Pituitary adenomas (PA) are the second most common intracranial tumors. These neoplasms are classified according to the hormone they produce. The majority of PA occur sporadically, and their molecular pathogenesis is incompletely understood. The present transcriptomic and methylomic analysis of PA revealed that they segregate into three molecular clusters according to the transcription factor driving their terminal differentiation. First cluster, driven by NR5A1, consists of clinically non-functioning PA (CNFPA), comprising gonadotrophinomas and null cell; the second cluster consists of clinically evident ACTH adenomas and silent corticotroph adenomas, driven by TBX19; and the third, POU1F1-driven TSH-, PRL- and GH-adenomas, segregated together. Genes such as CACNA2D4, EPHA4 and SLIT1, were upregulated in each of these three clusters, respectively. Pathway enrichment analysis revealed specific alterations of these clusters: calcium signaling pathway in CNFPA; renin-angiotensin system for ACTH-adenomas and fatty acid metabolism for the TSH-, PRL-, GH-cluster. Non-tumoral pituitary scRNAseq data confirmed that this clustering also occurs in normal cytodifferentiation. Deconvolution analysis identify potential mononuclear cell infiltrate in PA consists of dendritic, NK and mast cells. Our results are consistent with a divergent origin of PA, which segregate into three clusters that depend on the specific transcription factors driving late pituitary cytodifferentiation.

Lung cancer is one of the deadliest, most aggressive cancers. Abrupt changes in gene expression represent an important challenge to understand and fight the disease. Gene co-expression networks (GCNs) have been widely used to study the genomic regulatory landscape of human cancer. Here, based on 1,143 RNA-Seq experiments from the TCGA collaboration, we constructed GCN for the most common types of lung tumors: adenocarcinoma (TAD) and squamous cells (TSCs) as well as their respective control networks (NAD and NSC). We compared the number of intra-chromosome (cis-) and inter-chromosome (trans-) co-expression interactions in normal and cancer GCNs. We compared the number of shared interactions between TAD and TSC, as well as in NAD and NSC, to observe which phenotypes were more alike. By means of an over-representation analysis, we associated network topology features with biological functions. We found that TAD and TSC present mostly cis- small disconnected components, whereas in control GCNs, both types have a giant trans- component. In both cancer networks, we observed cis- components in which genes not only belong to the same chromosome but to the same cytoband or to neighboring cytobands. This supports the hypothesis that in lung cancer, gene co-expression is constrained to small neighboring regions. Despite this loss of distant co-expression observed in TAD and TSC, there are some remaining trans- clusters. These clusters seem to play relevant roles in the carcinogenic processes. For instance, some clusters in TAD and TSC are associated with the immune system, response to virus, or control of gene expression. Additionally, other non-enriched trans- clusters are composed of one gene and several associated pseudo-genes, as in the case of the FTH1 gene. The appearance of those common trans- clusters reflects that the gene co-expression program in lung cancer conserves some aspects for cell maintenance. Unexpectedly, 0.48% of the edges are shared between control networks; conversely, 35% is shared between lung cancer GCNs, a 73-fold larger intersection. This suggests that in lung cancer a process of de-differentiation may be occurring. To further investigate the implications of the loss of distant co-expression, it will become necessary to broaden the investigation with other omic-based approaches. However, the present approach provides a basis for future work toward an integrative perspective of abnormal transcriptional regulatory programs in lung cancer.

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Background SARS-CoV-2 , the etiological agent causing COVID-19, has infected more than 27 million people with over 894000 deaths worldwide since its emergence in December 2019. Factors for severe diseases, such as diabetes, hypertension, and obesity have been identified however, the precise pathogenesis is poorly understood. To understand its pathophysiology and to develop effective therapeutic strategies, it is essential to define the prevailing immune cellular subsets. Methods We performed whole circulating immune cells scRNAseq from five critically ill COVID-19 patients, trajectory and gene ontology analysis. Results Immature myeloid populations, such as promyelocytes-myelocytes, metamyelocytes, band neutrophils, monocytoid precursors, and activated monocytes predominated. The trajectory with pseudotime analysis supported the finding of immature cell states. While the gene ontology showed myeloid cell activation in immune response, DNA and RNA processing, defense response to the virus, and response to type 1 interferon. Lymphoid lineage was scarce. Expression of genes such as C/EBPβ, IRF1and FOSL2 potentially suggests the induction of trained immunity. Conclusions Our results uncover transcriptomic profiles related to immature myeloid lineages and suggest the potential induction of trained immunity.

Corticotroph cells give rise to aggressive and rare pituitary neoplasms comprising ACTH-producing adenomas resulting in Cushing disease (CD), clinically silent ACTH adenomas (SCA), Crooke cell adenomas (CCA) and ACTH-producing carcinomas (CA). The molecular pathogenesis of these tumors is still poorly understood. To better understand the genomic landscape of all the lesions of the corticotroph lineage, we sequenced the whole exome of three SCA, one CCA, four ACTH-secreting PA causing CD, one corticotrophinoma occurring in a CD patient who developed Nelson syndrome after adrenalectomy and one patient with an ACTH-producing CA. The ACTH-producing CA was the lesion with the highest number of single nucleotide variants (SNV) in genes such as USP8, TP53, AURKA, EGFR, HSD3B1 and CDKN1A. The USP8 variant was found only in the ACTH-CA and in the corticotrophinoma occurring in a patient with Nelson syndrome. In CCA, SNV in TP53, EGFR, HSD3B1 and CDKN1A SNV were present. HSD3B1 and CDKN1A SNVs were present in all three SCA, whereas in two of these tumors SNV in TP53, AURKA and EGFR were found. None of the analyzed tumors showed SNV in USP48, BRAF, BRG1 or CABLES1. The amplification of 17q12 was found in all tumors, except for the ACTH-producing carcinoma. The four clinically functioning ACTH adenomas and the ACTH-CA shared the amplification of 10q11.22 and showed more copy-number variation (CNV) gains and single-nucleotide variations than the nonfunctioning tumors.

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Background Pituitary adenomas (PA) are the second most common intracranial tumors and are classified according to hormone they produce, and the transcription factors they express. The majority of PA occur sporadically, and their molecular pathogenesis is incompletely understood. Methods Here we performed transcriptome and proteome analysis of tumors derived from POU1F1 (GH-, TSH-, and PRL-tumors, N = 16), NR5A1 (gonadotropes and null cells adenomas, n = 17) and TBX19 (ACTH-tumors, n = 6) lineages as well as from silent ACTH-tumors (n = 3) to determine expression of kinases, cyclins, CDKs and CDK inhibitors. Results The expression profiles of genes encoding kinases were distinctive for each of the three PA lineage: NR5A1-derived tumors showed upregulation of ETNK2 and PIK3C2G and alterations in MAPK, ErbB and RAS signaling, POU1F1-derived adenomas showed upregulation of PIP5K1B and NEK10 and alterations in phosphatidylinositol, insulin and phospholipase D signaling pathways and TBX19-derived adenomas showed upregulation of MERTK and STK17B and alterations in VEGFA-VEGFR, EGF-EGFR and Insulin signaling pathways. In contrast, the expression of the different genes encoding cyclins, CDK and CDK inhibitors among NR5A1-, POU1F1- and TBX19-adenomas showed only subtle differences. CDK9 and CDK18 were upregulated in NR5A1-adenomas, whereas CDK4 and CDK7 were upregulated in POUF1-adenomas. Conclusions The kinome of PA clusters these lesions into three distinct groups according to the transcription factor that drives their terminal differentiation. And these complexes could be harnessed as molecular therapy targets.