N-linked fucosylated glycans are biomarkers for prostate cancer with a neuroendocrine and metastatic phenotype.

Prostate cancer (PCa) is a heterogeneous disease with a spectrum of pathology and outcomes ranging from indolent to lethal. Although there have been recent advancements in prognostic tissue biomarkers, limitations still exist. We leveraged Matrix Assisted Laser Desorption Ionization (MALDI) imaging of formalin-fixed, paraffin embedded (FFPE) prostate cancer specimens to determine if N-linked glycans expressed in the extracellular matrix of lethal neuroendocrine prostate cancer were also expressed in conventional prostate adenocarcinomas that were associated with poor outcomes. We found that N-glycan fucosylation was abundant in neuroendocrine prostate cancer as well as adenocarcinomas at time of prostatectomy that eventually developed recurrent metastatic disease. Analysis of patient derived xenografts revealed that this fucosylation signature was enriched differently across metastatic disease organ sites, with the highest abundance in liver metastases. These data suggest that N-linked fucosylated glycans could be an early tissue biomarker for poor PCa outcomes. Implications: These studies identify that hyper-fucosylated N-linked glycans are enriched in neuroendocrine prostate cancer and conventional prostate adenocarcinomas that progress to metastatic disease, thus advancing biomarker discovery and providing insights into mechanisms underlying metastatic disease.

Determining the N-Glycan and Collagen/Extracellular Matrix Protein Compositions in a Novel Outcome Cohort of Prostate Cancer Tissue Microarrays Using MALDI-MSI.

Prostate cancer is a significant health concern, with metastasis posing major clinical challenges and resulting in poor patient outcome. Despite screening and treatment advances, a critical need for novel biomarkers to predict prostate cancer progression at the time of prostatectomy persists. Here, we assessed aberrant N-glycosylation patterns and alterations in extracellular matrix proteins as potential biomarkers of predicting prostate cancer severity in a unique patient outcome cohort. Tissue microarray slides were assembled from primary prostatectomy specimens that were categorized into "no evidence of disease (NED)" and "metastasis (MET)" designations based on >5-year disease progression outcomes. Serial mass spectrometry imaging techniques were performed to analyze N-glycans and extracellular matrix (ECM) components in formalin-fixed paraffin-embedded cores. The results revealed a significant upregulation of bisecting and multi-antennary core fucosylated N-glycans in MET tissues when compared to NED tissues. Alterations in ECM composition in both NED and MET cohorts were observed, particularly in collagen species and the amount of hydroxyproline content. Results suggest a coordinated alteration of ECM protein and glycosylation content in prostate cancer tissues can be predictive for post-prostatectomy disease progression.

Upgrading of Grade Group 1 Prostate Cancer at Prostatectomy: Germline Risk Factors in a Prospective Cohort.

BACKGROUND: Localized prostate tumors show significant spatial heterogeneity, with regions of high-grade disease adjacent to lower grade disease. Consequently, prostate cancer biopsies are prone to sampling bias, potentially leading to underestimation of tumor grade. To study the clinical, epidemiologic, and molecular hallmarks of this phenomenon, we conducted a prospective study of grade upgrading: differences in detected prostate cancer grade between biopsy and surgery. METHODS: We established a prospective, multi-institutional cohort of men with grade group 1 (GG1) prostate cancer on biopsy who underwent radical prostatectomy. Upgrading was defined as detection of GG2+ in the resected tumor. Germline DNA from 192 subjects was subjected to whole-genome sequencing to quantify ancestry, pathogenic variants in DNA damage response genes, and polygenic risk. RESULTS: Of 285 men, 67% upgraded at surgery. PSA density and percent of cancer in pre-prostatectomy positive biopsy cores were significantly associated with upgrading. No assessed genetic risk factor was predictive of upgrading, including polygenic risk scores for prostate cancer diagnosis. CONCLUSIONS: In a cohort of patients with low-grade prostate cancer, a majority upgraded at radical prostatectomy. PSA density and percent of cancer in pre-prostatectomy positive biopsy cores portended the presence of higher-grade disease, while germline genetics was not informative in this setting. Patients with low-risk prostate cancer, but elevated PSA density or percent cancer in positive biopsy cores, may benefit from repeat biopsy, additional imaging or other approaches to complement active surveillance. IMPACT: Further risk stratification of patients with low-risk prostate cancer may provide useful context for active surveillance decision-making.

