Neuropathological findings and staging in dementia

Dementia is a clinical syndrome with the development of impairment in multiple cognitive functions (including memory), severe enough to interfere with activities of daily living, as the main symptom. There are a large number of disorders that can lead to dementia, and neuropathological examination after death is necessary to determine the underlying cause with certainty. The overall aim of this thesis was to investigate neuropathological findings in patients with dementia and neuropathological staging of dementia disorders, the main potential gain being increased epidemiological knowledge and improved neuropathological dementia diagnostics. We could confirm the generally accepted opinion that on a neuropathological basis, Alzheimer’s disease (AD) is the most common dementia disorder, followed by vascular dementia (VaD) and mixed AD+VaD. Also, in a significant number of patients, the clinical dementia subtype diagnosis does not correspond with the neuropathological findings. Furthermore, degeneration of the nucleus locus coeruleus, often seen already macroscopically, generally indicates the diagnoses Lewy body disease (LBD) or AD among the demented, while a preserved locus coeruleus occurs mainly in VaD and frontotemporal lobar degeneration (FTLD). Moreover, various neuropathological staging systems for AD differ in procedure and targeted pathology, and the choice of system affects the judgement of Alzheimer pathology and hence the final diagnosis. Also, various neuropathological LBD staging systems differ in applicability and to some extent in the judgment of Lewy-related pathology

Spatial immunophenotyping of the tumour microenvironment in non–small cell lung cancer

Checkpoint therapy; Lung cancer; Tumour microenvironmentTerapia de puntos de control; Cáncer de pulmón; Microambiente tumoralTeràpia de punts de control; Càncer de pulmó; Microambient tumoralIntroduction: Immune cells in the tumour microenvironment are associated with prognosis and response to therapy. We aimed to comprehensively characterise the spatial immune phenotypes in the mutational and clinicopathological background of non-small cell lung cancer (NSCLC). Methods: We established a multiplexed fluorescence imaging pipeline to spatially quantify 13 immune cell subsets in 359 NSCLC cases: CD4 effector cells (CD4-Eff), CD4 regulatory cells (CD4-Treg), CD8 effector cells (CD8-Eff), CD8 regulatory cells (CD8-Treg), B-cells, natural killer cells, natural killer T-cells, M1 macrophages (M1), CD163+ myeloid cells (CD163), M2 macrophages (M2), immature dendritic cells (iDCs), mature dendritic cells (mDCs) and plasmacytoid dendritic cells (pDCs). Results: CD4-Eff cells, CD8-Eff cells and M1 macrophages were the most abundant immune cells invading the tumour cell compartment and indicated a patient group with a favourable prognosis in the cluster analysis. Likewise, single densities of lymphocytic subsets (CD4-Eff, CD4-Treg, CD8-Treg, B-cells and pDCs) were independently associated with longer survival. However, when these immune cells were located close to CD8-Treg cells, the favourable impact was attenuated. In the multivariable Cox regression model, including cell densities and distances, the densities of M1 and CD163 cells and distances between cells (CD8-Treg-B-cells, CD8-Eff-cancer cells and B-cells-CD4-Treg) demonstrated positive prognostic impact, whereas short M2-M1 distances were prognostically unfavourable. Conclusion: We present a unique spatial profile of the in situ immune cell landscape in NSCLC as a publicly available data set. Cell densities and cell distances contribute independently to prognostic information on clinical outcomes, suggesting that spatial information is crucial for diagnostic use.This study was partly supported by Swedish Cancer Society, The Lions Cancer Foundation Uppsala, Sweden, Selanders Foundation and The Sjöberg Foundation, Sweden

Mutational and gene fusion analyses of primary large cell and large cell neuroendocrine lung cancer.

Large cell carcinoma with or without neuroendocrine features (LCNEC and LC, respectively) constitutes 3-9% of non-small cell lung cancer but is poorly characterized at the molecular level. Herein we analyzed 41 LC and 32 LCNEC (including 15 previously reported cases) tumors using massive parallel sequencing for mutations in 26 cancer-related genes and gene fusions in ALK, RET, and ROS1. LC patients were additionally subdivided into three immunohistochemistry groups based on positive expression of TTF-1/Napsin A (adenocarcinoma-like, n = 24; 59%), CK5/P40 (squamous-like, n = 5; 12%), or no marker expression (marker-negative, n = 12; 29%). Most common alterations were TP53 (83%), KRAS (22%), MET (12%) mutations in LCs, and TP53 (88%), STK11 (16%), and PTEN (13%) mutations in LCNECs. In general, LCs showed more oncogene mutations compared to LCNECs. Immunomarker stratification of LC revealed oncogene mutations in 63% of adenocarcinoma-like cases, but only in 17% of marker-negative cases. Moreover, marker-negative LCs were associated with inferior overall survival compared with adenocarcinoma-like tumors (p = 0.007). No ALK, RET or ROS1 fusions were detected in LCs or LCNECs. Together, our molecular analyses support that LC and LCNEC tumors follow different tumorigenic paths and that LC may be stratified into molecular subgroups with potential implications for diagnosis, prognostics, and therapy decisions

Fibroblast subsets in non-small cell lung cancer : Associations with survival, mutations, and immune features

Background Cancer-associated fibroblasts (CAFs) are molecularly heterogeneous mesenchymal cells that interact with malignant cells and immune cells and confer anti- and protumorigenic functions. Prior in situ profiling studies of human CAFs have largely relied on scoring single markers, thus presenting a limited view of their molecular complexity. Our objective was to study the complex spatial tumor microenvironment of non-small cell lung cancer (NSCLC) with multiple CAF biomarkers, identify novel CAF subsets, and explore their associations with patient outcome. Methods Multiplex fluorescence immunohistochemistry was employed to spatially profile the CAF landscape in 2 population-based NSCLC cohorts (n = 636) using antibodies against 4 fibroblast markers: platelet-derived growth factor receptor-alpha (PDGFRA) and -beta (PDGFRB), fibroblast activation protein (FAP), and alpha-smooth muscle actin (alpha SMA). The CAF subsets were analyzed for their correlations with mutations, immune characteristics, and clinical variables as well as overall survival. Results Two CAF subsets, CAF7 (PDGFRA-/PDGFRB+/FAP+/alpha SMA+) and CAF13 (PDGFRA+/PDGFRB+/FAP-/alpha SMA+), showed statistically significant but opposite associations with tumor histology, driver mutations (tumor protein p53 [TP53] and epidermal growth factor receptor [EGFR]), immune features (programmed death-ligand 1 and CD163), and prognosis. In patients with early stage tumors (pathological tumor-node-metastasis IA-IB), CAF7 and CAF13 acted as independent prognostic factors. Conclusions Multimarker-defined CAF subsets were identified through high-content spatial profiling. The robust associations of CAFs with driver mutations, immune features, and outcome suggest CAFs as essential factors in NSCLC progression and warrant further studies to explore their potential as biomarkers or therapeutic targets. This study also highlights multiplex fluorescence immunohistochemistry-based CAF profiling as a powerful tool for the discovery of clinically relevant CAF subsets.Peer reviewe

Diagnostic gastrointestinal markers in primary lung cancer and pulmonary metastases

Funding Information: Open access funding provided by Lund University. The study was supported by Swedish governmental funding of clinical research (ALF), the Franke and Margareta Bergqvist Foundation, and the Swedish Cancer Society. The funding sources had no role in the design or conduct of the study. Publisher Copyright: © 2023, The Author(s).Histopathological diagnosis of pulmonary tumors is essential for treatment decisions. The distinction between primary lung adenocarcinoma and pulmonary metastasis from the gastrointestinal (GI) tract may be difficult. Therefore, we compared the diagnostic value of several immunohistochemical markers in pulmonary tumors. Tissue microarrays from 629 resected primary lung cancers and 422 resected pulmonary epithelial metastases from various sites (whereof 275 colorectal cancer) were investigated for the immunohistochemical expression of CDH17, GPA33, MUC2, MUC6, SATB2, and SMAD4, for comparison with CDX2, CK20, CK7, and TTF-1. The most sensitive markers for GI origin were GPA33 (positive in 98%, 60%, and 100% of pulmonary metastases from colorectal cancer, pancreatic cancer, and other GI adenocarcinomas, respectively), CDX2 (99/40/100%), and CDH17 (99/0/100%). In comparison, SATB2 and CK20 showed higher specificity, with expression in 5% and 10% of mucinous primary lung adenocarcinomas and both in 0% of TTF-1-negative non-mucinous primary lung adenocarcinomas (25-50% and 5-16%, respectively, for GPA33/CDX2/CDH17). MUC2 was negative in all primary lung cancers, but positive only in less than half of pulmonary metastases from mucinous adenocarcinomas from other organs. Combining six GI markers did not perfectly separate primary lung cancers from pulmonary metastases including subgroups such as mucinous adenocarcinomas or CK7-positive GI tract metastases. This comprehensive comparison suggests that CDH17, GPA33, and SATB2 may be used as equivalent alternatives to CDX2 and CK20. However, no single or combination of markers can categorically distinguish primary lung cancers from metastatic GI tract cancer.Peer reviewe

I am hiQ—a novel pair of accuracy indices for imputed genotypes

Background: Imputation of untyped markers is a standard tool in genome-wide association studies to close the gap between directly genotyped and other known DNA variants. However, high accuracy with which genotypes are imputed is fundamental. Several accuracy measures have been proposed and some are implemented in imputation software, unfortunately diversely across platforms. In the present paper, we introduce Iam hiQ, an independent pair of accuracy measures that can be applied to dosage files, the output of all imputation software. Iam (imputation accuracy measure) quantifies the average amount of individual-specific versus population-specific genotype information in a linear manner. hiQ (heterogeneity in quantities of dosages) addresses the inter-individual heterogeneity between dosages of a marker across the sample at hand. Results: Applying both measures to a large case–control sample of the International Lung Cancer Consortium (ILCCO), comprising 27,065 individuals, we found meaningful thresholds for Iam and hiQ suitable to classify markers of poor accuracy. We demonstrate how Manhattan-like plots and moving averages of Iam and hiQ can be useful to identify regions enriched with less accurate imputed markers, whereas these regions would by missed when applying the accuracy measure info (implemented in IMPUTE2). Conclusion: We recommend using Iam hiQ additional to other accuracy scores for variant filtering before stepping into the analysis of imputed GWAS data

Obesity, Metabolic Factors and Risk of Different Histological Types of Lung Cancer: A Mendelian Randomization Study

Background Assessing the relationship between lung cancer and metabolic conditions is challenging because of the confounding effect of tobacco. Mendelian randomization (MR), or the use of genetic instrumental variables to assess causality, may help to identify the metabolic drivers of lung cancer. Methods and findings We identified genetic instruments for potential metabolic risk factors and evaluated these in relation to risk using 29,266 lung cancer cases (including 11,273 adenocarcinomas, 7,426 squamous cell and 2,664 small cell cases) and 56,450 controls. The MR risk analysis suggested a causal effect of body mass index (BMI) on lung cancer risk for two of the three major histological subtypes, with evidence of a risk increase for squamous cell carcinoma (odds ratio (OR) [95% confidence interval (CI)] = 1.20 [1.01–1.43] and for small cell lung cancer (OR [95%CI] = 1.52 [1.15–2.00]) for each standard deviation (SD) increase in BMI [4.6 kg/m2]), but not for adenocarcinoma (OR [95%CI] = 0.93 [0.79–1.08]) (Pheterogeneity = 4.3x10-3). Additional analysis using a genetic instrument for BMI showed that each SD increase in BMI increased cigarette consumption by 1.27 cigarettes per day (P = 2.1x10-3), providing novel evidence that a genetic susceptibility to obesity influences smoking patterns. There was also evidence that low-density lipoprotein cholesterol was inversely associated with lung cancer overall risk (OR [95%CI] = 0.90 [0.84–0.97] per SD of 38 mg/dl), while fasting insulin was positively associated (OR [95%CI] = 1.63 [1.25–2.13] per SD of 44.4 pmol/l). Sensitivity analyses including a weighted-median approach and MR-Egger test did not detect other pleiotropic effects biasing the main results. Conclusions Our results are consistent with a causal role of fasting insulin and low-density lipoprotein cholesterol in lung cancer etiology, as well as for BMI in squamous cell and small cell carcinoma. The latter relation may be mediated by a previously unrecognized effect of obesity on smoking behavior

Obesity, metabolic factors and risk of different histological types of lung cancer: A Mendelian randomization study.

