The next horizon in precision oncology: Proteogenomics to inform cancer diagnosis and treatment.

When it comes to precision oncology, proteogenomics may provide better prospects to the clinical characterization of tumors, help make a more accurate diagnosis of cancer, and improve treatment for patients with cancer. This perspective describes the significant contributions of The Cancer Genome Atlas and the Clinical Proteomic Tumor Analysis Consortium to precision oncology and makes the case that proteogenomics needs to be fully integrated into clinical trials and patient care in order for precision oncology to deliver the right cancer treatment to the right patient at the right dose and at the right time.

The Cancer Genome Atlas: Creating Lasting Value beyond Its Data.

The Cancer Genome Atlas (TCGA) team now presents the Pan-Cancer Atlas, investigating different aspects of cancer biology by analyzing the data generated during the 10+ years of the TCGA project.

SnapShot: TCGA-Analyzed Tumors.

This SnapShot provides a list of the tumor types characterized by The Cancer Genome Atlas (TCGA) program. Key findings shown are the most relevant discoveries described in each marker paper for the tumor type.

Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics.

The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing.

Core Clinical Data Elements for Cancer Genomic Repositories: A Multi-stakeholder Consensus.

The Center for Medical Technology Policy and the Molecular Evidence Development Consortium gathered a diverse group of more than 50 stakeholders to develop consensus on a core set of data elements and values essential to understanding the clinical utility of molecularly targeted therapies in oncology.

The International Collaboration for Cancer Classification and Research.

Gaps in the translation of research findings to clinical management have been recognized for decades. They exist for the diagnosis as well as the management of cancer. The international standards for cancer diagnosis are contained within the World Health Organization (WHO) Classification of Tumours, published by the International Agency for Research on Cancer (IARC) and known worldwide as the WHO Blue Books. In addition to their relevance to individual patients, these volumes provide a valuable contribution to cancer research and surveillance, fulfilling an important role in scientific evidence synthesis and international standard setting. However, the multidimensional nature of cancer classification, the way in which the WHO Classification of Tumours is constructed, and the scientific information overload in the field pose important challenges for the translation of research findings to tumour classification and hence cancer diagnosis. To help address these challenges, we have established the International Collaboration for Cancer Classification and Research (IC3 R) to provide a forum for the coordination of efforts in evidence generation, standard setting and best practice recommendations in the field of tumour classification. The first IC3 R meeting, held in Lyon, France, in February 2019, gathered representatives of major institutions involved in tumour classification and related fields to identify and discuss translational challenges in data comparability, standard setting, quality management, evidence evaluation and copyright, as well as to develop a collaborative plan for addressing these challenges.

Epigenetic-mediated dysfunction of the bone morphogenetic protein pathway inhibits differentiation of glioblastoma-initiating cells.

Despite similarities between tumor-initiating cells with stem-like properties (TICs) and normal neural stem cells, we hypothesized that there may be differences in their differentiation potentials. We now demonstrate that both bone morphogenetic protein (BMP)-mediated and ciliary neurotrophic factor (CNTF)-mediated Jak/STAT-dependent astroglial differentiation is impaired due to EZH2-dependent epigenetic silencing of BMP receptor 1B (BMPR1B) in a subset of glioblastoma TICs. Forced expression of BMPR1B either by transgene expression or demethylation of the promoter restores their differentiation capabilities and induces loss of their tumorigenicity. We propose that deregulation of the BMP developmental pathway in a subset of glioblastoma TICs contributes to their tumorigenicity both by desensitizing TICs to normal differentiation cues and by converting otherwise cytostatic signals to proproliferative signals.

Heme oxygenase-1 expression in human gliomas and its correlation with poor prognosis in patients with astrocytoma.

