Abstract While numerous anti-angiogenic and immune targeting therapies have become standard-of-care treatments for oncology, predictive biomarkers for these agents have been either entirely lacking or challenged by inconsistencies across indications. We have developed and validated the Xerna TME Panel as a novel machine learning-based RNA-sequencing biomarker assay that guides patient selection for tumor microenvironment (TME)-targeted therapies across multiple tumor types. Gene expression data sets from both public sources and clinical practice representing over 5000 samples across 7 different tumor types were analyzed using the Xerna TME Panel. The Xerna TME Panel consists of an artificial neural net that learns complex gene expression interactions between angiogenesis and tumor immune biologies and robustly classifies patient samples into one of four TME biomarker subtypes: Angiogenesis (A), Immune Suppressed (IS), Immune Active (IA), or Immune Desert (ID). The vast majority (>75%) of all samples were assigned a TME class designation with confidence scores in the upper quartile and had nearly bimodal distributions for biomarker-positive versus -negative classifications. When compared to other independent gene signatures, such as those describing angiogenesis/mesenchymal biology, inflammation, and immune suppression, the expression profiles from the Xerna TME subtypes showed enrichment of those biological processes. Each TME subtype represented between ~15-40% of subjects of each tumor type, indicating balanced representation of subgroups within the patient populations. The Xerna TME designations were prognostic across tumor types, with “A” tumors generally associated with the worst survival and “IA” tumors associated with the best survival. The predictive ability of the Xerna TME Panel to enrich for tumor responses to targeted therapies in gastric cancer was also evaluated. In a ramucirumab+paclitaxel clinical cohort, the Xerna TME Panel high Angiogenesis score tumors (A and IS) demonstrated a 48% response rate compared to a 31% for low Angiogenesis score tumors (IA and ID). In an immune checkpoint inhibitor (ICI) cohort, high Immune score tumors (IA and IS) showed a response rate of 34% vs. 5% for low Immune score tumors (A and ID). Within the microsatellite stable patients (MSS), which historically have low response rates to ICIs, the Xerna TME Panel was able to enrich for responses between Immune high vs. Immune low score patients (25% vs. 3%). Currently in use to prospectively enroll patients into a Phase 3 ovarian cancer clinical trial and in development as a companion diagnostic (CDx) assay, the Xerna TME Panel is a robust, pan-cancer biomarker assay capable of characterizing TME dominant biologies to further advance the matching of patients with targeted therapeutics. Citation Format: Seema Iyer, Luka Ausec, Daniel Pointing, Matjaz Zganec, Robert Cvitkovic, Miha Stajdohar, Valerie Chamberlain Santos, Kerry Culm, Mokenge Malafa, Jeeyun Lee, Rafael Rosengarten, Laura Benjamin, Mark T. Uhlik. Xerna࣪ TME Panel: A pan-cancer RNA-based investigational assay designed to predict patient responses to angiogenic and immune targeted therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1232.
Development of the soil amoeba Dictyostelium discoideum is triggered by starvation. When placed on a solid substrate, the starving solitary amoebae cease growth, communicate via extracellular cAMP, aggregate by tens of thousands and develop into multicellular organisms. Early phases of the developmental program are often studied in cells starved in suspension while cAMP is provided exogenously. Previous studies revealed massive shifts in the transcriptome under both developmental conditions and a close relationship between gene expression and morphogenesis, but were limited by the sampling frequency and the resolution of the methods. Here, we combine the superior depth and specificity of RNA-seq-based analysis of mRNA abundance with high frequency sampling during filter development and cAMP pulsing in suspension. We found that the developmental transcriptome exhibits mostly gradual changes interspersed by a few instances of large shifts. For each time point we treated the entire transcriptome as single phenotype, and were able to characterize development as groups of similar time points separated by gaps. The grouped time points represented gradual changes in mRNA abundance, or molecular phenotype, and the gaps represented times during which many genes are differentially expressed rapidly, and thus the phenotype changes dramatically. Comparing developmental experiments revealed that gene expression in filter developed cells lagged behind those treated with exogenous cAMP in suspension. The high sampling frequency revealed many genes whose regulation is reproducibly more complex than indicated by previous studies. Gene Ontology enrichment analysis suggested that the transition to multicellularity coincided with rapid accumulation of transcripts associated with DNA processes and mitosis. Later development included the up-regulation of organic signaling molecules and co-factor biosynthesis. Our analysis also demonstrated a high level of synchrony among the developing structures throughout development. Our data describe D. discoideum development as a series of coordinated cellular and multicellular activities. Coordination occurred within fields of aggregating cells and among multicellular bodies, such as mounds or migratory slugs that experience both cell-cell contact and various soluble signaling regimes. These time courses, sampled at the highest temporal resolution to date in this system, provide a comprehensive resource for studies of developmental gene expression.
