In spite of the potentially groundbreaking environmental sentinel applications, studies of canine cancer data sources are often limited due to undercounting of cancer cases. This source of uncertainty might be further amplified through the process of spatial data aggregation, manifested as part of the modifiable areal unit problem (MAUP). In this study, we explore potential explanatory factors for canine cancer incidence retrieved from the Swiss Canine Cancer Registry (SCCR) in a regression modeling framework. In doing so, we also evaluate differences in statistical performance and associations resulting from a dasymetric refinement of municipal units to their portion of residential land. Our findings document severe underascertainment of cancer cases in the SCCR, which we linked to specific demographic characteristics and reduced use of veterinary care. These explanatory factors result in improved statistical performance when computed using dasymetrically refined units. This suggests that dasymetric mapping should be further tested in geographic correlation studies of canine cancer incidence and in future comparative studies involving human cancers.

Cancer encompasses a range of severe diseases characterized by uncontrolled cell growth and the potential for metastasis. Understanding the mechanism underlying tumorigenesis has been a central focus of cancer research. Self-propagating protein aggregates, known as prions, are linked to various biological functions and diseases, particularly those related to mammalian neurodegeneration. However, it remains unclear whether prion-like mechanisms contribute to tumorigenesis and cancer. Using a combined approach of algorithmic predictions, alongside genetic and biochemical experimentation, we identified numerous cancer-associated proteins prone to aggregation, many of which contain prion-like domains (PrLDs). These predictions were experimentally validated for both aggregation and prion-formation. We demonstrate that several PrLDs undergo nucleation-limited amyloid formation, which can alter protein activity in a mitotically heritable fashion. These include SSXT, a subunit of the chromatin-remodeling BAF (hSWI/SNF) complexes; CLOCK, a core component of the circadian clock; and EPN4, a clathrin-interacting protein involved in protein trafficking between the trans-Golgi network and endosomes. The prions formed by these PrLDs occurred in multiple variants and depended on Hsp104, a molecular chaperone with disaggregase activity. Our results reveal an inherent tendency for prion-like aggregation in human cancer-associated proteins, suggesting a potential role for such aggregation in the epigenetic changes driving tumorigenesis.

Analyses of clinically-relevant PTEN mutants and their aggregation potential.

According to Cognitive Market Research, the global Cancer Registry Software market size is USD 0.0711 billion in 2024 and will expand at the compound yearly growth rate (CAGR) of 10.3% from 2024 to 2031. Market Dynamics of Cancer Registry Software Market

Key Drivers for Cancer Registry Software Market

Increasing Emphasis on Cancer Research and Epidemiology - The increasing emphasis on cancer research and epidemiology acts as a key driving factor for the cancer registry software market. Governments, healthcare organizations, and research institutions worldwide invest significantly in cancer surveillance programs to track incidence, prevalence, and outcomes. Cancer registry software facilitates the collection, standardization, and analysis of vast amounts of cancer-related data, enabling researchers to identify trends, evaluate treatment efficacy, and develop strategies for prevention and early detection. The demand for robust cancer registry software solutions continues to rise as stakeholders recognize the importance of accurate, comprehensive data in advancing cancer research and improving patient outcomes.
The advancements in healthcare technology is anticipated to drive the Cancer Registry Software market's expansion in the years ahead.

Key Restraints for Cancer Registry Software Market

Complications in cancer registry data management and compliance retrains the market growth 

