Associations of Maternal Prenatal Smoking with Early Childhood Physical Aggression, Hyperactivity-Impulsivity, and Their Co-Occurrence

This study investigated associations between maternal prenatal smoking and physical aggression (PA), hyperactivity-impulsivity (HI) and co-occurring PA and HI between ages 17 and 42 months in a population sample of children born in Québec (Canada) in 1997/1998 (N=1745). Trajectory model estimation showed three distinct developmental patterns for PA and four for HI. Multinomial regression analyses showed that prenatal smoking significantly predicted children’s likelihood to follow different PA trajectories beyond the effects of other perinatal factors, parental psychopathology, family functioning and parenting, and socio-economic factors. However, prenatal smoking was not a significant predictor of HI in a model with the same control variables. Further multinomial regression analyses showed that, together with gender, presence of siblings and maternal hostile reactive parenting, prenatal smoking independently predicted co-occurring high PA and high HI compared to low levels of both behaviors, to high PA alone, and to high HI alone. These results show that maternal prenatal smoking predicts multiple behavior regulation problems in early childhood.

The literature on maternal prenatal smoking and externalizing behavior problems is clearly separated in two developmental periods. The bulk of the current literature on maternal prenatal smoking and externalizing behavior problems covers either childhood and adolescence or the preschool years. However, the rates of common childhood psychiatric disorders such as conduct disorder (CD) and attention deficit hyperactivity disorder (ADHD) and the patterns of comorbidity among them in early childhood are similar to those seen in later childhood (Egger & Angold, 2006). Limitations in current diagnostic criteria for early childhood psychopathology have resulted in a research focus on specific behaviors such as physical aggression (PA) and hyperactivity-impulsivity (HI) rather than clinical disorders in this age group. Both physical aggression and hyperactivity during early childhood appear to be typical precursors of full-blown CD and ADHD during the school years and beyond (Séguin, Nagin, Assaad, & Tremblay, 2004). A substantial number of studies show continuity between early and late childhood externalizing behavior problems (e.g. Campbell, Breaux, Ewing, & Szumowski, 1986; Keenan & Wakschlag, 2000) or have identified consistent developmental trajectories for PA and HI that start as early as age 1 1/2 years (e.g. Côté, Vaillancourt, LeBlanc, Nagin, & Tremblay, 2006; NICHD Early Child Care Research Network, 2004; Romano, Tremblay, Farhat, & Côté, 2006; Shaw, Lacourse, & Nagin, 2005).
In reviewing childhood and adolescence studies, maternal prenatal smoking has consistently been associated with CD- and ADHD- symptoms (for reviews, see Linnet et al., 2003; Wakschlag, Pickett, Cook, Benowitz, & Leventhal, 2002). The associations have been observed in clinical samples (e.g. Mick, Biederman, Faraone, Sayer, & Kleinman, 2002; Milberger, Biederman, Faraone, Chen, & Jones, 1996), ‘at-risk’ samples (e.g. Wakschlag & Hans, 2002) and large population-based samples (e.g. Braun, Kahn, Froehlich, Auinger, & Lanphaer, 2006; Kotimaa et al., 2003). Whereas these studies generally controlled for familial psychopathology and numerous environmental risk factors, there are also indications from studies using (large) twin samples that maternal prenatal smoking predicts children’s CD- and ADHD-symptoms beyond the effects of heritable risk (e.g. Button, Thapar, & McGuffin, 2005; Maughan, Taylor, Caspi, & Moffitt, 2004; Silberg et al., 2003; Thapar et al., 2003). Studies showing associations between maternal prenatal smoking and CD-symptoms and delinquency during adolescence and adulthood (Brennan, Grekin, Mortensen, & Mednick, 2002; Fergusson, Woodward, & Horwood, 1998; Räsänen et al., 1999) and studies showing behavior problems in children of mothers who smoked during pregnancy at different time points (e.g. Maughan et al., 2004; Wakschlag & Hans, 2002; Wakschlag, Pickett, Kasza, & Loeber, 2006) suggest long-lasting effects.
