ALK-positive histiocytosis is a rare subtype of histiocytic neoplasm first described in 2008 in 3 infants with multisystemic disease involving the liver and hematopoietic system. This entity has subsequently been documented in case reports and series to occupy a wider clinicopathologic spectrum with recurrent KIF5B-ALK fusions. The full clinicopathologic and molecular spectra of ALK-positive histiocytosis remain, however, poorly characterized. Here, we describe the largest study of ALK-positive histiocytosis to date, with detailed clinicopathologic data of 39 cases, including 37 cases with confirmed ALK rearrangements. The clinical spectrum comprised distinct clinical phenotypic groups: infants with multisystemic disease with liver and hematopoietic involvement, as originally described (Group 1A: 6/39), other patients with multisystemic disease (Group 1B: 10/39), and patients with single-system disease (Group 2: 23/39). Nineteen patients of the entire cohort (49%) had neurologic involvement (7 and 12 from Groups 1B and 2, respectively). Histology included classic xanthogranuloma features in almost one-third of cases, whereas the majority displayed a more densely cellular, monomorphic appearance without lipidized histiocytes but sometimes more spindled or epithelioid morphology. Neoplastic histiocytes were positive for macrophage markers and often conferred strong expression of phosphorylated extracellular signal-regulated kinase, confirming MAPK pathway activation. KIF5B-ALK fusions were detected in 27 patients, whereas CLTC-ALK, TPM3-ALK, TFG-ALK, EML4-ALK, and DCTN1-ALK fusions were identified in single cases. Robust and durable responses were observed in 11/11 patients treated with ALK inhibition, 10 with neurologic involvement. This study presents the existing clinicopathologic and molecular landscape of ALK-positive histiocytosis and provides guidance for the clinical management of this emerging histiocytic entity.

Fibrosis is a progressive biological condition, leading to organ dysfunction in various clinical settings. Although fibroblasts and macrophages are known as key cellular players for fibrosis development, a comprehensive functional model that considers their interaction in the metabolic/immunologic context of fibrotic tissue has not been set up. Here we show, by transcriptome-based mathematical modeling in an in vitro system that represents macrophage-fibroblast interplay and reflects the functional effects of inflammation, hypoxia and the adaptive immune context, that irreversible fibrosis development is associated with specific combinations of metabolic and inflammatory cues. The in vitro signatures are in good alignment with transcriptomic profiles generated on laser captured glomeruli and cortical tubule-interstitial area, isolated from human transplanted kidneys with advanced stages of glomerulosclerosis and interstitial fibrosis/tubular atrophy, two clinically relevant conditions associated with organ failure in renal allografts. The model we describe here is validated on tissue based quantitative immune-phenotyping of biopsies from transplanted kidneys, demonstrating its feasibility. We conclude that the combination of in vitro and in silico modeling represents a powerful systems medicine approach to dissect fibrosis pathogenesis, applicable to specific pathological conditions, and develop coordinated targeted approaches.

Delayed graft function (DGF) is a strong risk factor for development of interstitial fibrosis and tubular atrophy (IFTA) in kidney transplants. Quantitative assessment of inflammatory infiltrates in kidney biopsies of DGF patients can reveal predictive markers for IFTA development. In this study, we combined multiplex tyramide signal amplification (mTSA) and convolutional neural networks (CNNs) to assess the inflammatory microenvironment in kidney biopsies of DGF patients (n = 22) taken at 6 weeks post-transplantation. Patients were stratified for IFTA development (<10% versus ≥10%) from 6 weeks to 6 months post-transplantation, based on histopathological assessment by three kidney pathologists. One mTSA panel was developed for visualization of capillaries, T- and B-lymphocytes and macrophages and a second mTSA panel for T-helper cell and macrophage subsets. The slides were multi spectrally imaged and custom-made python scripts enabled conversion to artificial brightfield whole-slide images (WSI). We used an existing CNN for the detection of lymphocytes with cytoplasmatic staining patterns in immunohistochemistry and developed two new CNNs for the detection of macrophages and nuclear-stained lymphocytes. F1-scores were 0.77 (nuclear-stained lymphocytes), 0.81 (cytoplasmatic-stained lymphocytes), and 0.82 (macrophages) on a test set of artificial brightfield WSI. The CNNs were used to detect inflammatory cells, after which we assessed the peritubular capillary extent, cell density, cell ratios, and cell distance in the two patient groups. In this cohort, distance of macrophages to other immune cells and peritubular capillary extent did not vary significantly at 6 weeks post-transplantation between patient groups. CD163⁺ cell density was higher in patients with ≥10% IFTA development 6 months post-transplantation (p < 0.05). CD3⁺CD8⁻/CD3⁺CD8⁺ ratios were higher in patients with <10% IFTA development (p < 0.05). We observed a high correlation between CD163⁺ and CD4⁺GATA3⁺ cell density (R = 0.74, p < 0.001). Our study demonstrates that CNNs can be used to leverage reliable, quantitative results from mTSA-stained, multi spectrally imaged slides of kidney transplant biopsies.

Crowdsourcing in pathology has been performed on tasks that are assumed to be manageable by nonexperts. Demand remains high for annotations of more complex elements in digital microscopic images, such as anatomical structures. Therefore, this work investigates conditions to enable crowdsourced annotations of high-level image objects, a complex task considered to require expert knowledge. 76 medical students without specific domain knowledge who voluntarily participated in three experiments solved two relevant annotation tasks on histopathological images: (1) Labeling of images showing tissue regions, and (2) delineation of morphologically defined image objects. We focus on methods to ensure sufficient annotation quality including several tests on the required number of participants and on the correlation of participants’ performance between tasks. In a set up simulating annotation of images with limited ground truth, we validated the feasibility of a confidence score using full ground truth. For this, we computed a majority vote using weighting factors based on individual assessment of contributors against scattered gold standard annotated by pathologists. In conclusion, we provide guidance for task design and quality control to enable a crowdsourced approach to obtain accurate annotations required in the era of digital pathology.

Artificial intelligence (AI)–powered approaches are becoming increasingly used as histopathologic tools to extract subvisual features and improve diagnostic workflows. On the other hand, hi-plex approaches are widely adopted to analyze the immune ecosystem in tumor specimens. Here, we aimed at combining AI-aided histopathology and imaging mass cytometry (IMC) to analyze the ecosystem of non–small cell lung cancer (NSCLC). An AI-based approach was used on hematoxylin and eosin (H&E) sections from 158 NSCLC specimens to accurately identify tumor cells, both adenocarcinoma and squamous carcinoma cells, and to generate a classifier of tumor cell spatial clustering. Consecutive tissue sections were stained with metal-labeled antibodies and processed through the IMC workflow, allowing quantitative detection of 24 markers related to tumor cells, tissue architecture, CD45+ myeloid and lymphoid cells, and immune activation. IMC identified 11 macrophage clusters that mainly localized in the stroma, except for S100A8+ cells, which infiltrated tumor nests. T cells were preferentially localized in peritumor areas or in tumor nests, the latter being associated with better prognosis, and they were more abundant in highly clustered tumors. Integrated tumor and immune classifiers were validated as prognostic on whole slides. In conclusion, integration of AI-powered H&E and multiparametric IMC allows investigation of spatial patterns and reveals tissue relevant features with clinical relevance. Significance: Leveraging artificial intelligence–powered H&E analysis integrated with hi-plex imaging mass cytometry provides insights into the tumor ecosystem and can translate tumor features into classifiers to predict prognosis, genotype, and therapy response.