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Background: Breast cancer is the leading cause of cancer-related deaths in women, demanding new treatment options. With the advent of immune checkpoint blockade, immunotherapy emerged as a treatment option. In addition to lymphocytes, tumor-associated macrophages exert a significant, albeit controversial, impact on tumor development. Pro-inflammatory macrophages are thought to hinder, whereas anti-inflammatory macrophages promote tumor growth. However, molecular markers to identify prognostic macrophage populations remain elusive. Methods: We isolated two macrophage subsets, from 48 primary human breast tumors, distinguished by the expression of CD206. Their transcriptomes were analyzed via RNA-Seq, and potential prognostic macrophage markers were validated by PhenOptics in tissue microarrays of patients with invasive breast cancer. Results: Normal human breast tissue contained mainly CD206+ macrophages, while increased relative amounts of CD206− macrophages were observed in tumors. The presence of CD206+ macrophages correlated with a pronounced lymphocyte infiltrate and subsets of CD206+ macrophages, expressing SERPINH1 and collagen 1, or MORC4, were unexpectedly associated with improved survival of breast cancer patients. In contrast, MHCIIhi CD206− macrophages were linked with a poor survival prognosis. Conclusion: Our data highlight the heterogeneity of tumor-infiltrating macrophages and suggest the use of multiple phenotypic markers to predict the impact of macrophage subpopulations on cancer prognosis. We identified novel macrophage markers that correlate with the survival of patients with invasive mammary carcinoma.
Classical Hodgkin lymphoma (cHL) is one of the most common malignant lymphomas in Western Europe. The nodular sclerosing subtype of cHL (NS cHL) is characterised by a proliferation of fibroblasts in the tumour microenvironment, leading to fibrotic bands surrounding the lymphoma infiltrate. Several studies have described a crosstalk between the tumour cells of cHL, the Hodgkin- and Reed-Sternberg (HRS) cells, and cancerassociated fibroblasts (CAF). However, to date a deep molecular understanding of these fibroblasts is lacking. Aim of the present study therefore was a comprehensive
characterisation of these fibroblasts. Moreover, only a few studies describe the interplay of HRS cells and CAF. The paracrine communication and direct interaction of these two
cellular fractions have been investigated within this study. Finally, the influence of a few HRS cells within a lymph node orchestrate the mere alteration of its architecture and
morphology. Gene expression and methylation profiles of fibroblasts isolated from primary lymph node suspensions revealed persistent differences between fibroblasts obtained from NS cHL and lymphadenitis. NS cHL derived fibroblasts exhibit a myofibroblastic - inflammatory phenotype characterised by MYOCD, CNN1 and IL-6 expression. TIMP3, an inhibitor of matrix metalloproteinases, was strongly upregulated in NS cHL fibroblasts, likely contributing to the accumulation of collagen in sclerotic bands of NS cHL. Treatment by luteolin could reverse this fibroblast phenotype and decrease TIMP3 secretion. NS cHL fibroblasts showed enhanced proliferation when they were exposed to soluble factors released from HRS cells. For HRS cells, soluble
factors from fibroblasts were not sufficient to protect them from Brentuximab-Vedotin(BV) induced cell death. However, HRS cells adherent to fibroblasts were protected from BV-induced injury. The cHL specific interaction of both cell fractions reveals an initiation of inflammatory key regulators such as IL13 and IL4. Among important adhesion molecules known from literature the blocking of integrin beta 1 solely interrupted the adhesion of HRS cells to CAF. In summary, this study proves the stable reprograming of CAF phenotype and expression derived from NS cHL. It presents a suitable in vitro model for studying the interaction of HRS cells and CAF by paracrine factors and adherence. Most importantly the observations confirm the importance of fibroblasts for HRS cells´ inflammatory niche and cell survival associated with TIMP3 which probably acts as a major factor to the typical accumulation of fibrosis observed in NS cHL.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.