Challenges to Using Big Data in Cancer.

Big data in healthcare can enable unprecedented understanding of diseases and their treatment, particularly in oncology. These data may include electronic health records, medical imaging, genomic sequencing, payor records, and data from pharmaceutical research, wearables, and medical devices. The ability to combine datasets and use data across many analyses is critical to the successful use of big data and is a concern for those who generate and use the data. Interoperability and data quality continue to be major challenges when working with different healthcare datasets. Mapping terminology across datasets, missing and incorrect data, and varying data structures make combining data an onerous and largely manual undertaking. Data privacy is another concern addressed by the Health Insurance Portability and Accountability Act, the Common Rule, and the General Data Protection Regulation. The use of big data is now included in the planning and activities of the FDA and the European Medicines Agency. The willingness of organizations to share data in a precompetitive fashion, agreements on data quality standards, and institution of universal and practical tenets on data privacy will be crucial to fully realizing the potential for big data in medicine.

Case Studies for Overcoming Challenges in Using Big Data in Cancer.

The analysis of big healthcare data has enormous potential as a tool for advancing oncology drug development and patient treatment, particularly in the context of precision medicine. However, there are challenges in organizing, sharing, integrating, and making these data readily accessible to the research community. This review presents five case studies illustrating various successful approaches to addressing such challenges. These efforts are CancerLinQ, the American Association for Cancer Research Project GENIE, Project Data Sphere, the National Cancer Institute Genomic Data Commons, and the Veterans Health Administration Clinical Data Initiative. Critical factors in the development of these systems include attention to the use of robust pipelines for data aggregation, common data models, data deidentification to enable multiple uses, integration of data collection into physician workflows, terminology standardization and attention to interoperability, extensive quality assurance and quality control activity, incorporation of multiple data types, and understanding how data resources can be best applied. By describing some of the emerging resources, we hope to inspire consideration of the secondary use of such data at the earliest possible step to ensure the proper sharing of data in order to generate insights that advance the understanding and the treatment of cancer.

BloodPAC Data Commons for Liquid Biopsy Data.

PURPOSE: The Blood Profiling Atlas in Cancer (BloodPAC) Data Commons (BPDC) is being developed and is operated by the public-private BloodPAC Consortium to support the liquid biopsy community. It is an interoperable data commons with the ultimate aim of serving as a recognized source of valid scientific evidence for liquid biopsy assays for industry, academia, and standards and regulatory stakeholders. METHODS: The BPDC is implemented using the open source Gen3 data commons platform (https://gen3.org). In particular, the BPDC Data Exploration Portal, BPDC Data Submission Portal, the BPDC Workspace Hub, and the BloodPAC application programming interface (API) were all automatically generated from the BloodPAC Data Model using the Gen3 data commons platform. BPDC uses Gen3's implementation of the data commons framework services so that it can interoperate through secure, compliant APIs with other data commons using data commons framework service, such as National Cancer Institute's Cancer Research Data Commons. RESULTS: The BPDC contains 57 studies and projects spanning more than 4,100 cases. This amounts to 5,700 aliquots (blood plasma, serum, or a contrived sample) that have been subjected to a liquid biopsy assay, quantified, and then contributed by members of the BloodPAC Consortium. In all, there are more than 31,000 files in the commons as of December 2020. We describe the BPDC, the data it manages, the process that the BloodPAC Consortium used to develop it, and some of the applications that have been developed using its API. CONCLUSION: The BPDC has been the data platform used by BloodPAC during the past 4 years to manage the data for the consortium and to provide workspaces for its working groups.

Use of Wearable Activity Tracker in Patients With Cancer Undergoing Chemotherapy: Toward Evaluating Risk of Unplanned Health Care Encounters.

PURPOSE: Unplanned health care encounters (UHEs) such as emergency room visits can occur commonly during cancer chemotherapy treatments. Patients at an increased risk of UHEs are typically identified by clinicians using performance status (PS) assessments based on a descriptive scale, such as the Eastern Cooperative Oncology Group (ECOG) scale. Such assessments can be bias prone, resulting in PS score disagreements between assessors. We therefore propose to evaluate PS using physical activity measurements (eg, energy expenditure) from wearable activity trackers. Specifically, we examined the feasibility of using a wristband (band) and a smartphone app for PS assessments. METHODS: We conducted an observational study on a cohort of patients with solid tumor receiving highly emetogenic chemotherapy. Patients were instructed to wear the band for a 60-day activity-tracking period. During clinic visits, we obtained ECOG scores assessed by physicians, coordinators, and patients themselves. UHEs occurring during the activity-tracking period plus a 90-day follow-up period were later compiled. We defined our primary outcome as the percentage of patients adherent to band-wear ≥ 80% of 10 am to 8 pm for ≥ 80% of the activity-tracking period. In an exploratory analysis, we computed hourly metabolic equivalent of task (MET) and counted 10 am to 8 pm hours with > 1.5 METs as nonsedentary physical activity hours. RESULTS: Forty-one patients completed the study (56.1% female; 61.0% age 40-60 years); 68% were adherent to band-wear. ECOG score disagreement between assessors ranged from 35.3% to 50.0%. In our exploratory analysis, lower average METs and nonsedentary hours, but not higher ECOG scores, were associated with higher 150-day UHEs. CONCLUSION: The use of a wearable activity tracker is generally feasible in a similar population of patients with cancer. A larger randomized controlled trial should be conducted to confirm the association between lower nonsedentary hours and higher UHEs.

Quantified Kinematics to Evaluate Patient Chemotherapy Risks in Clinic.

PURPOSE: Performance status (PS) is a key factor in oncologic decision making, but conventional scales used to measure PS vary among observers. Consumer-grade biometric sensors have previously been identified as objective alternatives to the assessment of PS. Here, we investigate how one such biometric sensor can be used during a clinic visit to identify patients who are at risk for complications, particularly unexpected hospitalizations that may delay treatment or result in low physical activity. We aim to provide a novel and objective means of predicting tolerability to chemotherapy. METHODS: Thirty-eight patients across three centers in the United States who were diagnosed with a solid tumor with plans for treatment with two cycles of highly emetogenic chemotherapy were included in this single-arm, observational prospective study. A noninvasive motion-capture system quantified patient movement from chair to table and during the get-up-and-walk test. Activity levels were recorded using a wearable sensor over a 2-month period. Changes in kinematics from two motion-capture data points pre- and post-treatment were tested for correlation with unexpected hospitalizations and physical activity levels as measured by a wearable activity sensor. RESULTS: Among 38 patients (mean age, 48.3 years; 53% female), kinematic features from chair to table were the best predictors for unexpected health care encounters (area under the curve, 0.775 ± 0.029) and physical activity (area under the curve, 0.830 ± 0.080). Chair-to-table acceleration of the nonpivoting knee (t = 3.39; P = .002) was most correlated with unexpected health care encounters. Get-up-and-walk kinematics were most correlated with physical activity, particularly the right knee acceleration (t = -2.95; P = .006) and left arm angular velocity (t = -2.4; P = .025). CONCLUSION: Chair-to-table kinematics are good predictors of unexpected hospitalizations, whereas the get-up-and-walk kinematics are good predictors of low physical activity.

The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution.

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

Spatial and temporal tools for building a human cell atlas.

Improvements in the sensitivity, content, and throughput of microscopy, in the depth and throughput of single-cell sequencing approaches, and in computational and modeling tools for data integration have created a portfolio of methods for building spatiotemporal cell atlases. Challenges in this fast-moving field include optimizing experimental conditions to allow a holistic view of tissues, extending molecular analysis across multiple timescales, and developing new tools for 1) managing large data sets, 2) extracting patterns and correlation from these data, and 3) integrating and visualizing data and derived results in an informative way. The utility of these tools and atlases for the broader scientific community will be accelerated through a commitment to findable, accessible, interoperable, and reusable data and tool sharing principles that can be facilitated through coordination and collaboration between programs working in this space.

Patient reported outcomes can improve performance status assessment: a pilot study.

BACKGROUND: Patient performance status is routinely used in oncology to estimate physical functioning, an important factor in clinical treatment decisions and eligibility for clinical trials. However, validity and reliability data for ratings of performance status have not been optimal. This study recruited oncology patients who were about to begin emetogenic palliative or adjuvant chemotherapy for treatment of solid tumors. We employed actigraphy as the gold standard for physical activity level. Correspondences between actigraphy and oncologists' and patients' ratings of performance status were examined and compared with the correspondences of actigraphy and several patient reported outcomes (PROs). The study was designed to determine feasibility of the measurement approaches and if PROs can improve the accuracy of assessment of performance status. METHODS: Oncologists and patients made performance status ratings at visit 1. Patients wore an actigraph and entered weekly PROs on a smartphone app. Data for days 1-14 after visit 1 were analyzed. Chart reviews were conducted to tabulate all unexpected medical events across days 1-150. RESULTS: Neither oncologist nor patient ratings of performance status predicted steps/hour (actigraphy). The PROMIS® Physical Function PRO (average of Days 1, 7, 14) was associated with steps/hour at high (for men) and moderate (for women) levels; the PROMIS® Fatigue PRO predicted steps for men, but not for women. Unexpected medical events occurred in 57% of patients. Only body weight in female patients predicted events; oncologist and patient performance status ratings, steps/hour, and other PROs did not. CONCLUSIONS: PROMIS® Physical Function and Fatigue PROs show good correspondence with steps/hour making them easy, useful tools for oncologists to improve their assessment of performance status, especially for male patients. Female patients had lower levels of steps/hour than males and lower correlations among the predictors, suggesting the need for further work to improve performance status assessment in women. Assessment of pre-morbid sedentary behavior alongside current Physical Functioning and Fatigue PROs may allow for a more valid determination of disease-related activity level and performance status.

