Particle swarm optimization artificial intelligence technique for gene signature discovery in transcriptomic cohorts.

The development of gene signatures is key for delivering personalized medicine, despite only a few signatures being available for use in the clinic for cancer patients. Gene signature discovery tends to revolve around identifying a single signature. However, it has been shown that various highly predictive signatures can be produced from the same dataset. This study assumes that the presentation of top ranked signatures will allow greater efforts in the selection of gene signatures for validation on external datasets and for their clinical translation. Particle swarm optimization (PSO) is an evolutionary algorithm often used as a search strategy and largely represented as binary PSO (BPSO) in this domain. BPSO, however, fails to produce succinct feature sets for complex optimization problems, thus affecting its overall runtime and optimization performance. Enhanced BPSO (EBPSO) was developed to overcome these shortcomings. Thus, this study will validate unique candidate gene signatures for different underlying biology from EBPSO on transcriptomics cohorts. EBPSO was consistently seen to be as accurate as BPSO with substantially smaller feature signatures and significantly faster runtimes. 100% accuracy was achieved in all but two of the selected data sets. Using clinical transcriptomics cohorts, EBPSO has demonstrated the ability to identify accurate, succinct, and significantly prognostic signatures that are unique from one another. This has been proposed as a promising alternative to overcome the issues regarding traditional single gene signature generation. Interpretation of key genes within the signatures provided biological insights into the associated functions that were well correlated to their cancer type.

Using AI-Based Evolutionary Algorithms to Elucidate Adult Brain Tumor (Glioma) Etiology Associated with IDH1 for Therapeutic Target Identification.

Adult brain tumors (glioma) represent a cancer of unmet need where standard-of-care is non-curative; thus, new therapies are urgently needed. It is unclear whether isocitrate dehydrogenases (IDH1/2) when not mutated have any role in gliomagenesis or tumor growth. Nevertheless, IDH1 is overexpressed in glioblastoma (GBM), which could impact upon cellular metabolism and epigenetic reprogramming. This study characterizes IDH1 expression and associated genes and pathways. A novel biomarker discovery pipeline using artificial intelligence (evolutionary algorithms) was employed to analyze IDH-wildtype adult gliomas from the TCGA LGG-GBM cohort. Ninety genes whose expression correlated with IDH1 expression were identified from: (1) All gliomas, (2) primary GBM, and (3) recurrent GBM tumors. Genes were overrepresented in ubiquitin-mediated proteolysis, focal adhesion, mTOR signaling, and pyruvate metabolism pathways. Other non-enriched pathways included O-glycan biosynthesis, notch signaling, and signaling regulating stem cell pluripotency (PCGF3). Potential prognostic (TSPYL2, JAKMIP1, CIT, TMTC1) and two diagnostic (MINK1, PLEKHM3) biomarkers were downregulated in GBM. Their gene expression and methylation were negatively and positively correlated with IDH1 expression, respectively. Two diagnostic biomarkers (BZW1, RCF2) showed the opposite trend. Prognostic genes were not impacted by high frequencies of molecular alterations and only one (TMTC1) could be validated in another cohort. Genes with mechanistic links to IDH1 were involved in brain neuronal development, cell proliferation, cytokinesis, and O-mannosylation as well as tumor suppression and anaplerosis. Results highlight metabolic vulnerabilities and therapeutic targets for use in future clinical trials.

A bespoke target selection tool to guide biomarker discovery in tubo-ovarian cancer.

