PKD1 mutant clones within cirrhotic livers inhibit steatohepatitis without promoting cancer.

Somatic mutations in non-malignant tissues are selected for because they confer increased clonal fitness. However, it is uncertain whether these clones can benefit organ health. Here, ultra-deep targeted sequencing of 150 liver samples from 30 chronic liver disease patients revealed recurrent somatic mutations. PKD1 mutations were observed in 30% of patients, whereas they were only detected in 1.3% of hepatocellular carcinomas (HCCs). To interrogate tumor suppressor functionality, we perturbed PKD1 in two HCC cell lines and six in vivo models, in some cases showing that PKD1 loss protected against HCC, but in most cases showing no impact. However, Pkd1 haploinsufficiency accelerated regeneration after partial hepatectomy. We tested Pkd1 in fatty liver disease, showing that Pkd1 loss was protective against steatosis and glucose intolerance. Mechanistically, Pkd1 loss selectively increased mTOR signaling without SREBP-1c activation. In summary, PKD1 mutations exert adaptive functionality on the organ level without increasing transformation risk.

Netie: inferring the evolution of neoantigen-T cell interactions in tumors.

Neoantigens are the key targets of antitumor immune responses from cytotoxic T cells and play a critical role in affecting tumor progressions and immunotherapy treatment responses. However, little is known about how the interaction between neoantigens and T cells ultimately affects the evolution of cancerous masses. Here, we develop a hierarchical Bayesian model, named neoantigen-T cell interaction estimation (netie) to infer the history of neoantigen-CD8+ T cell interactions in tumors. Netie was systematically validated and applied to examine the molecular patterns of 3,219 tumors, compiled from a panel of 18 cancer types. We showed that tumors with an increase in immune selection pressure over time are associated with T cells that have an activation-related expression signature. We also identified a subset of exhausted cytotoxic T cells postimmunotherapy associated with tumor clones that newly arise after treatment. These analyses demonstrate how netie enables the interrogation of the relationship between individual neoantigen repertoires and the tumor molecular profiles. We found that a T cell inflammation gene expression profile (TIGEP) is more predictive of patient outcomes in the tumors with an increase in immune pressure over time, which reveals a curious synergy between T cells and neoantigen distributions. Overall, we provide a new tool that is capable of revealing the imprints left by neoantigens during each tumor's developmental process and of predicting how tumors will progress under further pressure of the host's immune system.

Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data.

Single-cell lineage tracing provides crucial insights into the fates of individual cells. Single-cell RNA sequencing (scRNA-seq) is commonly applied in modern biomedical research, but genetics-based lineage tracing for scRNA-seq data is still unexplored. Variant calling from scRNA-seq data uniquely suffers from "expressional drop-outs," including low expression and allelic bias in gene expression, which presents significant obstacles for lineage reconstruction. We introduce SClineager, which infers accurate evolutionary lineages from scRNA-seq data by borrowing information from related cells to overcome expressional drop-outs. We systematically validate SClineager and show that genetics-based lineage tracing is applicable for single-cell-sequencing studies of both tumor and non-tumor tissues using SClineager. Overall, our work provides a powerful tool that can be applied to scRNA-seq data to decipher the lineage histories of cells and that could address a missing opportunity to reveal valuable information from the large amounts of existing scRNA-seq data.

Deep learning-based prediction of the T cell receptor-antigen binding specificity.

Neoantigens play a key role in the recognition of tumor cells by T cells. However, only a small proportion of neoantigens truly elicit T cell responses, and fewer clues exist as to which neoantigens are recognized by which T cell receptors (TCRs). We built a transfer learning-based model, named pMHC-TCR binding prediction network (pMTnet), to predict TCR-binding specificities of neoantigens, and T cell antigens in general, presented by class I major histocompatibility complexes (pMHCs). pMTnet was comprehensively validated by a series of analyses, and showed advance over previous work by a large margin. By applying pMTnet in human tumor genomics data, we discovered that neoantigens were generally more immunogenic than self-antigens, but HERV-E, a special type of self-antigen that is re-activated in kidney cancer, is more immunogenic than neoantigens. We further discovered that patients with more clonally expanded T cells exhibiting better affinity against truncal, rather than subclonal, neoantigens, had more favorable prognosis and treatment response to immunotherapy, in melanoma and lung cancer but not in kidney cancer. Predicting TCR-neoantigen/antigen pairs is one of the most daunting challenges in modern immunology. However, we achieved an accurate prediction of the pairing only using the TCR sequence (CDR3ß), antigen sequence, and class I MHC allele, and our work revealed unique insights into the interactions of TCRs and pMHCs in human tumors using pMTnet as a discovery tool.

