Subtyping Tool Research Articles

Cytomorphological Features as a Subtyping Tool of Non-Small-Cell Lung Cancer in Brushing Bronchoscopic Samples.

Nowadays, the separation of adenocarcinomas (ADCs) and squamous cell carcinomas (SCCs) is crucial given that there are new specific targeted therapies. So, the aim of this study was to examine the differences in cytomorphological features between ADC and SCC in bronchoscopic brush samples. The retrospective study was conducted over a 3-year period at Western Balkan University Hospital. All brushing samples were analysed. According to the histopathological report, patients were classified into ADC and SCC groups. The cytomorphological features analysed in 95 samples were presence of necrosis, cell distribution, nuclear atypia, size of nuclei, and visibility of nucleoli. Statistical analysis was performed in JASP, and P values <0.05 were considered significant. The necrotic background was more frequent in SCC samples. Small clusters sized ≤200 µm were found in 17.95% of samples from the SCC group and 53.57% in the ADC group. Large clusters sized >400 µm were found in 43.59% in the SCC group, while in the ADC group, it was found in 5.36%. There were no differences in nuclear atypia between groups. Nuclei that were >5x lymphocyte size were found more often in samples from ADC than in the SCC group (37.50 vs 10.25%). In 89.75% of samples from the SCC group, nuclei were ≤5x lymphocyte sizes, while in the ADC group, the percentage was 63.5%. Nucleoli were more often visible in samples from the ADC group compared to the SCC group (92.86% vs 64.10%, P < 0,05). Small clusters, large nuclei, and visible nucleoli were more frequent in the ADC group (P < 0.05), while large clusters, small nuclei, and invisible nucleoli were more frequent in the SCC group (P < 0.05).

Development and validation of a 23-gene expression signature for molecular subtyping of medulloblastoma in a long-term Chinese cohort.

Medulloblastoma is the most common childhood malignant brain tumor andis a leading cause of cancer-related death in children. Recent transcriptional studies have shown that medulloblastomas comprise at least four molecular subgroups, each with distinct demographics, genetics, and clinical outcomes. Medulloblastoma subtyping has become critical for subgroup-specific therapies. The use of gene expression assays to determine the molecular subgroup of clinical specimens is a long-awaited application of molecular biology for this pediatric cancer. In the current study, we established a medulloblastoma transcriptome database of 460 samples retrieved from three published datasets (GSE21140, GSE37382, and GSE37418). With this database, we identified a 23-gene signature that is significantly associated with the medulloblastoma subgroups and achieved a classification accuracy of 95.2%. The 23-gene signature was further validated in a long-term cohort of 142 Chinese medulloblastoma patients. The 23-gene signature classified 21 patients as WNT (15%), 41 as SHH (29%), 16 as Group 3 (11%), and 64 as Group 4 (45%). For patients of WNT, SHH, Group 3, and Group 4, 5-year overall-survival rate reached 80%, 62%, 27%, and 47%, respectively (p < 0.0001), meanwhile 5-year progression-free survival reached 80%, 52%, 27%, and 45%, respectively (p < 0.0001). Besides, SHH/TP53-mutant tumors were associated with worse prognosis compared with SHH/TP53 wild-type tumors and other subgroups. We demonstrated that subgroup assignments by the 23-gene signature and Northcott's NanoString assay were highly comparable with a concordance rate of 96.4%. In conclusion, we present a novel gene signature that is capable of accurately and reliably assigning FFPE medulloblastoma samples to their molecular subgroup, which may serve as an auxiliary tool for medulloblastoma subtyping in the clinic. Future incorporation of this gene signature into prospective clinical trials is warranted to further evaluate its clinical.

Computer-Aided Multiphoton Microscopy Diagnosis of 5 Different Primary Architecture Subtypes of Meningiomas

Meningiomas rank among the most common intracranial tumors, and surgery stands as the primary treatment modality for meningiomas. The precise subtyping and diagnosis of meningiomas, both before and during surgery, play a pivotal role in enabling neurosurgeons choose the optimal surgical program. In this study, we utilized multiphoton microscopy (MPM) based on 2-photon excited fluorescence and second-harmonic generation to identify 5 common meningioma subtypes. The morphological features of these subtypes were depicted using the MPM multichannel mode. Additionally, we developed 2 distinct programs to quantify collagen content and blood vessel density. Furthermore, the lambda mode of the MPM characterized architectural and spectral features, from which 3 quantitative indicators were extracted. Moreover, we employed machine learning to differentiate meningioma subtypes automatically, achieving high classification accuracy. These findings demonstrate the potential of MPM as a noninvasive diagnostic tool for meningioma subtyping and diagnosis, offering improved accuracy and resolution compared with traditional methods.

