In the following maps, the U.S. states are divided into groups based on the rates at which people developed or died from cancer in 2013, the most recent year for which incidence data are available.

The rates are the numbers out of 100,000 people who developed or died from cancer each year.

Incidence Rates by State The number of people who get cancer is called cancer incidence. In the United States, the rate of getting cancer varies from state to state.

*Rates are per 100,000 and are age-adjusted to the 2000 U.S. standard population.

‡Rates are not shown if the state did not meet USCS publication criteria or if the state did not submit data to CDC.

†Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2016. Available at: http://www.cdc.gov/uscs.

Death Rates by State Rates of dying from cancer also vary from state to state.

*Rates are per 100,000 and are age-adjusted to the 2000 U.S. standard population.

†Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2016. Available at: http://www.cdc.gov/uscs.

Source: https://www.cdc.gov/cancer/dcpc/data/state.htm

Cancer Mortality & Incidence Rates: (Country LVL)

Investigating Cancer Trends over time

By Data Exercises [source]

About this dataset

This dataset is a comprehensive collection of data from county-level cancer mortality and incidence rates in the United States between 2000-2014. This data provides an unprecedented level of detail into cancer cases, deaths, and trends at a local level. The included columns include County, FIPS, age-adjusted death rate, average death rate per year, recent trend (2) in death rates, recent 5-year trend (2) in death rates and average annual count for each county. This dataset can be used to provide deep insight into the patterns and effects of cancer on communities as well as help inform policy decisions related to mitigating risk factors or increasing preventive measures such as screenings. With this comprehensive set of records from across the United States over 15 years, you will be able to make informed decisions regarding individual patient care or policy development within your own community!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides comprehensive US county-level cancer mortality and incidence rates from 2000 to 2014. It includes the mortality and incidence rate for each county, as well as whether the county met the objective of 45.5 deaths per 100,000 people. It also provides information on recent trends in death rates and average annual counts of cases over the five year period studied.

This dataset can be extremely useful to researchers looking to study trends in cancer death rates across counties. By using this data, researchers will be able to gain valuable insight into how different counties are performing in terms of providing treatment and prevention services for cancer patients and whether preventative measures and healthcare access are having an effect on reducing cancer mortality rates over time. This data can also be used to inform policy makers about counties needing more target prevention efforts or additional resources for providing better healthcare access within at risk communities.

When using this dataset, it is important to pay close attention to any qualitative columns such as “Recent Trend” or “Recent 5-Year Trend (2)” that may provide insights into long term changes that may not be readily apparent when using quantitative variables such as age-adjusted death rate or average deaths per year over shorter periods of time like one year or five years respectively. Additionally, when studying differences between different counties it is important to take note of any standard FIPS code differences that may indicate that data was collected by a different source with a difference methodology than what was used in other areas studied

Research Ideas

• Using this dataset, we can identify patterns in cancer mortality and incidence rates that are statistically significant to create treatment regimens or preventive measures specifically targeting those areas.
• This data can be useful for policymakers to target areas with elevated cancer mortality and incidence rates so they can allocate financial resources to these areas more efficiently.
• This dataset can be used to investigate which factors (such as pollution levels, access to medical care, genetic make up) may have an influence on the cancer mortality and incidence rates in different US counties

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: death .csv | Column name | Description | |:-------------------------------------------|:-------------------------------------------------------------------...

