Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers.

During the past two decades, computational analysis has become paramount for biological research. Advancements in high-throughput methods and computational tools resulted in the generation of large amounts of data from different omics fields (multi-omics), such as genomics, epigenomics, transcriptomics, and metabolomics. This plethora of large scale and diverse omics data is being driven by the understanding that a single-omic type does not provide adequate information and integrative analysis of multi-omics data is optimal to gain sufficiently meaningful insights into the actual biological mechanisms. Although various open-source tools have been developed for this purpose, multi-omics data integration and analysis are still beset by a number of problems, including software compatibility, complex parameter selection and creation of functional pipelines with multiple steps of analyses. In this work we present GenOptics, a novel visual analytics platform that aims to facilitate the integration and subsequent analysis of diverse multi-omics datasets as well as meta-data (e.g., clinical data), through a fully interactive environment. The platform comprises of two separate parts. The first incorporates asynchronous analyses of Next-Generation Sequencing raw data, including RNA-, Whole exome-, and ChIP-Seq, using workflows implemented with the Common Workflow Language (doi:10.6084/m9.figshare.3115156.v2) and Docker containers to automate software installation and confer cross-platform portability. The second part constitutes the analytical platform itself, designed to facilitate the execution of robust bioinformatics analyses by life scientists with minimal or no knowledge of programming. GenOptics constitutes an open source (https://genoptics.github.io/), computational biology platform for novel pattern and biomarker detection.

Given the limited information regarding the impact of BMI on treatment outcomes for nasopharyngeal carcinoma, we sought to examine the relationship between body mass index (BMI) and cancer control after radiotherapy. We compared clinic outcome information across BMI groups from 1,489 patients treated with radiotherapy between 1990 and 2003. Multivariate analysis was used to determine if BMI significantly predicted adverse recurrence. In comparison with normal group, there were statistical difference in age, T staging, N staging, and clinical staging (P<0.0001). In survival analysis, in comparison with under-weight group, we could found the hazard ratio was less than one, in the risk of death, cancer recurrence and local recurrence. Meanwhile, the hazard ratio gradually declined when the body weight increased. In univariate survival analysis, under-weight patient had a significant decrease in overall survival,(P<0.0001). When Cox regression model was applied to multivariate analysis, we could found age, T staging, N staging, and BMI grades could be a significant independent prognosis factors(P<0.05). Under-weight patients had a significant decrease in overall survival rate, distant metastasis failure-free survival, and local relapse-free survival. Pretherapy BMI grades could be a significant independent prognosis factors.

Adrenocortical carcinoma (ACC) is a rare and highly aggressive endocrine malignancy. Although surgery can cure localized disease, but the majority of patients experience recurrence of ACC. The 5-year survival rate of patients with metastatic ACC is <15%, and the prognosis is poor. Therefore, it is urgent to explore the potential diagnostic markers and therapeutic targets for ACC. Recently, it has been proved that non-coding RNA (ncRNAs) is widely involved in pathological and physiological processes, including tumorigenesis and development. Aberrantly expressed ncRNAs have been found to be involved in the pathogenesis of ACC. Here, we summarized the expression patterns and the molecular mechanism of the involvement of microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) in ACC development. To explore the clinical value of ncRNAs as noninvasive biomarkers of ACC, we also displayed the relationship between the expression level of ncRNAs and the diagnosis and prognosis of patients with ACC.

Adrenocortical carcinoma (ACC) is a tumor with poor prognosis in which overexpression of a panel of microRNAs has been associated with malignancy but a very limited number of investigations on their role in ACC pathogenesis have been conducted. We examined the involvement of miR-483-5p and miR-139-5p in adrenocortical cancer aggressiveness. Using bioinformatics predictions and mRNA/miRNA expression profiles, we performed an integrated analysis to identify inversely correlated miRNA-mRNA pairs in ACC. We identified N-myc downstream-regulated gene family members 2 and 4 (NDRG2 and NDRG4) as targets of miR-483-5p and miR-139-5p, respectively. NDRG2 and NDRG4 expressions were inversely correlated respectively with miR-483-5p and miR-139-5p levels in aggressive ACC samples from two independent cohorts of 20 and 44 ACC. Moreover, upregulation of miR-139-5p and downregulation of NDRG4 demonstrated a striking prognostic value. A direct interaction between miR-483-5p or miR-139-5p and their targets was demonstrated in reporter assays. Downregulation of miR-483-5p or miR-139-5p in the ACC cell lines NCI-H295R and SW13 increased NDRG2 or NDRG4 mRNA and protein expression, compromised adrenocortical cancer cell invasiveness and anchorage-independent growth. MiR-483-5p or miR-139-5p overexpression and NDRG2 or NDRG4 inhibition produce similar changes, which are rescued by NDRG2 or NDRG4 ectopic expression. We established that key factors mediating epithelial-to-mesenchymal transition are downstream effectors of miR-483-5p/NDRG2 and miR-139-5p/NDRG4 pathways. Collectively, our data show for the first time that miR-483-5p/NDRG2 and miR-139-5p/NDRG4 axes promote ACC aggressiveness, with potential implications for prognosis and therapeutic interventions in adrenocortical malignancies.

