AAV9:PKP2 improves heart function and survival in a Pkp2-deficient mouse model of arrhythmogenic right ventricular cardiomyopathy.

BACKGROUND: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a familial cardiac disease associated with ventricular arrhythmias and an increased risk of sudden cardiac death. Currently, there are no approved treatments that address the underlying genetic cause of this disease, representing a significant unmet need. Mutations in Plakophilin-2 (PKP2), encoding a desmosomal protein, account for approximately 40% of ARVC cases and result in reduced gene expression. METHODS: Our goal is to examine the feasibility and the efficacy of adeno-associated virus 9 (AAV9)-mediated restoration of PKP2 expression in a cardiac specific knock-out mouse model of Pkp2. RESULTS: We show that a single dose of AAV9:PKP2 gene delivery prevents disease development before the onset of cardiomyopathy and attenuates disease progression after overt cardiomyopathy. Restoration of PKP2 expression leads to a significant extension of lifespan by restoring cellular structures of desmosomes and gap junctions, preventing or halting decline in left ventricular ejection fraction, preventing or reversing dilation of the right ventricle, ameliorating ventricular arrhythmia event frequency and severity, and preventing adverse fibrotic remodeling. RNA sequencing analyses show that restoration of PKP2 expression leads to highly coordinated and durable correction of PKP2-associated transcriptional networks beyond desmosomes, revealing a broad spectrum of biological perturbances behind ARVC disease etiology. CONCLUSIONS: We identify fundamental mechanisms of PKP2-associated ARVC beyond disruption of desmosome function. The observed PKP2 dose-function relationship indicates that cardiac-selective AAV9:PKP2 gene therapy may be a promising therapeutic approach to treat ARVC patients with PKP2 mutations.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heart disease that leads to abnormal heartbeats and a higher risk of sudden cardiac death. ARVC is often caused by changes in a gene called PKP2, that then makes less PKP2 protein. PKP2 protein is important for the normal structure and function of the heart. Human ARVC characteristics can be mimicked in a mouse model missing this gene. Given no therapeutic option, our goal was to test if adding a working copy of PKP2 gene in the heart of this mouse model, using a technique called gene therapy that can deliver genes to cells, could improve heart function. Here, we show that a single dose of PKP2 gene therapy can improve heart function and heartbeats as well as extend lifespan in mice. PKP2 gene therapy may be a promising approach to treat ARVC patients with PKP2 mutations.

Targeting HDAC6 to treat heart failure with preserved ejection fraction in mice.

Heart failure with preserved ejection fraction (HFpEF) poses therapeutic challenges due to the limited treatment options. Building upon our previous research that demonstrates the efficacy of histone deacetylase 6 (HDAC6) inhibition in a genetic cardiomyopathy model, we investigate HDAC6's role in HFpEF due to their shared mechanisms of inflammation and metabolism. Here, we show that inhibiting HDAC6 with TYA-018 effectively reverses established heart failure and its associated symptoms in male HFpEF mouse models. Additionally, in male mice lacking Hdac6 gene, HFpEF progression is delayed and they are resistant to TYA-018's effects. The efficacy of TYA-018 is comparable to a sodium-glucose cotransporter 2 (SGLT2) inhibitor, and the combination shows enhanced effects. Mechanistically, TYA-018 restores gene expression related to hypertrophy, fibrosis, and mitochondrial energy production in HFpEF heart tissues. Furthermore, TYA-018 also inhibits activation of human cardiac fibroblasts and enhances mitochondrial respiratory capacity in cardiomyocytes. In this work, our findings show that HDAC6 impacts on heart pathophysiology and is a promising target for HFpEF treatment.

A clinically useful and biologically informative genomic classifier for papillary thyroid cancer.