Population based cancer incidence rates were abstracted from National Cancer Institute, State Cancer Profiles for all available counties in the United States for which data were available. This is a national county-level database of cancer data that are collected by state public health surveillance systems. All-site cancer is defined as any type of cancer that is captured in the state registry data, though non-melanoma skin cancer is not included. All-site age-adjusted cancer incidence rates were abstracted separately for males and females. County-level annual age-adjusted all-site cancer incidence rates for years 2006–2010 were available for 2687 of 3142 (85.5%) counties in the U.S. Counties for which there are fewer than 16 reported cases in a specific area-sex-race category are suppressed to ensure confidentiality and stability of rate estimates; this accounted for 14 counties in our study. Two states, Kansas and Virginia, do not provide data because of state legislation and regulations which prohibit the release of county level data to outside entities. Data from Michigan does not include cases diagnosed in other states because data exchange agreements prohibit the release of data to third parties. Finally, state data is not available for three states, Minnesota, Ohio, and Washington. The age-adjusted average annual incidence rate for all counties was 453.7 per 100,000 persons. We selected 2006–2010 as it is subsequent in time to the EQI exposure data which was constructed to represent the years 2000–2005. We also gathered data for the three leading causes of cancer for males (lung, prostate, and colorectal) and females (lung, breast, and colorectal). The EQI was used as an exposure metric as an indicator of cumulative environmental exposures at the county-level representing the period 2000 to 2005. A complete description of the datasets used in the EQI are provided in Lobdell et al. and methods used for index construction are described by Messer et al. The EQI was developed for the period 2000– 2005 because it was the time period for which the most recent data were available when index construction was initiated. The EQI includes variables representing each of the environmental domains. The air domain includes 87 variables representing criteria and hazardous air pollutants. The water domain includes 80 variables representing overall water quality, general water contamination, recreational water quality, drinking water quality, atmospheric deposition, drought, and chemical contamination. The land domain includes 26 variables representing agriculture, pesticides, contaminants, facilities, and radon. The built domain includes 14 variables representing roads, highway/road safety, public transit behavior, business environment, and subsidized housing environment. The sociodemographic environment includes 12 variables representing socioeconomics and crime. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., L. Messer, K. Rappazzo , C. Gray, S. Grabich , and D. Lobdell. County-level environmental quality and associations with cancer incidence#. Cancer. John Wiley & Sons Incorporated, New York, NY, USA, 123(15): 2901-2908, (2017).

Dataset Card for Lung Cancer

  Dataset Summary

The effectiveness of cancer prediction system helps the people to know their cancer risk with low cost and it also helps the people to take the appropriate decision based on their cancer risk status. The data is collected from the website online lung cancer prediction system .

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/virtual10/lungs_cancer.

Context

Millions of people around the world suffer from cancer without any hope of treatment due to the extravagant treatment costs.

Content

In this dataset you'll find the total money spent on treating different cancers.

Acknowledgements

This data comes from https://data.world/xprizeai-health/expenditures-for-cancer-care.

Death rate has been age-adjusted by the 2000 U.S. standard population. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Lung cancer is a leading cause of cancer-related death in the US. People who smoke have the greatest risk of lung cancer, though lung cancer can also occur in people who have never smoked. Most cases are due to long-term tobacco smoking or exposure to secondhand tobacco smoke. Cities and communities can take an active role in curbing tobacco use and reducing lung cancer by adopting policies to regulate tobacco retail; reducing exposure to secondhand smoke in outdoor public spaces, such as parks, restaurants, or in multi-unit housing; and improving access to tobacco cessation programs and other preventive services.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

https://www.cancerimagingarchive.net/wp-content/uploads/nctn-logo-300x108.png" alt="" width="300" height="108" />

Demographic Summary of Available Imaging

Characteristic	Value (N = 26254)
Age (years)	Mean ± SD: 61.4± 5 Median (IQR): 60 (57-65) Range: 43-75
Sex	Male: 15512 (59%) Female: 10742 (41%)
Race	White: 23969 (91.3%) Black: 1135 (4.3%) Asian: 547 (2.1%) American Indian/Alaska Native: 88 (0.3%) Native Hawaiian/Other Pacific Islander: 87 (0.3%) Unknown: 428 (1.6%)
Ethnicity	Not Available

Background: The aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. The advent of low-dose helical computed tomography (CT) altered the landscape of lung-cancer screening, with studies indicating that low-dose CT detects many tumors at early stages. The National Lung Screening Trial (NLST) was conducted to determine whether screening with low-dose CT could reduce mortality from lung cancer.

Methods: From August 2002 through April 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 U.S. medical centers. Participants were randomly assigned to undergo three annual screenings with either low-dose CT (26,722 participants) or single-view posteroanterior chest radiography (26,732). Data were collected on cases of lung cancer and deaths from lung cancer that occurred through December 31, 2009. This dataset includes the low-dose CT scans from 26,254 of these subjects, as well as digitized histopathology images from 451 subjects.

Results: The rate of adherence to screening was more than 90%. The rate of positive screening tests was 24.2% with low-dose CT and 6.9% with radiography over all three rounds. A total of 96.4% of the positive screening results in the low-dose CT group and 94.5% in the radiography group were false positive results. The incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose CT group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [CI], 1.03 to 1.23). There were 247 deaths from lung cancer per 100,000 person-years in the low-dose CT group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose CT screening of 20.0% (95% CI, 6.8 to 26.7; P=0.004). The rate of death from any cause was reduced in the low-dose CT group, as compared with the radiography group, by 6.7% (95% CI, 1.2 to 13.6; P=0.02).

Conclusions: Screening with the use of low-dose CT reduces mortality from lung cancer. (Funded by the National Cancer Institute; National Lung Screening Trial ClinicalTrials.gov number, NCT00047385).

Data Availability: A summary of the National Lung Screening Trial and its available datasets are provided on the Cancer Data Access System (CDAS). CDAS is maintained by Information Management System (IMS), contracted by the National Cancer Institute (NCI) as keepers and statistical analyzers of the NLST trial data. The full clinical data set from NLST is available through CDAS. Users of TCIA can download without restriction a publicly distributable subset of that clinical data, along with the CT and Histopathology images collected during the trial. (These previously were restricted.)

IntroductionSociodemographic disparities in genitourinary cancer-related mortality have been insufficiently studied, particularly across multiple cancer types. This study aimed to investigate gender, racial, and geographic disparities in mortality rates for the most common genitourinary cancers in the United States.MethodsMortality data for prostate, bladder, kidney, and testicular cancers were obtained from the Centers for Disease Control and Prevention (CDC) WONDER database between 1999 and 2020. Age-adjusted mortality rates (AAMRs) were analyzed by year, gender, race, urban–rural status, and geographic region using a significance level of p < 0.05.ResultsOverall, AAMRs for prostate, bladder, and kidney cancer declined significantly, while testicular cancer-related mortality remained stable. Bladder and kidney cancer AAMRs were 3–4 times higher in males than females. Prostate cancer mortality was highest in black individuals/African Americans and began increasing after 2015. Bladder cancer mortality decreased significantly in White individuals, Black individuals, African Americans, and Asians/Pacific Islanders but remained stable in American Indian/Alaska Natives. Kidney cancer-related mortality was highest in White individuals but declined significantly in other races. Testicular cancer mortality increased significantly in White individuals but remained stable in Black individuals and African Americans. Genitourinary cancer mortality decreased in metropolitan areas but either increased (bladder and testicular cancer) or remained stable (kidney cancer) in non-metropolitan areas. Prostate and kidney cancer mortality was highest in the Midwest, bladder cancer in the South, and testicular cancer in the West.DiscussionSignificant sociodemographic disparities exist in the mortality trends of genitourinary cancers in the United States. These findings highlight the need for targeted interventions and further research to address these disparities and improve outcomes for all populations affected by genitourinary cancers.

The Cancer Prevention Studies (CPS) aim to understand why and how certain people develop cancer while others remain cancer-free. In 1982, the CPS-II cohort was established and includes approximately 1.2 million men and women, aged at least 30 years, recruited by American Cancer Society (ACS) volunteers in all 50 states of the United States of America and Puerto Rico. Participants have been followed biannually for mortality. The CPS-II Nutrition Cohort was established as a subgroup of the larger CPS-II cohort, in which approximately 185,000 individuals have been followed biennially for cancer incidence, diet, and other exposures, since 1992. The CPS-II Lifelink Cohort/Biorepository was initiated in 1998, and collected blood samples from 40,000 participants and cheek cell samples from 70,000 participants in the CPS-II Nutrition Survey cohort.

WONDER online databases include county-level Compressed Mortality (death certificates) since 1979; county-level Multiple Cause of Death (death certificates) since 1999; county-level Natality (birth certificates) since 1995; county-level Linked Birth / Death records (linked birth-death certificates) since 1995; state & large metro-level United States Cancer Statistics mortality (death certificates) since 1999; state & large metro-level United States Cancer Statistics incidence (cancer registry cases) since 1999; state and metro-level Online Tuberculosis Information System (TB case reports) since 1993; state-level Sexually Transmitted Disease Morbidity (case reports) since 1984; state-level Vaccine Adverse Event Reporting system (adverse reaction case reports) since 1990; county-level population estimates since 1970. The WONDER web server also hosts the Data2010 system with state-level data for compliance with Healthy People 2010 goals since 1998; the National Notifiable Disease Surveillance System weekly provisional case reports since 1996; the 122 Cities Mortality Reporting System weekly death reports since 1996; the Prevention Guidelines database (book in electronic format) published 1998; the Scientific Data Archives (public use data sets and documentation); and links to other online data sources on the "Topics" page.

HRs and 95% CIs for the association between baseline and longitudinal pain scales and death, IRONMAN Registry 2017–2023 (N = 879; 137 deaths in White participants, 37 deaths in Black participants)

BackgroundColorectal cancer (CRC) incidence rates have increased in younger individuals worldwide. We examined the most recent early- and late-onset CRC rates for the US.MethodsAge-standardized incidence rates (ASIR, per 100,000) of CRC were calculated using the US Cancer Statistics Database’s high-quality population-based cancer registry data from the entire US population. Results were cross-classified by age (20-49 [early-onset] and 50-74 years [late-onset]), race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, American Indian/Alaskan Native, Asian/Pacific Islander), sex, anatomic location (proximal, distal, rectal), and histology (adenocarcinoma, neuroendocrine).ResultsDuring 2001 through 2018, early-onset CRC rates significantly increased among American Indians/Alaskan Natives, Hispanics, and Whites. Compared to Whites, early-onset CRC rates are now 21% higher in American Indians/Alaskan Natives and 6% higher in Blacks. Rates of early-onset colorectal neuroendocrine tumors have increased in Whites, Blacks, and Hispanics; early-onset colorectal neuroendocrine tumor rates are 2-times higher in Blacks compared to Whites. Late-onset colorectal adenocarcinoma rates are decreasing, while late-onset colorectal neuroendocrine tumor rates are increasing, in all racial/ethnic groups. Late-onset CRC rates remain 29% higher in Blacks and 15% higher in American Indians/Alaskan Natives compared to Whites. Overall, CRC incidence was higher in men than women, but incidence of early-onset distal colon cancer was higher in women.ConclusionsThe early-onset CRC disparity between Blacks and Whites has decreased, due to increasing rates in Whites—rates in Blacks have remained stable. However, rates of colorectal neuroendocrine tumors are increasing in Blacks. Blacks and American Indians/Alaskan Natives have the highest rates of both early- and late-onset CRC.ImpactOngoing prevention efforts must ensure access to and uptake of CRC screening for Blacks and American Indians/Alaskan Natives.

Demographic Trends and Health Outcomes in the U.S

Inequalities,Risk Factors and Access to Care

By Data Society [source]

About this dataset

This dataset contains key demographic, health status indicators and leading cause of death data to help us understand the current trends and health outcomes in communities across the United States. By looking at this data, it can be seen how different states, counties and populations have changed over time. With this data we can analyze levels of national health services use such as vaccination rates or mammography rates; review leading causes of death to create public policy initiatives; as well as identify risk factors for specific conditions that may be associated with certain populations or regions. The information from these files includes State FIPS Code, County FIPS Code, CHSI County Name, CHSI State Name, CHSI State Abbreviation, Influenza B (FluB) report count & expected cases rate per 100K population , Hepatitis A (HepA) Report Count & expected cases rate per 100K population , Hepatitis B (HepB) Report Count & expected cases rate per 100K population , Measles (Meas) Report Count & expected cases rate per 100K population , Pertussis(Pert) Report Count & expected case rate per 100K population , CRS report count & expected case rate per 100K population , Syphilis report count and expected case rate per 100k popuation. We also look at measures related to preventive care services such as Pap smear screen among women aged 18-64 years old check lower/upper confidence intervals seperately ; Mammogram checks among women aged 40-64 years old specified lower/upper conifence intervals separetly ; Colonosopy/ Proctoscpushy among men aged 50+ measured in lower/upper limits ; Pneumonia Vaccination amongst 65+ with loewr/upper confidence level detail Additionally we have some interesting trend indicating variables like measures of birth adn death which includes general fertility ratye ; Teen Birth Rate by Mother's age group etc Summary Measures covers mortality trend following life expectancy by sex&age categories Vressionable populations access info gives us insight into disablilty ratio + access to envtiromental issues due to poor quality housing facilities Finally Risk Factors cover speicfic hoslitic condtiions suchs asthma diagnosis prevelance cancer diabetes alcholic abuse smoking trends All these information give a good understanding on Healthy People 2020 target setings demograpihcally speaking hence will aid is generating more evience backed policies

