Temporal single-cell atlas of non-neuronal retinal cells reveals dynamic, coordinated multicellular responses to central nervous system injury.

Non-neuronal cells are key to the complex cellular interplay that follows central nervous system insult. To understand this interplay, we generated a single-cell atlas of immune, glial and retinal pigment epithelial cells from adult mouse retina before and at multiple time points after axonal transection. We identified rare subsets in naive retina, including interferon (IFN)-response glia and border-associated macrophages, and delineated injury-induced changes in cell composition, expression programs and interactions. Computational analysis charted a three-phase multicellular inflammatory cascade after injury. In the early phase, retinal macroglia and microglia were reactivated, providing chemotactic signals concurrent with infiltration of CCR2+ monocytes from the circulation. These cells differentiated into macrophages in the intermediate phase, while an IFN-response program, likely driven by microglia-derived type I IFN, was activated across resident glia. The late phase indicated inflammatory resolution. Our findings provide a framework to decipher cellular circuitry, spatial relationships and molecular interactions following tissue injury.

Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin.

Cell differentiation and function are regulated across multiple layers of gene regulation, including modulation of gene expression by changes in chromatin accessibility. However, differentiation is an asynchronous process precluding a temporal understanding of regulatory events leading to cell fate commitment. Here we developed simultaneous high-throughput ATAC and RNA expression with sequencing (SHARE-seq), a highly scalable approach for measurement of chromatin accessibility and gene expression in the same single cell, applicable to different tissues. Using 34,774 joint profiles from mouse skin, we develop a computational strategy to identify cis-regulatory interactions and define domains of regulatory chromatin (DORCs) that significantly overlap with super-enhancers. During lineage commitment, chromatin accessibility at DORCs precedes gene expression, suggesting that changes in chromatin accessibility may prime cells for lineage commitment. We computationally infer chromatin potential as a quantitative measure of chromatin lineage-priming and use it to predict cell fate outcomes. SHARE-seq is an extensible platform to study regulatory circuitry across diverse cells in tissues.

Disruption of the IL-33-ST2-AKT signaling axis impairs neurodevelopment by inhibiting microglial metabolic adaptation and phagocytic function.

To accommodate the changing needs of the developing brain, microglia must undergo substantial morphological, phenotypic, and functional reprogramming. Here, we examined whether cellular metabolism regulates microglial function during neurodevelopment. Microglial mitochondria bioenergetics correlated with and were functionally coupled to phagocytic activity in the developing brain. Transcriptional profiling of microglia with diverse metabolic profiles revealed an activation signature wherein the interleukin (IL)-33 signaling axis is associated with phagocytic activity. Genetic perturbation of IL-33 or its receptor ST2 led to microglial dystrophy, impaired synaptic function, and behavioral abnormalities. Conditional deletion of Il33 from astrocytes or Il1rl1, encoding ST2, in microglia increased susceptibility to seizures. Mechanistically, IL-33 promoted mitochondrial activity and phagocytosis in an AKT-dependent manner. Mitochondrial metabolism and AKT activity were temporally regulated in vivo. Thus, a microglia-astrocyte circuit mediated by the IL-33-ST2-AKT signaling axis supports microglial metabolic adaptation and phagocytic function during early development, with implications for neurodevelopmental and neuropsychiatric disorders.

Transcriptional Atlas of Intestinal Immune Cells Reveals that Neuropeptide α-CGRP Modulates Group 2 Innate Lymphoid Cell Responses.

Signaling abnormalities in immune responses in the small intestine can trigger chronic type 2 inflammation involving interaction of multiple immune cell types. To systematically characterize this response, we analyzed 58,067 immune cells from the mouse small intestine by single-cell RNA sequencing (scRNA-seq) at steady state and after induction of a type 2 inflammatory reaction to ovalbumin (OVA). Computational analysis revealed broad shifts in both cell-type composition and cell programs in response to the inflammation, especially in group 2 innate lymphoid cells (ILC2s). Inflammation induced the expression of exon 5 of Calca, which encodes the alpha-calcitonin gene-related peptide (α-CGRP), in intestinal KLRG1+ ILC2s. α-CGRP antagonized KLRG1+ ILC2s proliferation but promoted IL-5 expression. Genetic perturbation of α-CGRP increased the proportion of intestinal KLRG1+ ILC2s. Our work highlights a model where α-CGRP-mediated neuronal signaling is critical for suppressing ILC2 expansion and maintaining homeostasis of the type 2 immune machinery.