A Scaled Proteomic Discovery Study for Prostate Cancer Diagnostic Markers Using ProteographTM and Trapped Ion Mobility Mass Spectrometry.

There is a significant unmet need for clinical reflex tests that increase the specificity of prostate-specific antigen blood testing, the longstanding but imperfect tool for prostate cancer diagnosis. Towards this endpoint, we present the results from a discovery study that identifies new prostate-specific antigen reflex markers in a large-scale patient serum cohort using differentiating technologies for deep proteomic interrogation. We detect known prostate cancer blood markers as well as novel candidates. Through bioinformatic pathway enrichment and network analysis, we reveal associations of differentially abundant proteins with cytoskeletal, metabolic, and ribosomal activities, all of which have been previously associated with prostate cancer progression. Additionally, optimized machine learning classifier analysis reveals proteomic signatures capable of detecting the disease prior to biopsy, performing on par with an accepted clinical risk calculator benchmark.

The glycosylation landscape of prostate cancer tissues and biofluids.

An overview of the role of glycosylation in prostate cancer (PCa) development and progression is presented, focusing on recent advancements in defining the N-glycome through glycomic profiling and glycoproteomic methodologies. Glycosylation is a common post-translational modification typified by oligosaccharides attached N-linked to asparagine or O-linked to serine or threonine on carrier proteins. These attached sugars have crucial roles in protein folding and cellular recognition processes, such that altered glycosylation is a hallmark of cancer pathogenesis and progression. In the past decade, advancements in N-glycan profiling workflows using Matrix Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) technology have been applied to define the spatial distribution of glycans in PCa tissues. Multiple studies applying N-glycan MALDI-MSI to pathology-defined PCa tissues have identified significant alterations in N-glycan profiles associated with PCa progression. N-glycan compositions progressively increase in number, and structural complexity due to increased fucosylation and sialylation. Additionally, significant progress has been made in defining the glycan and glycopeptide compositions of prostatic-derived glycoproteins like prostate-specific antigen in tissues and biofluids. The glycosyltransferases involved in these changes are potential drug targets for PCa, and new approaches in this area are summarized. These advancements will be discussed in the context of the further development of clinical diagnostics and therapeutics targeting glycans and glycoproteins associated with PCa progression. Integration of large scale spatial glycomic data for PCa with other spatial-omic methodologies is now feasible at the tissue and single-cell levels.

Effects of vortioxetine on hippocampal-related cognitive impairment induced in rats by androgen deprivation as a model of prostate cancer treatment.

Advances in prostate cancer treatment have significantly improved survival, but quality of life for survivors remains an under-studied area of research. Androgen deprivation therapy (ADT) is a foundational treatment for advanced prostate cancer and is used as an adjuvant for prolonged periods in many high-risk, localized tumors. More than half of patients treated with ADT experience debilitating cognitive impairments in domains such as spatial learning and working memory. In this study, we investigated the effects of androgen deprivation on hippocampal-mediated cognition in rats. Vortioxetine, a multimodal antidepressant, has been shown to improve cognition in depressed patients. Thus, we also tested the potential efficacy of vortioxetine in restoring impaired cognition after ADT. We further investigated mechanisms that might contribute to these effects, measuring changes in the circuitry and gene expression within the dorsal hippocampus. ADT via surgical castration induced impairments in visuospatial cognition on the novel object location test and attenuated afferent-evoked local field potentials recorded in the CA1 region of the dorsal hippocampus. Chronic dietary administration of vortioxetine effectively reversed these deficits. Castration significantly altered gene expression in the hippocampus, whereas vortioxetine had little effect. Pathway analysis revealed that androgen depletion altered pathways related to synaptic plasticity. These results suggest that the hippocampus may be vulnerable to ADT, contributing to cognitive impairment in prostate cancer patients. Further, vortioxetine may be a candidate to improve cognition in patients who experience cognitive decline after androgen deprivation therapy for prostate cancer and may do so by restoring molecular and circuit-level plasticity-related mechanisms compromised by ADT.

