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Overlapping histological features between benign and malignant lesions and a lack of firm diagnostic criteria for malignancy result in high rates of inter-observer variation in the diagnosis of melanocytic lesions. We aimed to investigate the differential expression of five miRNAs (21, 200c, 204, 205, and 211) in benign naevi (n = 42), dysplastic naevi (n = 41), melanoma in situ (n = 42), and melanoma (n = 42) and evaluate their potential as diagnostic biomarkers of melanocytic lesions. Real-time PCR showed differential miRNA expression profiles between benign naevi; dysplastic naevi and melanoma in situ; and invasive melanoma. We applied a random forest machine learning algorithm to classify cases based on their miRNA expression profiles, which resulted in a ROC curve analysis of 0.99 for malignant melanoma and greater than 0.9 for all other groups. This indicates an overall very high accuracy of our panel of miRNAs as a diagnostic biomarker of benign, dysplastic, and malignant melanocytic lesions. However, the impact of variable lesion percentage and spatial expression patterns of miRNAs on these real-time PCR results was also considered. In situ hybridisation confirmed the expression of miRNA 21 and 211 in melanocytes, while demonstrating expression of miRNA 205 only in keratinocytes, thus calling into question its value as a biomarker of melanocytic lesions. In conclusion, we have validated some miRNAs, including miRNA 21 and 211, as potential diagnostic biomarkers of benign, dysplastic, and malignant melanocytic lesions. However, we also highlight the crucial importance of considering tissue morphology and spatial expression patterns when using molecular techniques for the discovery and validation of new biomarkers.

Sarcoma is a rare disease affecting both bone and connective tissue and with over 100 pathologic entities, differential diagnosis can be difficult. Complementing immune-histological diagnosis with current ancillary diagnostic techniques, including FISH and RT-PCR, can lead to inconclusive results in a significant number of cases. We describe here the design and validation of a novel sequencing tool to improve sarcoma diagnosis. A NGS DNA capture panel containing probes for 87 fusion genes and 7 genes with frequent copy number changes was designed and optimized. A cohort of 113 DNA samples extracted from soft-tissue and bone sarcoma FFPE material with clinical FISH and/or RT-PCR results positive for either a translocation or gene amplification was used for validation of the NGS method. Sarcoma-specific translocations or gene amplifications were confirmed in 110 out of 113 cases using FISH and/or RT-PCR as gold-standard. MDM2/CDK4 amplification and a total of 25 distinct fusion genes were identified in this cohort of patients using the NGS approach. Overall, the sensitivity of the NGS panel is 97% with a specificity of 100 and 0% failure rate. Targeted NGS appears to be a feasible and cost-effective approach to improve sarcoma subtype diagnosis with the ability to screen for a wide range of genetic aberrations in one test.

Background: Tumors of the pleura, such as metastatic lung cancer and mesothelioma, are amongst the most lethal and therapy-resistant tumors. The first manifestation of the disease is often pleural effusion, the first available material for diagnosis. The five-year survival rate is exceptionally low, around 10–20%, and only a small proportion of patients harbor mutations that allow targeted treatments. Almost all patients develop resistance to treatment, which is often palliative. There is therefore an urgent need to refine the selection of drugs and patients for personalized treatment. Methods: We isolated and cultured cells from pleural effusions in 3D cell aggregates and compared their drug sensitivity ex vivo to the clinical response to the same chemotherapeutic agents, combined with targeted sequencing and network analysis. Results: The ex vivo drug response showed a positive correlation with the treatment response and survival of patients in the clinic, with a stronger link to overall survival than to progression-free survival. Cryopreserved cells showed a similar response to freshly collected cells from the clinic. Conclusions: The findings advance the field of ex vivo screening and present an opportunity to combine strategies for functional precision medicine with comprehensive characterization of disease for improved treatment and future management of lung cancer.

Somatic mutations in mononucleotide repeats are commonly used to assess the mismatch repair status of tumours. Current tests focus on repeats with a length above 15bp, which tend to be somatically more unstable than shorter ones. These longer repeats also have a substantially higher PCR error rate, and tests that use capillary electrophoresis for fragment size analysis often require expert interpretation. In this communication, we present a panel of 17 short repeats (length 7-12bp) for sequence-based microsatellite instability (MSI) testing. Using a simple scoring procedure that incorporates the allelic distribution of the mutant repeats, and analysis of two cohort of tumours totalling 209 samples, we show that this panel is able to discriminate between MMR proficient and deficient tumours, even when constitutional DNA is not available. In the training cohort, the method achieved 100% concordance with fragment analysis, while in the testing cohort, 4 discordant samples were observed (corresponding to 97% concordance). Of these, 2 showed discrepancies between fragment analysis and immunohistochemistry and one was reclassified after re-testing using fragment analysis. These results indicate that our approach offers the option of a reliable, scalable routine test for MSI.

Current methods for analysing immunohistochemistry are labour-intensive and often confounded by inter-observer variability. Analysis is time consuming when identifying small clinically important cohorts within larger samples. This study trained QuPath, an open-source image analysis program, to accurately identify MLH1-deficient inflammatory bowel disease-associated colorectal cancers (IBD-CRC) from a tissue microarray containing normal colon and IBD-CRC. The tissue microarray (n = 162 cores) was immunostained for MLH1, digitalised, and imported into QuPath. A small sample (n = 14) was used to train QuPath to detect positive versus no MLH1 and tissue histology (normal epithelium, tumour, immune infiltrates, stroma). This algorithm was applied to the tissue microarray and correctly identified tissue histology and MLH1 expression in the majority of valid cases (73/99, 73.74%), incorrectly identified MLH1 status in one case (1.01%), and flagged 25/99 (25.25%) cases for manual review. Qualitative review found five reasons for flagged cores: small quantity of tissue, diverse/atypical morphology, excessive inflammatory/immune infiltrations, normal mucosa, or weak/patchy immunostaining. Of classified cores (n = 74), QuPath was 100% (95% CI 80.49, 100) sensitive and 98.25% (95% CI 90.61, 99.96) specific for identifying MLH1-deficient IBD-CRC; κ = 0.963 (95% CI 0.890, 1.036) (p < 0.001). This process could be efficiently automated in diagnostic laboratories to examine all colonic tissue and tumours for MLH1 expression.

Background Gorham-Stout disease is a rare condition characterized by vascular proliferation and the massive destruction of bone tissue. With less than 400 cases in the literature of Gorham-Stout syndrome, we performed a unique study combining whole-genome sequencing and RNA-Seq to probe the genomic features and differentially expressed pathways of a presented case, revealing new possible drivers and biomarkers of the disease. Case presentation We present a case report of a white 45-year-old female patient with marked bone loss of the left humerus associated with vascular proliferation, diagnosed with Gorham-Stout disease. The analysis of whole-genome sequencing showed a dominance of large structural DNA rearrangements. Particularly, rearrangements in chromosomes seven, twelve, and twenty could contribute to the development of the disease, especially a gene fusion involving ATG101 that could affect macroautophagy. The study of RNA-sequencing data from the patient uncovered the PI3K / AKT / mTOR pathway as the most affected signaling cascade in the Gorham-Stout lesional tissue. Furthermore, M2 macrophage infiltration was detected using immunohistochemical staining and confirmed by deconvolution of the RNA-seq expression data. Conclusions The way that DNA and RNA aberrations lead to Gorham-Stout disease is poorly understood due to the limited number of studies focusing on this rare disease. Our study provides the first glimpse into this facet of the disease, exposing new possible therapeutic targets and facilitating the clinicopathological diagnosis of Gorham-Stout disease.

The clinical utility of next-generation sequencing (NGS) for a diverse range of targets is expanding, increasing the need for multiplexed analysis of both DNA and RNA. However, translation into daily use requires a rigorous and comprehensive validation strategy. The aim of this clinical validation was to assess the performance of the Ion Torrent Personal Genome Machine (IonPGM™) and validate the Oncomine™ Focus DNA and RNA Fusion panels for clinical application in solid tumour testing of formalin-fixed, paraffin-embedded (FFPE) tissue. Using a mixture of routine FFPE and reference material across a variety of tissue and specimen types, we sequenced 86 and 31 samples on the Oncomine™ Focus DNA and RNA Fusion assays, respectively. This validation considered a number of parameters including the clinical robustness of the bioinformatics pipeline for variant detection and interpretation. The Oncomine™ Focus DNA assay had a sample and variant-based sensitivity of 99.1 and 97.1%, respectively, and an assay specificity of 100%. The Oncomine™ Focus Fusion panel had a good sensitivity and specificity based upon the samples assessed, however requires further validation to confirm findings due to limited sample numbers. We observed a good sequencing performance based upon amplicon, gene (hotspot variants within gene) and sample specific analysis with 92% of clinical samples obtaining an average amplicon coverage above 500X. Detection of some indels was challenging for the routine IonReporter™ workflow; however, the addition of NextGENe® software improved indel identification demonstrating the importance of both bench and bioinformatic validation. With an increasing number of clinically actionable targets requiring a variety of methodologies, NGS provides a cost-effective and time-saving methodology to assess multiple targets across different modalities. We suggest the use of multiple analysis software to ensure identification of clinically applicable variants.

