Poison Exon Splicing Regulates a Coordinated Network of SR Protein Expression during Differentiation and Tumorigenesis.

The RNA isoform repertoire is regulated by splicing factor (SF) expression, and alterations in SF levels are associated with disease. SFs contain ultraconserved poison exon (PE) sequences that exhibit greater identity across species than nearby coding exons, but their physiological role and molecular regulation is incompletely understood. We show that PEs in serine-arginine-rich (SR) proteins, a family of 14 essential SFs, are differentially spliced during induced pluripotent stem cell (iPSC) differentiation and in tumors versus normal tissues. We uncover an extensive cross-regulatory network of SR proteins controlling their expression via alternative splicing coupled to nonsense-mediated decay. We define sequences that regulate PE inclusion and protein expression of the oncogenic SF TRA2ß using an RNA-targeting CRISPR screen. We demonstrate location dependency of RS domain activity on regulation of TRA2ß-PE using CRISPR artificial SFs. Finally, we develop splice-switching antisense oligonucleotides to reverse the increased skipping of TRA2ß-PE detected in breast tumors, altering breast cancer cell viability, proliferation, and migration.

RNA expression of 6 genes from metastatic mucosal gastric cancer serves as the global prognostic marker for gastric cancer with functional validation.

BACKGROUND: Molecular analysis of advanced tumors can increase tumor heterogeneity and selection bias. We developed a robust prognostic signature for gastric cancer by comparing RNA expression between very rare early gastric cancers invading only mucosal layer (mEGCs) with lymph node metastasis (Npos) and those without metastasis (Nneg). METHODS: Out of 1003 mEGCs, all Npos were matched to Nneg using propensity scores. Machine learning approach comparing Npos and Nneg was used to develop prognostic signature. The function and robustness of prognostic signature was validated using cell lines and external datasets. RESULTS: Extensive machine learning with cross-validation identified the prognostic classifier consisting of four overexpressed genes (HDAC5, NPM1, DTX3, and PPP3R1) and two downregulated genes (MED12 and TP53), and enabled us to develop the risk score predicting poor prognosis. Cell lines engineered to high-risk score showed increased invasion, migration, and resistance to 5-FU and Oxaliplatin but maintained sensitivity to an HDAC inhibitor. Mouse models after tail vein injection of cell lines with high-risk score revealed increased metastasis. In three external cohorts, our risk score was identified as the independent prognostic factor for overall and recurrence-free survival. CONCLUSION: The risk score from the 6-gene classifier can successfully predict the prognosis of gastric cancer.

A comparison of DNA sequencing and gene expression profiling to assist tissue of origin diagnosis in cancer of unknown primary.

Cancer of unknown primary (CUP) is a syndrome defined by clinical absence of a primary cancer after standardised investigations. Gene expression profiling (GEP) and DNA sequencing have been used to predict primary tissue of origin (TOO) in CUP and find molecularly guided treatments; however, a detailed comparison of the diagnostic yield from these two tests has not been described. Here, we compared the diagnostic utility of RNA and DNA tests in 215 CUP patients (82% received both tests) in a prospective Australian study. Based on retrospective assessment of clinicopathological data, 77% (166/215) of CUPs had insufficient evidence to support TOO diagnosis (clinicopathology unresolved). The remainder had either a latent primary diagnosis (10%) or clinicopathological evidence to support a likely TOO diagnosis (13%) (clinicopathology resolved). We applied a microarray (CUPGuide) or custom NanoString 18-class GEP test to 191 CUPs with an accuracy of 91.5% in known metastatic cancers for high-medium confidence predictions. Classification performance was similar in clinicopathology-resolved CUPs - 80% had high-medium predictions and 94% were concordant with pathology. Notably, only 56% of the clinicopathology-unresolved CUPs had high-medium confidence GEP predictions. Diagnostic DNA features were interrogated in 201 CUP tumours guided by the cancer type specificity of mutations observed across 22 cancer types from the AACR Project GENIE database (77,058 tumours) as well as mutational signatures (e.g. smoking). Among the clinicopathology-unresolved CUPs, mutations and mutational signatures provided additional diagnostic evidence in 31% of cases. GEP classification was useful in only 13% of cases and oncoviral detection in 4%. Among CUPs where genomics informed TOO, lung and biliary cancers were the most frequently identified types, while kidney tumours were another identifiable subset. In conclusion, DNA and RNA profiling supported an unconfirmed TOO diagnosis in one-third of CUPs otherwise unresolved by clinicopathology assessment alone. DNA mutation profiling was the more diagnostically informative assay. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Local versus systemic control of bone and skeletal muscle mass by components of the transforming growth factor-ß signaling pathway.