Prevalence of genotoxic bacteria in men undergoing biopsy for prostate cancer.

BACKGROUND: New evidence suggests that bacteria-produced DNA toxins may have a role in the development or progression of prostate cancer. To determine the prevalence of these genes in a noninfection (i.e., colonized) state, we screened urine specimens in men before undergoing a biopsy for prostate cancer detection. METHODS: We developed a multiplex polymerase chain reaction using three of the most described bacterial genotoxin gene primers: Colibactin (polyketone synthase [pks] gene island: clbN and clbB), cytotoxic necrotizing factor (cnf1) toxin, and cytolethal distending toxin B (cdtB) represented gene islands. After calibration on Escherichia coli samples of known genotypes, we used a training and validation cohort. We performed multiplex testing on a training cohort of previously collected urine from 45 men undergoing prostate biopsy. For the validation cohort, we utilized baseline urine samples from a previous randomized clinical trial (n = 263) with known prostate cancer outcomes. RESULTS: The prevalence of four common bacterial genotoxin genes detected in the urine before prostate biopsy for prostate cancer is 8% (25/311). The prevalence of pks island (clbN and clbB), cnf1, and cdt toxin genes are 6.1%, 2.4%, and 1.7%, respectively. We found no association between urinary genotoxins and prostate cancer (p = 0.83). We did identify a higher proportion of low-grade cancer (92% vs. 44%) in those men positive for urinary genotoxin and higher-grade cancer in those genotoxin negative (8% vs. 56%, p = 0.001). CONCLUSIONS: The prevalence of urinary genotoxins is low and does not correspond to a prostate cancer diagnosis. The urine was taken at one point in time and does not rule out the possibility of previous exposure.

Targeted Mass Spectrometry Assays for Specific Quantification of Urinary proPSA Isoforms.

Prostate cancer (PCa) is the second leading cause of male cancer-related deaths in the United States. The pre-mature forms of prostate-specific antigen (PSA), proPSA, were shown to be associated with PCa. However, there is a technical challenge in the development of antibody-based immunoassays for specific recognition of each individual proPSA isoform. Herein, we report the development of highly specific, antibody-free, targeted mass spectrometry assays for simultaneous quantification of [-2], [-4], [-5], and [-7] proPSA isoforms in voided urine. The newly developed proPSA assays capitalize on Lys-C digestion to generate surrogate peptides with appropriate length (9-16 amino acids) along with long-gradient liquid chromatography separation. The assay utility of these isoform markers was evaluated in a cohort of 30 well-established clinical urine samples for distinguishing PCa patients from healthy controls. Under the 95% confidence interval, the combination of [-2] and [-4] proPSA isoforms yields the area under curve (AUC) of 0.86, and the AUC value for the combined all four isoforms was calculated to be 0.85. We have further verified [-2]proPSA, the dominant isoform, in an independent cohort of 34 clinical urine samples. Validation of proPSA isoforms in large-scale cohorts is needed to demonstrate their potential clinical utility.

Prospective comparison of restriction spectrum imaging and non-invasive biomarkers to predict upgrading on active surveillance prostate biopsy.

