Computational pathology: an evolving concept.

The initial enthusiasm about computational pathology (CP) and artificial intelligence (AI) was that they will replace pathologists entirely on the way to fully automated diagnostics. It is becoming clear that currently this is not the immediate model to pursue. On top of the legal and regulatory complexities surrounding its implementation, the majority of tested machine learning (ML)-based predictive algorithms do not display the exquisite performance needed to render them unequivocal, standalone decision makers for matters with direct implications to human health. We are thus moving into a different model of "computer-assisted diagnostics", where AI is there to provide support, rather than replacing, the pathologist. Herein we focus on the practical aspects of CP, from a pathologist perspective. There is a wide range of potential applications where CP can enhance precision of pathology diagnosis, tailor prognostic and predictive information, as well as save time. There are, however, a number of potential limitations for CP that currently hinder their wider adoption in the clinical setting. We address the key necessary steps towards clinical implementation of computational pathology, discuss the significant obstacles that hinders its adoption in the clinical context and summarize some proposed solutions. We conclude that the advancement of CP in the clinic is a promising resource-intensive endeavour that requires broad and inclusive collaborations between academia, industry, and regulatory bodies.

Delta SARS-CoV-2 variant is entirely substituted by the omicron variant during the fifth COVID-19 wave in Attica region.

Wastewater analysis is the most attractive alternative way for the quantification and variant profiling of SARS-CoV-2. Infection dynamics can be monitored by RT-qPCR assays while NGS can provide evidence for the presence of existing or new emerging SARS-CoV-2 variants. Herein, apart from the infection dynamic in Attica since June 1st, 2021, the monitoring of 9 mutations of the omicron and 4 mutations of the delta SARS-CoV-2 variants, utilizing both novel Nested-Seq and RT-PCR, is reported and the substitution of the delta variant (B.1.617.2) by the omicron variant (B.1.1.529) in Attica, Greece within approximately one month is highlighted. The key difference between the two methodologies is discovery power. RT-PCR can only detect known sequences cost-effectively, while NGS is a hypothesis-free approach that does not require prior knowledge to detect novel genes. Overall, the potential of wastewater genomic surveillance for the early discovery and monitoring of variants important for disease management at the community level is underlined. This is the first study, reporting the SARS-CoV-2 infection dynamic for an extended time period and the first attempt to monitor two of the most severe variants with two different methodologies in Greece.

Mucin 13 (MUC13) as a candidate biomarker for ovarian cancer detection: potential to complement CA125 in detecting non-serous subtypes.

OBJECTIVES: Ovarian cancer is the most lethal gynecological malignancy in developed countries. One of the key associations with the high mortality rate is diagnosis at late stages. This clinical limitation is primarily due to a lack of distinct symptoms and detection at the early stages. The ovarian cancer biomarker, CA125, is mainly effective for identifying serous ovarian carcinomas, leaving a gap in non-serous ovarian cancer detection. Mucin 13 (MUC13) is a transmembrane, glycosylated protein with aberrant expression in malignancies, including ovarian cancer. We explored the potential of MUC13 to complement CA125 as an ovarian cancer biomarker, by evaluating its ability to discriminate serous and non-serous subtypes of ovarian cancer at FIGO stages I-IV from benign conditions. METHODS: We used our newly developed, high sensitivity ELISA to measure MUC13 protein in a large, well-defined cohort of 389 serum samples from patients with ovarian cancer and benign conditions. RESULTS: MUC13 and CA125 serum levels were elevated in malignant compared to benign cases (p<0.0001). Receiver-operating characteristic (ROC) curve analysis showed similar area under the curve (AUC) of 0.74 (MUC13) and 0.76 (CA125). MUC13 concentrations were significantly higher in mucinous adenocarcinomas compared to benign controls (p=0.0005), with AUC of 0.80. MUC13 and CA125 showed significant elevation in early-stage cases (stage I-II) in relation to benign controls (p=0.0012 and p=0.014, respectively). CONCLUSIONS: We report the novel role of MUC13 as a serum ovarian cancer biomarker, where it could complement CA125 for detecting some subtypes of non-serous ovarian carcinoma and early-stage disease.

Distinct profiles of LRRK2 activation and Rab GTPase phosphorylation in clinical samples from different PD cohorts.

Despite several advances in the field, pharmacodynamic outcome measures reflective of LRRK2 kinase activity in clinical biofluids remain urgently needed. A variety of targets and approaches have been utilized including assessments of LRRK2 itself (levels, phosphorylation), or its substrates (e.g. Rab10 or other Rab GTPases). We have previously shown that intrinsic kinase activity of LRRK2 isolated from PBMCs of G2019S carriers is elevated, irrespective of disease status. In the present study we find that phosphorylation of Rab10 is also elevated in G2019S carriers, but only those with PD. Additionally, phosphorylation of this substrate is also elevated in two separate idiopathic PD cohorts, but not in carriers of the A53T mutation in α-synuclein. In contrast, Rab29 phosphorylation was specifically reduced in urinary exosomes from A53T and idiopathic PD patients. Taken together, our findings highlight the need for the assessment of multiple complimentary targets for a more comprehensive picture of the disease.

