Characterizing the invasive tumor front of aggressive uterine adenocarcinoma and leiomyosarcoma

The invasive tumor front (the tumor-host interface) is vitally important in malignant cell progression and metastasis. Tumor cell interactions with resident and infiltrating host cells and with the surrounding extracellular matrix and secreted factors ultimately determine the fate of the tumor. Herein we focus on the invasive tumor front, making an in-depth characterization of reticular fiber scaffolding, infiltrating immune cells, gene expression, and epigenetic profiles of classified aggressive primary uterine adenocarcinomas (24 patients) and leiomyosarcomas (11 patients). Sections of formalin-fixed samples before and after microdissection were scanned and studied. Reticular fiber architecture and immune cell infiltration were analyzed by automatized algorithms in colocalized regions of interest. Despite morphometric resemblance between reticular fibers and high presence of macrophages, we found some variance in other immune cell populations and distinctive gene expression and cell adhesion-related methylation signatures. Although no evident overall differences in immune response were detected at the gene expression and methylation level, impaired antimicrobial humoral response might be involved in uterine leiomyosarcoma spread. Similarities found at the invasive tumor front of uterine adenocarcinomas and leiomyosarcomas could facilitate the use of common biomarkers and therapies. Furthermore, molecular and architectural characterization of the invasive front of uterine malignancies may provide additional prognostic information beyond established prognostic factors

Amyloid PET as a marker of normal-appearing white matter early damage in multiple sclerosis: correlation with CSF β-amyloid levels and brain volumes

PURPOSE The disease course of multiple sclerosis (MS) is unpredictable, and reliable prognostic biomarkers are needed. Positron emission tomography (PET) with β-amyloid tracers is a promising tool for evaluating white matter (WM) damage and repair. Our aim was to investigate amyloid uptake in damaged (DWM) and normal-appearing WM (NAWM) of MS patients, and to evaluate possible correlations between cerebrospinal fluid (CSF) β-amyloid (Aβ) levels, amyloid tracer uptake, and brain volumes. METHODS Twelve MS patients were recruited and divided according to their disease activity into active and non-active groups. All participants underwent neurological examination, neuropsychological testing, lumbar puncture, brain magnetic resonance (MRI) imaging, and F-florbetapir PET. Aβ levels were determined in CSF samples from all patients. MRI and PET images were co-registered, and mean standardized uptake values (SUV) were calculated for each patient in the NAWM and in the DWM. To calculate brain volumes, brain segmentation was performed using statistical parametric mapping software. Nonparametric statistical analyses for between-group comparisons and regression analyses were conducted. RESULTS We found a lower SUV in DWM compared to NAWM (p < 0.001) in all patients. Decreased NAWM-SUV was observed in the active compared to non-active group (p < 0.05). Considering only active patients, NAWM volume correlated with NAWM-SUV (p = 0.01). Interestingly, CSF Aβ concentration was a predictor of both NAWM-SUV (r = 0.79; p = 0.01) and NAWM volume (r = 0.81, p = 0.01). CONCLUSIONS The correlation between CSF Aβ levels and NAWM-SUV suggests that the predictive role of β-amyloid may be linked to early myelin damage and may reflect disease activity and clinical progression

Serous borderline tumors of the ovary. A clinicopathologic, immunohistochemical, and quantitative study of 44 cases.