Adrenocortical carcinoma (ACC) is one of the deadliest endocrine malignancies and telomere maintenance by activated telomerase is critically required for ACC development and progression. Because telomerase reverse transcriptase (TERT) and regulator of telomere elongation helicase 1 (RTEL1) play key roles in telomere homeostasis, we determined their effect on ACC pathogenesis and outcomes. Analyses of TCGA and GEO datasets showed significantly higher expression of RTEL1 but not TERT in ACC tumors, compared to their benign or normal counterparts. Furthermore, gains/amplifications of both TERT and RTEL1 genes were widespread in ACC tumors and their expression correlated with their gene copy numbers. Higher expression of either TERT or RTEL1 was associated with shorter overall and progression-free survival (OS and PFS) in the TCGA ACC patient cohort, and higher levels of both TERT and RTEL1 mRNA predicted the shortest patient OS and PFS. However, multivariate analyses showed that only RTEL1 independently predicted patient OS and PFS. Gene set enrichment analysis further showed enrichments of wnt/β-catenin, MYC, glycolysis, MTOR, and DNA repair signaling pathways in ACC tumors expressing high TERT and RTEL1 mRNA levels. Taken together, TERT and RTEL1 promote ACC aggressiveness synergistically and may serve as prognostic factors and therapeutic targets for ACC.

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with an estimated incidence of 0.7-2 cases per million/year. The rarity of this disease, coupled with limited preclinical models and clinical trials, has hindered progress, resulting in poor outcomes, with a 5-year survival rate of approximately 35%. Currently, the only available curative treatment is complete surgical resection of the adrenal tumor. For unresectable or metastatic ACC, the current standard therapeutic modalities are mitotane, chemotherapy, radiotherapy and locoregional treatments; however, these are noncurative. Mitotane has an adrenolytic and anti-steroidogenic effect, and it is used in the adjuvant setting for high-risk patients, as systemic therapy for metastatic disease, and/or to control hormonal secretion. While key pathways in ACC pathogenesis have been identified as potential therapeutic targets, results with targeted therapies remain modest, showing that there is a clinical unmet need for novel treatments or new combinations of exiting drugs. Effective management requires a multidisciplinary team of experts to optimize outcomes for patients. This article presents a multidisciplinary consensus on the diagnosis, management, prognosis and follow-up of patients with ACC, and the approach to two special contexts, ACC in pregnant women and hormone-producing ACC. The consensus was coordinated by the Spanish Society of Endocrinology and Nutrition (SEEN) and the Spanish Group of Neuroendocrine and Endocrine Tumors (GETNE), with contribution from experts from related societies including the Spanish Association of Surgeons (AEC), Spanish Society of Urology (AEU), Anatomic-Pathology (SEAP), Nuclear Medicine (SEMNIM), Medical Oncology (SEOM) and Radiotherapeutic Oncology (SEOR).

