Appendiceal Cancer Consortium (APPECC)
Primary Contact Information
Andreana N. Holowatyj
Vanderbilt University Medical Center
Shanghai Men's Health Study (SMHS), Shanghai Women's Health Study (SWHS), Southern Community Cohort Study (SCCS)
Alternate Contact Information
We will apply for an R03 grant to support data preparation for each cohort and evaluate the association of lifestyle factors with appendiceal cancer risk in this proposed study. We will also apply for an R01 grant to support future studies of biomarkers.
In 2020, an estimated 333,680 cases of cancers of the digestive system will be diagnosed in the United States (US)—of which 2.3% (7,600 cases) include AC and other rare digestive system tumors. As a rare cancer, with an age-adjusted incidence rate of 0.12 per 1,000,000 person years, little is known about the risk factors and etiologies of AC. Among patients of all ages diagnosed with AC, incidence rates have been increasing over the last two and a half decades with unknown etiologies. Overall, the incidence of malignant AC increased 232% between 2000 and 2016 in the US, with rates rising in all age groups. However, rates of appendectomies—one of the most common gastrointestinal surgical procedures that often leads to diagnosis of incident AC—remained stable over this period. These findings have raised questions as to the causes that underlie this changing epidemiology and the rising burden of appendiceal cancers. A better understanding of factors driving this alarming increase among individuals of all ages diagnosed with AC and underlying etiologies of this disease are urgently needed to stop and reverse this trend.
The rarity of AC has also presented challenges in understanding disease pathogenesis, and in developing clinical management guidelines for AC. Currently, the National Comprehensive Cancer Network guidelines recommend treatment of AC cases with systemic therapy according to colon cancer guidelines. This is largely because of lack of robust data for AC and the treatment regimens are extrapolated from clinical studies related to colon cancer. In the absence of prognostic and predictive biomarkers and new therapeutic targets specific to AC, therapeutic advances in this malignancy remain very limited.
We recently conducted the first clinical study of AC among young patients using population-based data from the NIH/NCI’s SEER Program covering ~28% of the US population (Holowatyj AN, et al Gastroenterology. 2020. In press.). We identified 1,652 individuals diagnosed with AC between ages 20-49 years from 2000 to 2011. Over one-third of patients with early-onset AC (37.0%) were diagnosed before age 40 years. Cancer-specific survival at 5 years after early-onset AC diagnosis was 77.0% among NHWs, 64.5% among NHBs, and 79.2% among Hispanic patients. NHBs with early-onset AC had a significantly increased hazard of AC-specific death compared with NHWs after adjusting for age, sex, surgery, stage, and histological subtype, whereas there was no survival difference between NHWs and Hispanics. As such, the study of AC patients in cohorts with more comprehensive clinical and individual-level information is necessary to determine the extent to which access to healthcare, health behaviors, and potential environmental exposures may contribute to differences in survival outcomes among young patients with AC.
Characterization of appendiceal cancer biomarkers in multi-ethnic cohorts is also warranted to examine etiologies underlying appendiceal carcinogenesis and potential molecular heterogeneity in AC by age or racial/ethnic group. Differences in disease burden and survival by age of disease-onset and race or ethnicity can also inform the discovery of risk factors and tumor biomarkers, which would have implications for AC risk assessment, screening and surveillance, and treatment.
Our overarching goal is to understand risk factors, etiologies, and prognostic factors of appendiceal cancer and utilize this knowledge to: reverse the increasing disease burden as well as inform clinical, molecular and population-level features that contribute to appendiceal cancer disparities.
Our primary aim is to evaluate risk factors for appendiceal cancer and their association with prognostic outcomes in the APPECC cohort.
Future studies using the APPECC cohort will aim to discover biomarkers of appendiceal cancer and to determine whether these biomarkers are associated with appendiceal cancer risk factors and/or prognostic outcomes.
We will utilize a nested case-control study approach, including incident appendiceal cancer (AC) cases identified during follow-up of cohorts as well as their matched non-cancer controls, in order to facilitate future biomarker studies and to reduce the burden of cohorts to provide entire cohort data with a minimum gain in power. Incidence-density sampling will be used to randomly select controls from the same risk set as the index AC case. Non-cancer controls and cases will be individually matched 10:1 by race/ethnicity, sex, age, and date of baseline questionnaire collection. Additional criteria for a sub-set of these matched sets (for 4:1 matching) to facilitate future biomarker discovery include: date and time of day for baseline sample collection, age at baseline sample collection, type of sample collection, antibiotic use in the week prior to sample collection, and menopausal status at time of sample collection. Data will be pooled and harmonized across all cohorts.
Means and percentages of selected baseline characteristics for cases and controls will be calculated and compared using t-tests and chi-square tests for continuous and categorical variables, respectively. Odds ratios (OR) and 95% confidence intervals will be used to measure the association of appendiceal cancer with baseline questionnaire features (e.g. diet, physical activity, anthropometrics, smoking history, medical history, other lifestyle factors etc.), after adjusting for potential confounders (e.g. age, race). We will also explore heterogeneity by sex and race/ethnicity by including their interaction effect term in the models. If significant, stratified analyses will follow. Hazards ratios (HR) and 95% confidence intervals will be used to examine associations with prognostic outcomes with baseline questionnaire features after adjusting for potential confounders. Missing covariate data will be handled using multiple imputation that incorporates predictive mean matching and flexible additive imputation models as implemented in the aregImpute function in Hmisc R package.
As a rare cancer, with an age-adjusted incidence rate of 0.12 per 1,000,000 person years, the study of appendiceal cancer is limited in individual cohorts. By leveraging a cohort consortium approach and pooling cases from large cohort studies, we will have sufficient statistical power to discover appendiceal cancer risk factors and etiologies.
For our nested case-control study, we request all available, verified cases of appendiceal cancer from participating cohorts.
Incidence-density sampling will then be used to randomly select non-cancer controls from the same risk set as the index AC case. Non-cancer controls and cases will be individually matched 10:1 by race/ethnicity, sex, age, and date of baseline questionnaire collection. Additional criteria for a sub-set of these matched sets (for 4:1 matching) to facilitate biomarker discovery in future studies include: date and time of day for baseline sample collection, age at baseline sample collection, type of sample collection, antibiotic use in the week prior to sample collection, and menopausal status at time of sample collection. Data will be pooled and harmonized across all cohorts.
We request all available incidence and mortality data for appendiceal cancer (ICD-O code: C18.1) cases, including clinical and pathological information (e.g. tumor histology, age at cancer diagnosis, stage) and information on prognostic outcomes.
We request all available baseline questionnaire data for appendiceal cancer cases and non-cancer controls to discover appendiceal cancer risk factors.
We request all available baseline questionnaire data to examine potential covariates (e.g. diet, physical activity, anthropometrics, smoking history, medical history, other lifestyle factors etc.).