DOG-1 positive primary acinic cell carcinoma of the lung and investigation of molecular status.

Primary acinic cell carcinoma (ACC) of the lung is an extremely rare neoplasm that more often arises near to a right bronchus. It is characterized by two populations of clear and dark eosinophilic cells, arranged in a glandular acinar pattern. Mitosis are rare and tumor cells show small and eccentric nuclei. Positive stain for PAS, PAS-D, cytokeratin, A1AT and A1ACT is reported, while TTF1, p40, synaptophysin, SMA, and S100 are substantially negative. DOG-1 positive stain was observed in ACC of the salivary glands and its negativity was proposed to distinguish between primary and metastatic ACC of the lung. Here, we report the 30th case of primary ACC of the lung, describing the immunohistochemical positivity for DOG-1 and the molecular status of the neoplasm for the first time.

Exploring the Role of the MUTYH Gene in Breast, Ovarian and Endometrial Cancer.

BACKGROUND: MUTYH germline monoallelic variants have been detected in a number of patients affected by breast/ovarian cancer or endometrial cancer, suggesting a potential susceptibility role, though their significance remains elusive since the disease mechanism is normally recessive. Hence, the aim of this research was to explore the hypothesis that a second hit could have arisen in the other allele in the tumor tissue. METHODS: we used Sanger sequencing and immunohistochemistry to search for a second MUTYH variant in the tumoral DNA and to assess protein expression, respectively. RESULTS: we detected one variant of unknown significance, one variant with conflicting interpretation of pathogenicity and three benign/likely benign variants; the MUTYH protein was not detected in the tumor tissue of half of the patients, and in others, its expression was reduced. CONCLUSIONS: our results fail to demonstrate that germinal monoallelic MUTYH variants increase cancer risk through a LOH (loss of heterozygosity) mechanism in the somatic tissue; however, the absence or partial loss of the MUTYH protein in many tumors suggests its dysregulation regardless of MUTYH genetic status.

Non-Small Cell Lung Cancer Testing on Reference Specimens: An Italian Multicenter Experience.

INTRODUCTION: Biomarker testing is mandatory for the clinical management of patients with advanced non-small cell lung cancer (NSCLC). Myriads of technical platforms are now available for biomarker analysis with differences in terms of multiplexing capability, analytical sensitivity, and turnaround time (TAT). We evaluated the technical performance of the diagnostic workflows of 24 representative Italian institutions performing molecular tests on a series of artificial reference specimens built to mimic routine diagnostic samples. METHODS: Sample sets of eight slides from cell blocks of artificial reference specimens harboring exon 19 EGFR (epidermal growth factor receptor) p.E746_AT50del, exon 2 KRAS (Kirsten rat sarcoma viral oncogene homologue) p.G12C, ROS1 (c-ros oncogene 1)-unknown gene fusion, and MET (MET proto-oncogene, receptor tyrosine kinase) Δ exon 14 skipping were distributed to each participating institution. Two independent cell block specimens were validated by the University of Naples Federico II before shipment. Methodological and molecular data from reference specimens were annotated. RESULTS: Overall, a median DNA concentration of 3.3 ng/µL (range 0.1-10.0 ng/µL) and 13.4 ng/µL (range 2.0-45.8 ng/µL) were obtained with automated and manual technical procedures, respectively. RNA concentrations of 5.7 ng/µL (range 0.2-11.9 ng/µL) and 9.3 ng/µL (range 0.5-18.0 ng/µL) were also detected. KRAS exon 2 p.G12C, EGFR exon 19 p.E736_A750del hotspot mutations, and ROS1 aberrant transcripts were identified in all tested cases, whereas 15 out of 16 (93.7%) centers detected MET exon 14 skipping mutation. CONCLUSIONS: Optimized technical workflows are crucial in the decision-making strategy of patients with NSCLC. Artificial reference specimens enable optimization of diagnostic workflows for predictive molecular analysis in routine clinical practice.

Feasibility of Comprehensive Genomic Profiling (CGP) in Real-Life Clinical Practice.

