Signature For Stratification Research Articles

N6-methyladenosine modified lncRNAs signature for stratification of biochemical recurrence in prostate cancer.

Nonmutational epigenetic reprogramming is a crucial mechanism contributing to the pronounced heterogeneity of prostate cancer (PCa). Among these mechanisms, N6-methyladenosine (m6A)-modified long non-coding RNAs (lncRNAs) have emerged as key players. However, the precise roles of m6A-modified lncRNAs in PCa remain to be elucidated. In this study, methylated RNA immunoprecipitation sequencing (MeRIP-seq) was conducted on primary and metastatic PCa samples, leading to the identification of 21 lncRNAs exhibiting differential methylation and expression patterns. We further established a PCa prognostic signature, named m6A-modified lncRNA score (mLs), based on 9 differential methylated lncRNAs in 4 multicenter cohorts. The high mLs score cohort exhibited a tendency for earlier biochemical recurrence (BCR) compared to the low mLs score cohort. Remarkably, the predictive performance of the mLs score surpassed that of five previously reported lncRNA-based signatures. Functional enrichment analysis underscored a negative correlation between the mLs score and lipid metabolism. Additionally, through MeRIP-qPCR, we pinpointed a hub gene, MIR210HG, which was validated through in vitro and in vivo experiments. These findings collectively illuminate the landscape of m6A-methylated lncRNAs in PCa tissue via MeRIP-seq and harness this information to prognosticate PCa outcomes using the mLs score. Furthermore, our study validates, both experimentally and mechanistically, the facilitative role of MIR210HG in driving PCa progression.

A Novel HOXA10-Associated 5-Gene-Based Prognostic Signature for Stratification of Short-term Survivors of Pancreatic Ductal Adenocarcinoma.

Despite the significant association of molecular subtypes with poor prognosis in patients with pancreatic ductal adenocarcinoma (PDAC), few efforts have been made to identify the underlying pathway(s) responsible for this prognosis. Identifying a clinically relevant prognosis-based gene signature may be the key to improving patient outcomes. We analyzed the transcriptomic profiles of treatment-naïve surgically resected short-term survivor (STS) and long-term survivor (LTS) tumors (GSE62452) for expression and survival, followed by validation in several datasets. These results were corroborated by IHC analysis of PDAC-resected STS and LTS tumors. The mechanism of this differential survival was investigated using CIBERSORT and pathway analyses. We identified a short-surviving prognostic subtype of PDAC with a high degree of significance (P = 0.018). One hundred thirty genes in this novel subtype were found to be regulated by a master regulator, homeobox gene HOXA10, and a 5-gene signature derived from these genes, including BANF1, EIF4G1, MRPS10, PDIA4, and TYMS, exhibited differential expression in STSs and a strong association with poor survival. This signature was further associated with the proportion of T cells and macrophages found in STSs and LTSs, demonstrating a potential role in PDAC immunosuppression. Pathway analyses corroborated these findings, revealing that this HOXA10-driven prognostic signature is associated with immune suppression and enhanced tumorigenesis. Overall, these findings reveal the presence of a HOXA10-associated prognostic subtype that can be used to differentiate between STS and LTS patients of PDAC and inform on the molecular interactions that play a role in this poor prognosis.

Identification and verification of an ALYREF-involved 5-methylcytosine based signature for stratification of prostate cancer patients and prediction of clinical outcome and response to therapies

ObjectivesDue to the heterogeneity of PCa, the clinical indicators used for PCa can't satisfy risk prognostication and personalized treatment. It is imperative to develop novel biomarkers for prognosis prediction and therapy response in PCa. Accumulating evidence shows that non-mutational epigenetic reprogramming, independent from genomic instability and mutation, serves as a newly added hallmark in cancer progression.MethodsIn this study, we integrated multi-center cohorts (N > 1300) to develop a RNA 5-methylcytosine regulator-based signature, the m5C score. We performed unsupervised clustering and LASSO regression to identify novel m5C-related subtypes and calculate the m5C score. Then we assessed the role of m5C cluster and m5C score in several clinical aspects such as prognosis in various molecular subtypes, responses to chemotherapy, androgen receptor signaling inhibitor (ARSI) therapy and immunotherapy in PCa. Finally, we validated the cancer-promoting performance of ALYREF through clinical data analysis and experiments in vivo and in vitro.ResultsThe investigation revealed that the m5C score could accurately predict the biochemical recurrence (BCR) in different subtypes (the PAM50 subtypes and immunophenotypes) and the responses to chemotherapy, ARSI therapy, and immunotherapy (PD1/PD-L1). A high m5C score indicated a poor BCR prognosis in every subtype of PCa, unfavorable responses in ARSI therapy and immunotherapy (PD1/PD-L1). Moreover, the m5C reader gene termed ALYREF, yielding the highest weighed coefficient, promoted PCa progression through in silico analysis and experimental validations (in vivo and in vitro).ConclusionsThe m5C signature can function in many aspects of PCa, such as the development and prognosis of the disease, and multiple therapy responses. Further, the m5C reader, ALYREF, was identified as a prognostic biomarker and a potential therapeutic target for PCa. The m5C signature could act as a brand-new tool for predicting the prognosis of patients in different molecular subtypes and patients’ therapy responses and promoting customized treatments.