DNA Sensing in Mismatch Repair-Deficient Tumor Cells Is Essential for Anti-tumor Immunity.

Increased neoantigens in hypermutated cancers with DNA mismatch repair deficiency (dMMR) are proposed as the major contributor to the high objective response rate in anti-PD-1 therapy. However, the mechanism of drug resistance is not fully understood. Using tumor models defective in the MMR gene Mlh1 (dMLH1), we show that dMLH1 tumor cells accumulate cytosolic DNA and produce IFN-ß in a cGAS-STING-dependent manner, which renders dMLH1 tumors slowly progressive and highly sensitive to checkpoint blockade. In neoantigen-fixed models, dMLH1 tumors potently induce T cell priming and lose resistance to checkpoint therapy independent of tumor mutational burden. Accordingly, loss of STING or cGAS in tumor cells decreases tumor infiltration of T cells and endows resistance to checkpoint blockade. Clinically, downregulation of cGAS/STING in human dMMR cancers correlates with poor prognosis. We conclude that DNA sensing within tumor cells is essential for dMMR-triggered anti-tumor immunity. This study provides new mechanisms and biomarkers for anti-dMMR-cancer immunotherapy.

Immune Checkpoint Inhibition is Safe and Effective for Liver Cancer Prevention in a Mouse Model of Hepatocellular Carcinoma.

Cirrhosis is a high-risk state for hepatocellular carcinoma (HCC) development and represents an opportunity to prevent cancer. In the precancerous state of cirrhosis, there is an accumulation of neoantigens that may be specifically targetable through immunotherapy. We asked whether immune checkpoint inhibition could prevent tumorigenesis in a mouse model of diethylnitrosamine and carbon tetrachloride-induced HCC. We found that initiation of anti-PD-1 therapy prior to tumorigenesis could prevent up to 46% of liver tumors. This significant reduction in tumor burden was accompanied by infiltration of CD4+ Th cells and CD8+ cytotoxic T cells into the liver parenchyma. Importantly, anti-PD-1 therapy did not exacerbate liver dysfunction or worsen overall health in this liver disease model. Given the safety and preservation of quality of life observed with long-term immunotherapy use, an immunotherapy chemoprevention strategy is likely associated with a low risk-to-benefit ratio and high value care in select patients. These results encourage a prevention trial in cirrhotic patients with the highest risk of developing HCC.See related Spotlight by Mohammed et al., p. 897.

Effect of Supplementary Materials on the Autogenous Shrinkage of Cement Paste.

In recent years more and more attention has been given to autogenous shrinkage due to the increasing use of high-performance concrete, which always contains supplementary materials. With the addition of supplementary materials-e.g., fly ash and blast furnace slag-internal relative humidity, chemical shrinkage and mechanical properties of cement paste will be affected. These properties significantly influence the autogenous shrinkage of cement paste. In this study, three supplementary materials-i.e., silica fume, fly ash and blast furnace slag-are investigated. Measurements of final setting time, internal relative humidity, chemical shrinkage, compressive strength and autogenous deformation of the cement pastes with and without supplementary materials are presented. Two water-binder ratios, 0.3 and 0.4, are considered. The effects of different supplementary materials on autogenous shrinkage of cement paste are discussed.

DNA Repair Gene Mutations as Predictors of Immune Checkpoint Inhibitor Response beyond Tumor Mutation Burden.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy, but prediction of their benefit is challenging. Neoantigens generated through impaired non-mismatch DNA repair may result in greater ICI activity. By analyzing 1,661 ICI-treated patients, we show that deletions and mutations in nucleotide excision repair (NER) and homologous repair (HR) pathways are predictors of ICI benefit independent of tumor mutation burden and tumor type. NER and HR mutations are also associated with objective response rates to ICIs in esophagogastric and non-small-cell lung cancers. In a cohort of 40,181 unique patients, NER and HR mutations are present in 3.4% and 13.9% of cancers, respectively. These results indicate that NER and HR gene mutations occur in a subpopulation of cancer patients and may aid patient selection for ICI therapy. Assessing NER and HR mutations in the context of other biomarkers may yield powerful predictors of ICI activity across different cancer types.

Bayesian multiple instance regression for modeling immunogenic neoantigens.