BACKGROUND: Protocol-based active surveillance (AS) biopsies have led to poor compliance. To move to risk-based protocols, more accurate imaging biomarkers are needed to predict upgrading on AS prostate biopsy. We compared restriction spectrum imaging (RSI-MRI) generated signal maps as a biomarker to other available non-invasive biomarkers to predict upgrading or reclassification on an AS biopsy. METHODS: We prospectively enrolled men on prostate cancer AS undergoing repeat biopsy from January 2016 to June 2019 to obtain an MRI and biomarkers to predict upgrading. Subjects underwent a prostate multiparametric MRI and a short duration, diffusion-weighted enhanced MRI called RSI to generate a restricted signal map along with evaluation of 30 biomarkers (14 clinico-epidemiologic features, 9 molecular biomarkers, and 7 radiologic-associated features). Our primary outcome was upgrading or reclassification on subsequent AS prostate biopsy. Statistical analysis included operating characteristic improvement using AUROC and AUPRC. RESULTS: The individual biomarker with the highest area under the receiver operator characteristic curve (AUC) was RSI-MRI (AUC = 0.84; 95% CI: 0.71-0.96). The best non-imaging biomarker was prostate volume-corrected Prostate Health Index density (PHI, AUC = 0.68; 95% CI: 0.53-0.82). Non-imaging biomarkers had a negligible effect on predicting upgrading at the next biopsy but did improve predictions of overall time to progression in AS. CONCLUSIONS: RSI-MRI, PIRADS, and PHI could improve the predictive ability to detect upgrading in AS. The strongest predictor of clinically significant prostate cancer on AS biopsy was RSI-MRI signal output.

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction.

BACKGROUND: We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user. METHODS: Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach. RESULTS: Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history. CONCLUSION: Developers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors.

Prediction of future risk of any and higher-grade prostate cancer based on the PLCO and SELECT trials.

BACKGROUND: A model was built that characterized effects of individual factors on five-year prostate cancer (PCa) risk in the Prostate, Lung, Colon, and Ovarian Cancer Screening Trial (PLCO) and the Selenium and Vitamin E Cancer Prevention Trial (SELECT). This model was validated in a third San Antonio Biomarkers of Risk (SABOR) screening cohort. METHODS: A prediction model for 1- to 5-year risk of developing PCa and Gleason > 7 PCa (HG PCa) was built on PLCO and SELECT using the Cox proportional hazards model adjusting for patient baseline characteristics. Random forests and neural networks were compared to Cox proportional hazard survival models, using the trial datasets for model building and the SABOR cohort for model evaluation. The most accurate prediction model is included in an online calculator. RESULTS: The respective rates of PCa were 8.9%, 7.2%, and 11.1% in PLCO (n = 31,495), SELECT (n = 35,507), and SABOR (n = 1790) over median follow-up of 11.7, 8.1 and 9.0 years. The Cox model showed higher prostate-specific antigen (PSA), BMI and age, and African American race to be associated with PCa and HGPCa. Five-year risk predictions from the combined SELECT and PLCO model effectively discriminated risk in the SABOR cohort with C-index 0.76 (95% CI [0.72, 0.79]) for PCa, and 0.74 (95% CI [0.65,0.83]) for HGPCa. CONCLUSIONS: A 1- to 5-year PCa risk prediction model developed from PLCO and SELECT was validated with SABOR and implemented online. This model can individualize and inform shared screening decisions.

Hereditary Cancer Gene Variants in Hispanic Men With a Personal or Family History of Prostate Cancer.