RNA editing, epitranscriptomics, and processing in cancer progression.

The transcriptome is extensively and dynamically regulated by a network of RNA modifying factors. RNA editing enzymes APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like) and ADAR (adenosine deaminase, RNA-specific) irreversibly recode primary RNA sequences, whereas newly described methylases (writers) and de-methylases (erasers) dynamically alter RNA molecules in response to environmental conditions. RNA modifications can affect RNA splicing, nuclear-cytoplasmic transport, translation, and regulation of gene expression by RNA interference. In addition, tRNA base modifications, processing, and regulated cleavage have been shown to alter global patterns of mRNA translation in response to cellular stress pathways. Recent studies, some of which were discussed at this workshop, have rekindled interest in the emerging roles of RNA modifications in health and disease. On September 10th, 2014, the Division of Cancer Biology, NCI sponsored a workshop to explore the role of epitranscriptomic RNA modifications and tRNA processing in cancer progression. The workshop attendees spanned a scientific range including chemists, virologists, and RNA and cancer biologists. The goal of the workshop was to explore the interrelationships between RNA editing, epitranscriptomics, and RNA processing and the enzymatic pathways that regulate these activities in cancer initiation and progression. At the conclusion of the workshop, a general discussion focused on defining the major challenges and opportunities in this field, as well as identifying the tools, technologies, resources and community efforts required to accelerate research in this emerging area.

Measuring the evolution and output of cross-disciplinary collaborations within the NCI Physical Sciences-Oncology Centers Network.

Development of effective quantitative indicators and methodologies to assess the outcomes of cross-disciplinary collaborative initiatives has the potential to improve scientific program management and scientific output. This article highlights an example of a prospective evaluation that has been developed to monitor and improve progress of the National Cancer Institute Physical Sciences-Oncology Centers (PS-OC) program. Study data, including collaboration information, was captured through progress reports and compiled using the web-based analytic database: Interdisciplinary Team Reporting, Analysis, and Query Resource. Analysis of collaborations was further supported by data from the Thomson Reuters Web of Science database, MEDLINE database, and a web-based survey. Integration of novel and standard data sources was augmented by the development of automated methods to mine investigator pre-award publications, assign investigator disciplines, and distinguish cross-disciplinary publication content. The results highlight increases in cross-disciplinary authorship collaborations from pre- to post-award years among the primary investigators and confirm that a majority of cross-disciplinary collaborations have resulted in publications with cross-disciplinary content that rank in the top third of their field. With these evaluation data, PS-OC Program officials have provided ongoing feedback to participating investigators to improve center productivity and thereby facilitate a more successful initiative. Future analysis will continue to expand these methods and metrics to adapt to new advances in research evaluation and changes in the program.

Genome-wide protein-DNA binding dynamics suggest a molecular clutch for transcription factor function.

Dynamic access to genetic information is central to organismal development and environmental response. Consequently, genomic processes must be regulated by mechanisms that alter genome function relatively rapidly. Conventional chromatin immunoprecipitation (ChIP) experiments measure transcription factor occupancy, but give no indication of kinetics and are poor predictors of transcription factor function at a given locus. To measure transcription-factor-binding dynamics across the genome, we performed competition ChIP (refs 6, 7) with a sequence-specific Saccharomyces cerevisiae transcription factor, Rap1 (ref. 8). Rap1-binding dynamics and Rap1 occupancy were only weakly correlated (R(2) = 0.14), but binding dynamics were more strongly linked to function than occupancy. Long Rap1 residence was coupled to transcriptional activation, whereas fast binding turnover, which we refer to as 'treadmilling', was linked to low transcriptional output. Thus, DNA-binding events that seem identical by conventional ChIP may have different underlying modes of interaction that lead to opposing functional outcomes. We propose that transcription factor binding turnover is a major point of regulation in determining the functional consequences of transcription factor binding, and is mediated mainly by control of competition between transcription factors and nucleosomes. Our model predicts a clutch-like mechanism that rapidly engages a treadmilling transcription factor into a stable binding state, or vice versa, to modulate transcription factor function.

