FuncPEP v2.0: An Updated Database of Functional Short Peptides Translated from Non-Coding RNAs.

Over the past decade, there have been reports of short novel functional peptides (less than 100 aa in length) translated from so-called non-coding RNAs (ncRNAs) that have been characterized using mass spectrometry (MS) and large-scale proteomics studies. Therefore, understanding the bivalent functions of some ncRNAs as transcripts that encode both functional RNAs and short peptides, which we named ncPEPs, will deepen our understanding of biology and disease. In 2020, we published the first database of functional peptides translated from non-coding RNAs-FuncPEP. Herein, we have performed an update including the newly published ncPEPs from the last 3 years along with the categorization of host ncRNAs. FuncPEP v2.0 contains 152 functional ncPEPs, out of which 40 are novel entries. A PubMed search from August 2020 to July 2023 incorporating specific keywords was performed and screened for publications reporting validated functional peptides derived from ncRNAs. We did not observe a significant increase in newly discovered functional ncPEPs, but a steady increase. The novel identified ncPEPs included in the database were characterized by a wide array of molecular and physiological parameters (i.e., types of host ncRNA, species distribution, chromosomal density, distribution of ncRNA length, identification methods, molecular weight, and functional distribution across humans and other species). We consider that, despite the fact that MS can now easily identify ncPEPs, there still are important limitations in proving their functionality.

RETRACTED: Liu et al. Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images. Cancers 2023, 15, 4044.

The journal and authors wish to retract the article entitled 'Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images' cited above [...].

PCA-Plus: Enhanced principal component analysis with illustrative applications to batch effects and their quantitation.

Background: Principal component analysis (PCA), a standard approach to analysis and visualization of large datasets, is commonly used in biomedical research for detecting similarities and differences among groups of samples. We initially used conventional PCA as a tool for critical quality control of batch and trend effects in multi-omic profiling data produced by The Cancer Genome Atlas (TCGA) project of the NCI. We found, however, that conventional PCA visualizations were often hard to interpret when inter-batch differences were moderate in comparison with intra-batch differences; it was also difficult to quantify batch effects objectively. We, therefore, sought enhancements to make the method more informative in those and analogous settings. Results: We have developed algorithms and a toolbox of enhancements to conventional PCA that improve the detection, diagnosis, and quantitation of differences between or among groups, e.g., groups of molecularly profiled biological samples. The enhancements include (i) computed group centroids; (ii) sample-dispersion rays; (iii) differential coloring of centroids, rays, and sample data points; (iii) trend trajectories; and (iv) a novel separation index (DSC) for quantitation of differences among groups. Conclusions: PCA-Plus has been our most useful single tool for analyzing, visualizing, and quantitating batch effects, trend effects, and class differences in molecular profiling data of many types: mRNA expression, microRNA expression, DNA methylation, and DNA copy number. An early version of PCA-Plus has been used as the central graphical visualization in our MBatch package for near-real-time surveillance of data for analysis working groups in more than 70 TCGA, PanCancer Atlas, PanCancer Analysis of Whole Genomes, and Genome Data Analysis Network projects of the NCI. The algorithms and software are generic, hence applicable more generally to other types of multivariate data as well. PCA-Plus is freely available in a down-loadable R package at our MBatch website.

Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images.

