICI. Such high-dimensional pathology maps showed that Tcy gradually evolve toward an exhausted phenotype as they approach and infiltrate the tumor. Moreover, a key cellular interaction network functionally linked Tcy and PD-L1(+) macrophages. Mapping the respective spatial distributions of these two cell populations predicted response to anti-PD-1 immunotherapy with high confidence. These results suggest that baseline measurements of the spatial context should be integrated in the design of predictive biomarkers to identify patients likely to benefit from ICI. SIGNIFICANCE: This study shows that spatial characterization can address the challenge of finding efficient biomarkers, revealing that localization of macrophages and T cells in melanoma predicts patient response to ICI. See related commentary by Smalley and Smalley, p. 319

Age-related remodelling of the blood immunological portrait and the local tumor immune response in patients with luminal breast cancer

Objectives: Aging is associated with altered immune function and chronic low-grade inflammation, referred to as immunosenescence. As breast cancer is an age-related disease, the impact of aging on tumor immune responses may have important consequences. However, effects of immunosenescence on breast tumor immune infiltration remain largely unknown. Methods: This exploratory study investigated a broad panel of immune/senescence markers in peripheral blood and in the tumor microenvironment of young, middle-aged and old patients diagnosed with early invasive luminal (hormone-sensitive, HER2-negative) breast cancer. In the old group, G8-scores were computed as a correlate for clinical frailty. Results: Significant age-related changes in plasma levels of several inflammatory mediators (IL-1α, IP-10, IL-8, MCP-1, CRP), immune checkpoint markers (Gal-9, sCD25, TIM-3, PD-L1), IGF-1 and circulating miRs (miR-18a, miR-19b, miR-20, miR-155, miR-195 and miR-326) were observed. Shifts were observed in distinct peripheral blood mononuclear cell populations, particularly naive CD8+ T-cells. At the tumor level, aging was associated with lower total lymphocytic infiltration, together with decreased abundance of several immune cell markers, especially CD8. The relative fractions of cell subsets in the immune infiltrate were also altered. Clinical frailty was associated with higher frequencies of exhausted/senescent (CD27-CD28- and/or CD57+) terminally differentiated CD8+ cells in the blood and with increased tumor infiltration by FOXP3+ cells. Conclusion: Aging and frailty are associated with profound changes of the blood and tumor immune profile in luminal breast cancer, pointing to a different interplay between tumor cells, immune cells and inflammatory mediators at higher age.status: publishe