Although antibody response to SARS-CoV-2 can be detected early during the infection, several outstanding questions remain to be addressed regarding the magnitude and persistence of antibody titer against different viral proteins and their correlation with the strength of the immune response. An ELISA assay has been developed by expressing and purifying the recombinant SARS-CoV-2 Spike Receptor Binding Domain (RBD), Soluble Ectodomain (Spike), and full length Nucleocapsid protein (N). Sera from healthcare workers affected by non-severe COVID-19 were longitudinally collected over four weeks, and compared to sera from patients hospitalized in Intensive Care Units (ICU) and SARS-CoV-2-negative subjects for the presence of IgM, IgG and IgA antibodies as well as soluble pro-inflammatory mediators in the sera. Non-hospitalized subjects showed lower antibody titers and blood pro-inflammatory cytokine profiles as compared to patients in Intensive Care Units (ICU), irrespective of the antibodies tested. Noteworthy, in non-severe COVID-19 infections, antibody titers against RBD and Spike, but not against the N protein, as well as pro-inflammatory cytokines decreased within a month after viral clearance. Thus, rapid decline in antibody titers and in pro-inflammatory cytokines may be a common feature of non-severe SARS-CoV-2 infection, suggesting that antibody-mediated protection against re-infection with SARS-CoV-2 is of short duration. These results suggest caution in using serological testing to estimate the prevalence of SARS-CoV-2 infection in the general population.

One of the most relevant prognostic factors in cancer staging is the presence of lymph node (LN) metastasis. Evaluating lymph nodes for the presence of metastatic cancerous cells can be a lengthy, monotonous, and error-prone process. Owing to digital pathology, artificial intelligence (AI) applied to whole slide images (WSIs) of lymph nodes can be exploited for the automatic detection of metastatic tissue. The aim of this study was to review the literature regarding the implementation of AI as a tool for the detection of metastases in LNs in WSIs. A systematic literature search was conducted in PubMed and Embase databases. Studies involving the application of AI techniques to automatically analyze LN status were included. Of 4584 retrieved articles, 23 were included. Relevant articles were labeled into three categories based upon the accuracy of AI in evaluating LNs. Published data overall indicate that the application of AI in detecting LN metastases is promising and can be proficiently employed in daily pathology practice.

Background. Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and “omics” data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. Methods. In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting “Big Data” and the analytic approaches used for analysis. Results. These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies. Conclusions. The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies.

Aberrations in histone post-translational modifications (PTMs), as well as in the histone modifying enzymes (HMEs) that catalyze their deposition and removal, have been reported in many tumors and many epigenetic inhibitors are currently under investigation for cancer treatment. Therefore, profiling epigenetic features in cancer could have important implications for the discovery of both biomarkers for patient stratification and novel epigenetic targets. In this study, we employed mass spectrometry-based approaches to comprehensively profile histone H3 PTMs in a panel of normal and tumoral tissues for different cancer types, identifying various changes, some of which appear to be a consequence of the increased proliferation rate of tumors, while others are cell-cycle independent. Histone PTM changes found in tumors partially correlate with alterations of the gene expression profiles of HMEs obtained from publicly available data and are generally lost in culture conditions. Through this analysis, we identified tumor- and subtype-specific histone PTM changes, but also widespread changes in the levels of histone H3 K9me3 and K14ac marks. In particular, H3K14ac showed a cell-cycle independent decrease in all the seven tumor/tumor subtype models tested and could represent a novel epigenetic hallmark of cancer.

Somatic mutations in PIK3CA are present in ~40% breast cancers (BC); their detection in hormone receptor (HR)+/HER2− tumors allows for selecting patients with advanced disease eligible for PIK3CA targeting with alpelisib. The choice of what type of PIK3CA testing approach to adopt and which tissue sample to analyze is a new task in breast pathology. In this methodological study, we sought to assess the performance of next-generation sequencing (NGS) and RT-PCR for PIK3CA testing on archival formalin-fixed paraffin-embedded (FFPE) primary tumors and corresponding metastases. Sixteen HR+/HER2− BC with known PIK3CA-mutated status (ex. 7, 9, and 20) on metastatic samples by means of amplicon-based targeted NGS were selected, and n = 13 of these samples were re-tested with a commercially available CE-IVD RT-PCR assay. All available primary tumors (n = 8) were tested with both methods. NGS detected mutations in all samples, while RT-PCR in n = 2 sample-pairs and overall, in n = 5/8 (62.5%) primary tumors and 7/13 (53.8%) metastases (κ = 0.09; 95% CI, −0.69–0.87). Slight agreement (κ = 0; 95% CI, −0.59–0.59) was observed between NGS and RT-PCR, with the former being generally more sensitive in cases with low DNA quality and quantity. Post hoc visual inspection of the RT-PCR data increased the concordance to 76.9%. Targeted NGS offers reliable and robust PIK3CA testing on both tumor and metastasis FFPE samples; the accuracy of RT-PCR depends on the DNA quantity and quality. In HR+/HER2− BC, both the selection of the PIK3CA testing strategy of FFPE tissues and which sample to analyze should consider several technical parameters and should be tailored for each case.

Breast cancer during pregnancy (PrBC) is a rare tumor with only a little information on its immune landscape. Here, we sought to characterize the cellular composition of the tumor microenvironment (TME) of PrBC and identify its differences from early-onset breast cancer (EOBC) in non-pregnant women. A total of 83 PrBC and 89 EOBC were selected from our Institutional registry and subjected to tumor-infiltrating lymphocytes (TILs) profiling and immunohistochemistry for CD4, CD8, forkhead box P3 (FOXP3), and programmed death-ligand 1 (PD-L1) (clone 22C3). A significantly lower frequency of hormone receptor (HR)-positive tumors was observed in PrBC. The prevalence of low/null PD-L1 and CD8+TILs was higher in PrBC than in the controls, specifically in HR+/HER2– breast cancers. PrBC had a significantly higher risk of relapse and disease-related death, compared to EOBC. The presence of TILs and each TIL subpopulation were significantly associated with disease relapse. Moreover, the death rate was higher in PrBC with CD8+ TILs. The TME of PrBC is characterized by specific patterns of TIL subpopulations with significant biological and prognostic roles. Routine assessment of TILs and TILs subtyping in these patients would be a valid addition to the pathology report that might help identify clinically relevant subsets of women with PrBC.