Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer.

We report a comprehensive analysis of 412 muscle-invasive bladder cancers characterized by multiple TCGA analytical platforms. Fifty-eight genes were significantly mutated, and the overall mutational load was associated with APOBEC-signature mutagenesis. Clustering by mutation signature identified a high-mutation subset with 75% 5-year survival. mRNA expression clustering refined prior clustering analyses and identified a poor-survival "neuronal" subtype in which the majority of tumors lacked small cell or neuroendocrine histology. Clustering by mRNA, long non-coding RNA (lncRNA), and miRNA expression converged to identify subsets with differential epithelial-mesenchymal transition status, carcinoma in situ scores, histologic features, and survival. Our analyses identified 5 expression subtypes that may stratify response to different treatments.

Author Correction: PAK signalling drives acquired drug resistance to MAPK inhibitors in BRAF-mutant melanomas.

In this Letter, two relevant references were omitted; see the accompanying Amendment for details. The original Letter has not been corrected.

Next-generation characterization of the Cancer Cell Line Encyclopedia.

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.

Exosomal PD-L1 contributes to immunosuppression and is associated with anti-PD-1 response.

Tumour cells evade immune surveillance by upregulating the surface expression of programmed death-ligand 1 (PD-L1), which interacts with programmed death-1 (PD-1) receptor on T cells to elicit the immune checkpoint response1,2. Anti-PD-1 antibodies have shown remarkable promise in treating tumours, including metastatic melanoma2-4. However, the patient response rate is low4,5. A better understanding of PD-L1-mediated immune evasion is needed to predict patient response and improve treatment efficacy. Here we report that metastatic melanomas release extracellular vesicles, mostly in the form of exosomes, that carry PD-L1 on their surface. Stimulation with interferon-γ (IFN-γ) increases the amount of PD-L1 on these vesicles, which suppresses the function of CD8 T cells and facilitates tumour growth. In patients with metastatic melanoma, the level of circulating exosomal PD-L1 positively correlates with that of IFN-γ, and varies during the course of anti-PD-1 therapy. The magnitudes of the increase in circulating exosomal PD-L1 during early stages of treatment, as an indicator of the adaptive response of the tumour cells to T cell reinvigoration, stratifies clinical responders from non-responders. Our study unveils a mechanism by which tumour cells systemically suppress the immune system, and provides a rationale for the application of exosomal PD-L1 as a predictor for anti-PD-1 therapy.

RPPA SPACE: an R package for normalization and quantitation of Reverse-Phase Protein Array data.

SUMMARY: Reverse-Phase Protein Array (RPPA) is a robust high-throughput, cost-effective platform for quantitatively measuring proteins in biological specimens. However, converting raw RPPA data into normalized, analysis-ready data remains a challenging task. Here, we present the RPPA SPACE (RPPA Superposition Analysis and Concentration Evaluation) R package, a substantially improved successor to SuperCurve, to meet that challenge. SuperCurve has been used to normalize over 170 000 samples to date. RPPA SPACE allows exclusion of poor-quality samples from the normalization process to improve the quality of the remaining samples. It also features a novel quality-control metric, 'noise', that estimates the level of random errors present in each RPPA slide. The noise metric can help to determine the quality and reliability of the data. In addition, RPPA SPACE has simpler input requirements and is more flexible than SuperCurve, it is much faster with greatly improved error reporting. AVAILABILITY AND IMPLEMENTATION: The standalone RPPA SPACE R package, tutorials and sample data are available via https://rppa.space/, CRAN (https://cran.r-project.org/web/packages/RPPASPACE/index.html) and GitHub (https://github.com/MD-Anderson-Bioinformatics/RPPASPACE). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PAK signalling drives acquired drug resistance to MAPK inhibitors in BRAF-mutant melanomas.

Targeted BRAF inhibition (BRAFi) and combined BRAF and MEK inhibition (BRAFi and MEKi) therapies have markedly improved the clinical outcomes of patients with metastatic melanoma. Unfortunately, the efficacy of these treatments is often countered by the acquisition of drug resistance. Here we investigated the molecular mechanisms that underlie acquired resistance to BRAFi and to the combined therapy. Consistent with previous studies, we show that resistance to BRAFi is mediated by ERK pathway reactivation. Resistance to the combined therapy, however, is mediated by mechanisms independent of reactivation of ERK in many resistant cell lines and clinical samples. p21-activated kinases (PAKs) become activated in cells with acquired drug resistance and have a pivotal role in mediating resistance. Our screening, using a reverse-phase protein array, revealed distinct mechanisms by which PAKs mediate resistance to BRAFi and the combined therapy. In BRAFi-resistant cells, PAKs phosphorylate CRAF and MEK to reactivate ERK. In cells that are resistant to the combined therapy, PAKs regulate JNK and ß-catenin phosphorylation and mTOR pathway activation, and inhibit apoptosis, thereby bypassing ERK. Together, our results provide insights into the molecular mechanisms underlying acquired drug resistance to current targeted therapies, and may help to direct novel drug development efforts to overcome acquired drug resistance.

A Heuristic-Based Adaptive Iterated Greedy Algorithm for Lot-Streaming Hybrid Flow Shop Scheduling Problem with Consistent and Intermingled Sub-Lots.

Owing to the different quantities and processing times of sub-lots, intermingling sub-lots with each other, instead of fixing the production sequence of sub-lots of a lot as in the existing studies, is a more practical approach to lot-streaming flow shops. Hence, a lot-streaming hybrid flow shop scheduling problem with consistent and intermingled sub-lots (LHFSP-CIS) was studied. A mixed integer linear programming (MILP) model was established, and a heuristic-based adaptive iterated greedy algorithm (HAIG) with three modifications was designed to solve the problem. Specifically, a two-layer encoding method was proposed to decouple the sub-lot-based connection. Two heuristics were embedded in the decoding process to reduce the manufacturing cycle. Based on this, a heuristic-based initialization is proposed to improve the performance of the initial solution; an adaptive local search with four specific neighborhoods and an adaptive strategy has been structured to improve the exploration and exploitation ability. Besides, an acceptance criterion of inferior solutions has been improved to promote global optimization ability. The experiment and the non-parametric Kruskal-Wallis test (p = 0) showed the significant advantages of HAIG in effectiveness and robustness compared with five state-of-the-art algorithms. An industrial case study verifies that intermingling sub-lots is an effective technique to enhance the utilization ratio of machines and shorten the manufacturing cycle.

Research on the Surface Deformation, Fault Rupture, and Coseismic Geohazard of the 2022 Luding Mw 6.8 Earthquake.

An Mw 6.8 earthquake occurred in Luding County, Ganzi Tibetan Autonomous Prefecture, Sichuan Province, on 5 September 2022. This seismic event triggered numerous coseismic geohazards in the seismic zone. In this study, the ascending- and descending-track synthetic aperture radar (SAR) images observed by the Sentinel-1A satellite are utilized to extract the coseismic surface deformation of the Luding earthquake. Subsequently, a faulting model is estimated based on the elastic dislocation theory, under the constraint of the InSAR observation. Additionally, the POT technique was employed to detect coseismic geohazards. High-spatial-resolution optical remote sensing images served to validate the reliability of the detection results. The coseismic interferometric synthetic aperture radar (InSAR) deformation field indicated a maximum deformation of ~190 mm and ~140 mm along the ascending and descending tracks, respectively. The estimated best-fitting faulting model suggests that the optimal seismogenic fault strike and dip angles are 169.3° and 70°, respectively. The fault slip predominantly exhibits left-lateral strike-slip characteristics and is concentrated at depths of 3-12 km. The estimated maximum fault slip was 2.67 m, occurring at a depth of 7 km. The pixel offset tracking (POT) result derived from the pre- and post-earthquake SAR images found a total of 245 medium- to large-scale coseismic geohazards, with a verification rate from optical images exceeding 64%. The distribution of these geohazards is notably dense within the significant fault rupture segment. Geohazards on the fault hanging wall are densely packed, whereas landslides along the Dadu River's fault footwall are also notably frequent.

Recurrent Neural Network Methods for Extracting Dynamic Balance Variables during Gait from a Single Inertial Measurement Unit.

Monitoring dynamic balance during gait is critical for fall prevention in the elderly. The current study aimed to develop recurrent neural network models for extracting balance variables from a single inertial measurement unit (IMU) placed on the sacrum during walking. Thirteen healthy young and thirteen healthy older adults wore the IMU during walking and the ground truth of the inclination angles (IA) of the center of pressure to the center of mass vector and their rates of changes (RCIA) were measured simultaneously. The IA, RCIA, and IMU data were used to train four models (uni-LSTM, bi-LSTM, uni-GRU, and bi-GRU), with 10% of the data reserved to evaluate the model errors in terms of the root-mean-squared errors (RMSEs) and percentage relative RMSEs (rRMSEs). Independent t-tests were used for between-group comparisons. The sensitivity, specificity, and Pearson's r for the effect sizes between the model-predicted data and experimental ground truth were also obtained. The bi-GRU with the weighted MSE model was found to have the highest prediction accuracy, computational efficiency, and the best ability in identifying statistical between-group differences when compared with the ground truth, which would be the best choice for the prolonged real-life monitoring of gait balance for fall risk management in the elderly.