The software market for cancer registries experiences significant constraints, and this mainly stems from working and data management difficulties for healthcare organizations. Difficulty in organizing timely follow-ups and cancer referrals is one of the most pertinent issues, and this is usually compounded by the scarce availability of Oncology Data Specialist-Certified (ODS-C) experts. These experts are responsible for accurate data entry, interpretation, and reporting. But the lack of certified professionals leads to delays and inefficiencies, impacting the overall functionality and reliability of cancer registry systems. Additionally, the disintegration of cancer data within different healthcare systems creates another layer of complexity. In most instances, data need to be aggregated manually from multiple sources or accessed through disparate Electronic Medical Records (EMRs). This fragmented methodology not only maximizes the possibility of errors but also delays data collection and analysis, making the software less effective. Second, cancer registry requirements differ dramatically between organizations. Whereas some organizations need a full-suite solution, others might need more modular or customized software according to their respective workflows and capacities. Since organizational requirements change over time, inflexible or unnecessarily sophisticated solutions may not be able to keep pace, resulting in underutilization. All these considerations cumulatively prevent cancer registry software from seamlessly integrating and scaling, thus inhibiting market expansion.

Opportunity

Rising demand for the cloud-based software is an opportunity for the market 

The increasing use of cloud-based cancer registry software represents a major opportunity for the market for cancer registry software. The solutions are being increasingly preferred for their scalability, cost savings, and capacity to offer remote access attributes that are becoming more of a requirement in today's digitally oriented healthcare landscape. Cloud-based systems enable real-time data consolidation from multiple health centers, making it easier for clinicians, researchers, and public health agencies to collaborate easily. This ability is particularly important for successful oncologic disease monitoring, where accurate and timely sharing of data is essential to track cancer trends, patient outcomes, and treatment effectiveness. For instance, in November 2024, the CDC's National Program of Cancer Registries (NPCR) released the Cancer Surveillance Cloud-Based Computing Platform to advance oncologic disease data collection, editing, and storage. The cloud-based environment provides real-time access to data, automates essential processes, and enhances data security, driving the utilization of cancer registry software for efficient trend analysis and public health decision-making based on data. (Source - https://www.cdc.gov/national-program-cancer-registries/data-modernization/cloud-based-computing....

Cloud based data science infrastructure that provides secure access to cancer research data from NCI programs and key external cancer programs. Serves as coordinated resource for public data sharing of NCI funded programs. Users can explore and use analytical and visualization tools for data analysis. Enables to search and aggregate data across repositories including Cancer Data Service, Clinical Trial Data Commons, Genomic Data Commons, Imaging Data Commons, Integrated Canine Data Commons, Proteomic Data Commons.

Context

This data comes from aggregation of the tables available on the NIH's National Cancer Institutes State Cancer Profiles, specifically with their incidence tables.

The objective of the State Cancer Profiles Web site is to provide a system to characterize the cancer burden in a standardized manner in order to motivate action, integrate surveillance into cancer control planning, characterize areas and demographic groups, and expose health disparities. The focus is on cancer sites for which there are evidence based control interventions. Interactive graphics and maps provide visual support for deciding where to focus cancer control efforts.

Content

This data has cancer Incidence rates broken down by US County and includes data aggregated from 2012-2016. It has both incidence rates per 100k as well as yearly totals averaged over that period

Potential Future Work

This data is summarized across other potentially illuminating fields. The State Cancer Profiles can be further broken down by cancer area, race/ethnicity, sex, age, and stage. If more fidelity on the data would be helpful please add it to the discussion section and I can work on adding it!

Data Use Restrictions

Read Carefully Before Using

By using these data, you signify your agreement to comply with the following statutorily based requirements.

The Public Health Service Act (42 U.S.C. 242m(d)) provides that the data collected by the National Center for Health Statistics (NCHS) may be used only for the purpose for which they were obtained; any effort to determine the identity of any reported cases, or to use the information for any purpose other than for statistical reporting and analysis, is against the law. The National Program of Cancer Registries (NPCR), Centers for Disease Control and Prevention (CDC), has obtained an assurance of confidentiality pursuant to Section 308(d) of the Public Health Service Act, 42 U.S.C. 242m(d). This assurance provides that identifiable or potentially identifiable data collected by the NPCR may be used only for the purpose for which they were obtained unless the person or establishment from which they were obtained has consented to such use. Any effort to determine the identity of any reported cases, or to use the information for any purpose other than statistical reporting and analysis, is a violation of the assurance.

