• 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • 2021-03
  • Paclitaxel (Taxol) Styrene propylene dichloride and polycycl


    Styrene, propylene dichloride, and polycyclic organic matter had suggestive associations for overall and ER+ breast cancer. In a study using MCF-7 estrogen-sensitive human breast cancer cells, styrene oli-gomers increased cell proliferation, demonstrated estrogenicity, and had an affinity to estrogen receptor alpha (Ohyama et al., 2001). Pro-pylene dichloride has been classified by IARC as Group 1 (carcinogenic to humans) (International Agency for Research on Cancer, n.d.). In mice it has been shown to increase oxidative stress through cytochrome P450 metabolism (Toyooka et al., 2017) and to induce mammary adeno-carcinoma (International Agency for Research on Cancer, 2017). The primary components of polycyclic organic matter are polycyclic aro-matic hydrocarbons, which have shown estrogenic and anti-estrogenic activity in vitro (Zhang et al., 2016; Santodonato, 1997; Fertuck et al., 2001; Arcaro et al., 1999; Kummer et al., 2008) and have been asso-ciated with breast cancer risk in previous studies (White et al., 2016; Bonner et al., 2005; Gammon et al., 2002; Mordukhovich et al., 2016).
    Acrylamide and benzidine were associated with increased ER− breast cancer risk, but other air toxics, particularly 2,4-toluene diiso-cyanate, benzene, chloroprene, ethylbenzene, and styrene, were asso-ciated with reduced ER− breast cancer risk. These inverse associations were unexpected. However, a few studies on air pollution have also found inverse associations for some other pollutants with ER− breast cancer (Hart et al., 2018; Amadou et al., 2019; White et al., 2019) so this area warrants further study, particularly in a population with a greater number of ER− cancers.
    The finding of effect-measure modification by BMI for multiple air toxics is consistent with our a priori hypothesis that associations would
    Table 5 Additive and multiplicative effect measure modification by BMI for the associationsa between hazardous air pollutants and breast cancer risk, the Sister Study.
    BMI (kg/m2) Air toxic Cases (N) Single referent HRs (95% CIs) Additive ICR (95% CI) Stratified HRs (95% CIs) Multiplicative RHR (95% CI) p interaction
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    Table 5 (continued)
    BMI (kg/m2) Air toxic Cases (N) Single referent HRs (95% CIs) Additive ICR (95% CI) Stratified HRs (95% CIs) Multiplicative RHR (95% CI) p interaction
    Vinylidene chloride
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    Table 5 (continued)
    BMI (kg/m2) Air toxic Cases (N) Single referent HRs (95% CIs) Additive ICR (95% CI) Stratified HRs (95% CIs) Multiplicative RHR (95% CI) p interaction
    Abbreviations: BMI, body mass index; ICR, interaction Paclitaxel (Taxol) ratio; RHR, ratio of hazard ratios. a Models adjusted for race, residence type, education, and smoking status.
    be stronger among women who are overweight/obese. Obesity, which is associated with postmenopausal breast cancer (White et al., 2015; Fortner et al., 2016), leads to increases in oxidative stress and in-flammation (Fortner et al., 2016; Weichenthal et al., 2014; Morris et al., 2011). Given that air toxics also increase oxidative stress, a synergy between the oxidative stress and inflammation from obesity and air toxics is plausible (Weichenthal et al., 2014; Dubowsky et al., 2006). Additionally, obesity has been shown to impair the oxidant defense system which could make obese individuals more susceptible to the oxidative stress from air toxics (Weichenthal et al., 2014; Savini et al., 2013). Given that over 70% of US adults are overweight/obese (National Institute of Diabetes and Digestive and Kidney Diseases, n.d.) and the prevalence continues to increase (Hales et al., 2018), this is an important, potentially vulnerable subgroup that should be considered in the regulation of air toxics.
    Our use of classification tree methods was useful in recognizing complex relations between air toxics and covariates in our population and also supported findings from the single pollutant analyses. Similar to the single pollutant analysis, methylene chloride appeared to be an air toxic of relevance for breast cancer; among women < 58.7 years of age, breast cancer risk was higher among those with higher vs. lower methylene chloride. Further, among women > 58.7 years of age and with a BMI < 29.7 kg/m2, breast cancer risk was elevated among those with higher vs. lower methylene chloride. However, the best cut-point identified by the classification tree was at a high concentration in the methylene chloride distribution. Therefore, although the 4th quintile was most strongly associated with breast cancer risk in the single pol-lutant models, the multipollutant models indicated that very high levels may also be of relevance (which could have been masked in the 5th quintile as a whole in the single pollutant models). Due to the ease with which CART handles non-linear and non-additive associations, CART methods were able to identify this grouping. While it was a strength that CART identified important cutpoints that would not have been found using traditional regression methods, the results based on these splits should be interpreted cautiously as the number of women im-pacted by them is small. Propylene dichloride and styrene were also identified as important in the formation of multipollutant groups in the classification tree and showed some evidence of association with breast cancer risk in the single pollutant models. The tree methodology was used as a tool for exploring complex relations between the air toxics that may reflect harmful co-exposures for breast cancer of interest for future evaluation. We considered different combinations of the number of total terminal nodes, minimum number of observations in a node, and maximum depth before settling on our final tree that was not too large to lose interpretability, but still identified potentially important groups. Generally, the nodes at the top of our tree were most robust Paclitaxel (Taxol) to changes in stopping parameters, but the known instability of trees to changes in parameters is a limitation worth noting (Strobl et al., 2009). However, we emphasize that this analysis was exploratory as CART does not provide measures of statistical precision and the size of the tree is controlled by investigator-specified parameters.