Relative algebraic K-theory and algebraic cyclic homology

Since its introduction over 40 years ago algebraic K-theory, which provides powerful invariants, still remains hard to compute. The subject of this work is the construction of an isomorphism between relative algebraic K-groups and relative algebraic cyclic homology in low dimensions, for certain nilpotent ideals. This isomorphism generalizes the Theorem of Goodwillie concerning rational algebras and provides a more accessible alternative to topological cyclic homology for the computation of algebraic K-groups. Following roughly the strategy of Goodwillie, the proof is structured into several parts of varying interdependencies. First, we construct a natural isomorphism between group homology and Lie ring homology of certain associated groups and Lie rings. This represents an integral generalization of a Theorem of Pickel concerning nilpotent groups and also provides a strategy for an integral version of the Theorem of Lazard concerning p-valued groups, which both considered homology with rational coefficients. The theory provides a bridge in form of a natural logarithm map from the homology of the multiplicative to that of the additive K-theory. Second, we prove that the low-dimensional homotopy groups of an infinite loop space can be identified with the primitive part of its homology by using an improved version of the Hurewicz map. This represents a variant of a Theorem of Beilinson linking both objects up to isogeny. We apply this to the infinite loop space of relative K-theory. Similarly by using an additive analogue we compute the primitive part of the homology of the Lie algebra homology of matrices as cyclic homology. This can be considered as an integral generalization of the Theorem of Loday, Quillen and Tsygan. Combining the single steps we are constructing the desired isomorphism between K-theory and cyclic homology and also compare it with the negative Chern character. Alongside the proofs we provide a comprehensive collection of required abstract tools of simplicial homotopy theory. As an application of the main theorem we compute the lower relative K-groups of truncated polynomial rings over a subring of the rationals. This shows that our Theorem can be used to obtain new results in the computation of K-groups

Mutational Landscape and Expression of PD-L1 in Patients with Non-Small Cell Lung Cancer Harboring Genomic Alterations of the MET gene

BACKGROUND Mesenchymal-to-epithelial transition (MET) exon 14 skipping mutations and MET gene amplification occur in 3-5% of non-small cell lung cancer (NSCLC) patients. Tyrosine kinase inhibitors (TKIs) targeting MET alterations have shown promising results in these patients. OBJECTIVE The aim of this study was to describe the genomic profile, PD-L1 expression and clinicopathological features of MET dysregulated NSCLC. PATIENTS AND METHODS We identified 188 patients with advanced-stage NSCLC with data on MET expression by immunohistochemistry (IHC). IHC for PD-L1 expression was performed in 131 patient samples, and next-generation sequencing (NGS) analysis was performed in 109 patient samples. RESULTS MET exon 14 skipping alterations were identified in 16 (14.7%) samples, MET amplifications with cut-off ≥4 copy number variations were identified in 11 (10.1%) samples, and an oncogenic MET mutation (MET p.D1228N) was identified in 1 (0.9%) sample. 12/15 tumors (80.0%) harboring MET exon 14 alterations and 7/11 (63.6%) MET-amplified tumors expressed PD-L1 in ≥1% of tumor cells. Tumors harboring MET exon 14 skipping alterations expressed PD-L1 more frequently than MET wild-type IHC-positive tumors (p = 0.045). Twenty-five percent of MET exon 14-altered cases and 33% of MET-amplified cases harbored potentially targetable oncogenic co-mutations in KRAS, BRAF, and EGFR. The most frequent co-occurring mutations in all MET-altered tumors were TP53, KRAS, BRAF, and CDK4. CONCLUSIONS We demonstrated that MET exon 14 skipping alterations and MET amplification are not mutually exclusive to other oncogenic co-mutations, and report the association of genomic MET alterations with PD-L1 expression. Since genomic MET alterations are emerging targets requiring upfront treatment, optimal understanding of the co-mutational landscape for this patient population is needed

Single-cell proteomics defines the cellular heterogeneity of localized prostate cancer

Localized prostate cancer exhibits multiple genomic alterations and heterogeneity at the proteomic level. Single-cell technologies capture important cell-to-cell variability responsible for heterogeneity in biomarker expression that may be overlooked when molecular alterations are based on bulk tissue samples. This study aims to identify prognostic biomarkers and describe the heterogeneity of prostate cancer and the associated microenvironment by simultaneously quantifying 36 proteins using single-cell mass cytometry analysis of over 1.6 million cells from 58 men with localized prostate cancer. We perform this task, using a high-dimensional clustering pipeline named Franken to describe subpopulations of immune, stromal, and prostate cells, including changes occurring in tumor tissues and high-grade disease that provide insights into the coordinated progression of prostate cancer. Our results further indicate that men with localized disease already harbor rare subpopulations that typically occur in castration-resistant and metastatic disease

Language Learning and Language Use - Applied Linguistics Approaches. Selected Papers from the 3rd Junior Research Meeting of Applied Linguistics

Kersten S, Ludwig C, Meer D, Rüschoff B, eds. Language Learning and Language Use - Applied Linguistics Approaches. Selected Papers from the 3rd Junior Research Meeting of Applied Linguistics. Duisburg: Universitätsverlag Rhein Ruhr; 2012

A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients

Microscopy image data of human cancers provide detailed phenotypes of spatially and morphologically intact tissues at single-cell resolution, thus complementing large-scale molecular analyses, e.g., next generation sequencing or proteomic profiling. Here we describe a high-resolution tissue microarray (TMA) image dataset from a cohort of 71 prostate tissue samples, which was hybridized with bright-field dual colour chromogenic and silver in situ hybridization probes for the tumour suppressor gene PTEN. These tissue samples were digitized and supplemented with expert annotations, clinical information, statistical models of PTEN genetic status, and computer source codes. For validation, we constructed an additional TMA dataset for 424 prostate tissues, hybridized with FISH probes for PTEN, and performed survival analysis on a subset of 339 radical prostatectomy specimens with overall, disease-specific and recurrence-free survival (maximum 167 months). For application, we further produced 6,036 image patches derived from two whole slides. Our curated collection of prostate cancer data sets provides reuse potential for both biomedical and computational studies

Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity

Recent large-scale genome analyses of human tissue samples have uncovered a high degree of genetic alterations and tumour heterogeneity in most tumour entities, independent of morphological phenotypes and histopathological characteristics. Assessment of genetic copy-number variation (CNV) and tumour heterogeneity by fluorescence in situ hybridization (ISH) provides additional tissue morphology at single-cell resolution, but it is labour intensive with limited throughput and high inter-observer variability. We present an integrative method combining bright-field dual-colour chromogenic and silver ISH assays with an image-based computational workflow (ISHProfiler), for accurate detection of molecular signals, high-throughput evaluation of CNV, expressive visualization of multi-level heterogeneity (cellular, inter- and intra-tumour heterogeneity), and objective quantification of heterogeneous genetic deletions (PTEN) and amplifications (19q12, HER2) in diverse human tumours (prostate, endometrial, ovarian and gastric), using various tissue sizes and different scanners, with unprecedented throughput and reproducibility

Correction: Automated Gleason grading of prostate cancer tissue microarrays via deep learning (vol 8, 12054, 2018)

ISSN:2045-232

Author Correction / 2019: Automated Gleason grading of prostate cancer tissue microarrays via deep learning

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