JNCI Cancer Spectrum最新文献

Background: Prior studies evaluating racial disparities in cancer outcomes used regional measures of deprivation when accounting for socioeconomic status, which lack granularity. We evaluated differences in prostate cancer mortality between Black and White men using individual home prices in addition to regional metrics to understand the impact of individual wealth on prostate cancer outcomes.

Methods: Individuals diagnosed with prostate cancer between January 2004 and December 2016 in the Ohio Cancer Incidence Surveillance System were included. Individual home addresses were linked to the Area Deprivation Indices and home prices using data from an online real estate marketplace. Using inverse probability weighting to balance patient characteristics, we assessed differences in prostate cancer-specific mortality or other-cause mortality between Black and White men after accounting for clinical characteristics and social determinants of health (insurance, area deprivation, and home price).

Results: We identified 70 660 (85%) White and 12 192 (15%) Black men with prostate cancer. Black race was associated with a higher risk of prostate cancer-specific mortality in models that adjusted for age and year at diagnosis (subdistribution hazard ratio = 1.45, 95% CI = 1.45 to 1.57) and with the addition of cancer variables (subdistribution hazard ratio = 1.16, 95% CI = 1.06 to 1.26). In models that incorporate social determinants of health, however, rates of prostate cancer-specific mortality and other-cause mortality were not statistically significantly higher for Black men (subdistribution hazard ratios = 1.10, 95% CI = 0.98 to 1.24, and 1.02, 95% CI = 0.95 to 1.09), respectively.

Conclusions: After accounting for clinical characteristics and social determinants of health at the individual level, Black men were not at increased risk of prostate cancer mortality relative to White men.

Background: Use of a commercial database to obtain residential history information in environmental epidemiologic studies of cancer can lead to information bias if data availability varies by individual sociodemographic factors or case status. Residential data that are not missing at random and data that are discordant with cancer registry or birth record address data can impact subsequent exposure assessments. In our study of childhood cancers, we aimed to determine if the availability of residential history information differs by case status or other potential confounders and if there was agreement with cancer registry and birth records address data.

Methods: We worked with LexisNexis to retrieve residential histories for mothers of 3573 childhood cancer cases and 7160 controls born 2000-2015 in Los Angeles and Orange Counties in Southern California. We used linear regression to determine independent predictors of having residential history returned by LexisNexis. We assessed concordance between maternal address at birth and child's address at diagnosis available from registry data and the LexisNexis residential history by comparing street addresses and geocoded coordinates.

Results: Maternal characteristics (birthplace, race and ethnicity, education, insurance provider) and child's case status were associated with the mother having any address returned by LexisNexis. When comparing geocoded coordinates of cases, less than 10% of LexisNexis addresses during the diagnosis year matched cancer registry addresses. Birth record addresses matched LexisNexis-provided addresses for 47% of mothers.

Conclusion(s): This study elucidates potential implications of using commercial databases such as LexisNexis to reconstruct residential histories and derive exposure measures in cancer case-control studies.

Numerous exercise oncology trials have been completed, greatly informing exercise recommendations for patients with cancer. Exercise medicine can be administered in various types, doses, and schedules at various time points. Advancing precision exercise medicine requires understanding of how the effects of different exercise interventions vary by characteristics of individual patients. The Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS) study provides an international infrastructure and shared database to perform pooled analyses of individual patient data (IPD) from multiple randomized controlled trials. This commentary aims to highlight the value of pooled IPD analyses, summarize key findings from published pooled IPD analyses on the effects of physical exercise on various outcomes, and provide guidance to advance precision exercise medicine for patients with cancer. POLARIS currently includes IPD from 52 exercise trials. Findings to date indicate that exercise interventions in patients with cancer have beneficial effects on physical fitness, fatigue, health-related quality of life, self-reported cognition (posttreatment), sleep disturbances, and symptoms of anxiety and depression. Additionally, it was determined that the exercise effects varied by characteristics of the patients, including the initial value of the outcome, age, marital status, and education level, and by characteristics of the intervention, including exercise supervision and specificity. Future research opportunities to advance precision exercise medicine for patients with cancer include pooling of trial data from understudied populations, data on clinical outcomes, and biomarkers, as well as applying machine learning models for identifying combinations of covariables that modify intervention effects and predictions of individual treatment effects.

Background: A prediction model for estimating risk of therapy-related myeloid neoplasms (tMNs), a late effect with a high mortality after chemotherapy and/or radiation, is currently unavailable. Ability to predict risk at initial cancer presentation can be key for early detection and risk mitigation.

Methods: Using SEER-Medicare linked database, 970 390 adults diagnosed with first primary cancer from 2000 to 2011 (with follow-up through 2015) were selected. The sample was divided into training (n = 582 234) and validation cohorts (n = 388 156). Various tMN risk factors were used for the development of tMN prediction model: the Therapy-Related Myeloid Neoplasm Risk Score (TMNRS). TMNRS was created as a simple arithmetic sum of independent predictors of tMN weighted according to the adjusted hazard ratio from the Cox proportional hazards analysis.

Results: In addition to the known risk factors of chemotherapy and radiation exposure, history of autoimmune disease and granulocyte-colony stimulating factor exposure emerged as consistent predictors of tMN after each of the 5 cancers in the study. Cancer survivors were categorized into distinct risk groups with variable risk of tMN.

Conclusion: TMNRS provides a simple and convenient office-based mechanism to identify solid cancer patients at variable risks of tMN development. This risk assessment tool provides preliminary insights that may contribute to future research on the management of patients, particularly those receiving adjuvant therapies. Further investigation is required to fully evaluate its clinical utility and potential effects on patient care.

Background: Bile acids are produced in the liver and are important for lipid digestion. Higher-circulating bile acid levels, however, have been linked to metabolic disorders, inflammation, and gut microbiota dysbiosis, which have been implicated in liver carcinogenesis. To date, few epidemiological studies have explored the association between circulating bile acids and liver cancer risk.

Methods: We conducted a nested case-control study among 12 prospective cohort studies located in the United States. Fifteen prediagnostic circulating bile acids were measured from blood samples among 872 individuals who developed liver cancer and 872 matched control participants. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using multivariable-adjusted conditional logistic regression analysis of circulating bile acid levels and liver cancer risk.

Results: Primary conjugated bile acid concentrations were positively associated with higher risk of liver cancer (OR per doubling in concentrations [log2] and 95% CI of glycocholic acid: 1.32, 1.24 to 1.40; glycochenodeoxycholic acid: 1.33, 1.24 to 1.43; taurocholic acid: 1.28, 1.22 to 1.35; and taurchenodeoxycholic acid: 1.32, 1.24 to 1.39). Secondary conjugated bile acids were also positively associated with liver cancer risk (doubling of concentrations OR ranged from 1.11 to 1.22). Unconjugated bile acid concentrations were generally not associated with liver cancer risk, except lithocholic acid (OR per doubling: 1.27, 1.16 to 1.39). When analyses were separated into the 2 main subtypes of liver cancer, hepatocellular carcinoma (HCC; 438 cases/438 controls) and intrahepatic cholangiocarcinoma (ICC; 111 cases/111 controls), significant heterogeneity was observed for primary conjugated bile acid concentrations (all P < .001) that showed positive significant associations with HCC but not ICC.

Conclusions: These results suggest that bile acids may be important markers of HCC risk and contribute to hepatocarcinogenesis; however, further research using serial measurements is needed.

Background: The surrogacy of progression-free survival (PFS) for overall survival (OS) at the trial-level in randomized clinical trials (RCTs) testing immune checkpoint inhibitors (ICIs) in patients with advanced non-small cell lung cancers (NSCLC) is influenced by several clinical-pathological factors. However, potential heterogeneity of PFS surrogacy according to patients' sex has never been investigated.

Methods: RCTs testing ICIs as monotherapy or combined with chemotherapy in patients with advanced NSCLC reporting hazard ratios (HRs) for PFS and OS according to patients' sex were included. The main objective was to assess sex-based heterogeneity in the trial-level association between PFS (surrogate endpoint) and OS (reference endpoint), overall and in subgroups defined by treatment type (ICIs as monotherapy vs ICIs plus chemotherapy). We used the coefficient of determination (R2) to quantify surrogacy.

Results: Twenty RCTs, for a total of 7528 male and 3008 female patients, were included. Overall, the association between OS-HR and PFS-HR was moderate: the R2 from a model adjusted by the type of treatment administered in the experimental arm was 0.69 (95% confidence interval [CI] = 0.34 to 0.88). Sex-disaggregated analysis showed heterogeneity in PFS surrogacy: the association was strong in male patients (adjusted R2 = 0.77; 95% CI = 0.56 to 0.89), but poor in female (adjusted R2 = 0.31, 95% CI = 0.03 to 0.63). Consistent results were obtained in subgroups analyses by treatment type, and in cross-validation analysis.

Conclusions: In RCTs testing ICIs alone or combined with chemotherapy in patients with advanced NSCLC, PFS is a robust surrogate endpoint for OS in male patients but not in female.

Background: Older adults with advanced prostate cancer and type 2 diabetes mellitus are underrepresented in trials of androgen receptor pathway inhibitors. This study examined changes in unplanned hospitalization rates in patients receiving androgen receptor pathway inhibitors by type 2 diabetes mellitus status and assessed if unplanned hospitalization varies according to androgen receptor pathway inhibitors.

Methods: This population-based study of advanced prostate cancer patients aged older than 66 years used Surveillance, Epidemiology, and End Results-Medicare data. Prepost androgen receptor pathway inhibitor initiation changes and androgen receptor pathway inhibitor differences in unplanned hospitalization rates were estimated by adjusted incidence rate ratio with considerations for interactions between period, androgen receptor pathway inhibitor, and type 2 diabetes mellitus status. Linear contrasts were used to estimate and test conditional incidence rate ratios. Tests were 2-sided, and a P value less than .05 was considered statistically significant.

Results: The study included 12 240 patients: 3160 (25.8%) with type 2 diabetes mellitus, 7191 (58.8%) received abiraterone acetate with prednisone, and 5049 (41.2%) received enzalutamide. Unplanned hospitalization rates increased after androgen receptor pathway inhibitor initiation by 65% among patients with type 2 diabetes mellitus complications (adjusted incidence rate ratio = 1.65, 95% confidence interval [CI] = 1.37 to 1.98) and 109% in nondiabetics (adjusted incidence rate ratio = 2.09, 95% CI = 1.94 to 2.26). Among patients with type 2 diabetes mellitus without complications, the increase in unplanned hospitalization rates depended on the androgen receptor pathway inhibitor initiated: 103% after abiraterone acetate with prednisone (adjusted incidence rate ratio = 2.03, 95% CI = 1.70 to 2.43) and 47% after enzalutamide (adjusted incidence rate ratio = 1.47, 95% CI = 1.21 to 1.80) and a 38% greater increase in unplanned hospitalization rates after abiraterone acetate with prednisone than enzalutamide (ratio of abiraterone acetate with prednisone adjusted incidence rate ratio divided by enzalutamide adjusted incidence rate ratio = 1.38, 95% CI = 1.06 to 1.80).

Conclusions: All patients had higher unplanned hospitalization rates after androgen receptor pathway inhibitor. Our findings highlight the importance of using real-world data to better understand the interplay between preexisting health conditions and treatment outcomes, a critical step toward precision medicine.

Background: Despite long-standing efforts to improve breast cancer care quality, wide performance gaps persist. We sought to identify regions demonstrating meaningfully low performance and characterize health-system and health-profession factors associated with geospatial disparities.

Methods: We used the Surveillance, Epidemiology, and End Results-Medicare linked database and the Health Resources and Services Administration area health resource file to characterize performance across health-care service areas using 4 metrics: diagnosis stage, chemotherapy, radiation, and endocrine therapy. We used principal component analysis to identify health-care facility and provider characteristics associated with performance; and hierarchical multivariable modeling to attribute total variance proportionally to 5 domains: patient characteristics, health service area region, health-care facility and provider characteristics, randomness, and unexplained.

Results: Among 31,571 women aged 66-79 diagnosed 2007-2013 with stage I-III breast cancer and treated with surgery, 61% had stage I disease, 23% received chemotherapy, 54% received radiation therapy, and 42% received endocrine therapy. Health system factors explained more variance for endocrine therapy (21%), chemotherapy (12%), and radiation therapy (12%), compared with geospatial region or patient characteristics. Health profession factors were associated with quality for stage, radiation therapy, and chemotherapy; health-care facility factors were associated with quality for stage, endocrine therapy, and chemotherapy. Patient characteristics explained <5% of observed variance.

Conclusions: Reassuringly, only a small number of regions demonstrated suboptimal breast cancer care. Optimal performance was associated with multidisciplinary teams and facilities with robust resources and higher volumes. Incorporating geospatial and health system factors into quality measurement efforts could foster the design of impactful quality improvement programs.