An APRI+ALBI Based Multivariable Model as Preoperative Predictor for Posthepatectomy Liver Failure.

OBJECTIVE AND BACKGROUND Clinically significant posthepatectomy liver failure (PHLF B+C) remains the main cause of mortality after major hepatic resection. This study aimed to establish an APRI+ALBI, aspartate aminotransferase to platelet ratio (APRI) combined with albumin-bilirubin grade (ALBI), based multivariable model (MVM) to predict PHLF and compare its performance to indocyanine green clearance (ICG-R15 or ICG-PDR) and albumin-ICG evaluation (ALICE). METHODS 12,056 patients from the National Surgical Quality Improvement Program (NSQIP) database were used to generate a MVM to predict PHLF B+C. The model was determined using stepwise backwards elimination. Performance of the model was tested using receiver operating characteristic curve analysis and validated in an international cohort of 2,525 patients. In 620 patients, the APRI+ALBI MVM, trained in the NSQIP cohort, was compared with MVM's based on other liver function tests (ICG clearance, ALICE) by comparing the areas under the curve (AUC). RESULTS A MVM including APRI+ALBI, age, sex, tumor type and extent of resection was found to predict PHLF B+C with an AUC of 0.77, with comparable performance in the validation cohort (AUC 0.74). In direct comparison with other MVM's based on more expensive and time-consuming liver function tests (ICG clearance, ALICE), the APRI+ALBI MVM demonstrated equal predictive potential for PHLF B+C. A smartphone application for calculation of the APRI+ALBI MVM was designed. CONCLUSION Risk assessment via the APRI+ALBI MVM for PHLF B+C increases preoperative predictive accuracy and represents an universally available and cost-effective risk assessment prior to hepatectomy, facilitated by a freely available smartphone app

Unique genomic profile of fibrolamellar hepatocellular carcinoma

BACKGROUND & AIMS: Fibrolamellar hepatocellular carcinoma (FLC) is a rare primary hepatic cancer that develops in children and young adults without cirrhosis. Little is known about its pathogenesis, and it can be treated only with surgery. We performed an integrative genomic analysis of a large series of patients with FLC to identify associated genetic factors. METHODS: By using 78 clinically annotated FLC samples, we performed whole-transcriptome (n = 58), single-nucleotide polymorphism array (n = 41), and next-generation sequencing (n = 48) analyses; we also assessed the prevalence of the DNAJB1-PRKACA fusion transcript associated with this cancer (n = 73). We performed class discovery using non-negative matrix factorization, and functional annotation using gene-set enrichment analyses, nearest template prediction, ingenuity pathway analyses, and immunohistochemistry. The genomic identification of significant targets in a cancer algorithm was used to identify chromosomal aberrations, MuTect and VarScan2 were used to identify somatic mutations, and the random survival forest was used to determine patient prognoses. Findings were validated in an independent cohort. RESULTS: Unsupervised gene expression clustering showed 3 robust molecular classes of tumors: the proliferation class (51% of samples) had altered expression of genes that regulate proliferation and mammalian target of rapamycin signaling activation; the inflammation class (26% of samples) had altered expression of genes that regulate inflammation and cytokine enriched production; and the unannotated class (23% of samples) had a gene expression signature that was not associated previously with liver tumors. Expression of genes that regulate neuroendocrine function, as well as histologic markers of cholangiocytes and hepatocytes, were detected in all 3 classes. FLCs had few copy number variations; the most frequent were focal amplification at 8q24.3 (in 12.5% of samples), and deletions at 19p13 (in 28% of samples) and 22q13.32 (in 25% of samples). The DNAJB1-PRKACA fusion transcript was detected in 79% of samples. FLC samples also contained mutations in cancer-related genes such as BRCA2 (in 4.2% of samples), which are uncommon in liver neoplasms. However, FLCs did not contain mutations most commonly detected in liver cancers. We identified an 8-gene signature that predicted survival of patients with FLC. CONCLUSIONS: In a genomic analysis of 78 FLC samples, we identified 3 classes based on gene expression profiles. FLCs contain mutations and chromosomal aberrations not previously associated with liver cancer, and almost 80% contain the DNAJB1-PRKACA fusion transcript. By using this information, we identified a gene signature that is associated with patient survival time

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

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

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

Molecular characterization and clinical relevance of metabolic expression subtypes in human cancers.

Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1—master regulators of carbohydrate metabolic subtypes-modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility