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.

Data for: Investigating the aggregation and prionogenic properties of human cancer-related proteins

https://doi.org/10.5061/dryad.905qfttw0

Description of the data and file structure

Table S1 shows computational analysis results to rank the aggregation propensity and prionogenicity of 9620 cancer-associated human proteins in this study. In the csv data sheet, FI MaxRun, PASTA Energy, and PASTA Disorder are for folding/free energy/disorder; and PLAAC Score and PAPA Score are used to measure the prionogenicity of a PrLD. Columns D-H are for normalized scores (0-1) and column I is weighted score - average of the normalized scores with distinct weighting factors considering equal importance of folding/free energy/disorder and prionogenicity. Table S2 and S3 include information of plasmids and primers used in this study, respectively. N/A, information unavailable.

Code/software

Microsoft Office Excel or .cvs combatable programs.

While studies on pathological protein aggregation are largely limited to neurodegenerative disease, emerging evidence suggests that other diseases are also associated with pathogenic protein aggregation. For example, tumor suppressor protein p53, and its mutant conformers, undergo protein aggregation, exacerbating the cancer phenotype. These findings raise the possibility that inactivation of tumor suppressors via protein aggregation may participate in cancer and other disease pathologies. Since tumor suppressor protein PTEN has similar functions to p53, and is mutated in multiple diseases, we examined the aggregation propensity of PTEN wild-type and 1523 clinically relevant PTEN mutants. Applying computational tools to PTEN mutation databases revealed that PTEN wild-type protein can aggregate under physiological conditions, and 274 distinct PTEN mutants had increased aggregation propensity. To understand the mechanism underlying PTEN conformer aggregation, we analyzed the physicochemical properties of these 274 PTEN mutants and defined their aggregation potential. We conclude that increased aggregation propensity of select PTEN mutants may contribute to disease phenotypes. Our studies have built the foundation for interrogating the aggregation potential of these select mutants in cancers and in PTENopathies. Elucidating the pathogenic mechanisms associated with aggregation-prone PTEN conformers will aid in developing therapies that target PTEN-aggregates in multiple diseases. Communicated by Ramaswamy H. Sarma

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....

This is a peer-reviewed supplementary table for the article 'Single-arm oncology trials and the nature of external controls arms' published in the Journal of Comparative Effectiveness Research.Supplementary Table 1: Comparing cancer types of focus among the 15 example studies.Summary: Aim: Single-arm trials with external control arms (ECAs) have gained popularity in oncology. ECAs may consist of primary data from previous trials, electronic health records (EHRs) or aggregate data from the literature. We sought to provide a description of how such studies achieve similarity of patients, comparability of data quality and outcome assessment. Materials & methods: In a stratified convenience sample of 15 studies, five used primary data from trials as ECAs, five used secondary data from EHRs and five used aggregate data from the literature. Data were collected from the published literature and public web resources, blinded to the eventual approval decision. Results: Studies using ECAs from primary data and EHR data displayed methods to achieve comparability of information, including matched baseline characteristics. Aggregate data from published studies did not attempt to match covariates. The EHR controls often showed calendar time overlap for collecting information while trial data were mostly historic. Outcome data were not consistently reported across studies. US FDA approval was only seen when primary data from trials or EHR data were used as the ECA, however no ECA in this sample directly contributed to approval. Discussion: In this nonsystematic review of ECAs for single-arm trials, the ECAs derived from primary data collected by other trials or EHRs show patterns of patient comparability, time overlap, and realistic methodological approaches to achieving balance between treatment arms. They are often submitted to regulators while literature-derived aggregate findings as ECA may serve as benchmarks for pipeline decisions.

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.

Posterior summary of simulated lung cancer BYM model with covariates.

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”).