The clinicopathologic features of 44 serous borderline tumors (SBT) of the ovary were evaluated. Nineteen were Stages II and III, and 9 had invasive peritoneal implants. All 19 patients received chemotherapy and 4, who had invasive implants, died of disease after 3, 4.3, 8, and 9 years. The other 25 patients were free of tumor 1-14 years (mean, 5.3 years) after surgery. Coexpression of low molecular weight keratins (AE1, CAM 5.2) and vimentin was found in all tumors and their implants. No significant differences were found between SBT with different volume-corrected mitotic indices (M/Vi) with respect to gross features, presence or absence of implants, stage, and survival. Cytometric DNA analysis also was performed on the primary ovarian tumors and the implants. Twenty-one primary tumors had diploid or tetraploid histograms, and 23 had aneuploid histograms. DNA ploidy of the primary ovarian tumors did not correlate with gross features, the presence or absence of implants, M/Vi, stage, and survival. The data from this study confirm that most SBT are clinically benign, but SBT with invasive implants may behave aggressively. Expression of intermediate filaments, M/Vi, and DNA ploidy evaluation of the primary ovarian tumors seem to be of no value in predicting prognosis. However, four of seven patients with aneuploid DNA implants died of tumor

Molecular basis of tumor heterogeneity in endometrial carcinosarcoma

Endometrial carcinosarcoma (ECS) represents one of the most extreme examples of tumor heterogeneity among human cancers. ECS is a clinically aggressive, high-grade, metaplastic carcinoma. At the morphological level, intratumor heterogeneity in ECS is due to an admixture of epithelial (carcinoma) and mesenchymal (sarcoma) components that can include heterologous tissues, such as skeletal muscle, cartilage, or bone. Most ECSs belong to the copy-number high serous-like molecular subtype of endometrial carcinoma, characterized by the TP53 mutation and the frequently accompanied by a large number of gene copy-number alterations, including the amplification of important oncogenes, such as CCNE1 and c-MYC. However, a proportion of cases (20%) probably represent the progression of tumors initially belonging to the copy-number low endometrioid-like molecular subtype (characterized by mutations in genes such as PTEN, PI3KCA, or ARID1A), after the acquisition of the TP53 mutations. Only a few ECS belong to the microsatellite-unstable hypermutated molecular type and the POLE-mutated, ultramutated molecular type. A common characteristic of all ECSs is the modulation of genes involved in the epithelial to mesenchymal process. Thus, the acquisition of a mesenchymal phenotype is associated with a switch from E- to N-cadherin, the up-regulation of transcriptional repressors of E-cadherin, such as Snail Family Transcriptional Repressor 1 and 2 (SNAI1 and SNAI2), Zinc Finger E-Box Binding Homeobox 1 and 2 (ZEB1 and ZEB2), and the down-regulation, among others, of members of the miR-200 family involved in the maintenance of an epithelial phenotype. Subsequent differentiation to different types of mesenchymal tissues increases tumor heterogeneity and probably modulates clinical behavior and therapy response

NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images

Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tu- mor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and pro- vides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet un- supervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates several novel and state-of- the-art deep learning techniques, such as patch-level contrastive learning, multi-level graph embeddings, a novel max-sum pooling operation, or a metric that quantifies the relevance that each microenvironment element has in the individual predictions. We validate NaroNet using synthetic data simulating multiplex- immunostained images where a patient label is artificially associated to the -adjustable- probabilistic inci- dence of different microenvironment elements. We then apply our model to two sets of images of human cancer tissues: 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancer patients; and 382 35-plex mass cytometry images from 215 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions of relevant clinical information while associating those predictions to the presence of specific microenvironment elements

NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images

Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tu- mor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and pro- vides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet un- supervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates several novel and state-of- the-art deep learning techniques, such as patch-level contrastive learning, multi-level graph embeddings, a novel max-sum pooling operation, or a metric that quantifies the relevance that each microenvironment element has in the individual predictions. We validate NaroNet using synthetic data simulating multiplex- immunostained images where a patient label is artificially associated to the -adjustable- probabilistic inci- dence of different microenvironment elements. We then apply our model to two sets of images of human cancer tissues: 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancer patients; and 382 35-plex mass cytometry images from 215 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions of relevant clinical information while associating those predictions to the presence of specific microenvironment elements

SEOM clinical guidelines for endometrial cancer (2017)