Skeletal muscle and bone homeostasis are regulated by members of the myostatin/GDF-11/activin branch of the transforming growth factor-ß superfamily, which share many regulatory components, including inhibitory extracellular binding proteins and receptors that mediate signaling. Here, we present the results of genetic studies demonstrating a critical role for the binding protein follistatin (FST) in regulating both skeletal muscle and bone. Using an allelic series corresponding to varying expression levels of endogenous Fst, we show that FST acts in an exquisitely dose-dependent manner to regulate both muscle mass and bone density. Moreover, by employing a genetic strategy to target Fst expression only in the posterior (caudal) region of the animal, we show that the effects of Fst loss are mostly restricted to the posterior region, implying that locally produced FST plays a much more important role than circulating FST with respect to regulation of muscle and bone. Finally, we show that targeting receptors for these ligands specifically in osteoblasts leads to dramatic increases in bone mass, with trabecular bone volume fraction being increased by 12- to 13-fold and bone mineral density being increased by 8- to 9-fold in humeri, femurs, and lumbar vertebrae. These findings demonstrate that bone, like muscle, has an enormous inherent capacity for growth that is normally kept in check by this signaling system and suggest that the extent to which this regulatory mechanism may be used throughout the body to regulate tissue mass may be more significant than previously appreciated.

Harnessing large language models (LLMs) for candidate gene prioritization and selection.

BACKGROUND: Feature selection is a critical step for translating advances afforded by systems-scale molecular profiling into actionable clinical insights. While data-driven methods are commonly utilized for selecting candidate genes, knowledge-driven methods must contend with the challenge of efficiently sifting through extensive volumes of biomedical information. This work aimed to assess the utility of large language models (LLMs) for knowledge-driven gene prioritization and selection. METHODS: In this proof of concept, we focused on 11 blood transcriptional modules associated with an Erythroid cells signature. We evaluated four leading LLMs across multiple tasks. Next, we established a workflow leveraging LLMs. The steps consisted of: (1) Selecting one of the 11 modules; (2) Identifying functional convergences among constituent genes using the LLMs; (3) Scoring candidate genes across six criteria capturing the gene's biological and clinical relevance; (4) Prioritizing candidate genes and summarizing justifications; (5) Fact-checking justifications and identifying supporting references; (6) Selecting a top candidate gene based on validated scoring justifications; and (7) Factoring in transcriptome profiling data to finalize the selection of the top candidate gene. RESULTS: Of the four LLMs evaluated, OpenAI's GPT-4 and Anthropic's Claude demonstrated the best performance and were chosen for the implementation of the candidate gene prioritization and selection workflow. This workflow was run in parallel for each of the 11 erythroid cell modules by participants in a data mining workshop. Module M9.2 served as an illustrative use case. The 30 candidate genes forming this module were assessed, and the top five scoring genes were identified as BCL2L1, ALAS2, SLC4A1, CA1, and FECH. Researchers carefully fact-checked the summarized scoring justifications, after which the LLMs were prompted to select a top candidate based on this information. GPT-4 initially chose BCL2L1, while Claude selected ALAS2. When transcriptional profiling data from three reference datasets were provided for additional context, GPT-4 revised its initial choice to ALAS2, whereas Claude reaffirmed its original selection for this module. CONCLUSIONS: Taken together, our findings highlight the ability of LLMs to prioritize candidate genes with minimal human intervention. This suggests the potential of this technology to boost productivity, especially for tasks that require leveraging extensive biomedical knowledge.