The stress response factors Yap6, Cin5, Phd1, and Skn7 direct targeting of the conserved co-repressor Tup1-Ssn6 in S. cerevisiae.

Maintaining the proper expression of the transcriptome during development or in response to a changing environment requires a delicate balance between transcriptional regulators with activating and repressing functions. The budding yeast transcriptional co-repressor Tup1-Ssn6 is a model for studying similar repressor complexes in multicellular eukaryotes. Tup1-Ssn6 does not bind DNA directly, but is directed to individual promoters by one or more DNA-binding proteins, referred to as Tup1 recruiters. This functional architecture allows the Tup1-Ssn6 to modulate the expression of genes required for the response to a variety of cellular stresses. To understand the targeting or the Tup1-Ssn6 complex, we determined the genomic distribution of Tup1 and Ssn6 by ChIP-chip. We found that most loci bound by Tup1-Ssn6 could not be explained by co-occupancy with a known recruiting cofactor and that deletion of individual known Tup1 recruiters did not significantly alter the Tup1 binding profile. These observations suggest that new Tup1 recruiting proteins remain to be discovered and that Tup1 recruitment typically depends on multiple recruiting cofactors. To identify new recruiting proteins, we computationally screened for factors with binding patterns similar to the observed Tup1-Ssn6 genomic distribution. Four top candidates, Cin5, Skn7, Phd1, and Yap6, all known to be associated with stress response gene regulation, were experimentally confirmed to physically interact with Tup1 and/or Ssn6. Incorporating these new recruitment cofactors with previously characterized cofactors now explains the majority of Tup1 targeting across the genome, and expands our understanding of the mechanism by which Tup1-Ssn6 is directed to its targets.

Analysis of the Basidiomycete Coprinopsis cinerea reveals conservation of the core meiotic expression program over half a billion years of evolution.

Coprinopsis cinerea (also known as Coprinus cinereus) is a multicellular basidiomycete mushroom particularly suited to the study of meiosis due to its synchronous meiotic development and prolonged prophase. We examined the 15-hour meiotic transcriptional program of C. cinerea, encompassing time points prior to haploid nuclear fusion though tetrad formation, using a 70-mer oligonucleotide microarray. As with other organisms, a large proportion (∼20%) of genes are differentially regulated during this developmental process, with successive waves of transcription apparent in nine transcriptional clusters, including one enriched for meiotic functions. C. cinerea and the fungi Saccharomyces cerevisiae and Schizosaccharomyces pombe diverged ∼500-900 million years ago, permitting a comparison of transcriptional programs across a broad evolutionary time scale. Previous studies of S. cerevisiae and S. pombe compared genes that were induced upon entry into meiosis; inclusion of C. cinerea data indicates that meiotic genes are more conserved in their patterns of induction across species than genes not known to be meiotic. In addition, we found that meiotic genes are significantly more conserved in their transcript profiles than genes not known to be meiotic, which indicates a remarkable conservation of the meiotic process across evolutionarily distant organisms. Overall, meiotic function genes are more conserved in both induction and transcript profile than genes not known to be meiotic. However, of 50 meiotic function genes that were co-induced in all three species, 41 transcript profiles were well-correlated in at least two of the three species, but only a single gene (rad50) exhibited coordinated induction and well-correlated transcript profiles in all three species, indicating that co-induction does not necessarily predict correlated expression or vice versa. Differences may reflect differences in meiotic mechanisms or new roles for paralogs. Similarities in induction, transcript profiles, or both, should contribute to gene discovery for orthologs without currently characterized meiotic roles.

A positive but complex association between meiotic double-strand break hotspots and open chromatin in Saccharomyces cerevisiae.

During meiosis, chromatin undergoes extensive changes to facilitate recombination, homolog pairing, and chromosome segregation. To investigate the relationship between chromatin organization and meiotic processes, we used formaldehyde-assisted isolation of regulatory elements (FAIRE) to map open chromatin during the transition from mitosis to meiosis in the budding yeast Saccharomyces cerevisiae. We found that meiosis-induced opening of chromatin is associated with meiotic DSB hotpots. The positive association between open chromatin and DSB hotspots is most prominent 3 h into meiosis, when the early meiotic genes DMC1 and HOP1 exhibit maximum transcription and the early recombination genes SPO11 and RAD51 are strongly up-regulated. While the degree of chromatin openness is positively associated with the occurrence of recombination hotspots, many hotspots occur outside of open chromatin. Of particular interest, many DSB hotspots that fell outside of meiotic open chromatin nonetheless occurred in chromatin that had recently been open during mitotic growth. Finally, we find evidence for meiosis-specific opening of chromatin at the regions adjacent to boundaries of subtelomeric sequences, which exhibit specific crossover control patterns hypothesized to be regulated by chromatin.