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

What the Dataset Contains

This dataset contains valuable information about public health relevant to each county in the United States, broken down into 9 indicator domains: Demographics, Leading Causes of Death, Summary Measures of Health, Measures of Birth and Death Rates, Relative Health Importance, Vulnerable Populations and Environmental Health Conditions, Preventive Services Use Data from BRFSS Survey System Data , Risk Factors and Access to Care/Health Insurance Coverage & State Developed Types of Measurements such as CRS with Multiple Categories Identified for Each Type . The data includes indicators such as percentages or rates for influenza (FLU), hepatitis (HepA/B), measles(MEAS) pertussis(PERT), syphilis(Syphilis) , cervical cancer (CI_Min_Pap_Smear - CI_Max\Pap \Smear), breast cancer (CI\Min Mammogram - CI \Max \Mammogram ) proctoscopy (CI Min Proctoscopy - CI Max Proctoscopy ), pneumococcal vaccinations (Ci min Pneumo Vax - Ci max Pneumo Vax )and flu vaccinations (Ci min Flu Vac - Ci Max Flu Vac). Additionally , it provides information on leading causes of death at both county levels & national level including age-adjusted mortality rates due to suicide among teens aged between 15-19 yrs per 100000 population etc.. Furthermore , summary measures such as age adjusted percentage who consider their physical health fair or poor are provided; vulnerable populations related indicators like relative importance score for disabled adults ; preventive service use related ones ranging from self reported vaccination coverage among men40-64 yrs old against hepatitis B virus etc...

Getting Started With The Dataset

To get started with exploring this dataset first your need to understand what each column in the table represents: State FIPS Code identifies a unique identifier used by various US government agencies which denote states . County FIPS code denotes counties wi...

Stratified analyses for the association between pain scales at enrollment and death, IRONMAN Registry 2017–2023

Average pain at study enrollment by disease state at enrollment and self-reported race (N = 4 pain scales)

I was interested in investigating cancer incidence levels in the US by looking at how they vary by race or state. All the data is collected online from Centers for Disease Control and Prevention, State Cancer Profiles, and United States Census Bureau. This dataset can be used to answer questions on the correlation between poverty levels, insurance levels and cancer incidence levels. Further, one can find which cancers affect a certain race more or a certain state.

BackgroundOvarian cancer contributes significantly to the morbidity and mortality rates for women worldwide. As observed with other types of cancer, health disparities disproportionately affect ovarian cancer incidence rates and outcomes, especially in African American and older women. However, the trends in ovarian cancer mortality rates up until 2023 with regard to various demographic identifiers have not been fully elucidated, which this study aims to rectify.MethodsMortality trends due to malignant neoplasms of the ovary in individuals 25 and older in the US from 1999 to 2023 were analyzed using the Centers for Disease Control Wide Ranging Online Data for Epidemiological Research (CDC WONDER) database. Trends in age-adjusted mortality rate (AAMR) were analyzed on the basis of race, 10-year age-group, region and urban/rural designation.ResultsBetween 1999 and 2023, the AAMR related to malignant neoplasms of the ovary fell from 14.62 in 1999 to 9.52 in 2023. All races analyzed saw a decrease in overall mortality related to malignant neoplasms of the ovary, with the largest decrease being observed in White patients (AAPC: -1.78). Regionally, the Northeast (AAPC: -1.95), Midwest (AAPC: -1.99), South (AAPC: -1.72), and West (AAPC: -1.73) regions of the United States (US) all saw reduced ovarian neoplasm mortality rates. Similarly, rates also decreased in urban (AAPC: -1.83) and rural (AAPC: -1.75) localities, as well as in each ten-year age category analyzed, with the largest decrease seen in the 55–64 years old category (AAPC: -2.15). States such as Delaware, South Carolina, and Idaho experienced some of the largest decreases in AAMR, whereas the District of Columbia saw an increase in AAMR during this period.ConclusionsOver the last twenty-years, mortality rates for malignant neoplasms of the ovary have declined, with the largest decreases being seen in White patients, those residing in the Midwest, urban locality, and women between 55–64 years olds. While mortality rates have declined, health disparities still continue to negatively affect ovarian cancer outcomes, and more research is needed to improve accessibility, availability, and affordability of care for patients.

