A Study Design to Harmonize Patient-Reported Outcomes Across Data Sets.

PURPOSE: Using patient-reported outcomes (PROs) provides important insights from the patient's perspective and can be valuable to monitor and manage treatment-related adverse events during cancer treatment. Additionally, the digital administration of PROs (electronic PROs [ePROs]) provides real-time updates to clinical care teams on treatment-related symptoms in-between clinic visits. However, given the variability in the methodology and timing of the data collection, using and harmonizing these data across different systems remains challenging. Identifying data elements to capture and operating procedures for harmonization across ePRO tools will expedite efforts to generate relevant and robust data on use of ePRO data in clinical care. METHODS: Friends of Cancer Research assembled a consortium of project partners from key health care sectors to align on a framework for ePRO data capture across ePRO tools and assessment of the impact of ePRO data capture on patient outcomes. RESULTS: We identified challenges and opportunities to align ePRO data capture across ePRO tools and aligned on key data elements for assessing the impact of ePRO data capture on patient care and outcomes. Ultimately, we proposed a study protocol to leverage ePRO data for symptom and adverse event management to measure real-world effectiveness of ePRO tool implementation on patient care and outcomes. CONCLUSION: This work provides considerations for harmonizing ePRO data sets and a common framework to align across multiple ePRO tools to assess the value of ePROs for improving patient outcomes. Future efforts to interpret evidence and evaluate the impact of ePRO tools on patient outcomes will be aided by improved alignment across studies.

A functional genomic approach to actionable gene fusions for precision oncology.

Fusion genes represent a class of attractive therapeutic targets. Thousands of fusion genes have been identified in patients with cancer, but the functional consequences and therapeutic implications of most of these remain largely unknown. Here, we develop a functional genomic approach that consists of efficient fusion reconstruction and sensitive cell viability and drug response assays. Applying this approach, we characterize ~100 fusion genes detected in patient samples of The Cancer Genome Atlas, revealing a notable fraction of low-frequency fusions with activating effects on tumor growth. Focusing on those in the RTK-RAS pathway, we identify a number of activating fusions that can markedly affect sensitivity to relevant drugs. Last, we propose an integrated, level-of-evidence classification system to prioritize gene fusions systematically. Our study reiterates the urgent clinical need to incorporate similar functional genomic approaches to characterize gene fusions, thereby maximizing the utility of gene fusions for precision oncology.

A digital highway for data fluidity and data equity in precision medicine.

Recent technological advances continue to expand the universe of big data in biomedicine along the four axes of variety, veracity, volume, and velocity, fueling innovations in research and discovery while transforming care delivery. These advances allow quantitative capture of multimodal health, behavioral, social, and environmental data from n-of-all in near real-time to support the development of new therapies and personalization of treatment decisions for the n-of-one. Application of advanced analytical methods, including artificial intelligence and machine learning, to these modern data assets can greatly propel our understanding of health and disease, accelerating the development of safer and more effective anticancer therapies. In this perspective, we rationalize the creation of a universally accessible digital highway system as a foundational infrastructure to enable data fluidity in an equitable manner. An interoperable and integrated digital inter-state highway can facilitate efficient derivation of insights from biomedical big data to improve health outcomes and ensure that the U.S. remains at the leading-edge innovations in technology, advanced analytics, and precision medicine.

Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer.

Chromatin is folded into successive layers to organize linear DNA. Genes within the same topologically associating domains (TADs) demonstrate similar expression and histone-modification profiles, and boundaries separating different domains have important roles in reinforcing the stability of these features. Indeed, domain disruptions in human cancers can lead to misregulation of gene expression. However, the frequency of domain disruptions in human cancers remains unclear. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), which aggregated whole-genome sequencing data from 2,658 cancers across 38 tumor types, we analyzed 288,457 somatic structural variations (SVs) to understand the distributions and effects of SVs across TADs. Notably, SVs can lead to the fusion of discrete TADs, and complex rearrangements markedly change chromatin folding maps in the cancer genomes. Notably, only 14% of the boundary deletions resulted in a change in expression in nearby genes of more than twofold.

Global impact of somatic structural variation on the DNA methylome of human cancers.