Systematic identification of balanced transposition polymorphisms in Saccharomyces cerevisiae.

High-throughput techniques for detecting DNA polymorphisms generally do not identify changes in which the genomic position of a sequence, but not its copy number, varies among individuals. To explore such balanced structural polymorphisms, we used array-based Comparative Genomic Hybridization (aCGH) to conduct a genome-wide screen for single-copy genomic segments that occupy different genomic positions in the standard laboratory strain of Saccharomyces cerevisiae (S90) and a polymorphic wild isolate (Y101) through analysis of six tetrads from a cross of these two strains. Paired-end high-throughput sequencing of Y101 validated four of the predicted rearrangements. The transposed segments contained one to four annotated genes each, yet crosses between S90 and Y101 yielded mostly viable tetrads. The longest segment comprised 13.5 kb near the telomere of chromosome XV in the S288C reference strain and Southern blotting confirmed its predicted location on chromosome IX in Y101. Interestingly, inter-locus crossover events between copies of this segment occurred at a detectable rate. The presence of low-copy repetitive sequences at the junctions of this segment suggests that it may have arisen through ectopic recombination. Our methodology and findings provide a starting point for exploring the origins, phenotypic consequences, and evolutionary fate of this largely unexplored form of genomic polymorphism.

Repression of the yeast HO gene by the MATalpha2 and MATa1 homeodomain proteins.

The HO gene in Saccharomyces cerevisiae is regulated by a large and complex promoter that is similar to promoters in higher order eukaryotes. Within this promoter are 10 potential binding sites for the a1-alpha2 heterodimer, which represses HO and other haploid-specific genes in diploid yeast cells. We have determined that a1-alpha2 binds to these sites with differing affinity, and that while certain strong-affinity sites are crucial for repression of HO, some of the weak-affinity sites are dispensable. However, these weak-affinity a1-alpha2-binding sites are strongly conserved in related yeast species and have a role in maintaining repression upon the loss of strong-affinity sites. We found that these weak sites are sufficient for a1-alpha2 to partially repress HO and recruit the Tup1-Cyc8 (Tup1-Ssn6) co-repressor complex to the HO promoter. We demonstrate that the Swi5 activator protein is not bound to URS1 in diploid cells, suggesting that recruitment of the Tup1-Cyc8 complex by a1-alpha2 prevents DNA binding by activator proteins resulting in repression of HO.

Analysis of the meiotic role of the mitochondrial ribosomal proteins Mrps17 and Mrpl37 in Saccharomyces cerevisiae.

Sporulation in the yeast Saccharomyces cerevisiae is a complex and tightly regulated pathway that involves the induction of a large number of genes. We have identified MRPS17 in a cDNA library enriched for sporulation-specific genes. Homology searches show that the first one-third of Mrps17 has strong sequence similarity to bacterial S17 proteins, suggesting that Mrps17 is a potential mitochondrial ribosomal protein. This is further supported by the fact that mrps17Delta cells are respiratory-deficient and that a Mrps17-GFP fusion localizes to the mitochondria. We have confirmed by Northern blot analysis that both MRPS17 and MRPL37 are strongly induced during the middle stages of sporulation and that this induction is dependent on the presence of a middle sporulation element (MSE) in the promoters of these genes. Interestingly, we found that Mrps17 and Mrpl37, but not other mitochondrial ribosomal proteins, accumulate during the middle stages of sporulation. These results suggest that Mrps17 and Mrpl37 may have additional meiosis-specific roles.

Progress and challenges in profiling the dynamics of chromatin and transcription factor binding with DNA microarrays.

ChIP-chip, or chromatin immunoprecipitation followed by DNA microarray analysis, has proven to be an efficient means of mapping protein-genome interactions. Recent experiments using this tool are beginning to reveal the complex dynamics of transcription factor binding and chromatin organization, and how these processes interact with each other to generate a cellular response to environmental and developmental cues. Data derived from this approach, particularly data involving chromatin components and histone modifications, might be affected by assumptions underlying the procedure, and the data might be made more useful by adoption of standardized whole-genome microarray platforms.