Identification of a Novel Ferroptosis-Related Gene Signature for Predicting Prognosis and Responsiveness to Immunotherapy in Hepatocellular Carcinoma.

Ferroptosis has been reported to regulate multiple biological behaviors. However, the prognostic and oncologic values of ferroptosis-related genes (FRGs) have not been comprehensively elucidated in hepatocellular carcinoma (HCC). Here, we aimed to construct FRGs-associated signature for stratification of the prognosis of HCC patients. A list of FRGs was generated from FerrDb. Public databases were used to extract expression matrices and clinical information. TCGA cohort was randomly divided into a training set and a validation set. Prognostic signature for Overall Survival (OS) was established in training set and validated in internal cohorts (TCGA validation set and entire set) and external cohort (ICGC cohort). Additionally, the role of signature in HCC was well investigated by analysis of mutations, gene set enrichment analysis (GSEA), analysis of immune infiltrates, and analysis of response to immune checkpoint blockade (ICB) treatment. The oncogenic effects of ZFP69B on HCC were also investigated in vitro. We identified 12 FRGs-based signature for OS with LASSO regression. Patients were partitioned into different risk groups based on the signature. Overall, patients in different groups had different prognosis. The signature independently predicted OS in multivariate Cox regression analyses. Anti-tumor immune cells including activated CD8 T cells, cytolytic activity, and Th1 cells were negatively correlated with risk score in both TCGC and ICGC cohorts. Furthermore, low-risk patients responded better to ICB than high-risk patients. In addition, knockdown of ZFP69B reduced proliferation, migration, and invasion, and promoted erastin-induced ferroptosis of HCC cells. We developed a prognostic signature based on FRGs to predict OS of HCC patients. And the signature may facilitate clinicians in identifying those who are likely to benefit from ICIs. The results also indicated that ZFP69B might regulate the process of ferroptosis and could be used as a novel potential target for HCC.

Identification and Validation of a DNA Damage Repair-Related Signature for Diffuse Large B-Cell Lymphoma.

Background Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin's lymphoma in adults, whose prognostic scoring system remains to be improved. Dysfunction of DNA repair genes is closely associated with the development and prognosis of diffuse large B-cell lymphoma. The aim of this study was to establish and validate a DNA repair-related gene signature associated with the prognosis of DLBCL and to investigate the clinical predictive value of this signature. Methods DLBCL cases were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. One hundred ninety-nine DNA repair-related gene sets were retrieved from the GeneCards database. The LASSO Cox regression was used to generate the DNA repair-related gene signature. Subsequently, the level of immune cell infiltration and the correlation between the gene signature and immune cells were analyzed using the CIBERSORT algorithm. Based on the Genomics of Drug Sensitivity in Cancer (GDSC) database, the relationship between the signature and drug sensitivity was analyzed, and together with the nomogram and gene set variation analysis (GSVA), the value of the signature for clinical application was evaluated. Results A total of 14 DNA repair genes were screened out and included in the final risk model. Subgroup analysis of the training and validation cohorts showed that the risk model accurately predicted overall survival of DLBCL patients, with patients in the high-risk group having a worse prognosis than patients in the low-risk group. Subsequently, the risk score was confirmed as an independent prognostic factor by multivariate analysis. Furthermore, by CIBERSORT analysis, we discovered that immune cells, such as regulatory T cells (Tregs), activated memory CD4+ T cells, and gamma delta T cells showed significant differences between the high- and low-risk groups. In addition, we found some interesting associations of our signature with immune checkpoint genes (CD96, TGFBR1, and TIGIT). By analyzing drug sensitivity data in the GDSC database, we were able to identify potential therapeutics for DLBCL patients stratified according to our signature. Conclusions Our study identified and validated a 14-DNA repair-related gene signature for stratification and prognostic prediction of DLBCL patients, which might guide clinical personalization of treatment.

Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq.

BackgroundStudies have shown that hepatocellular carcinoma (HCC) heterogeneity is a main cause leading to failure of treatment. Technology of single-cell sequencing (scRNA) could more accurately reveal the essential characteristics of tumor genetics.MethodsFrom the Gene Expression Omnibus (GEO) database, HCC scRNA-seq data were extracted. The FindCluster function was applied to analyze cell clusters. Autophagy-related genes were acquired from the MSigDB database. The ConsensusClusterPlus package was used to identify molecular subtypes. A prognostic risk model was built with the Least Absolute Shrinkage and Selection Operator (LASSO)–Cox algorithm. A nomogram including a prognostic risk model and multiple clinicopathological factors was constructed.ResultsEleven cell clusters labeled as various cell types by immune cell markers were obtained from the combined scRNA-seq GSE149614 dataset. ssGSEA revealed that autophagy-related pathways were more enriched in malignant tumors. Two autophagy-related clusters (C1 and C2) were identified, in which C1 predicted a better survival, enhanced immune infiltration, and a higher immunotherapy response. LASSO–Cox regression established an eight-gene signature. Next, the HCCDB18, GSA14520, and GSE76427 datasets confirmed a strong risk prediction ability of the signature. Moreover, the low-risk group had enhanced immune infiltration and higher immunotherapy response. A nomogram which consisted of RiskScore and clinical features had better prediction ability.ConclusionTo precisely assess the prognostic risk, an eight-gene prognostic stratification signature was developed based on the heterogeneity of HCC immune cells.

Nyssorhynchus darlingi genome-wide studies related to microgeographic dispersion and blood-seeking behavior

BackgroundIn Brazil, malaria is concentrated in the Amazon Basin, where more than 99% of the annual cases are reported. The main goal of this study was to investigate the population structure and genetic association of the biting behavior of Nyssorhynchus (also known as Anopheles) darlingi, the major malaria vector in the Amazon region of Brazil, using low-coverage genomic sequencing data.MethodsSamples were collected in the municipality of Mâncio Lima, Acre state, Brazil between 2016 and 2017. Different approaches using genotype imputation and no gene imputation for data treatment and low-coverage sequencing genotyping were performed. After the samples were genotyped, population stratification analysis was performed.ResultsWeak but statistically significant stratification signatures were identified between subpopulations separated by distances of approximately 2–3 km. Genome-wide association studies (GWAS) were performed to compare indoor/outdoor biting behavior and blood-seeking at dusk/dawn. A statistically significant association was observed between biting behavior and single nucleotide polymorphism (SNP) markers adjacent to the gene associated with cytochrome P450 (CYP) 4H14, which is associated with insecticide resistance. A statistically significant association between blood-seeking periodicity and SNP markers adjacent to genes associated with the circadian cycle was also observed.ConclusionThe data presented here suggest that low-coverage whole-genome sequencing with adequate processing is a powerful tool to genetically characterize vector populations at a microgeographic scale in malaria transmission areas, as well as for use in GWAS. Female mosquitoes entering houses to take a blood meal may be related to a specific CYP4H14 allele, and female timing of blood-seeking is related to circadian rhythm genes.Graphical

Prognostic and predictive value of a metabolic risk score model in breast cancer: an immunogenomic landscape analysis.

Breast cancer is a kind of malignant tumor that occurs in breast tissue, which is the most common cancer in women. Cellular metabolism is a critical determinant of the viability and function of cancer cells in tumor microenvironment. In this study, based on the gene expression profile of metabolism-related genes, the prognostic value of 20 metabolic pathways in patients with breast cancer was identified. A universal risk stratification signature that relies on 20 metabolic pathways was established and validated in training cohort, two testing cohorts and The Cancer Genome Atlas pan cancer cohort. Then, the relationship between metabolic risk score subtype, prognosis, immune infiltration level, cancer genotypes and their impact on therapeutic benefit were characterized. Results demonstrated that the patients with the low metabolic risk score subtype displayed good prognosis, high level of immune infiltration and exhibited a favorable response to neoadjuvant chemotherapy and immunotherapy. Taken together, the work presented in this study may deepen the understanding of metabolic hallmarks of breast cancer, and may provide some valuable information for personalized therapies in patients with breast cancer.