Although there are indications that maternal prenatal smoking is more strongly or even exclusively related to CD symptoms (Wakschlag & Hans, 2002; Wakschlag, Leventhal, Pine, Pickett, & Carter, 2006; Wakschlag, Pickett et al., 2006), only few studies tested for specificity of the relations between maternal prenatal smoking and CD- or ADHD-symptoms (Button et al., 2005; Mick et al., 2002; Thapar et al., 2003). This is an important matter considering the high comorbidity of these behavior problems (e.g. Jensen, Martin, & Cantwell, 1997). Mick et al. (2002) found a robust link with ADHD-symptoms after controlling for CD-symptoms in a clinical population. In that study, children aged 6 to 17 years with ADHD and non-ADHD controls were compared after statistical control for CD. In contrast, Thapar et al.’s (2003) and Button et al.’s (2005) population-based twin study using the Greater Manchester Twin Register did not select on either CD- or ADHD-symptoms. Whereas Thapar et al. showed that prenatal maternal smoking predicted a unique proportion of the variance in ADHD-symptoms after control for CD-symptoms in children between 5 and 16 years, Button et al. (2005) tested a more complex model showing unique variance for both CD- and ADHD-symptoms in children 5 to 18 years of age. Although the aforementioned studies used some form of statistical control for the symptoms associated with the ‘other’ disorder, only one study to date examined a relation between prenatal maternal smoking and co-occurring behavior problems (Wakschlag, Pickett et al., 2006). In that study maternal prenatal smoking predicted the co-occurrence of Oppositional Defiant Disorder (ODD) and ADHD when high levels of both were contrasted with absence of both. This suggests that maternal prenatal smoking, to the extent that it is a causal factor, may affect behavior much more seriously than initially shown in specificity studies. However, such a conclusion may be premature because that analysis did not inform us if maternal prenatal smoking was related specifically to the co-occurrence of both behavior problems or whether it was driven by its association with either behavior problem because we do not know if the combined group differed from the ODD-only or ADHD-only groups. Thus whether maternal prenatal smoking is related to the co-occurrence of behavior problems remains an open question in this literature.
In contrast to childhood and adolescence studies, there is a more limited number of early childhood studies and they tend to focus on symptoms rather than diagnoses. Tremblay et al. (2004) reported an association with early childhood trajectories of PA across time, and Romano et al. (2006) found an association between maternal prenatal smoking and hyperactive symptoms from age 2 to 7, but both studies focused on one externalizing behavior problem. Other studies focused on multiple behaviors. Orlebeke, Knol, and Verhulst (1997) and Williams et al. (1998), using the Child Behavior Checklist (CBCL) in large cohort studies of 3-year-old and 4 to 6-year old children, respectively, found associations with externalizing behavior problems (aggressive, overactive, oppositional) but not with internalizing behavior problems (withdrawn, anxious, depressed). Day, Richardson, Goldschmidt, and Cornelius (2000) reported significant associations of third trimester exposure with scores on each of the subscales of the Toddler Behavior Checklist (Physical Aggression, Oppositional Behavior, Immaturity, and Emotionality) and Activity level assessed with the Routh Activity Scale. Wakschlag, Leventhal et al. (2006) noted a persistent association between maternal prenatal smoking and disruptive behaviors during early childhood. None of these multiple behavior studies examined co-occurrence. Thus there also remains a need in the early childhood literature to examine whether maternal prenatal smoking is specifically associated with the co-occurrence of problem behaviors.
From a developmental perspective, early onset of externalizing behavior has been associated with the poorest outcomes (Brame, Nagin, & Tremblay, 2001; Lacourse et al., 2006; Moffitt, 1993; Woodward, Fergusson, & Horwood, 2002). In addition, co-occurring externalizing behaviors during middle childhood have been associated with the poorest behavioral outcomes during adolescence and adulthood (Lacourse et al., 2006; Lahey, McBurnett, & Loeber, 2000; Séguin et al., 2004). However, co-occurring behavior problems may be etiologically different from individual behavior problems, with their own social, sociodemographic and biological precursors (Waschbusch, 2002). Yet, we know very little about the prenatal markers of co-occurring behavior problems in general. Given that maternal prenatal smoking has been clearly associated with several single externalizing problems across development, in the present study, we test the hypothesis that maternal prenatal smoking is associated with co-occurring externalizing problems. Specifically we predict that maternal prenatal smoking will be associated to the co-occurrence of PA and HI in contrast with the absence of both, PA-only, and HI-only in a large early childhood sample.