[Retrospective on ESP Courses in the Global English Era].

With the rise of the medical tourism industry in Taiwan and changes in the country's population structure, nurses are facing greater challenges than ever before. Both professional knowledge and English proficiency are indispensable. Various types of English for Specific Purposes (ESP) courses have emerged to assist healthcare professionals to build their English abilities and cope with the changing demands of their profession. However, related research indicates that the deficient state of English communication skills among nurses in Taiwan may hinder the country's ability to effectively promote medical internationalization and handle ongoing changes in its population structure. To effectively face this predicament, educators must re-examine the current design of ESP courses. Therefore, this article was developed to explore ESP courses from the three perspectives of language descriptions, needs analysis, and learning theories. Furthermore, the concepts and research related to these three perspectives, including the nurse-patient relationship, community of practice, situated learning, and English as a medium of instruction, were reviewed. Some insights into how these concepts may be applied to ESP courses are also proposed with the goals of better incorporating the needs of learners into course designs and placing learners at the center of language learning.

Identification of potential gene markers in gestational diabetes mellitus.

This study aims to investigate underlying mechanisms of gestational diabetes mellitus (GDM). In this work, the GSE70493 dataset from GDM and control samples was acquired from Gene Expression Omnibus (GEO) database. Afterward, differentially expressed genes (DEGs) were screened between GDM and control samples. Subsequently, functional enrichment analysis and protein-protein interaction (PPI) network analysis of these DEGs were carried out. Furthermore, significant sub-modules were identified, and the functional analysis was also performed. Finally, we undertook a quantitative real-time polymerase chain reaction (qRT-PCR) with the purpose of confirming several key genes in GDM development. There were totally 528 up-regulated and 684 down-regulated DEGs between GDM and healthy samples. The functional analyses suggested that the above genes were dramatically enriched in type 1 diabetes mellitus (T1DM) process and immune-related pathways. Moreover, PPI analysis revealed that several members of human leukocyte antigen (HLA) superfamily, including down-regulated HLA-DQA1, HLA-DRB1, HLA-DPA1, and HLA-DQB1 served as hub genes. In addition, six significant sub-clusters were extracted and functional analysis suggested that these four genes in sub-module 1 were also associated with immune and T1DM-related pathways. Finally, they were also confirmed by qRT-PCR array. Besides, the four members of HLA superfamily might be implicated with molecular mechanisms of GDM, contributing to a deeper understanding of GDM development.

Frequent post-operative monitoring of colorectal cancer using individualised ctDNA validated by multiregional molecular profiling.

BACKGROUND: Circulating tumour DNA (ctDNA) is known as a tumour-specific personalised biomarker, but the mutation-selection criteria from heterogeneous tumours remain a challenge. METHODS: We conducted multiregional sequencing of 42 specimens from 14 colorectal tumours of 12 patients, including two double-cancer cases, to identify mutational heterogeneity to develop personalised ctDNA assays using 175 plasma samples. RESULTS: "Founder" mutations, defined as a mutation that is present in all regions of the tumour in a binary manner (i.e., present or absent), were identified in 12/14 tumours. In contrast, "truncal" mutations, which are the first mutation that occurs prior to the divergence of branches in the phylogenetic tree using variant allele frequency (VAF) as continuous variables, were identified in 12/14 tumours. Two tumours without founder and truncal mutations were hypermutators. Most founder and truncal mutations exhibited higher VAFs than "non-founder" and "branch" mutations, resulting in a high chance to be detected in ctDNA. In post-operative long-term observation for 10/12 patients, early relapse prediction, treatment efficacy and non-relapse corroboration were achievable from frequent ctDNA monitoring. CONCLUSIONS: A single biopsy is sufficient to develop custom dPCR probes for monitoring tumour burden in most CRC patients. However, it may not be effective for those with hypermutated tumours.

TOP1 modulation during melanoma progression and in adaptative resistance to BRAF and MEK inhibitors.

