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.

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.

Immunohistochemical phenotyping of T cells, granulocytes, and phagocytes in the muscle of cancer patients: association with radiologically defined muscle mass and gene expression.

BACKGROUND: Inflammation is a recognized contributor to muscle wasting. Research in injury and myopathy suggests that interactions between the skeletal muscle and immune cells confer a pro-inflammatory environment that influences muscle loss through several mechanisms; however, this has not been explored in the cancer setting. This study investigated the local immune environment of the muscle by identifying the phenotype of immune cell populations in the muscle and their relationship to muscle mass in cancer patients. METHODS: Intraoperative muscle biopsies were collected from cancer patients (n = 30, 91% gastrointestinal malignancies). Muscle mass was assessed histologically (muscle fiber cross-sectional area, CSA; µm2) and radiologically (lumbar skeletal muscle index, SMI; cm2/m2 by computed tomography, CT). T cells (CD4 and CD8) and granulocytes/phagocytes (CD11b, CD14, and CD15) were assessed by immunohistochemistry. Microarray analysis was conducted in the muscle of a second cancer patient cohort. RESULTS: T cells (CD3+), granulocytes/phagocytes (CD11b+), and CD3-CD4+ cells were identified. Muscle fiber CSA (µm2) was positively correlated (Spearman's r = > 0.45; p = < 0.05) with the total number of T cells, CD4, and CD8 T cells and granulocytes/phagocytes. In addition, patients with the smallest SMI exhibited fewer CD8 T cells within their muscle. Consistent with this, further exploration with gene correlation analyses suggests that the presence of CD8 T cells is negatively associated (Pearson's r = ≥ 0.5; p = <0.0001) with key genes within muscle catabolic pathways for signaling (ACVR2B), ubiquitin proteasome (FOXO4, TRIM63, FBXO32, MUL1, UBC, UBB, UBE2L3), and apoptosis/autophagy (CASP8, BECN1, ATG13, SIVA1). CONCLUSION: The skeletal muscle immune environment of cancer patients is comprised of immune cell populations from the adaptive and innate immunity. Correlations of T cells, granulocyte/phagocytes, and CD3-CD4+ cells with muscle mass measurements indicate a positive relationship between immune cell numbers and muscle mass status in cancer patients. Further exploration with gene correlation analyses suggests that the presence of CD8 T cells is negatively correlated with components of muscle catabolism.

Clinical and biological characterization of skeletal muscle tissue biopsies of surgical cancer patients.

BACKGROUND: Researchers increasingly use intraoperative muscle biopsy to investigate mechanisms of skeletal muscle atrophy in patients with cancer. Muscles have been assessed for morphological, cellular, and biochemical features. The aim of this study was to conduct a state-of-the-science review of this literature and, secondly, to evaluate clinical and biological variation in biopsies of rectus abdominis (RA) muscle from a cohort of patients with malignancies. METHODS: Literature was searched for reports on muscle biopsies from patients with a cancer diagnosis. Quality of reports and risk of bias were assessed. Data abstracted included patient characteristics and diagnoses, sample size, tissue collection and biobanking procedures, and results. A cohort of cancer patients (n = 190, 88% gastrointestinal malignancies), who underwent open abdominal surgery as part of their clinical care, consented to RA biopsy from the site of incision. Computed tomography (CT) scans were used to quantify total abdominal muscle and RA cross-sectional areas and radiodensity. Biopsies were assessed for muscle fibre area (µm2 ), fibre types, myosin heavy chain isoforms, and expression of genes selected for their involvement in catabolic pathways of muscle. RESULTS: Muscle biopsy occurred in 59 studies (total N = 1585 participants). RA was biopsied intraoperatively in 40 studies (67%), followed by quadriceps (26%; percutaneous biopsy) and other muscles (7%). Cancer site and stage, % of male participants, and age were highly variable between studies. Details regarding patient medical history and biopsy procedures were frequently absent. Lack of description of the population(s) sampled and low sample size contributed to low quality and risk of bias. Weight-losing cases were compared with weight stable cancer or healthy controls without considering a measure of muscle mass in 21 out of 44 studies. In the cohort of patients providing biopsy for this study, 78% of patients had preoperative CT scans and a high proportion (64%) met published criteria for sarcopenia. Fibre type distribution in RA was type I (46% ± 13), hybrid type I/IIA (1% ± 1), type IIA (36% ± 10), hybrid type IIA/D (15% ± 14), and type IID (2% ± 5). Sexual dimorphism was prominent in RA CT cross-sectional area, mean fibre cross-sectional area, and in expression of genes associated with muscle growth, apoptosis, and inflammation (P < 0.05). Medical history revealed multiple co-morbid conditions and medications. CONCLUSIONS: Continued collaboration between researchers and cancer surgeons enables a more complete understanding of mechanisms of cancer-associated muscle atrophy. Standardization of biobanking practices, tissue manipulation, patient characterization, and classification will enhance the consistency, reliability, and comparability of future studies.