BACKGROUND: Ovarian cancer remains the leading gynecological cause of cancer mortality. Predicting the sensitivity of ovarian cancer to chemotherapy at the time of pathological diagnosis is a goal of precision medicine research that we have addressed in this study using a novel deep-learning neural network framework to analyze the histopathological images. METHODS: We have developed a method based on the Inception V3 deep learning algorithm that complements other methods for predicting response to standard platinum-based therapy of the disease. For the study, we used histopathological H&E images (pre-treatment) of high-grade serous carcinoma from The Cancer Genome Atlas (TCGA) Genomic Data Commons portal to train the Inception V3 convolutional neural network system to predict whether cancers had independently been labeled as sensitive or resistant to subsequent platinum-based chemotherapy. The trained model was then tested using data from patients left out of the training process. We used receiver operating characteristic (ROC) and confusion matrix analyses to evaluate model performance and Kaplan-Meier survival analysis to correlate the predicted probability of resistance with patient outcome. Finally, occlusion sensitivity analysis was piloted as a start toward correlating histopathological features with a response. RESULTS: The study dataset consisted of 248 patients with stage 2 to 4 serous ovarian cancer. For a held-out test set of forty patients, the trained deep learning network model distinguished sensitive from resistant cancers with an area under the curve (AUC) of 0.846 ± 0.009 (SE). The probability of resistance calculated from the deep-learning network was also significantly correlated with patient survival and progression-free survival. In confusion matrix analysis, the network classifier achieved an overall predictive accuracy of 85% with a sensitivity of 73% and specificity of 90% for this cohort based on the Youden-J cut-off. Stage, grade, and patient age were not statistically significant for this cohort size. Occlusion sensitivity analysis suggested histopathological features learned by the network that may be associated with sensitivity or resistance to the chemotherapy, but multiple marker studies will be necessary to follow up on those preliminary results. CONCLUSIONS: This type of analysis has the potential, if further developed, to improve the prediction of response to therapy of high-grade serous ovarian cancer and perhaps be useful as a factor in deciding between platinum-based and other therapies. More broadly, it may increase our understanding of the histopathological variables that predict response and may be adaptable to other cancer types and imaging modalities.

SPOP Mutations Target STING1 Signaling in Prostate Cancer and Create Therapeutic Vulnerabilities to PARP Inhibitor-Induced Growth Suppression.

PURPOSE: Speckle-type POZ protein (SPOP) is important in DNA damage response (DDR) and maintenance of genomic stability. Somatic heterozygous missense mutations in the SPOP substrate-binding cleft are found in up to 15% of prostate cancers. While mutations in SPOP predict for benefit from androgen receptor signaling inhibition (ARSi) therapy, outcomes for patients with SPOP-mutant (SPOPmut) prostate cancer are heterogeneous and targeted treatments for SPOPmut castrate-resistant prostate cancer (CRPC) are lacking. EXPERIMENTAL DESIGN: Using in silico genomic and transcriptomic tumor data, proteomics analysis, and genetically modified cell line models, we demonstrate mechanistic links between SPOP mutations, STING signaling alterations, and PARP inhibitor vulnerabilities. RESULTS: We demonstrate that SPOP mutations are associated with upregulation of a 29-gene noncanonical (NC) STING (NC-STING) signature in a subset of SPOPmut, treatment-refractory CRPC patients. We show in preclinical CRPC models that SPOP targets and destabilizes STING1 protein, and prostate cancer-associated SPOP mutations result in upregulated NC-STING-NF-κB signaling and macrophage- and tumor microenvironment (TME)-facilitated reprogramming, leading to tumor cell growth. Importantly, we provide in vitro and in vivo mechanism-based evidence that PARP inhibitor (PARPi) treatment results in a shift from immunosuppressive NC-STING-NF-κB signaling to antitumor, canonical cGAS-STING-IFNß signaling in SPOPmut CRPC and results in enhanced tumor growth inhibition. CONCLUSIONS: We provide evidence that SPOP is critical in regulating immunosuppressive versus antitumor activity downstream of DNA damage-induced STING1 activation in prostate cancer. PARPi treatment of SPOPmut CRPC alters this NC-STING signaling toward canonical, antitumor cGAS-STING-IFNß signaling, highlighting a novel biomarker-informed treatment strategy for prostate cancer.

RPPA SPACE: an R package for normalization and quantitation of Reverse-Phase Protein Array data.

SUMMARY: Reverse-Phase Protein Array (RPPA) is a robust high-throughput, cost-effective platform for quantitatively measuring proteins in biological specimens. However, converting raw RPPA data into normalized, analysis-ready data remains a challenging task. Here, we present the RPPA SPACE (RPPA Superposition Analysis and Concentration Evaluation) R package, a substantially improved successor to SuperCurve, to meet that challenge. SuperCurve has been used to normalize over 170 000 samples to date. RPPA SPACE allows exclusion of poor-quality samples from the normalization process to improve the quality of the remaining samples. It also features a novel quality-control metric, 'noise', that estimates the level of random errors present in each RPPA slide. The noise metric can help to determine the quality and reliability of the data. In addition, RPPA SPACE has simpler input requirements and is more flexible than SuperCurve, it is much faster with greatly improved error reporting. AVAILABILITY AND IMPLEMENTATION: The standalone RPPA SPACE R package, tutorials and sample data are available via https://rppa.space/, CRAN (https://cran.r-project.org/web/packages/RPPASPACE/index.html) and GitHub (https://github.com/MD-Anderson-Bioinformatics/RPPASPACE). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fibroblast Growth Factor Receptor 1 Drives the Metastatic Progression of Prostate Cancer.

BACKGROUND: No curative therapy is currently available for metastatic prostate cancer (PCa). The diverse mechanisms of progression include fibroblast growth factor (FGF) axis activation. OBJECTIVE: To investigate the molecular and clinical implications of fibroblast growth factor receptor 1 (FGFR1) and its isoforms (α/ß) in the pathogenesis of PCa bone metastases. DESIGN, SETTING, AND PARTICIPANTS: In silico, in vitro, and in vivo preclinical approaches were used. RNA-sequencing and immunohistochemical (IHC) studies in human samples were conducted. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: In mice, bone metastases (chi-square/Fisher's test) and survival (Mantel-Cox) were assessed. In human samples, FGFR1 and ladinin 1 (LAD1) analysis associated with PCa progression were evaluated (IHC studies, Fisher's test). RESULTS AND LIMITATIONS: FGFR1 isoform expression varied among PCa subtypes. Intracardiac injection of mice with FGFR1-expressing PC3 cells reduced mouse survival (α, p < 0.0001; ß, p = 0.032) and increased the incidence of bone metastases (α, p < 0.0001; ß, p = 0.02). Accordingly, IHC studies of human castration-resistant PCa (CRPC) bone metastases revealed significant enrichment of FGFR1 expression compared with treatment-naïve, nonmetastatic primary tumors (p = 0.0007). Expression of anchoring filament protein LAD1 increased in FGFR1-expressing PC3 cells and was enriched in human CRPC bone metastases (p = 0.005). CONCLUSIONS: FGFR1 expression induces bone metastases experimentally and is significantly enriched in human CRPC bone metastases, supporting its prometastatic effect in PCa. LAD1 expression, found in the prometastatic PCa cells expressing FGFR1, was also enriched in CRPC bone metastases. Our studies support and provide a roadmap for the development of FGFR blockade for advanced PCa. PATIENT SUMMARY: We studied the role of fibroblast growth factor receptor 1 (FGFR1) in prostate cancer (PCa) progression. We found that PCa cells with high FGFR1 expression increase metastases and that FGFR1 expression is increased in human PCa bone metastases, and identified genes that could participate in the metastases induced by FGFR1. These studies will help pinpoint PCa patients who use fibroblast growth factor to progress and will benefit by the inhibition of this pathway.

ATR Inhibition Induces CDK1-SPOP Signaling and Enhances Anti-PD-L1 Cytotoxicity in Prostate Cancer.

PURPOSE: Despite significant benefit for other cancer subtypes, immune checkpoint blockade (ICB) therapy has not yet been shown to significantly improve outcomes for men with castration-resistant prostate cancer (CRPC). Prior data have shown that DNA damage response (DDR) deficiency, via genetic alteration and/or pharmacologic induction using DDR inhibitors (DDRi), may improve ICB response in solid tumors in part due to induction of mitotic catastrophe and innate immune activation. Discerning the underlying mechanisms of this DDRi-ICB interaction in a prostate cancer-specific manner is vital to guide novel clinical trials and provide durable clinical responses for men with CRPC. EXPERIMENTAL DESIGN: We treated prostate cancer cell lines with potent, specific inhibitors of ATR kinase, as well as with PARP inhibitor, olaparib. We performed analyses of cGAS-STING and DDR signaling in treated cells, and treated a syngeneic androgen-indifferent, prostate cancer model with combined ATR inhibition and anti-programmed death ligand 1 (anti-PD-L1), and performed single-cell RNA sequencing analysis in treated tumors. RESULTS: ATR inhibitor (ATRi; BAY1895433) directly repressed ATR-CHK1 signaling, activated CDK1-SPOP axis, leading to destabilization of PD-L1 protein. These effects of ATRi are distinct from those of olaparib, and resulted in a cGAS-STING-initiated, IFN-ß-mediated, autocrine, apoptotic response in CRPC. The combination of ATRi with anti-PD-L1 therapy resulted in robust innate immune activation and a synergistic, T-cell-dependent therapeutic response in our syngeneic mouse model. CONCLUSIONS: This work provides a molecular mechanistic rationale for combining ATR-targeted agents with immune checkpoint blockade for patients with CRPC. Multiple early-phase clinical trials of this combination are underway.

PARP and CDK4/6 Inhibitor Combination Therapy Induces Apoptosis and Suppresses Neuroendocrine Differentiation in Prostate Cancer.

We analyzed the efficacy and mechanistic interactions of PARP inhibition (PARPi; olaparib) and CDK4/6 inhibition (CDK4/6i; palbociclib or abemaciclib) combination therapy in castration-resistant prostate cancer (CRPC) and neuroendocrine prostate cancer (NEPC) models. We demonstrated that combined olaparib and palbociblib or abemaciclib treatment resulted in synergistic suppression of the p-Rb1-E2F1 signaling axis at the transcriptional and posttranslational levels, leading to disruption of cell-cycle progression and inhibition of E2F1 gene targets, including genes involved in DDR signaling/damage repair, antiapoptotic BCL-2 family members (BCL-2 and MCL-1), CDK1, and neuroendocrine differentiation (NED) markers in vitro and in vivo In addition, olaparib + palbociclib or olaparib + abemaciclib combination treatment resulted in significantly greater growth inhibition and apoptosis than either single agent alone. We further showed that PARPi and CDK4/6i combination treatment-induced CDK1 inhibition suppressed p-S70-BCL-2 and increased caspase cleavage, while CDK1 overexpression effectively prevented the downregulation of p-S70-BCL-2 and largely rescued the combination treatment-induced cytotoxicity. Our study defines a novel combination treatment strategy for CRPC and NEPC and demonstrates that combination PARPi and CDK4/6i synergistically promotes suppression of the p-Rb1-E2F1 axis and E2F1 target genes, including CDK1 and NED proteins, leading to growth inhibition and increased apoptosis in vitro and in vivo Taken together, our results provide a molecular rationale for PARPi and CDK4/6i combination therapy and reveal mechanism-based clinical trial opportunities for men with NEPC.

Methylation-eQTL analysis in cancer research.

MOTIVATION: DNA methylation is a key epigenetic factor regulating gene expression. While promoter methylation has been well studied, recent publications have revealed that functionally important methylation also occurs in intergenic and distal regions, and varies across genes and tissue types. Given the growing importance of inter-platform integrative genomic analyses, there is an urgent need to develop methods to discover and characterize gene-level relationships between methylation and expression. RESULTS: We introduce a novel sequential penalized regression approach to identify methylation-expression quantitative trait loci (methyl-eQTLs), a term that we have coined to represent, for each gene and tissue type, a sparse set of CpG loci best explaining gene expression and accompanying weights indicating direction and strength of association. Using TCGA and MD Anderson colorectal cohorts to build and validate our models, we demonstrate our strategy better explains expression variability than current commonly used gene-level methylation summaries. The methyl-eQTLs identified by our approach can be used to construct gene-level methylation summaries that are maximally correlated with gene expression for use in integrative models, and produce a tissue-specific summary of which genes appear to be strongly regulated by methylation. Our results introduce an important resource to the biomedical community for integrative genomics analyses involving DNA methylation. AVAILABILITY AND IMPLEMENTATION: We produce an R Shiny app (https://rstudio-prd-c1.pmacs.upenn.edu/methyl-eQTL/) that interactively presents methyl-eQTL results for colorectal, breast and pancreatic cancer. The source R code for this work is provided in the Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development and Validation of a Gene Signature Classifier for Consensus Molecular Subtyping of Colorectal Carcinoma in a CLIA-Certified Setting.

PURPOSE: Consensus molecular subtyping (CMS) of colorectal cancer has potential to reshape the colorectal cancer landscape. We developed and validated an assay that is applicable on formalin-fixed, paraffin-embedded (FFPE) samples of colorectal cancer and implemented the assay in a Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory. EXPERIMENTAL DESIGN: We performed an in silico experiment to build an optimal CMS classifier using a training set of 1,329 samples from 12 studies and validation set of 1,329 samples from 14 studies. We constructed an assay on the basis of NanoString CodeSets for the top 472 genes, and performed analyses on paired flash-frozen (FF)/FFPE samples from 175 colorectal cancers to adapt the classifier to FFPE samples using a subset of genes found to be concordant between FF and FFPE, tested the classifier's reproducibility and repeatability, and validated in a CLIA-certified laboratory. We assessed prognostic significance of CMS in 345 patients pooled across three clinical trials. RESULTS: The best classifier was weighted support vector machine with high accuracy across platforms and gene lists (>0.95), and the 472-gene model outperforming existing classifiers. We constructed subsets of 99 and 200 genes with high FF/FFPE concordance, and adapted FFPE-based classifier that had strong classification accuracy (>80%) relative to "gold standard" CMS. The classifier was reproducible to sample type and RNA quality, and demonstrated poor prognosis for CMS1-3 and good prognosis for CMS2 in metastatic colorectal cancer (P < 0.001). CONCLUSIONS: We developed and validated a colorectal cancer CMS assay that is ready for use in clinical trials, to assess prognosis in standard-of-care settings and explore as predictor of therapy response.

Downregulation of 15-hydroxyprostaglandin dehydrogenase during acquired tamoxifen resistance and association with poor prognosis in ERα-positive breast cancer.

AIM: Tamoxifen (TAM) resistance remains a clinical issue in breast cancer. The authors previously reported that 15-hydroxyprostaglandin dehydrogenase (HPGD) was significantly downregulated in tamoxifen-resistant (TAMr) breast cancer cell lines. Here, the authors investigated the relationship between HPGD expression, TAM resistance and prediction of outcome in breast cancer. METHODS: HPGD overexpression and silencing studies were performed in isogenic TAMr and parental human breast cancer cell lines to establish the impact of HPGD expression on TAM resistance. HPGD expression and clinical outcome relationships were explored using immunohistochemistry and in silico analysis. RESULTS: Restoration of HPGD expression and activity sensitised TAMr MCF-7 cells to TAM and 17ß-oestradiol, whilst HPGD silencing in parental MCF-7 cells reduced TAM sensitivity. TAMr cells released more prostaglandin E2 (PGE2) than controls, which was reduced in TAMr cells stably transfected with HPGD. Exogenous PGE2 signalled through the EP4 receptor to reduce breast cancer cell sensitivity to TAM. Decreased HPGD expression was associated with decreased overall survival in ERα-positive breast cancer patients. CONCLUSIONS: HPGD downregulation in breast cancer is associated with reduced response to TAM therapy via PGE2-EP4 signalling and decreases patient survival. The data offer a potential target to develop combination therapies that may overcome acquired tamoxifen resistance.

FuncPEP: A Database of Functional Peptides Encoded by Non-Coding RNAs.

Non-coding RNAs (ncRNAs) are essential players in many cellular processes, from normal development to oncogenic transformation. Initially, ncRNAs were defined as transcripts that lacked an open reading frame (ORF). However, multiple lines of evidence suggest that certain ncRNAs encode small peptides of less than 100 amino acids. The sequences encoding these peptides are known as small open reading frames (smORFs), many initiating with the traditional AUG start codon but terminating with atypical stop codons, suggesting a different biogenesis. The ncRNA-encoded peptides (ncPEPs) are gradually becoming appreciated as a new class of functional molecules that contribute to diverse cellular processes, and are deregulated in different diseases contributing to pathogenesis. As multiple publications have identified unique ncPEPs, we appreciated the need for assembling a new web resource that could gather information about these functional ncPEPs. We developed FuncPEP, a new database of functional ncRNA encoded peptides, containing all experimentally validated and functionally characterized ncPEPs. Currently, FuncPEP includes a comprehensive annotation of 112 functional ncPEPs and specific details regarding the ncRNA transcripts that encode these peptides. We believe that FuncPEP will serve as a platform for further deciphering the biologic significance and medical use of ncPEPs.

The MD Anderson Prostate Cancer Patient-derived Xenograft Series (MDA PCa PDX) Captures the Molecular Landscape of Prostate Cancer and Facilitates Marker-driven Therapy Development.