Clinical management of papillary thyroid cancer depends on estimations of prognosis. Standard care, which relies on prognostication based on clinicopathologic features, is inaccurate. We applied a machine learning algorithm (HighLifeR) to 502 cases annotated by The Cancer Genome Atlas Project to derive an accurate molecular prognostic classifier. Unsupervised analysis of the 82 genes that were most closely associated with recurrence after surgery enabled the identification of three unique molecular subtypes. One subtype had a high recurrence rate, an immunosuppressed microenvironment, and enrichment of the EZH2-HOTAIR pathway. Two other unique molecular subtypes with a lower rate of recurrence were identified, including one subtype with a paucity of BRAFV600E mutations and a high rate of RAS mutations. The genomic risk classifier, in addition to tumor size and lymph node status, enabled effective prognostication that outperformed the American Thyroid Association clinical risk stratification. The genomic classifier we derived can potentially be applied preoperatively to direct clinical decision-making. Distinct biological features of molecular subtypes also have implications regarding sensitivity to radioactive iodine, EZH2 inhibitors, and immune checkpoint inhibitors.

Improved Cardiac Function in Postischemic Rats Using an Optimized Cardiac Reprogramming Cocktail Delivered in a Single Novel Adeno-Associated Virus.

BACKGROUND: Cardiac reprogramming is a technique to directly convert nonmyocytes into myocardial cells using genes or small molecules. This intervention provides functional benefit to the rodent heart when delivered at the time of myocardial infarction or activated transgenically up to 4 weeks after myocardial infarction. Yet, several hurdles have prevented the advancement of cardiac reprogramming for clinical use. METHODS: Through a combination of screening and rational design, we identified a cardiac reprogramming cocktail that can be encoded in a single adeno-associated virus. We also created a novel adeno-associated virus capsid that can transduce cardiac fibroblasts more efficiently than available parental serotypes by mutating posttranslationally modified capsid residues. Because a constitutive promoter was needed to drive high expression of these cell fate-altering reprogramming factors, we included binding sites to a cardiomyocyte-restricted microRNA within the 3' untranslated region of the expression cassette that limits expression to nonmyocytes. After optimizing this expression cassette to reprogram human cardiac fibroblasts into induced cardiomyocyte-like cells in vitro, we also tested the ability of this capsid/cassette combination to confer functional benefit in acute mouse myocardial infarction and chronic rat myocardial infarction models. RESULTS: We demonstrated sustained, dose-dependent improvement in cardiac function when treating a rat model 2 weeks after myocardial infarction, showing that cardiac reprogramming, when delivered in a single, clinically relevant adeno-associated virus vector, can support functional improvement in the postremodeled heart. This benefit was not observed with GFP (green fluorescent protein) or a hepatocyte reprogramming cocktail and was achieved even in the presence of immunosuppression, supporting myocyte formation as the underlying mechanism. CONCLUSIONS: Collectively, these results advance the application of cardiac reprogramming gene therapy as a viable therapeutic approach to treat chronic heart failure resulting from ischemic injury.

Phenotypic screening with deep learning identifies HDAC6 inhibitors as cardioprotective in a BAG3 mouse model of dilated cardiomyopathy.

Dilated cardiomyopathy (DCM) is characterized by reduced cardiac output, as well as thinning and enlargement of left ventricular chambers. These characteristics eventually lead to heart failure. Current standards of care do not target the underlying molecular mechanisms associated with genetic forms of heart failure, driving a need to develop novel therapeutics for DCM. To identify candidate therapeutics, we developed an in vitro DCM model using induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) deficient in B-cell lymphoma 2 (BCL2)-associated athanogene 3 (BAG3). With these BAG3-deficient iPSC-CMs, we identified cardioprotective drugs using a phenotypic screen and deep learning. From a library of 5500 bioactive compounds and siRNA validation, we found that inhibiting histone deacetylase 6 (HDAC6) was cardioprotective at the sarcomere level. We translated this finding to a BAG3 cardiomyocyte-knockout (BAG3cKO) mouse model of DCM, showing that inhibiting HDAC6 with two isoform-selective inhibitors (tubastatin A and a novel inhibitor TYA-018) protected heart function. In BAG3cKO and BAG3E455K mice, HDAC6 inhibitors improved left ventricular ejection fraction and reduced left ventricular diameter at diastole and systole. In BAG3cKO mice, TYA-018 protected against sarcomere damage and reduced Nppb expression. Based on integrated transcriptomics and proteomics and mitochondrial function analysis, TYA-018 also enhanced energetics in these mice by increasing expression of targets associated with fatty acid metabolism, protein metabolism, and oxidative phosphorylation. Our results demonstrate the power of combining iPSC-CMs with phenotypic screening and deep learning to accelerate drug discovery, and they support developing novel therapies that address underlying mechanisms associated with heart disease.