Introduction: Cancers presenting at advanced stages inherently have poor prognosis. High grade serous carcinoma (HGSC) is the most common and aggressive form of tubo-ovarian cancer. Clinical tests to accurately diagnose and monitor this condition are lacking. Hence, development of disease-specific tests are urgently required. Methods: The molecular profile of HGSC during disease progression was investigated in a unique patient cohort. A bespoke data browser was developed to analyse gene expression and DNA methylation datasets for biomarker discovery. The Ovarian Cancer Data Browser (OCDB) is built in C# with a.NET framework using an integrated development environment of Microsoft Visual Studio and fast access files (.faf). The graphical user interface is easy to navigate between four analytical modes (gene expression; methylation; combined gene expression and methylation data; methylation clusters), with a rapid query response time. A user should first define a disease progression trend for prioritising results. Single or multiomics data are then mined to identify probes, genes and methylation clusters that exhibit the desired trend. A unique scoring system based on the percentage change in expression/methylation between disease stages is used. Results are filtered and ranked using weighting and penalties. Results: The OCDB's utility for biomarker discovery is demonstrated with the identified target OSR2. Trends in OSR2 repression and hypermethylation with HGSC disease progression were confirmed in the browser samples and an independent cohort using bioassays. The OSR2 methylation biomarker could discriminate HGSC with high specificity (95%) and sensitivity (93.18%). Conclusions: The OCDB has been refined and validated to be an integral part of a unique biomarker discovery pipeline. It may also be used independently to aid identification of novel targets. It carries the potential to identify further biomarker assays that can reduce type I and II errors within clinical diagnostics.

Evolutionary genetic algorithm identifies IL2RB as a potential predictive biomarker for immune-checkpoint therapy in colorectal cancer.

Identifying robust predictive biomarkers to stratify colorectal cancer (CRC) patients based on their response to immune-checkpoint therapy is an area of unmet clinical need. Our evolutionary algorithm Atlas Correlation Explorer (ACE) represents a novel approach for mining The Cancer Genome Atlas (TCGA) data for clinically relevant associations. We deployed ACE to identify candidate predictive biomarkers of response to immune-checkpoint therapy in CRC. We interrogated the colon adenocarcinoma (COAD) gene expression data across nine immune-checkpoints (PDL1, PDCD1, CTLA4, LAG3, TIM3, TIGIT, ICOS, IDO1 and BTLA). IL2RB was identified as the most common gene associated with immune-checkpoint genes in CRC. Using human/murine single-cell RNA-seq data, we demonstrated that IL2RB was expressed predominantly in a subset of T-cells associated with increased immune-checkpoint expression (P < 0.0001). Confirmatory IL2RB immunohistochemistry (IHC) analysis in a large MSI-H colon cancer tissue microarray (TMA; n = 115) revealed sensitive, specific staining of a subset of lymphocytes and a strong association with FOXP3+ lymphocytes (P < 0.0001). IL2RB mRNA positively correlated with three previously-published gene signatures of response to immune-checkpoint therapy (P < 0.0001). Our evolutionary algorithm has identified IL2RB to be extensively linked to immune-checkpoints in CRC; its expression should be investigated for clinical utility as a potential predictive biomarker for CRC patients receiving immune-checkpoint blockade.

Defining the molecular evolution of extrauterine high grade serous carcinoma.

OBJECTIVE: High grade serous carcinoma (HGSC) is the most common and most aggressive, subtype of epithelial ovarian cancer. It presents as advanced stage disease with poor prognosis. Recent pathological evidence strongly suggests HGSC arises from the fallopian tube via the precursor lesion; serous tubal intraepithelial carcinoma (STIC). However, further definition of the molecular evolution of HGSC has major implications for both clinical management and research. This study aims to more clearly define the molecular pathogenesis of HGSC. METHODS: Six cases of HGSC were identified at the Northern Ireland Gynaecological Cancer Centre (NIGCC) that each contained ovarian HGSC (HGSC), omental HGSC (OMT), STIC, normal fallopian tube epithelium (FTE) and normal ovarian surface epithelium (OSE). The relevant formalin-fixed paraffin embedded (FFPE) tissue samples were retrieved from the pathology archive via the Northern Ireland Biobank following attaining ethical approval (NIB11:005). Full microarray-based gene expression profiling was performed on the cohort. The resulting data was analysed bioinformatically and the results were validated in a HGSC-specific in-vitro model. RESULTS: The carcinogenesis of HGSC was investigated and showed the molecular profile of HGSC to be more closely related to normal FTE than OSE. STIC lesions also clustered closely with HGSC, indicating a common molecular origin. CONCLUSION: This study provides strong evidence suggesting that extrauterine HGSC arises from the fimbria of the distal fallopian tube. Furthermore, several potential pathways were identified which could be targeted by novel therapies for HGSC. These findings have significant translational relevance for both primary prevention and clinical management of the disease.

NUQA: Estimating Cancer Spatial and Temporal Heterogeneity and Evolution through Alignment-Free Methods.

Longitudinal next-generation sequencing of cancer patient samples has enhanced our understanding of the evolution and progression of various cancers. As a result, and due to our increasing knowledge of heterogeneity, such sampling is becoming increasingly common in research and clinical trial sample collections. Traditionally, the evolutionary analysis of these cohorts involves the use of an aligner followed by subsequent stringent downstream analyses. However, this can lead to large levels of information loss due to the vast mutational landscape that characterizes tumor samples. Here, we propose an alignment-free approach for sequence comparison-a well-established approach in a range of biological applications including typical phylogenetic classification. Such methods could be used to compare information collated in raw sequence files to allow an unsupervised assessment of the evolutionary trajectory of patient genomic profiles. In order to highlight this utility in cancer research we have applied our alignment-free approach using a previously established metric, Jensen-Shannon divergence, and a metric novel to this area, Hellinger distance, to two longitudinal cancer patient cohorts in glioma and clear cell renal cell carcinoma using our software, NUQA. We hypothesize that this approach has the potential to reveal novel information about the heterogeneity and evolutionary trajectory of spatiotemporal tumor samples, potentially revealing early events in tumorigenesis and the origins of metastases and recurrences. Key words: alignment-free, Hellinger distance, exome-seq, evolution, phylogenetics, longitudinal.

ACE: A Workbench Using Evolutionary Genetic Algorithms for Analyzing Association in TCGA.

Modern methods of acquiring molecular data have improved rapidly in recent years, making it easier for researchers to collect large volumes of information. However, this has increased the challenge of recognizing interesting patterns within the data. Atlas Correlation Explorer (ACE) is a user-friendly workbench for seeking associations between attributes in The Cancer Genome Atlas (TCGA) database. It allows any combination of clinical and genomic data streams to be searched using an evolutionary algorithm approach. To showcase ACE, we assessed which RNA sequencing transcripts were associated with estrogen receptor (ESR1) in the TCGA breast cancer cohort. The analysis revealed already well-established associations with XBP1 and FOXA1, but also identified a strong association with CT62, a potential immunotherapeutic target with few previous associations with breast cancer. In conclusion, ACE can produce results for very large searches in a short time and will serve as an increasingly useful tool for biomarker discovery in the big data era. SIGNIFICANCE: ACE uses an evolutionary algorithm approach to perform large searches for associations between any combinations of data in the TCGA database.

Impact of Variable RNA-Sequencing Depth on Gene Expression Signatures and Target Compound Robustness: Case Study Examining Brain Tumor (Glioma) Disease Progression.

PURPOSE: Gene expression profiling can uncover biologic mechanisms underlying disease and is important in drug development. RNA sequencing (RNA-seq) is routinely used to assess gene expression, but costs remain high. Sample multiplexing reduces RNAseq costs; however, multiplexed samples have lower cDNA sequencing depth, which can hinder accurate differential gene expression detection. The impact of sequencing depth alteration on RNA-seq-based downstream analyses such as gene expression connectivity mapping is not known, where this method is used to identify potential therapeutic compounds for repurposing. METHODS: In this study, published RNA-seq profiles from patients with brain tumor (glioma) were assembled into two disease progression gene signature contrasts for astrocytoma. Available treatments for glioma have limited effectiveness, rendering this a disease of poor clinical outcome. Gene signatures were subsampled to simulate sequencing alterations and analyzed in connectivity mapping to investigate target compound robustness. RESULTS: Data loss to gene signatures led to the loss, gain, and consistent identification of significant connections. The most accurate gene signature contrast with consistent patient gene expression profiles was more resilient to data loss and identified robust target compounds. Target compounds lost included candidate compounds of potential clinical utility in glioma (eg, suramin, dasatinib). Lost connections may have been linked to low-abundance genes in the gene signature that closely characterized the disease phenotype. Consistently identified connections may have been related to highly expressed abundant genes that were ever-present in gene signatures, despite data reductions. Potential noise surrounding findings included false-positive connections that were gained as a result of gene signature modification with data loss. CONCLUSION: Findings highlight the necessity for gene signature accuracy for connectivity mapping, which should improve the clinical utility of future target compound discoveries.

Bcl-xL as a poor prognostic biomarker and predictor of response to adjuvant chemotherapy specifically in BRAF-mutant stage II and III colon cancer.

Purpose: BRAF mutation occurs in 8-15% of colon cancers (CC), and is associated with poor prognosis in metastatic disease. Compared to wild-type BRAF (BRAFWT) disease, stage II/III CC patients with BRAF mutant (BRAFMT) tumors have shorter overall survival after relapse; however, time-to-relapse is not significantly different. The aim of this investigation was to identify, and validate, novel predictors of relapse of stage II/III BRAFMT CC. Experimental design: We used gene expression data from a cohort of 460 patients (GSE39582) to perform a supervised classification analysis based on risk-of-relapse within BRAFMT stage II/III CC, to identify transcriptomic biomarkers associated with prognosis within this genotype. These findings were validated using immunohistochemistry in an independent population-based cohort of Stage II/III CC (n = 691), applying Cox proportional hazards analysis to determine associations with survival. Results: High gene expression levels of Bcl-xL, a key regulator of apoptosis, were associated with increased risk of relapse, specifically in BRAFMT tumors (HR = 8.3, 95% CI 1.7-41.7), but not KRASMT/BRAFWT or KRASWT/BRAFWT tumors. High Bcl-xL protein expression in BRAFMT, untreated, stage II/III CC was confirmed to be associated with an increased risk of death in an independent cohort (HR = 12.13, 95% CI 2.49-59.13). Additionally, BRAFMT tumors with high levels of Bcl-xL protein expression appeared to benefit from adjuvant chemotherapy (P for interaction = 0.006), indicating the potential predictive value of Bcl-xL expression in this setting. Conclusions: These findings provide evidence that Bcl-xL gene and/or protein expression identifies a poor prognostic subgroup of BRAFMT stage II/III CC patients, who may benefit from adjuvant chemotherapy.

Anti-mullerian hormone as a marker of ovarian reserve.

OBJECTIVE: To analyse the usefulness of plasma anti-mullerian hormone (AMH) measurement as a tool for assessing ovarian reserve in a general infertility population. MATERIALS AND METHODS: Plasma AMH levels were analysed in 238 women aged 18-46 years during day 3-5 of their menstrual cycle. All 238 patients had follicle stimulating hormone (FSH) levels less than 10 i.u./L, suggesting normal ovarian reserve on traditional FSH criteria. Eighty-seven patients gave their consent to correlate their AMH levels with IVF oocyte retrieval outcome. Patients producing > or = 8 oocytes were classified as having normal ovarian reserve, while those producing < or = 4 oocytes were classified as having poor ovarian reserve. RESULTS: Plasma AMH levels remained relatively static (20-25 pmol/L) from 18 to 29 years of age. By 30 years of age, plasma AMH levels start to drop rapidly, reaching only 10 pmol/L by 37 years. Despite this 50% fall in AMH levels between 29 and 37 years of age, minimal changes in FSH levels were observed. Using a cut off value of 8.1 pmol/L, plasma AMH assessment could predict poor ovarian reserve on a subsequent IVF cycle with a sensitivity of 80% and a specificity of 85%. CONCLUSIONS: Plasma AMH assessments are superior to FSH in identifying women with reduced ovarian reserve. Anti-mullerian hormone assessment should be considered as a useful adjunct to FSH/oestradiol levels and antral follicle count when estimating ovarian reserve.