Abstract Background Mitochondrial genomes (mtDNA) of numerous sponges have been sequenced as part of an ongoing effort to resolve the class-level phylogeny of the Porifera, as well as to place the various lower metazoan groups on the animal-kingdom tree. Most recently, the partial mtDNA of two glass sponges, class Hexactinellida, were reported. While previous phylogenetic estimations based on these data remain uncertain due to insufficient taxon sampling and accelerated rates of evolution, the mtDNA molecules themselves reveal interesting traits that may be unique to hexactinellids. Here we determined the first complete mitochondrial genome of a hexactinellid sponge, Aphrocallistes vastus , and compared it to published poriferan mtDNAs to further describe characteristics specific to hexactinellid and other sponge mitochondrial genomes. Results The A. vastus mtDNA consisted of a 17,427 base pair circular molecule containing thirteen protein-coding genes, divergent large and small subunit ribosomal RNAs, and a reduced set of 18 tRNAs. The A. vastus mtDNA showed a typical hexactinellid nucleotide composition and shared a large synteny with the other sequenced glass sponge mtDNAs. It also contained an unidentified open reading frame and large intergenic space region. Two frameshifts, in the cox3 and nad6 genes, were not corrected by RNA editing, but rather possessed identical shift sites marked by the extremely rare tryptophan codon (UGG) followed by the common glycine codon (GGA) in the +1 frame. Conclusion Hexactinellid mtDNAs have shown similar trends in gene content, nucleotide composition, and codon usage, and have retained a large gene syntenty. Analysis of the mtDNA of A. vastus has provided evidence diagnostic for +1 programmed translational frameshifting, a phenomenon disparately reported throughout the animal kingdom, but present in the hexactinellid mtDNAs that have been sequenced to date.
Abstract Breakthroughs in targeted KRAS therapeutics (KRASi) have the potential to transform the treatment landscape for several of the most common cancers including lung, colorectal, and pancreatic. Despite the recent approvals of KRASi and the anticipation of more to come, both the rate of patient response and the durability of these responses remain significant areas requiring improvement. Biomarkers that can predict response to KRASi and guide effective patient selection and drug combination strategies will be key to realizing the full potential of this emerging therapeutic field. While most biomarkers predominantly rely on a single analyte (e.g. KRAS mutation status), Genialis’ biomarkers are constructed using high-dimensional and/or multimodal data that capture the underlying biological complexity unique to each individual patient. Genialis' ResponderID™ is a machine learning-based biomarker discovery framework that models fundamental aspects of cancer biology to predict the clinical benefit based on the patient’s own biology. Here we report progress towards the development of a first-in-class, RNA-based biomarker, ResponderID™ KRAS, capable of stratifying KRAS G12C inhibitor response in lung cancer patients using RNA sequencing data. Trained on thousands of lung cancer samples, our biomarker models therapeutic response by unifying two core KRAS biologic axes, dependency and activation, to identify those patients most likely to respond. The performance characteristics of ResponderID™ KRAS thus far has been evaluated on a real world dataset of lung cancer patients treated with Sotorasib. ResponderID™ KRAS serves as an independent biomarker designed to inform clinical trial design, select for therapeutic efficacy, identify rational combination strategies, and expedite approvals across various therapeutic contexts. Citation Format: Josh Wheeler, Anže Lovše, Klemen Žiberna, Miha Štajdohar, Luka Ausec, Janez Kokošar, Daniel Pointing, Aditya Pai, Rafael Rosengarten, Mark Uhlik. ResponderID™ KRAS: Biology-driven machine learning to personalize KRAS inhibitor therapeutics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6446.