The relationship between tumor immune responses and tumor neoantigens is one of the most fundamental and unsolved questions in tumor immunology, and is the key to understanding the inefficiency of immunotherapy observed in many cancer patients. However, the properties of neoantigens that can elicit immune responses remain unclear. This biological problem can be represented and solved under a multiple instance learning framework, which seeks to model multiple instances (neoantigens) within each bag (patient specimen) with the continuous response (T cell infiltration) observed for each bag. To this end, we develop a Bayesian multiple instance regression method, named BMIR, using a Gaussian distribution to address continuous responses and latent binary variables to model primary instances in bags. By means of such Bayesian modeling, BMIR can learn a function for predicting the bag-level responses and for identifying the primary instances within bags, as well as give access to Bayesian statistical inference, which are elusive in existing works. We demonstrate the superiority of BMIR over previously proposed optimization-based methods for multiple instance regression through simulation and real data analyses. Our method is implemented in R package entitled "BayesianMIR" and is available at https://github.com/inmybrain/BayesianMIR.

Tumor neoantigenicity assessment with CSiN score incorporates clonality and immunogenicity to predict immunotherapy outcomes.

Lack of responsiveness to checkpoint inhibitors is a central problem in the modern era of cancer immunotherapy. Tumor neoantigens are critical targets of the host antitumor immune response, and their presence correlates with the efficacy of immunotherapy treatment. Many studies involving assessment of tumor neoantigens principally focus on total neoantigen load, which simplistically treats all neoantigens equally. Neoantigen load has been linked with treatment response and prognosis in some studies but not others. We developed a Cauchy-Schwarz index of Neoantigens (CSiN) score to better account for the degree of concentration of immunogenic neoantigens in truncal mutations. Unlike total neoantigen load determinations, CSiN incorporates the effect of both clonality and MHC binding affinity of neoantigens when characterizing tumor neoantigen profiles. By analyzing the clinical responses in 501 treated patients with cancer (with most receiving checkpoint inhibitors) and the overall survival of 1978 patients with cancer at baseline, we showed that CSiN scores predict treatment response to checkpoint inhibitors and prognosis in patients with melanoma, lung cancer, and kidney cancer. CSiN score substantially outperformed prior genetics-based prediction methods of responsiveness and fills an important gap in research involving assessment of tumor neoantigen burden.

Mice With Increased Numbers of Polyploid Hepatocytes Maintain Regenerative Capacity But Develop Fewer Hepatocellular Carcinomas Following Chronic Liver Injury.

BACKGROUND & AIMS: Thirty to 90% of hepatocytes contain whole-genome duplications, but little is known about the fates or functions of these polyploid cells or how they affect development of liver disease. We investigated the effects of continuous proliferative pressure, observed in chronically damaged liver tissues, on polyploid cells. METHODS: We studied Rosa-rtTa mice (controls) and Rosa-rtTa;TRE-short hairpin RNA mice, which have reversible knockdown of anillin, actin binding protein (ANLN). Transient administration of doxycycline increases the frequency and degree of hepatocyte polyploidy without permanently altering levels of ANLN. Mice were then given diethylnitrosamine and carbon tetrachloride (CCl4) to induce mutations, chronic liver damage, and carcinogenesis. We performed partial hepatectomies to test liver regeneration and then RNA-sequencing to identify changes in gene expression. Lineage tracing was used to rule out repopulation from non-hepatocyte sources. We imaged dividing hepatocytes to estimate the frequency of mitotic errors during regeneration. We also performed whole-exome sequencing of 54 liver nodules from patients with cirrhosis to quantify aneuploidy, a possible outcome of polyploid cell divisions. RESULTS: Liver tissues from control mice given CCl4 had significant increases in ploidy compared with livers from uninjured mice. Mice with knockdown of ANLN had hepatocyte ploidy above physiologic levels and developed significantly fewer liver tumors after administration of diethylnitrosamine and CCl4 compared with control mice. Increased hepatocyte polyploidy was not associated with altered regenerative capacity or tissue fitness, changes in gene expression, or more mitotic errors. Based on lineage-tracing experiments, non-hepatocytes did not contribute to liver regeneration in mice with increased polyploidy. Despite an equivalent rate of mitosis in hepatocytes of differing ploidies, we found no lagging chromosomes or micronuclei in mitotic polyploid cells. In nodules of human cirrhotic liver tissue, there was no evidence of chromosome-level copy number variations. CONCLUSIONS: Mice with increased polyploid hepatocytes develop fewer liver tumors following chronic liver damage. Remarkably, polyploid hepatocytes maintain the ability to regenerate liver tissues during chronic damage without generating mitotic errors, and aneuploidy is not commonly observed in cirrhotic livers. Strategies to increase numbers of polypoid hepatocytes might be effective in preventing liver cancer.

Uncovering Biological Factors That Regulate Hepatocellular Carcinoma Growth Using Patient-Derived Xenograft Assays.