Identification of a Four Hypoxia-Associated Long Non-Coding RNA Signature and Establishment of a Nomogram Predicting Prognosis of Clear Cell Renal Cell Carcinoma

To identify novel hypoxia-associated long non-coding RNAs (lncRNAs) as potential biomarkers, we developed a risk stratification signature and constructed a prognosis prediction nomogram of clear cell renal cell carcinoma (ccRCC). Hypoxia-related lncRNAs were identified through Pearson correlation analysis between the expression profiles of hypoxia-related differentially expressed genes and lncRNAs from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset. Then, a signature of four key lncRNAs (COMETT, EMX2OS, AC026462.3, and HAGLR) was developed. The four lncRNAs were downregulated in high-grade, advanced stage, and high-risk ccRCC. The signature had an independent and long-standing prognosis prediction ability up to a 10-year follow-up. Notably, the risk score was significantly positively correlated with the infiltration abundances of six immune cells from the Tumor IMmune Estimation Resource (TIMER). The gene set enrichment analysis (GSEA) also suggested that the signature was involved in metabolism and tumorigenesis, which were closely related to the hypoxic tumor microenvironment. Ultimately, a nomogram of signature, age, stage, and grade, was built to predict the individual long-term survival possibility. Finally, the expressions of four lncRNAs were validated by quantitative real-time PCR (qRT-PCR). Our study identified a four-lncRNA signature and established a prognostic nomogram that reliably predicts survival in ccRCC. The findings may be beneficial to therapeutic customization and medical decision-making.

An immune-related seven-lncRNA signature for head and neck squamous cell carcinoma.

In this study, we developed a long noncoding RNA (lncRNA)‐based prognostic signature for stratification of patients with head a nd neck squamous cell carcinoma (HNSCC). In total, 493 HNSCC samples obtained from the Cancer Genome Atlas database were divided into training and testing cohorts (3:2 ratio). We identified 3913 immune‐related lncRNAs in the HNSCC training cohort by Pearson correlation analysis; only seven were independently associated with overall survival and were used to develop an immune‐related lncRNA prognostic signature (IRLPS) grouping of HNSCC patients into high‐ and low‐IRLPS subgroups. Univariate and multivariate Cox analyses revealed that low‐IRLPS patients had a better prognosis in all the cohorts, which was retained after stratification by sex, grade, and HPV status. Although the TNM stage was also an independent prognostic factor, the IRLPS had a better discriminability with higher AUC at the 3‐ and 5‐year follow‐ups in all cohorts. Low‐IRLPS samples had more immune cell infiltration and were enriched in immune‐related pathways, while high‐ IRLPS samples were enriched in metabolic pathways. A nomogram constructed including age, TNM stage, and IRLPS showed good calibration. Thus, IRLPS improves the prognostic prediction and also distinguishes different tumor microenvironment (TME) in HNSCC patients.

Analysis of ovarian cancer cell secretome during epithelial to mesenchymal transition reveals a protein signature associated with advanced stages of ovarian tumors

Ovarian cancer (OvCA) is the most lethal neoplasia among gynecologic malignancies and faces high rates of new cases particularly in South America. In special, the High Grade Serous Ovarian Carcinoma (HGSC) presents very poor prognosis with deaths caused mainly by metastasis. Among several mechanisms involved in metastasis, the Epithelial to Mesenchymal Transition (EMT) molecular reprogramming represents a model for latest stages of cancer progression. EMT promotes important cellular changes in cellular adhesion and cell-cell communication, which particularly depends on the paracrine signaling from neighbor cells. Considering the importance of cellular communication during EMT and metastasis, here we analyzed the changes in the secretome of the ovarian cancer cell line Caov-3 induced to EMT by Epidermal Growth Factor (EGF). Using a combination of GEL-LC-MS/MS and stable isotopic metabolic labelling (SILAC), we identified up-regulated candidates during EMT as a starting point to identify relevant proteins for HGSC. Based on public databases, our candidate proteins were validated and prioritized for further analysis. Importantly, several of the protein candidates were associated with cellular vesicles, which are important to the cell-cell communication and metastasis. Furthermore, the association of candidate proteins with gene expression data uncovered a subset of proteins correlated with the mesenchymal subtype of ovarian cancer. Based on this relevant molecular signature for aggressive ovarian cancer, supported by protein and gene expression data, we developed a targeted proteomic method to evaluate individual OvCA clinical samples. The quantitative information obtained for 33 peptides, representative of 18 proteins, was able to segregate HGSC from other tumor types. Our study highlighted the richness of the secretome and EMT to reveal relevant proteins for HGSC, which could be used in further studies and larger patient cohorts as a potential stratification signature for ovarian cancer tumor that could guide clinical conduct for patient treatment.

