Molecular subtyping of bladder cancer using Kohonen self-organizing maps

Assessment of Prostate and Bladder Cancer Genomic Biomarkers Using Artificial Intelligence: a Systematic Review

PURPOSE OF REVIEW

The aim of the systematic review is to assess AI's capabilities in the genetics of prostate cancer (PCa) and bladder cancer (BCa) to evaluate target groups for such analysis as well as to assess its prospects in daily practice.

RECENT FINDINGS

In total, our analysis included 27 articles: 10 articles have reported on PCa and 17 on BCa, respectively. The AI algorithms added clinical value and demonstrated promising results in several fields, including cancer detection, assessment of cancer development risk, risk stratification in terms of survival and relapse, and prediction of response to a specific therapy. Besides clinical applications, genetic analysis aided by the AI shed light on the basic urologic cancer biology. We believe, our results of the AI application to the analysis of PCa, BCa data sets will help to identify new targets for urological cancer therapy. The integration of AI in genomic research for screening and clinical applications will evolve with time to help personalizing chemotherapy, prediction of survival and relapse, aid treatment strategies such as reducing frequency of diagnostic cystoscopies, and clinical decision support, e.g., by predicting immunotherapy response. These factors will ultimately lead to personalized and precision medicine thereby improving patient outcomes.

Metabolic profile predicts incident cancer: A large-scale population study in the UK Biobank

BACKGROUND AND AIMS

Analyses to predict the risk of cancer typically focus on single biomarkers, which do not capture their complex interrelations. We hypothesized that the use of metabolic profiles may provide new insights into cancer prediction.

METHODS

We used information from 290,888 UK Biobank participants aged 37 to 73 years at baseline. Metabolic subgroups were defined based on clustering of biochemical data using an artificial neural network approach and examined for their association with incident cancers identified through linkage to cancer registry. In addition, we evaluated associations between 38 individual biomarkers and cancer risk.

RESULTS

In total, 21,973 individuals developed cancer during the follow-up (median 3.87 years, interquartile range [IQR] = 2.03-5.58). Compared to the metabolically favorable subgroup (IV), subgroup III (defined as "high BMI, C-reactive protein & cystatin C") was associated with a higher risk of obesity-related cancers (hazard ratio [HR] = 1.26, 95 % CI = 1.21 to 1.32) and hematologic-malignancies (e.g., lymphoid leukemia: HR = 1.83, 95%CI = 1.44 to 2.33). Subgroup II ("high triglycerides & liver enzymes") was strongly associated with liver cancer risk (HR = 5.70, 95%CI = 3.57 to 9.11). Analysis of individual biomarkers showed a positive association between testosterone and greater risks of hormone-sensitive cancers (HR per SD higher = 1.32, 95%CI = 1.23 to 1.44), and liver cancer (HR = 2.49, 95%CI =1.47 to 4.24). Many liver tests were individually associated with a greater risk of liver cancer with the strongest association observed for gamma-glutamyl transferase (HR = 2.40, 95%CI = 2.19 to 2.65).

CONCLUSIONS

Metabolic profile in middle-to-older age can predict cancer incidence, in particular risk of obesity-related cancer, hematologic malignancies, and liver cancer. Elevated values from liver tests are strong predictors for later risk of liver cancer.

Bladder cancer and human papillomavirus association: a systematic review and meta-analysis

BACKGROUND

The possible association of human papillomavirus (HPV) and bladder cancer has been controversial. Older findings suggest a significant association between the virus and bladder cancer. The aim of this study was to evaluate the data from the last ten years to estimate the prevalence of the virus in bladder cancer patients and to assess the association between the virus and cancer.

METHOD

A search of major databases was conducted to retrieve published English language studies between January 2011 and March 2021. In the present study overall prevalence of the virus in bladder cancer patients was estimated along with the prevalence of subgroups. Also, the possible associations between the prevalence of the virus and bladder cancer and the possible impact of variables in the geographical area and the type of sample were measured by comprehensive meta-analysis software (V2.2, BIOSTAT).

RESULTS

Unlike previous studies, despite the relatively high prevalence of the virus [pooled prevalence: 14.3% (95% CI 8.9-22.2%)] no significant association was found between HPV and bladder cancer (OR 2.077, 95% CI 0.940-4.587). No significant association was found between geographical area (except Asia) and type of sample with bladder cancer.

CONCLUSIONS

Given the significant prevalence, despite the insignificance of the association between virus and cancer, it seems that more studies with case-control design are needed to elucidate this association.

Nonnegative matrix factorization-based bioinformatics analysis reveals that TPX2 and SELENBP1 are two predictors of the inner sub-consensuses of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a heterogeneous disease. However the inner sub‐groups of LUAD have not been fully studied. Markers predicted the sub‐groups and prognosis of LUAD are badly needed.

