A Polymerase-chain-reaction Assay for the Specific Identification of Transcripts Encoded by Individual Carcinoembryonic Antigen (CEA)-gene-family Members

Carcinoembryonic antigen (CEA) is a tumor marker that belongs to a family of closely related molecules with variable expression patterns. We have developed sets of oligonucleotide primers for the specific amplification of transcripts from individual CEA-family members using the reverse transcriptase/ polymerase chain reaction (RT/PCR). Specific primer sets were designed for CEA, non-specific cross-reacting antigen (NCA), biliary glycoprotein (BGP), carcinoembryonic antigen gene-family members 1, 6 and 7 (CGMI, CGM6 and CGM7), and one set for all pregnancy-specific glycoprotein (PSG) transcripts. Primers were first tested for their specificity against individual cDNA clones and product-hybridization with internal, transcript-specific oligonucleotides. Total RNA from 12 brain and 63 gynecological tumors were then tested for expression of CEA-related transcripts. None were found in tumors located in the brain, including various mesenchymal and neuro-epithelial tumors. CEA and NCA transcripts were, however, present in an adenocarcinoma located in the nasal sinuses. In ovarian mucinous adenocarcinomas, we always found co-expression of CEA and NCA transcripts, and occasionally BGP mRNA. CEA-related transcripts were also found in some serous, endometrioid and clear-cell ovarian carcinomas. CEA, NCA and BGP transcripts were present in endometrial carcinomas of the uterus and cervical carcinomas, whereas uterine leiomyomas were completely negative. No transcripts were found from CGM 1, CGM6, CGM7 or from PSG genes in any of the tumors tested. The PCR data were compared with immunohistochemical investigations of ovarian tumors at the protein level using CEA (26/3/13)-, NCA-50/90 (9A6FR) and NCA-95 (80H3)-specific monoclonal antibodies

Adult-type granulosa cell tumor of the ovary : a FOXL2-centric disease

Adult-type granulosa cell tumors (aGCTs) account for 90% of malignant ovarian sex cord-stromal tumors and 2-5% of all ovarian cancers. These tumors are usually diagnosed at an early stage and are treated with surgery. However, one-third of patients relapse between 4 and 8 years after initial diagnosis, and there are currently no effective treatments other than surgery for these relapsed patients. As the majority of aGCTs (>95%) harbor a somatic mutation in FOXL2 (c.C402G; p.C134W), the aim of this study was to identify genetic mutations besides FOXL2 C402G in aGCTs that could explain the clinical diversity of this disease. Whole-genome sequencing of 10 aGCTs and their matched normal blood was performed to identify somatic mutations. From this analysis, a custom amplicon-based panel was designed to sequence 39 genes of interest in a validation cohort of 83 aGCTs collected internationally. KMT2D inactivating mutations were present in 10 of 93 aGCTs (10.8%), and the frequency of these mutations was similar between primary and recurrent aGCTs. Inactivating mutations, including a splice site mutation in candidate tumor suppressor WNK2 and nonsense mutations in PIK3R1 and NLRC5, were identified at a low frequency in our cohort. Missense mutations were identified in cell cycle-related genes TP53, CDKN2D, and CDK1. From these data, we conclude that aGCTs are comparatively a homogeneous group of tumors that arise from a limited set of genetic events and are characterized by the FOXL2 C402G mutation. Secondary mutations occur in a subset of patients but do not explain the diverse clinical behavior of this disease. As the FOXL2 C402G mutation remains the main driver of this disease, progress in the development of therapeutics for aGCT would likely come from understanding the functional consequences of the FOXL2 C402G mutation.Peer reviewe

Reactive oxygen species produced by myeloid cells in psoriasis as a potential biofactor contributing to the development of vascular inflammation.

