Autophagy enables microglia to engage amyloid plaques and prevents microglial senescence.

Dysfunctional autophagy has been implicated in the pathogenesis of Alzheimer's disease (AD). Previous evidence suggested disruptions of multiple stages of the autophagy-lysosomal pathway in affected neurons. However, whether and how deregulated autophagy in microglia, a cell type with an important link to AD, contributes to AD progression remains elusive.

Targeting ERRα promotes cytotoxic effects against acute myeloid leukemia through suppressing mitochondrial oxidative phosphorylation.

Acute myeloid leukemia (AML) is an aggressive blood cancer with poor clinical outcomes. Emerging data suggest that mitochondrial oxidative phosphorylation (mtOXPHOS) plays a significant role in AML tumorigenesis, progression, and resistance to chemotherapies. However, how the mtOXPHOS is regulated in AML cells is not well understood.

Transcriptional Circuitry of NKX2-1 and SOX1 Defines an Unrecognized Lineage Subtype of Small-Cell Lung Cancer.

The current molecular classification of small-cell lung cancer (SCLC) on the basis of the expression of four lineage transcription factors still leaves its major subtype SCLC-A as a heterogeneous group, necessitating more precise characterization of lineage subclasses. To refine the current SCLC classification with epigenomic profiles and to identify features of the redefined SCLC subtypes. We performed unsupervised clustering of epigenomic profiles on 25 SCLC cell lines.

HBV genome-enriched single cell sequencing revealed heterogeneity in HBV-driven hepatocellular carcinoma (HCC).

Background: Hepatitis B virus (HBV) related hepatocellular carcinoma (HCC) is heterogeneous and frequently contains multifocal tumors, but how the multifocal tumors relate to each other in terms of HBV integration and other genomic patterns is not clear.

Methods: To interrogate heterogeneity of HBV-HCC, we developed a HBV genome enriched single cell sequencing (HGE-scSeq) procedure and a computational method to identify HBV integration sites and infer DNA copy number variations (CNVs).

Results: We performed HGE-scSeq on 269 cells from four tumor sites and two tumor thrombi of a HBV-HCC patient.

Integrative network analysis of early-stage lung adenocarcinoma identifies aurora kinase inhibition as interceptor of invasion and progression.

Here we focus on the molecular characterization of clinically significant histological subtypes of early-stage lung adenocarcinoma (esLUAD), which is the most common histological subtype of lung cancer. Within lung adenocarcinoma, histology is heterogeneous and associated with tumor invasion and diverse clinical outcomes. We present a gene signature distinguishing invasive and non-invasive tumors among esLUAD.

Global DNA methylation of WTC prostate cancer tissues show signature differences compared to non-exposed cases.

There is increased incidence of prostate cancer (PC) among World Trade Center (WTC)-exposed responders and community members, with preliminary evidence suggestive of more aggressive disease. While previous research is supportive of differences in DNA methylation and gene expression as a consequence of WTC exposure, as measured in blood of healthy individuals, the epigenetics of WTC PC tissues has yet to be explored. Patients were recruited from the World Trade Center Health Program.

Early-Stage Lung Adenocarcinoma MDM2 Genomic Amplification Predicts Clinical Outcome and Response to Targeted Therapy.

Lung cancer is the most common cause of cancer-related deaths in both men and women, accounting for one-quarter of total cancer-related mortality globally. Lung adenocarcinoma is the major subtype of non-small cell lung cancer (NSCLC) and accounts for around 40% of lung cancer cases. Lung adenocarcinoma is a highly heterogeneous disease and patients often display variable histopathological morphology, genetic alterations, and genomic aberrations.

Proteogenomic characterization of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications.

A community effort to identify and correct mislabeled samples in proteogenomic studies.

Sample mislabeling or misannotation has been a long-standing problem in scientific research, particularly prevalent in large-scale, multi-omic studies due to the complexity of multi-omic workflows. There exists an urgent need for implementing quality controls to automatically screen for and correct sample mislabels or misannotations in multi-omic studies. Here, we describe a crowdsourced precisionFDA NCI-CPTAC Multi-omics Enabled Sample Mislabeling Correction Challenge, which provides a framework for systematic benchmarking and evaluation of mislabel identification and correction methods for integrative proteogenomic studies.

Proteogenomic and metabolomic characterization of human glioblastoma.

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology.

Prototypical oncogene family Myc defines unappreciated distinct lineage states of small cell lung cancer.

Comprehensive genomic analyses of small cell lung cancer (SCLC) have revealed frequent mutually exclusive genomic amplification of MYC family members. Hence, it has been long suggested that they are functionally equivalent; however, more recently, their expression has been associated with specific neuroendocrine markers and distinct histopathology. Here, we explored a previously undescribed role of L-Myc and c-Myc as lineage-determining factors contributing to SCLC molecular subtypes and histology.

Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma.