Knockout of USP19 Deubiquitinating Enzyme Prevents Muscle Wasting by Modulating Insulin and Glucocorticoid Signaling.

Muscle atrophy arises because of many chronic illnesses, as well as from prolonged glucocorticoid treatment and nutrient deprivation. We previously demonstrated that the USP19 deubiquitinating enzyme plays an important role in chronic glucocorticoid- and denervation-induced muscle wasting. However, the mechanisms by which USP19 exerts its effects remain unknown. To explore this further, we fasted mice for 48 hours to try to identify early differences in the response of wild-type and USP19 knockout (KO) mice that could yield insights into the mechanisms of USP19 action. USP19 KO mice manifested less myofiber atrophy in response to fasting due to increased rates of protein synthesis. Insulin signaling was enhanced in the KO mice, as revealed by lower circulating insulin levels, increased insulin-stimulated glucose disposal and phosphorylation of Akt and S6K in muscle, and improved overall glucose tolerance. Glucocorticoid signaling, which is essential in many conditions of atrophy, was decreased in KO muscle, as revealed by decreased expression of glucocorticoid receptor (GR) target genes upon both fasting and glucocorticoid treatment. This decreased GR signaling was associated with lower GR protein levels in the USP19 KO muscle. Restoring the GR levels in USP19-deficient muscle was sufficient to abolish the protection from myofiber atrophy. Expression of GR target genes also correlated with that of USP19 in human muscle samples. Thus, USP19 modulates GR levels and in so doing may modulate both insulin and glucocorticoid signaling, two critical pathways that control protein turnover in muscle and overall glucose homeostasis.

Sarcopenia and myosteatosis are accompanied by distinct biological profiles in patients with pancreatic and periampullary adenocarcinomas.

INTRODUCTION: Pancreatic and periampullary adenocarcinomas are associated with abnormal body composition visible on CT scans, including low muscle mass (sarcopenia) and low muscle radiodensity due to fat infiltration in muscle (myosteatosis). The biological and clinical correlates to these features are poorly understood. METHODS: Clinical characteristics and outcomes were studied in 123 patients who underwent pancreaticoduodenectomy for pancreatic or non-pancreatic periampullary adenocarcinoma and who had available preoperative CT scans. In a subgroup of patients with pancreatic cancer (n = 29), rectus abdominus muscle mRNA expression was determined by cDNA microarray and in another subgroup (n = 29) 1H-NMR spectroscopy and gas chromatography-mass spectrometry were used to characterize the serum metabolome. RESULTS: Muscle mass and radiodensity were not significantly correlated. Distinct groups were identified: sarcopenia (40.7%), myosteatosis (25.2%), both (11.4%). Fat distribution differed in these groups; sarcopenia associated with lower subcutaneous adipose tissue (P<0.0001) and myosteatosis associated with greater visceral adipose tissue (P<0.0001). Sarcopenia, myosteatosis and their combined presence associated with shorter survival, Log Rank P = 0.005, P = 0.06, and P = 0.002, respectively. In muscle, transcriptomic analysis suggested increased inflammation and decreased growth in sarcopenia and disrupted oxidative phosphorylation and lipid accumulation in myosteatosis. In the circulating metabolome, metabolites consistent with muscle catabolism associated with sarcopenia. Metabolites consistent with disordered carbohydrate metabolism were identified in both sarcopenia and myosteatosis. DISCUSSION: Muscle phenotypes differ clinically and biologically. Because these muscle phenotypes are linked to poor survival, it will be imperative to delineate their pathophysiologic mechanisms, including whether they are driven by variable tumor biology or host response.

Small RNAome profiling from human skeletal muscle: novel miRNAs and their targets associated with cancer cachexia.

BACKGROUND: MicroRNAs (miRs) are small non-coding RNAs that regulate gene (mRNA) expression. Although the pathological role of miRs have been studied in muscle wasting conditions such as myotonic and muscular dystrophy, their roles in cancer cachexia (CC) are still emerging. OBJECTIVES: The objectives are (i) to profile human skeletal muscle expressed miRs; (ii) to identify differentially expressed (DE) miRs between cachectic and non-cachectic cancer patients; (iii) to identify mRNA targets for the DE miRs to gain mechanistic insights; and (iv) to investigate if miRs show potential prognostic and predictive value. METHODS: Study subjects were classified based on the international consensus diagnostic criteria for CC. Forty-two cancer patients were included, of which 22 were cachectic cases and 20 were non-cachectic cancer controls. Total RNA isolated from muscle biopsies were subjected to next-generation sequencing. RESULTS: A total of 777 miRs were profiled, and 82 miRs with read counts of ≥5 in 80% of samples were retained for analysis. We identified eight DE miRs (up-regulated, fold change of ≥1.4 at P < 0.05). A total of 191 potential mRNA targets were identified for the DE miRs using previously described human skeletal muscle mRNA expression data (n = 90), and a majority of them were also confirmed in an independent mRNA transcriptome dataset. Ingenuity pathway analysis identified pathways related to myogenesis and inflammation. qRT-PCR analysis of representative miRs showed similar direction of effect (P < 0.05), as observed in next-generation sequencing. The identified miRs also showed prognostic and predictive value. CONCLUSIONS: In all, we identified eight novel miRs associated with CC.