Therefore users will: - Use the data for statistical reporting and analysis only. - Make no attempt to learn the identity of any person or establishment included in these data. - Make no disclosure or other use of the identity of any person or establishment discovered inadvertently, and advise the appropriate contact for the data provider. In addition to immediately notifying "Contact Us" of the potential disclosure, - For mortality data, notify the Confidentiality Officer at the National Center for Health Statistics (Alvan O. Zarate, Ph.D.), 3311 Toledo Road, Rm 7116, Hyattsville, MD 20782, Phone: 301-458-4601, Fax: 301-458-4021) - For incidence data notify both the Federal agency that provided the data and notify the relevant state or metropolitan area cancer registryExternal Web Site Policy, of any such discovery. - For CDC's National Program of Cancer Registries (NPCR) areas, notify the Associate Director for Science, Office of Science Policy and Technology Transfer, CDC, Mailstop D-50, 1600 Clifton Road, N.E., Atlanta, Georgia, 30333, Phone: 404-639-7240) - For NCI's Surveillance, Epidemiology, and End Results (SEER) Program registry areas, notify the Branch Chief of the Cancer Statistics Branch of the Surveillance Research Program, Division of Cancer Control and Population Sciences, NCI, BG 9609 MSC 9760, 9609 Medical Center Drive, Bethesda, MD 20892-9760, Phone: 301-496-8510, Fax: 301-496-9949.

Many cancer predisposition syndromes are rare or have incomplete penetrance, and traditional epidemiological tools are not well suited for their detection. Here we have used an approach that employs the entire population based data in the Finnish Cancer Registry (FCR) for analyzing familial aggregation of all types of cancer, in order to find evidence for previously unrecognized cancer susceptibility conditions. We performed a systematic clustering of 878,593 patients in FCR based on family name at birth, municipality of birth, and tumor type, diagnosed between years 1952 and 2011. We also estimated the familial occurrence of the tumor types using cluster score that reflects the proportion of patients belonging to the most significant clusters compared to all patients in Finland. The clustering effort identified 25,910 birth name-municipality based clusters representing 183 different tumor types characterized by topography and morphology. We produced information about familial occurrence of hundreds of tumor types, and many of the tumor types with high cluster score represented known cancer syndromes. Unexpectedly, Kaposi sarcoma (KS) also produced a very high score (cluster score 1.91, p-value <0.0001). We verified from population records that many of the KS patients forming the clusters were indeed close relatives, and identified one family with five affected individuals in two generations and several families with two first degree relatives. Our approach is unique in enabling systematic examination of a national epidemiological database to derive evidence of aberrant familial aggregation of all tumor types, both common and rare. It allowed effortless identification of families displaying features of both known as well as potentially novel cancer predisposition conditions, including striking familial aggregation of KS. Further work with high-throughput methods should elucidate the molecular basis of the potentially novel predisposition conditions found in this study.

Breast cancer cells closely interact with different cell types of the surrounding adipose tissue to favor invasive growth and metastasis. Extracellular vesicles (EVs) are nanometer-sized vesicles secreted by different cell types that shuttle proteins and nucleic acids to establish cell-cell communication. To study the role of EVs released by cancer-associated adipose tissue in breast cancer progression and metastasis a standardized EV isolation protocol that obtains pure EVs and maintains their functional characteristics is required. We implemented differential ultracentrifugation as a pre-enrichment step followed by OptiPrep density gradient centrifugation (dUC-ODG) to isolate EVs from the conditioned medium of cancer-associated adipose tissue. A combination of immune-electron microscopy, nanoparticle tracking analysis (NTA) and Western blot analysis identified EVs that are enriched in flotillin-1, CD9 and CD63, and sized between 20 and 200 nm with a density of 1.076–1.125 g/ml. The lack of protein aggregates and cell organelle proteins confirmed the purity of the EV preparations. Next, we evaluated whether dUC-ODG isolated EVs are functionally active. ZR75.1 breast cancer cells treated with cancer-associated adipose tissue-secreted EVs from breast cancer patients showed an increased phosphorylation of CREB. MCF-7 breast cancer cells treated with adipose tissue-derived EVs exhibited a stronger propensity to form cellular aggregates. In conclusion, dUC-ODG purifies EVs from conditioned medium of cancer-associated adipose tissue, and these EVs are morphologically intact and biologically active.

The Get Data Out programme from the National Disease Registration Service publishes detailed statistics about small groups of cancer patients in a way that ensures patient anonymity is maintained. The 19 cancer sites currently covered by Get Data Out are: ‘Bladder, urethra, renal pelvis and ureter’, ‘Bone’, ‘Brain’, ‘Eye’, ‘Blood cancer (haematological neoplasms)’, ‘Blood cancer (haematological neoplasm) transformations’, ‘Head and neck’, ‘Kaposi sarcoma’, ‘Kidney’, ‘Liver and biliary tract’, ‘Lung, mesothelioma, and other thoracic', Oesophagus and stomach’, ‘Ovary’, ‘Pancreas’, ‘Prostate’, ‘Sarcoma’, ‘Skin tumours’, ‘Soft tissue’, ‘Testes’. Anonymisation standards are designed into the data by aggregation at the outset. Patients diagnosed with a certain type of tumour are divided into many smaller groups, each of which contains approximately 100 patients with the same characteristics. These groups are aimed to be clinically meaningful and differ across cancer sites. For each group of patients, Get Data Out routinely publish statistics about incidence, routes to diagnosis, treatments and survival. This release covers the addition of the diagnosis year 2022 for treatment, plus a refresh of the 2013-2021 treatment data. This is also a first release of a new 'Visualisations' tab on our dashboard which will allow the user to explore the GDO data in graphical and tabular form. Users will now be able to select a single GDO group using drop down menus and display figures of incidence, demographic, treatment, routes to diagnosis, and survival statistics by diagnosis year. Finally, this is a small update to the 2013-2022 incidence data to include more age standardised rates (ASRs) for gender specific groups (genital skin groups for example which previously did not have an ASR published). All releases and documentation are available on the Get Data Out dashboard. Before using the data, we recommend that you read the 'Introduction', 'FAQs' and 'Known limitations' tabs. The data is available in an open format for anyone to access and use. We hope that by releasing anonymous detailed data like this we can help researchers, the public and patients themselves discover more about cancer. If you have feedback or any other queries about Get Data Out, please email us at NDRSenquires@nhs.net and mention 'Get Data Out' in your email.

Population and number of lung cancer diagnoses by geographic levels of aggregation.

BackgroundSpatial data are often aggregated by area to protect the confidentiality of individuals and aid the calculation of pertinent risks and rates. However, the analysis of spatially aggregated data is susceptible to the modifiable areal unit problem (MAUP), which arises when inference varies with boundary or aggregation changes. While the impact of the MAUP has been examined previously, typically these studies have focused on well-populated areas. Understanding how the MAUP behaves when data are sparse is particularly important for countries with less populated areas, such as Australia. This study aims to assess different geographical regions’ vulnerability to the MAUP when data are relatively sparse to inform researchers’ choice of aggregation level for fitting spatial models.MethodsTo understand the impact of the MAUP in Queensland, Australia, the present study investigates inference from simulated lung cancer incidence data using the five levels of spatial aggregation defined by the Australian Statistical Geography Standard. To this end, Bayesian spatial BYM models with and without covariates were fitted.Results and conclusionThe MAUP impacted inference in the analysis of cancer counts for data aggregated to coarsest areal structures. However, area structures with moderate resolution were not greatly impacted by the MAUP, and offer advantages in terms of data sparsity, computational intensity and availability of data sets.