Protein-Protein, Genetic, and Chemical Interactions for Lin TW (2015):Galectin-3 Binding Protein and Galectin-1 Interaction in Breast Cancer Cell Aggregation and Metastasis. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Galectin-3 binding protein (Gal-3BP) is a large hyperglycosylated protein that acts as a ligand for several galectins through glycan-dependent interactions. Gal-3BP can induce galectin-mediated tumor cell aggregation to increase the survival of cancer cells in the bloodstream during the metastatic process. However, the galectin interacting with Gal-3BP and its binding specificity has not been identified and structurally elucidated, mainly due to the limitation of mass spectrometry in glycan sequencing. To understand the role of Gal-3BP, we here used liquid chromatography-mass spectrometry combined with specific exoglycosidase reactions to determine the sequences of N-glycans on Gal-3BP from MCF-7 and MDA-MB-231 cells, especially the sequences with terminal sialylation and fucosylation, and addition of LacNAc repeat structures. The N-glycans from both strains are complex type with terminal α2,3-sialidic acid and core fucose linkages, with additional α1,2- and α1,3 fucose linkages found in MCF-7 cells. Compared with that from MCF-7, the Gal-3BP from MDA-MB-231 cells had fewer tetra-antennary structures, only α1,6-linked core fucoses, and more LacNAc repeat structures; the MDA-MB-231 cells had no surface galectin-3 but used surface galectin-1 for interaction with Gal-3BP to form large oligomers and cell aggregates. This study elucidates the specificity of Gal-3BP interacting with galectin-1 and the role of Gal-3BP in cancer cell aggregation and metastasis.

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.

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 4 of CPES are recorded , alongside characteristics of the patient who has completed the survey.

Wave 4 of the National Cancer Patient Experience Survey is limited to patients discharged from cancer care between 01/09/2013 – 30/11/2013.

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”).

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+)

• 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).

This statistic shows the rate of mortality to cancer incidence excluding non-melanoma skin cancer (NMSC) in England in 2016, by region and gender. Cancer is an aggregation of diseases in which cells within the body grow abnormally, often spreading to other parts of the body. In this year, in the north east of England 51 percent of males and 46 percent females who were diagnosed died as a result of cancer, excluding NMSC, this is the highest recorded rate among males and females.

BackgroundCancer is a leading cause of death, especially among women, with cancers like breast, ovarian, and cervical cancer presenting unique diagnostic and treatment challenges. Systemic inflammation plays a significant role in cancer progression, affecting both tumor development and therapeutic outcomes. Despite the established link between inflammation and cancer, comprehensive studies on the prognostic value of the Aggregate Index of Systemic Inflammation (AISI) in female cancer patients are lacking. This study explores the association between AISI and mortality outcomes, including all-cause and cardiovascular mortality, in female cancer patients.MethodsThis study analyzes data from the NHANES database and Dandong Central Hospital. Kaplan-Meier survival curves and multivariable Cox proportional hazards regression analyses were used to assess the relationship between AISI and all-cause and cardiovascular mortality. Restricted cubic spline plots and subgroup analyses were applied to explore potential interactions.ResultsElevated AISI levels were strongly associated with increased all-cause and cardiovascular mortality. Patients in the highest AISI quartile demonstrated significantly higher mortality risks compared to those in the lowest quartile. ROC curve analysis indicated superior predictive performance of AISI over SII. Restricted cubic spline plots revealed a linear relationship, with mortality risk notably increasing when AISI levels were elevated.ConclusionAISI is a robust predictor of all-cause and cardiovascular mortality in female cancer patients. Its ease of measurement and strong prognostic value make it a valuable tool for risk assessment and management in this population.

Near-real time aggregated cancer activity data from 8 major sites across the UK, for the purpose of testing the effect of the COVID-19 pandemic on cancer diagnostic and cancer treatment pathways.

Gold complexes have been recognized as potential anticancer agents against various kinds of diseases due to their inherent suppressions of antioxidant thioredoxin reductase (TrxR) activity. Herein, a powerful aggregation-induced emission luminogen (AIEgen), TBP-Au, was designed and synthesized by integrating an anticancer Au(I) moiety with an AIE-active photosensitizer (TBP), in which both the production and consumption routes of reactive oxygen species (ROS) were elaborately considered simultaneously to boost the anticancer efficacy. It has been demonstrated that TBP-Au could realize superior two-photon fluorescence imaging in tumor tissues with high resolution and deep penetration as well as long-term imaging in live animals due to its AIE property. In addition, the introduction of a special Au(I) moiety could tune the organelle specificity and efficiently facilitate the ROS-determined photodynamic therapy (PDT). More impressively, TBP-Au could efficiently eliminate cancer cells under light irradiation through the preconceived synergetic approaches from the PDT and the effective suppression of TrxR, demonstrating that TBP-Au holds great potential for precise cancer theranostics.