Coliphage cocktails for controlling antimicrobial-resistant Escherichia coli: emphasizing polyphage and multihost interactions at different levels of multiplicity of infection.

AIM: This study elucidates the in-vitro bactericidal effectiveness of polyphage cocktail combinations of 2, 4, 6, 8, and 10 individual coliphages against a cocktail of 20 AMR Escherichia coli. METHODS AND RESULTS: Different polyphage cocktails viz., 45 two-phage combinations, 28 four-phage combinations, 15 six-phage combinations, 6 eight-phage combinations, and 1 ten-phage combination were formulated using a pool of ten coliphages that were isolated from two different geographical locations (East and West coasts of India). The different polyphage cocktails were tested at four different levels of Multiplicity of Infection (MOI) viz., MOI-1, MOI-10, MOI-100, and MOI-1000. All the 2, 4, 6, 8, and 10-phage cocktails were found to be effective in controlling the growth of a cocktail of 20 AMR bacteria when tested at MOI-1000 and MOI-100 but variations in antibacterial activity were observed at lower MOIs of 10 and 1. The ten coliphage cocktail showed lytic activity against 100% of AMR E. coli from farmed brackish water shrimp, 96% of laboratory collection of AMR E. coli, 92% of AMR E. coli from farmed freshwater fish, and 85% of AMR E. coli from market shrimp. CONCLUSION: Polyphage cocktails of 2, 4, 6, 8, and 10 coliphages applied at an MOI of 1000 effectively suppressed the growth of antimicrobial-resistant E. coli. The results indicated phage-phage synergy in the lytic activity of several coliphage combinations at higher MOIs of 1000 and 100 while phage-phage antagonism was evidenced at lower MOIs of 10 and 1.

Tetracycline resistance potential of heterotrophic bacteria isolated from freshwater fin-fish aquaculture system.

AIMS: This study investigated the tetracycline resistance potential of heterotrophic bacteria isolated from twenty-four freshwater fin-fish culture ponds in Andhra Pradesh, India. METHODS AND RESULTS: A total of 261 tetracycline resistant bacteria (tetR) were recovered from pond water, pond sediment, fish gills, fish intestine, and fish feed. Bacteria with high tetracycline resistance (tetHR) (n = 30) that were resistant to tetracycline concentrations above 128  µg mL-1 were predominantly Lactococcus garvieae followed by Enterobacter spp., Lactococcus lactis, Enterobacter hormaechei, Staphylococcus arlettae, Streptococcus lutetiensis, Staphylococcus spp., Brevundimonas faecalis, Exiguobacterium profundum, Lysinibacillus spp., Stutzerimonas stutzeri, Enterobacter cloacae, and Lactococcus taiwanensis. Resistance to 1024 µg mL-1 of tetracycline was observed in L. garvieae, S. arlettae, Enterobacter spp., B. faecalis. Tet(A) (67%) was the predominant resistance gene in tetHR followed by tet(L), tet(S), tet(K), and tet(M). At similar concentrations of exposure, tetracycline procured at the farm level (69.5% potency) exhibited lower inhibition against tetHR bacteria compared to pure tetracycline (99% potency). The tetHR bacteria showed higher cross-resistance to furazolidone (100%) followed by co-trimoxazole (47.5%) and enrofloxacin (11%). CONCLUSIONS: The maximum threshold of tetracycline resistance at 1024 µg mL-1 was observed in S. arlettae, Enterobacter spp., B. faecalis, and L. garvieae and tet(A) was the major determinant found in this study.

Targeting myostatin/activin A protects against skeletal muscle and bone loss during spaceflight.