With the advent of high throughput genome-wide assays it has become possible to simultaneously measure multiple types of genomic data. Several projects like TCGA, ICGC, NCI-60 has generated comprehensive, multi-dimensional maps of the key genomic changes like MiRNA, MRNA, proteomics etc. from cancer samples[2,4]. These genomic data can be used for classifying tumour types[5]. Integrative analysis of these data from multiple sources can potentially provide additional biological insights, but methods to do any such analysis are lacking. One of the widely used solutions to handle high dimension data is by removing redundant information in the integrated sample. Most of the expressed genes are overlapped and can be projected onto lower dimension, and then be used to classify different tumor types, without the loss of any/much information. Sufficient dimension reduction (SDR) [1], a supervised dimension reduction approach, can be ideal to achieve such a goal. In this paper, we propose a novel integrative SDR method that can reduce dimensions of multiple data types simultaneously while sharing common latent structures to improve prediction and interpretation. In particular, we extend the sliced inverse regression (SIR) technique, a major SDR method, to integrate multiple omits data for simultaneous dimension reduction. SIR is a supervised dimension reduction method that assumes that the outcome variable Y depends on the predictor variable X through d unknown linear combinations of the predictor[3]. The predictor variable is replaced by its projection into a lower dimension subspace of the predictor space without the loss of information. The aim is to find the intersection of all the subspaces δ called the central susbspace (CS) of the predictor space satisfying the property Y ╨ X| Pδ X. To integrate multiple types of data, we propose and implement a new integrative sufficient dimension reduction method extending SIR[3], called integrative SIR. The main idea is that we take into account all the multi-omics data information simultaneously while finding a basis matrix for each data type with some sharing latent structures. Finally, we get d dimension data which is much smaller than the original data dimension. The reduced dimension d was achieved by cross validation. To demonstrate the integrated analysis of multi-omics data, we applied and compared conventional SIR and integrative SIR to analyze MRNA, MiRNA and proteomics expression profile of a subset of cell lines from the NCI-60 panel. The data used is taken from [6]. The outcomes we have to classify are CNS, Leukemia and Melanoma tumor types. We pre-screened 400 variables from each data type with the criteria of high variance. To find classification error, we performed random forest classification after we applied to each method with leave-one-out cross-validation. As a result, we found out that integrative SIR leads to less classification error as compared to conventional SIR. To summarize, we proposed a new integrative SIR method, a supervised dimension reduction technique for integrative analysis of multi-omics data types. Unlike conventional SDR methods, the new approach can reduce the dimensions of multiple omics data simultaneously while sharing common latent structures across data types without losing any information in prediction. By efficiently capturing the common information, our numerical study shows that integrative SIR classifies tumor types more accurately as compared to conventional SDR methods.

The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as computational instability and variable redundancy. Most multi-omics data analyses apply single supervised analysis, repeatedly, for dimensional reduction and variable selection. However, these approaches cannot avoid the problems of redundancy and collinearity of variables. In this study, we propose a novel approach using blockwise component analysis. This would solve the limitations of current methods by applying variable clustering and sparse principal component (sPC) analysis. Our approach consists of two stages. The first stage identifies homogeneous variable blocks, and then extracts sPCs, for each omics dataset. The second stage merges sPCs from each omics dataset, and then constructs a prediction model. We also propose a graphical method showing the results of sparse PCA and model fitting, simultaneously. We applied the proposed methodology to glioblastoma multiforme data from The Cancer Genome Atlas. The comparison with other existing approaches showed that our proposed methodology is more easily interpretable than other approaches, and has comparable predictive power, with a much smaller number of variables.

Iron Overload Decreases Engraftment of Mice Receiving Allogeneic or Syngeneic Bone Marrow Transplant

Lnc-IRF2-3 and Lnc-ZNF667-AS1 were recently studied as a positive biomarker for many tumor cells. However, experimental studies found that they are associated with worse outcomes in B-CLL. A prospective case study was conducted on 135 B-CLL patients that were compared to thirty healthy controls. The patients were followed up for 40months and quantitative measurements of Lnc-IRF2-3 and Lnc-ZNF667-AS1 were measured and compared between the two groups as well as high-risk and low low-risk B-CLL. Lnc-IRF2-3 and Lnc-ZNF667-AS1 had a high specificity (94% and 85%) and sensitivity (85%, 87%), respectively, to differentiate B-CLL from healthy controls. Furthermore, they showed high expression levels in high-risk CLL groups. For survival analysis, there was a negative correlation between overall survival (OS) and progression-free survival (PFS) and both biomarkers. However, it was not evident in multivariate Cox regression analysis; in patients with Lnc-IRF2-3 expression level, >67 had a significant decrease in OS and PFS. However, there is no significant effect for high expression levels of Lnc-ZNF667-AS1 on OS (P=.16) or PFS (P=.48). The Lnc-IRF2-3 and Lnc-ZNF667-AS1 are promising prognostic biomarkers in B-CLL.

With the rapid development of multi-omics technologies and accumulation of large-scale bio-datasets, many studies have conducted a more comprehensive understanding of human diseases and drug sensitivity from multiple biomolecules, such as DNA, RNA, proteins and metabolites. Using single omics data is difficult to systematically and comprehensively analyze the complex disease pathology and drug pharmacology. The molecularly targeted therapy-based approaches face some challenges, such as insufficient target gene labeling ability, and no clear targets for non-specific chemotherapeutic drugs. Consequently, the integrated analysis of multi-omics data has become a new direction for scientists to explore the mechanism of disease and drug. However, the available drug sensitivity prediction models based on multi-omics data still have problems such as overfitting, lack of interpretability, difficulties in integrating heterogeneous data, and the prediction accuracy needs to be improved. In this paper, we proposed a novel drug sensitivity prediction (NDSP) model based on deep learning and similarity network fusion approaches, which extracts drug targets using an improved sparse principal component analysis (SPCA) method for each omics data, and construct sample similarity networks based on the sparse feature matrices. Furthermore, the fused similarity networks are put into a deep neural network for training, which greatly reduces the data dimensionality and weakens the risk of overfitting problem. We use three omics of data, RNA sequence, copy number aberration and methylation, and select 35 drugs from Genomics of Drug Sensitivity in Cancer (GDSC) for experiments, including Food and Drug Administration (FDA)-approved targeted drugs, FDA-unapproved targeted drugs and non-specific therapies. Compared with some current deep learning methods, our proposed method can extract highly interpretable biological features to achieve highly accurate sensitivity prediction of targeted and non-specific cancer drugs, which is beneficial for the development of precision oncology beyond targeted therapy.