The clonal and mutational evolution spectrum of primary triple-negative breast cancers.

Primary triple-negative breast cancers (TNBCs), a tumour type defined by lack of oestrogen receptor, progesterone receptor and ERBB2 gene amplification, represent approximately 16% of all breast cancers. Here we show in 104 TNBC cases that at the time of diagnosis these cancers exhibit a wide and continuous spectrum of genomic evolution, with some having only a handful of coding somatic aberrations in a few pathways, whereas others contain hundreds of coding somatic mutations. High-throughput RNA sequencing (RNA-seq) revealed that only approximately 36% of mutations are expressed. Using deep re-sequencing measurements of allelic abundance for 2,414 somatic mutations, we determine for the first time-to our knowledge-in an epithelial tumour subtype, the relative abundance of clonal frequencies among cases representative of the population. We show that TNBCs vary widely in their clonal frequencies at the time of diagnosis, with the basal subtype of TNBC showing more variation than non-basal TNBC. Although p53 (also known as TP53), PIK3CA and PTEN somatic mutations seem to be clonally dominant compared to other genes, in some tumours their clonal frequencies are incompatible with founder status. Mutations in cytoskeletal, cell shape and motility proteins occurred at lower clonal frequencies, suggesting that they occurred later during tumour progression. Taken together, our results show that understanding the biology and therapeutic responses of patients with TNBC will require the determination of individual tumour clonal genotypes.

TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data.

The evolution of cancer genomes within a single tumor creates mixed cell populations with divergent somatic mutational landscapes. Inference of tumor subpopulations has been disproportionately focused on the assessment of somatic point mutations, whereas computational methods targeting evolutionary dynamics of copy number alterations (CNA) and loss of heterozygosity (LOH) in whole-genome sequencing data remain underdeveloped. We present a novel probabilistic model, TITAN, to infer CNA and LOH events while accounting for mixtures of cell populations, thereby estimating the proportion of cells harboring each event. We evaluate TITAN on idealized mixtures, simulating clonal populations from whole-genome sequences taken from genomically heterogeneous ovarian tumor sites collected from the same patient. In addition, we show in 23 whole genomes of breast tumors that the inference of CNA and LOH using TITAN critically informs population structure and the nature of the evolving cancer genome. Finally, we experimentally validated subclonal predictions using fluorescence in situ hybridization (FISH) and single-cell sequencing from an ovarian cancer patient sample, thereby recapitulating the key modeling assumptions of TITAN.

densityCut: an efficient and versatile topological approach for automatic clustering of biological data.

MOTIVATION: Many biological data processing problems can be formalized as clustering problems to partition data points into sensible and biologically interpretable groups. RESULTS: This article introduces densityCut, a novel density-based clustering algorithm, which is both time- and space-efficient and proceeds as follows: densityCut first roughly estimates the densities of data points from a K-nearest neighbour graph and then refines the densities via a random walk. A cluster consists of points falling into the basin of attraction of an estimated mode of the underlining density function. A post-processing step merges clusters and generates a hierarchical cluster tree. The number of clusters is selected from the most stable clustering in the hierarchical cluster tree. Experimental results on ten synthetic benchmark datasets and two microarray gene expression datasets demonstrate that densityCut performs better than state-of-the-art algorithms for clustering biological datasets. For applications, we focus on the recent cancer mutation clustering and single cell data analyses, namely to cluster variant allele frequencies of somatic mutations to reveal clonal architectures of individual tumours, to cluster single-cell gene expression data to uncover cell population compositions, and to cluster single-cell mass cytometry data to detect communities of cells of the same functional states or types. densityCut performs better than competing algorithms and is scalable to large datasets. AVAILABILITY AND IMPLEMENTATION: Data and the densityCut R package is available from https://bitbucket.org/jerry00/densitycut_dev CONTACT: : condon@cs.ubc.ca or sshah@bccrc.ca or jiaruid@cs.ubc.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An RCOR1 loss-associated gene expression signature identifies a prognostically significant DLBCL subgroup.