BACKGROUND: Mutations in several common hereditary cancer genes are associated with prostate cancer, but there is limited information on the prevalence of these mutations in Hispanic men. MATERIALS AND METHODS: We selected men at high risk for genetic mutations from 1515 Hispanic men enrolled in the San Antonio Biomarkers of Risk for prostate cancer (SABOR) cohort. Inclusion criteria included men with a diagnosis of prostate cancer or a first-degree family history of prostate cancer. We performed germline genetic testing using the Color Genomics platform, sequencing 30 genes associated with hereditary cancer risk. Additionally, we assessed ancestral informative markers to determine the admixture of the ethnically unique cohort. RESULTS: Of the 275 subjects who met selection criteria, 263 patients had sufficient samples for sequencing. We identified 3.8% of patients (10 of 263) with a pathogenic or likely pathogenic mutation in the 30 genes tested, of whom 70% would not have met established criteria for genetic testing. Six of these mutations were in BRCA1/2 or ATM. There was a significant inverse association between the percentage of Native American ancestry and the risk of prostate cancer, OR 0.11 (95% CI 0.02-0.76, P = .025). CONCLUSION: Hispanic men with either a personal or family history of prostate cancer carry mutations in hereditary cancer genes at a significant rate, on par with non-Hispanic counterparts with similar risk factors.

Prostate cancer risk stratification improvement across multiple ancestries with new polygenic hazard score.

BACKGROUND: Prostate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets. METHODS: In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured. RESULTS: The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively. CONCLUSIONS: We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.

Defining the Impact of Family History on Detection of High-grade Prostate Cancer in a Large Multi-institutional Cohort.

BACKGROUND: The risk of high-grade prostate cancer, given a family history of cancer, has been described in the general population, but not among men selected for prostate biopsy in an international cohort. OBJECTIVE: To estimate the risk of high-grade prostate cancer on biopsy based on a family history of cancer. DESIGN, SETTING, AND PARTICIPANTS: This is a multicenter study of men undergoing prostate biopsy from 2006 to 2019, including 12 sites in North America and Europe. All sites recorded first-degree prostate cancer family histories; four included more detailed data on the number of affected relatives, second-degree relatives with prostate cancer, and breast cancer family history. OUTCOMES MEASUREMENTS AND STATISTICAL ANALYSIS: Multivariable logistic regressions evaluated odds of high-grade (Gleason grade group ≥2) prostate cancer. Separate models were fit for family history definitions, including first- and second-degree prostate cancer and breast cancer family histories. RESULTS AND LIMITATIONS: A first-degree prostate cancer family history was available for 15 799 men, with a more detailed family history for 4617 (median age 65 yr, both cohorts). Adjusted odds of high-grade prostate cancer were 1.77 times greater (95% confidence interval [CI] 1.57-2.00, p < 0.001, risk ratio [RR] = 1.40) with first-degree prostate cancer, 1.38 (95% CI 1.07-1.77, p = 0.011, RR = 1.22) for second-degree prostate cancer, and 1.30 (95% CI 1.01-1.67, p = 0.040, RR = 1.18) for first-degree breast cancer family histories. Interaction terms revealed that the effect of a family history did not differ based on prostate-specific antigen but differed based on age. This study is limited by missing data on race and prior negative biopsy. CONCLUSIONS: Men with indications for biopsy and a family history of prostate or breast cancer can be counseled that they have a moderately increased risk of high-grade prostate cancer, independent of other risk factors. PATIENT SUMMARY: In a large international series of men selected for prostate biopsy, finding a high-grade prostate cancer was more likely in men with a family history of prostate or breast cancer.

Performance of African-ancestry-specific polygenic hazard score varies according to local ancestry in 8q24.

BACKGROUND: We previously developed an African-ancestry-specific polygenic hazard score (PHS46+African) that substantially improved prostate cancer risk stratification in men with African ancestry. The model consists of 46 SNPs identified in Europeans and 3 SNPs from 8q24 shown to improve model performance in Africans. Herein, we used principal component (PC) analysis to uncover subpopulations of men with African ancestry for whom the utility of PHS46+African may differ. MATERIALS AND METHODS: Genotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Genetic variation in a window spanning 3 African-specific 8q24 SNPs was estimated using 93 PCs. A Cox proportional hazards framework was used to identify the pair of PCs most strongly associated with the performance of PHS46+African. A calibration factor (CF) was formulated using Cox coefficients to quantify the extent to which the performance of PHS46+African varies with PC. RESULTS: CF of PHS46+African was strongly associated with the first and twentieth PCs. Predicted CF ranged from 0.41 to 2.94, suggesting that PHS46+African may be up to 7 times more beneficial to some African men than others. The explained relative risk for PHS46+African varied from 3.6% to 9.9% for individuals with low and high CF values, respectively. By cross-referencing our data set with 1000 Genomes, we identified significant associations between continental and calibration groupings. CONCLUSION: We identified PCs within 8q24 that were strongly associated with the performance of PHS46+African. Further research to improve the clinical utility of polygenic risk scores (or models) is needed to improve health outcomes for men of African ancestry.