Adrenocortical carcinoma (ACC) is a rare aggressive cancer with heterogeneous behaviour. Disease surveillance relies on frequent imaging, which comes with significant radiation exposure. The aim of the study was to investigate the role of circulating cell-free DNA (ccfDNA)-related biomarkers (BMs) for prognostication and monitoring of ACC. We investigated 34 patients with ACC and 23 healthy subjects (HSs) as controls. Circulating cell-free DNA was extracted by commercial kits and ccfDNA concentrations were quantified by fluorimeter (BM1). Targeted sequencing was performed using a customized panel of 27 ACC-specific genes. Leucocyte DNA was used to discriminate somatic variants (BM2), while tumour DNA was sequenced in 22/34 cases for comparison. Serial ccfDNA samples were collected during follow-up in 19 ACC patients (median period 9 months) and analysed in relationship with standard radiological imaging. Circulating cell-free DNA concentrations were higher in ACC than HS (mean ± SD, 1.15 ± 1.56 vs 0.05 ± 0.05 ng/µL, P < .0001), 96% of them being above the cut-off of 0.146 ng/µL (mean HS + 2 SD, positive BM1). At ccfDNA sequencing, 47% of ACC showed at least 1 somatic mutation (positive BM2). A combined ccfDNA-BM score was strongly associated with both progression-free and overall survival (hazard ratio [HR] = 2.63; 95% CI, 1.13-6.13; P = .010, and HR = 5.98; 95% CI, 2.29-15.6; P = .0001, respectively). During disease monitoring, positive BM2 showed the best specificity (100%) and sensitivity (67%) to detect ACC recurrence or progress compared with BM1. ccfDNA-related BMs are frequently detected in ACC patients and represent a promising, minimally invasive tool to predict clinical outcome and complement surveillance imaging. Our findings will be validated in a larger cohort of ACCs with long-term follow-up.

Objectives: Positive lymph node status in breast cancer is known to be associated with poor outcomes when compared with node-negative disease, but the effect of lymph node status on outcomes in inflammatory breast cancer (IBC) has not been evaluated. This study was designed to investigate the association between lymph-node status and overall survival (OS) in individuals with inflammatory breast cancer using prospective data from the Surveillance, Epidemiology, and End Results (SEER) database. Methods: We identified 750 patients in the April 2012 edition of the SEER 17 registry who had non-metastatic IBC diagnosed from 1973–2008. Patients were included only if they met a stringent definition of IBC (ICD-O-2 morphology code 8503) and their pathologic nodal status was known. Patients who did not receive mastectomy as part of their local therapy were excluded, to minimize the likelihood of inadvertently including patients with metastatic disease at or shortly after diagnosis. A total of 711 patients were deemed evaluable for analysis (145 node-negative, 566 node-positive). Survival analysis was performed using the Kaplan–Meier method. Cox proportional hazard regression was performed to evaluate univariate and multivariate associations between treatment and OS. Information regarding receipt of systemic therapy and human epidermal growth factor receptor 2 (HER2) status was not available in this version of the SEER database. Results: Positive lymph node status was associated with a significant decrease in OS (p = 0.01) when compared with node negative status. In lymph node-positive patients, ER or PR positivity was associated with better OS than ER or PR negativity (adjusted HR 0.56 (p = 0.005) for ER+ vs. ER−, and adjusted HR 0.55 (p = 0.009) for PR+vs. PR−). In patients with positive lymph nodes, the combination of surgery and radiation therapy improved overall survival when compared with surgery alone (adjusted HR 0.56, p = 0.003). In node-negative patients, the combination of surgery and radiation therapy was not clearly superior to surgery alone, possibly due to the rarity of node-negative IBC (adjusted HR 0.53, 95% CI 0.23–1.19, p = 0.13). Conclusions: Our findings provide a better understanding of the characteristics of inflammatory breast cancer, and creates the opportunity for future studies to evaluate the prognostic significance and treatment implications of lymph node status in inflammatory breast cancer. Our study is limited by lack of knowledge of use of systemic therapy, the HER2 status for each patient, and information regarding locoregional recurrence. However, institutions participating in the SEER registry are likely to follow guidelines set forth by the National Comprehensive Cancer Network, recommending induction chemotherapy followed by surgery followed by radiation for IBC. Nearly 80% of the IBC patients included in this study had nodal metastasis, reflecting the inherently aggressive biology of this disease. Further studies are required to characterize the biology of IBC and guide the optimal treatment of this disease. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P3-10-07.