Effective treatment of diffuse large B-cell lymphoma (DLBCL) is plagued by heterogeneous responses to standard therapy, and molecular mechanisms underlying unfavorable outcomes in lymphoma patients remain elusive. Here, we profiled 148 genomes with 91 matching transcriptomes in a DLBCL cohort treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP) to uncover molecular subgroups linked to treatment failure. Systematic integration of high-resolution genotyping arrays and RNA sequencing data revealed novel deletions in RCOR1 to be associated with unfavorable progression-free survival (P = .001). Integration of expression data from the clinical samples with data from RCOR1 knockdowns in the lymphoma cell lines KM-H2 and Raji yielded an RCOR1 loss-associated gene signature comprising 233 genes. This signature identified a subgroup of patients with unfavorable overall survival (P = .023). The prognostic significance of the 233-gene signature for overall survival was reproduced in an independent cohort comprising 195 R-CHOP-treated patients (P = .039). Additionally, we discovered that within the International Prognostic Index low-risk group, the gene signature provides additional prognostic value that was independent of the cell-of-origin phenotype. We present a novel and reproducible molecular subgroup of DLBCL that impacts risk-stratification of R-CHOP-treated DLBCL patients and reveals a possible new avenue for therapeutic intervention strategies.

Integrative analysis of genome-wide loss of heterozygosity and monoallelic expression at nucleotide resolution reveals disrupted pathways in triple-negative breast cancer.

Loss of heterozygosity (LOH) and copy number alteration (CNA) feature prominently in the somatic genomic landscape of tumors. As such, karyotypic aberrations in cancer genomes have been studied extensively to discover novel oncogenes and tumor-suppressor genes. Advances in sequencing technology have enabled the cost-effective detection of tumor genome and transcriptome mutation events at single-base-pair resolution; however, computational methods for predicting segmental regions of LOH in this context are not yet fully explored. Consequently, whole transcriptome, nucleotide-level resolution analysis of monoallelic expression patterns associated with LOH has not yet been undertaken in cancer. We developed a novel approach for inference of LOH from paired tumor/normal sequence data and applied it to a cohort of 23 triple-negative breast cancer (TNBC) genomes. Following extensive benchmarking experiments, we describe the nucleotide-resolution landscape of LOH in TNBC and assess the consequent effect of LOH on the transcriptomes of these tumors using RNA-seq-derived measurements of allele-specific expression. We show that the majority of monoallelic expression in the transcriptomes of triple-negative breast cancer can be explained by genomic regions of LOH and establish an upper bound for monoallelic expression that may be explained by other tumor-specific modifications such as epigenetics or mutations. Monoallelically expressed genes associated with LOH reveal that cell cycle, homologous recombination and actin-cytoskeletal functions are putatively disrupted by LOH in TNBC. Finally, we show how inference of LOH can be used to interpret allele frequencies of somatic mutations and postulate on temporal ordering of mutations in the evolutionary history of these tumors.

Mutational and structural analysis of diffuse large B-cell lymphoma using whole-genome sequencing.

Diffuse large B-cell lymphoma (DLBCL) is a genetically heterogeneous cancer composed of at least 2 molecular subtypes that differ in gene expression and distribution of mutations. Recently, application of genome/exome sequencing and RNA-seq to DLBCL has revealed numerous genes that are recurrent targets of somatic point mutation in this disease. Here we provide a whole-genome-sequencing-based perspective of DLBCL mutational complexity by characterizing 40 de novo DLBCL cases and 13 DLBCL cell lines and combining these data with DNA copy number analysis and RNA-seq from an extended cohort of 96 cases. Our analysis identified widespread genomic rearrangements including evidence for chromothripsis as well as the presence of known and novel fusion transcripts. We uncovered new gene targets of recurrent somatic point mutations and genes that are targeted by focal somatic deletions in this disease. We highlight the recurrence of germinal center B-cell-restricted mutations affecting genes that encode the S1P receptor and 2 small GTPases (GNA13 and GNAI2) that together converge on regulation of B-cell homing. We further analyzed our data to approximate the relative temporal order in which some recurrent mutations were acquired and demonstrate that ongoing acquisition of mutations and intratumoral clonal heterogeneity are common features of DLBCL. This study further improves our understanding of the processes and pathways involved in lymphomagenesis, and some of the pathways mutated here may indicate new avenues for therapeutic intervention.

