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This paper summarizes the recent activities of the Chromosome-Centric Human Proteome Project (C-HPP) consortium, which develops new technologies to identify yet-to-be annotated proteins (termed "missing proteins") in biological samples that lack sufficient experimental evidence at the protein level for confident protein identification. The C-HPP also aims to identify new protein forms that may be caused by genetic variability, post-translational modifications, and alternative splicing. Proteogenomic data integration forms the basis of the C-HPP's activities; therefore, we have summarized some of the key approaches and their roles in the project. We present new analytical technologies that improve the chemical space and lower detection limits coupled to bioinformatics tools and some publicly available resources that can be used to improve data analysis or support the development of analytical assays. Most of this paper's content has been compiled from posters, slides, and discussions presented in the series of C-HPP workshops held during 2014. All data (posters, presentations) used are available at the C-HPP Wiki (http://c-hpp.webhosting.rug.nl/) and in the Supporting Information.

Background: Alzheimer's disease (AD) is the most common neurodegenerative disorder. Depositions of amyloid β peptide (Aβ) and tau protein are among the major pathological hallmarks of AD. Aβ and tau burden follows predictable spatial patterns during the progression of AD. Nevertheless, it remains obscure why certain brain regions are more vulnerable than others; to investigate this and dysregulated pathways during AD progression, a mass spectrometry-based proteomics study was performed. Methods: In total 103 tissue samples from regions early (entorhinal and parahippocampal cortices-medial temporal lobe (MTL)) and late affected (temporal and frontal cortices-neocortex) by tau pathology were subjected to label-free quantitative proteomics analysis. Results: Considering dysregulated proteins during AD progression, the majority (625 out of 737 proteins) was region specific, while some proteins were shared between regions (101 proteins altered in two areas and 11 proteins altered in three areas). Analogously, many dysregulated pathways during disease progression were exclusive to certain regions, but a few pathways altered in two or more areas. Changes in protein expression indicate that synapse loss occurred in all analyzed regions, while translation dysregulation was preponderant in entorhinal, parahippocampal and frontal cortices. Oxidative phosphorylation impairment was prominent in MTL. Differential proteomic analysis of brain areas in health state (controls) showed higher metabolism and increased expression of AD-related proteins in the MTL compared to the neocortex. In addition, several proteins that differentiate brain regions in control tissue were dysregulated in AD. Conclusions: This work provides the comparison of proteomic changes in brain regions affected by tau pathology at different stages of AD. Although we identified commonly regulated proteins and pathways during disease advancement, we found that the dysregulated processes are predominantly region specific. In addition, a distinct proteomic signature was found between MTL and neocortex in healthy subjects that might be related to AD

The tissue distribution and prognostic relevance of subtype‐specific proteins (ASCL1, NEUROD1, POU2F3, YAP1) present an evolving area of research in small‐cell lung cancer (SCLC). The expression of subtype‐specific transcription factors and P53 and RB1 proteins were measured by immunohistochemistry (IHC) in 386 surgically resected SCLC samples. Correlations between subtype‐specific proteins and in vitro efficacy of various therapeutic agents were investigated by proteomics and cell viability assays in 26 human SCLC cell lines. Besides SCLC‐A (ASCL1‐dominant), SCLC‐AN (combined ASCL1/NEUROD1), SCLC‐N (NEUROD1‐dominant), and SCLC‐P (POU2F3‐dominant), IHC and cluster analyses identified a quadruple‐negative SCLC subtype (SCLC‐QN). No unique YAP1‐subtype was found. The highest overall survival rates were associated with non‐neuroendocrine subtypes (SCLC‐P and SCLC‐QN) and the lowest with neuroendocrine subtypes (SCLC‐A, SCLC‐N, SCLC‐AN). In univariate analyses, high ASCL1 expression was associated with poor prognosis and high POU2F3 expression with good prognosis. Notably, high ASCL1 expression influenced survival outcomes independently of other variables in a multivariate model. High POU2F3 and YAP1 protein abundances correlated with sensitivity and resistance to standard‐of‐care chemotherapeutics, respectively. Specific correlation patterns were also found between the efficacy of targeted agents and subtype‐specific protein abundances. In conclusion, we investigated the clinicopathological relevance of SCLC molecular subtypes in a large cohort of surgically resected specimens. Differential IHC expression of ASCL1, NEUROD1, and POU2F3 defines SCLC subtypes. No YAP1‐subtype can be distinguished by IHC. High POU2F3 expression is associated with improved survival in a univariate analysis, whereas elevated ASCL1 expression is an independent negative prognosticator. Proteomic and cell viability assays of human SCLC cell lines revealed distinct vulnerability profiles defined by transcription regulators. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.