HIV-1 Subtype Diversity and Factors Affecting Drug Resistance among Patients with Virologic Failure in Antiretroviral Therapy in Hainan Province, China, 2014–2020

ObjectiveThis study aimed to determine the HIV-1 subtype distribution and HIV drug resistance (HIVDR) in patients with ART failure from 2014 to 2020 in Hainan, China. MethodsA 7-year cross-sectional study was conducted among HIV/AIDS patients with ART failure in Hainan. We used online subtyping tools and the maximum likelihood phylogenetic tree to confirm the HIV subtypes with pol sequences. Drug resistance mutations (DRMs) were analyzed using the Stanford University HIV Drug Resistance Database. ResultsA total of 307 HIV-infected patients with ART failure were included, and 241 available pol sequences were obtained. Among 241 patients, CRF01_AE accounted for 68.88%, followed by CRF07_BC (17.00%) and eight other subtypes (14.12%). The overall prevalence of HIVDR was 61.41%, and the HIVDR against non-nucleoside reverse transcriptase inhibitors (NNRTIs), nucleotide reverse transcriptase inhibitors (NRTIs), and protease inhibitors (PIs) were 59.75%, 45.64%, and 2.49%, respectively. Unemployed patients, hypoimmunity or opportunistic infections in individuals, and samples from 2017 to 2020 increased the odd ratios of HIVDR. Also, HIVDR was less likely to affect female patients. The common DRMs to NNRTIs were K103N (21.99%) and Y181C (20.33%), and M184V (28.21%) and K65R (19.09%) were the main DRMs against NRTIs. ConclusionThe present study highlights the HIV-1 subtype diversity in Hainan and the importance of HIVDR surveillance over a long period.

Composite Biomarker Image for Advanced Visualization in Histopathology.

Immunohistochemistry (IHC) biomarkers are essential tools for reliable cancer diagnosis and subtyping. It requires cross-staining comparison among Whole Slide Images (WSIs) of IHCs and hematoxylin and eosin (H&E) slides. Currently, pathologists examine the visually co-localized areas across IHC and H&E glass slides for a final diagnosis, which is a tedious and challenging task. Moreover, visually inspecting different IHC slides back and forth to analyze local co-expressions is inherently subjective and prone to error, even when carried out by experienced pathologists. Relying on digital pathology, we propose "Composite Biomarker Image" (CBI) in this work. CBI is a single image that can be composed using different filtered IHC biomarker images for better visualization. We present a CBI image produced in two steps by the proposed solution for better visualization and hence more efficient clinical workflow. In the first step, IHC biomarker images are aligned with the H&E images using one coordinate system and orientation. In the second step, the positive or negative IHC regions from each biomarker image (based on the pathologists' recommendation) are filtered and combined into one image using a fuzzy inference system. For evaluation, the resulting CBI images, from the proposed system, were evaluated qualitatively by the expert pathologists. The CBI concept helps the pathologists to identify the suspected target tissues more easily, which could be further assessed by examining the actual WSIs at the same suspected regions.

Molecular characteristics and zoonotic potential of enteric protists in domestic dogs and cats in Egypt.