Ovarian and endometrial endometrioid carcinomas have distinct CTNNB1 and PTEN mutation profiles.

Ovarian endometrioid carcinomas and endometrial endometrioid carcinomas share many histological and molecular alterations. These similarities are likely due to a common endometrial epithelial precursor cell of origin, with most ovarian endometrioid carcinomas arising from endometriosis. To directly compare the mutation profiles of two morphologically similar tumor types, endometrial endometrioid carcinomas (n=307) and ovarian endometrioid carcinomas (n=33), we performed select exon capture sequencing on a panel of genes: ARID1A, PTEN, PIK3CA, KRAS, CTNNB1, PPP2R1A, TP53. We found that PTEN mutations are more frequent in low-grade endometrial endometrioid carcinomas (67%) compared with low-grade ovarian endometrioid carcinomas (17%) (P<0.0001). By contrast, CTNNB1 mutations are significantly different in low-grade ovarian endometrioid carcinomas (53%) compared with low-grade endometrial endometrioid carcinomas (28%) (P<0.0057). This difference in CTNNB1 mutation frequency may be reflective of the distinct microenvironments; the epithelial cells lining an endometriotic cyst within the ovary are exposed to a highly oxidative environment that promotes tumorigenesis. Understanding the distinct mutation patterns found in the PI3K and Wnt pathways of ovarian and endometrial endometrioid carcinomas may provide future opportunities for stratifying patients for targeted therapeutics.

Intratumoral heterogeneity in a minority of ovarian low-grade serous carcinomas.

BACKGROUND: Ovarian low-grade serous carcinoma (LGSC) has fewer mutations than ovarian high-grade serous carcinoma (HGSC) and a less aggressive clinical course. However, an overwhelming majority of LGSC patients do not respond to conventional chemotherapy resulting in a poor long-term prognosis comparable to women diagnosed with HGSC. KRAS and BRAF mutations are common in LGSC, leading to clinical trials targeting the MAPK pathway. We assessed the stability of targetable somatic mutations over space and/or time in LGSC, with a view to inform stratified treatment strategies and clinical trial design. METHODS: Eleven LGSC cases with primary and recurrent paired samples were identified (stage IIB-IV). Tumor DNA was isolated from 1-4 formalin-fixed paraffin-embedded tumor blocks from both the primary and recurrence (n = 37 tumor and n = 7 normal samples). Mutational analysis was performed using the Ion Torrent AmpliSeqTM Cancer Panel, with targeted validation using Fluidigm-MiSeq, Sanger sequencing and/or Raindance Raindrop digital PCR. RESULTS: KRAS (3/11), BRAF (2/11) and/or NRAS (1/11) mutations were identified in five unique cases. A novel, non-synonymous mutation in SMAD4 was observed in one case. No somatic mutations were detected in the remaining six cases. In two cases with a single matched primary and recurrent sample, two KRAS hotspot mutations (G12V, G12R) were both stable over time. In three cases with multiple samplings from both the primary and recurrent surgery some mutations (NRAS Q61R, BRAF V600E, SMAD4 R361G) were stable across all samples, while others (KRAS G12V, BRAF G469V) were unstable. CONCLUSIONS: Overall, the majority of cases with detectable somatic mutations showed mutational stability over space and time while one of five cases showed both temporal and spatial mutational instability in presumed drivers of disease. Investigation of additional cases is required to confirm whether mutational heterogeneity in a minority of LGSC is a general phenomenon that should be factored into the design of clinical trials and stratified treatment for this patient population.

Distinct evolutionary trajectories of primary high-grade serous ovarian cancers revealed through spatial mutational profiling.