Immunogenicity of SARS-CoV-2 messenger RNA vaccines in patients with cancer.

Patients with cancer experience a higher burden of SARS-CoV-2 infection, disease severity, complications, and mortality, than the general population. SARS-CoV-2 mRNA vaccines are highly effective in the general population; however, few data are available on their efficacy in patients with cancer. Using a prospective cohort, we assessed the seroconversion rates and anti-SARS-CoV-2 spike protein antibody titers following the first and second dose of BNT162b2 and mRNA-1273 SARS-CoV-2 vaccines in patients with cancer in US and Europe from January to April 2021. Among 131 patients, most (94%) achieved seroconversion after receipt of two vaccine doses. Seroconversion rates and antibody titers in patients with hematological malignancy were significantly lower than those with solid tumors. None of the patients with history of anti-CD-20 antibody in the 6 months before vaccination developed antibody response. Antibody titers were highest for clinical surveillance or endocrine therapy groups and lowest for cytotoxic chemotherapy or monoclonal antibody groups.

Regulation of telomere homeostasis and genomic stability in cancer by N 6-adenosine methylation (m6A).

The role of RNA methylation on N 6-adenosine (m6A) in cancer has been acknowledged, but the underlying mechanisms remain obscure. Here, we identified homeobox containing 1 (HMBOX1) as an authentic target mRNA of m6A machinery, which is highly methylated in malignant cells compared to the normal counterparts and subject to expedited degradation upon the modification. m6A-mediated down-regulation of HMBOX1 causes telomere dysfunction and inactivation of p53 signaling, which leads to chromosome abnormalities and aggressive phenotypes. CRISPR-based, m6A-editing tools further prove that the methyl groups on HMBOX1 per se contribute to the generation of altered cancer genome. In multiple types of human cancers, expression of the RNA methyltransferase METTL3 is negatively correlated with the telomere length but favorably with fractions of altered cancer genome, whereas HMBOX1 mRNA levels show the opposite patterns. Our work suggests that the cancer-driving genomic alterations may potentially be fixed by rectifying particular epitranscriptomic program.

Adiponectin Alleviates Diet-Induced Inflammation in the Liver by Suppressing MCP-1 Expression and Macrophage Infiltration.

Adiponectin is an adipokine that exerts insulin-sensitizing and anti-inflammatory roles in insulin target tissues including liver. While the insulin-sensitizing function of adiponectin has been extensively investigated, the precise mechanism by which adiponectin alleviates diet-induced hepatic inflammation remains elusive. Here, we report that hepatocyte-specific knockout (KO) of the adaptor protein APPL2 enhanced adiponectin sensitivity and prevented mice from developing high-fat diet-induced inflammation, insulin resistance, and glucose intolerance, although it caused fatty liver. The improved anti-inflammatory and insulin-sensitizing effects in the APPL2 hepatocyte-specific KO mice were largely reversed by knocking out adiponectin. Mechanistically, hepatocyte APPL2 deficiency enhances adiponectin signaling in the liver, which blocks TNF-α-stimulated MCP-1 expression via inhibiting the mTORC1 signaling pathway, leading to reduced macrophage infiltration and thus reduced inflammation in the liver. With results taken together, our study uncovers a mechanism underlying the anti-inflammatory role of adiponectin in the liver and reveals the hepatic APPL2-mTORC1-MCP-1 axis as a potential target for treating overnutrition-induced inflammation in the liver.