Endometrial cancer (EC) is the most common gynecological cancer in developed countries. Most patients are diagnosed at an early stage with a low risk of relapse. However, there is a group of patients with a high risk of relapse and poor prognosis. Despite the recent publication of randomized trials, the adjuvant treatment of high-risk EC is still to be defned and there are many open questions about the best approach and the right timing. Unfortunately, the survival of metastatic or recurrent EC is short, due to the poor results of chemotherapy and the lack of a second line of treatment. Advances in the knowledge of the molecular abnormalities in EC have permitted the development of promising targeted therapies

Molecular basis of tumor heterogeneity in endometrial carcinosarcoma

Endometrial carcinosarcoma (ECS) represents one of the most extreme examples of tumor heterogeneity among human cancers. ECS is a clinically aggressive, high-grade, metaplastic carcinoma. At the morphological level, intratumor heterogeneity in ECS is due to an admixture of epithelial (carcinoma) and mesenchymal (sarcoma) components that can include heterologous tissues, such as skeletal muscle, cartilage, or bone. Most ECSs belong to the copy-number high serous-like molecular subtype of endometrial carcinoma, characterized by the TP53 mutation and the frequently accompanied by a large number of gene copy-number alterations, including the amplification of important oncogenes, such as CCNE1 and c-MYC. However, a proportion of cases (20%) probably represent the progression of tumors initially belonging to the copy-number low endometrioid-like molecular subtype (characterized by mutations in genes such as PTEN, PI3KCA, or ARID1A), after the acquisition of the TP53 mutations. Only a few ECS belong to the microsatellite-unstable hypermutated molecular type and the POLE-mutated, ultramutated molecular type. A common characteristic of all ECSs is the modulation of genes involved in the epithelial to mesenchymal process. Thus, the acquisition of a mesenchymal phenotype is associated with a switch from E- to N-cadherin, the up-regulation of transcriptional repressors of E-cadherin, such as Snail Family Transcriptional Repressor 1 and 2 (SNAI1 and SNAI2), Zinc Finger E-Box Binding Homeobox 1 and 2 (ZEB1 and ZEB2), and the down-regulation, among others, of members of the miR-200 family involved in the maintenance of an epithelial phenotype. Subsequent differentiation to different types of mesenchymal tissues increases tumor heterogeneity and probably modulates clinical behavior and therapy response

Early evolutionary divergence between papillary and anaplastic thyroid cancers

Background: Papillary thyroid cancer (PTC) is the most common thyroid carcinoma and exhibits an almost uniformly good prognosis, while anaplastic thyroid cancer (ATC) is less frequent and is one of the most aggressive cancers usually resistant to conventional treatment. Current hypothesis posits that ATC derives from PTC through the progressive acquisition of a discrete number of genomic alterations and implies that the mutational landscape of ATC resembles that of PTC. However, the clinical behaviour of ATC and PTC is radically different. We decided to address the disconnection between the clinical behaviour of ATC and PTC and the proposed model of the progressive development of ATC from PTC. Patients and methods: We carried out exome sequencing of DNA from 14 ATC specimens including three cases of concomitant ATC and PTC as well as their corresponding normal DNA from 14 patients. The sequencing results were validated using droplet digital PCR. We carried out immunohistochemistry and immunofluorescence studies of the concomitant ATC and PTC cases. In addition, we integrated our sequencing results with the existing TCGA data. Results: Most of the somatic mutations identified in the ATC component differed from the ones in PTC in the cases of concomitant ATC and PTC. The trunks of the phylogenetic trees representing the somatic mutations were short with long branches. In one case of concomitant PTC and ATC specimens, we observed an infiltration of PTC cells within the ATC component. Moreover, we integrated our results with data obtained from TCGA and observed that the most frequent mutations found in ATC presented high cancer cell fraction values and were significantly different from the PTC ones. Conclusion: ATC diverge from PTC early in tumour development and both tumour types evolve independently. Our work allows the understanding of the relationship between ATC and PTC facilitating the clinical management of these malignancies