Dictyostelium discoideum, a soil-dwelling social amoeba, is a model for the study of numerous biological processes. Research in the field has benefited mightily from the adoption of next-generation sequencing for genomics and transcriptomics. Dictyostelium biologists now face the widespread challenges of analyzing and exploring high dimensional data sets to generate hypotheses and discovering novel insights. We present dictyExpress (2.0), a web application designed for exploratory analysis of gene expression data, as well as data from related experiments such as Chromatin Immunoprecipitation sequencing (ChIP-Seq). The application features visualization modules that include time course expression profiles, clustering, gene ontology enrichment analysis, differential expression analysis and comparison of experiments. All visualizations are interactive and interconnected, such that the selection of genes in one module propagates instantly to visualizations in other modules. dictyExpress currently stores the data from over 800 Dictyostelium experiments and is embedded within a general-purpose software framework for management of next-generation sequencing data. dictyExpress allows users to explore their data in a broader context by reciprocal linking with dictyBase—a repository of Dictyostelium genomic data. In addition, we introduce a companion application called GenBoard, an intuitive graphic user interface for data management and bioinformatics analysis. dictyExpress and GenBoard enable broad adoption of next generation sequencing based inquiries by the Dictyostelium research community. Labs without the means to undertake deep sequencing projects can mine the data available to the public. The entire information flow, from raw sequence data to hypothesis testing, can be accomplished in an efficient workspace. The software framework is generalizable and represents a useful approach for any research community. To encourage more wide usage, the backend is open-source, available for extension and further development by bioinformaticians and data scientists.
Recent advances in Synthetic Biology have yielded standardized and automatable DNA assembly protocols that enable a broad range of biotechnological research and development. Unfortunately, the experimental design required for modern scar-less multipart DNA assembly methods is frequently laborious, time-consuming, and error-prone. Here, we report the development and deployment of a web-based software tool, j5, which automates the design of scar-less multipart DNA assembly protocols including SLIC, Gibson, CPEC, and Golden Gate. The key innovations of the j5 design process include cost optimization, leveraging DNA synthesis when cost-effective to do so, the enforcement of design specification rules, hierarchical assembly strategies to mitigate likely assembly errors, and the instruction of manual or automated construction of scar-less combinatorial DNA libraries. Using a GFP expression testbed, we demonstrate that j5 designs can be executed with the SLIC, Gibson, or CPEC assembly methods, used to build combinatorial libraries with the Golden Gate assembly method, and applied to the preparation of linear gene deletion cassettes for E. coli. The DNA assembly design algorithms reported here are generally applicable to broad classes of DNA construction methodologies and could be implemented to supplement other DNA assembly design tools. Taken together, these innovations save researchers time and effort, reduce the frequency of user design errors and off-target assembly products, decrease research costs, and enable scar-less multipart and combinatorial DNA construction at scales unfeasible without computer-aided design.
The Asia-Pacific Hematology Consortium (APHCON), in partnership with MDRingTM, a mobile global physician education network, has initiated a detailed longitudinal study of physician knowledge and practice preferences in the Asia-Pacific sphere. The first dataset comes from a series of surveys answered by delegates at the APHCON Bridging The Gap (BTG) conference in Beijing in January, 2015. In this report we present our findings regarding diagnosis and treatment of multiple myeloma (MM). We aim to create a conduit for physicians in this region to share their experiences with the rest of the world, to identify areas of consensus and best practices, and to highlight opportunities for improvement in communication, education and patient care.