Integrative molecular and clinical profiling of acral melanoma links focal amplification of 22q11.21 to metastasis.

Acral melanoma, the most common melanoma subtype among non-White individuals, is associated with poor prognosis. However, its key molecular drivers remain obscure. Here, we perform integrative genomic and clinical profiling of acral melanomas from 104 patients treated in North America (n = 37) or China (n = 67). We find that recurrent, late-arising focal amplifications of cytoband 22q11.21 are a leading determinant of inferior survival, strongly associated with metastasis, and linked to downregulation of immunomodulatory genes associated with response to immune checkpoint blockade. Unexpectedly, LZTR1 - a known tumor suppressor in other cancers - is a key candidate oncogene in this cytoband. Silencing of LZTR1 in melanoma cell lines causes apoptotic cell death independent of major hotspot mutations or melanoma subtypes. Conversely, overexpression of LZTR1 in normal human melanocytes initiates processes associated with metastasis, including anchorage-independent growth, formation of spheroids, and an increase in MAPK and SRC activities. Our results provide insights into the etiology of acral melanoma and implicate LZTR1 as a key tumor promoter and therapeutic target.

Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes.

Drug-induced cardiotoxicity and hepatotoxicity are major causes of drug attrition. To decrease late-stage drug attrition, pharmaceutical and biotechnology industries need to establish biologically relevant models that use phenotypic screening to detect drug-induced toxicity in vitro. In this study, we sought to rapidly detect patterns of cardiotoxicity using high-content image analysis with deep learning and induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). We screened a library of 1280 bioactive compounds and identified those with potential cardiotoxic liabilities in iPSC-CMs using a single-parameter score based on deep learning. Compounds demonstrating cardiotoxicity in iPSC-CMs included DNA intercalators, ion channel blockers, epidermal growth factor receptor, cyclin-dependent kinase, and multi-kinase inhibitors. We also screened a diverse library of molecules with unknown targets and identified chemical frameworks that show cardiotoxic signal in iPSC-CMs. By using this screening approach during target discovery and lead optimization, we can de-risk early-stage drug discovery. We show that the broad applicability of combining deep learning with iPSC technology is an effective way to interrogate cellular phenotypes and identify drugs that may protect against diseased phenotypes and deleterious mutations.

Germline genetic contribution to the immune landscape of cancer.

Understanding the contribution of the host's genetic background to cancer immunity may lead to improved stratification for immunotherapy and to the identification of novel therapeutic targets. We investigated the effect of common and rare germline variants on 139 well-defined immune traits in ∼9000 cancer patients enrolled in TCGA. High heritability was observed for estimates of NK cell and T cell subset infiltration and for interferon signaling. Common variants of IFIH1, TMEM173 (STING1), and TMEM108 were associated with differential interferon signaling and variants mapping to RBL1 correlated with T cell subset abundance. Pathogenic or likely pathogenic variants in BRCA1 and in genes involved in telomere stabilization and Wnt-ß-catenin also acted as immune modulators. Our findings provide evidence for the impact of germline genetics on the composition and functional orientation of the tumor immune microenvironment. The curated datasets, variants, and genes identified provide a resource toward further understanding of tumor-immune interactions.

Type 3 Inositol 1,4,5-Trisphosphate Receptor Is Increased and Enhances Malignant Properties in Cholangiocarcinoma.

Cholangiocarcinoma (CCA) is the second most common malignancy arising in the liver. It carries a poor prognosis, in part because its pathogenesis is not well understood. The type 3 inositol 1,4,5-trisphosphate receptor (ITPR3) is the principal intracellular calcium ion (Ca2+ ) release channel in cholangiocytes, and its increased expression has been related to the pathogenesis of malignancies in other types of tissues, so we investigated its role in CCA. ITPR3 expression was increased in both hilar and intrahepatic CCA samples as well as in CCA cell lines. Deletion of ITPR3 from CCA cells impaired proliferation and cell migration. A bioinformatic analysis suggested that overexpression of ITPR3 in CCA would have a mitochondrial phenotype, so this was also examined. ITPR3 normally is concentrated in a subapical region of endoplasmic reticulum (ER) in cholangiocytes, but both immunogold electron microscopy and super-resolution microscopy showed that ITPR3 in CCA cells was also in regions of ER in close association with mitochondria. Deletion of ITPR3 from these cells impaired mitochondrial Ca2+ signaling and led to cell death. Conclusion: ITPR3 expression in cholangiocytes becomes enhanced in CCA. This contributes to malignant features, including cell proliferation and migration and enhanced mitochondrial Ca2+ signaling.