Among the physiological consequences of extended spaceflight are loss of skeletal muscle and bone mass. One signaling pathway that plays an important role in maintaining muscle and bone homeostasis is that regulated by the secreted signaling proteins, myostatin (MSTN) and activin A. Here, we used both genetic and pharmacological approaches to investigate the effect of targeting MSTN/activin A signaling in mice that were sent to the International Space Station. Wild type mice lost significant muscle and bone mass during the 33 d spent in microgravity. Muscle weights of Mstn-/- mice, which are about twice those of wild type mice, were largely maintained during spaceflight. Systemic inhibition of MSTN/activin A signaling using a soluble form of the activin type IIB receptor (ACVR2B), which can bind each of these ligands, led to dramatic increases in both muscle and bone mass, with effects being comparable in ground and flight mice. Exposure to microgravity and treatment with the soluble receptor each led to alterations in numerous signaling pathways, which were reflected in changes in levels of key signaling components in the blood as well as their RNA expression levels in muscle and bone. These findings have implications for therapeutic strategies to combat the concomitant muscle and bone loss occurring in people afflicted with disuse atrophy on Earth as well as in astronauts in space, especially during prolonged missions.

Comprehensive Molecular Characterization of Adenocarcinoma of the Gastroesophageal Junction Between Esophageal and Gastric Adenocarcinomas.

OBJECTIVE: To investigate the molecular characteristics of AGEJ compared with EAC and gastric adenocarcinoma. SUMMARY OF BACKGROUND DATA: Classification of AGEJ based on differential molecular characteristics between EAC and gastric adenocarcinoma has been long-standing controversy but rarely conducted due to anatomical ambiguity and epidemiologic difference. METHODS: The molecular classification model with Bayesian compound covariate predictor was developed based on differential mRNA expression of EAC (N = 78) and GCFB (N = 102) from the Cancer Genome Atlas (TCGA) cohort. AGEJ/cardia (N = 48) in TCGA cohort and AGEJ/upper third GC (N = 46 pairs) in Seoul National University cohort were classified into the EAC-like or GCFB-like groups whose genomic, transcriptomic, and proteomic characteristics were compared. RESULTS: AGEJ in both cohorts was similarly classified as EAC-like (31.2%) or GCFB-like (68.8%) based on the 400-gene classifier. The GCFB-like group showed significantly activated phosphoinositide 3-kinase-AKT signaling with decreased expression of ERBB2. The EAC-like group presented significantly different alternative splicing including the skipped exon of RPS24, a significantly higher copy number amplification including ERBB2 amplification, and increased protein expression of ERBB2 and EGFR compared with GCFB-like group. High-throughput 3D drug test using independent cell lines revealed that the EAC-like group showed a significantly better response to lapatinib than the GCFB-like group (P = 0.015). CONCLUSIONS: AGEJ was the combined entity of the EAC-like and GCFB-like groups with consistently different molecular characteristics in both Seoul National University and TCGA cohorts. The EAC-like group with a high Bayesian compound covariate predictor score could be effectively targeted by dual inhibition of ERBB2 and EGFR.

SFRP4 drives invasion in gastric cancer and is an early predictor of recurrence.

OBJECTIVE: Gastric cancer patients generally have a poor outcome, particularly those with advanced-stage disease which is defined by the increased invasion of cancer locally and is associated with higher metastatic potential. This study aimed to identify genes that were functional in the most fundamental hallmark of cancer, namely invasion. We then wanted to assess their value as biomarkers of gastric cancer progression and recurrence. DESIGN: Data from a cohort of patients profiled on cDNA expression arrays was interrogated using K-means analysis. This genomic approach classified the data based on patterns of gene expression allowing the identification of the genes most correlated with the invasion of GC. We evaluated the functional role of a key protein from this analysis in invasion and as a biomarker of recurrence after curative resection. RESULTS: Expression of secreted frizzled-related protein 4 (SFRP4) was identified as directly proportional to gastric cancer invasion. This finding was validated in multiple, independent datasets and its functional role in invasion was also confirmed using invasion assays. A change in serum levels of SFRP4 after curative resection, when coupled with AJCC stage, can accurately predict the risk of disease recurrence after curative therapy in an assay we termed PredictR. CONCLUSIONS: This simple ELISA-based assay can help predict recurrence of disease after curative gastric cancer surgery irrespective of adjuvant therapy. The results require further evaluation in a prospective trial but would help in the rational prescription of cancer therapies and surveillance to prevent under or over treatment of patients after curative resection.