Domestic dogs and cats can be a source of human infection by a wide diversity of zoonotic pathogens including parasites. Genotyping and subtyping tools are useful in assessing the true public health relevance of canine and feline infections by these pathogens. This study investigated the occurrence, genetic diversity, and zoonotic potential of common diarrhea-causing enteric protist parasites in household dogs and cats in Egypt, a country where this information is particularly scarce. In this prospective, cross-sectional study a total of 352 individual fecal samples were collected from dogs (n = 218) and cats (n = 134) in three Egyptian governorates (Dakahlia, Gharbeya, and Giza) during July-December 2021. Detection and identification of Cryptosporidium spp., Giardia duodenalis, Enterocytozoon bieneusi, and Blastocystis sp. were carried out by PCR and Sanger sequencing. Basic epidemiological variables (geographical origin, sex, age, and breed) were examined for association with occurrence of infection by enteric protists. The overall prevalence rates of Cryptosporidium spp. and G. duodenalis were 1.8% (95% CI: 0.5-4.6) and 38.5% (95% CI: 32.0-45.3), respectively, in dogs, and 6.0% (95% CI: 2.6-11.4) and 32.1% (95% CI: 24.3-40.7), respectively, in cats. All canine and feline fecal samples analyzed tested negative for E. bieneusi and Blastocystis sp. Dogs from Giza governorate and cats from Dakahlia governorate were at higher risk of infection by Cryptosporidium spp. (p = 0.0006) and G. duodenalis (p = 0.00001), respectively. Sequence analyses identified host-adapted Cryptosporidium canis (n = 4, one of them belonging to novel subtype XXe2) and G. duodenalis assemblages C (n = 1) and D (n = 3) in dogs. In cats the zoonotic C. parvum (n = 5) was more prevalent than host-adapted C. felis (n = 1). Household dogs had a limited (but not negligible) role as source of human giardiasis and cryptosporidiosis, but the unexpected high frequency of zoonotic C. parvum in domestic cats might be a public health concern. This is the first molecular-based description of Cryptosporidium spp. infections in cats in the African continent to date. Molecular epidemiological data provided here can assist health authorities and policy makers in designing and implementing effective campaigns to minimize the transmission of enteric protists in Egypt.

Genetic characterization of HIV-1 viruses among cases with antiretroviral therapy failure in Suzhou City, China

BackgroundThis retrospective study aimed to characterize the distribution of HIV-1 genotypes and the prevalence of drug resistance mutations in people with antiretroviral treatment (ART) failure in Suzhou City, China.MethodsPol gene of HIV-1 viruses in blood samples of EDTA anticoagulants from 398 patients with failed antiviral treatment was successfully amplified by using an in-house assay. Drug resistance mutations were analyzed by using the Stanford HIV Drug Resistance Database system (https://hivdb.stanford.edu/hivdb/by-mutations/). HIV-1 genotypes were determined by the REGA HIV subtyping tool (version 3.46, https://www.genomedetective.com/app/typingtool/hiv). Near full-length genomes (NFLG) of HIV-1 viruses were obtained by next generation sequencing method.ResultsSequences analysis of the pol gene revealed that CRF 01_AE (57.29%, 228/398) was the dominant subtype circulating in Suzhou City, followed by CRF 07_BC (17.34%, 69/398), subtype B (7.54%, 30/398), CRF 08_BC (6.53%, 26/398), CRF 67_01B (3.02%, 12/398) and CRF55_01B (2.51%, 10/398). The overall prevalence of drug-resistant mutations in cases with ART failure was 64.57% (257/398), including 45.48% (181/398) for nucleotide reverse transcriptase inhibitors (NRTIs) mutations, 63.32% (252/398) for non-nucleoside reverse transcriptase inhibitors (NNRTIs) mutations, and 3.02% (12/398) for protease inhibitors (PIs) mutations. Ten near full-length genomes (NFLG) of HIV-1 viruses were identified, including six recombinants of CRF 01_AE and subtype B, two recombinants of CRF 01_AE, subtype B and subtype C sequences, one recombinant of CRF 01_AE and subtype C and one recombinant of CRF 01_AE, subtype A1 and subtype C.ConclusionsThe high prevalence of drug-resistant HIV-1 viruses was a serious challenge for HIV prevention and treatment of people with HIV infection. Treatment regimens for ART failure patients should be adjusted over time based on the outcome of drug resistance tests. NFLG sequencing facilitates the identification of new recombinants of HIV-1.