Human-specific polymorphic pseudogenization of SIGLEC12 protects against advanced cancer progression.

Compared with our closest living evolutionary cousins, humans appear unusually prone to develop carcinomas (cancers arising from epithelia). The SIGLEC12 gene, which encodes the Siglec-XII protein expressed on epithelial cells, has several uniquely human features: a fixed homozygous missense mutation inactivating its natural ligand recognition property; a polymorphic frameshift mutation eliminating full-length protein expression in ~60%-70% of worldwide human populations; and, genomic features suggesting a negative selective sweep favoring the pseudogene state. Despite the loss of canonical sialic acid binding, Siglec-XII still recruits Shp2 and accelerates tumor growth in a mouse model. We hypothesized that dysfunctional Siglec-XII facilitates human carcinoma progression, correlating with known tumorigenic signatures of Shp2-dependent cancers. Immunohistochemistry was used to detect Siglec-XII expression on tissue microarrays. PC-3 prostate cancer cells were transfected with Siglec-XII and transcription of genes enriched with Siglec-XII was determined. Genomic SIGLEC12 status was determined for four different cancer cohorts. Finally, a dot blot analysis of human urinary epithelial cells was established to determine the Siglec-XII expressors versus non-expressors. Forced expression in a SIGLEC12 null carcinoma cell line enriched transcription of genes associated with cancer progression. While Siglec-XII was detected as expected in ~30%-40% of normal epithelia, ~80% of advanced carcinomas showed strong expression. Notably, >80% of late-stage colorectal cancers had a functional SIGLEC12 allele, correlating with overall increased mortality. Thus, advanced carcinomas are much more likely to occur in individuals whose genomes have an intact SIGLEC12 gene, likely because the encoded Siglec-XII protein recruits Shp2-related oncogenic pathways. The finding has prognostic, diagnostic, and therapeutic implications.

Comparison of rectal swab, glove tip, and participant-collected stool techniques for gut microbiome sampling.

BACKGROUND: Studies of the gut microbiome are becoming increasingly important. Such studies require stool collections that can be processed or frozen in a timely manner so as not to alter the microbial content. Due to the logistical difficulties of home-based stool collection, there has been a challenge in selecting the appropriate sample collection technique and comparing results from different microbiome studies. Thus, we compared stool collection and two alternative clinic-based fecal microbiome collection techniques, including a newer glove-based collection method. RESULTS: We prospectively enrolled 22 adult men from our prostate cancer screening cohort SABOR (San Antonio Biomarkers of Risk for prostate cancer) in San Antonio, TX, from 8/2018 to 4/2019. A rectal swab and glove tip sample were collected from each participant during a one-time visit to our clinics. A single stool sample was collected at the participant's home. DNA was isolated from the fecal material and 16 s rRNA sequencing of the V1-V2 and V3-V4 regions was performed. We found the gut microbiome to be similar in richness and evenness, noting no differences in alpha diversity among the collection methods. The stool collection method, which remains the gold-standard method for the gut microbiome, proved to have different community composition compared to swab and glove tip techniques (p< 0.001) as measured by Bray-Curtis and unifrac distances. There were no significant differences in between the swab and glove tip samples with regard to beta diversity (p> 0.05). Despite differences between home-based stool and office-based fecal collection methods, we noted that the distance metrics for the three methods cluster by participant indicating within-person similarities. Additionally, no taxa differed among the methods in a Linear Discriminant Analysis Effect Size (LEfSe) analysis comparing all-against-all sampling methods. CONCLUSION: The glove tip method provides similar gut microbiome results as rectal swab and stool microbiome collection techniques. The addition of a new office-based collection technique could help easy and practical implementation of gut microbiome research studies and clinical practice.