BACKGROUND AND AIMS: Several major factors limit our understanding of hepatocellular carcinoma (HCC). First, human HCCs are infrequently biopsied for diagnosis and thus are not often biologically interrogated. Second, HCC initiation and progression are strongly influenced by the cirrhotic microenvironment, and the exact contributions of intrinsic and extrinsic tumor factors are unclear. A powerful approach to examine the personalized biology of liver cancers and the influence of host tissues is with patient-derived xenograft (PDX) models. In Asia, HCCs from patients with hepatitis B virus have been efficiently converted into PDXs, but few parallel efforts from the west have been reported. APPROACH AND RESULTS: In a large-scale analysis, we implanted 93 HCCs and 8 cholangiocarcinomas (CCAs) to systematically analyze host factors and to define an optimized platform for PDX development from both surgical and biopsy samples. NOD Scid IL-2Rγ-/- (NSG) mice that had undergone partial hepatectomy (PHx) represented the best combination of engraftability, growth, and passageability, but overall rates were low and indicative of a unique intrinsic biology for HCCs in the United States. PDX models preserved the histology and genetic features of parental tumors, and ultimately, eight models were usable for preclinical studies. Intriguingly, HCC PDXs were differentially sensitive to regorafenib and sorafenib, and CCA PDXs were also highly sensitive to regorafenib. CONCLUSIONS: PDX models functionalize early and advanced stage HCCs and revealed unique biological features of liver cancers from the United States.

Somatic Mutations Increase Hepatic Clonal Fitness and Regeneration in Chronic Liver Disease.

Normal tissues accumulate genetic changes with age, but it is unknown if somatic mutations promote clonal expansion of non-malignant cells in the setting of chronic degenerative diseases. Exome sequencing of diseased liver samples from 82 patients revealed a complex mutational landscape in cirrhosis. Additional ultra-deep sequencing identified recurrent mutations in PKD1, PPARGC1B, KMT2D, and ARID1A. The number and size of mutant clones increased as a function of fibrosis stage and tissue damage. To interrogate the functional impact of mutated genes, a pooled in vivo CRISPR screening approach was established. In agreement with sequencing results, examination of 147 genes again revealed that loss of Pkd1, Kmt2d, and Arid1a promoted clonal expansion. Conditional heterozygous deletion of these genes in mice was also hepatoprotective in injury assays. Pre-malignant somatic alterations are often viewed through the lens of cancer, but we show that mutations can promote regeneration, likely independent of carcinogenesis.

Creatine maintains intestinal homeostasis and protects against colitis.

Creatine, a nitrogenous organic acid, replenishes cytoplasmic ATP at the expense of mitochondrial ATP via the phosphocreatine shuttle. Creatine levels are maintained by diet and endogenous synthesis from arginine and glycine. Glycine amidinotransferase (GATM) catalyzes the rate-limiting step of creatine biosynthesis: the transfer of an amidino group from arginine to glycine to form ornithine and guanidinoacetate. We screened 36,530 third-generation germline mutant mice derived from N-ethyl-N-nitrosourea-mutagenized grandsires for intestinal homeostasis abnormalities after oral administration of dextran sodium sulfate (DSS). Among 27 colitis susceptibility phenotypes identified and mapped, one was strongly correlated with a missense mutation in Gatm in a recessive model of inheritance, and causation was confirmed by CRISPR/Cas9 gene targeting. Supplementation of homozygous Gatm mutants with exogenous creatine ameliorated the colitis phenotype. CRISPR/Cas9-targeted (Gatmc/c ) mice displayed a normal peripheral immune response and immune cell homeostasis. However, the intestinal epithelium of the Gatmc/c mice displayed increased cell death and decreased proliferation during DSS treatment. In addition, Gatmc/c colonocytes showed increased metabolic stress in response to DSS with higher levels of phospho-AMPK and lower levels of phosphorylation of mammalian target of rapamycin (phospho-mTOR). These findings establish an in vivo requirement for rapid replenishment of cytoplasmic ATP within colonic epithelial cells in the maintenance of the mucosal barrier after injury.

A numerical algorithm for magnetohydrodynamics of ablated materials.

A numerical algorithm for the simulation of magnetohydrodynamics in partially ionized ablated material is described. For the hydro part, the hyperbolic conservation laws with electromagnetic terms is solved using techniques developed for free surface flows; for the electromagnetic part, the electrostatic approximation is applied and an elliptic equation for electric potential is solved. The algorithm has been implemented in the frame of front tracking, which explicitly tracks geometrically complex evolving interfaces. An elliptic solver based on the embedded boundary method were implemented for both two- and three-dimensional simulations. A surface model on the interface between the solid target and the ablated vapor has also been developed as well as a numerical model for the equation of state which accounts for atomic processes in the ablated material. The code has been applied to simulations of the pellet ablation in a magnetically confined plasma and the laser-ablated plasma plume expansion in magnetic fields.