Aims

To identify biomarkers associated with the sub‐groups and prognosis of LUAD.

Materials and Methods

Using nonnegative matrix factorization (NMF) clustering, LUAD patients from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) datasets and LUAD cell lines from Genomics of Drug Sensitivity in Cancer (GDSC) dataset were divided into different sub‐consensuses based on the gene expression profiling. The overall survival of LUAD patients in each sub‐consensus was determined by Kaplan‐Meier survival analysis. The common genes which were differentially expressed in each sub‐consensus of LUAD patients and LUAD cell lines were identified using TBtools. The predictive accuracy of TPX2 and SELENBP1 for theinner sub‐consensuses of LUAD was determined by Receiver operator characteristic (ROC) analysis. The Kaplan‐Meier survival analysis was also used to test the prognostic significance of TPX2 and SELENBP1 in LUAD patients.

Results

Using nonnegative matrix factorization clustering, LUAD patients in The Cancer Genome Atlas (TCGA), GSE30219, GSE42127, GSE50081, GSE68465, and GSE72094 datasets were divided into three sub‐consensuses. Sub‐consensus3 LUAD patients were with low overall survival and were with high TP53 mutations. Similarly, LUAD cell lines were also divided into three sub‐consensuses by NMF method, and sub‐consensus2 cell lines were resistant to EGFR inhibitors. Identification of the common genes which were differentially expressed in different sub‐consensuses of LUAD patients and LUAD cell lines revealed that TPX2 was highly expressed in sub‐consensus3 LUAD patients and sub‐consensus2 LUAD cell lines. On the contrary, SELENBP1 was highly expressed in sub‐consensus1 LUAD patients and sub‐consensus1 LUAD cell lines. The expression levels of TPX2 and SELENBP1 could distinguish sub‐consensus3 LUAD patients or sub‐consensus2 LUAD cell lines from other sub‐consensuses of LUAD patients or cell lines. Moreover, compared with normal lung tissues, TPX2 was highly expressed, while, SELENBP1 was lowly expressed in LUAD tissues. Furthermore, the higher expression levels of TPX2 were associated with the lower relapse‐free survival and the lower overall survival of LUAD patients. While, the higher expression levels of SELENBP1 were associated with the higher relapse‐free survival and higher overall survival. At last, we showed that TP53 mutant LUAD patients were with higher TPX2 and lower SELENBP1 expressions.

Discussion

Both iCluster and NMF method are proved to be robust LUAD classification systems. However, the LUAD patients in different iclusters had no significant clinical overall survival, while, sub‐consensus3 LUAD patients from NMF classification were with lower overall survival than other sub‐consensuses.

Conclusions

By integrated analysis of 1765 LUAD patients and 64 LUAD cell lines, we showed that NMF was a robust inner sub‐consensuses classification method of LUAD. TPX2 and SELENBP1 were differentially expressed in different LUAD sub‐ consensuses, and predicted the inner sub‐consensuses of LUAD with high accuracy. TPX2 was an unfavorable prognostic biomarker of LUAD which was up‐regulated in LUAD tissues and associated with the low overall survival of LUAD. SELENBP1 was a favorable prognostic biomarker of LUAD which was down‐regulated in LUAD tissues and associated with the prolonged overall survival of LUAD.

Analysis of CDKN2A gene alterations in recurrent and non-recurrent meningioma

PURPOSE

Assessment of the risk of recurrence is essential to determine the therapeutic strategy of meningioma treatment. Many relapsing or aggressive meningiomas show elevated mitotic and/or Ki67 indices, reflecting cell cycle deregulation. As CDKN2A is a key tumor suppressor gene involved in cell cycle control, we investigated whether CDKN2A alterations may be involved in tumor recurrence.

METHODS

We carried out a comparative analysis of 17 recurrent and 13 non-recurrent meningiomas. CDKN2A single nucleotide variations (SNVs), deletions, methylation status of the promotor, and p16 expression were investigated. Results were correlated with the recurrent or non-recurrent status and clinicopathological data.

RESULTS

We identified a CDKN2A SNV (NM_000077, exon2, c.G442A, p.Ala148Thr) in five meningiomas that was significantly associated with recurrence (p = 0.03). This mutation, confirmed by Sanger sequencing and referenced in the COSMIC database in various cancers, has not been reported in meningioma. The presence of one of the three following CDKN2A alterations-p.(Ala148Thr) mutation, whole homozygous or heterozygous gene loss, or promotor methylation > 8%-was observed in 13 of the 17 relapsing meningiomas and was strongly associated with recurrence (p < 0.0001) and a Ki67 labeling index > 7% (p = 0.004).

CONCLUSION

We report an undescribed p.(Ala148Thr) CDKN2A mutation in meningioma that was only present in relapsing tumors. In our series, CDKN2A gene alterations were only found in recurrent meningiomas. However, our results need to be evaluated on a larger series to ensure that these CDKN2A alterations can be used as biomarkers of recurrence in meningioma.