BACKGROUND: Genomic rearrangements exert a heavy influence on the molecular landscape of cancer. New analytical approaches integrating somatic structural variants (SSVs) with altered gene features represent a framework by which we can assign global significance to a core set of genes, analogous to established methods that identify genes non-randomly targeted by somatic mutation or copy number alteration. While recent studies have defined broad patterns of association involving gene transcription and nearby SSV breakpoints, global alterations in DNA methylation in the context of SSVs remain largely unexplored. RESULTS: By data integration of whole genome sequencing, RNA sequencing, and DNA methylation arrays from more than 1400 human cancers, we identify hundreds of genes and associated CpG islands (CGIs) for which the nearby presence of a somatic structural variant (SSV) breakpoint is recurrently associated with altered expression or DNA methylation, respectively, independently of copy number alterations. CGIs with SSV-associated increased methylation are predominantly promoter-associated, while CGIs with SSV-associated decreased methylation are enriched for gene body CGIs. Rearrangement of genomic regions normally having higher or lower methylation is often involved in SSV-associated CGI methylation alterations. Across cancers, the overall structural variation burden is associated with a global decrease in methylation, increased expression in methyltransferase genes and DNA damage response genes, and decreased immune cell infiltration. CONCLUSION: Genomic rearrangement appears to have a major role in shaping the cancer DNA methylome, to be considered alongside commonly accepted mechanisms including histone modifications and disruption of DNA methyltransferases.

Applying Artificial Intelligence to Address the Knowledge Gaps in Cancer Care.

BACKGROUND: Rapid advances in science challenge the timely adoption of evidence-based care in community settings. To bridge the gap between what is possible and what is practiced, we researched approaches to developing an artificial intelligence (AI) application that can provide real-time patient-specific decision support. MATERIALS AND METHODS: The Oncology Expert Advisor (OEA) was designed to simulate peer-to-peer consultation with three core functions: patient history summarization, treatment options recommendation, and management advisory. Machine-learning algorithms were trained to construct a dynamic summary of patients cancer history and to suggest approved therapy or investigative trial options. All patient data used were retrospectively accrued. Ground truth was established for approximately 1,000 unique patients. The full Medline database of more than 23 million published abstracts was used as the literature corpus. RESULTS: OEA's accuracies of searching disparate sources within electronic medical records to extract complex clinical concepts from unstructured text documents varied, with F1 scores of 90%-96% for non-time-dependent concepts (e.g., diagnosis) and F1 scores of 63%-65% for time-dependent concepts (e.g., therapy history timeline). Based on constructed patient profiles, OEA suggests approved therapy options linked to supporting evidence (99.9% recall; 88% precision), and screens for eligible clinical trials on ClinicalTrials.gov (97.9% recall; 96.9% precision). CONCLUSION: Our results demonstrated technical feasibility of an AI-powered application to construct longitudinal patient profiles in context and to suggest evidence-based treatment and trial options. Our experience highlighted the necessity of collaboration across clinical and AI domains, and the requirement of clinical expertise throughout the process, from design to training to testing. IMPLICATIONS FOR PRACTICE: Artificial intelligence (AI)-powered digital advisors such as the Oncology Expert Advisor have the potential to augment the capacity and update the knowledge base of practicing oncologists. By constructing dynamic patient profiles from disparate data sources and organizing and vetting vast literature for relevance to a specific patient, such AI applications could empower oncologists to consider all therapy options based on the latest scientific evidence for their patients, and help them spend less time on information "hunting and gathering" and more time with the patients. However, realization of this will require not only AI technology maturation but also active participation and leadership by clincial experts.

A Pan-Cancer Compendium of Genes Deregulated by Somatic Genomic Rearrangement across More Than 1,400 Cases.

A systematic cataloging of genes affected by genomic rearrangement, using multiple patient cohorts and cancer types, can provide insight into cancer-relevant alterations outside of exomes. By integrative analysis of whole-genome sequencing (predominantly low pass) and gene expression data from 1,448 cancers involving 18 histopathological types in The Cancer Genome Atlas, we identified hundreds of genes for which the nearby presence (within 100 kb) of a somatic structural variant (SV) breakpoint is associated with altered expression. While genomic rearrangements are associated with widespread copy-number alteration (CNA) patterns, approximately 1,100 genes-including overexpressed cancer driver genes (e.g., TERT, ERBB2, CDK12, CDK4) and underexpressed tumor suppressors (e.g., TP53, RB1, PTEN, STK11)-show SV-associated deregulation independent of CNA. SVs associated with the disruption of topologically associated domains, enhancer hijacking, or fusion transcripts are implicated in gene upregulation. For cancer-relevant pathways, SVs considerably expand our understanding of how genes are affected beyond point mutation or CNA.

