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-..., , , # 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. ,

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.

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

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.

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....

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.

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

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.

The global Real-World Evidence (RWE) solutions market is experiencing robust growth, projected to reach $1.47 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 9.40% from 2025 to 2033. This expansion is driven by several key factors. The increasing adoption of RWE by pharmaceutical and medical device companies to support regulatory submissions and accelerate drug development is a significant driver. Furthermore, the growing volume of readily available electronic health records (EHRs), claims data, and patient-generated health data (PGHD) fuels the market's expansion. The shift towards value-based healthcare models, emphasizing real-world outcomes, further necessitates the use of RWE solutions for better patient care and cost-effectiveness. Technological advancements in data analytics and artificial intelligence (AI) are also instrumental in enhancing the capabilities of RWE platforms, making them more efficient and insightful. Market segmentation reveals significant contributions from oncology, immunology, and cardiovascular disease therapeutic areas, while healthcare payers and providers are key end-users leveraging these solutions. North America currently holds a substantial market share, though robust growth is anticipated in Asia-Pacific regions driven by rising healthcare expenditure and technological adoption. The market's growth, however, is not without challenges. Data privacy and security concerns surrounding the use of patient-level data remain a significant restraint. The complexity of integrating diverse data sources and ensuring data quality can also pose hurdles. Regulatory landscapes vary across regions, creating inconsistencies that impact market penetration. Nevertheless, ongoing efforts towards standardization and the development of robust data governance frameworks are mitigating these concerns, paving the way for continued market expansion. The competitive landscape is dynamic, with a mix of established players and emerging companies offering diverse solutions, ranging from data aggregation and analytics platforms to specialized consulting services. The market's trajectory suggests a promising future for RWE solutions as they become increasingly integral to healthcare research, drug development, and regulatory decision-making. Recent developments include: In December 2021, EVERSANA signed an agreement with Janssen Research & Development LLC (Janssen) to drive evidence-based development of Janssen therapies, treatments, and patient support models., In October 2021, Real-World Evidence Transparency Initiative launched the Real-World Evidence Registry to establish a culture of transparency for the analysis and reporting of Real-World Evidence in healthcare and health research. The Real-World Evidence Transparency Initiative is a partnership between ISPOR, the International Society for Pharmacoepidemiology, the Duke-Margolis Center for Health Policy, and the National Pharmaceutical Council.. Key drivers for this market are: Shift From Volume- to Value-based Care, Increasing Aging Population and Prevalence of Chronic Diseases. Potential restraints include: Shift From Volume- to Value-based Care, Increasing Aging Population and Prevalence of Chronic Diseases. Notable trends are: Oncology is Anticipated to be the Dominant Segment During the Forecast Period.

Remote Oncology Symptom Management Market Outlook

As per our latest research, the global remote oncology symptom management market size reached USD 1.42 billion in 2024, reflecting the rapid adoption of digital health solutions among oncology providers and patients worldwide. The market is poised for robust expansion, with a projected CAGR of 14.7% from 2025 to 2033. By the end of 2033, the market is forecasted to achieve a value of USD 4.39 billion. This significant growth is primarily driven by the increasing prevalence of cancer, the rising need for continuous patient monitoring, and the integration of advanced technologies such as artificial intelligence (AI) and telemedicine within oncology care pathways.

One of the most critical growth factors for the remote oncology symptom management market is the global rise in cancer incidence and survivorship. As cancer becomes a chronic condition for many, there is a growing demand for solutions that enable real-time symptom tracking and timely intervention. The integration of remote monitoring tools allows healthcare providers to detect and address symptoms such as pain, fatigue, and nausea promptly, preventing complications and reducing hospital admissions. This shift towards proactive care management not only improves patient outcomes but also optimizes healthcare resource utilization, making remote symptom management indispensable in modern oncology practice.

Technological advancements and digital transformation in healthcare are further accelerating market growth. The proliferation of smartphones, wearable devices, and user-friendly health apps has empowered patients to report symptoms from the comfort of their homes. Cloud-based platforms and AI-driven analytics facilitate the aggregation and interpretation of patient data, enabling personalized care plans and predictive symptom management. Additionally, interoperability with electronic health records (EHRs) ensures seamless communication between patients and multidisciplinary oncology teams, fostering a more holistic approach to cancer care. These innovations are attracting significant investments from both public and private sectors, further propelling market expansion.

The ongoing shift towards value-based care and patient-centered models is also a key driver. Payers and healthcare systems are increasingly recognizing the cost-effectiveness of remote oncology symptom management, as it reduces emergency room visits, shortens hospital stays, and enhances patient satisfaction. Regulatory support, such as reimbursement for remote monitoring services and the relaxation of telehealth restrictions, has further encouraged adoption. Furthermore, the COVID-19 pandemic underscored the necessity of remote care, prompting permanent changes in clinical workflows and solidifying the role of digital symptom management in oncology.

Regionally, North America dominates the remote oncology symptom management market, owing to its advanced healthcare infrastructure, high digital literacy, and favorable reimbursement landscape. Europe follows closely, driven by robust government initiatives and widespread adoption of telemedicine. The Asia Pacific region is emerging as a high-growth market, fueled by rising cancer prevalence, increasing healthcare investments, and rapid technological adoption. Latin America and the Middle East & Africa are also witnessing steady growth, although challenges such as limited digital infrastructure and regulatory complexities persist. Overall, the global market is characterized by dynamic regional trends, with each geography contributing uniquely to the market’s evolution.

Component Analysis

The component segment of the remote oncology symptom management market is broadly categorized into software, hardware, and services. Software solutions form the backbone of this market, accounting for the largest revenue share in 2024. These platforms offer functionalities such as symptom tracking, patient communication, data analytics, and integration w

Protein-drug conjugates (PDCs) constitute rapidly growing group of precise anticancer agents. Receptor-mediated endocytosis is a critical step in PDC action, which ensures delivery of PDC into cancer cell interior. Here we report TriF_HS-MMAE, the first multivalent PDC specifically targeting heparan sulphate proteoglycans (HSPGs) overexpressed by pancreatic cancer cells, which induces ultra fast and highly efficient aggregation dependent endocytosis (ADE) of HSPGs. Using high content screening with near kinome-wide library of inhibitors we identified signaling pathways that govern ADE of HSPGs and we discovered cascades that selectively operate in pancreatic cancer versus healthy cells. We show then that priming cells with identified endocytic modulators improves selectivity of the TriF_HS-MMAE conjugate. Overall, these findings provide insights into signaling/endocytosis/PDC interplay and for development of specific therapy of pancreatic cancer.

These data are raw data from publication by Chorążewska, Ciura et al: Priming cells with modulators of cellular signaling ensures enhanced selectivity of HSPG-specific protein-drug conjugate delivery.

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.

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.

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.

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”).

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.

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.

AI-Enabled Drug Discovery and Clinical Trials Market is estimated to be USD 19.8 Billion by 2035 and is anticipated to register a CAGR of 30.9% during the forecast period.