Correction: Identification of a Novel, EBV-based Antibody Risk Stratification Signature for Early Detection of Nasopharyngeal Carcinoma in Taiwan

In the original version of [this article][1] ([1][2]), the left panel of Fig. 2 was incorrectly labeled: the BFRF1 IgG antibody was incorrectly labeled as the BALF2 IgG antibody, and the corresponding sentence in the text was also incorrect. These errors have been corrected in the latest online HTML

Validation of an Epstein-Barr Virus Antibody Risk Stratification Signature for Nasopharyngeal Carcinoma by Use of Multiplex Serology.

Serological testing for nasopharyngeal carcinoma (NPC) has recently been reinvigorated by the implementation of novel Epstein-Barr virus (EBV)-specific IgA and IgG antibodies from a proteome array. Although proteome arrays are well suited for comprehensive antigen selection, they are not applicable for large-scale studies. We adapted a 13-marker EBV antigen signature for NPC risk identified by proteome arrays to multiplex serology to establish an assay for large-scale studies. Taiwanese NPC cases (n = 175) and matched controls (n = 175) were used for assay validation. Spearman's correlation was calculated, and the diagnostic value of all multiplex markers was assessed independently using the area under the receiver operating characteristic curve (AUC). Two refined signatures were identified using stepwise logistic regression and internally validated with 10-fold cross validation. Array and multiplex serology showed strong correlation for each individual EBV marker, as well as for a 13-marker combined model on continuous data. Two refined signatures with either four (LF2 and BGLF2 IgG, LF2 and BMRF1 IgA) or two (LF2 and BGLF2 IgG) antibodies on dichotomous data were identified as the most parsimonious set of serological markers able to distinguish NPC cases from controls with AUCs of 0.992 (95% confidence interval [CI], 0.983 to 1.000) and 0.984 (95% CI, 0.971 to 0.997), respectively. Neither differed significantly from the 13-marker model (AUC, 0.992; 95% CI, 0.982 to 1.000). All models were internally validated. Multiplex serology successfully validated the original EBV proteome microarray data. Two refined signatures of four and two antibodies were capable of detecting NPC with 99.2% and 98.4% accuracy.

A Circulating miRNA Signature for Stratification of Breast Lesions among Women with Abnormal Screening Mammograms.

Although mammography is the gold standard for breast cancer screening, the high rates of false-positive mammograms remain a concern. Thus, there is an unmet clinical need for a non-invasive and reliable test to differentiate between malignant and benign breast lesions in order to avoid subjecting patients with abnormal mammograms to unnecessary follow-up diagnostic procedures. Serum samples from 116 malignant breast lesions and 64 benign breast lesions were comprehensively profiled for 2,083 microRNAs (miRNAs) using next-generation sequencing. Of the 180 samples profiled, three outliers were removed based on the principal component analysis (PCA), and the remaining samples were divided into training (n = 125) and test (n = 52) sets at a 70:30 ratio for further analysis. In the training set, significantly differentially expressed miRNAs (adjusted p < 0.01) were identified after correcting for multiple testing using a false discovery rate. Subsequently, a predictive classification model using an eight-miRNA signature and a Bayesian logistic regression algorithm was developed. Based on the receiver operating characteristic (ROC) curve analysis in the test set, the model could achieve an area under the curve (AUC) of 0.9542. Together, this study demonstrates the potential use of circulating miRNAs as an adjunct test to stratify breast lesions in patients with abnormal screening mammograms.