International challenges have become the standard for validation of biomedical image analysis methods. We argue, though, that the actual performance even of the winning algorithms on ���real-world��� clinical data often remains unclear, as the data included in these challenges are usually acquired in very controlled settings at few institutions. The seemingly obvious solution of just collecting increasingly more data from more institutions in such challenges does not scale well due to privacy and ownership hurdles. As the first challenge to ever be proposed for federated learning in medicine, the Federated Tumor Segmentation (FeTS) challenge 2021 intends to address these hurdles, both for the creation and the evaluation of tumor segmentation models. Specifically, the FeTS 2021 challenge uses clinically acquired, multi-institutional MRI scans from the BraTS challenge, as well as from various remote independent institutions included in the collaborative network of a real-world federation (www.fets.ai). The FeTS challenge focuses on the construction and evaluation of a consensus model for the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas [1]. Compared to the BraTS challenge [2-4], the ultimate goal of FeTS is 1) the creation of a consensus segmentation model that has gained knowledge from data of multiple institutions without pooling their data together (i.e., by retaining the data within each institution), and 2) the evaluation of segmentation models in such a federated configuration (i.e., in the wild). The FeTS 2021 challenge is structured in two tasks: Task 1 ("Federated Training") aims at effective weight aggregation methods for the creation of a consensus model given a pre-defined segmentation algorithm for training, while also (optionally) accounting for network outages. Task 2 ("Federated Evaluation") aims at robust segmentation algorithms, given a pre-defined weight aggregation method, evaluated during the testing phase on unseen datasets from various remote independent institutions of the collaborative network of the fets.ai federation. To prepare for both these tasks, the participants can use the information provided on data origin and acquisition settings during the training phase of the challenge. We intend to add a third task in the FeTS challenge 2022 to account for adversaries during the training phase. The clinical relevance and importance of the FeTS challenge is that it addresses challenges related to privacy, legal, bureaucratic, and ownership concerns. Ground truth reference annotations are created and approved by expert neuroradiologists for every subject included in the training, validation, and testing datasets to quantitatively evaluate the performance of the participating algorithms. Participants are free to choose whether they want to focus on only one or multiple tasks. References [1] M.J.Sheller, et al. "Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data." Scientific reports. 10:1-12, 2020. DOI: 10.1038/s41598-020-69250-1 [2] B. H. Menze, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging 34(10):1993-2024, 2015. DOI: 10.1109/TMI.2014.2377694 [3] S.Bakas, et al., ���Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge���, arXiv preprint arXiv:1811.02629 [4] S. Bakas, et al., "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data, 4:170117, 2017. DOI: 10.1038/sdata.2017.117 [5] T. Rohlfing, et al. The SRI24 multichannel atlas of normal adult human brain structure. Hum Brain Mapp. 31(5):798-819, 2010. [6] Duan R, et al. PALM: Patient-centered Treatment Ranking via Large-scale Multivariate Network Meta-analysis. medRxiv. 2020. [7] A. L. Simpson et al., ���A large annotated medical image dataset for the development and evaluation of segmentation algorithms,��� arXiv:1902.09063 [8] M. Wiesenfarth, et al. ���Methods and open-source toolkit for analyzing and visualizing challenge results,��� arXiv:1910.05121. [9] L. Maier-Hein, et al., ���Why rankings of biomedical image analysis competitions should be interpreted with care,��� Nat. Commun., 9(1):1���13, 2018. DOI: 10.1038/s41467-018-07619-7 [10] R.Cox, et al. ���A (Sort of) new image data format standard: NIfTI-1: WE 150���, Neuroimage, 22, 2004. [11] S.Thakur, et al. ���Brain Extraction on MRI Scans in Presence of Diffuse Glioma: Multi-institutional Performance Evaluation of Deep Learning Methods and Robust Modality-Agnostic Training���, NeuroImage, 220: 117081, 2020. DOI: 10.1016/j.neuroimage.2020.117081

