Competing-risks nomogram for predicting cancer-specific death in upper tract urothelial carcinoma: a population-based analysis

ObjectiveThis study aimed to use a competing-risks model to establish a nomogram to accurately analyse the prognostic factors for upper tract urothelial carcinoma (UTUC) cancer-specific death (CSD).DesignRetrospective observational cohort study.SettingThe programme has yielded a database of all patients with cancer in 18 defined geographical regions of the USA.ParticipantsWe selected patients with UTUC from the latest edition of the Surveillance, Epidemiology, and End Results database from 1975 to 2016. After excluding patients with unknown histological grade, tumour size and lymph node status, 2576 patients were finally selected.Primary and secondary outcome measuresWe used the Fine-Gray proportional subdistribution hazards model for multivariate analysis and compared the results with cause-specific hazards model. We finally constructed a nomogram for 3-year, 5-year and 8-year CSD rates and tested these rates in a validation cohort.ResultsThe proportional subdistribution hazards model showed that sex, tumour size, distant metastasis, surgery status, number of lymph nodes positive (LNP) and lymph nodes ratio (LNR) were independent prognostic factors for CSD. All significant factors associated with CSD were included in the nomogram. The 3-year, 5-year and 8-year concordance indexes were 0.719, 0.702 and 0.692 in the training cohort and 0.701, 0.675 and 0.668 in the validation cohort, respectively.ConclusionsThe competing-risks model showed that sex, tumour size, distant metastasis, surgery status, LNP and LNR were associated with CSD. The nomogram predicts the probability of CSD in patients with UTUC at 3, 5 and 8 years, which may help clinicians in predicting survival probabilities in individual patients.

Elevated risk of colorectal, liver, and pancreatic cancers among HCV, HBV and/or HIV (co)infected individuals in a population based cohort in Canada

Introduction:Studies of the impact of hepatitis C virus (HCV), hepatitis B virus (HBV) and HIV mono and co-infections on the risk of cancer, particularly extra-hepatic cancer, have been limited and inconsistent in their findings.Methods:In the British Columbia Hepatitis Testers Cohort, we assessed the risk of colorectal, liver, and pancreatic cancers in association with HCV, HBV and HIV infection status. Using Fine and Gray adjusted proportional subdistribution hazards models, we assessed the impact of infection status on each cancer, accounting for competing mortality risk. Cancer occurrence was ascertained from the BC Cancer Registry.Results:Among 658,697 individuals tested for the occurrence of all three infections, 1407 colorectal, 1294 liver, and 489 pancreatic cancers were identified. Compared to uninfected individuals, the risk of colorectal cancer was significantly elevated among those with HCV (Hazard ration [HR] 2.99; 95% confidence interval [CI] 2.55–3.51), HBV (HR 2.47; 95% CI 1.85–3.28), and HIV mono-infection (HR 2.30; 95% CI 1.47–3.59), and HCV/HIV co-infection. The risk of liver cancer was significantly elevated among HCV and HBV mono-infected and all co-infected individuals. The risk of pancreatic cancer was significantly elevated among individuals with HCV (HR 2.79; 95% CI 2.01–3.70) and HIV mono-infection (HR 2.82; 95% CI 1.39–5.71), and HCV/HBV co-infection.Discussion/Conclusion:Compared to uninfected individuals, the risk of colorectal, pancreatic and liver cancers was elevated among those with HCV, HBV and/or HIV infection. These findings highlight the need for targeted cancer prevention and diligent clinical monitoring for hepatic and extrahepatic cancers in infected populations.

Measuring mercaptopurine (6MP) adherence using red cell 6MP metabolite levels in children with acute lymphoblastic leukemia (ALL): A COG AALL03N1 study.

10514 Background: Non-adherence to 6MP (monitored with medication event monitoring system [MEMs]) is associated with an increased risk of relapse in children with ALL.(JAMA Oncol, 2015) Self-report over-estimates true medication intake, particularly in non-adherent patients.(Blood, 2017) However, monitoring adherence using MEMs is logistically difficult. We investigated whether red cell 6MP metabolite levels (thioguanine nucleotide [TGN] and methylated mercaptopurine [MMP]) taken together, could identify non-adherent patients. Methods: The analysis included children with ALL in maintenance. To minimize variability in TGN and MMP levels due to pharmacogenetics, we excluded TPMT heterozygotes and homozygote mutants. We also excluded Asians to remove variability due to NUDT15. TGN and MMP levels were drawn at 6 consecutive monthly time points for each patient and averaged. TGN and MMP levels (pmol/8 x 108red cells) were standardized, adjusted for 6MP dose intensity, and then analyzed using cluster analysis (Spath, H. [1980]). Results: The 373 patients eligible for analysis yielded 5 clusters. Cluster #1 (n = 119; mean MMP: 15,656; mean TGN: 158); Cluster #2 (n = 211; MMP: 6,042; TGN: 135); and 3 very small outlying clusters (total N = 43). Adjusting for age, sex, race/ethnicity, cytogenetics and NCI risk, we found that patients in Cluster #2 were 2.6 times as likely to be non-adherent (MEMs-based adherence < 95%) compared to Cluster #1 (95% CI 1.5-4.4; P= 0.0007). Mean MEMs-based adherence was significantly higher for patients in Cluster #1 (94.3%) when compared to those in Cluster #2 (87.8%, p = 0.0002). Using Fine-Gray proportional subdistribution hazards models for competing risks and adjusting for clinical and sociodemographic factors, we found that patients in Cluster #2 were at a 2.3-fold higher risk of relapse compared with those in Cluster #1 (95%CI, 1.0-6.4, p = 0.058). Conclusions: These findings illustrate the potential for using a combination of red cell TGN and MMP levels in identifying non-adherent patients. We propose to use these and clinical and demographic factors associated with non-adherence in creating an adherence calculator.