Introduction Most predictive biomarkers approved for clinical use measure single analytes such as genetic alteration or protein overexpression. We developed and validated a novel biomarker with the aim of achieving broad clinical utility. The Xerna™ TME Panel is a pan-tumor, RNA expression-based classifier, designed to predict response to multiple tumor microenvironment (TME)-targeted therapies, including immunotherapies and anti-angiogenic agents. Methods The Panel algorithm is an artificial neural network (ANN) trained with an input signature of 124 genes that was optimized across various solid tumors. From the 298-patient training data, the model learned to discriminate four TME subtypes: Angiogenic (A), Immune Active (IA), Immune Desert (ID), and Immune Suppressed (IS). The final classifier was evaluated in four independent clinical cohorts to test whether TME subtype could predict response to anti-angiogenic agents and immunotherapies across gastric, ovarian, and melanoma datasets. Results The TME subtypes represent stromal phenotypes defined by angiogenesis and immune biological axes. The model yields clear boundaries between biomarker-positive and -negative and showed 1.6-to-7-fold enrichment of clinical benefit for multiple therapeutic hypotheses. The Panel performed better across all criteria compared to a null model for gastric and ovarian anti-angiogenic datasets. It also outperformed PD-L1 combined positive score (>1) in accuracy, specificity, and positive predictive value (PPV), and microsatellite-instability high (MSI-H) in sensitivity and negative predictive value (NPV) for the gastric immunotherapy cohort. Discussion The TME Panel’s strong performance on diverse datasets suggests it may be amenable for use as a clinical diagnostic for varied cancer types and therapeutic modalities.
Abstract The most utilized targeted therapies in colorectal cancer (CRC) are focused on EGFR inhibition and anti-angiogenesis. In the ~5% of patients with microsatellite instability (MSI-H) or high tumor mutational burden (TMB), checkpoint inhibitors (CPIs) have been approved. Oncxerna has developed an RNA expression-based approach to characterize the ‘dominant' biology of a patient's tumor microenvironment with the diagnostic hypothesis to prospectively pair those patients with therapies and known mechanism of action that directly target these biologies. We developed an RNA-based gene expression panel (TME Panel-1) and machine learning (ML) algorithms to prospectively predict a patient's response to anti-angiogenesis or immune modulators, such as CPIs. In this study, we explore the potential of the TME Panel-1 to identify dominant biologies present in colorectal cancer specimens procured from the Wood-Hudson Cancer Research Lab. Total RNA expression counts from FFPE slides were analyzed with the ML algorithms and used to assign each sample into one of four subgroups. The respective prevalence of the subgroups are similar to those observed in gastric cancer and ovarian cancer samples, suggesting that the TME-Panel 1 has potential to be used to develop pan-tumor diagnostics. We will present these results, correlations with clinical outcomes and other relevant biomarkers for CRC. In summary, we conclude that RNA-based descriptors of biology may be a useful approach to enrich for better response to targeted therapies whose mechanism of action is to modify the TME biology. Citation Format: Kristen Strand-Tibbitts, Kerry Culm-Merdek, Valerie Chamberlain Santps, Laura Benjamin, Julia Carter, Larry Douglass, Roman Luštrik, Robert Cvitkovič, Luka Ausec, Rafael Rosengarten. Development of an RNA based diagnostic panel to the tumor microenvironment to match cancer therapies for colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 348.
The evolution of ANTP genes in the Metazoa has been the subject of conflicting hypotheses derived from full or partial gene sequences and genomic organization in higher animals. Whole genome sequences have recently filled in some crucial gaps for the basal metazoan phyla Cnidaria and Porifera. Here we analyze the complete genome of Trichoplax adhaerens, representing the basal metazoan phylum Placozoa, for its set of ANTP class genes. The Trichoplax genome encodes representatives of Hox/ParaHox-like, NKL, and extended Hox genes. This repertoire possibly mirrors the condition of a hypothetical cnidarian-bilaterian ancestor. The evolution of the cnidarian and bilaterian ANTP gene repertoires can be deduced by a limited number of cis-duplications of NKL and "extended Hox" genes and the presence of a single ancestral "ProtoHox" gene.
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