In melanomas, therapy resistance can arise due to a combination of genetic, epigenetic and phenotypic mechanisms. Due to its crucial role in DNA supercoil relaxation, TOP1 is often considered an essential chemotherapeutic target in cancer. However, how TOP1 expression and activity might differ in therapy sensitive versus resistant cell types is unknown. Here we show that TOP1 expression is increased in metastatic melanoma and correlates with an invasive gene expression signature. More specifically, TOP1 expression is highest in cells with the lowest expression of MITF, a key regulator of melanoma biology. Notably, TOP1 and DNA Single-Strand Break Repair genes are downregulated in BRAFi- and BRAFi/MEKi-resistant cells and TOP1 inhibition decreases invasion markers only in BRAFi/MEKi-resistant cells. Thus, we show three different phenotypes related to TOP1 levels: i) non-malignant cells with low TOP1 levels; ii) metastatic cells with high TOP1 levels and high invasiveness; and iii) BRAFi- and BRAFi/MEKi-resistant cells with low TOP1 levels and high invasiveness. Together, these results highlight the potential role of TOP1 in melanoma progression and resistance.

TCPA v3.0: An Integrative Platform to Explore the Pan-Cancer Analysis of Functional Proteomic Data.

Reverse-phase protein arrays represent a powerful functional proteomics approach to characterizing cell signaling pathways and understanding their effects on cancer development. Using this platform, we have characterized ∼8,000 patient samples of 32 cancer types through The Cancer Genome Atlas and built a widely used, open-access bioinformatic resource, The Cancer Proteome Atlas (TCPA). To maximize the utility of TCPA, we have developed a new module called "TCGA Pan-Cancer Analysis," which provides comprehensive protein-centric analyses that integrate protein expression data and other TCGA data across cancer types. We further demonstrate the value of this module by examining the correlations of RPPA proteins with significantly mutated genes, assessing the predictive power of somatic copy-number alterations, DNA methylation, and mRNA on protein expression, inferring the regulatory effects of miRNAs on protein expression, constructing a co-expression network of proteins and pathways, and identifying clinically relevant protein markers. This upgraded TCPA (v3.0) will provide the cancer research community with a more powerful tool for studying functional proteomics and making translational impacts.

Systematic characterization of A-to-I RNA editing hotspots in microRNAs across human cancers.

RNA editing, a widespread post-transcriptional mechanism, has emerged as a new player in cancer biology. Recent studies have reported key roles for individual miRNA editing events, but a comprehensive picture of miRNA editing in human cancers remains largely unexplored. Here, we systematically characterized the miRNA editing profiles of 8595 samples across 20 cancer types from miRNA sequencing data of The Cancer Genome Atlas and identified 19 adenosine-to-inosine (A-to-I) RNA editing hotspots. We independently validated 15 of them by perturbation experiments in several cancer cell lines. These miRNA editing events show extensive correlations with key clinical variables (e.g., tumor subtype, disease stage, and patient survival time) and other molecular drivers. Focusing on the RNA editing hotspot in miR-200b, a key tumor metastasis suppressor, we found that the miR-200b editing level correlates with patient prognosis opposite to the pattern observed for the wild-type miR-200b expression. We further experimentally showed that, in contrast to wild-type miRNA, the edited miR-200b can promote cell invasion and migration through its impaired ability to inhibit ZEB1/ZEB2 and acquired concomitant ability to repress new targets, including LIFR, a well-characterized metastasis suppressor. Our study highlights the importance of miRNA editing in gene regulation and suggests its potential as a biomarker for cancer prognosis and therapy.

Implementation of a Multiplex and Quantitative Proteomics Platform for Assessing Protein Lysates Using DNA-Barcoded Antibodies.

Molecular analysis of tumors forms the basis for personalized cancer medicine and increasingly guides patient selection for targeted therapy. Future opportunities for personalized medicine are highlighted by the measurement of protein expression levels via immunohistochemistry, protein arrays, and other approaches; however, sample type, sample quantity, batch effects, and "time to result" are limiting factors for clinical application. Here, we present a development pipeline for a novel multiplexed DNA-labeled antibody platform which digitally quantifies protein expression from lysate samples. We implemented a rigorous validation process for each antibody and show that the platform is amenable to multiple protocols covering nitrocellulose and plate-based methods. Results are highly reproducible across technical and biological replicates, and there are no observed "batch effects" which are common for most multiplex molecular assays. Tests from basal and perturbed cancer cell lines indicate that this platform is comparable to orthogonal proteomic assays such as Reverse-Phase Protein Array, and applicable to measuring the pharmacodynamic effects of clinically-relevant cancer therapeutics. Furthermore, we demonstrate the potential clinical utility of the platform with protein profiling from breast cancer patient samples to identify molecular subtypes. Together, these findings highlight the potential of this platform for enhancing our understanding of cancer biology in a clinical translation setting.

Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma.

BACKGROUND: Papillary renal-cell carcinoma, which accounts for 15 to 20% of renal-cell carcinomas, is a heterogeneous disease that consists of various types of renal cancer, including tumors with indolent, multifocal presentation and solitary tumors with an aggressive, highly lethal phenotype. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist. METHODS: We performed comprehensive molecular characterization of 161 primary papillary renal-cell carcinomas, using whole-exome sequencing, copy-number analysis, messenger RNA and microRNA sequencing, DNA-methylation analysis, and proteomic analysis. RESULTS: Type 1 and type 2 papillary renal-cell carcinomas were shown to be different types of renal cancer characterized by specific genetic alterations, with type 2 further classified into three individual subgroups on the basis of molecular differences associated with patient survival. Type 1 tumors were associated with MET alterations, whereas type 2 tumors were characterized by CDKN2A silencing, SETD2 mutations, TFE3 fusions, and increased expression of the NRF2-antioxidant response element (ARE) pathway. A CpG island methylator phenotype (CIMP) was observed in a distinct subgroup of type 2 papillary renal-cell carcinomas that was characterized by poor survival and mutation of the gene encoding fumarate hydratase (FH). CONCLUSIONS: Type 1 and type 2 papillary renal-cell carcinomas were shown to be clinically and biologically distinct. Alterations in the MET pathway were associated with type 1, and activation of the NRF2-ARE pathway was associated with type 2; CDKN2A loss and CIMP in type 2 conveyed a poor prognosis. Furthermore, type 2 papillary renal-cell carcinoma consisted of at least three subtypes based on molecular and phenotypic features. (Funded by the National Institutes of Health.).

Identification and validation of a prognostic proteomic signature for cervical cancer.

OBJECTIVE: To date, The Cancer Genome Atlas (TCGA) has provided the most extensive molecular characterization of invasive cervical cancer (ICC). Analysis of reverse phase protein array (RPPA) data from TCGA samples showed that cervical cancers could be stratified into 3 clusters exhibiting significant differences in survival outcome: hormone, EMT, and PI3K/AKT. The goals of the current study were to: 1) validate the TCGA RPPA results in an independent cohort of ICC patients and 2) to develop and validate an algorithm encompassing a small antibody set for clinical utility. METHODS: Subjects consisted of 2 ICC patient cohorts with accompanying RPPA and clinical-pathologic data: 155 samples from TCGA (TCGA-155) and 61 additional, unique samples (MCW-61). Using data from 173 common RPPA antibodies, we replicated Silhouette clustering analysis in both ICC cohorts. Further, an index score for each patient was calculated from the survival-associated antibodies (SAAs) identified using Random survival forests (RSF) and the Cox proportional hazard regression model. Kaplan-Meier survival analysis and the log-rank test were performed to assess and compare cluster or risk group survival outcome. RESULTS: In addition to validating the prognostic ability of the proteomic clusters reported by TCGA, we developed an algorithm based on 22 unique antibodies (SAAs) that stratified women with ICC into low-, medium-, or high-risk survival groups. CONCLUSIONS: We provide a signature of 22 antibodies which accurately predicted survival outcome in 2 separate groups of ICC patients. Future studies examining these candidate biomarkers in additional ICC cohorts is warranted to fully determine their clinical potential.

Two phases of mitogenic signaling unveil roles for p53 and EGR1 in elimination of inconsistent growth signals.

Normal cells require continuous exposure to growth factors in order to cross a restriction point and commit to cell-cycle progression. This can be replaced by two short, appropriately spaced pulses of growth factors, where the first pulse primes a process, which is completed by the second pulse, and enables restriction point crossing. Through integration of comprehensive proteomic and transcriptomic analyses of each pulse, we identified three processes that regulate restriction point crossing: (1) The first pulse induces essential metabolic enzymes and activates p53-dependent restraining processes. (2) The second pulse eliminates, via the PI3K/AKT pathway, the suppressive action of p53, as well as (3) sets an ERK-EGR1 threshold mechanism, which digitizes graded external signals into an all-or-none decision obligatory for S phase entry. Together, our findings uncover two gating mechanisms, which ensure that cells ignore fortuitous growth factors and undergo proliferation only in response to consistent mitogenic signals.