A survey of neural network-based cancer prediction models from microarray data

Neural networks are powerful tools used widely for building cancer prediction models from microarray data. We review the most recently proposed models to highlight the roles of neural networks in predicting cancer from gene expression data. We identified articles published between 2013-2018 in scientific databases using keywords such as cancer classification, cancer analysis, cancer prediction, cancer clustering and microarray data. Analyzing the studies reveals that neural network methods have been either used for filtering (data engineering) the gene expressions in a prior step to prediction; predicting the existence of cancer, cancer type or the survivability risk; or for clustering unlabeled samples. This paper also discusses some practical issues that can be considered when building a neural network-based cancer prediction model. Results indicate that the functionality of the neural network determines its general architecture. However, the decision on the number of hidden layers, neurons, hypermeters and learning algorithm is made using trail-and-error techniques.

Computational methods to identify bimodal gene expression and facilitate personalized treatment in cancer patients

Standard methods for detecting cancer-associated genes rely on comparison of sample means between cancer patients and healthy controls. While such methods have successfully identified several oncogenes and tumor suppressor genes, they neglect to account for heterogeneity within the cancer population. Genetic mutations, translocations, and amplifications are often inconsistent across tumors, and instead they often affect smaller subsets of patients. This concept gives rise to the idea of bimodally expressed genes, or genes that display two modes of expression within one population. Analysis of bimodal gene expression has been explored via a variety of techniques including test statistics and clustering. In this review, we summarize the methodologies used to quantify bimodal gene expression and address the utility of these genes in patient stratification and specialized therapeutics in breast and lung cancer. Finally we discuss the limitations and future directions for bimodal genes in the era of high-throughput sequencing and personalized medicine.

Use of self-organizing maps for analyzing the behavior of canines displaced towards midline under interceptive treatment

Background

Displaced maxillary permanent canine is one of the more frequent findings in canine eruption process and it’s easy to be outlined and early diagnosed by means of x-ray images. Late diagnosis frequently needs surgery to rescue the impacted permanent canine. In many cases, interceptive treatment to redirect canine eruption is needed. However, some patients treated by interceptive means end up requiring fenestration to orthodontically guide the canine to its normal occlusal position. It would be interesting, therefore, to discover the dental characteristics of patients who will need additional surgical treatment to interceptive treatment.

Material and Methods

To study the dental characteristics associated with canine impaction, conventional statistics have traditionally been used. This approach, although serving to illustrate many features of this problem, has not provided a satisfactory response or not provided an overall idea of the characteristics of these types of patients, each one of them with their own particular set of variables. Faced with this situation, and in order to analyze the problem of impaction despite interceptive treatment, we have used an alternative method for representing the variables that have an influence on this syndrome. This method is known as Self-Organizing Maps (SOM), a method used for analyzing problems with multiple variables.

Results

We analyzed 78 patients with a PMC angulation higher than 100º. All of them were subject to interceptive treatment and in 21 cases it was necessary to undertake the above-mentioned fenestration to achieve the final eruption of the canine.

Conclusions

In this study, we describe the process of debugging variables and selecting the appropriate number of cells in SOM so as to adequately visualize the problem posed and the dental characteristics of patients with regard to a greater or lesser probability of the need for fenestration.

Key words:Interceptive orthodontic treatment, altered eruption, impacted canines, neuronal networks, self-organizing maps.

The promise of multi-omics and clinical data integration to identify and target personalized healthcare approaches in autism spectrum disorders

Complex diseases are caused by a combination of genetic and environmental factors, creating a difficult challenge for diagnosis and defining subtypes. This review article describes how distinct disease subtypes can be identified through integration and analysis of clinical and multi-omics data. A broad shift toward molecular subtyping of disease using genetic and omics data has yielded successful results in cancer and other complex diseases. To determine molecular subtypes, patients are first classified by applying clustering methods to different types of omics data, then these results are integrated with clinical data to characterize distinct disease subtypes. An example of this molecular-data-first approach is in research on Autism Spectrum Disorder (ASD), a spectrum of social communication disorders marked by tremendous etiological and phenotypic heterogeneity. In the case of ASD, omics data such as exome sequences and gene and protein expression data are combined with clinical data such as psychometric testing and imaging to enable subtype identification. Novel ASD subtypes have been proposed, such as CHD8, using this molecular subtyping approach. Broader use of molecular subtyping in complex disease research is impeded by data heterogeneity, diversity of standards, and ineffective analysis tools. The future of molecular subtyping for ASD and other complex diseases calls for an integrated resource to identify disease mechanisms, classify new patients, and inform effective treatment options. This in turn will empower and accelerate precision medicine and personalized healthcare.