Abstract

The AURORA US Metastatic Breast Cancer project is funded by the Breast Cancer Research Foundation (BCRF) Evelyn H. Lauder Founder's Fund for Metastatic Breast Cancer Research. This multi-center effort was conducted within the Translational Breast Cancer Research Consortium (TBCRC) and cancer researchers to better understand the metastatic process through the study of both the primary and metastatic tissue. In the retrospective phase, 55 patients with 31 primary tissues and 102 metastases were profiled using whole genome DNA sequencing, whole exome DNA sequencing, DNA methylation arrays, and RNA sequencing. The related molecular data are hosted in dbGaP and GEO.

H&E slides are available for 184 specimens (17 samples have 2 images, 12 have 3 images). H&E were performed on 53 primary breast cancer tissues, 99 metastatic samples, and 32 adjacent normal tissues. HLA-A immunofluorescence was performed on 37 samples.

Introduction

The Aurora US metastatic breast cancer project is a partnership between the Breast Cancer Research Foundation (BCRF) and the Translational Breast Cancer Research Consortium (TBCRC). Metastatic breast cancer is currently incurable. To better understand why breast cancers spread and to improve treatment options for metastatic breast cancer, Aurora US proposed to study the metastatic process through molecular characterization of paired primary and metastatic samples. Limited prior research has been performed on large cohorts of paired samples. In the retrospective phase, investigators profiled 55 patients with 1-9 metastases to understand key molecular features associated with metastasis. This study found that subtype switching from primary to metastatic disease occurred in ~30% of cases. Interestingly, the basal-like molecular subtype rarely changed. HLA-A was found to be downregulated in metastasis compared to their paired primary tumors through a variety of mechanisms including, DNA hypermethylation or focal deletion.

A prospective clinical trial is underway to increase our cohort of metastatic breast cancer (NCT03737695). Additional information can be found at the Aurora US website: https://auroraus.org/about/

Methods

The following subsections provide information about how the data were selected, acquired and prepared for publication.

Subject Inclusion and Exclusion Criteria

TBCRC participating sites identified retrospective cases of individuals diagnosed with metastatic breast cancer with availability of tissue from both primary and metastatic specimens. Pathology quality control was performed on each specimen. H&E sections from each sample were subjected to independent pathology review to confirm the tumor specimen was histologically consistent to the reported histology. Tumor samples with ≥30% tumor nuclei and normal tissue with 0% tumor nuclei were submitted for nucleic acid extraction.

Data Acquisition

Histologic H&E images were scanned with a Leica Aperio scanner at 40X and the pixel aspect ratio varies by image. File type submitted is svs files.

HLA-A immunofluorescence was performed on 37 samples. FFPE tissue was sectioned at 4 µm and stained with a CK/HLA-A assay developed and optimized at Vanderbilt University Medical Center using tyramine signal amplification for increased antigen sensitivity. Sections were deparaffinized. Antigen retrieval was performed with citrate buffer at pH 6. Endogen peroxidase was blocked with hydrogen peroxide, and protein block was applied. Sections were then incubated with the first primary antibody, pan-cytokeratin (pan-CK) AE1/AE3 Biocare, at 1:1,600 overnight at 4 °C, followed by incubation with the secondary antibody conjugated with horseradish peroxidase. TSA reagent was applied according to manufacturer’s recommendations. After washing, antigen retrieval and protein block steps, the second primary antibody, HLA-A C6 Santa Cruz at 1:1,300, was incubated overnight as described. Counterstaining was performed with DAPI for nuclei identification. Tonsil and placenta tissue were used as positive- and negative-control tissues. Whole slide images were scanned with an Axioscan Z1 at 20X. File type submitted is tif files.

