Ancient mitochondrial and modern whole genomes unravel massive genetic diversity loss during near extinction of Alpine ibex

Population bottlenecks can have dramatic consequences for the health and long-term survival of a species. Understanding of historic population size and standing genetic variation prior to a contraction allows estimating the impact of a bottleneck on the species' genetic diversity. Although historic population sizes can be modelled based on extant genomics, uncertainty is high for the last 10–20 millenia. Hence, integrating ancient genomes provides a powerful complement to retrace the evolution of genetic diversity through population fluctuations. Here, we recover 15 high-quality mitogenomes of the once nearly extinct Alpine ibex spanning 8601 BP to 1919 CE and combine these with 60 published modern whole genomes. Coalescent demography simulations based on modern whole genomes indicate population fluctuations coinciding with the last major glaciation period. Using our ancient and historic mitogenomes, we investigate the more recent demographic history of the species and show that mitochondrial haplotype diversity was reduced to a fifth of the prebottleneck diversity with several highly differentiated mitochondrial lineages having coexisted historically. The main collapse of mitochondrial diversity coincides with elevated human population growth during the last 1–2 kya. After recovery, one lineage was spread and nearly fixed across the Alps due to recolonization efforts. Our study highlights that a combined approach integrating genomic data of ancient, historic and extant populations unravels major long-term population fluctuations from the emergence of a species through its near extinction up to the recent past

Genomic Characterization of Cholangiocarcinoma in Primary Sclerosing Cholangitis Reveals Therapeutic Opportunities

Background and Aims Lifetime risk of biliary tract cancer (BTC) in primary sclerosing cholangitis (PSC) may exceed 20%, and BTC is currently the leading cause of death in patients with PSC. To open new avenues for management, we aimed to delineate clinically relevant genomic and pathological features of a large panel of PSC-associated BTC (PSC-BTC). Approach and Results We analyzed formalin-fixed, paraffin-embedded tumor tissue from 186 patients with PSC-BTC from 11 centers in eight countries with all anatomical locations included. We performed tumor DNA sequencing at 42 clinically relevant genetic loci to detect mutations, translocations, and copy number variations, along with histomorphological and immunohistochemical characterization. Regardless of the anatomical localization, PSC-BTC exhibited a uniform molecular and histological characteristic similar to extrahepatic cholangiocarcinoma. We detected a high frequency of genomic alterations typical of extrahepatic cholangiocarcinoma, such asTP53(35.5%),KRAS(28.0%),CDKN2A(14.5%), andSMAD4(11.3%), as well as potentially druggable mutations (e.g.,HER2/ERBB2). We found a high frequency of nontypical/nonductal histomorphological subtypes (55.2%) and of the usually rare BTC precursor lesion, intraductal papillary neoplasia (18.3%). Conclusions Genomic alterations in PSC-BTC include a significant number of putative actionable therapeutic targets. Notably, PSC-BTC shows a distinct extrahepatic morpho-molecular phenotype, independent of the anatomical location of the tumor. These findings advance our understanding of PSC-associated cholangiocarcinogenesis and provide strong incentives for clinical trials to test genome-based personalized treatment strategies in PSC-BTC.Peer reviewe

Population genomics of the critically endangered kākāpō

Summary The kākāpō is a flightless parrot endemic to New Zealand. Once common in the archipelago, only 201 individuals remain today, most of them descending from an isolated island population. We report the first genome-wide analyses of the species, including a high-quality genome assembly for kākāpō, one of the first chromosome-level reference genomes sequenced by the Vertebrate Genomes Project (VGP). We also sequenced and analyzed 35 modern genomes from the sole surviving island population and 14 genomes from the extinct mainland population. While theory suggests that such a small population is likely to have accumulated deleterious mutations through genetic drift, our analyses on the impact of the long-term small population size in kākāpō indicate that present-day island kākāpō have a reduced number of harmful mutations compared to mainland individuals. We hypothesize that this reduced mutational load is due to the island population having been subjected to a combination of genetic drift and purging of deleterious mutations, through increased inbreeding and purifying selection, since its isolation from the mainland ∼10,000 years ago. Our results provide evidence that small populations can survive even when isolated for hundreds of generations. This work provides key insights into kākāpō breeding and recovery and more generally into the application of genetic tools in conservation efforts for endangered species

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation