Genomic and proteomic analysis with dynamically growing self organising tree (DGSOT) for measuring clinical outcomes of cancer

Genomics and proteomics microarray technologies are used for analysing molecular and cellular expressions of cancer. This creates a challenge for analysis and interpretation of the data generated as it is produced in large volumes. The current review describes a combined system for genetic, molecular interpretation and analysis of genomics and proteomics technologies that offers a wide range of interpreted results. Artificial neural network systems technology has the type of programmes to best deal with these large volumes of analytical data. The artificial system to be recommended here is to be determined from the analysis and selection of the best of different available technologies currently being used or reviewed for microarray data analysis. The system proposed here is a tree structure, a new hierarchical clustering algorithm called a dynamically growing self-organizing tree (DGSOT) algorithm, which overcomes drawbacks of traditional hierarchical clustering algorithms. The DGSOT algorithm combines horizontal and vertical growth to construct a mutlifurcating hierarchical tree from top to bottom to cluster the data. They are designed to combine the strengths of Neural Networks (NN), which have speed and robustness to noise, and hierarchical clustering tree structure which are minimum prior requirement for number of clusters specification and training in order to output results of interpretable biological context. The combined system will generate an output of biological interpretation of expression profiles associated with diagnosis of disease (including early detection, molecular classification and staging), metastasis (spread of the disease to non-adjacent organs and/or tissues), prognosis (predicting clinical outcome) and response to treatment; it also gives possible therapeutic options ranking them according to their benefits for the patient.Key words: Genomics, proteomics, microarray, dynamically growing self-organizing tree (DGSOT)

Using Molecular Markers to Help Predict Who Will Fail after Radical Prostatectomy

Recent phase III trial data clearly demonstrate that adjuvant therapy can reduce recurrence and increase survival after prostatectomy for prostate cancer. There is great interest in being able to accurately predict who is at risk of failure to avoid treating those who may not benefit. The standard markers consisting of prostate specific antigen (PSA), Gleason score, and pathological stage are not very specific, so there is an unmet need for other markers to aid in prognostic stratification. Numerous studies have been conducted with various markers and more recently gene signatures, but it is unclear whether any of them are really useful. We conducted a comprehensive review of the literature to determine the current status of molecular markers in predicting outcome after radical prostatectomy

Image analysis reveals molecularly distinct patterns of TILs in NSCLC associated with treatment outcome.

Despite known histological, biological, and clinical differences between lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), relatively little is known about the spatial differences in their corresponding immune contextures. Our study of over 1000 LUAD and LUSC tumors revealed that computationally derived patterns of tumor-infiltrating lymphocytes (TILs) on H&E images were different between LUAD (N = 421) and LUSC (N = 438), with TIL density being prognostic of overall survival in LUAD and spatial arrangement being more prognostically relevant in LUSC. In addition, the LUAD-specific TIL signature was associated with OS in an external validation set of 100 NSCLC treated with more than six different neoadjuvant chemotherapy regimens, and predictive of response to therapy in the clinical trial CA209-057 (n = 303). In LUAD, the prognostic TIL signature was primarily comprised of CD4+ T and CD8+ T cells, whereas in LUSC, the immune patterns were comprised of CD4+ T, CD8+ T, and CD20+ B cells. In both subtypes, prognostic TIL features were associated with transcriptomics-derived immune scores and biological pathways implicated in immune recognition, response, and evasion. Our results suggest the need for histologic subtype-specific TIL-based models for stratifying survival risk and predicting response to therapy. Our findings suggest that predictive models for response to therapy will need to account for the unique morphologic and molecular immune patterns as a function of histologic subtype of NSCLC

FOXA1 mutations alter pioneering activity, differentiation and prostate cancer phenotypes.

Mutations in the transcription factor FOXA1 define a unique subset of prostate cancers but the functional consequences of these mutations and whether they confer gain or loss of function is unknown1-9. Here, by annotating the landscape of FOXA1 mutations from 3,086 human prostate cancers, we define two hotspots in the forkhead domain: Wing2 (around 50% of all mutations) and the highly conserved DNA-contact residue R219 (around 5% of all mutations). Wing2 mutations are detected in adenocarcinomas at all stages, whereas R219 mutations are enriched in metastatic tumours with neuroendocrine histology. Interrogation of the biological properties of wild-type FOXA1 and fourteen FOXA1 mutants reveals gain of function in mouse prostate organoid proliferation assays. Twelve of these mutants, as well as wild-type FOXA1, promoted an exaggerated pro-luminal differentiation program, whereas two different R219 mutants blocked luminal differentiation and activated a mesenchymal and neuroendocrine transcriptional program. Assay for transposase-accessible chromatin using sequencing (ATAC-seq) of wild-type FOXA1 and representative Wing2 and R219 mutants revealed marked, mutant-specific changes in open chromatin at thousands of genomic loci and exposed sites of FOXA1 binding and associated increases in gene expression. Of note, ATAC-seq peaks in cells expressing R219 mutants lacked the canonical core FOXA1-binding motifs (GTAAAC/T) but were enriched for a related, non-canonical motif (GTAAAG/A), which was preferentially activated by R219-mutant FOXA1 in reporter assays. Thus, FOXA1 mutations alter its pioneering function and perturb normal luminal epithelial differentiation programs, providing further support for the role of lineage plasticity in cancer progression

MiR-133b Targets Antiapoptotic Genes and Enhances Death Receptor-Induced Apoptosis

Despite the importance of microRNAs (miRs) for regulation of the delicate balance between cell proliferation and death, evidence for their specific involvement during death receptor (DR)-mediated apoptosis is scarce. Transfection with miR-133b rendered resistant HeLa cells sensitive to tumor necrosis factor-alpha (TNFα)-induced cell death. Similarly, miR-133b caused exacerbated proapoptotic responses to TNF-related apoptosis-inducing ligand (TRAIL) or an activating antibody to Fas/CD95. Comprehensive analysis, encompassing global RNA or protein expression profiling performed by microarray experiments and pulsed stable isotope labeling with amino acids in cell culture (pSILAC), led to the discovery of the antiapoptotic protein Fas apoptosis inhibitory molecule (FAIM) as immediate miR-133b target. Moreover, miR-133b impaired the expression of the detoxifying protein glutathione-S-transferase pi (GSTP1). Expression of miR-133b in tumor specimens of prostate cancer patients was significantly downregulated in 75% of the cases, when compared with matched healthy tissue. Furthermore, introduction of synthetic miR-133b into an ex-vivo model of prostate cancer resulted in impaired proliferation and cellular metabolic activity. PC3 cells were also sensitized to apoptotic stimuli after transfection with miR-133b similar to HeLa cells. These data reveal the ability of a single miR to influence major apoptosis pathways, suggesting an essential role for this molecule during cellular transformation, tumorigenesis and tissue homeostasis

Discovering cancer-associated transcripts by RNA sequencing

High-throughput sequencing of poly-adenylated RNA (RNA-Seq) in human cancers shows remarkable potential to identify uncharacterized aspects of tumor biology, including gene fusions with therapeutic significance and disease markers such as long non-coding RNA (lncRNA) species. However, the analysis of RNA-Seq data places unprecedented demands upon computational infrastructures and algorithms, requiring novel bioinformatics approaches. To meet these demands, we present two new open-source software packages - ChimeraScan and AssemblyLine - designed to detect gene fusion events and novel lncRNAs, respectively. RNA-Seq studies utilizing ChimeraScan led to discoveries of new families of recurrent gene fusions in breast cancers and solitary fibrous tumors. Further, ChimeraScan was one of the key components of the repertoire of computational tools utilized in data analysis for MI-ONCOSEQ, a clinical sequencing initiative to identify potentially informative and actionable mutations in cancer patients’ tumors. AssemblyLine, by contrast, reassembles RNA sequencing data into full-length transcripts ab initio. In head-to-head analyses AssemblyLine compared favorably to existing ab initio approaches and unveiled abundant novel lncRNAs, including antisense and intronic lncRNAs disregarded by previous studies. Moreover, we used AssemblyLine to define the prostate cancer transcriptome from a large patient cohort and discovered myriad lncRNAs, including 121 prostate cancer-associated transcripts (PCATs) that could potentially serve as novel disease markers. Functional studies of two PCATs - PCAT-1 and SChLAP1 - revealed cancer-promoting roles for these lncRNAs. PCAT1, a lncRNA expressed from chromosome 8q24, promotes cell proliferation and represses the tumor suppressor BRCA2. SChLAP1, located in a chromosome 2q31 ‘gene desert’, independently predicts poor patient outcomes, including metastasis and cancer-specific mortality. Mechanistically, SChLAP1 antagonizes the genome-wide localization and regulatory functions of the SWI/SNF chromatin-modifying complex. Collectively, this work demonstrates the utility of ChimeraScan and AssemblyLine as open-source bioinformatics tools. Our applications of ChimeraScan and AssemblyLine led to the discovery of new classes of recurrent and clinically informative gene fusions, and established a prominent role for lncRNAs in coordinating aggressive prostate cancer, respectively. We expect that the methods and findings described herein will establish a precedent for RNA-Seq-based studies in cancer biology and assist the research community at large in making similar discoveries.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120814/1/mkiyer_1.pd

Identification of prostate cancer diagnostic and prognostic biomarkers in urine expression data with a focus on extracellular vesicles

Prostate Cancer (PCa) is a major clinical problem worldwide with considerable variability in clinical outcome of patients. PCa diagnostics and prognostics currently lack specific and sensitive clinical biomarkers and treatment is not well individualised. The PCA3 test, amongst others, highlights the utility of urine in PCa diagnostics and prognostics. Urine contains cells and extracellular vesicles (EV) that originate in the prostate. There are many areas of the PCa clinical process that could be aided with an expression based urine test, including diagnosis, prognosis and response to therapy. NanoString data (167 transcripts) from 485 EV RNA samples were collected from PCa patients and used to build models that would aid in PCa diagnosis and prognosis i.e. i) PCa (low- (L), intermediate-(I), and high-risk(H)) vs CB (Clinically Benign/No evidence for cancer), ii) high-risk PCa vs CB, and iii) trend in expression across CB>L>I>H. These models were validated in 235 samples, with AUCs of i) 0.851 ii) 0.897 and iii) 0.709, respectively. The potential of using urine EVs to predict patient response to treatments was also investigated. In a pilot data set a signature of seven transcripts was identified that could optimally predict progression of patients on hormone therapy (p = 2.3x10-05; HR = 0.04288). Models were also built using NanoString data from 92 cell RNA samples. Intercomparing expression data from matched cell and EV fractions of urine showed that transcripts significantly higher in the EV samples were associated with the prostate, PCa and cancer in general, supporting them as a viable source of biomarkers in the clinical management of PCa. In conclusion my analyses have demonstrated the utility of examining urine RNA for the diagnosis and prognosis of PCa. My studies have formed the basis of the production of a Prostate Urine Risk test that is currently under development at UEA