High-grade serous ovarian cancer (HGSC) is characterized by poor outcome, often attributed to the emergence of treatment-resistant subclones. We sought to measure the degree of genomic diversity within primary, untreated HGSCs to examine the natural state of tumour evolution prior to therapy. We performed exome sequencing, copy number analysis, targeted amplicon deep sequencing and gene expression profiling on 31 spatially and temporally separated HGSC tumour specimens (six patients), including ovarian masses, distant metastases and fallopian tube lesions. We found widespread intratumoural variation in mutation, copy number and gene expression profiles, with key driver alterations in genes present in only a subset of samples (eg PIK3CA, CTNNB1, NF1). On average, only 51.5% of mutations were present in every sample of a given case (range 10.2-91.4%), with TP53 as the only somatic mutation consistently present in all samples. Complex segmental aneuploidies, such as whole-genome doubling, were present in a subset of samples from the same individual, with divergent copy number changes segregating independently of point mutation acquisition. Reconstruction of evolutionary histories showed one patient with mixed HGSC and endometrioid histology, with common aetiologic origin in the fallopian tube and subsequent selection of different driver mutations in the histologically distinct samples. In this patient, we observed mixed cell populations in the early fallopian tube lesion, indicating that diversity arises at early stages of tumourigenesis. Our results revealed that HGSCs exhibit highly individual evolutionary trajectories and diverse genomic tapestries prior to therapy, exposing an essential biological characteristic to inform future design of personalized therapeutic solutions and investigation of drug-resistance mechanisms.

An esophagus cell atlas reveals dynamic rewiring during active eosinophilic esophagitis and remission.

Coordinated cell interactions within the esophagus maintain homeostasis, and disruption can lead to eosinophilic esophagitis (EoE), a chronic inflammatory disease with poorly understood pathogenesis. We profile 421,312 individual cells from the esophageal mucosa of 7 healthy and 15 EoE participants, revealing 60 cell subsets and functional alterations in cell states, compositions, and interactions that highlight previously unclear features of EoE. Active disease displays enrichment of ALOX15+ macrophages, PRDM16+ dendritic cells expressing the EoE risk gene ATP10A, and cycling mast cells, with concomitant reduction of TH17 cells. Ligand-receptor expression uncovers eosinophil recruitment programs, increased fibroblast interactions in disease, and IL-9+IL-4+IL-13+ TH2 and endothelial cells as potential mast cell interactors. Resolution of inflammation-associated signatures includes mast and CD4+ TRM cell contraction and cell type-specific downregulation of eosinophil chemoattractant, growth, and survival factors. These cellular alterations in EoE and remission advance our understanding of eosinophilic inflammation and opportunities for therapeutic intervention.

JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data.

MOTIVATION: Identification of somatic single nucleotide variants (SNVs) in tumour genomes is a necessary step in defining the mutational landscapes of cancers. Experimental designs for genome-wide ascertainment of somatic mutations now routinely include next-generation sequencing (NGS) of tumour DNA and matched constitutional DNA from the same individual. This allows investigators to control for germline polymorphisms and distinguish somatic mutations that are unique to the tumour, thus reducing the burden of labour-intensive and expensive downstream experiments needed to verify initial predictions. In order to make full use of such paired datasets, computational tools for simultaneous analysis of tumour-normal paired sequence data are required, but are currently under-developed and under-represented in the bioinformatics literature. RESULTS: In this contribution, we introduce two novel probabilistic graphical models called JointSNVMix1 and JointSNVMix2 for jointly analysing paired tumour-normal digital allelic count data from NGS experiments. In contrast to independent analysis of the tumour and normal data, our method allows statistical strength to be borrowed across the samples and therefore amplifies the statistical power to identify and distinguish both germline and somatic events in a unified probabilistic framework. AVAILABILITY: The JointSNVMix models and four other models discussed in the article are part of the JointSNVMix software package available for download at http://compbio.bccrc.ca CONTACT: sshah@bccrc.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data.