Whole-genome sequencing reveals novel tandem-duplication hotspots and a prognostic mutational signature in gastric cancer

Genome-wide analysis of genomic signatures might reveal novel mechanisms for gastric cancer (GC) tumorigenesis. Here, we analysis structural variations (SVs) and mutational signatures via whole-genome sequencing of 168 GCs. Our data demonstrates diverse models of complex SVs operative in GC, which lead to high-level amplification of oncogenes. We find varying proportion of tandem-duplications (TDs) among individuals and identify 24 TD hotspots involving well-established cancer genes such as CCND1, ERBB2 and MYC. Specifically, we nominate a novel hotspot involving the super-enhancer of ZFP36L2 presents in approximately 10% GCs from different cohorts, the oncogenic role of which is further confirmed by experimental data. In addition, our data reveal a mutational signature, specifically occurring in noncoding region, significantly enriched in tumors with cadherin 1 mutations, and associated with poor prognoses. Collectively, our data suggest that TDs might serve as an important mechanism for cancer gene activation and provide a novel signature for stratification.

Harnessing synthetic lethality to predict the response to cancer treatment

While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and by predicting drug efficacy in mouse xenograft models. We then experimentally test a select set of predicted cSLi via new screening experiments, validating their predicted context-specific sensitivity in hypoxic vs normoxic conditions and demonstrating cSLi’s utility in predicting synergistic drug combinations. We show that cSLi can successfully predict patients’ drug treatment response and provide patient stratification signatures. ISLE thus complements existing actionable mutation-based methods for precision cancer therapy, offering an opportunity to expand its scope to the whole genome.

Identification of a Novel, EBV-Based Antibody Risk Stratification Signature for Early Detection of Nasopharyngeal Carcinoma in Taiwan.

Background Epstein-Barr virus (EBV) is necessary for the development of nasopharyngeal carcinoma (NPC). By adulthood, approximately 90% of individuals test EBV-positive, but only a fraction develop cancer. Factors that identify which individuals are most likely to develop disease, including differential antibody response to the virus, could facilitate detection at early stages when treatment is most effective.Methods We measured anti-EBV IgG and IgA antibody responses in 607 Taiwanese individuals. Antibodies were measured using a custom protein microarray targeting 199 sequences from 86 EBV proteins. Variation in response patterns between NPC cases and controls was used to develop an antibody-based risk score for predicting NPC. The overall accuracy [area under the curve (AUC)] of this risk score, and its performance relative to currently used biomarkers, was evaluated in two independent Taiwanese cohorts.Findings Levels of 60 IgA and 73 IgG anti-EBV antibodies differed between stage I/IIa NPC cases and controls (P < 0.0002). Risk prediction analyses identified antibody targets that best discriminated NPC status-BXLF1, LF2,BZLF1, BRLF1, EAd, BGLF2, BPLF1, BFRF1, and BORF1. When combined with currently used VCA/EBNA1 IgA biomarkers, the resulting risk score predicted NPC with 93% accuracy (95% CI, 87%-98%) in the general Taiwanese population, a significant improvement beyond current biomarkers alone (82%; 95% CI, 75%-90%, P ≤ 0.01). This EBV-based risk score also improved NPC prediction in genetically high-risk families (89%; 95% CI, 82%-96%) compared with current biomarkers (78%; 95% CI, 66%-90%, P ≤ 0.03).Interpretation We identified NPC-related differences in 133 anti-EBV antibodies and developed a risk score using this microarray dataset that targeted immune responses against EBV proteins from all stages of the viral life cycle, significantly improving the ability to predict NPC. Clin Cancer Res; 24(6); 1305-14. ©2017 AACR.

Androgen Receptor Deregulation Drives Bromodomain-Mediated Chromatin Alterations in Prostate Cancer

Global changes in chromatin accessibility may drive cancer progression by reprogramming transcription factor (TF) binding. In addition, histone acetylation readers such as bromodomain-containing protein 4 (BRD4) have been shown to associate with these TFs and contribute to aggressive cancers including prostate cancer (PC). Here, we show that chromatin accessibility defines castration-resistant prostate cancer (CRPC). We show that the deregulation of androgen receptor (AR) expression is a driver of chromatin relaxation and that AR/androgen-regulated bromodomain-containing proteins (BRDs) mediate this effect. We also report that BRDs are overexpressed in CRPCs and that ATAD2 and BRD2 have prognosticvalue. Finally, we developed gene stratification signature (BROMO-10) for bromodomain response and PC prognostication, to inform current and future trials with drugs targeting these processes. Our findings provide a compelling rational for combination therapy targeting bromodomains in selected patients in which BRD-mediated TF binding is enhanced or modified as cancer progresses.