BACKGROUND: Assessing the relationship between lung cancer and metabolic conditions is challenging because of the confounding effect of tobacco. Mendelian randomization (MR), or the use of genetic instrumental variables to assess causality, may help to identify the metabolic drivers of lung cancer. METHODS AND FINDINGS: We identified genetic instruments for potential metabolic risk factors and evaluated these in relation to risk using 29,266 lung cancer cases (including 11,273 adenocarcinomas, 7,426 squamous cell and 2,664 small cell cases) and 56,450 controls. The MR risk analysis suggested a causal effect of body mass index (BMI) on lung cancer risk for two of the three major histological subtypes, with evidence of a risk increase for squamous cell carcinoma (odds ratio (OR) [95% confidence interval (CI)] = 1.20 [1.01-1.43] and for small cell lung cancer (OR [95%CI] = 1.52 [1.15-2.00]) for each standard deviation (SD) increase in BMI [4.6 kg/m2]), but not for adenocarcinoma (OR [95%CI] = 0.93 [0.79-1.08]) (Pheterogeneity = 4.3x10-3). Additional analysis using a genetic instrument for BMI showed that each SD increase in BMI increased cigarette consumption by 1.27 cigarettes per day (P = 2.1x10-3), providing novel evidence that a genetic susceptibility to obesity influences smoking patterns. There was also evidence that low-density lipoprotein cholesterol was inversely associated with lung cancer overall risk (OR [95%CI] = 0.90 [0.84-0.97] per SD of 38 mg/dl), while fasting insulin was positively associated (OR [95%CI] = 1.63 [1.25-2.13] per SD of 44.4 pmol/l). Sensitivity analyses including a weighted-median approach and MR-Egger test did not detect other pleiotropic effects biasing the main results. CONCLUSIONS: Our results are consistent with a causal role of fasting insulin and low-density lipoprotein cholesterol in lung cancer etiology, as well as for BMI in squamous cell and small cell carcinoma. The latter relation may be mediated by a previously unrecognized effect of obesity on smoking behavior

The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types

Background: The development of a reactive tumour stroma is a hallmark of tumour progression and pronounced tumour stroma is generally considered to be associated with clinical aggressiveness. The variability between tumour types regarding stroma fraction, and its prognosis associations, have not been systematically analysed.Methods: Using an objective machine-learning method we quantified the tumour stroma in 16 solid cancer types from 2732 patients, representing retrospective tissue collections of surgically resected primary tumours. Image analysis performed tissue segmentation into stromal and epithelial compartment based on pan-cytokeratin staining and autofluorescence patterns.Findings: The stroma fraction was highly variable within and across the tumour types, with kidney cancer showing the lowest and pancreato-biliary type periampullary cancer showing the highest stroma proportion (median 19% and 73% respectively). Adjusted Cox regression models revealed both positive (pancreato-biliary type periampullary cancer and oestrogen negative breast cancer, HR(95%CI)=0.56(0.34-0.92) and HR (95%CI)=0.41(0.17-0.98) respectively) and negative (intestinal type periampullary cancer, HR(95%CI)=3.59 (1.49-8.62)) associations of the tumour stroma fraction with survival.Interpretation: Our study provides an objective quantification of the tumour stroma fraction across major types of solid cancer. Findings strongly argue against the commonly promoted view of a general associations between high stroma abundance and poor prognosis. The results also suggest that full exploitation of the prognostic potential of tumour stroma requires analyses that go beyond determination of stroma abundance.</p