New genetic signatures associated with cancer cachexia as defined by low skeletal muscle index and weight loss.

BACKGROUND: Cachexia affects the majority with advanced cancer. Based on current demographic and clinical factors, it is not possible to predict who will develop cachexia or not. Such variation may, in part, be due to genotype. It has recently been proposed to extend the diagnostic criteria for cachexia to include a direct measure of low skeletal muscle index (LSMI) in addition to weight loss (WL). We aimed to explore our panel of candidate single nucleotide polymorphism (SNPs) for association with WL +/- computerized tomography-defined LSMI. We also explored whether the transcription in muscle of identified genes was altered according to such cachexia phenotype METHODS: A retrospective cohort study design was used. Analysis explored associations of candidate SNPs with WL (n = 1276) and WL + LSMI (n = 943). Human muscle transcriptome (n = 134) was analysed using an Agilent platform. RESULTS: Single nucleotide polymorphisms in the following genes showed association with WL alone: GCKR, LEPR, SELP, ACVR2B, TLR4, FOXO3, IGF1, CPN1, APOE, FOXO1, and GHRL. SNPs in LEPR, ACVR2B, TNF, and ACE were associated with concurrent WL + LSMI. There was concordance between muscle-specific expression for ACVR2B, FOXO1 and 3, LEPR, GCKR, and TLR4 genes and LSMI and/or WL (P < 0.05). CONCLUSIONS: The rs1799964 in the TNF gene and rs4291 in the ACE gene are new associations when the definition of cachexia is based on a combination of WL and LSMI. These findings focus attention on pro-inflammatory cytokines and the renin-angiotensin system as biomarkers/mediators of muscle wasting in cachexia.

Inactivation of the ubiquitin-specific protease 19 deubiquitinating enzyme protects against muscle wasting.

The ubiquitin system plays a critical role in muscle wasting. Previous work has focused on the roles of ubiquitination. However, a role for deubiquitination in this process has not been established. Because ubiquitin-specific protease (USP)19 deubiquitinating enzyme is induced in skeletal muscle in many catabolic conditions, we generated USP19 knockout (KO) mice. These mice lost less muscle mass than wild-type (WT) animals in response to glucocorticoids, a common systemic cause of muscle atrophy as well as in response to denervation, a model of disuse atrophy. KO mice retained more strength and had less myofiber atrophy with both type I and type IIb fibers being protected. Rates of muscle protein synthesis were similar in WT and KO mice, suggesting that the sparing of atrophy was attributed to suppressed protein degradation. Consistent with this, expression of the ubiquitin ligases MuRF1 and MAFbx/atrogin-1 as well as several autophagy genes was decreased in the muscles of catabolic KO mice. Expression of USP19 correlates with that of MuRF1 and MAFbx/atrogin-1 in skeletal muscles from patients with lung cancer or gastrointestinal cancer, suggesting that USP19 is involved in human muscle wasting. Inhibition of USP19 may be a useful approach to the treatment of many muscle-wasting conditions.

Development of a new equation to estimate creatinine clearance in cancer patients.

PURPOSE: Determining renal function is important for chemotherapy eligibility and dosing. Measured creatinine clearance (mCrCl) is the gold standard but is cumbersome. Equations estimating CrCl (eCrCl) based on serum creatinine (SCr) produce widely varying estimates. Considering that SCr is derived from skeletal muscle, this study prospectively developed a new eCrCl equation in cancer patients using CT-defined muscle surface area (MSA) and evaluated its utility in a separate, retrospective series. METHODS: In a prospective, observational cohort study of cancer patients, mCrCl by 24-h urine collection was correlated with CT-determined MSA to create an equation for eCrCl [muscle surface area (cm(2)) × 42/SCr]. eCrCl by Wright, Cockcroft-Gault (CG), CKD-EPI, MDRD, and MSA was compared to mCrCl to determine fit. MSA-eCrCl was used to simulate carboplatin dosing in a retrospective series of advanced non-small cell lung cancer (NSCLC). RESULTS: Prospectively, 22 patients were accrued and evaluable (12 males; median age 69). MSA-eCrCl correlated stronger (r (2) 0.80) than current equations (r (2) 0.47-0.69) with mCrCl. In calculating carboplatin doses for 89 NSCLC patients with MSA and CG-eCrCl, median error of CG-determined carboplatin dose was 5.5 % (range -19.0 to 44.2 %), assuming that MSA was better at estimating CrCl. Forty-two patients (47 %) received doses that varied ≥10 % of what was calculated by MSA. CONCLUSIONS: We propose a new formula for eCrCl in patients that appears more accurate than current formulae and may have implications for chemotherapy efficacy and toxicity. Studies to validate this formula are under way.