In human glioma tumors, heme oxygenase-1 (HO-1) has been shown to be upregulated both when compared with normal brain tissues and also during oligodendroglioma progression. The cell types that express HO-1 have been shown to be mainly macrophages/microglia and T cells. However, many other reports also demonstrated that cell lines derived from glioma tumors and astrocytes express HO-1 after the occurrence of a wide variety of cell injuries and stressors. In addition, the significance of HO-1 upregulation in glioma had not, so far, been addressed. We therefore aimed at investigating the expression and significance of HO-1 in human glial tumors. For this purpose, we performed a wide screening of HO-1 expression in gliomas by using tissue microarrays containing astrocytomas, oligodendrogliomas, mixed tumors, and normal brain tissues. We subsequently correlated protein expression with patient clinicopathological data. We found differences in HO-1 positivity rates between non-malignant brain (22 %) and gliomas (54%, p = 0.01). HO-1 was expressed by tumor cells and showed cytoplasmic localization, although 19% of tumor samples also depicted nuclear staining. Importantly, a significant decrease in the overall survival time of grade II and III astrocytoma patients with HO-1 expression was observed. This result was validated at the mRNA level in a cohort of 105 samples. However, no association of HO-1 nuclear localization with patient survival was detected. In vitro experiments aimed at investigating the role of HO-1 in glioma progression showed that HO-1 modulates glioma cell proliferation, but has no effects on cellular migration. In conclusion, our results corroborate the higher frequency of HO-1 protein expression in gliomas than in normal brain, demonstrate that HO-1 is expressed by glial malignant cells, and show an association of HO-1 expression with patients' shorter survival time.

Vitamin D receptor expression is associated with improved overall survival in human glioblastoma multiforme.

Vitamin D and its analogs have been shown to display anti-proliferative effects in a wide variety of cancer types including glioblastoma multiforme (GBM). These anticancer effects are mediated by its active metabolite, 1α, 25-dihydroxyvitamin D3 (calcitriol) acting mainly through vitamin D receptor (VDR) signaling. In addition to its involvement in calcitriol action, VDR has also been demonstrated to be useful as a prognostic factor for some types of cancer. However, to our knowledge, there are no studies evaluating the expression of VDR protein and its association with outcome in gliomas. Therefore, we investigated VDR expression by using immunohistochemical analysis in human glioma tissue microarrays, and analyzed the association between VDR expression and clinico-pathological parameters. We further investigated the effects of genetic and pharmacologic modulation of VDR on survival and migration of glioma cell lines. Our data demonstrate that VDR is increased in tumor tissues when compared with VDR in non-malignant brains, and that VDR expression is associated with an improved outcome in patients with GBM. We also show that both genetic and pharmacologic modulation of VDR modulates GBM cellular migration and survival and that VDR is necessary for calcitriol-mediated effects on migration. Altogether these results provide some limited evidence supporting a role for VDR in glioma progression.

Resource requirements to accelerate clinical applications of next generation sequencing and radiomics: Workshop commentary and review.

The National Institutes of Health (NIH)/U.S. Food and Drug Administration (FDA) Joint Leadership Council Next-Generation Sequencing (NGS) and Radiomics Working Group (NGS&R WG) was formed by the NIH/FDA Joint Leadership Council to promote the development and validation of innovative NGS tests, radiomic tools, and associated data analysis and interpretation enhanced by artificial intelligence (AI) and machine-learning (ML) technologies. A two-day workshop was held on September 29-30, 2021 to convene members of the scientific community to discuss how to overcome the "ground truth" gap that has frequently been acknowledged as one of the limiting factors impeding high-quality research, development, validation, and regulatory science in these fields. This report provides a summary of the resource gaps identified by the WG and attendees, highlights existing resources and the ways they can potentially be leveraged to accelerate growth in these fields, and presents opportunities to support NGS and radiomic tool development and validation using technologies such as AI and ML.

NCI Cancer Research Data Commons: Resources to Share Key Cancer Data.

Since 2014, the NCI has launched a series of data commons as part of the Cancer Research Data Commons (CRDC) ecosystem housing genomic, proteomic, imaging, and clinical data to support cancer research and promote data sharing of NCI-funded studies. This review describes each data commons (Genomic Data Commons, Proteomic Data Commons, Integrated Canine Data Commons, Cancer Data Service, Imaging Data Commons, and Clinical and Translational Data Commons), including their unique and shared features, accomplishments, and challenges. Also discussed is how the CRDC data commons implement Findable, Accessible, Interoperable, Reusable (FAIR) principles and promote data sharing in support of the new NIH Data Management and Sharing Policy. See related articles by Brady et al., p. 1384, Pot et al., p. 1396, and Kim et al., p. 1404.

The Childhood Cancer Data Initiative: Using the Power of Data to Learn From and Improve Outcomes for Every Child and Young Adult With Pediatric Cancer.