This dataset presents the footprint of cancer incidence statistics in Australia for all cancers combined. The data spans the years 2006-2010 and is aggregated to the 2011 Public Health Information …Show full descriptionThis dataset presents the footprint of cancer incidence statistics in Australia for all cancers combined. The data spans the years 2006-2010 and is aggregated to the 2011 Public Health Information Development Unit (PHIDU) Population Health Areas (PHA), based on the 2011 Australian Statistical Geography Standard (ASGS). Incidence data refer to the number of new cases of cancer diagnosed in a given time period. It does not refer to the number of people newly diagnosed (because one person can be diagnosed with more than one cancer in a year). Cancer incidence data come from the Australian Institute of Health and Welfare (AIHW) 2012 Australian Cancer Database (ACD). For further information about this dataset, please visit: Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books. Australian Cancer Database 2012 Data Quality Statement. Please note: AURIN has spatially enabled the original data using the PHIDU - PHAs. Due to changes in geographic classifications over time, long-term trends are not available. Values assigned to "n.p." in the original data have been removed from the data. The Australian and jurisdictional totals include people who could not be assigned to a PHA. The number of people who could not be assigned a PHA is less than 1% of the total. The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory). The ACD records all primary cancers except for basal and squamous cell carcinomas of the skin (BCCs and SCCs). These cancers are not notifiable diseases and are not collected by the state and territory cancer registries. The diseases coded to ICD-10 codes D45-D46, D47.1 and D47.3-D47.5, which cover most of the myelodysplastic and myeloproliferative cancers, were not considered cancer at the time the ICD-10 was first published and were not routinely registered by all Australian cancer registries. The ACD contains all cases of these cancers which were diagnosed from 1982 onwards and which have been registered but the collection is not considered complete until 2003 onwards. Note that the incidence data presented are for 2006-2010 because 2011 and 2012 data for NSW and ACT were not able to be provided for the 2012 ACD. Copyright attribution: Government of the Commonwealth of Australia - Australian Institute of Health and Welfare, (2016): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia (CC BY-NC-SA 3.0 AU)