Evaluation of viral concentration and extraction methods for SARS-CoV-2 recovery from wastewater using droplet digital and quantitative RT-PCR.

The ongoing pandemic caused by the emergence of SARS-CoV-2 has resulted in millions of deaths worldwide despite the various measures announced by the authorities. Wastewater-based epidemiology has the ability to provide a day-to-day estimation of the number of infected people in a fast and cost-effective manner. However, owing to the complex nature of wastewater, wastewater monitoring for viral genome copies is affected by the extensive viral fragmentation that takes place all the way to the sewage and the analytical lab. The aim of this study was to evaluate different methodologies for the concentration and extraction of viruses in wastewaters and to select and improve an option that maximizes the recovery of SARS-CoV-2. We compare 5 different concentration methods and 4 commercially available kits for the RNA extraction. To evaluate the performance and the recovery of these, SARS-CoV-2 isolated from patients was used as a spike control. Additionally, the presence of SARS-CoV-2 in all wastewater samples was determined using reverse transcription quantitative PCR (RT-qPCR) and reverse transcription droplet digital PCR (RT-ddPCR), targeting three genetic markers (N1, N2 and N3). Using spiked samples, recoveries were estimated 2.1-37.6% using different extraction kits and 0.1-2.1% using different concentration kits. It was found that a direct capture-based method, evaluated against a variety of concentration methods, is the best in terms of recovery, time and cost. Interestingly, we noticed a good agreement between the results provided by RT-qPCR and RT-ddPCR in terms of recovery. This evaluation can serve as a guide for laboratories establishing a protocol to perform wastewater monitoring of SARS-CoV-2. Overall, data presented here reinforces the validity of WBE for SARS-CoV-2 surveillance, uncovers potential caveats in the selection of concentration and extraction protocols and points towards optimal solutions to maximize its potential.

Defining (and blocking) neuronal death in Parkinson's disease: Does it matter what we call it?

Parkinson's disease (PD) is the second most common neurodegenerative disease, comprised of both familial and idiopathic forms, behind only Alzheimer's disease (AD). The disease is characterized, regardless of the pathogenesis, primarily by a loss of DA neurons in the ventral midbrain as well as noradrenergic neurons of the locus coeruleus; however, by the time symptoms manifest, considerable neuronal loss in both areas has occurred. Neuroprotective strategies thus have to be paired with more sensitive and specific biomarker assays that can identify early at-risk patients in order to initiate disease-modifying therapies at an earlier stage in the disease. Complicating this is the fact that multiple forms of cell death mediate the neuronal loss; however, with a common underlying element that the cell death is considered a "regulated" form of cell death, in contrast to an un-controlled necrotic cell death process. In this review we focus our discussion on several categories of regulated cell death in the context of PD: apoptosis, necroptosis, pyroptosis, and autophagic cell death. In clinical studies as well as experimental in vivo models of PD, there is evidence for a role of each of these forms of cell death in the loss of midbrain DA neurons, and specific therapeutic strategies have been proposed and tested. What remains unclear however is the relative contributions of these distinct forms of cell death to the overall loss of DA neurons, whether they occur at different stages of the disease, or whether specific sub-regions within the midbrain are more susceptible to specific death triggers and pathways.

Proteome-wide onco-proteogenomic somatic variant identification in ER-positive breast cancer.

BACKGROUND: Recent advances in mass spectrometric instrumentation and bioinformatics have critically contributed to the field of proteogenomics. Nonetheless, whether that integrative approach has reached the point of maturity to effectively reveal the flow of genetic variants from DNA to proteins still remains elusive. The objective of this study was to detect somatically acquired protein variants in breast cancer specimens for which full genome and transcriptome data was already available (BASIS cohort). METHODS: LC-MS/MS shotgun proteomic results of 21 breast cancer tissues were coupled to DNA sequencing data to identify variants at the protein level and finally were used to associate protein expression with gene expression levels. RESULTS: Here we report the observation of three sequencing-predicted single amino acid somatic variants. The sensitivity of single amino acid variant (SAAV) detection based on DNA sequencing-predicted single nucleotide variants was 0.4%. This sensitivity was increased to 0.6% when all the predicted variants were filtered for MS "compatibility" and was further increased to 2.9% when only proteins with at least one wild type peptide detected were taken into account. A correlation of mRNA abundance and variant peptide detection revealed that transcripts for which variant proteins were detected ranked among the top 6.3% most abundant transcripts. The variants were detected in highly abundant proteins as well, thus establishing transcript and protein abundance and MS "compatibility" as the main factors affecting variant onco-proteogenomic identification. CONCLUSIONS: While proteomics fails to identify the vast majority of exome DNA variants in the resulting proteome, its ability to detect a small subset of SAAVs could prove valuable for precision medicine applications.

