Minimum follow-up time required for the estimation of statistical cure of cancer patients: verification using data from 42 cancer sites in the SEER database

BACKGROUND: The present commonly used five-year survival rates are not adequate to represent the statistical cure. In the present study, we established the minimum number of years required for follow-up to estimate statistical cure rate, by using a lognormal distribution of the survival time of those who died of their cancer. We introduced the term, threshold year, the follow-up time for patients dying from the specific cancer covers most of the survival data, leaving less than 2.25% uncovered. This is close enough to cure from that specific cancer. METHODS: Data from the Surveillance, Epidemiology and End Results (SEER) database were tested if the survival times of cancer patients who died of their disease followed the lognormal distribution using a minimum chi-square method. Patients diagnosed from 1973–1992 in the registries of Connecticut and Detroit were chosen so that a maximum of 27 years was allowed for follow-up to 1999. A total of 49 specific organ sites were tested. The parameters of those lognormal distributions were found for each cancer site. The cancer-specific survival rates at the threshold years were compared with the longest available Kaplan-Meier survival estimates. RESULTS: The characteristics of the cancer-specific survival times of cancer patients who died of their disease from 42 cancer sites out of 49 sites were verified to follow different lognormal distributions. The threshold years validated for statistical cure varied for different cancer sites, from 2.6 years for pancreas cancer to 25.2 years for cancer of salivary gland. At the threshold year, the statistical cure rates estimated for 40 cancer sites were found to match the actuarial long-term survival rates estimated by the Kaplan-Meier method within six percentage points. For two cancer sites: breast and thyroid, the threshold years were so long that the cancer-specific survival rates could yet not be obtained because the SEER data do not provide sufficiently long follow-up. CONCLUSION: The present study suggests a certain threshold year is required to wait before the statistical cure rate can be estimated for each cancer site. For some cancers, such as breast and thyroid, the 5- or 10-year survival rates inadequately reflect statistical cure rates, and highlight the need for long-term follow-up of these patients

Modeling the effect of age in T1-2 breast cancer using the SEER database

BACKGROUND: Modeling the relationship between age and mortality for breast cancer patients may have important prognostic and therapeutic implications. METHODS: Data from 9 registries of the Surveillance, Epidemiology, and End Results Program (SEER) of the United States were used. This study employed proportional hazards to model mortality in women with T1-2 breast cancers. The residuals of the model were used to examine the effect of age on mortality. This procedure was applied to node-negative (N0) and node-positive (N+) patients. All causes mortality and breast cancer specific mortality were evaluated. RESULTS: The relationship between age and mortality is biphasic. For both N0 and N+ patients among the T1-2 group, the analysis suggested two age components. One component is linear and corresponds to a natural increase of mortality with each year of age. The other component is quasi-quadratic and is centered around age 50. This component contributes to an increased risk of mortality as age increases beyond 50. It suggests a hormonally related process: the farther from menopause in either direction, the more prognosis is adversely influenced by the quasi-quadratic component. There is a complex relationship between hormone receptor status and other prognostic factors, like age. CONCLUSION: The present analysis confirms the findings of many epidemiological and clinical trials that the relationship between age and mortality is biphasic. Compared with older patients, young women experience an abnormally high risk of death. Among elderly patients, the risk of death from breast cancer does not decrease with increasing age. These facts are important in the discussion of options for adjuvant treatment with breast cancer patients

The rs4646 and rs12592697 Polymorphisms in CYP19A1 Are Associated with Disease Progression among Patients with Breast Cancer from Different Racial/ethnic Backgrounds.

Given the racial/ethnic disparities in breast cancer, we evaluated the association between CYP19A1 single nucleotide polymorphisms (SNPs) on disease progression in women with breast cancer from different racial/ethnic backgrounds. This is a cross-sectional analysis of data from 327 women with breast cancer in the Expanded Breast Cancer Registry program of the University of New Mexico. Stored DNA samples were analyzed for CYP19A1 SNPs using a custom designed microarray panel. Genotype-phenotype correlations were analyzed. Of the 384 SNPs, 2 were associated with clinically significant outcomes, the rs4646 and rs12592697. The T allele for the rs4646 was associated with advanced stage of the disease at the time of presentation (odds ratio OR:1.8, confidence intervals CI: 1.05-3.13, p<0.05) and a more progressive disease (OR: 2.1 CI: 1.1-4.0, p=0.04). For the rs12592697, the variant T allele was more frequent in Hispanic women and associated with a more progressive disease (OR: 2.05 CI: 1.0-4.0, p=0.04). However, further analysis according to menopausal status showed that the association between these 2 SNPs with disease progression or the stage at diagnosis are confined only to postmenopausal women. The odds ratios of disease progression among postmenopausal women carrying the T allele for the rs4646 and rs12592697 are 3.05 (1.21, 7.74, p=0.02) and 3.80 (1.24, 11.6, p=0.02), respectively. Regardless, differences in disease progression among the different genotypes for both SNPs disappeared after adjustment for treatment. In summary, the rs4646 and the rs12592697 SNPs in CYP19A1 are associated with differences in disease progression in postmenopausal women. However, treatment appears to mitigate the differences in genetic risk.ClinicalTrials.govs Identifier: NCT00322894(https://clinicaltrials.gov/ct2/show/NCT00322894?term=new+mexico+breast+cancer+registry&rank=1