EpiMix is an integrative tool for epigenomic subtyping using DNA methylation

DNA methylation (DNAme) is a major epigenetic factor influencing gene expression with alterations leading to cancer and immunological and cardiovascular diseases. Recent technological advances have enabled genome-wide profiling of DNAme in large human cohorts. There is a need for analytical methods that can more sensitively detect differential methylation profiles present in subsets of individuals from these heterogeneous, population-level datasets. We developed an end-to-end analytical framework named "EpiMix" for population-level analysis of DNAme and gene expression. Compared with existing methods, EpiMix showed higher sensitivity in detecting abnormal DNAme that was present in only small patient subsets. We extended the model-based analyses of EpiMix to cis-regulatory elements within protein-coding genes, distal enhancers, and genes encoding microRNAs and long non-coding RNAs (lncRNAs). Using cell-type-specific data from two separate studies, we discover epigenetic mechanisms underlying childhood food allergy and survival-associated, methylation-driven ncRNAs in non-small cell lung cancer.

Longitudinal follow-up reveals occurrence of successive Cryptosporidium bovis and Cryptosporidium ryanae infections by different subtype families in dairy cattle

Cryptosporidium bovis and Cryptosporidium ryanae are common species causing cryptosporidiosis in cattle. Data accumulated thus far indicate that the infection patterns of the two species could be different between areas with and without Cryptosporidium parvum. To better understand the infection dynamics of these two species, cross-sectional and longitudinal studies of Cryptosporidium spp. were conducted using genotyping and subtyping tools. In the cross-sectional survey, analysis of 634 faecal samples from two farms identified only C. bovis and C. ryanae in pre-weaned calves. Two birth cohorts of 61 and 78 calves were followed longitudinally over a 12 month period, which revealed the shedding of C. bovis oocysts started at 1–2 weeks of age and peaked initially at 6–8 weeks of age. Altogether calves experienced four infections by six subtype families of C. bovis, with each infection caused by different subtype families. In contrast, the shedding of C. ryanae oocysts started at 2–4 weeks of age, and the two infections were caused by different subtype families. The cumulative incidence of C. bovis infection was 100% (58/58, 32/32) on both farms, compared with 84.4–98.3% (27/32 and 57/58) for C. ryanae infection. Overall, the mean duration of oocyst shedding in the cohort studies was 3.8–4.0 weeks for C. bovis compared with 2.1 weeks for C. ryanae. The oocyst shedding intensity was high (mean oocysts per gram of faeces was over 105) during the first infection with each species but became significantly lower in the later infections. Cryptosporidium ryanae was associated with the occurrence of diarrhea on one farm, while C. bovis was not. The data indicate that there is an early occurrence of C. bovis and C. ryanae in pre-weaned calves with high infection intensity in the absence of C. parvum. Calves infected with the same Cryptosporidium sp. multiple times could be associated with the presence of subtype-specific immunity.

Low level of HIV-1C integrase strand transfer inhibitor resistance mutations among recently diagnosed ART-naive Ethiopians

With the widespread use of Integrase strand transfer inhibitors (INSTIs), surveillance of HIV-1 pretreatment drug resistance is critical in optimizing antiretroviral treatment efficacy. However, despite the introduction of these drugs, data concerning their resistance mutations (RMs) is still limited in Ethiopia. Thus, this study aimed to assess INSTI RMs and polymorphisms at the gene locus coding for Integrase (IN) among viral isolates from ART-naive HIV-1 infected Ethiopian population. This was a cross-sectional study involving isolation of HIV-1 from plasma of 49 newly diagnosed drug-naive HIV-1 infected individuals in Addis-Ababa during the period between June to December 2018. The IN region covering the first 263 codons of blood samples was amplified and sequenced using an in-house assay. INSTIs RMs were examined using calibrated population resistance tool version 8.0 from Stanford HIV drug resistance database while both REGA version 3 online HIV-1 subtyping tool and the jumping profile Hidden Markov Model from GOBICS were used to examine HIV-1 genetic diversity. Among the 49 study participants, 1 (1/49; 2%) harbored a major INSTIs RM (R263K). In addition, blood specimens from 14 (14/49; 28.5%) patients had accessory mutations. Among these, the M50I accessory mutation was observed in a highest frequency (13/49; 28.3%) followed by L74I (1/49; 2%), S119R (1/49; 2%), and S230N (1/49; 2%). Concerning HIV-1 subtype distribution, all the entire study subjects were detected to harbor HIV-1C strain as per the IN gene analysis. This study showed that the level of primary HIV-1 drug resistance to INSTIs is still low in Ethiopia reflecting the cumulative natural occurrence of these mutations in the absence of selective drug pressure and supports the use of INSTIs in the country. However, continues monitoring of drug resistance should be enhanced since the virus potentially develop resistance to this drug classes as time goes by.