BRCA1 loss activates cathepsin L–mediated degradation of 53BP1 in breast cancer cells

Loss of 53BP1 rescues BRCA1 deficiency and is associated with BRCA1-deficient and triple-negative breast cancers (TNBC) and with resistance to genotoxic drugs. The mechanisms responsible for decreased 53BP1 transcript and protein levels in tumors remain unknown. Here, we demonstrate that BRCA1 loss activates cathepsin L (CTSL)-mediated degradation of 53BP1. Activation of this pathway rescued homologous recombination repair and allowed BRCA1-deficient cells to bypass growth arrest. Importantly, depletion or inhibition of CTSL with vitamin D or specific inhibitors stabilized 53BP1 and increased genomic instability in response to radiation and poly(adenosine diphosphate-ribose) polymerase inhibitors, compromising proliferation. Analysis of human breast tumors identified nuclear CTSL as a positive biomarker for TNBC, which correlated inversely with 53BP1. Importantly, nuclear levels of CTSL, vitamin D receptor, and 53BP1 emerged as a novel triple biomarker signature for stratification of patients with BRCA1-mutated tumors and TNBC, with potential predictive value for drug response. We identify here a novel pathway with prospective relevance for diagnosis and customization of breast cancer therapy.

The Last Glacial Maximum at 44°S documented by a 10Be moraine chronology at Lake Ohau, Southern Alps of New Zealand

Determining whether glaciers registered the classic Last Glacial Maximum (LGM; ∼26,500–∼19,000 yrs ago) coevally between the hemispheres can help to discriminate among hypothesized drivers of ice-age climate. Here, we present a record of glacier behavior from the Southern Alps of New Zealand during the ‘local LGM’ (LLGM). We used 10Be surface-exposure dating methods and detailed glacial geomorphologic mapping to produce a robust chronology of well-preserved terminal moraines deposited during the LLGM near Lake Ohau on central South Island. We then used a glaciological model to estimate a LLGM glacier snowline and atmospheric temperature from the Ohau glacier record.Seventy-three 10Be surface-exposure ages place culminations of terminal moraine construction, and hence completions of glacier advances to positions outboard of present-day Lake Ohau, at 138,600 ± 10,600 yrs, 32,520 ± 970 yrs ago, 27,400 ± 1300 yrs ago, 22,510 ± 660 yrs ago, and 18,220 ± 500 yrs ago. Recessional moraines document glacier recession into the Lake Ohau trough by 17,690 ± 350 yrs ago. Exposure of an ice-molded bedrock bench located inboard of the innermost LLGM moraines by 17,380 ± 510 yrs ago indicates that the ice tongue had receded about 40% of its overall length by that time. Comparing our chronology with distances of retreat suggests that the Ohau glacier terminus receded at a mean net rate of about 77 m yr−1 and its surface lowered by 200 m between 17,690 and 17,380 yrs ago. A long-term continuation of ice retreat in the Ohau glacier catchment is implied by moraine records at the head of Irishman Stream valley, a tributary of the Ohau glacier valley. The Irishman Stream cirque glacier advanced to produce a set of Lateglacial moraines at 13,000 ± 500 yrs ago, implying that the cirque glacier was less extensive prior to that advance.We employed a glaciological model, fit to these mapped and dated LLGM moraines, to derive snowline elevations and temperature parameters from the Ohau glacier record. The modeling experiments indicate that a snowline lowering of 920 ± 50 m and temperature depression of 6.25 ± 0.5 °C below modern values allows for the Ohau glacier to grow to an equilibrium position within the LGM moraine belt. Taken together with a glaciological simulation reported from the Irishman Stream valley, snowlines and temperatures increased by at least ∼520 m and ∼3.6 °C, respectively, between ∼18,000 and ∼13,000 yrs ago.Climate parameters derived from the Ohau glacier reconstruction are similar to those derived from glacier records from Patagonia, to air temperature indicators from Antarctica, as well as to sea-surface temperature and stratification signatures of the Southern Ocean. We think that the best explanation for the observed southern LLGM is that southern winter duration modulated Southern Ocean sea ice, which in turn influenced Southern Ocean stratification and made the surface ocean cooler. Orbitally induced cooling of the Southern Ocean provides an explanation for the LLGM in the Southern Alps having been coincident with the northern LGM. We argue further that the global effect of North Atlantic stadials led to disturbance of Southern Ocean stratification, southward shifts of the subtropical front, and retreat of Southern Alps glaciers. Collapse of Southern Ocean stratification during Heinrich Stadial-1, along with attendant sea-surface warming, triggered the onset of the Last Glacial termination in the Southern Alps of New Zealand.