Exploring the potential of mucin 13 (MUC13) as a biomarker for carcinomas and other diseases.

BACKGROUND: Mucin 13 (MUC13) is a cell surface glycoprotein aberrantly expressed in a variety of epithelial carcinomas. Thus far, the role of MUC13 in various diseases remains elusive. To the best of our knowledge, this is the first study to examine the potential of MUC13 as a serum biomarker in a variety of carcinomas and other conditions. METHODS: We developed a recombinant MUC13 protein, mouse monoclonal antibodies and enzyme immunoassay (ELISA) for MUC13. We used this assay to measure MUC13 levels in the supernatants of cancer cell lines and a large cohort of serum samples from healthy and diseased individuals. RESULTS: MUC13 is secreted from cancer cell lines, with highest levels found in ovarian cancer cell lines. MUC13 levels in human sera were significantly increased in patients with renal failure and 20%-30% of patients with ovarian, liver, lung and other cancers. MUC13 was also elevated in 70% of patients with active cutaneous melanoma, but not uveal melanoma. Furthermore, we identified significant MUC13 elevations in the serum of patients with vasculitis (ANCA-positive) autoantibodies, but not in those with inflammatory bowel disease. CONCLUSIONS: Serum MUC13 is frequently elevated not only in a variety of malignant cases but also in some benign pathologies, thus appearing to be a non-specific disease biomarker. Nonetheless, serum MUC13 is clearly highly elevated in some carcinoma patients, and its relationship with tumor progression in this context warrant further research. Future studies that examine the correlation between serum MUC13 levels to stage of cancer could elucidate prognostic potential.

Onco-proteogenomics: Multi-omics level data integration for accurate phenotype prediction.

The overall goal of translational oncology is to identify molecular alterations indicative of cancer or of responsiveness to specific therapeutic regimens. While next-generation sequencing has played a pioneering role in this quest, the latest advances in proteomic technologies promise to provide a holistic approach to the further elucidation of tumor biology. Genetic information may be written in DNA and flow from DNA to RNA to protein, according to the central dogma of molecular biology, but the observed phenotype is dictated predominantly by the DNA protein coding region-derived proteotype. Proteomics holds the potential to bridge the gap between genotype and phenotype, because the powerful analytical tool of mass spectrometry has reached a point of maturity to serve this purpose effectively. This integration of "omics" data has given birth to the novel field of onco-proteogenomics, which has much to offer to precision medicine and personalized patient management. Here, we review briefly how each "omics" technology has individually contributed to cancer research, discuss technological and computational advances that have contributed to the realization of onco-proteogenomics, and summarize current and future translational applications.

Variant peptide detection utilizing mass spectrometry: laying the foundations for proteogenomic identification and validation.

BACKGROUND: Proteogenomics is an emerging field at the intersection of genomics and proteomics. Many variant peptides corresponding to single nucleotide variations (SNVs) are associated with specific diseases. The aim of this study was to demonstrate the feasibility of proteogenomic-based variant peptide detection in disease models and clinical specimens. METHODS: We sought to detect p53 single amino acid variant (SAAV) peptides in breast cancer tumor samples that have been previously subjected to sequencing analysis. Initially, two cancer cell lines having a cellular tumor antigen p53 (TP53) mutation and one wild type for TP53 were analyzed by selected reaction monitoring (SRM) assays as controls. One pool of wild type and one pool of mutated for TP53 cytosolic extracts were assayed with a shotgun proteogenomic workflow. Furthermore, 18 individual samples having a mutation in TP53 were assayed by SRM. RESULTS: Two mutant p53 peptides were successfully detected in two cancer cell lines as expected from their DNA sequence. Wild type p53 peptides were detected in both cytosolic pools, however, none of the mutant p53 peptides were identified. Mutations at the protein level were detected in two cytosolic extracts and whole tumor lysates from the same patients by SRM analysis. Six thousand and six hundred and twenty eight non-redundant proteins were identified in the two cytosolic pools, thus greatly improving a previously reported cytosolic proteome. CONCLUSIONS: In the current study we show the great potential of using proteogenomics for the direct identification of cancer-associated mutations in clinical samples and we discuss current limitations and future perspectives.

An integrated cell line-based discovery strategy identified follistatin and kallikrein 6 as serum biomarker candidates of breast carcinoma.

