IMPORTANCECancer treatment delay has been reported to variably impact cancer-specific survival and coronavirus disease 2019 (COVID-19)-specific mortality during the severe acute respiratory syndrome coronavirus 2 pandemic. During the pandemic, treatment delay is being recommended in a nonquantitative, nonobjective, and nonpersonalized manner, and this approach may be associated with suboptimal outcomes. Quantitative integration of cancer mortality estimates and data on the consequences of treatment delay is needed to aid treatment decisions and improve patient outcomes.OBJECTIVE To obtain quantitative integration of cancer-specific and COVID-19-specific mortality estimates that can be used to make optimal decisions for individual patients and optimize resource allocation. DESIGN, SETTING, AND PARTICIPANTSIn this decision analytical model, age-specific and stage-specific estimates of overall survival pre-COVID-19 were adjusted by the probability of COVID-19 (individualized by county, treatment-specific variables, hospital exposure frequency, and COVID-19 infectivity estimates), COVID-19 mortality (individualized by age-specific, comorbidity-specific, and treatment-specific variables), and delay of cancer treatment (impact and duration). These model estimates were integrated into a web application (OncCOVID) to calculate estimates of the cumulative overall survival and restricted mean survival time of patients who received immediate vs delayed cancer treatment. Using currently available information about COVID-19, a susceptible-infectedrecovered model that accounted for the increased risk among patients at health care treatment centers was developed. This model integrated the data on cancer mortality and the consequences of treatment delay to aid treatment decisions. Age-specific and cancer stage-specific estimates of overall survival pre-COVID-19 were extracted from the Surveillance, Epidemiology, and End Results database for 691 854 individuals with 25 cancer types who received cancer diagnoses in 2005 to 2006. Data from 5 436 896 individuals in the National Cancer Database were used to estimate the independent impact of treatment delay by cancer type and stage. In addition, data from 275 patients in a nested case-control study were used to estimate the COVID-19 mortality rate by age group and number of comorbidities. Data were analyzed from March 17 to May 21, 2020. EXPOSURES COVID-19 and cancer.MAIN OUTCOMES AND MEASURES Estimates of restricted mean survival time after the receipt of immediate vs delayed cancer treatment.
In men with recurrent prostate cancer, addition of long-term antiandrogen therapy to salvage radiotherapy (SRT) was associated with overall survival (OS) in the NRG/RTOG 9601 study. However, hormone therapy has associated morbidity, and there are no validated predictive biomarkers to identify which patients derive most benefit from treatment.OBJECTIVE To examine the role of pre-SRT prostate-specific antigen (PSA) levels to personalize hormone therapy use with SRT.INTERVENTIONS Men were randomized to SRT plus high-dose nonsteroidal antiandrogen (bicalutamide, 150 mg/d) or placebo for 2 years. DESIGN, SETTING, AND PARTICIPANTSIn this secondary analysis of the multicenter RTOG 9601 double-blind, placebo-controlled randomized clinical trial conducted from 1998 to 2003 by a multinational cooperative group, men with a positive surgical margin or pathologic T3 disease after radical prostatectomy with pre-SRT PSA of 0.2 to 4.0 ng/mL were included. Analysis was performed between March 4, 2019, and December 20, 2019. MAIN OUTCOMES AND MEASURESThe primary outcome was overall survival (OS). Secondary end points included distant metastasis (DM), other-cause mortality (OCM), and grades 3 to 5 cardiac and neurologic toxic effects. Subgroup analyses were performed using the protocol-specified PSA stratification variable (1.5 ng/mL) and additional PSA cut points, including test for interaction. Competing risk analyses were performed for DM and other-cause mortality (OCM).RESULTS Overall, 760 men with PSA elevation after radical prostatectomy for prostate cancer were included. The median (range) age of particpants was 65 (40-83) years. Antiandrogen assignment was associated with an OS benefit in the PSA stratum greater than 1.5 ng/mL (n = 118) with a 25% 12-year absolute benefit (hazard ratio [HR], 0.45; 95% CI, 0.25-0.81), but not in the PSA of 1.5 ng/mL or less stratum (n = 642) (1% 12-year absolute difference; HR, 0.87; 95% CI, 0.66-1.16). In a subanalysis of men with PSA of 0.61 to 1.5 (n = 253), there was an OS benefit associated with antiandrogen assignment (HR, 0.61; 95% CI, 0.39-0.94). In those receiving early SRT (PSA Յ0.6 ng/mL, n = 389), there was no improvement in OS (HR, 1.16; 95% CI, 0.79-1.70), an increased OCM hazard (subdistribution HR, 1.94; 95% CI, 1.17-3.20; P = .01), and an increased odds of late grades 3 to 5 cardiac and neurologic toxic effects (odds ratio, 3.57; 95% CI, 1.09-15.97; P = .05). CONCLUSIONS AND RELEVANCEThese results suggest that pre-SRT PSA level may be a prognostic biomarker for outcomes of antiandrogen treatment with SRT. In patients receiving late SRT (PSA >0.6 ng/mL, hormone therapy was associated with improved outcomes. In men receiving early SRT (PSA Յ0.6 ng/mL), long-term antiandrogen treatment was not associated with improved OS. Future randomized clinical trials are needed to determine hormonal therapy benefit in this population.
PURPOSE Hepatocellular carcinoma (HCC) is characterized by a poor prognosis and a high recurrence rate. The tumor immune microenvironment in HCC has been characterized as shifted toward immunosuppression. We conducted a genomic data-driven classification of immune microenvironment HCC subtypes. In addition, we demonstrated their prognostic value and suggested a potential therapeutic targeting strategy. METHODS RNA sequencing data from The Cancer Genome Atlas–Liver Hepatocellular Carcinoma was used (n = 366). Abundance of immune cells was imputed using CIBERSORT and visualized using unsupervised hierarchic clustering. Overall survival (OS) was analyzed using Kaplan-Meier estimates and Cox regression. Differential expression and gene set enrichment analyses were conducted on immune clusters with poor OS and high programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) coexpression. A scoring metric combining differentially expressed genes and immune cell content was created, and its prognostic value and immune checkpoint blockade response prediction was evaluated. RESULTS Two clusters were characterized by macrophage enrichment, with distinct M0Hi and M2Hi subtypes. M2Hi ( P = .038) and M0Hi ( P = .018) were independently prognostic for OS on multivariable analysis. Kaplan-Meier estimates demonstrated that patients in M0Hi and M2Hi treated with sorafenib had decreased OS ( P = .041), and angiogenesis hallmark genes were enriched in the M0Hi group. CXCL6 and POSTN were overexpressed in both the M0Hi and the PD-1Hi/PD-L1Hi groups. A score consisting of CXCL6 and POSTN expression and absolute M0 macrophage content was discriminatory for OS (intermediate: hazard ratio [HR], 1.59; P ≤ .001; unfavorable: HR, 2.08; P = .04). CONCLUSION Distinct immune cell clusters with macrophage predominance characterize an aggressive HCC phenotype, defined molecularly by angiogenic gene enrichment and clinically by poor prognosis and sorafenib response. This novel immunogenomic signature may aid in stratification of unresectable patients to receive checkpoint inhibitor and antiangiogenic therapy combinations.
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