Discordant inheritance of chromosomal and extrachromosomal DNA elements contributes to dynamic disease evolution in glioblastoma.

To understand how genomic heterogeneity of glioblastoma (GBM) contributes to poor therapy response, we performed DNA and RNA sequencing on GBM samples and the neurospheres and orthotopic xenograft models derived from them. We used the resulting dataset to show that somatic driver alterations including single-nucleotide variants, focal DNA alterations and oncogene amplification on extrachromosomal DNA (ecDNA) elements were in majority propagated from tumor to model systems. In several instances, ecDNAs and chromosomal alterations demonstrated divergent inheritance patterns and clonal selection dynamics during cell culture and xenografting. We infer that ecDNA was unevenly inherited by offspring cells, a characteristic that affects the oncogenic potential of cells with more or fewer ecDNAs. Longitudinal patient tumor profiling found that oncogenic ecDNAs are frequently retained throughout the course of disease. Our analysis shows that extrachromosomal elements allow rapid increase of genomic heterogeneity during GBM evolution, independently of chromosomal DNA alterations.

PAF promotes stemness and radioresistance of glioma stem cells.

An integrated genomic and functional analysis to elucidate DNA damage signaling factors promoting self-renewal of glioma stem cells (GSCs) identified proliferating cell nuclear antigen (PCNA)-associated factor (PAF) up-regulation in glioblastoma. PAF is preferentially overexpressed in GSCs. Its depletion impairs maintenance of self-renewal without promoting differentiation and reduces tumor-initiating cell frequency. Combined transcriptomic and metabolomic analyses revealed that PAF supports GSC maintenance, in part, by influencing DNA replication and pyrimidine metabolism pathways. PAF interacts with PCNA and regulates PCNA-associated DNA translesion synthesis (TLS); consequently, PAF depletion in combination with radiation generated fewer tumorspheres compared with radiation alone. Correspondingly, pharmacological impairment of DNA replication and TLS phenocopied the effect of PAF depletion in compromising GSC self-renewal and radioresistance, providing preclinical proof of principle that combined TLS inhibition and radiation therapy may be a viable therapeutic option in the treatment of glioblastoma multiforme (GBM).

Genomic and immune heterogeneity are associated with differential responses to therapy in melanoma.

Appreciation for genomic and immune heterogeneity in cancer has grown though the relationship of these factors to treatment response has not been thoroughly elucidated. To better understand this, we studied a large cohort of melanoma patients treated with targeted therapy or immune checkpoint blockade (n = 60). Heterogeneity in therapeutic responses via radiologic assessment was observed in the majority of patients. Synchronous melanoma metastases were analyzed via deep genomic and immune profiling, and revealed substantial genomic and immune heterogeneity in all patients studied, with considerable diversity in T cell frequency, and few shared T cell clones (<8% on average) across the cohort. Variables related to treatment response were identified via these approaches and through novel radiomic assessment. These data yield insight into differential therapeutic responses to targeted therapy and immune checkpoint blockade in melanoma, and have key translational implications in the age of precision medicine.

PRKCI promotes immune suppression in ovarian cancer.

A key feature of high-grade serous ovarian carcinoma (HGSOC) is frequent amplification of the 3q26 locus harboring PRKC-ι (PRKCI). Here, we show that PRKCI is also expressed in early fallopian tube lesions, called serous tubal intraepithelial carcinoma. Transgenic mouse studies establish PRKCI as an ovarian cancer-specific oncogene. Mechanistically, we show that the oncogenic activity of PRKCI relates in part to the up-regulation of TNFα to promote an immune-suppressive tumor microenvironment characterized by an abundance of myeloid-derived suppressor cells and inhibition of cytotoxic T-cell infiltration. Furthermore, system-level and functional analyses identify YAP1 as a downstream effector in tumor progression. In human ovarian cancers, high PRKCI expression also correlates with high expression of TNFα and YAP1 and low infiltration of cytotoxic T cells. The PRKCI-YAP1 regulation of the tumor immunity provides a therapeutic strategy for highly lethal ovarian cancer.

Modeling Genomic Instability and Selection Pressure in a Mouse Model of Melanoma.