Whole-genome characterization of chemoresistant ovarian cancer.

Patients with high-grade serous ovarian cancer (HGSC) have experienced little improvement in overall survival, and standard treatment has not advanced beyond platinum-based combination chemotherapy, during the past 30 years. To understand the drivers of clinical phenotypes better, here we use whole-genome sequencing of tumour and germline DNA samples from 92 patients with primary refractory, resistant, sensitive and matched acquired resistant disease. We show that gene breakage commonly inactivates the tumour suppressors RB1, NF1, RAD51B and PTEN in HGSC, and contributes to acquired chemotherapy resistance. CCNE1 amplification was common in primary resistant and refractory disease. We observed several molecular events associated with acquired resistance, including multiple independent reversions of germline BRCA1 or BRCA2 mutations in individual patients, loss of BRCA1 promoter methylation, an alteration in molecular subtype, and recurrent promoter fusion associated with overexpression of the drug efflux pump MDR1.

ABCC4/MRP4 contributes to the aggressiveness of Myc-associated epithelial ovarian cancer.

Epithelial ovarian cancer (EOC) is a complex disease comprising discrete histological and molecular subtypes, for which survival rates remain unacceptably low. Tailored approaches for this deadly heterogeneous disease are urgently needed. Efflux pumps belonging to the ATP-binding cassette (ABC) family of transporters are known for roles in both drug resistance and cancer biology and are also highly targetable. Here we have investigated the association of ABCC4/MRP4 expression to clinical outcome and its biological function in endometrioid and serous tumors, common histological subtypes of EOC. We found high expression of ABCC4/MRP4, previously shown to be directly regulated by c-Myc/N-Myc, was associated with poor prognosis in endometrioid EOC (P = .001) as well as in a subset of serous EOC with a "high-MYCN" profile (C5/proliferative; P = .019). Transient siRNA-mediated suppression of MRP4 in EOC cells led to reduced growth, migration and invasion, with the effects being most pronounced in endometrioid and C5-like serous cells compared to non-C5 serous EOC cells. Sustained knockdown of MRP4 also sensitized endometrioid cells to MRP4 substrate drugs. Furthermore, suppression of MRP4 decreased the growth of patient-derived EOC cells in vivo. Together, our findings provide the first evidence that MRP4 plays an important role in the biology of Myc-associated ovarian tumors and highlight this transporter as a potential therapeutic target for EOC.

Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes.

Blood glucose levels are tightly controlled by the coordinated action of at least four cell types constituting pancreatic islets. Changes in the proportion and/or function of these cells are associated with genetic and molecular pathophysiology of monogenic, type 1, and type 2 (T2D) diabetes. Cellular heterogeneity impedes precise understanding of the molecular components of each islet cell type that govern islet (dys)function, particularly the less abundant delta and gamma/pancreatic polypeptide (PP) cells. Here, we report single-cell transcriptomes for 638 cells from nondiabetic (ND) and T2D human islet samples. Analyses of ND single-cell transcriptomes identified distinct alpha, beta, delta, and PP/gamma cell-type signatures. Genes linked to rare and common forms of islet dysfunction and diabetes were expressed in the delta and PP/gamma cell types. Moreover, this study revealed that delta cells specifically express receptors that receive and coordinate systemic cues from the leptin, ghrelin, and dopamine signaling pathways implicating them as integrators of central and peripheral metabolic signals into the pancreatic islet. Finally, single-cell transcriptome profiling revealed genes differentially regulated between T2D and ND alpha, beta, and delta cells that were undetectable in paired whole islet analyses. This study thus identifies fundamental cell-type-specific features of pancreatic islet (dys)function and provides a critical resource for comprehensive understanding of islet biology and diabetes pathogenesis.

A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification.

BACKGROUND: The advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification. RESULTS: We have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment. CONCLUSIONS: Based on our study, we found that when marker genes are expressed at fold change of 4 or more, either Seurat or SIMLR algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fold change of 2, choice of the single cell algorithm is dependent on the number of single cells isolated and rarity of cell types to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in the design of single cell experiments.

Tracing source, distribution and health risk of potentially harmful elements (PHEs) in street dust of Durgapur, India.

Street dust samples from Durgapur, the steel city of eastern India, were collected from five different land use patterns, i.e., national highways, urban residential area, sensitive area, industrial area and busy traffic zone during summer, monsoon, and winter to analyze the pollution characteristics, chemical fractionation, source apportionment and health risk of heavy metals (HMs). The samples were fractionated into ≤ 53 µm and analyzed for potentially harmful elements (PHEs) viz. Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn. Summer season indicated higher concentrations of PHEs when compared to the other two seasons. Mean enrichment factor (EF), geo-accumulation index (Igeo), and contamination factor (CF) were high for Cd followed by Pb during all the three season in Durgapur. Chemical fractionation was executed in order to obtain distribution patterns of PHEs and to evaluate their bioavailable fractions in street dust samples. Mn was found to be highly bioavailable and bioavailability of the PHEs were in the order of Mn > Zn > Pb > Ni > Cd > Cu > Fe > Cr. Principal Component Analysis (PCA), cluster analysis, correlation analysis indicated the main sources of PHEs could be industrial, especially coal powered thermal plant, iron and steel industries and cement industries and vehicular. Multivariate analysis of variance (MANOVA) indicated that sites, seasons and their interaction were significantly affected by different PHEs as a whole. The health risk was calculated with total metal as well as mobile fraction of PHEs, which indicated that the actual non-carcinogenic risk due to bioavailable PHEs was less (HI < 1) when compared to total concentrations of PHEs. Carcinogenic risk was observed for total Cr in street dust (Child: 4.6E-06; Adult: 3.6E-06).

Expression and function of ABCG2 and XIAP in glioblastomas.

Despite multimodal treatment that includes surgery, radiation and chemotherapy, virtually all glioblastomas (GBM) recur, indicating that these interventions are insufficient to eradicate all malignant cells. To identify potential new therapeutic targets in GBMs, we examined the expression and function of proteins that are associated with therapy resistance and cancer cell survival. We measured the expression of eight such proteins in 50 GBM samples by immunohistochemistry and analyzed patient survival. We report that GBM patients with high expression of ABCG2 (also called BCRP) or XIAP at the protein level had worse survival than those with low expression. The adjusted hazard ratio for ABCG2 was 2.35 and for XIAP was 2.65. Since glioma stem cells (GSCs) have been shown to be more resistant than bulk tumor cells to anti-cancer therapies and to express high levels of these proteins, we also sought to determine if ABCG2 and XIAP have functional roles in GSCs. We used small molecule inhibitors to treat patient-derived GBM tumorspheres in vitro and observed that inhibitors of ABCG2, Ko143 and fumitremorgin, significantly reduced self-renewal. These results suggest that ABCG2 and XIAP proteins may be useful indicators of patient survival and that inhibition of ABCG2 may be a promising therapeutic strategy in GBMs.

Bioavailability and health risk of some potentially toxic elements (Cd, Cu, Pb and Zn) in street dust of Asansol, India.