Veterinary Oncology Information Systems Market Outlook

According to our latest research, the veterinary oncology information systems market size reached USD 162.5 million in 2024, with a robust compound annual growth rate (CAGR) of 10.9% projected from 2025 to 2033. By 2033, the market is forecasted to achieve a value of USD 410.6 million. This significant growth is primarily driven by the rising prevalence of cancer in companion and livestock animals, increasing pet ownership, and the growing adoption of advanced digital healthcare solutions in veterinary practices. As per the latest research, the market is experiencing a surge in demand for efficient data management, precision diagnostics, and personalized treatment planning, which are essential for improving animal health outcomes and streamlining veterinary oncology workflows.

One of the primary growth factors for the veterinary oncology information systems market is the escalating incidence of cancer among companion animals such as dogs and cats. With pet owners becoming more aware of the importance of early diagnosis and effective treatment, there has been a notable rise in veterinary visits and cancer screenings. The increasing emotional and financial investment in pet health has led to a greater demand for specialized oncology care, which in turn necessitates advanced information systems capable of managing complex data, tracking treatment protocols, and facilitating communication between veterinarians and pet owners. These systems enable veterinary professionals to provide tailored cancer care, improve survival rates, and enhance the overall quality of life for animals undergoing oncology treatments.

Another crucial driver is the technological evolution within veterinary healthcare infrastructure. The integration of artificial intelligence, machine learning, and cloud computing into veterinary oncology information systems is transforming the way veterinary practices operate. These technological advancements allow for real-time data sharing, remote consultations, and seamless integration with diagnostic imaging and laboratory results. The shift towards cloud-based solutions is particularly noteworthy, as it offers scalability, data security, and cost-effectiveness for veterinary clinics and hospitals, regardless of their size. This digital transformation is not only improving operational efficiency but is also enabling veterinary oncologists to access and analyze large datasets, leading to more accurate diagnoses and personalized treatment regimens for animal patients.

The growing emphasis on research and development in veterinary oncology is further propelling market expansion. Academic research institutes and veterinary teaching hospitals are increasingly adopting oncology information systems to support clinical trials, data collection, and collaborative research initiatives. These systems facilitate the aggregation and analysis of large-scale clinical data, which is vital for understanding cancer epidemiology in animals and developing innovative therapies. Moreover, the collaboration between veterinary professionals, pharmaceutical companies, and technology providers is fostering the development of new software features and service models tailored to the unique needs of veterinary oncology. This collaborative ecosystem is driving continuous innovation and ensuring that veterinary oncology information systems remain at the forefront of digital healthcare transformation in the animal health sector.

The introduction of Veterinary Cloud EMR systems is revolutionizing the way veterinary oncology practices manage patient data and clinical workflows. By leveraging cloud-based platforms, veterinary clinics can access electronic medical records (EMR) from anywhere, facilitating seamless communication and collaboration among veterinary teams. These systems offer enhanced data security, automatic updates, and scalability, making them an attractive option for practices of all sizes. The ability to integrate with diagnostic tools and provide real-time access to patient information ensures that veterinarians can deliver timely and accurate cancer care. As the demand for digital solutions in veterinary oncology continues to grow, Veterinary Cloud EMR systems are poised to play a pivotal role in enhancing operational efficiency and improving patient outcomes.

Overview

NCI held a Workshop on Semantics to support the NCI Cancer Research Data Commons (CRDC) in May 2018 at the National Cancer Institute in Rockville, MD. This workshop brought together experts in various areas of semantics, data integration and harmonization, Natural Language Processing (NLP) and other relevant areas to discuss and gather recommendations on semantic support for the CRDC.