Transmitted HIV Drug Resistance in Bulgaria Occurs in Clusters of Individuals from Different Transmission Groups and Various Subtypes (2012-2020).

Transmitted HIV drug resistance in Bulgaria was first reported in 2015 using data from 1988-2011. We determined the prevalence of surveillance drug resistance mutations (SDRMs) and HIV-1 genetic diversity in Bulgaria during 2012-2020 using polymerase sequences from 1053 of 2010 (52.4%) antiretroviral therapy (ART)-naive individuals. Sequences were analyzed for DRM using the WHO HIV SDRM list implemented in the calculated population resistance tool at Stanford University. Genetic diversity was inferred using automated subtyping tools and phylogenetics. Cluster detection and characterization was performed using MicrobeTrace. The overall rate of SDRMs was 5.7% (60/1053), with 2.2% having resistance to nucleoside reverse transcriptase inhibitors (NRTIs), 1.8% to non-nucleoside reverse transcriptase inhibitors (NNRTIs), 2.1% to protease inhibitors (PIs), and 0.4% with dual-class SDRMs. We found high HIV-1 diversity, with the majority being subtype B (60.4%), followed by F1 (6.9%), CRF02_AG (5.2%), A1 (3.7%), CRF12_BF (0.8%), and other subtypes and recombinant forms (23%). Most (34/60, 56.7%) of the SDRMs were present in transmission clusters of different subtypes composed mostly of male-to-male sexual contact (MMSC), including a 14-member cluster of subtype B sequences from 12 MMSC and two males reporting heterosexual contact; 13 had the L90M PI mutation and one had the T215S NRTI SDRM. We found a low SDRM prevalence amid high HIV-1 diversity among ART-naive patients in Bulgaria during 2012-2020. The majority of SDRMs were found in transmission clusters containing MMSC, indicative of onward spread of SDRM in drug-naive individuals. Our study provides valuable information on the transmission dynamics of HIV drug resistance in the context of high genetic diversity in Bulgaria, for the development of enhanced prevention strategies to end the epidemic.

Variabilities in global DNA methylation and β-sheet richness establish spectroscopic landscapes among subtypes of pancreatic cancer

PurposeKnowledge about pancreatic cancer (PC) biology has been growing rapidly in recent decades. Nevertheless, the survival of PC patients has not greatly improved. The development of a novel methodology suitable for deep investigation of the nature of PC tumors is of great importance. Molecular imaging techniques, such as Fourier transform infrared (FTIR) spectroscopy and Raman hyperspectral mapping (RHM) combined with advanced multivariate data analysis, were useful in studying the biochemical composition of PC tissue.MethodsHere, we evaluated the potential of molecular imaging in differentiating three groups of PC tumors, which originate from different precursor lesions. Specifically, we comprehensively investigated adenocarcinomas (ACs): conventional ductal AC, intraductal papillary mucinous carcinoma, and ampulla of Vater AC. FTIR microspectroscopy and RHM maps of 24 PC tissue slides were obtained, and comprehensive advanced statistical analyses, such as hierarchical clustering and nonnegative matrix factorization, were performed on a total of 211,355 Raman spectra. Additionally, we employed deep learning technology for the same task of PC subtyping to enable automation. The so-called convolutional neural network (CNN) was trained to recognize spectra specific to each PC group and then employed to generate CNN-prediction-based tissue maps. To identify the DNA methylation spectral markers, we used differently methylated, isolated DNA and compared the observed spectral differences with the results obtained from cellular nuclei regions of PC tissues.ResultsThe results showed significant differences among cancer tissues of the studied PC groups. The main findings are the varying content of β-sheet-rich proteins within the PC cells and alterations in the relative DNA methylation level. Our CNN model efficiently differentiated PC groups with 94% accuracy. The usage of CNN in the classification task did not require Raman spectral data preprocessing and eliminated the need for extensive knowledge of statistical methodologies.ConclusionsMolecular spectroscopy combined with CNN technology is a powerful tool for PC detection and subtyping. The molecular fingerprint of DNA methylation and β-sheet cytoplasmic proteins established by our results is different for the main PC groups and allowed the subtyping of pancreatic tumors, which can improve patient management and increase their survival. Our observations are of key importance in understanding the variability of PC and allow translation of the methodology into clinical practice by utilizing liquid biopsy testing.