MOTIVATION: The study of cancer genomes now routinely involves using next-generation sequencing technology (NGS) to profile tumours for single nucleotide variant (SNV) somatic mutations. However, surprisingly few published bioinformatics methods exist for the specific purpose of identifying somatic mutations from NGS data and existing tools are often inaccurate, yielding intolerably high false prediction rates. As such, the computational problem of accurately inferring somatic mutations from paired tumour/normal NGS data remains an unsolved challenge. RESULTS: We present the comparison of four standard supervised machine learning algorithms for the purpose of somatic SNV prediction in tumour/normal NGS experiments. To evaluate these approaches (random forest, Bayesian additive regression tree, support vector machine and logistic regression), we constructed 106 features representing 3369 candidate somatic SNVs from 48 breast cancer genomes, originally predicted with naive methods and subsequently revalidated to establish ground truth labels. We trained the classifiers on this data (consisting of 1015 true somatic mutations and 2354 non-somatic mutation positions) and conducted a rigorous evaluation of these methods using a cross-validation framework and hold-out test NGS data from both exome capture and whole genome shotgun platforms. All learning algorithms employing predictive discriminative approaches with feature selection improved the predictive accuracy over standard approaches by statistically significant margins. In addition, using unsupervised clustering of the ground truth 'false positive' predictions, we noted several distinct classes and present evidence suggesting non-overlapping sources of technical artefacts illuminating important directions for future study. AVAILABILITY: Software called MutationSeq and datasets are available from http://compbio.bccrc.ca.

Use of mutation profiles to refine the classification of endometrial carcinomas.

The classification of endometrial carcinomas is based on pathological assessment of tumour cell type; the different cell types (endometrioid, serous, carcinosarcoma, mixed, undifferentiated, and clear cell) are associated with distinct molecular alterations. This current classification system for high-grade subtypes, in particular the distinction between high-grade endometrioid (EEC-3) and serous carcinomas (ESC), is limited in its reproducibility and prognostic abilities. Therefore, a search for specific molecular classifiers to improve endometrial carcinoma subclassification is warranted. We performed target enrichment sequencing on 393 endometrial carcinomas from two large cohorts, sequencing exons from the following nine genes: ARID1A, PPP2R1A, PTEN, PIK3CA, KRAS, CTNNB1, TP53, BRAF, and PPP2R5C. Based on this gene panel, each endometrial carcinoma subtype shows a distinct mutation profile. EEC-3s have significantly different frequencies of PTEN and TP53 mutations when compared to low-grade endometrioid carcinomas. ESCs and EEC-3s are distinct subtypes with significantly different frequencies of mutations in PTEN, ARID1A, PPP2R1A, TP53, and CTNNB1. From the mutation profiles, we were able to identify subtype outliers, ie cases diagnosed morphologically as one subtype but with a mutation profile suggestive of a different subtype. Careful review of these diagnostically challenging cases suggested that the original morphological classification was incorrect in most instances. The molecular profile of carcinosarcomas suggests two distinct mutation profiles for these tumours: endometrioid-type (PTEN, PIK3CA, ARID1A, KRAS mutations) and serous-type (TP53 and PPP2R1A mutations). While this nine-gene panel does not allow for a purely molecularly based classification of endometrial carcinoma, it may prove useful as an adjunct to morphological classification and serve as an aid in the classification of problematic cases. If used in practice, it may lead to improved diagnostic reproducibility and may also serve to stratify patients for targeted therapeutics.

The improvement of agronomic performances in the cold weather conditions for perennial wheatgrass by crossing Thinopyrum intermedium with wheat-Th. intermedium partial amphiploids.

Thinopyrum intermedium (2n=6x=42, StStJrJrJvsJvs) is resistant or tolerant to biotic and abiotic stresses, making it suitable for developing perennial crops and forage. Through five cycles of selection, we developed 24 perennial wheatgrass lines, designated 19HSC-Q and 20HSC-Z, by crossing wheat-Th. intermedium partial amphiploids with Th. intermedium. The cold resistance, morphological performance, chromosome composition, and yield components of these perennial lines were investigated from 2019 to 2022. Six lines of 19HSC-Q had higher 1,000-kernel weight, grains per spike, and tiller number than Th. intermedium, as well as surviving -30°C in winter. Lines 19HSC-Q14, 19HSC-Q18, and 19HSC-Q20 had the best performances for grain number per spike and 1,000-kernel weight. The 20HSC-Z lines, 20HSC-Z1, 20HSC-Z2, and 20HSC-Z3, were able to survive in the cold winter in Harbin and had been grown for two years. Sequential multicolor GISH analysis revealed that the Jvs subgenome of Th. intermedium were divided into two karyotypes, three pairs of type-I Jvs chromosomes and four pairs of type-II Jvs chromosomes. Both Th. intermedium and the 24 advanced perennial wheatgrass lines had similar chromosome compositions, but the translocations among subgenome chromosomes were detected in some lines with prominent agronomic traits, such as 19HSC-Q11, 19HSC-Q14, 19HSC-Q18, 19HSC-Q20, and the three 20HSC-Z lines. The chromosome aberrations were distinguished into two types: the large fragment translocation with St-Jr, Jvs-St, Jr-IIJvs, and Jvs-Jr and the small fragment introgression of Jr-St, St-IJvs, and Jvs-Jr. These chromosomal variations can be used to further analyze the relationship between the subgenomes and phenotypes of Th. intermedium. The results of this study provide valuable materials for the next selection cycle of cold-resistant perennial wheatgrass.

