Magnetic resonance imaging (MRI) of pharmacological ascorbate-induced iron redox state as a biomarker in subjects undergoing radio-chemotherapy.

Pharmacological ascorbate (P-AscH-) combined with standard of care (SOC) radiation and temozolomide is being evaluated in a phase 2 clinical trial (NCT02344355) in the treatment of glioblastoma (GBM). Previously published data demonstrated that paramagnetic iron (Fe3+) catalyzes ascorbate's oxidation to form diamagnetic iron (Fe2+). Because paramagnetic Fe3+ may influence relaxation times observed in MR imaging, quantitative MR imaging of P-AscH--induced changes in redox-active Fe was assessed as a biomarker for therapy response. Gel phantoms containing either Fe3+ or Fe2+ were imaged with T2* and quantitative susceptibility mapping (QSM). Fifteen subjects receiving P-AscH- plus SOC underwent T2* and QSM imaging four weeks into treatment. Subjects were scanned: pre-P-AscH- infusion, post-P-AscH- infusion, and post-radiation (3-4 h between scans). Changes in T2* and QSM relaxation times in tumor and normal tissue were calculated and compared to changes in Fe3+ and Fe2+ gel phantoms. A GBM mouse model was used to study the relationship between the imaging findings and the labile iron pool. Phantoms containing Fe3+ demonstrated detectable changes in T2* and QSM relaxation times relative to Fe2+ phantoms. Compared to pre-P-AscH-, GBM T2* and QSM imaging were significantly changed post-P-AscH- infusion consistent with conversion of Fe3+ to Fe2+. No significant changes in T2* or QSM were observed in normal brain tissue. There was moderate concordance between T2* and QSM changes in both progression free survival and overall survival. The GBM mouse model showed similar results with P-AscH- inducing greater changes in tumor labile iron pools compared to the normal tissue. CONCLUSIONS: T2* and QSM MR-imaging responses are consistent with P-AscH- reducing Fe3+ to Fe2+, selectively in GBM tumor volumes and represent a potential biomarker of response. This study is the first application using MR imaging in humans to measure P-AscH--induced changes in redox-active iron.

Chapter 3: Cohort life tables by smoking status, removing lung cancer as a cause of death.

The purpose of this study was to develop life tables by smoking status removing lung cancer as a cause of death. These life tables are inputs to studies that compare the effectiveness of lung cancer treatments or interventions, and provide a way to quantify time until death from causes other than lung cancer. The study combined actuarial and statistical smoothing methods, as well as data from multiple sources, to develop separate life tables by smoking status, birth cohort, by single year of age, and by sex. For current smokers, separate life tables by smoking quintiles were developed based on the average number of cigarettes smoked per day by birth cohort. The end product is the creation of six non-lung-cancer life tables for males and six tables for females: five current smoker quintiles and one for never smokers. Tables for former smokers are linear combinations of the appropriate table based on the current smoker quintile before quitting smoking and the never smoker probabilities, plus added covariates for the smoking quit age and time since quitting.

Lung cancer death rates in lifelong nonsmokers.

BACKGROUND: Few studies have directly measured the age-, sex-, and race-specific risks of lung cancer incidence and mortality among never tobacco smokers. Such data are needed to quantify the risks associated with smoking and to understand racial and sex disparities and temporal trends that are due to factors other than active smoking. METHODS: We measured age-, sex-, and race-specific rates (per 100,000 person-years at risk) of death from lung cancer among more than 940,000 adults who reported no history of smoking at enrollment in either of two large American Cancer Society Cancer Prevention Study cohorts during 1959-1972 (CPS-I) and 1982-2000 (CPS-II). We compared lung cancer death rates between men and women and between African Americans and whites and analyzed temporal trends in lung cancer death rates among never smokers across the two studies by using directly age-standardized rates as well as Poisson and Cox proportional hazards regression analyses. All statistical tests were two-sided. RESULTS: The age-standardized lung cancer death rates among never-smoking men and women in CPS-II were 17.1 and 14.7 per 100,000 person-years, respectively. Men who had never smoked had higher age-standardized lung cancer death rates than women in both studies (CPS-I: hazard ratio [HR] = 1.52, 95% confidence interval [CI] = 1.28 to 1.79; CPS-II: HR = 1.21, 95% CI = 1.09 to 1.36). The rate was higher among African American women than white women in CPS-II (HR = 1.43, CI = 1.11 to 1.85). A small temporal increase (CPS-II versus CPS-I) in lung cancer mortality was seen for white women (HR = 1.25, CI = 1.12 to 1.41) and African American women (HR = 1.22, CI = 0.64 to 2.33), but not for white men (HR = 0.89, CI = 0.74 to 1.08). Among white and African American women combined, the temporal increase was statistically significant only among those aged 70-84 years (P < .001). CONCLUSIONS: Contrary to clinical perception, the lung cancer death rate is not higher in female than in male never smokers and shows little evidence of having increased over time in the absence of smoking. Factors that affect the interpretation of lung cancer trends are discussed. Our novel finding that lung cancer mortality is higher among African American than white women never smokers should be confirmed in other studies.

Lung cancer mortality is related to age in addition to duration and intensity of cigarette smoking: an analysis of CPS-I data.

OBJECTIVES: Models previously developed for predicting lung cancer mortality from cigarette smoking intensity and duration based on aggregated prospective mortality data have employed a study of British doctors and have assumed a uniform age of initiation of smoking. We reexamined these models using the American Cancer Society's Cancer Prevention Study I data that include a range of ages of initiation to assess the importance of an additional term for age. METHODS: Model parameters were estimated by maximum likelihood, and model fit was assessed by residual analysis, likelihood ratio tests, and chi(2) goodness-of-fit tests. RESULTS: Examination of the residuals of a model proposed by Doll and Peto with the Cancer Prevention Study I data suggested that a better fitting model might be obtained by including an additional term specifying the ages when smoking exposure occurred. An extended model with terms for cigarettes smoked per day, duration of smoking, and attained age was found to fit statistically significantly better than the Doll and Peto model (P < 0.001) and to fit well in an absolute sense (goodness-of-fit; P = 0.34). Finally, a model proposed by Moolgavkar was examined and found not to fit as well as the extended model, although it included similar terms (goodness-of-fit; P = 0.007). CONCLUSIONS: The addition of age, or another measure of the timing of the exposure to smoking, improves the prediction of lung cancer mortality with Doll and Peto's multiplicative power model.