Identification of HIV-1 subtype CRF18_cpx in a patient with multidrug resistance in KwaZulu-Natal, South Africa: An epidemiological worry?

The most common HIV-1 subtype in South Africa is subtype C, and detection of other subtypes is rare. We report the first known case of HIV-1 subtype CRF18_cpx identified in KwaZulu-Natal, South Africa, the epicenter of the HIV epidemic, through HIV drug resistance genotyping.The 31-year-old female patient was initiated on fixed dose combination (tenofovir, emtricitabine, efavirenz) in 2017, with failure to achieve virologic suppression. Change to second-line antiretroviral therapy (tenofovir, emtricitabine and ritonavir-boosted lopinavir) was made in 2018. Adherence was impacted by lopinavir-induced diarrhoea. In 2020, she was switched to zidovudine, lamivudine and ritonavir-boosted atazanavir. Virologic failure persisted, and HIV drug resistance genotyping in April 2021 showed multidrug resistance. Third-line regimen (tenofovir, lamivudine, dolutegravir and ritonavir-boosted darunavir) was commenced in May 2021 and virologic suppression was achieved.Co-incidentally we discovered from the HIV drug resistance sequence report that the patient was infected with HIV-1 subtype CRF18_cpx. Analysis of the HIV sequence using REGA HIV-1 Subtyping Tool confirmed sequence subtype assignment as HIV-1 CRF 18_cpx (bootstrap confidence = 90%). Subtyping was also confirmed with the COMET HIV-1 tool, HIV BLAST tool and phylogenetic analysis.Based on our incidental finding, CRF18_cpx may be circulating locally as the patient had no travel history. Introduction of non-subtype C and recombinant viruses are expected to have increased in South Africa due to increased travel. This highlights the need to characterize subtype diversity in South Africa, particularly in KwaZulu-Natal, as HIV subtype can influence pathogenesis, treatment response, drug resistance and efforts towards vaccine development.

Deep Learning-Assisted Quantitative Susceptibility Mapping as a Tool for Grading and Molecular Subtyping of Gliomas.

The online version contains supplementary material available at 10.1007/s43657-022-00087-6.

S-55-2: ROLE OF AVS IN PA SUBTYPING-NOT THE PUREST GOLD STANDARD

Primary aldosteronism (PA) has been traditionally grouped into unilateral aldosterone producing adenomas (APA) and bilateral forms. PA subtyping still relies on adrenal vein sampling (AVS), which is invasive, costly, and scantly available. This session will focus on the evolution of steroid profiling by mass spectrometry as a non-invasive tool for PA subtyping.

Updated Parkinson's disease motor subtypes classification and correlation to cerebrospinal homovanillic acid and 5-hydroxyindoleacetic acid levels

IntroductionMotor classifications of Parkinson's Disease (PD) have been widely used. This paper aims to update a subtype classification using the MDS-UPDRS-III and determine if cerebrospinal neurotransmitter profiles (HVA and 5-HIAA) differ between these subtypes in a cohort from the Parkinson's Progression Marker Initiative (PPMI). MethodsUPDRS and MDS-UPDRS scores were collected for 20 PD patients. Akinetic-rigid (AR), Tremor-dominant (TD), and Mixed (MX) subtypes were calculated using a formula derived from UPDRS, and a new ratio was developed for subtyping patients with the MDS-UPDRS. This new formula was subsequently applied to 95 PD patients from the PPMI dataset, and subtyping was correlated to neurotransmitter levels.Data were analyzed using receiver operating characteristic models and ANOVA. ResultsCompared to previous UPDRS classifications, the new MDS-UPDRS TD/AR ratios produced significant areas under the curve (AUC) for each subtype. The optimal sensitivity and specificity cutoff scores were ≥0.82 for TD, ≤0.71 for AR, and >0.71 and <0.82 for Mixed. Analysis of variance showed that the AR group had significantly lower HVA and 5-HIAA levels than the TD and HC groups. A logistic model using neurotransmitter levels and MDS-UPDRS-III could predict the subtype classification. ConclusionsThis MDS-UPDRS motor classification system provides a method to transition from the original UPDRS to the new MDS-UPDRS. It is a reliable and quantifiable subtyping tool for monitoring disease progression. The TD subtype is associated with lower motor scores and higher HVA levels, while the AR subtype is associated with higher motor scores and lower 5-HIAA levels.