BRCA1 and BRCA2 mutations correlate with TP53 abnormalities and presence of immune cell infiltrates in ovarian high-grade serous carcinoma.

We characterized BRCA1 and BRCA2 status (mutation/methylation) in a consecutive series of cases of ovarian carcinoma in order to identify differences in clinicopathological features, molecular characteristics, and outcome between the pelvic high-grade serous cancers with (i) germline or somatic mutations in BRCA1 or BRCA2, (ii) methylation of BRCA1, and (iii) normal BRCA1 or BRCA2. In all, 131 women were identified prospectively, who were undergoing surgical staging and agreed to germline testing for BRCA1 and BRCA2 mutations. Histopathology, germline and somatic BRCA1 or BRCA2 mutations, BRCA1 methylation, and BRCA1 and BRCA2 mRNA expression levels distinguished four subgroups. In all, 103 cases were high-grade serous carcinoma and of these 31 (30%) had germline or somatic BRCA1 or BRCA2 mutations (20% BRCA1 and 10% BRCA2) (group 1), 21 (20%) had methylation of BRCA1 (group 2), and in 51 (50%) there was no BRCA loss (group 3). Group 4 consisted of 28 cases of non-high-grade serous, none of which had BRCA loss. BRCA1 and BRCA2 mRNA expression levels correlated with designated group (P=0.0008). Among high-grade serous carcinomas, there were no differences between groups 1-3 with respect to stage, ascites, CA125 level, platinum sensitivity, cytoreduction rate, neoadjuvant chemotherapy, or survival. Tumors with BRCA1 or BRCA2 mutations had increased immune infiltrates (CD20 and TIA-1) compared with high-grade serous without mutations (P=0.034, 0.027). TP53 expression differed between groups (P<0.0001), with abnormal TP53 expression in 49/50 tumors from groups 1 and 2. Wild-type TP53 expression was associated with worse outcome in high-grade serous (P<0.001). BRCA loss (mutation/methylation) is a common event in the pelvic high-grade serous (50%). TP53 abnormalities and increased immune cell infiltrates are significantly more common in high-grade serous with germline and somatic mutations in BRCA1 or BRCA2, compared with tumors lacking BRCA abnormalities.

An adaptive approach to denoising tandem mass spectra.

In our recent work on denoising, a linear combination of five features was used to adjust the peak intensities in tandem mass spectra. Although the method showed a promise, the coefficients (weights) of the linear combination were fixed and determined empirically. In this paper, we proposed an adaptive approach for estimating these weights. The proposed approach: (i) calculates the score for each peak in a data set with the previous empirically determined weights, (ii) selects the training data set based on the scores of peaks, (iii) applies the linear discriminant analysis to the training data set and takes the solution of linear discriminant analysis as the new weights, (iv) calculates the score again with the new weights, (v) repeats (ii)-(iv) until the weights have no significant change. After getting the final weights, the proposed approach follows the previous methods. The proposed approach was applied to two tandem mass spectra data sets: ISB (with low resolution) and TOV-Q (with high resolution) to evaluate its performance. The results show that about 66% of peaks (likely noise peaks) can be removed and that the number of peptides identified by MASCOT increases by 14 and 23.4% for ISB and TOV-Q data set, respectively, compared to the previous work.