Street dust samples were collected from five different types of land use patterns (busy traffic zone, urban residential area, national highways, industrial area and sensitive area) in a medium sized industrial city Asansol, India. The samples were fractionated into ≤53µm and analyzed for potential toxic elements (PTEs) viz. Zn, Cd, Pb and Cu. The mean total concentration of Zn, Cd, Pb and Cu in the urban street dust samples were 192, 0.75, 110 and 132mgkg-1 respectively. Chemical speciation was performed for PTEs to evaluate the bio-available fractions. Cu was mostly associated with organic matter phase while Zn, Pb and Cd with residual phase. Mean mobility factor (MF) for heavy metals in Asansol was Zn (54.6%)>Pb (49.1%)>Cu (25.3%)>Cd (22.7%). Geo-chemical indices such as Enrichment Factor (EF), geo-accumulation index (Igeo) and contamination Factor (CF) were in the order of Pb>Cd>Zn>Cu. Cluster analysis was done to understand the similarities among the sites. The risks of all metals was calculated with mobile fraction, which indicated actual risk due to PTEs was less (HI<1).

Efficient molecular subtype classification of high-grade serous ovarian cancer.

High-grade serous carcinomas (HGSCs) account for approximately 70% of all epithelial ovarian cancers diagnosed. Using microarray gene expression profiling, we previously identified four molecular subtypes of HGSC: C1 (mesenchymal), C2 (immunoreactive), C4 (differentiated), and C5 (proliferative), which correlate with patient survival and have distinct biological features. Here, we describe molecular classification of HGSC based on a limited number of genes to allow cost-effective and high-throughput subtype analysis. We determined a minimal signature for accurate classification, including 39 differentially expressed and nine control genes from microarray experiments. Taqman-based (low-density arrays and Fluidigm), fluorescent oligonucleotides (Nanostring), and targeted RNA sequencing (Illumina) assays were then compared for their ability to correctly classify fresh and formalin-fixed, paraffin-embedded samples. All platforms achieved > 90% classification accuracy with RNA from fresh frozen samples. The Illumina and Nanostring assays were superior with fixed material. We found that the C1, C2, and C4 molecular subtypes were largely consistent across multiple surgical deposits from individual chemo-naive patients. In contrast, we observed substantial subtype heterogeneity in patients whose primary ovarian sample was classified as C5. The development of an efficient molecular classifier of HGSC should enable further biological characterization of molecular subtypes and the development of targeted clinical trials.

The genomic landscape of phaeochromocytoma.

Phaeochromocytomas (PCCs) and paragangliomas (PGLs) are rare neural crest-derived tumours originating from adrenal chromaffin cells or extra-adrenal sympathetic and parasympathetic tissues. More than a third of PCC/PGL cases are associated with heritable syndromes involving 13 or more known genes. These genes have been broadly partitioned into two groups based on pseudo-hypoxic and receptor tyrosine kinase (RTK) signalling pathways. Many of these genes can also become somatically mutated, although up to one third of sporadic cases have no known genetic driver. Furthermore, little is known of the genes that co-operate with known driver genes to initiate and drive tumourigenesis. To explore the genomic landscape of PCC/PGL, we applied exome sequencing, high-density SNP-array analysis, and RNA sequencing to 36 PCCs and four functional PGL tumours. All tumours displayed low mutation frequency, in contrast to frequent large segmental copy-number alterations, aneuploidy, and evidence for chromothripsis in one case. Multi-region sampling of one benign familial PCC tumour provided evidence for the timing of mutations during tumourigenesis and ongoing clonal evolution. Thirty-one of 40 (77.5%) cases could be explained by germline or somatic mutations or structural alterations affecting known PCC/PGL genes. Deleterious somatic mutations were also identified in known tumour-suppressor genes associated with genome maintenance and epigenetic modulation. A multitude of other genes were also found mutated that are likely important for normal neuroendocrine cell function. We revisited the gene-expression subtyping of PCC/PGL by integrating published microarray data with our RNA-seq data, enabling the identification of six robust gene-expression subtypes. The majority of cases in our cohort with no identifiable driver mutation were classified into a gene-expression subtype bearing similarity to MAX mutant PCC/PGL. Our data suggest there are yet unknown PCC/PGL cancer genes that can phenocopy MAX mutant PCC/PGL tumours. This study provides new insight into the molecular diversity and genetic origins of PCC/PGL tumours.