The workshop goals were to:
1) Identify high-level requirements to address semantic needs and potential approaches for evaluation testing of the Cancer Data Aggregator (CDA)

2) A set of options for using and/or extending current methods and resources (e.g. NLP) to

• support semantic query capabilities

• facilitate metadata annotation

• minimize efforts for data validation and submission

3) Develop recommendations to support ongoing engagement with the community to ensure the semantics underlying the CDA improve and evolve as people contribute to and use the CDA

Participants

In total, 33 participants attended the meeting, coming from various backgrounds including clinicians, ontologists, bioinformaticians, data scientists, and project managers. Participants had expertise in semantic technologies, software and infrastructure development, data standards, data integration, clinical research, open source tool development.

Competency Queries

At the workshop, participants were asked to brainstorm ‘competency queries’, potential queries or questions that they would ask the future Cancer Data Aggregator (CDA) in order to retrieve data from across the CRDC. At the workshop, the breakout groups documented 237 queries for the CDA.

Competency queries are often used to inform requirements to build a data model and/or ontology. They can help inform the scope of the model:what queries should the model support; 2) the content of the model, in terms of what types of entity types and attributes are needed to answer these queries; 3) the structure of the model in terms of what types of relationships between entities are needed to efficiently answer queries; 4) the semantics of the data, meaning which terminologies/ontologies would be useful for representing data to support query needs; 5) how to test and improve a completed model to ensure it can efficiently support queries determined to be in scope.

After the workshop, a small subgroup assessed, organized and summarized the 237 queries that were noted at the workshop. A spreadsheet was created containing the queries and the keywords in each query were highlighted. From the highlighted keywords, a column was added to capture the core search parameters or classifications for each query. In evaluating the queries it was observed that some were really not a query, but expressed various observations about the data that one might hope to make. For example “Patients with a certain temporal pattern of diagnoses, both cancer and comorbidities”. This submission indicates that the data returned would need to include diagnosis and other conditions and can help to inform the requirements for CRDC data models.

Using the information from the keyword analysis, the queries were initially categorized across various classifications, such as queries that included exposure information, diagnosis or cancer types, anatomical location of tumor, etc. In total, the queries were classified amongst 25 different parameters, or an ‘other’ category, where the query did not fit the classification scheme, or was out of scope. To further refine this list into a more manageable list, 82 representative queries were pulled out, with at least 2 examples from every classification parameter. The goal was to identify a minimal or at least smaller subset that was still representative. This list of 82 queries was then reviewed with a larger group of experts and further refined. Additional classification parameters were added, for a total of 31 parameters and an ‘other’ category. Some classifications were subdivided into more granular classifications, such as treatment was subdivided into surgery/radiation and protocols/regimens.

In classifying each query, the exact words from each query that fit the classification scheme were noted. For example, consider the query, “What environmental exposures are typically associated with the development of salivary gland cancer?”; this query is classified as an exposure (environmental exposure), a diagnosis or specific cancer type (salivary gland cancer), and a tumor location (salivary gland).

For a central query to be effective, we felt that the use of preferred terms for each classification parameter would be useful, so each was mapped to a relevant terminology or ontology. For example, exposure data is represented in the Environmental and Exposures Ontology (ECTO), as well as NCIt. The Uber Anatomy Ontology (Uberon) contains classifications of anatomical structures, which can be used to classify tumor locations. Many of the parameters covered by specialized terminologies are also covered by the NCIt. In some cases, the parameters were covered by multiple ontologies. At some point a preferred terminology will need to be selected for each parameter, perhaps informed by an assessment of what is being used in the CRDC data. It is likely that all terminologies and ontologies will need to be extended to cover all the terminology needed. Mappings between these terminologies and those used in the CRDC data will need to be developed.

Finally, categories were prioritized based on how relevant and feasible they were for the CRDC as either high priority, nice to have or low priority.