IntroductionMismatch Repair (MMR) deficient colorectal cancers are an important cohort to study as these patients have a high neo-epitope load, suggesting they may respond well to immunotherapy. We previously reported deficient MMR due to loss of MLH1 expression occurs in >20% of all inflammatory bowel disease-associated colorectal cancers (IBD-CRC). Manually identifying MHL1 deficient tumours from immunostained samples is time-consuming and labour-intensive. Therefore, we aimed to train QuPath, a digital image analysis program, to accurately identify MLH1 deficient IBD-CRCs from a tissue microarray (TMA) containing normal colon and IBD-CRC.MethodsA TMA (n=162 cores) containing normal colon and IBD-CRC was immunostained for MLH1, digitalised and imported into QuPath. A representative sample (14 sections of 500μm x 500μm) was used to train a QuPath algorithm to identify tissue histology (normal epithelium, tumour, immune infiltrate or stroma) and MLH1 expression pattern. The algorithm was applied to the whole TMA. Accuracy level for histology and MLH1 classification was set at >75% of cells per category; cores where neither category met this threshold were flagged for review and these cores were histopathologically reviewed qualitatively. Data were exported to IBM®SPSS® (V25.0) for statistical analysis.ResultsTissue histology and MLH1 expression was correctly identified in 87/113 (77%) cases, misclassified in 1/113 (0.9%) case and flagged for manual review in 25/113 (22.1%) cases. Reasons cores were flagged for review included: small quantity of tissue, atypical morphology, excessive immune infiltrate, normal epithelium and patchy immunostaining. Of cores not flagged for review (n=88), the algorithm was highly sensitive (100%) and specific (98.6%) for identifying MLH1-deficient IBD-CRC, κ=0.967 (95%CI 0.902-1.032) (p<0.001).ConclusionsMLH1 deficient IBD-CRC can be accurately identified using our QuPath algorithm, with an acceptable proportion of equivocal samples highlighted for manual evaluation. This process could be efficiently automated in conventional NHS IBD-CRC surveillance and treatment programmes to examine all colonic tissue and tumour specimens for MLH1 expression, thus identifying patients who may respond well to immunotherapy. This approach now needs to be validated using endoscopic biopsies.

Background Tumour budding (TB), lymphatic vessel density (LVD) and lymphatic vessel invasion (LVI) have shown promise as prognostic factors in colorectal cancer (CRC) but reproducibility using conventional histopathology is challenging. We demonstrate image analysis methodology to quantify the histopathological features which could permit standardisation across institutes and aid risk stratification of Dukes B patients. Methods Multiplexed immunofluorescence of pan-cytokeratin, D2-40 and DAPI identified epithelium, lymphatic vessels and all nuclei respectively in tissue sections from 50 patients diagnosed with Dukes A (n = 13), Dukes B (n = 29) and Dukes C (n = 8) CRC. An image analysis algorithm was developed and performed, on digitised images of the CRC tissue sections, to quantify TB, LVD, and LVI at the invasive front. Results TB (HR =5.7; 95% CI, 2.38-13.8), LVD (HR =5.1; 95% CI, 2.04-12.99) and LVI (HR =9.9; 95% CI, 3.57-27.98) were successfully quantified through image analysis and all were shown to be significantly associated with poor survival, in univariate analyses. LVI (HR =6.08; 95% CI, 1.17-31.41) is an independent prognostic factor within the study and was correlated to both TB (Pearson r =0.71, p <0.0003) and LVD (Pearson r =0.69, p <0.0003). Conclusion We demonstrate methodology through image analysis which can standardise the quantification of TB, LVD and LVI from a single tissue section while decreasing observer variability. We suggest this technology is capable of stratifying a high risk Dukes B CRC subpopulation and we show the three histopathological features to be of prognostic significance.