UNLABELLED: Secreted proteins constitute a relevant source of putative cancer biomarkers. Here, we compared the secretome of a series of four genetically-related breast cancer cell lines as a model of aggressiveness using quantitative mass spectrometry. 537 proteins (59.5% of the total identified proteins) predicted to be released or shed from cells were identified. Using a scoring system based on i) iTRAQ value, ii) breast cancer tissue mRNA expression levels, and iii) immunohistochemical staining (public database), a short list of 10 candidate proteins was selected. Using specific ELISA assays, the expression level of the top five proteins was measured in a verification set of 56 patients. The four significantly differentially expressed proteins were then validated in a second independent set of 353 patients. Finally, follistatin (FST) and kallikrein 6 (KLK6) in serum were significantly higher (p-value < 0.0001) in invasive breast cancer patients compared with non-cancerous controls. Using specific cut-off values, FST distinguished breast cancer samples from healthy controls with a sensitivity of 65% and an accuracy of 68%, whereas KLK6 achieved a sensitivity of 55% and an accuracy of 61%. Therefore, we concluded that FST and KLK6 may have significance in breast cancer detection. BIOLOGICAL SIGNIFICANCE: Discovery of new serum biomarkers that exhibit increased sensitivity and specificity compared to current biomarkers appears to be an essential field of research in cancer. Most biological markers show insufficient diagnostic sensitivity for early breast cancer detection and, for the majority of them, their concentrations are elevated only in metastatic forms of the disease. It is therefore essential to identify clinically reliable biomarkers and develop effective approaches for cancer diagnosis. One promising approach in this field is the study of secreted proteins through proteomic analysis of in vitro progression breast cancer models. Here we have shown that FST and KLK6 are elevated in breast cancer patient serum compared to healthy controls. We expect that our discovery strategy will help to identify cancer-specific and body-fluid-accessible biomarkers.

A rapid and accurate closed-tube Methylation-Sensitive High Resolution Melting Analysis assay for the semi-quantitative determination of SOX17 promoter methylation in clinical samples.

INTRODUCTION: SOX17 promoter methylation can provide important prognostic information in cancer. We developed a novel semi-quantitative MS-HRMA assay for SOX17 promoter methylation. METHODS: The assay was optimized by using synthetic control samples and validated by analyzing 165 clinical samples: a) 107 formalin fixed paraffin embedded (FFPEs) samples of patients with early breast cancer, b) 27 FFPE samples of patients with metastatic breast cancer, c) 15 reduction mammoplasty specimens obtained from healthy women and d) 16 genomic DNA samples isolated from healthy blood donors. Comparison with real time MSP was also performed. RESULTS: The assay is highly specific and sensitive and provides a semi-quantitative estimation of SOX17 promoter methylation. SOX17 promoter was found methylated in 96/134 (71.6%) breast cancer samples, while none of the 31 non-cancerous samples tested was positive (0%). SOX17 promoter methylation levels varied significantly among samples. When 165 clinical samples were analyzed both by MS-HRMA and real time MSP results were significantly comparable (concordance: 146/165, 88.5%). CONCLUSIONS: This novel MS-HRMA assay for SOX17 promoter methylation is closed-tube, highly sensitive, specific, cost-effective, rapid and easy-to-perform. It gives comparable results to Real-Time MSP in less time, while it offers the advantage of additionally providing an estimation of SOX17 promoter methylation levels.

A closed-tube methylation-sensitive high resolution melting assay (MS-HRMA) for the semi-quantitative determination of CST6 promoter methylation in clinical samples.

BACKGROUND: CST6 promoter is highly methylated in cancer, and its detection can provide important prognostic information in breast cancer patients. The aim of our study was to develop a Methylation-Sensitive High Resolution Melting Analysis (MS-HRMA) assay for the investigation of CST6 promoter methylation. METHODS: We designed primers that amplify both methylated and unmethylated CST6 sequences after sodium bisulfate (SB) treatment and used spiked control samples of fully methylated to unmethylated SB converted genomic DNA to optimize the assay. We first evaluated the assay by analyzing 36 samples (pilot training group) and further analyzed 80 FFPES from operable breast cancer patients (independent group). MS-HRMA assay results for all 116 samples were compared with Methylation-Specific PCR (MSP) and the results were comparable. RESULTS: The developed assay is highly specific and sensitive since it can detect the presence of 1% methylated CST6 sequence and provides additionally a semi-quantitative estimation of CST6 promoter methylation. CST6 promoter was methylated in 39/80 (48.75%) of FFPEs with methylation levels being very different among samples. MS-HRMA and MSP gave comparable results when all samples were analyzed by both assays. CONCLUSIONS: The developed MS-HRMA assay for CST6 promoter methylation is closed tube, highly sensitive, cost-effective, rapid and easy-to-perform. It gives comparable results to MSP in less time, while it offers the advantage of additionally providing an estimation of the level of methylation.