Psoriasis is an immune-mediated inflammatory skin disease driven by interleukin-17A (IL-17A) and associated with cardiovascular dysfunction. We used a severe psoriasis mouse model of keratinocyte IL-17A overexpression (K14-IL-17Aind/+ , IL-17Aind/+ control mice) to investigate the activity of neutrophils and a potential cellular interconnection between skin and vasculature. Levels of dermal reactive oxygen species (ROS) and their release by neutrophils were measured by lucigenin-/luminol-based assays, respectively. Quantitative RT-PCR determined neutrophilic activity and inflammation-related markers in skin and aorta. To track skin-derived immune cells, we used PhAM-K14-IL-17Aind/+ mice allowing us to mark all cells in the skin by photoconversion of a fluorescent protein to analyze their migration into spleen, aorta, and lymph nodes by flow cytometry. Compared to controls, K14-IL-17Aind/+ mice exhibited elevated ROS levels in the skin and a higher neutrophilic oxidative burst accompanied by the upregulation of several activation markers. In line with these results psoriatic mice displayed elevated expression of genes involved in neutrophil migration (e.g., Cxcl2 and S100a9) in skin and aorta. However, no direct immune cell migration from the psoriatic skin into the aortic vessel wall was observed. Neutrophils of psoriatic mice showed an activated phenotype, but no direct cellular migration from the skin to the vasculature was observed. This suggests that highly active vasculature-invading neutrophils must originate directly from the bone marrow. Hence, the skin-vasculature crosstalk in psoriasis is most likely based on the systemic effects of the autoimmune skin disease, emphasizing the importance of a systemic therapeutic approach for psoriasis patients

Cancer tissue classification using supervised machine learning applied to maldi mass spectrometry imaging

Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) can determine the spatial distribution of analytes such as protein distributions in a tissue section according to their mass-to-charge ratio. Here, we explored the clinical potential of machine learning (ML) applied to MALDI MSI data for cancer diagnostic classification using tissue microarrays (TMAs) on 302 colorectal (CRC) and 257 endometrial cancer (EC)) patients. ML based on deep neural networks discriminated colorectal tumour from normal tissue with an overall accuracy of 98% in balanced cross-validation (98.2% sensitivity and 98.6% specificity). Moreover, our machine learning approach predicted the presence of lymph node metastasis (LNM) for primary tumours of EC with an accuracy of 80% (90% sensitivity and 69% specificity). Our results demonstrate the capability of MALDI MSI for complementing classic histopathological examination for cancer diagnostic applications.Paul Mittal, Mark R. Condina, Manuela Klingler-Hoffmann, Gurjeet Kaur, Martin K. Oehler, Oliver M. Siebe

Targeted deep sequencing of mucinous ovarian tumors reveals multiple overlapping RAS-pathway activating mutations in borderline and cancerous neoplasms

Background: Mucinous ovarian tumors represent a distinct histotype of epithelial ovarian cancer. The rarest (2-4 % of ovarian carcinomas) of the five major histotypes, their genomic landscape remains poorly described. We undertook hotspot sequencing of 50 genes commonly mutated in human cancer across 69 mucinous ovarian tumors. Our goals were to establish the overall frequency of cancer-hotspot mutations across a large cohort, especially those tumors previously thought to be “RAS-pathway alteration negative”, using highly-sensitive next-generation sequencing as well as further explore a small number of cases with apparent heterogeneity in RAS-pathway activating alterations. Methods: Using the Ion Torrent PGM platform, we performed next generation sequencing analysis using the v2 Cancer Hotspot Panel. Regions of disparate ERBB2-amplification status were sequenced independently for two mucinous carcinoma (MC) cases, previously established as showing ERBB2 amplification/overexpression heterogeneity, to assess the hypothesis of subclonal populations containing either KRAS mutation or ERBB2 amplification independently or simultaneously. Results: We detected mutations in KRAS, TP53, CDKN2A, PIK3CA, PTEN, BRAF, FGFR2, STK11, CTNNB1, SRC, SMAD4, GNA11 and ERBB2. KRAS mutations remain the most frequently observed alteration among MC (64.9 %) and mucinous borderline tumors (MBOT) (92.3 %). TP53 mutation occurred more frequently in carcinomas than borderline tumors (56.8 % and 11.5 %, respectively), and combined IHC and mutation data suggest alterations occur in approximately 68 % of MC and as many as 20 % of MBOT. Proven and potential RAS-pathway activating changes were observed in all but one MC. Concurrent ERBB2 amplification and KRAS mutation were observed in a substantial number of cases (7/63 total), as was co-occurrence of KRAS and BRAF mutations (one case). Microdissection of ERBB2-amplified regions of tumors harboring KRAS mutation suggests these alterations are occurring in the same cell populations, while consistency of KRAS allelic frequency in both ERBB2 amplified and non-amplified regions suggests this mutation occurred in advance of the amplification event. Conclusions: Overall, the prevalence of RAS-alteration and striking co-occurrence of pathway “double-hits” supports a critical role for tumor progression in this ovarian malignancy. Given the spectrum of RAS-activating mutations, it is clear that targeting this pathway may be a viable therapeutic option for patients with recurrent or advanced stage mucinous ovarian carcinoma, however caution should be exercised in selecting one or more personalized therapeutics given the frequency of non-redundant RAS-activating alterations