Tumor evolution is an iterative process of selection for pro-oncogenic aberrations. This process can be accelerated by genomic instability, but how it interacts with different selection bottlenecks to shape the evolving genomic landscape remains understudied. Here, we assessed tumor initiation and therapy resistance bottlenecks in mouse models of melanoma, with or without genomic instability. At the initiation bottleneck, whole-exome sequencing revealed that drug-naive tumors were genomically silent, and this was surprisingly unaffected when genomic instability was introduced via telomerase inactivation. We hypothesize that the strong engineered alleles created low selection pressure. At the therapy resistance bottleneck, strong selective pressure was applied using a BRAF inhibitor. In the absence of genomic instability, tumors acquired a non-genomic drug resistance mechanism. By contrast, telomerase-deficient, drug-resistant melanomas acquired highly recurrent copy number gains. These proof-of-principle experiments demonstrate how different selection pressures can interact with genomic instability to impact tumor evolution.

A Pan-Cancer Proteogenomic Atlas of PI3K/AKT/mTOR Pathway Alterations.

Molecular alterations involving the PI3K/AKT/mTOR pathway (including mutation, copy number, protein, or RNA) were examined across 11,219 human cancers representing 32 major types. Within specific mutated genes, frequency, mutation hotspot residues, in silico predictions, and functional assays were all informative in distinguishing the subset of genetic variants more likely to have functional relevance. Multiple oncogenic pathways including PI3K/AKT/mTOR converged on similar sets of downstream transcriptional targets. In addition to mutation, structural variations and partial copy losses involving PTEN and STK11 showed evidence for having functional relevance. A substantial fraction of cancers showed high mTOR pathway activity without an associated canonical genetic or genomic alteration, including cancers harboring IDH1 or VHL mutations, suggesting multiple mechanisms for pathway activation.

Biomarker Accessible and Chemically Addressable Mechanistic Subtypes of BRAF Melanoma.

Genomic diversity among melanoma tumors limits durable control with conventional and targeted therapies. Nevertheless, pathologic activation of the ERK1/2 pathway is a linchpin tumorigenic mechanism associated with the majority of primary and recurrent disease. Therefore, we sought to identify therapeutic targets that are selectively required for tumorigenicity in the presence of pathologic ERK1/2 signaling. By integration of multigenome chemical and genetic screens, recurrent architectural variants in melanoma tumor genomes, and patient outcome data, we identified two mechanistic subtypes of BRAFV600 melanoma that inform new cancer cell biology and offer new therapeutic opportunities. Subtype membership defines sensitivity to clinical MEK inhibitors versus TBK1/IKBKε inhibitors. Importantly, subtype membership can be predicted using a robust quantitative five-feature genetic biomarker. This biomarker, and the mechanistic relationships linked to it, can identify a cohort of best responders to clinical MEK inhibitors and identify a cohort of TBK1/IKBKε inhibitor-sensitive disease among nonresponders to current targeted therapy.Significance: This study identified two mechanistic subtypes of melanoma: (1) the best responders to clinical BRAF/MEK inhibitors (25%) and (2) nonresponders due to primary resistance mechanisms (9.9%). We identified robust biomarkers that can detect these subtypes in patient samples and predict clinical outcome. TBK1/IKBKε inhibitors were selectively toxic to drug-resistant melanoma. Cancer Discov; 7(8); 832-51. ©2017 AACR.See related commentary by Jenkins and Barbie, p. 799This article is highlighted in the In This Issue feature, p. 783.

Systematic Epigenomic Analysis Reveals Chromatin States Associated with Melanoma Progression.

The extent and nature of epigenomic changes associated with melanoma progression is poorly understood. Through systematic epigenomic profiling of 35 epigenetic modifications and transcriptomic analysis, we define chromatin state changes associated with melanomagenesis by using a cell phenotypic model of non-tumorigenic and tumorigenic states. Computation of specific chromatin state transitions showed loss of histone acetylations and H3K4me2/3 on regulatory regions proximal to specific cancer-regulatory genes in important melanoma-driving cell signaling pathways. Importantly, such acetylation changes were also observed between benign nevi and malignant melanoma human tissues. Intriguingly, only a small fraction of chromatin state transitions correlated with expected changes in gene expression patterns. Restoration of acetylation levels on deacetylated loci by histone deacetylase (HDAC) inhibitors selectively blocked excessive proliferation in tumorigenic cells and human melanoma cells, suggesting functional roles of observed chromatin state transitions in driving hyperproliferative phenotype. Through these results, we define functionally relevant chromatin states associated with melanoma progression.