We present a proteogenomic study of 108 human papilloma virus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs). Proteomic analysis systematically catalogs HNSCC-associated proteins and phosphosites, prioritizes copy number drivers, and highlights an oncogenic role for RNA processing genes. Proteomic investigation of mutual exclusivity between FAT1 truncating mutations and 11q13.

Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer.

We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses.

Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma.

To explore the biology of lung adenocarcinoma (LUAD) and identify new therapeutic opportunities, we performed comprehensive proteogenomic characterization of 110 tumors and 101 matched normal adjacent tissues (NATs) incorporating genomics, epigenomics, deep-scale proteomics, phosphoproteomics, and acetylproteomics. Multi-omics clustering revealed four subgroups defined by key driver mutations, country, and gender. Proteomic and phosphoproteomic data illuminated biology downstream of copy number aberrations, somatic mutations, and fusions and identified therapeutic vulnerabilities associated with driver events involving KRAS, EGFR, and ALK.

A reference profile-free deconvolution method to infer cancer cell-intrinsic subtypes and tumor-type-specific stromal profiles.

Background: Patient stratification based on molecular subtypes is an important strategy for cancer precision medicine. Deriving clinically informative cancer molecular subtypes from transcriptomic data generated on whole tumor tissue samples is a non-trivial task, especially given the various non-cancer cellular elements intertwined with cancer cells in the tumor microenvironment.

Methods: We developed a computational deconvolution method, DeClust, that stratifies patients into subtypes based on cancer cell-intrinsic signals identified by distinguishing cancer-type-specific signals from non-cancer signals in bulk tumor transcriptomic data.

Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma.

To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules.

A Network Analysis of Multiple Myeloma Related Gene Signatures.

Multiple myeloma (MM) is the second most prevalent hematological cancer. MM is a complex and heterogeneous disease, and thus, it is essential to leverage omics data from large MM cohorts to understand the molecular mechanisms underlying MM tumorigenesis, progression, and drug responses, which may aid in the development of better treatments. In this study, we analyzed gene expression, copy number variation, and clinical data from the Multiple Myeloma Research Consortium (MMRC) dataset and constructed a multiple myeloma molecular causal network (M3CN).

Epigenomic Profiling Discovers Trans-lineage SOX2 Partnerships Driving Tumor Heterogeneity in Lung Squamous Cell Carcinoma.

Molecular characterization of lung squamous cell carcinoma (LUSC), one of the major subtypes of lung cancer, has not sufficiently improved its nonstratified treatment strategies over decades. Accumulating evidence suggests that lineage-specific transcriptional regulators control differentiation states during cancer evolution and underlie their distinct biological behaviors. In this study, by investigating the super-enhancer landscape of LUSC, we identified a previously undescribed "neural" subtype defined by Sox2 and a neural lineage factor Brn2, as well as the classical LUSC subtype defined by Sox2 and its classical squamous partner p63.

A probabilistic multi-omics data matching method for detecting sample errors in integrative analysis.

Background: Data errors, including sample swapping and mis-labeling, are inevitable in the process of large-scale omics data generation. Data errors need to be identified and corrected before integrative data analyses where different types of data are merged on the basis of the annotated labels. Data with labeling errors dampen true biological signals.

Oncogenic role of SFRP2 in p53-mutant osteosarcoma development via autocrine and paracrine mechanism.

Osteosarcoma (OS), the most common primary bone tumor, is highly metastatic with high chemotherapeutic resistance and poor survival rates. Using induced pluripotent stem cells (iPSCs) generated from Li-Fraumeni syndrome (LFS) patients, we investigate an oncogenic role of secreted frizzled-related protein 2 (SFRP2) in p53 mutation-associated OS development. Interestingly, we find that high SFRP2 expression in OS patient samples correlates with poor survival.

Temporal genetic association and temporal genetic causality methods for dissecting complex networks.

A large amount of panomic data has been generated in populations for understanding causal relationships in complex biological systems. Both genetic and temporal models can be used to establish causal relationships among molecular, cellular, or phenotypical traits, but with limitations. To fully utilize high-dimension temporal and genetic data, we develop a multivariate polynomial temporal genetic association (MPTGA) approach for detecting temporal genetic loci (teQTLs) of quantitative traits monitored over time in a population and a temporal genetic causality test (TGCT) for inferring causal relationships between traits linked to the locus.

A pilot systematic genomic comparison of recurrence risks of hepatitis B virus-associated hepatocellular carcinoma with low- and high-degree liver fibrosis.

Background: Chronic hepatitis B virus (HBV) infection leads to liver fibrosis, which is a major risk factor in hepatocellular carcinoma (HCC) and an independent risk factor of recurrence after HCC tumor resection. The HBV genome can be inserted into the human genome, and chronic inflammation may trigger somatic mutations. However, how HBV integration and other genomic changes contribute to the risk of tumor recurrence with regards to the different degree of liver fibrosis is not clearly understood.