A strategy for early detection of response to chemotherapy drugs based on treatment-related changes in the metabolome.

We describe a biomarker-based approach to delivering chemotherapy that entails monitoring treatment changes in the circulating metabolome that reflect efficacy. In-vitro, multiple tumor cell lines were exposed to numerous chemotherapeutics. Supernatants were collected at baseline and 72 hours post treatment. MTT assays were used to quantify growth inhibition. Clinical samples were derived from a phase II clinical trial of second-line axitinib in patients with advanced hepatocellular carcinoma. Sera were collected at baseline and 2-4 weeks after treatment initiation. Response to therapy was estimated by CT scan at 8 weeks. Samples were analyzed by gas chromatography-mass spectrometry to identify metabolomic changes associated with response. In vitro, we found drug-specific and generalizable patterns of change in the extracellular metabolome accompany growth inhibition. A cell death signature was also identified. This approach was also applied to clinical samples. While the in vitro signatures were detectable in vivo, a more robust signal was identified clinically that appeared within 4 weeks of administering drug that distinguished individuals with a treatment response. These changes were extinguished as tumor growth resumed. Serial monitoring of the metabolome during chemotherapy is a means to follow treatment efficacy and emergence of resistance, informing the oncologist whether to modify treatment.

Gas Chromatography-Mass Spectrometry and Analysis of the Serum Metabolomic Profile Through Extraction and Derivatization of Polar Metabolites.

Metabolite profiling in complex biological matrices such as serum requires high-throughput technologies capable of accurate and reproducible quantitative analysis and detection of slight differences in metabolite concentrations. Gas chromatography-mass spectrometry (GC-MS) is widely used for characterizing the metabolome. This chapter summarizes the necessary preparatory steps required to profile the metabolome using GC-MS. While this chapter focuses on evaluating polar metabolites in serum samples, the methods can be adapted to quantify nonpolar metabolites in other biological matrices.

Host phenotype is associated with reduced survival independent of tumour biology in patients with colorectal liver metastases.

BACKGROUND: Most prognostic scoring systems for colorectal liver metastases (CRLMs) account for factors related to tumour biology. Little is known about the effects of the host phenotype to the tumour. Our objective was to delineate the relationship of systemic inflammation and body composition features [i.e. low skeletal muscle mass (sarcopenia) and low visceral adipose tissue (VAT)], two well-described host phenotypes in cancer. METHODS: Clinical data and pre-operative blood samples were collected from 99 patients who underwent resection of CRLM. Pre-operative computed tomography scans were available for 97 patients; body composition was analysed at the L3 level, stratified for sex and age. Clinicopathological variables, serum C-reactive protein (CRP), and various body composition variables were evaluated. Overall survival was evaluated as a function of these same variables in multivariate Cox regression analysis. RESULTS: Skeletal muscle was significantly correlated with VAT (r = 0.46, P < 0.001). Of patients with sarcopenia, 35 (65%) also had low VAT. C-reactive protein was elevated (≥5 mg/mL) in 42 patients (43.3%). Elevated CRP was more common in patients with sarcopenia (73.8% vs. 51.1%, P = 0.029). The most significant prognostic factors were the coincidence of elevated CRP and adverse body composition features (sarcopenia and/or low VAT; hazard ratio 4.3, 95% confidence interval 1.5-13.0, P = 0.008), as well as Fong clinical prognostic score (hazard ratio 2.9, 95% confidence interval 1.5-5.5, P = 0.002). CONCLUSIONS: Body composition in patients with CRLM is not directly linked to the presence of systemic inflammation. However, when systemic inflammation coincides with sarcopenia and/or low VAT, prognosis is adversely affected, independent of the Fong clinical prognostic score.

A Framework for Development of Useful Metabolomic Biomarkers and Their Effective Knowledge Translation.