Effects of sample size on differential gene expression, rank order and prediction accuracy of a gene signature.

Top differentially expressed gene lists are often inconsistent between studies and it has been suggested that small sample sizes contribute to lack of reproducibility and poor prediction accuracy in discriminative models. We considered sex differences (69♂, 65 ♀) in 134 human skeletal muscle biopsies using DNA microarray. The full dataset and subsamples (n = 10 (5 ♂, 5 ♀) to n = 120 (60 ♂, 60 ♀)) thereof were used to assess the effect of sample size on the differential expression of single genes, gene rank order and prediction accuracy. Using our full dataset (n = 134), we identified 717 differentially expressed transcripts (p<0.0001) and we were able predict sex with ~90% accuracy, both within our dataset and on external datasets. Both p-values and rank order of top differentially expressed genes became more variable using smaller subsamples. For example, at n = 10 (5 ♂, 5 ♀), no gene was considered differentially expressed at p<0.0001 and prediction accuracy was ~50% (no better than chance). We found that sample size clearly affects microarray analysis results; small sample sizes result in unstable gene lists and poor prediction accuracy. We anticipate this will apply to other phenotypes, in addition to sex.

Prediction of skeletal muscle and fat mass in patients with advanced cancer using a metabolomic approach.

Urine and plasma metabolites originate from endogenous metabolic pathways in different organs and exogenous sources (diet). Urine and plasma were obtained from advanced cancer patients and investigated to determine if variations in lean and fat mass, dietary intake, and energy metabolism relate to variation in metabolite profiles. Patients (n = 55) recorded their diets for 3 d and after an overnight fast they were evaluated by DXA and indirect calorimetry. Metabolites were measured by NMR and direct injection MS. Three algorithms were used [partial least squares discriminant-analysis, support vector machines (SVM), and least absolute shrinkage and selection operator] to relate patients' plasma/urine metabolic profile with their dietary/physiological assessments. Leave-one-out cross-validation and permutation testing were conducted to determine statistical validity. None of the algorithms, using 63 urine metabolites, could learn to predict variations in individual's resting energy expenditure, respiratory quotient, or their intake of total energy, fat, sugar, or carbohydrate. Urine metabolites predicted appendicular lean tissue (skeletal muscle) with excellent cross-validation accuracy (98% using SVM). Total lean tissue correlated highly with appendicular muscle (Pearson r = 0.98; P < 0.0001) and gave similar cross-validation accuracies. Fat mass was effectively predicted using the 63 urine metabolites or the 143 plasma metabolites, exclusively. In conclusion, in this population, lean and fat mass variation could be effectively predicted using urinary metabolites, suggesting a potential role for metabolomics in body composition research. Furthermore, variation in lean and fat mass potentially confounds metabolomic studies attempting to characterize diet or disease conditions. Future studies should account or correct for such variation.

Understanding the human salivary metabolome.

Saliva is a readily accessible biofluid that is important for the overall health, aiding in the chewing, swallowing, and tasting of food as well as the regulation mouth flora. As a first step to determining and understanding the human saliva metabolome, we have measured salivary metabolite concentrations under a variety of conditions in a healthy population with reasonably good oral hygiene. Using (1)H NMR spectroscopy, metabolite concentrations were measured in resting (basal) and stimulated saliva from the same subject and compared in a cohort of healthy male non-smoking subjects (n = 62). Almost all metabolites were higher in the unstimulated saliva when compared to the stimulated saliva. Comparison of the salivary metabolite profile of male smokers and non-smokers (n = 46) revealed citrate, lactate, pyruvate, and sucrose to be higher and formate to be lower in concentration in smokers compared with non-smokers (p < 0.05). Gender differences were also investigated (n = 40), and acetate, formate, glycine, lactate, methanol, propionate, propylene glycol, pyruvate, succinate, and taurine were significantly higher in concentration in male saliva compared to female saliva (p < 0.05). These results show that differences between male and female, stimulated and unstimulated, as well as smoking status may be observed in the salivary metabolome.