Wang, Ming

Abstract Background: Sexual and gender minority (SGM) representation is growing in the United States. This study estimated the future SGM cancer burden amidst shifting cancer patterns. Methods: Age-specific incidence data for breast, prostate, colorectal, lung, and melanoma were obtained from the Surveillance, Epidemiology, and End Results Program’s 12-registry database. Nordpred age-period-cohort models projected incidence through 2040 to 2044. Rates were applied to US Census population estimates and generation- and sex-specific SGM identification proportions from Gallup’s 2023 to 2024 surveys. Prophet was used for sensitivity analysis. Results: At ages 55 to 59, breast cancer cases among Millennial SGMs were 3,672% higher than among Silent Generation SGMs (34,850 vs. 924 cases), melanoma cases were 1,061% higher (5,526 vs. 476 cases), and colorectal cancer cases were 2,206% higher (23,594 vs. 1,023 cases). Compared with Generation X, Millennial prostate cancer cases were 142% higher (8,100 vs. 3,344 cases), and lung cancer cases were 169% higher (5,451 vs. 2,029 cases). By 2040 to 2044, Millennials were projected to account for 68.4% of breast cancer cases and 59.7% of colorectal cancer cases among SGMs. Conclusions: At ages 55 to 59, the 5-year cancer burden was projected to be 15 times higher among Millennials than Silent Generation SGMs (77,521 vs. 5,134 cases), driven primarily by the larger SGM population in younger generations. Oncology systems will need to prepare for the increase in cancer care demand. Impact: These findings identify a substantial generational shift in the SGM cancer burden and may inform oncology workforce capacity, culturally responsive cancer care, and healthcare system preparation for this growing population.

PURPOSE Although outcomes for nonmetastatic cancers have improved since the 1970s, it remains uncertain whether long-term survival has improved among patients presenting with de novo metastatic disease, defined as cancer presenting with distant metastases at the time of initial diagnosis. This study evaluated changes in 5-year survival among patients with de novo metastatic cancer diagnosed in 1976 versus 2015, with a minimum 5-year follow-up through 1981 and 2020, respectively. METHODS Population-based data were obtained from the SEER database. Patients diagnosed with de novo metastatic cancer in 1976 (n = 11,864) and 2015 (n = 26,324) were included. Five-year overall survival (OS) was compared between cohorts, and multivariable logistic regression identified demographic and clinical factors associated with survival. RESULTS The 5-year OS increased significantly from 10.6% in 1976-1981 to 23.6% in 2015-2020 ( P < .001). Survival gains were largest in breast cancer (12.6%-34.2%; P < .001), ovarian cancer (14.4%-32.5%; P < .001), colorectal cancer (3.7%-15.0%; P < .001), and hematologic malignancies (24.3%-53.9%; P < .001). Minimal improvement occurred in pancreatic cancer (0.25%-1.6%; P = .007) and small cell lung cancer (0.8%-3.1%; P = .008). Multivariable analysis demonstrated that older age, male sex, and Black race were associated with lower odds of 5-year survival (all P < .01) CONCLUSION Over the past four decades, long-term survival among patients with de novo metastatic cancer has more than doubled; however, progress remains uneven across cancer types. Gains appear concentrated in malignancies with substantial therapeutic advances, whereas others show persistently poor outcomes. Continued efforts are needed to address disparities and improve survival in resistant cancer types.

Objectives: Early identification of patients living with cancer at higher risk of death from an infection is critical in infection mortality prevention. We characterize patients living with cancer at the highest risk of dying from an infection. Methods: 7,529,481 US cancer survivors (1992 to 2015) were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Standardized mortality ratios (SMRs) and 95% CIs were calculated. Fine-gray survival analysis was performed to calculate hazard ratios by adjusting for the effects of competing risks (eg, deaths due to causes other than infection). Results: Among 7,529,481 patients living with cancer (1992 to 2015), 101,167 (1.3%) died of infection. The rate of infection-specific mortality was 27.19/10,000 person-years, with an SMR of 3.29 (95% CI: 3.26-3.32, P <0.001). Patients who were older, male, black, and unmarried were at a greater risk of fatal infection. Overall, the risk of infection-specific mortality for patients living with cancer is greatest 1 year after diagnosis compared with the general population (SMR: 8.68, 95% CI: 8.53-8.84; P< 0.0001), and this risk decreases with follow-up time (SMR at >10 y after diagnosis: 2.93, 95% CI: 2.87-3.00; P< 0.0001). Among patients with Hodgkin Lymphoma, Non-Hodgkin Lymphoma, and Kaposi Sarcoma, 9.2%, 11.5%, and 82.2% of all deaths within the first year after cancer diagnosis occurred due to acute infectious disease. In contrast, for patients with liver cancer, the relative percentage of infection-specific mortality increases with follow-up time from 3.5% at <1 year after cancer diagnosis and 10.4% at 10+ years of follow-up. Conclusion: The results of this study characterize infection mortality in patients living with cancer, which can guide more targeted research and interventions in this population.

Objectives: Deaths from an unknown cause are difficult to adjudicate and oncologic studies of comparative effectiveness often demonstrate inconsistencies in incorporating these deaths and competing events (eg, heart disease and stroke) in their analyses. In this study, we identify cancer patients most at risk for death of an unknown cause. Methods: This retrospective, population-based study used cancer registry data from the Surveillance, Epidemiology, and End Results database (1992-2015). The absolute rate of unknown causes of death (COD) cases stratified by sex, marital status, race, treatment, and cancer site were calculated and a multivariable logistic regression model was applied to obtain adjusted odds ratios with 95% CIs. Results: Out of 7,154,779 cancer patients across 22 cancer subtypes extracted from Surveillance, Epidemiology, and End Results, 3,448,927 died during follow-up and 276,068 (7.4%) of these deaths were from unknown causes. Patients with an unknown COD had a shorter mean survival time compared with patients with known COD (36.3 vs 65.7 mo, P < 0.001). The contribution of unknown COD to total mortality was highest in patients with more indolent cancers (eg, prostate [12.7%], thyroid [12.3%], breast [10.7%]) and longer follow-up (eg, >5 to 10 y). One, 3, and 5-year cancer-specific survival (CSS) calculations including unknown COD were significantly decreased compared with CSS estimates excluding cancer patients with unknown COD. Conclusion: Of the patients, 7.4% died of unknown causes during follow-up and the proportion of death was higher with longer follow-up and among more indolent cancers. The attribution of high percentages of unknown COD to cancer or non-cancer causes could impact population-based cancer registry studies or clinical trial outcomes with respect to measures involving CSS and mortality.

Abstract Acute pancreatitis (AP) is a disease characterized by an acute inflammatory phase followed by a convalescent phase. Diabetes mellitus (DM) was historically felt to be a transient phenomenon related to acute inflammation; however, it is increasingly recognized as an important late and chronic complication. There are several challenges that have prevented precisely determining the incidence rate of DM after AP and understanding the underlying mechanisms. The DREAM (Diabetes RElated to Acute Pancreatitis and its Mechanisms) Study is a prospective cohort study designed to address these and other knowledge gaps to provide the evidence needed to screen for, prevent, and treat DM after AP. In the following article, we summarize literature regarding the epidemiology of DM after AP and provide the rationale and an overview of the DREAM study.

AbstractAlthough cancer is highly heterogeneous, all metastatic cancer is considered American Joint Committee on Cancer (AJCC) Stage IV disease. The purpose of this project was to redefine staging of metastatic cancer. Internal validation of nationally representative patient data from the National Cancer Database (n = 461 357; 2010‐2013), and external validation using the Surveillance, Epidemiology and End Results database (n = 106 595; 2014‐2015) were assessed using the concordance index for evaluation of survival prediction. A Cox proportional hazards model was used for overall survival by considering identified phenotypes (latent classes) and other confounding variables. Latent class analysis was performed for phenotype identification, where Bayesian information criterion (BIC) and sample‐size‐adjusted BIC were used to select the optimal number of distinct clusters. Kappa coefficients assessed external cluster validation. Latent class analysis identified five metastatic phenotypes with differences in overall survival (P < .0001): (Stage IVA) nearly exclusive bone‐only metastases (n = 59 049, 12.8%; median survival 12.7 months; common in lung, breast and prostate cancers); (IVB) predominant lung metastases (n = 62 491, 13.5%; 11.4 months; common in breast, stomach, kidney, ovary, uterus, thyroid, cervix and soft tissue cancers); (IVC) predominant liver/lung metastases (n = 130 014, 28.2%; 7.0 months; common in colorectum, pancreatic, lung, esophagus and stomach cancers); (IVD) bone/liver/lung metastases predominant over brain (n = 61 004, 13.2%; 5.9 months; common in lung and breast cancers); and (IVE) brain/lung metastases predominant over bone/liver (n = 148 799, 32.3%; 5.7 months; lung cancer and melanoma). Long‐term survivors were identified, particularly in Stages IVA‐B. A pan‐cancer nomogram model to predict survival (STARS: site, tumor, age, race, sex) was created, validated and provides 13% better prognostication than AJCC: 1‐month concordance index of 0.67 (95% confidence interval [CI]: 0.66‐0.67) vs 0.61 (95% CI: 0.60‐0.61). STARS is simple, uses easily accessible variables, better prognosticates survival outcomes and provides a platform to develop novel metastasis‐directed clinical trials.

Abstract Background We hypothesized that the addition of receptor tyrosine kinase inhibitors (RTKis, e.g., lapatinib, erlotinib, cetuximab, bevacizumab, panitumumab) to radiotherapy-based treatment for solid tumors does not increase overall survival but may increase toxicity. Methods Population, Intervention, Control, Outcome, Study Design; Preferred Reporting Items for Systematic Reviews and Meta-Analyses; and Meta-analysis of Observational Studies in Epidemiology methods were used to identify prospective randomized studies including patients with solid tumor cancers treated with radiotherapy with or without RTKis. Extracted variables included use of radiotherapy vs chemoradiotherapy, RTKi type (antibody vs small molecule), outcomes, and toxicities. The primary endpoint was overall survival; the secondary endpoint was grade 3+ toxicity. Random-effects meta-analyses were performed for each outcome measure. All statistical tests were 2-sided. Results A total of 405 studies met the initial search criteria, of which 13 prospective randomized trials of radiotherapy with or without RTKi met the inclusion criteria, encompassing 5678 patients. The trials included cancers of the head and neck (6 trials, 3295 patients), esophagus (3 trials, 762 patients), lung (2 trials, 550 patients), and brain (2 trials, 1542 patients). Three studies evaluated a small molecule and radiotherapy in 949 patients, and 10 studies evaluated antibodies and radiotherapy in 4729 patients. The addition of RTKis to radiotherapy-based treatment did not improve overall survival (hazard ratio = 1.02, 95% confidence interval = 0.90 to 1.15, P = .76) but increased grade 3+ toxicity (relative risk = 1.18, 95% confidence interval = 1.06 to 1.33, P = .009). Conclusions The addition of RTKis to radiotherapy does not improve survival and worsens toxicity.