Despite the significant advantages of metabolomic biomarkers, no diagnostic tests based on metabolomics have been introduced to clinical use. There are many reasons for this, centered around substantial obstacles in developing clinically useful metabolomic biomarkers. Most significant is the need for interdisciplinary teams with expertise in metabolomics, analysis of complex clinical and metabolomic data, and clinical care. Importantly, the clinical need must precede biomarker discovery, and the experimental design for discovery and validation must reflect the purpose of the biomarker. Standard operating procedures for procuring and handling samples must be developed from the beginning, to ensure experimental integrity. Assay design is another challenge, as there is not much precedent informing this. Another obstacle is that it is not yet clear how to protect any intellectual property related to metabolomic biomarkers. Viewing a metabolomic biomarker as a natural phenomenon would inhibit patent protection and potentially stifle commercial interest. However, demonstrating that a metabolomic biomarker is actually a derivative of a natural phenomenon that requires innovation would enhance investment in this field. Finally, effective knowledge translation strategies must be implemented, which will require engagement with end users (clinicians and lab physicians), patient advocate groups, policy makers, and payer organizations. Addressing each of these issues comprises the framework for introducing a metabolomic biomarker to practice.

The Immune Landscape of Cancer.

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-ß dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.

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.

Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas.

We analyzed 921 adenocarcinomas of the esophagus, stomach, colon, and rectum to examine shared and distinguishing molecular characteristics of gastrointestinal tract adenocarcinomas (GIACs). Hypermutated tumors were distinct regardless of cancer type and comprised those enriched for insertions/deletions, representing microsatellite instability cases with epigenetic silencing of MLH1 in the context of CpG island methylator phenotype, plus tumors with elevated single-nucleotide variants associated with mutations in POLE. Tumors with chromosomal instability were diverse, with gastroesophageal adenocarcinomas harboring fragmented genomes associated with genomic doubling and distinct mutational signatures. We identified a group of tumors in the colon and rectum lacking hypermutation and aneuploidy termed genome stable and enriched in DNA hypermethylation and mutations in KRAS, SOX9, and PCBP1.

A quantitative multimodal metabolomic assay for colorectal cancer.

BACKGROUND: Early diagnosis of colorectal cancer (CRC) simplifies treatment and improves treatment outcomes. We previously described a diagnostic metabolomic biomarker derived from semi-quantitative gas chromatography-mass spectrometry. Our objective was to determine whether a quantitative assay of additional metabolomic features, including parts of the lipidome could enhance diagnostic power; and whether there was an advantage to deriving a combined diagnostic signature with a broader metabolomic representation. METHODS: The well-characterized Biocrates P150 kit was used to quantify 163 metabolites in patients with CRC (N = 62), adenoma (N = 31), and age- and gender-matched disease-free controls (N = 81). Metabolites included in the analysis included phosphatidylcholines, sphingomyelins, acylcarnitines, and amino acids. Using a training set of 32 CRC and 21 disease-free controls, a multivariate metabolomic orthogonal partial least squares (OPLS) classifier was developed. An independent set of 28 CRC and 20 matched healthy controls was used for validation. Features characterizing 31 colorectal adenomas from their healthy matched controls were also explored, and a multivariate OPLS classifier for colorectal adenoma could be proposed. RESULTS: The metabolomic profile that distinguished CRC from controls consisted of 48 metabolites (R2Y = 0.83, Q2Y = 0.75, CV-ANOVA p-value < 0.00001). In this quantitative assay, the coefficient of variance for each metabolite was <10%, and this dramatically enhanced the separation of these groups. Independent validation resulted in AUROC of 0.98 (95% CI, 0.93-1.00) and sensitivity and specificity of 93% and 95%. Similarly, we were able to distinguish adenoma from controls (R2Y = 0.30, Q2Y = 0.20, CV-ANOVA p-value = 0.01; internal AUROC = 0.82 (95% CI, 0.72-0.93)). When combined with the previously generated GC-MS signatures for CRC and adenoma, the candidate biomarker performance improved slightly. CONCLUSION: The diagnostic power for metabolomic tests for colorectal neoplasia can be improved by utilizing a multimodal approach and combining metabolites from diverse chemical classes. In addition, quantification of metabolites enhances separation of disease-specific metabolomic profiles. Our future efforts will be focused on developing a quantitative assay for the metabolites comprising the optimal diagnostic biomarker.