Data-driven basic, translational, and clinical research has resulted in improved outcomes for children, adolescents, and young adults (AYAs) with pediatric cancers. However, challenges in sharing data between institutions, particularly in research, prevent addressing substantial unmet needs in children and AYA patients diagnosed with certain pediatric cancers. Systematically collecting and sharing data from every child and AYA can enable greater understanding of pediatric cancers, improve survivorship, and accelerate development of new and more effective therapies. To accomplish this goal, the Childhood Cancer Data Initiative (CCDI) was launched in 2019 at the National Cancer Institute. CCDI is a collaborative community endeavor supported by a 10-year, $50-million (in US dollars) annual federal investment. CCDI aims to learn from every patient diagnosed with a pediatric cancer by designing and building a data ecosystem that facilitates data collection, sharing, and analysis for researchers, clinicians, and patients across the cancer community. For example, CCDI's Molecular Characterization Initiative provides comprehensive clinical molecular characterization for children and AYAs with newly diagnosed cancers. Through these efforts, the CCDI strives to provide clinical benefit to patients and improvements in diagnosis and care through data-focused research support and to build expandable, sustainable data resources and workflows to advance research well past the planned 10 years of the initiative. Importantly, if CCDI demonstrates the success of this model for pediatric cancers, similar approaches can be applied to adults, transforming both clinical research and treatment to improve outcomes for all patients with cancer.

Just Add Data: automated predictive modeling for knowledge discovery and feature selection.

Fully automated machine learning (AutoML) for predictive modeling is becoming a reality, giving rise to a whole new field. We present the basic ideas and principles of Just Add Data Bio (JADBio), an AutoML platform applicable to the low-sample, high-dimensional omics data that arise in translational medicine and bioinformatics applications. In addition to predictive and diagnostic models ready for clinical use, JADBio focuses on knowledge discovery by performing feature selection and identifying the corresponding biosignatures, i.e., minimal-size subsets of biomarkers that are jointly predictive of the outcome or phenotype of interest. It also returns a palette of useful information for interpretation, clinical use of the models, and decision making. JADBio is qualitatively and quantitatively compared against Hyper-Parameter Optimization Machine Learning libraries. Results show that in typical omics dataset analysis, JADBio manages to identify signatures comprising of just a handful of features while maintaining competitive predictive performance and accurate out-of-sample performance estimation.

Analysis of germline-driven ancestry-associated gene expression in cancers.

Differential mRNA expression between ancestry groups can be explained by both genetic and environmental factors. We outline a computational workflow to determine the extent to which germline genetic variation explains cancer-specific molecular differences across ancestry groups. Using multi-omics datasets from The Cancer Genome Atlas (TCGA), we enumerate ancestry-informative markers colocalized with cancer-type-specific expression quantitative trait loci (e-QTLs) at ancestry-associated genes. This approach is generalizable to other settings with paired germline genotyping and mRNA expression data for a multi-ethnic cohort. For complete details on the use and execution of this protocol, please refer to Carrot-Zhang et al. (2020), Robertson et al. (2021), and Sayaman et al. (2021).

Integrative modeling identifies genetic ancestry-associated molecular correlates in human cancer.

Cellular and molecular aberrations contribute to the disparity of human cancer incidence and etiology between ancestry groups. Multiomics profiling in The Cancer Genome Atlas (TCGA) allows for querying of the molecular underpinnings of ancestry-specific discrepancies in human cancer. Here, we provide a protocol for integrative associative analysis of ancestry with molecular correlates, including somatic mutations, DNA methylation, mRNA transcription, miRNA transcription, and pathway activity, using TCGA data. This protocol can be generalized to analyze other cancer cohorts and human diseases. For complete details on the use and execution of this protocol, please refer to Carrot-Zhang et al. (2020).

The Exceptional Responders Initiative: Feasibility of a National Cancer Institute Pilot Study.