PURPOSE: Advances in prostate cancer lag behind other tumor types partly due to the paucity of models reflecting key milestones in prostate cancer progression. Therefore, we develop clinically relevant prostate cancer models. EXPERIMENTAL DESIGN: Since 1996, we have generated clinically annotated patient-derived xenografts (PDXs; the MDA PCa PDX series) linked to specific phenotypes reflecting all aspects of clinical prostate cancer. RESULTS: We studied two cell line-derived xenografts and the first 80 PDXs derived from 47 human prostate cancer donors. Of these, 47 PDXs derived from 22 donors are working models and can be expanded either as cell lines (MDA PCa 2a and 2b) or PDXs. The histopathologic, genomic, and molecular characteristics (androgen receptor, ERG, and PTEN loss) maintain fidelity with the human tumor and correlate with published findings. PDX growth response to mouse castration and targeted therapy illustrate their clinical utility. Comparative genomic hybridization and sequencing show significant differences in oncogenic pathways in pairs of PDXs derived from different areas of the same tumor. We also identified a recurrent focal deletion in an area that includes the speckle-type POZ protein-like (SPOPL) gene in PDXs derived from seven human donors of 28 studied (25%). SPOPL is a SPOP paralog, and SPOP mutations define a molecular subclass of prostate cancer. SPOPL deletions are found in 7% of The Cancer Genome Atlas prostate cancers, which suggests that our cohort is a reliable platform for targeted drug development. CONCLUSIONS: The MDA PCa PDX series is a dynamic resource that captures the molecular landscape of prostate cancers progressing under novel treatments and enables optimization of prostate cancer-specific, marker-driven therapy.

The Structure-Function Relationship of Angular Estrogens and Estrogen Receptor Alpha to Initiate Estrogen-Induced Apoptosis in Breast Cancer Cells.

High-dose synthetic estrogen therapy was the standard treatment of advanced breast cancer for three decades until the discovery of tamoxifen. A range of substituted triphenylethylene synthetic estrogens and diethylstilbestrol were used. It is now known that low doses of estrogens can cause apoptosis in long-term estrogen deprived (LTED) breast cancer cells resistant to antiestrogens. This action of estrogen can explain the reduced breast cancer incidence in postmenopausal women over 60 who are taking conjugated equine estrogens and the beneficial effect of low-dose estrogen treatment of patients with acquired aromatase inhibitor resistance in clinical trials. To decipher the molecular mechanism of estrogens at the estrogen receptor (ER) complex by different types of estrogens-planar [17ß-estradiol (E2)] and angular triphenylethylene (TPE) derivatives-we have synthesized a small series of compounds with either no substitutions on the TPE phenyl ring containing the antiestrogenic side chain of endoxifen or a free hydroxyl. In the first week of treatment with E2 the LTED cells undergo apoptosis completely. By contrast, the test TPE derivatives act as antiestrogens with a free para-hydroxyl on the phenyl ring that contains an antiestrogenic side chain in endoxifen. This inhibits early E2-induced apoptosis if a free hydroxyl is present. No substitution at the site occupied by the antiestrogenic side chain of endoxifen results in early apoptosis similar to planar E2 The TPE compounds recruit coregulators to the ER differentially and predictably, leading to delayed apoptosis in these cells. SIGNIFICANCE STATEMENT: In this paper we investigate the role of the structure-function relationship of a panel of synthetic triphenylethylene (TPE) derivatives and a novel mechanism of estrogen-induced cell death in breast cancer, which is now clinically relevant. Our study indicates that these TPE derivatives, depending on the positioning of the hydroxyl groups, induce various conformations of the estrogen receptor's ligand-binding domain, which in turn produces differential recruitment of coregulators and subsequently different apoptotic effects on the antiestrogen-resistant breast cancer cells.

Back to the Colorectal Cancer Consensus Molecular Subtype Future.

PURPOSE OF REVIEW: This review seeks to provide an informed prospective on the advances in molecular profiling and analysis of colorectal cancer (CRC). The goal is to provide a historical context and current summary on how advances in gene and protein sequencing technology along with computer capabilities led to our current bioinformatic advances in the field. RECENT FINDINGS: An explosion of knowledge has occurred regarding genetic, epigenetic, and biochemical alterations associated with the evolution of colorectal cancer. This has led to the realization that CRC is a heterogeneous disease with molecular alterations often dictating natural history, response to treatment, and outcome. The consensus molecular subtypes (CMS) classification classifies CRC into four molecular subtypes with distinct biological characteristics, which may form the basis for clinical stratification and subtype-based targeted intervention. This review summarizes new developments of a field moving "Back to the Future." CRC molecular subtyping will better identify key subtype specific therapeutic targets and responses to therapy.