Clinical:

Clinical data for each case was captured in a redcap database. Data collected included details for patient demographics (age, gender, race, ethnicity, family history of breast or ovarian cancer, known BRCA1/2 mutations), primary diagnosis and clinical staging information, surgery and pathologic staging, metastatic diagnosis and pathology, and treatment information.

Data Analysis

H&E:

Pathology quality control (QC) was performed on each tumor specimen and normal tissue specimen as an initial QC step. Hematoxylin and eosin-stained sections from each sample were subjected to independent pathology review to confirm that the tumor specimen was histologically consistent to the reported histology. The percent tumor nuclei, percent necrosis and other pathology annotations were also assessed.

HLA-A immunofluorescence:

Automated quantification was performed via a pathologist-supervised machine learning algorithm using QuPath software. Cell segmentation was determined on DAPI. Object classifiers were trained on annotated training regions from control tissue and tumor samples to define cellular phenotypes. Tumor cells were defined by pan-CK expression and subcellular characteristics. Once the algorithm was performing at a satisfactory level, it was used for batch analysis. For cases with low, heterogenous or null CK expression in which the classifier performance was not optimal, tumor areas were manually annotated. Out-of-focus areas, tissue folds, necrosis, normal breast and in situ carcinoma were excluded from the analysis. Single-cell data were exported from QuPath, and mean HLA-A intensity on tumor cells was further calculated in R.

Usage Notes

File Naming Schema:

This study aimed to identify the trends in the incidence of thymic cancer, i.e., thymoma, thymic carcinoma, and thymic neuroendocrine tumor, in the United States. Data from the United States Cancer Statistics (USCS) database (2001–2015) and those from the Surveillance, Epidemiology, and End Results (SEER) database (SEER 9 [1973–2015], SEER 13 [1992–2015], and SEER 18 [2000–2015]) were used in this study. All incidences were per 100,000 population at risk. The trends in incidence were described as annual percent change (APC) using the Joinpoint regression program. Data from the USCS (2001–2015) database showed an increase in thymic cancer diagnosis with an APC of 4.89% from 2001 to 2006, which is mainly attributed to the significant increase in the incidence of thymoma and thymic carcinoma particularly in women. The incidence of thymic cancer did not increase from 2006 to 2015, which may be attributed to the increase in the diagnosis of thymic carcinoma from 2004 to 2015, with a concomitant decrease in thymoma from 2008 to 2015. Before declining, the age-specific incidence of thymic cancer peaked at ages 70–74 years, with a peak incidence at 1.06 per 100,000 population, and decreased in older age groups. The incidence of thymic cancer was higher in men than in women. Asian/Pacific Islanders had the highest incidence of thymoma, followed by black and then white people. The incidence of thymic carcinoma increased from 2004 to 2015, with a concomitant decrease in thymoma from 2008 to 2015. Asian/Pacific Islanders had the highest incidence of thymoma than other races.

PurposeAlthough the adverse effects of atrial fibrillation (AF) on cancers have been well reported, the relationship between the AF and the adverse outcomes in prostate cancer (PC) remains inconclusive. This study aimed to explore the prevalence of AF and evaluate the relationship between AF and clinical outcomes in PC patients.MethodsPatients diagnosed with PC between 2008 and 2017 were identified from the National Inpatient Sample database. The trends in AF prevalence were compared among PC patients and their subgroups. Multivariable regression models were used to assess the associations between AF and in-hospital mortality, length of hospital stay, total cost, and other clinical outcomes.Results256,239 PC hospitalizations were identified; 41,356 (83.8%) had no AF and 214,883 (16.2%) had AF. AF prevalence increased from 14.0% in 2008 to 20.1% in 2017 (P < .001). In-hospital mortality in PC inpatients with AF increased from 5.1% in 2008 to 8.1% in 2017 (P < .001). AF was associated with adverse clinical outcomes, such as in-hospital mortality, congestive heart failure, pulmonary circulation disorders, renal failure, fluid and electrolyte disorders, cardiogenic shock, higher total cost, and longer length of hospital stay.ConclusionsThe prevalence of AF among inpatients with PC increased from 2008 to 2017. AF was associated with poor prognosis and higher health resource utilization. Better management strategies for patients with comorbid PC and AF, particularly in older individuals, are required.