The Get Data Out programme from the National Disease Registration Service publishes detailed statistics about small groups of cancer patients in a way that ensures patient anonymity is maintained. The Get Data Out programme currently covers 15 cancer sites. This data release updates the incidence data for all 15 sites to cover 2013-2020 (previous data covered 2013-2019) and also adds new cancer sites ‘Liver and biliary tract’, 'Haematological malignancies' and 'Haematological malignancy transformations'. The 18 cancer sites now covered by Get Data Out are: ‘Bladder, Urethra, Renal Pelvis and Ureter’, ‘Bone cancer’, ‘Brain, meningeal and other primary CNS tumours’, ‘Eye cancer’, 'Haematological malignancies', 'Haematological malignancy transformations', ‘Head and neck’, ‘Kaposi sarcoma’, ‘Kidney’, 'Liver and Biliary tract', ‘Oesophageal and Stomach’, ‘Ovary, fallopian tube and primary peritoneal carcinomas’, ‘Pancreas’, ‘Prostate’, ‘Sarcoma’, ‘Skin tumours’, ‘Soft tissue and peripheral nerve cancer’, ‘Testicular tumours including post-pubertal teratomas’. Anonymisation standards are designed into the data by aggregation at the outset. Patients diagnosed with a certain type of tumour are divided into many smaller groups, each of which contains approximately 100 patients with the same characteristics. These groups are aimed to be clinically meaningful and differ across cancer sites. For each group of patients, Get Data Out routinely publish statistics about incidence, routes to diagnosis, treatments and survival. All releases and documentation are available on the Get Data Out main technical page. Before using the data, we recommend that you read the guide for first time users. The data is available in an open format for anyone to access and use. We hope that by releasing anonymous detailed data like this we can help researchers, the public and patients themselves discover more about cancer. If you have feedback or any other queries about Get Data Out, please email us at NDRSenquires@nhs.net and mention 'Get Data Out' in your email.

BackgroundRegional genomic copy number alterations (CNA) are observed in the vast majority of cancers. Besides specifically targeting well-known, canonical oncogenes, CNAs may also play more subtle roles in terms of modulating genetic potential and broad gene expression patterns of developing tumors. Any significant differences in the overall CNA patterns between different cancer types may thus point towards specific biological mechanisms acting in those cancers. In addition, differences among CNA profiles may prove valuable for cancer classifications beyond existing annotation systems.Principal FindingsWe have analyzed molecular-cytogenetic data from 25579 tumors samples, which were classified into 160 cancer types according to the International Classification of Disease (ICD) coding system. When correcting for differences in the overall CNA frequencies between cancer types, related cancers were often found to cluster together according to similarities in their CNA profiles. Based on a randomization approach, distance measures from the cluster dendrograms were used to identify those specific genomic regions that contributed significantly to this signal. This approach identified 43 non-neutral genomic regions whose propensity for the occurrence of copy number alterations varied with the type of cancer at hand. Only a subset of these identified loci overlapped with previously implied, highly recurrent (hot-spot) cytogenetic imbalance regions.ConclusionsThus, for many genomic regions, a simple null-hypothesis of independence between cancer type and relative copy number alteration frequency can be rejected. Since a subset of these regions display relatively low overall CNA frequencies, they may point towards second-tier genomic targets that are adaptively relevant but not necessarily essential for cancer development.

National Cancer Registration and Analysis Service (NCRAS). (2018). Cancer Registration: Frequency of tumours diagnosed by route to diagnosis, per Government Office Region (GOR) for 38 cancer sites (2006-2013) [Data set]. Public Health England. https://doi.org/10.25503/3xn3-jp97