Methods Study population This study was based on data derived from 1,000 NSCLC cases and 1,000 cancer-free controls, frequency-matched by age, gender, and smoking status as previously described.1 All cases were recruited at Massachusetts General Hospital (MGH) from 1992–2004, were >18 years old, and had newly diagnosed, histologically confirmed primary NSCLC. Controls were healthy, non-blood-related family members and friends of patients with cancer or with cardiothoracic conditions undergoing surgery. Histological classification was done by two staff pulmonary pathologists at MGH according to the International Classification of Diseases for Oncology (ICD-O3). For histology analysis, the following codes were used: adenocarcinoma, 8140/3, 8250/3, 8260/3, 8310/3, 8480/3, and 8560/3; large cell carcinoma, 8012/3 and 8031/3; squamous cell carcinoma, 8070/3, 8071/3, 8072/3, and 8074/3; and other non-small cell carcinomas, 8010/3, 8020/3, 8021/3, 8032/3, and 8230/3. The Institutional Review Board of MGH and the Human Subjects Committee of the Harvard School of Public Health approved the study, and all participants signed consent forms. GWAS dataset DNA was extracted from peripheral white blood cells using standard protocols and was genotyped using the Human610-Quad BeadChip (Illumina, San Diego, CA). Before association tests, we conducted a systematic quality evaluation of raw genotyping data according to a general quality control (QC) procedure described by Anderson et al.2 Briefly, unqualified samples were excluded if they fit the following QC criteria: (i) overall genotype completion rates <95%; (ii) gender discrepancies; (iii) unexpected duplicates or probable relatives (based on pairwise identity by state value, PI_HAT in PLINK > 0.185); or (iv) heterozygosity rates >6 standard deviations from the mean. Unqualified SNPs were excluded if they fit the following QC criteria: (i) overall genotype completion rates <95%; (ii) gender discrepancies; (iii) unexpected duplicates or probable relatives (based on pairwise identity by state value, PI_HAT in PLINK > 0.185); (iv) heterozygosity rates >6 standard deviations from the mean; or (v) individuals were non-Caucasians (using the HapMap release 23, including JPT, CEPH, CEU, and YRI populations as reference). Unqualified SNPs were excluded if they fit the following QC criteria: (i) not mapped on autosomes; (ii) call rate <95% in all GWAS samples; (iii) MAF < 0.01; or (iv) genotype distributions deviated from those expected by Hardy-Weinberg equilibrium (p < 1.0×10-6). After quality evaluation, we had a dataset of 984 cases and 970 controls with 543,697 autosomal SNPs for epistasis analysis. Transcriptomic data analysis Although FFPE profiles and external data were generated from different platforms, we used DNA-Chip Analyzer 2006 (dChip, http://www.dchip.org) software, which applied an invariant set of genes for normalization and calculation of expression values across all microarrays, to normalize raw microarray signals. This analysis assumed that a subset of genes had constant expression among all cell subtypes.3 Only paired tumor and non-affected tissue samples were used in the analysis, including 18 FFPE adenocarcinomas, 8 FFPE squamous cell carcinomas, 33 snap-frozen adenocarcinomas (GSE10072), and 32 snap-frozen squamous cell carcinomas (GSE18842). Targeted and whole exome sequencing Targeted and whole exome sequencing was performed at the Center for Inherited Disease Research. Ninety-nine custom regions were targeted for a total of 17.26Mb of custom content that was then added to the Agilent SureSelect XT Human All Exome v5 capture product as one pool of capture probes. The Agilent Sure Design Software (https://earray.chem.agilent.com/suredesign/index.htm) was used to design the custom content in a tiered fashion, reducing stringency parameters for each subsequent pass of uncovered target regions not covered by a probe in the previous pass (Pass 1 - 1x tiling, most stringent masking, max performance boosting; Pass 2 - 1x tiling, moderate stringent masking, balanced boosting; Pass 3 - 1x tiling, least stringent masking, no probe boosting). Probes from the third pass were filtered using BLAT to identify any potentially problematic probes that could affect selection due to homology 4. Threshold settings within BLAT were set to 100.
1ug of genomic DNA was sheared using the Covaris E210 instrument (Covaris), shear time 80 seconds. A hybrid protocol for library preparation and whole exome enrichment was developed at CIDR based on methods and parameters from Fisher et al. 5, applied to the reagents, volumes and parameters from the Agilent SureSelect XT kit and automated protocol l (p/n G7550-90000 revision B) and substituting Herculase enzyme with Kapa Biosystem HiFi Hotstart Ready Mix. Libraries were sequenced on the HiSeq2500, 125 bp paired end runs and sequencing chemistry kits HiSeq PE Cluster Kit v4 and HiSeq SBS kit v4. BAM 6 files were created by aligning FASTQ 7 files to GRCh37 8, 9 with BWA mem v0.7.8 and using GATK’s best practices 10, 11 with v3.3-0. Joint sample variant calling and variant site filtering was performed using GATK’s HaplotypeCaller and Variant Quality Score recalibration according to their best practices 12. Genotypes for biallelic SNPs were further refined using CalculateGenotypePosteriors and allele frequency information from 1000 genomes phase 3 data 13 as well as the Exome Aggregation Consortium data 14. The mean on-target coverage (regions covered by probes in the bait pool) was 52X for each sequencing experiment and greater than 97% of on-target bases had a depth greater than 10X. Additional quality control was performed by the Genetic Analysis Center at the University of Washington. Sample quality metrics were analyzed using PCA to identify multi-variate outliers (which were excluded from downstream analysis) and to verify the absence of notable batch effects. A small number of first and second degree relatives were identified and excluded from downstream analysis. PCA of genotypes were used to define a homogeneous set of samples for Hardy-Weinberg equilibrium (HWE) testing. The quality of genotype calls was evaluated using concordance with array data, duplicate concordance, HWE deviation and Mendelian errors. These evaluations showed that the genotype refinement step improved quality An unsupervised variant quality metric was defined from PCA of variant-level quality metrics and found to be comparable to the VQSR metric. Both metrics were used to filter variants for association testing.