Background Claudin-18 (CLDN18) is a highly specific tight junction protein of the gastric mucosa. An isoform of CLDN18, the Claudin 18.2, has recently emerged as an innovative drug target for metastatic gastric cancer. Methods We investigated the immunohistochemical profile of CLDN18, p53, p16, E-cadherin, MSH2, MSH6, MLH1, PSM2, HER2, and PDL-1 in a large series of 523 primary gastric carcinomas (GCs; n = 408) and gastro-oesophageal carcinomas (GECs; n = 115) and 135 matched and synchronous nodal metastases. The status of HER2 and EBER by means of chromogenic in situ hybridisation (CISH) was also evaluated. Results High membranous CLDN18 expression was present in 150/510 (29.4%) primary cases and in 45/132 (34.1%) metastases. An abnormal expression (i.e. nuclear and/or cytoplasmic) was observed in 115 (22.5%) primary cases and in 33 (25.0%) metastases. A 38.8% of the cases showed significant CLDN18 intratumoural variability among the different tissue microarray cores obtained from the same tumour. Positive membrane CLDN18 expression was statistically associated with non-antral GCs (p = 0.016), Lauren diffuse type (p = 0.009), and with EBV-associated cancers (p < 0.001). Conclusions CLDN18 is frequently expressed in gastric and gastro-oesophageal cancers; further studies should investigate the prognostic significance of CLDN18 heterogeneity in order to implement its test into clinical practice.

Colorectal cancer (CRC) is a complex and molecularly heterogeneous disease representing one of the most frequent causes of cancer-related death worldwide. About 8–15% of CRCs harbor a mutation inBRAFgene, a proto-oncogene involved in cell proliferation, differentiation and survival through the MAPK signaling cascade. The acquisition ofBRAFmutation is an early event in the “serrated” CRC carcinogenetic pathway and is associated with specific and aggressive clinico-pathological and molecular features. Despite that the presence ofBRAFmutation is a well-recognized negative prognostic biomarker in metastatic CRC (mCRC), a great heterogeneity in survival outcome characterizes these patients, due to the complex, and still not completely fully elucidated, interactions between the clinical, genetic and epigenetic landscape ofBRAFmutations. Because of the great aggressiveness ofBRAF-mutated mCRCs, only 60% of patients can receive a second-line chemotherapy; so intensive combined and tailored first-line approach could be a potentially effective strategy, but to minimize the selective pressure of resistant clones and to reduce side effects, a better stratification of patients bearingBRAFmutations is needed.

Purpose: In-depth characterization of recurrent glioblastoma (rGBM) might contribute to a better understanding of the mechanisms behind tumor progression and enable rGBM treatment with targeted drugs. Experimental Design: In this study, GBM samples were collected at diagnosis and recurrence from adult patients treated with Stupp protocol. Expression of mismatch repair (MMR) proteins was evaluated by IHC, followed by whole exome sequencing (WES) of tumor samples showing loss of MSH6 reactivity. Established genetic, epigenetic, and immunologic markers were assessed by standard methods and correlated with loss of MMR proteins and patient survival. Results: Expression of MMR proteins was partially or completely lost in 25.9% rGBM samples. Specifically, 12 samples showed partial or total MSH6 expression reduction. Conversely, 96.4% of GBM samples at diagnosis expressed MMR markers. WES disclosed lack of variants in MMR genes in primary samples, whereas two MSH6-negative rGBM samples shared a c.3438+1G>A* splicing MSH6 variant with a potential loss of function effect. MSH6-negative rGBM specimens had high tumor mutational burden (TMB), but no microsatellite instability. In contrast, GBM samples with partial loss of MMR proteins disclosed low TMB. MMR-deficient rGBM showed significant telomere shortening and MGMT methylation and are characterized by highly heterogeneous MHC class I expression. Conclusions: Multilevel profiling of MMR-deficient rGBM uncovered hypermutated genotype uncoupled from enriched expression of immune-related markers. Assessment of MHC class I expression and TMB should be included in protocols aiming to identify rGBM patients potentially eligible for treatment with drugs targeting immune-checkpoint inhibitors.

Glioblastoma (GBM) is the most common primary malignant brain tumour in adults. Despite the combination of novel therapeutical approaches, it remains a deadly malignancy with an abysmal prognosis. GBM is a polymorphic tumour from both molecular and histological points of view. It consists of different malignant cells and various stromal cells, contributing to tumour initiation, progression, and treatment response. GBM’s microenvironment is multifaceted and is made up of soluble factors, extracellular matrix components, tissue-resident cell types (e.g., neurons, astrocytes, endothelial cells, pericytes, and fibroblasts) together with resident (e.g., microglia) or recruited (e.g., bone marrow-derived macrophages) immune cells. These latter constitute the so-called immune microenvironment, accounting for a substantial GBM’s tumour volume. Despite the abundance of immune cells, an intense state of tumour immunosuppression is promoted and developed; this represents the significant challenge for cancer cells’ immune-mediated destruction. Though literature data suggest that distinct GBM’s subtypes harbour differences in their microenvironment, its role in treatment response remains obscure. However, an in-depth investigation of GBM’s microenvironment may lead to novel therapeutic opportunities to improve patients’ outcomes. This review will elucidate the GBM’s microenvironment composition, highlighting the current state of the art in immunotherapy approaches. We will focus on novel strategies of active and passive immunotherapies, including vaccination, gene therapy, checkpoint blockade, and adoptive T-cell therapies.

Gleason score, a measure of prostate tumor differentiation, is the strongest predictor of lethal prostate cancer at the time of diagnosis. Metabolomic profiling of tumor and of patient serum could identify biomarkers of aggressive disease and lead to the development of a less-invasive assay to perform active surveillance monitoring. Metabolomic profiling of prostate tissue and serum samples was performed. Metabolite levels and metabolite sets were compared across Gleason scores. Machine learning algorithms were trained and tuned to predict transformation or differentiation status from metabolite data. A total of 135 metabolites were significantly different (Padjusted < 0.05) in tumor versus normal tissue, and pathway analysis identified one sugar metabolism pathway (Padjusted = 0.03). Machine learning identified profiles that predicted tumor versus normal tissue (AUC of 0.82 ± 0.08). In tumor tissue, 25 metabolites were associated with Gleason score (unadjusted P < 0.05), 4 increased in high grade while the remainder were enriched in low grade. While pyroglutamine and 1,5-anhydroglucitol were correlated (0.73 and 0.72, respectively) between tissue and serum from the same patient, no metabolites were consistently associated with Gleason score in serum. Previously reported as well as novel metabolites with differing abundance were identified across tumor tissue. However, a “metabolite signature” for Gleason score was not obtained. This may be due to study design and analytic challenges that future studies should consider.Implications: Metabolic profiling can distinguish benign and neoplastic tissues. A novel unsupervised machine learning method can be utilized to achieve this distinction.

The processes of recurrence and metastasis, through which cancer relapses locally or spreads to distant sites in the body, accounts for more than 90% of cancer-related deaths. At present there are very few treatment options for patients at this stage of their disease. The main obstacle to successfully treat advanced cancer is the cells' ability to change in ways that make them resistant to treatment. Understanding the cellular mechanisms that mediate this cancer cell plasticity may lead to improved patient survival. Epigenetic reprogramming, together with tumor microenvironment, drives such dynamic mechanisms favoring tumor heterogeneity, and cancer cell plasticity. In addition, the development of new approaches that can report on cancer plasticity in their native environment have profound implications for studying cancer biology and monitoring tumor progression. We herein provide an overview of recent advancements in understanding the mechanisms regulating cell plasticity and current strategies for their monitoring and therapy management.