In advanced or metastatic settings, Comprehensive Genomic Profiling (CGP) allows the evaluation of thousands of gene alterations with the goal of offering new opportunities for personalized treatment in solid tumors. This study evaluated the CGP Success Rate in a real-life cohort of 184 patients enrolled in a prospective clinical trial. CGP data were compared with the routine molecular testing strategy adopted in-house. Sample age, tumor area, and the percentage of tumor nuclei were recorded for CGP analysis. We found that 150/184 (81.5%) samples resulted in satisfying CGP reports. The CGP Success Rate was higher in samples from surgical specimens (96.7%) and in specimens that had been stored (sample age) for less than six months (89.4%). Among the inconclusive CGP reports, 7/34 (20.6%) were optimal samples, according to CGP sample requirements. Moreover, with the in-house molecular testing approach, we could obtain clinically relevant molecular data in 25/34 (73.5%) samples that had inconclusive CGP reports. In conclusion, despite the fact that CGP offers specific therapeutical options in selected patients, our data suggest that the standard molecular testing strategy should not be replaced in routine molecular profiling.

Deep pathomics: A new image-based tool for predicting response to treatment in stage III non-small cell lung cancer.

Despite the advantages offered by personalized treatments, there is presently no way to predict response to chemoradiotherapy in patients with non-small cell lung cancer (NSCLC). In this exploratory study, we investigated the application of deep learning techniques to histological tissue slides (deep pathomics), with the aim of predicting the response to therapy in stage III NSCLC. We evaluated 35 digitalized tissue slides (biopsies or surgical specimens) obtained from patients with stage IIIA or IIIB NSCLC. Patients were classified as responders (12/35, 34.7%) or non-responders (23/35, 65.7%) based on the target volume reduction shown on weekly CT scans performed during chemoradiation treatment. Digital tissue slides were tested by five pre-trained convolutional neural networks (CNNs)-AlexNet, VGG, MobileNet, GoogLeNet, and ResNet-using a leave-two patient-out cross validation approach, and we evaluated the networks' performances. GoogLeNet was globally found to be the best CNN, correctly classifying 8/12 responders and 10/11 non-responders. Moreover, Deep-Pathomics was found to be highly specific (TNr: 90.1) and quite sensitive (TPr: 0.75). Our data showed that AI could surpass the capabilities of all presently available diagnostic systems, supplying additional information beyond that currently obtainable in clinical practice. The ability to predict a patient's response to treatment could guide the development of new and more effective therapeutic AI-based approaches and could therefore be considered an effective and innovative step forward in personalised medicine.

Clinical, pathological, and PAM50 gene expression features of HER2-low breast cancer.

Novel antibody-drug conjugates against HER2 are showing high activity in HER2-negative breast cancer (BC) with low HER2 expression (i.e., 1+ or 2+ and lack of ERBB2 amplification). However, the clinical and molecular features of HER2-low BC are yet to be elucidated. Here, we collected retrospective clinicopathological and PAM50 data from 3,689 patients with HER2-negative disease and made the following observations. First, the proportion of HER2-low was higher in HR-positive disease (65.4%) than triple-negative BC (TNBC, 36.6%). Second, within HR-positive disease, ERBB2 and luminal-related genes were more expressed in HER2-low than HER2 0. In contrast, no gene was found differentially expressed in TNBC according to HER2 expression. Third, within HER2-low, ERBB2 levels were higher in HR-positive disease than TNBC. Fourth, HER2-low was not associated with overall survival in HR-positive disease and TNBC. Finally, the reproducibility of HER2-low among pathologists was suboptimal. This study emphasizes the large biological heterogeneity of HER2-low BC, and the need to implement reproducible and sensitive assays to measure low HER2 expression.

The lncRNA HOTAIR transcription is controlled by HNF4α-induced chromatin topology modulation.

The expression of the long noncoding RNA HOTAIR (HOX Transcript Antisense Intergenic RNA) is largely deregulated in epithelial cancers and positively correlates with poor prognosis and progression of hepatocellular carcinoma and gastrointestinal cancers. Furthermore, functional studies revealed a pivotal role for HOTAIR in the epithelial-to-mesenchymal transition, as this RNA is causal for the repressive activity of the master factor SNAIL on epithelial genes. Despite the proven oncogenic role of HOTAIR, its transcriptional regulation is still poorly understood. Here hepatocyte nuclear factor 4-α (HNF4α), as inducer of epithelial differentiation, was demonstrated to directly repress HOTAIR transcription in the mesenchymal-to epithelial transition. Mechanistically, HNF4α was found to cause the release of a chromatin loop on HOTAIR regulatory elements thus exerting an enhancer-blocking activity.