BACKGROUND: Tumor molecular profiling from patients experiencing exceptional responses to systemic therapy may provide insights into cancer biology and improve treatment tailoring. This pilot study evaluates the feasibility of identifying exceptional responders retrospectively, obtaining pre-exceptional response treatment tumor tissues, and analyzing them with state-of-the-art molecular analysis tools to identify potential molecular explanations for responses. METHODS: Exceptional response was defined as partial (PR) or complete (CR) response to a systemic treatment with population PR or CR rate less than 10% or an unusually long response (eg, duration >3 times published median). Cases proposed by patients' clinicians were reviewed by clinical and translational experts. Tumor and normal tissue (if possible) were profiled with whole exome sequencing and, if possible, targeted deep sequencing, RNA sequencing, methylation arrays, and immunohistochemistry. Potential germline mutations were tracked for relevance to disease. RESULTS: Cases reflected a variety of tumors and standard and investigational treatments. Of 520 cases, 476 (91.5%) were accepted for further review, and 222 of 476 (46.6%) proposed cases met requirements as exceptional responders. Clinical data were obtained from 168 of 222 cases (75.7%). Tumor was provided from 130 of 168 cases (77.4%). Of 117 of the 130 (90.0%) cases with sufficient nucleic acids, 109 (93.2%) were successfully analyzed; 6 patients had potentially actionable germline mutations. CONCLUSION: Exceptional responses occur with standard and investigational treatment. Retrospective identification of exceptional responders, accessioning, and sequencing of pretreatment archived tissue is feasible. Data from molecular analyses of tumors, particularly when combining results from patients who received similar treatments, may elucidate molecular bases for exceptional responses.

Molecular Features of Cancers Exhibiting Exceptional Responses to Treatment.

A small fraction of cancer patients with advanced disease survive significantly longer than patients with clinically comparable tumors. Molecular mechanisms for exceptional responses to therapy have been identified by genomic analysis of tumor biopsies from individual patients. Here, we analyzed tumor biopsies from an unbiased cohort of 111 exceptional responder patients using multiple platforms to profile genetic and epigenetic aberrations as well as the tumor microenvironment. Integrative analysis uncovered plausible mechanisms for the therapeutic response in nearly a quarter of the patients. The mechanisms were assigned to four broad categories-DNA damage response, intracellular signaling, immune engagement, and genetic alterations characteristic of favorable prognosis-with many tumors falling into multiple categories. These analyses revealed synthetic lethal relationships that may be exploited therapeutically and rare genetic lesions that favor therapeutic success, while also providing a wealth of testable hypotheses regarding oncogenic mechanisms that may influence the response to cancer therapy.

Rembrandt: helping personalized medicine become a reality through integrative translational research.

Finding better therapies for the treatment of brain tumors is hampered by the lack of consistently obtained molecular data in a large sample set and the ability to integrate biomedical data from disparate sources enabling translation of therapies from bench to bedside. Hence, a critical factor in the advancement of biomedical research and clinical translation is the ease with which data can be integrated, redistributed, and analyzed both within and across functional domains. Novel biomedical informatics infrastructure and tools are essential for developing individualized patient treatment based on the specific genomic signatures in each patient's tumor. Here, we present Repository of Molecular Brain Neoplasia Data (Rembrandt), a cancer clinical genomics database and a Web-based data mining and analysis platform aimed at facilitating discovery by connecting the dots between clinical information and genomic characterization data. To date, Rembrandt contains data generated through the Glioma Molecular Diagnostic Initiative from 874 glioma specimens comprising approximately 566 gene expression arrays, 834 copy number arrays, and 13,472 clinical phenotype data points. Data can be queried and visualized for a selected gene across all data platforms or for multiple genes in a selected platform. Additionally, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-anomaly pairs to facilitate the discovery of novel biomarkers and therapeutic targets. We believe that Rembrandt represents a prototype of how high-throughput genomic and clinical data can be integrated in a way that will allow expeditious and efficient translation of laboratory discoveries to the clinic.

Gene pathways and subnetworks distinguish between major glioma subtypes and elucidate potential underlying biology.

Molecular diagnostic tools are increasingly being used in an attempt to classify primary human brain tumors more accurately. While methods that are based on the analysis of individual gene expression prove to be useful for diagnostic purposes, they are devoid of biological significance since tumorgenesis is a concerted deregulation of multiple pathways rather than single genes. In a proof of concept, we utilize two large clinical data sets and show that the elucidation of enriched pathways and small differentially expressed sub-networks of protein interactions allow a reliable classification of glioblastomas and oligodendrogliomas. Applying a feature selection method, we observe that an optimized subset of pathways and subnetworks significantly improves the prediction accuracy. By determining the enrichment of altered genes in pathways and subnetworks we show that optimized subsets of genes rarely seem to be a target of genomic alteration. Our results suggest that groups of genes play a decisive role for the phenotype of the underlying tumor samples that can be utilized to reliably distinguish tumor types. In the absence of enrichment of genes that are genomically altered we assume that genetic changes largely exert an indirect rather than direct regulatory influence on a number of tumor-defining regulatory networks.