Discrimination of grade 2 and 3 cervical intraepithelial neoplasia by means of analysis of water soluble proteins recovered from cervical biopsies

<p>Abstract</p> <p>Background</p> <p>Cervical intraepithelial neoplasia (CIN) grades 2 and 3 are usually grouped and treated in the same way as "high grade", in spite of their different risk to cancer progression and spontaneous regression rates. CIN2-3 is usually diagnosed in formaldehyde-fixed paraffin embedded (FFPE) punch biopsies. This procedure virtually eliminates the availability of water-soluble proteins which could have diagnostic and prognostic value.</p> <p>Aim</p> <p>To investigate whether a water-soluble protein-saving biopsy processing method followed by a proteomic analysis of supernatant samples using LC-MS/MS (LTQ Orbitrap) can be used to distinguish between CIN2 and CIN3.</p> <p>Methods</p> <p>Fresh cervical punch biopsies from 20 women were incubated in RPMI1640 medium for 24 hours at 4°C for protein extraction and subsequently subjected to standard FFPE processing. P16 and Ki67-supported histologic consensus review CIN grade (CIN2, n = 10, CIN3, n = 10) was assessed by independent gynecological pathologists. The biopsy supernatants were depleted of 7 high abundance proteins prior to uni-dimensional LC-MS/MS analysis for protein identifications.</p> <p>Results</p> <p>The age of the patients ranged from 25-40 years (median 29.7), and mean protein concentration was 0.81 mg/ml (range 0.55 - 1.14). After application of multistep identification criteria, 114 proteins were identified, including proteins like vimentin, actin, transthyretin, apolipoprotein A-1, Heat Shock protein beta 1, vitamin D binding protein and different cytokeratins. The identified proteins are annotated to metabolic processes (36%), signal transduction (27%), cell cycle processes (15%) and trafficking/transport (9%). Using binary logistic regression, Cytokeratin 2 was found to have the strongest independent discriminatory power resulting in 90% overall correct classification.</p> <p>Conclusions</p> <p>114 proteins were identified in supernatants from fresh cervical biopsies and many differed between CIN2 and 3. Cytokeratin 2 is the strongest discriminator with 90% overall correct classifications.</p

High frequency microsatellite instability has a prognostic value in endometrial endometrioid adenocarcinoma, but only in FIGO stage 1 cases.

OBJECTIVES: to analyze the prognostic value of microsatellite instability (MSI) in a population-based study of FIGO stage 1-4 endometrial endometrioid adenocarcinomas. STUDY DESIGN: survival analysis in 273 patients of MSI status and clinico-pathologic features. Using a highly sensitive pentaplex polymerase chain reaction to establish MSI status, cases were divided into microsatellite stable (MSS), MSI-low (MSI-L, 1 marker positive) and MSI-high (MSI-H, 2-5 markers positive). RESULTS: after 61 months median follow-up (1-209), 34 (12.5%) of the patients developed metastases but only 6.4% of the FIGO 1. MSI (especially as MSI-H vs. MSS/MSI-Lcombined) was prognostic in FIGO 1 but not in FIGO 2-4. The 5 and 10 year recurrence-free survival rates were 98% and 95% in the MSS/MSI-L vs. 85% and 73% in the MSI-H patients (p=0.005). CONCLUSIONS: MSI-H status assessed by pentaplex polymerase chain reaction is an indicator of poor prognosis in FIGO 1, but not in FIGO 2-4 endometrial endometrioid adenocarcinomas.Peer Reviewe

Mitochondrial 2,4-dienoyl-CoA Reductase Deficiency in Mice Results in Severe Hypoglycemia with Stress Intolerance and Unimpaired Ketogenesis