Interactive Clustered Heat Map Builder: An easy web-based tool for creating sophisticated clustered heat maps.

Clustered heat maps are the most frequently used graphics for visualization and interpretation of genome-scale molecular profiling data in biology.  Construction of a heat map generally requires the assistance of a biostatistician or bioinformatics analyst capable of working in R or a similar programming language to transform the study data, perform hierarchical clustering, and generate the heat map.  Our web-based Interactive Heat Map Builder can be used by investigators with no bioinformatics experience to generate high-caliber, publication quality maps.  Preparation of the data and construction of a heat map is rarely a simple linear process.  Our tool allows a user to move back and forth iteratively through the various stages of map generation to try different options and approaches.  Finally, the heat map the builder creates is available in several forms, including an interactive Next-Generation Clustered Heat Map that can be explored dynamically to investigate the results more fully.

Targeting the MYCN-PARP-DNA Damage Response Pathway in Neuroendocrine Prostate Cancer.

Purpose: We investigated MYCN-regulated molecular pathways in castration-resistant prostate cancer (CRPC) classified by morphologic criteria as adenocarcinoma or neuroendocrine to extend the molecular phenotype, establish driver pathways, and identify novel approaches to combination therapy for neuroendocrine prostate cancer (NEPC).Experimental Design and Results: Using comparative bioinformatics analyses of CRPC-Adeno and CRPC-Neuro RNA sequence data from public data sets and a panel of 28 PDX models, we identified a MYCN-PARP-DNA damage response (DDR) pathway that is enriched in CRPC with neuroendocrine differentiation (NED) and CRPC-Neuro. ChIP-PCR assay revealed that N-MYC transcriptionally activates PARP1, PARP2, BRCA1, RMI2, and TOPBP1 through binding to the promoters of these genes. MYCN or PARP1 gene knockdown significantly reduced the expression of MYCN-PARP-DDR pathway genes and NED markers, and inhibition with MYCNsi and/or PARPsi, BRCA1si, or RMI2si significantly suppressed malignant activities, including cell viability, colony formation, and cell migration, in C4-2b4 and NCI-H660 cells. Targeting this pathway with AURKA inhibitor PHA739358 and PARP inhibitor olaparib generated therapeutic effects similar to those of gene knockdown in vitro and significantly suppressed tumor growth in both C4-2b4 and MDACC PDX144-13C subcutaneous models in vivoConclusions: Our results identify a novel MYCN-PARP-DDR pathway that is driven by N-MYC in a subset of CRPC-Adeno and in NEPC. Targeting this pathway using in vitro and in vivo CRPC-Adeno and CRPC-Neuro models demonstrated a novel therapeutic strategy for NEPC. Further investigation of N-MYC-regulated DDR gene targets and the biological and clinical significance of MYCN-PARP-DDR signaling will more fully elucidate the importance of the MYCN-PARP-DDR signaling pathway in the development and maintenance of NEPC. Clin Cancer Res; 24(3); 696-707. ©2017 AACR.

A Galaxy Implementation of Next-Generation Clustered Heatmaps for Interactive Exploration of Molecular Profiling Data.

Clustered heatmaps are the most frequently used graphics for visualization of molecular profiling data in biology. However, they are generally rendered as static, or only modestly interactive, images. We have now used recent advances in web technologies to produce interactive "next-generation" clustered heatmaps (NG-CHM) that enable extreme zooming and navigation without loss of resolution. NG-CHMs also provide link-outs to additional information sources and include other features that facilitate deep exploration of the biology behind the image. Here, we describe an implementation of the NG-CHM system in the Galaxy bioinformatics platform. We illustrate the algorithm and available computational tool using RNA-seq data from The Cancer Genome Atlas program's Kidney Clear Cell Carcinoma project. Cancer Res; 77(21); e23-26. ©2017 AACR.