Endometrial Cancer Molecular Risk Stratification is Equally Prognostic for Endometrioid Ovarian Carcinoma.

PURPOSE: Endometrioid ovarian carcinoma (ENOC) is generally associated with a more favorable prognosis compared with other ovarian carcinomas. Nonetheless, current patient treatment continues to follow a "one-size-fits-all" approach. Even though tumor staging offers stratification, personalized treatments remain elusive. As ENOC shares many clinical and molecular features with its endometrial counterpart, we sought to investigate The Cancer Genome Atlas-inspired endometrial carcinoma (EC) molecular subtyping in a cohort of ENOC. EXPERIMENTAL DESIGN: IHC and mutation biomarkers were used to segregate 511 ENOC tumors into four EC-inspired molecular subtypes: low-risk POLE mutant (POLEmut), moderate-risk mismatch repair deficient (MMRd), high-risk p53 abnormal (p53abn), and moderate-risk with no specific molecular profile (NSMP). Survival analysis with established clinicopathologic and subtype-specific features was performed. RESULTS: A total of 3.5% of cases were POLEmut, 13.7% MMRd, 9.6% p53abn, and 73.2% NSMP, each showing distinct outcomes (P < 0.001) and survival similar to observations in EC. Median OS was 18.1 years in NSMP, 12.3 years in MMRd, 4.7 years in p53abn, and not reached for POLEmut cases. Subtypes were independent of stage, grade, and residual disease in multivariate analysis. CONCLUSIONS: EC-inspired molecular classification provides independent prognostic information in ENOC. Our findings support investigating molecular subtype-specific management recommendations for patients with ENOC; for example, subtypes may provide guidance when fertility-sparing treatment is desired. Similarities between ENOC and EC suggest that patients with ENOC may benefit from management strategies applied to EC and the opportunity to study those in umbrella trials

Prognostic gene expression signature for high-grade serous ovarian cancer.

BACKGROUND: Median overall survival (OS) for women with high-grade serous ovarian cancer (HGSOC) is ∼4 years, yet survival varies widely between patients. There are no well-established, gene expression signatures associated with prognosis. The aim of this study was to develop a robust prognostic signature for OS in patients with HGSOC. PATIENTS AND METHODS: Expression of 513 genes, selected from a meta-analysis of 1455 tumours and other candidates, was measured using NanoString technology from formalin-fixed paraffin-embedded tumour tissue collected from 3769 women with HGSOC from multiple studies. Elastic net regularization for survival analysis was applied to develop a prognostic model for 5-year OS, trained on 2702 tumours from 15 studies and evaluated on an independent set of 1067 tumours from six studies. RESULTS: Expression levels of 276 genes were associated with OS (false discovery rate \u3c 0.05) in covariate-adjusted single-gene analyses. The top five genes were TAP1, ZFHX4, CXCL9, FBN1 and PTGER3 (P \u3c 0.001). The best performing prognostic signature included 101 genes enriched in pathways with treatment implications. Each gain of one standard deviation in the gene expression score conferred a greater than twofold increase in risk of death [hazard ratio (HR) 2.35, 95% confidence interval (CI) 2.02-2.71; P \u3c 0.001]. Median survival [HR (95% CI)] by gene expression score quintile was 9.5 (8.3 to -), 5.4 (4.6-7.0), 3.8 (3.3-4.6), 3.2 (2.9-3.7) and 2.3 (2.1-2.6) years. CONCLUSION: The OTTA-SPOT (Ovarian Tumor Tissue Analysis consortium - Stratified Prognosis of Ovarian Tumours) gene expression signature may improve risk stratification in clinical trials by identifying patients who are least likely to achieve 5-year survival. The identified novel genes associated with the outcome may also yield opportunities for the development of targeted therapeutic approaches