Background and Aims Patients on dialysis are at substantially higher risk for developing cognitive impairment compared with the general population. At the same time cognitive impairment is a strong and independent risk factor for all-cause mortality. Given that the approaches used to access the prevalence of cognitive disfunction widely vary in different patient populations, their true burden in hemodialysis patients remains poorly understood. The aim of our study was to determine the association between cognitive impairment and overall survival and mortality in patients undergoing hemodialysis treatment. Method A total of 69 chronic hemodialysis patients (47 men and 22 women) were included. The median age of patients was 61 [47-69] years. The median duration of hemodialysis treatment was 32 [21-72] months. The study of cognitive status was conducted using the Montreal Cognitive Assessment (MoCA) questionnaire. The test results are between 0 and 30 points. A score of 26 points or more is considered the norm, less than 26 indicates the presence of cognitive impairment. The end point was a fatal outcome from acute pathology or progression of a chronic disease. The analysis of survival and mortality was evaluated using survival tables, the construction of Kaplan-Mayer curves with the Mantel-Cox log–rank criterion. The risks of the influence of cognitive impairment on mortality were assessed using Cox regression. Results During the 24-month follow-up period, 23 patients died. Structure of mortality over the observation period: acute coronary syndrome – 6 (26 %), COVID-19 – 6 (26 %), acute cerebrovascular accident – 2 (9%), thromboembolic complications – 3 (13%), another causes – 6 (26 %). In patients with cognitive impairment on hemodialysis, the average survival value was 16.33 months ± 1.70 months [CI 95% 12.99-19.66] and 20.48 months±1.32 months [CI 95% 17.88-23.07] in patients without cognitive impairment. According to the Mantel-Cox log-rank criterion, there was a statistically significant decrease in overall survival in patients with cognitive impairment, P = .02. The mortality rate of dialysis patients with cognitive impairment was 51%, without cognitive impairment – 19%. Relative risk of death in the group of patients with cognitive impairment using Cox regression: Hazard ratio (HR) :2.90 [95% CI 1.11-7.37, P = .03]. Conclusion Our results support the notion that cognitive impairment may be an independent predictor of all-cause mortality in hemodialysis patients. There are limitations on the sample size of patients in our study. Further larger-scale observations a large cohort of patients are required.

BackgroundThe role of cancer cell FOXP3 in tumorigenesis is conflicting. We aimed to study FOXP3 expression and regulation, function and clinical implication in human non-small cell lung cancer (NSCLC).MethodsOne hundred and six patients with histologically-confirmed NSCLC who underwent surgery were recruited for the study. Tumor samples and NSCLC cell lines were used to examine FOXP3 and its related molecules. Various cell functions related to tumorigenesis were performed. In vivo mouse tumor xenograft was used to confirm the in vitro results.ResultsNSCLC patients with the high level of FOXP3 had a significant decrease in overall survival and recurrence-free survival. FOXP3 overexpression significantly induced cell proliferation, migration, and invasion, whereas its inhibition impaired its oncogenic function. In vivo studies confirmed that FOXP3 promoted tumor growth and metastasis. The ectopic expression of FOXP3 induced epithelial–mesenchymal transition (EMT) with downregulation of E-cadherin and upregulation of N-cadherin, vimentin, snail, slug, and MMP9. The oncogenic effects by FOXP3 could be attributed to FOX3-mediated activation of Wnt/β-catenin signaling, as FOXP3 increased luciferase activity of Topflash reporter and upregulated Wnt signaling target genes including c-Myc and Cyclin D1 in NSCLC cells. Co-immunoprecipitation results further indicated that FOXP3 could physically interacted with β-catenin and TCF4 to enhance the functions of β-catenin and TCF4, inducing transcription of Wnt target genes to promote cell proliferation, invasion and EMT induction.ConclusionsFOXP3 can act as a co-activator to facilitate the Wnt-b-catenin signaling pathway, inducing EMT and tumor growth and metastasis in NSCLC.