Total incident tumours (malignant and in situ) by Route to Diagnosis per Government Office Region. The data is restricted to the England resident population diagnosed between 01/01 2006 and 31/12/2013. 38 cancer sites are identified and the data is split by age at diagnosis (aggregated to 20 year age band) -- TOTAL (total number of patients) --FINAL_ROUTE (One of eight Routes to Diagnosis- methodology for the assignment of each route is described in Elliss-Brookes L, McPhail S, Greenslade M, Shelton J, Hiom S, Richards M (2012) Routes to diagnosis for cancer – determining the patient journey using multiple routine data sets. British Journal of Cancer 107: 1220–1226.) -- DIAGDATEYEAR (Year of diagnosis) -- RTD_GROUPS_BREAKDOWN_2013 (Description of site of tumour (topography)) -- GOR_CODE (in accordance with GOR classifications for April 1996-July 1998. See www.ons.gov.uk/methodology/geography/ukgeographies/administrativegeography/england) -- GOR_NAME (Description of GOR_CODE for each Government Office Region) -- AGE (aggregated in 4 categories: 0-39, 40-59, 60-79, 80+)

The English Cancer Patient Experience Survey (CPES) is commissioned by NHS England and administered on their behalf by an external survey provider organisation (Quality Health). The survey provides insights into the care experienced by cancer patients across England who were treated as day cases or inpatients. Data from CPES has been linked to cancer registration records recorded by the National Cancer Registration and Analysis Service (the cancer registry in England). Individual responses to Wave 2 of CPES are recorded , alongside characteristics of the patient who has completed the survey. Wave 2 of the National Cancer Patient Experience Survey is limited to patients discharged from cancer care between 01/09/2011 – 30/11/2011. Data within the file: --PATIENT_PSEUDO_ID (Project specific Pseudonymised Patient ID) GENDER (coded Male, Female) --QUINTILE2010 (Deprivation quintile [1-5], describing the Income Deprivation Domain where 1= least deprived and 5= most deprived) --FINAL_ROUTE (One of eight Routes to Diagnosis- methodology for the assignment of each route is described in Elliss-Brookes L, McPhail S, Greenslade M, Shelton J, Hiom S, Richards M (2012) Routes to diagnosis for cancer – determining the patient journey using multiple routine data sets. British Journal of Cancer 107: 1220–1226.) --AGE (aggregated in 4 categories: <55, 55-64, 65-74, 75+) --STAGE (stage of the cancer coded as I, II, III, IV, missing) --CANCER_SITE (Cancer sites coded in accordance with ICD 10: C00-C14, C15, C16, C18, C19-C20, C25, C33-C34, C43, C49, C50, C54, C56, C61, C64, C67, C73, C82, C83, C85, C90, C91-C95, D05 and ‘all other ICD-10 codes’ Specific disclosure controls applied: --Gender omitted from the data specification in the following cancer sites: • Female only for C50, D05 and C73 • Male only for C49 --Self-reported ethnicity (from the CPES surveys) aggregated into white British / non-white British / not specified. --Self-reported ethnicity omitted for C49, C64, C73 (replaced as “missing”).

• Frequency counts of cancer patients receiving radiotherapy, who were resident in England, and began treatment with radiotherapy between 1st April 2016 and 31st March 2017. All data is derived from the patient-level Radiotherapy Treatment Dataset (RTDS) data recorded by National Cancer Registration and Analysis Service (NCRAS). • Data is aggregated using anatomical site groups according to the International Classification of Diseases (ICD) ICD-10 codes, except where otherwise stated. Groupings are identified in tab: ‘Reference – tumour groupings.’ Data is further aggregated by age at treatment start date (presented as 5 year age bands) and sex (male/female). • Treatments are categorised by modality or method of treatment, as (1) external beam radiotherapy (EBRT or Teletherapy) and (2) internal brachytherapy

Whenever it is possible and practicable to do so, data released by PHE will be anonymous and made available under an Open Government License. To render the data anonymous it must be stripped of direct identifiers and privacy by design methods applied in line with the rules layed out in the ISB Anonymisation Standard for Publishing Health and Social Care Data Specification (2013).

Here we present a harmonized dataset including tumors from 41,539 individuals across 825 species. Each tumor is assigned the NCBI taxonomy ID for the species where it was found; a tissue-type ID specifying body site if applicable; and a cell type ID.

Posterior summary of simulated lung cancer BYM model with covariates.