Prognostic gene expression signature for high-grade serous ovarian cancer

BACKGROUND:Median overall survival (OS) for women with high-grade serous ovarian cancer (HGSOC) is ∼4 years, yet survival varies widely between patients. There are no well-established, gene expression signatures associated with prognosis. The aim of this study was to develop a robust prognostic signature for OS in patients with HGSOC. PATIENTS AND METHODS:Expression of 513 genes, selected from a meta-analysis of 1455 tumours and other candidates, was measured using NanoString technology from formalin-fixed paraffin-embedded tumour tissue collected from 3769 women with HGSOC from multiple studies. Elastic net regularization for survival analysis was applied to develop a prognostic model for 5-year OS, trained on 2702 tumours from 15 studies and evaluated on an independent set of 1067 tumours from six studies. RESULTS:Expression levels of 276 genes were associated with OS (false discovery rate &lt; 0.05) in covariate-adjusted single-gene analyses. The top five genes were TAP1, ZFHX4, CXCL9, FBN1 and PTGER3 (P &lt; 0.001). The best performing prognostic signature included 101 genes enriched in pathways with treatment implications. Each gain of one standard deviation in the gene expression score conferred a greater than twofold increase in risk of death [hazard ratio (HR) 2.35, 95% confidence interval (CI) 2.02-2.71; P &lt; 0.001]. Median survival [HR (95% CI)] by gene expression score quintile was 9.5 (8.3 to -), 5.4 (4.6-7.0), 3.8 (3.3-4.6), 3.2 (2.9-3.7) and 2.3 (2.1-2.6) years. CONCLUSION:The OTTA-SPOT (Ovarian Tumor Tissue Analysis consortium - Stratified Prognosis of Ovarian Tumours) gene expression signature may improve risk stratification in clinical trials by identifying patients who are least likely to achieve 5-year survival. The identified novel genes associated with the outcome may also yield opportunities for the development of targeted therapeutic approaches

Prognostic gene expression signature for high-grade serous ovarian cancer.

BACKGROUND: Median overall survival (OS) for women with high-grade serous ovarian cancer (HGSOC) is ∼4 years, yet survival varies widely between patients. There are no well-established, gene expression signatures associated with prognosis. The aim of this study was to develop a robust prognostic signature for OS in patients with HGSOC. PATIENTS AND METHODS: Expression of 513 genes, selected from a meta-analysis of 1455 tumours and other candidates, was measured using NanoString technology from formalin-fixed paraffin-embedded tumour tissue collected from 3769 women with HGSOC from multiple studies. Elastic net regularization for survival analysis was applied to develop a prognostic model for 5-year OS, trained on 2702 tumours from 15 studies and evaluated on an independent set of 1067 tumours from six studies. RESULTS: Expression levels of 276 genes were associated with OS (false discovery rate < 0.05) in covariate-adjusted single-gene analyses. The top five genes were TAP1, ZFHX4, CXCL9, FBN1 and PTGER3 (P < 0.001). The best performing prognostic signature included 101 genes enriched in pathways with treatment implications. Each gain of one standard deviation in the gene expression score conferred a greater than twofold increase in risk of death [hazard ratio (HR) 2.35, 95% confidence interval (CI) 2.02-2.71; P < 0.001]. Median survival [HR (95% CI)] by gene expression score quintile was 9.5 (8.3 to -), 5.4 (4.6-7.0), 3.8 (3.3-4.6), 3.2 (2.9-3.7) and 2.3 (2.1-2.6) years. CONCLUSION: The OTTA-SPOT (Ovarian Tumor Tissue Analysis consortium - Stratified Prognosis of Ovarian Tumours) gene expression signature may improve risk stratification in clinical trials by identifying patients who are least likely to achieve 5-year survival. The identified novel genes associated with the outcome may also yield opportunities for the development of targeted therapeutic approaches