Whole Genome Sequencing as the Ultimate Genomic Subtyping Tool for the Identification and Control of Listeria monocytogenes in the RTE Food Chain

Whole Genome Sequencing as the Ultimate Genomic Subtyping Tool for the Identification and Control of <i>Listeria monocytogenes</i> in the RTE Food Chain

Identification of a minimum number of genes to predict triple-negative breast cancer subgroups from gene expression profiles

BackgroundTriple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it, and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (basal-like 1 (BL1); basal-like 2 (BL2); mesenchymal (M); immunomodulatory (IM); mesenchymal stem-like (MSL); and luminal androgen receptor (LAR)), to be able to accurately predict them.MethodsLehman’s TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458), and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used, and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA data set.ResultsWe identified two signatures with the 120 and 81 top up- and downregulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88.6 to 89.4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets were complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug–target interactions were found among the upregulated genes in the M, IM and MSL subsets.ConclusionsOur study took full advantage of available TNBC data sets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amount of information from several data sets has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets.

Glioblastoma Molecular Classification Tool Based on mRNA Analysis: From Wet-Lab to Subtype.

Most glioblastoma studies incorporate the layer of tumor molecular subtype based on the four-subtype classification system proposed in 2010. Nevertheless, there is no universally recognized and convenient tool for glioblastoma molecular subtyping, and each study applies a different set of markers and/or approaches that cause inconsistencies in data comparability and reproducibility between studies. Thus, this study aimed to create an applicable user-friendly tool for glioblastoma classification, with high accuracy, while using a significantly smaller number of variables. The study incorporated a TCGA microarray, sequencing datasets, and an independent cohort of 56 glioblastomas (LUHS cohort). The models were constructed by applying the Agilent G4502 dataset, and they were tested using the Affymetrix HG-U133a and Illumina Hiseq cohorts, as well as the LUHS cases. Two classification models were constructed by applying a logistic regression classification algorithm, based on the mRNA levels of twenty selected genes. The classifiers were translated to a RT-qPCR assay and validated in an independent cohort of 56 glioblastomas. The classification accuracy of the 20-gene and 5-gene classifiers varied between 90.7-91% and 85.9-87.7%, respectively. With this work, we propose a cost-efficient three-class (classical, mesenchymal, and proneural) tool for glioblastoma molecular classification based on the mRNA analysis of only 5-20 genes, and we provide the basic information for classification performance starting from the wet-lab stage. We hope that the proposed classification tool will enable data comparability between different research groups.

Categorization of tinnitus listeners with a focus on cochlear synaptopathy.

Tinnitus is a complex and not yet fully understood phenomenon. Often the treatments provided are effective only for subgroups of sufferers. We are presently not able to predict benefit with the currently available diagnostic tools and analysis methods. Being able to identify and specifically treat sub-categories of tinnitus would help develop and implement more targeted treatments with higher success rate. In this study we use a clustering analysis based on 17 predictors to cluster an audiologically homogeneous group of normal hearing participants, both with and without tinnitus. The predictors have been chosen to be either tinnitus-specific measures or measures that are thought to be connected to cochlear synaptopathy. Our aim was to identify a subgroup of participants with characteristics consistent with the current hypothesized impact of cochlear synaptopathy. Our results show that this approach can separate the listeners into different clusters. But not in all cases could the tinnitus sufferers be separated from the control group. Another challenge is the use of categorical measures which seem to dominate the importance analysis of the factors. The study showed that data-driven clustering of a homogeneous listener group based on a mixed set of experimental outcome measures is a promising tool for tinnitus sub-typing, with the caveat that sample sizes might need to be sufficiently high, and higher than in the present study, to keep a meaningful sample size after clustering.