The mitochondrial β-oxidation system is one of the central metabolic pathways of energy metabolism in mammals. Enzyme defects in this pathway cause fatty acid oxidation disorders. To elucidate the role of 2,4-dienoyl-CoA reductase (DECR) as an auxiliary enzyme in the mitochondrial β-oxidation of unsaturated fatty acids, we created a DECR–deficient mouse line. In Decr−/− mice, the mitochondrial β-oxidation of unsaturated fatty acids with double bonds is expected to halt at the level of trans-2, cis/trans-4-dienoyl-CoA intermediates. In line with this expectation, fasted Decr−/− mice displayed increased serum acylcarnitines, especially decadienoylcarnitine, a product of the incomplete oxidation of linoleic acid (C18:2), urinary excretion of unsaturated dicarboxylic acids, and hepatic steatosis, wherein unsaturated fatty acids accumulate in liver triacylglycerols. Metabolically challenged Decr−/− mice turned on ketogenesis, but unexpectedly developed hypoglycemia. Induced expression of peroxisomal β-oxidation and microsomal ω-oxidation enzymes reflect the increased lipid load, whereas reduced mRNA levels of PGC-1α and CREB, as well as enzymes in the gluconeogenetic pathway, can contribute to stress-induced hypoglycemia. Furthermore, the thermogenic response was perturbed, as demonstrated by intolerance to acute cold exposure. This study highlights the necessity of DECR and the breakdown of unsaturated fatty acids in the transition of intermediary metabolism from the fed to the fasted state

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

Novel immunohistochemistry-based signatures to predict metastatic site of triple-negative breast cancers

Background: Although distant metastasis (DM) in breast cancer (BC) is the most lethal form of recurrence and the most commonunderlying cause of cancer related deaths, the outcome following the development of DM is related to the site of metastasis.Triple negative BC (TNBC) is an aggressive form of BC characterised by early recurrences and high mortality. Athough multiplevariables can be used to predict the risk of metastasis, few markers can predict the specific site of metastasis. This study aimed atidentifying a biomarker signature to predict particular sites of DM in TNBC.Methods: A clinically annotated series of 322 TNBC were immunohistochemically stained with 133 biomarkers relevant to BC, todevelop multibiomarker models for predicting metastasis to the bone, liver, lung and brain. Patients who experienced metastasisto each site were compared with those who did not, by gradually filtering the biomarker set via a two-tailed t-test and Coxunivariate analyses. Biomarker combinations were finally ranked based on statistical significance, and evaluated in multivariableanalyses.Results: Our final models were able to stratify TNBC patients into high risk groups that showed over 5, 6, 7 and 8 times higher riskof developing metastasis to the bone, liver, lung and brain, respectively, than low-risk subgroups. These models for predictingsite-specific metastasis retained significance following adjustment for tumour size, patient age and chemotherapy status.Conclusions: Our novel IHC-based biomarkers signatures, when assessed in primary TNBC tumours, enable prediction of specificsites of metastasis, and potentially unravel biomarkers previously unknown in site tropism

Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests

The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion which relies on the recognition of patterns and clinical context for the detection of specific diseases. In the study, we aimed to explore the accuracy and inter-rater variability of pulmonologists when interpreting PFTs and compared it against that of artificial intelligence (AI)-based software which was developed and validated in more than 1500 historical patient cases. 120 pulmonologists from 16 European hospitals evaluated 50 cases comprising with PFT and clinical information resulting in 6000 independent interpretations. AI software examined the same data. ATS/ERS guidelines were used as the gold standard for PFT pattern interpretation. The gold standard for diagnosis was derived from clinical history, PFT and all additional tests. The pattern recognition of PFTs by pulmonologists (senior 73%, junior 27%) matched the guidelines in 74.4% (±5.9) of the cases (range: 56-88%). The inter-rater variability of 0.67 (kappa) pointed to a common agreement. Pulmonologists made correct diagnoses in 44.6% (±8.7) of the cases (range: 24-62%) with a large inter-rater variability (kappa= 0.35). The AI-based software perfectly matched the PFT pattern interpretations (100%) and assigned a correct diagnosis in 82% of all cases (p<0.0001 for both measures). The interpretation of PFTs by pulmonologists leads to marked variations and errors. AI-based software provides more accurate interpretations and may serve as a powerful decision support tool to improve clinical practice