High competing risks minimize real-world utility of adjuvant targeted therapy in renal cell carcinoma: a population-based analysis

Abstract

ObjectiveTo utilize a population-based approach to address the role of adjuvant TT in the management of RCC.MethodsPatients with RCC (2006-2013) in the SEER database were stratified by metastatic disease at the time of diagnosis (cM0/cM1). cM0 patients following surgical excision were stratified into low and high-risk (ASSURE and S-TRAC criteria). Multivariable analyses performed to identify predictors of TT receipt; Fine and Gray competing risks analyses used to identify predictors of cancer-specific mortality (CSM). Subset analyses included patients with clear cell histology and high-risk cM0. Survival analyses were used to evaluate overall survival (OS) and cancer-specific survival (CSS) for all cohorts, stratified on TT receipt.Results79,926 patients included (71,682 cM0, 8,244 cM1); median follow-up for the entire cohort was 40.1 months. Of 31,453 patients with histologic grade data, 18,328 and 13,125 were low- and high-risk cM0, respectively. TT utilization in cM1 patients peaked at 50.6% and was associated with reduced CSM (HR 0.73, p<0.01). In contrast, TT utilization (presumed salvage therapy) never exceeded 2.2% in the entire cM0 cohort and 3.5% in the high-risk cM0 cohort. On competing risks analysis, TT receipt was associated with increased CSM in all cohorts.ConclusionWhen compared to the cM1 patients, TT receipt in cM0 patients does not provide any cancer-specific survival benefit, even in the small percentage of patients that eventually progress to metastatic disease. Competing risks mortality further limit any potential benefit in this population. Based on current evidence, adjuvant TT cannot be recommended for RCC patients.

Risk of secondary malignancies after pelvic radiation: A population-based analysis.

“…For each primary disease site, demographics were compared with and without stratification by radiation receipt. Overall survival (OS) was calculated as median OS (months) and 5yr OS (%), stratified by SEER summary stage (localized, regional, and distant) [11]. Data from the American Cancer Society (ACS) [12,13] were reported in a similar fashion to provide external validation.…”

“…Demographic variables of interest included age at diagnosis, gender, race, insurance, marital status, and region based on the SEER registry. Utilizing prior literature [11], a countylevel socioeconomic measure was created.…”

“…For primary malignancies, clinical variables of interest included tumor-node-metastasis (TNM), SEER summary stage (localized, regional, and distant) [11], and survival. Radiation receipt was documented in patients who underwent any form of RT targeting the primary malignancy, including brachytherapy, as well as in patients who received radiation before, during, and/or after surgery.…”

Upgrading on radical prostatectomy specimens of very low- and low-risk prostate cancer patients on active surveillance: A population-level analysis

“…whether PCa was the first cancer diagnosis the patient received or whether he had previously been diagnosed with a different, unrelated malignancy), cT stage (cT1a-b, cT1c, or cT2a), prostate-specific antigen (PSA) level at diagnosis (0-2, 2-5, or 5-10 ng/ml), GS on prostate biopsy or transurethral resection of prostate, and number of positive and examined prostate cores/specimens. County-level socioeconomic status [1 st (lowest), 2 nd , 3 rd , or 4 th (highest)] was derived from: percentage of individuals (i) with less than a high school education, (ii) below the poverty line, (iii) unemployed, (iv) foreign born, and (v) median household income) (9). Percent positive cores variable was derived from number of cores/specimen positive and examined.…”

Association of extended core sampling with delayed intervention and pathologic outcomes for active surveillance patients

“…Evaluable baseline sociodemographic variables included: year of diagnosis, age, race, marital status, and county-level socioeconomic status (SES), derived from the percentage of individuals in each county who were unemployed, below the poverty line, foreign-born, had less than a high school education, and the median household income. 17,18 Patient-level comorbidity data (e.g., Charlson Comorbidity Index scores) was not available.…”