Fully automated real-time PCR for EGFR testing in non-small cell lung carcinoma.

Molecular testing for mutations in the EGFR gene is commonplace for patients with non-small cell lung cancer (NSCLC). These patients are often very sick and management decisions need to be made urgently. In many cases, the results of molecular testing are needed the same day, in order to start targeted therapy and allow maximum benefit for patients. The Idylla™ EGFR Mutation Test offers rapid results within three hours of requesting. This study aimed to assess the concordance of Idylla™ EGFR Mutation Test results with current standard tests. Forty formalin-fixed, paraffin-embedded NSCLC tumour cases (20 EGFR mutant and EGFR 20 wild type) were analysed by the Idylla™ EGFR Mutation Test (CE-IVD) and compared with PCR and NGS methodologies. The overall concordance between Idylla™ and standard testing was 92.5% (95% CI 80.14% to 97.42%) and the specificity of Idylla™ was 100% (95% CI 83.89% to 100%). The sensitivity was affected by loss of tumour content in tissue blocks in a small number of NGS cases; however, comparing Idylla™ with PCR alone, there was 100% concordance (95% CI 89.85% to 100%). The Idylla™ EGFR Mutation Test shows comparative accuracy to routine PCR testing for the most common EGFR mutations in NSCLC. The Idylla™ also offers significantly reduced turn-around times compared with existing modalities and therefore the platform would be a useful addition to many molecular diagnostics units

Homogeneous nucleation in associated vapors. I. Acetic acid

Homogeneous nucleation measurements on acetic acid vapor are reported. The presence of the relatively stable association clusters tends to stabilize the vapor with regard to homogeneous nucleation. The variation of the critical supersaturation with temperature for acetic acid vapor was found to agree well with the predictions of the Katz–Saltsburg–Reiss theory for nucleation in associated vapors

Evaluation of relative yeast cell surface hydrophobicity measured by flow cytometry.

OBJECTIVE: To develop an efficient method for evaluating cell surface hydrophobicity and to apply the method to demonstrate the effects of fungal growth conditions on cell surface properties. METHODS: Yeast isolates were suspended in phosphate-buffered saline and mixed with deep blue-dyed polystyrene microspheres. Flow cytometry was used to detect the degree of microsphere binding to yeast cells. Different strains of yeast were compared for intrinsic microsphere binding activity and changes in growth conditions were invoked to modify the relative surface hydrophobicity. RESULTS: Commercially available blue-dyed polystyrene microspheres showed strong fluorescence in the FL3 channel, whereas yeast cells did not show appreciable FL3 fluorescence. Microspheres and yeast were generally distinguishable on the basis of size revealed by forward light scatter. This method showed a wide variation in intrinsic cell surface hydrophobicity among Candida albicans strains. Likewise, variation in hydrophobicity of non-albicans yeast species was observed. Growth on solid media, incubation at 25 degrees C, or 250 mg/dl glucose concentration increased hydrophobicity compared with growth in liquid media, incubation at 37 degrees C, or 50 mg/dl glucose, respectively. Growth in 1 x 10(-9) M estradiol had no appreciable effect on hydrophobicity. CONCLUSIONS: Stained latex microspheres fluoresced in the FL3 channel of the flow cytometer and bound to yeast cells to an extent related to the surface hydrophobicity of the yeast. Binding detected by flow cytometry showed that clinical yeast isolates varied in intrinsic binding capacity and this binding ability was altered by different growth conditions. The implications for virulence regulation among yeast isolates are discussed

The diagnostic molecular pathology of colorectal carcinoma using automated PCR

BACKGROUND: Diagnostic molecular testing in colorectal cancer (CRC) offers a number of benefits including predicting prognosis, directing targeted therapies and screening for hereditary cancer syndromes. Molecular testing however is expensive, requires specialist facilities and staff and is time consuming, limiting its widespread availability. The Idylla System is an automated testing platform that could overcome these issues. AIMS: To appraise the suitability of the Idylla System for use in clinical practice by evaluating the system’s accuracy and financial impact. HYPOTHESIS: The Idylla System has high accuracy for detecting mutations in BRAF, KRAS and NRAS genes in CRC resection tissue and is a cost-effective alternative to current testing platforms. METHODS: Ethical approval was granted by Oxfordshire Research and Ethics Committee A (reference: 04/Q1604/21). Diagnostic accuracy was determined for the Idylla System in detecting BRAF and KRAS mutations with a comparison against conventional polymerase chain reaction (PCR). Further validations were also performed for BRAF, KRAS and NRAS mutation testing against NGS and IHC methods. An audit of the molecular diagnostics workload was carried out and a cost-analysis performed. RESULTS: The Idylla system had a sensitivity of 100.0% (95% CI: 88.3% to 100.0%) and a specificity of up to 100.0% (95% CI: 94.7% to 100.0%) for detecting BRAF mutations and a sensitivity of 100.0% (95% CI: 79.6% to 100.0%) and a specificity of up to 92.9% (95% CI: 68.5% to 98.7%) for detecting KRAS Mutations. There was 100% concordance for NRAS testing. A cost-analysis estimated that the Idylla System could save from around £12,000 to anywhere up to £40,000 per year in some centres. CONCLUSIONS: The results support the hypothesis that the Idylla System is an accurate system for detecting relevant mutations in CRC and demonstrate the system to be cost-effective. The Idylla system is therefore suitable for use in routine clinical practice

Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice

The use of artificial intelligence will likely transform clinical practice over the next decade and the early impact of this will likely be the integration of image analysis and machine learning into routine histopathology. In the UK and around the world, a digital revolution is transforming the reporting practice of diagnostic histopathology and this has sparked a proliferation of image analysis software tools. While this is an exciting development that could discover novel predictive clinical information and potentially address international pathology work-force shortages, there is a clear need for a robust and evidence-based framework in which to develop these new tools in a collaborative manner that meets regulatory approval. With these issues in mind, the NCRI Cellular Molecular Pathology (CM-Path) initiative and the British In Vitro Diagnostics Association (BIVDA) has set out a roadmap to help academia, industry and clinicians develop new software tools to the point of approved clinical use. This article is protected by copyright. All rights reserved. [Abstract copyright: This article is protected by copyright. All rights reserved.

Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer

Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H&E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H&E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out

Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer

Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H&E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H&E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out

Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer

Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H&E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H&E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out