Methods
Participants
The children of this study were born in 1997/1998 in the province of Québec, Canada and participate in the Québec Longitudinal Study of Children’s Development. This sample excluded very remote regions of the province populated mainly by aboriginal people (2.1% of live births), babies for whom gestational age could not be computed (1.3%), babies born in a different territory but whose parents reside in Québec (4.5%), and very premature babies (<24 weeks) and babies for whom there were delays in filing the birth records in the Master Birth Registry on time for the first assessment, i.e. babies born after 42 weeks gestation (0.1%; for full details, see Jetté & Des Groseilliers, 2000). A total of 2940 infants met inclusion criteria and were selected through a region-based stratification procedure (Jetté & Des Groseilliers, 2000). Of this original selection, a number of families could not be included in the initial 5 month-assessment for the following reasons: (1) Families not found on time (incorrect address/tel no.) (n=172, 5.9%); (2) Families excluded (total n=93, 3.2%) because of death of the baby (n=5), because of participation in other longitudinal studies (n=5), because they had no command of either English or French language (n=81), or because the instruments of the study were not designed to adequately measure development of children with severe physical or mental handicaps (n=2); (3) Families that could not be reached (n=14, 0.5%); (4) Families who declined participating (n=438, 16.4%). 2223 Families (75.6%) took part in the first assessment, which took place when the infants were 5 months old. Demographic characteristics of this Québec sample were comparable to those of a large Canadian sample consisting of 13,439 households which contains sufficient samples from each of the 10 Canadian provinces (Human Resources Development Canada, 1996: National Longitudinal Survey of Children and Youth, 1994–1995). Assessments relevant to this study took place at 17, 30 and 42 months. Parental informed consent was obtained before every assessment. 478 participants had either dropped out since the initial assessment at 5 months or had missing values for PA, HI or one or more predictor variables. Thus, the final study sample consisted of 1745 children (78.5% of families enrolled at 5 months). Table 1 indicates that, despite the fact there was a slight tendency for less advantaged families to drop out or have missing values on relevant variables, demographic characteristics were largely similar for in- and excluded families.Table 1Demographic characteristics of families enrolled in the Québec Longitudinal Study of Children’s Development (QLSCD 1) (n max=2223) and families included in the present study (QLSCD 2: n=1745)QLSCD 1QLSCD 2
n
%
n
%Sex of child Male1,12351.288450.7 Female1,06948.886149.3Age of mother at birth of first child 21 years or less47122.237221.3 Older than 21 years164877.8137378.7Mother graduated from high school Yes186584.0150486.2 No35516.024113.8Family status Two parents161779.2140780.6 One parent42420.833819.4Number of siblings in the home None83040.671340.9 One or more121559.4103259.1Family income Less than 30,000$63929.346826.8 30,000$–50,000$62528.752730.2 50,000$–80,000$61128.049928.6 More than 80,000$30714.125114.4Data courtesy of the Institut de la Statistique du Québec
Measurements
Maternal prenatal smoking
When the child was 5 months of age, mothers filled out a number of questionnaires. One set of questions concerned substance use (alcohol, tobacco, and illegal drugs) during pregnancy. The questions assessing smoking behavior during pregnancy were straightforward, ‘Did you smoke during pregnancy?’ and ‘How many cigarettes/day did you smoke whilst pregnant?’ We also asked when during pregnancy the mother had smoked, i.e. (only) in the first, second or third trimester or throughout pregnancy. These questions are similar to those found in most other assessment instruments (e.g., the Centers for Disease Control and Prevention (CDC) Pregnancy Risk Assessment Monitoring System (PRAMS) Questionnaire, see Beck et al., 2002) and like those found in most other studies (particularly those assessing large samples, e.g. Button et al., 2005; Fergusson et al., 1998; Maughan et al., 2004; Thapar et al., 2003; Wakschlag et al., 1997; Wakschlag & Hans, 2002). Because amount of cigarettes reportedly smoked tends to be a ‘rounded’ number, e.g. 5, 10, 15, etc, mothers were classified into 1 of 4 groups (0, 1–9, 10–19, or ≥20 cigarettes/day). This or similar classifications have been used in most other studies investigating smoking during pregnancy (e.g. Button et al., 2005; Fergusson et al., 1998; Maughan et al., 2004; Thapar et al., 2003; Wakschlag et al., 1997; Wakschlag & Hans, 2002). Although there is a risk for a social desirability bias, several studies have indicated a relatively strong association between retrospective self-report and blood/urine cotinine-levels (i.e. the main nicotine metabolite) (e.g. Law et al., 2003; Pickett, Rathouz, Kasza, Wakschlag, & Wright, 2005). Further reliance on self-report measures may be inferred from the strong relation between the amount reportedly smoked during pregnancy and birth weight (e.g. Huijbregts et al., 2006; Kramer et al., 2001). Other studies have shown that whereas for some prenatal exposures (e.g. alcohol, drugs) higher levels were reported postnatally than antenatally, this was not the case for tobacco exposure, which might indicate that smoking is considered a more habitual behavior that is more reliably recalled (e.g. Jacobsen, Chiodo, Sokol, & Jacobson, 2002). For the current analyses, 1307 (74.9%) mothers reported not to have smoked during pregnancy, 202 (11.6%) reported to have smoked 1–9 cigarettes/day, 174 (10%) between 10 and 19, and 62 (3.6%) reported to have smoked 20 or more cigarettes/day during pregnancy.
Physical aggression and hyperactivity-impulsivity
Maternal ratings of child behavior were obtained with the use of an early childhood behavior scale from the Canadian National Longitudinal Study of Children and Youth (Statistics Canada, 1995), which incorporates items from the Child Behavior Checklist for Ages 2–3 (Achenbach, Edelbrock, & Howell, 1987), the Ontario Child Health Study Scales (Offord, Boyle, & Racine, 1989), and the Preschool Behavior Questionnaire (Tremblay, Desmarais-Gervais, Gagnon, & Charlebois, 1987). In order to assess PA mothers were asked at 17, 30 and 42 months to indicate whether the child: (1) hits, bites, kicks; (2) fights; and (3) bullies others. The items for HI were: (1) can’t sit still, is restless (or hyperactive), (2) fidgets, (3) cannot settle down to do anything for more than a few moments, (4) is impulsive, acts without thinking, and (5) has difficulty waiting for turn in games. The first three representing hyperactivity, and the latter two representing impulsivity. The items were scored as follows: never or not true (score = 0), sometimes or somewhat true (score = 1), or often or very true (score = 2). The items were summed to obtain PA (range = 0 to 6) and HI scores (range = 0 to 10). The internal consistency values (Cronbach’s α) for PA were 0.80 at 17 months, 0.82 at 30 months, and 0.72 at 42 months. For HI they were 0.75, 0.75, and 0.71, respectively. The scales were shown to have good discriminant validity in the prediction of different types of adolescent criminal behaviors (Broidy et al., 2003; Nagin & Tremblay, 1999), and although correlated, the PA and HI scales were related in predictable ways with mother and child reports of CD and ADHD, respectively (Séguin et al., 2004). Their validity in early childhood is also well established (Tremblay et al., 2004; Romano et al., 2006).
Control variables
Potential confounders of associations between maternal prenatal smoking and early behavior problems were selected from four key domains: demographic factors (age at birth of first child, family status (separation/divorce: yes/no), presence of siblings, family income, maternal education); perinatal factors (alcohol and drug exposure during pregnancy, birth weight); family functioning and parenting (hostile reactive parenting, responsiveness, involvement), and parental background and mental health (mother’s and father’s history of antisocial behavior, maternal depression). Mothers provided information on most of these factors when their child was 5 months of age and again when their child was 17 months of age.
Family income was indicated on a 7-point scale (1 = less than $10,000 (Canadian) to 8 = more than $80,000, as was maternal education (1 = no high school diploma to 7 = university degree). The poverty line in Canada is situated at around $15,000 per capita (Statistics Canada, 2006).
Perinatal factors included exposure to illegal drugs (yes/no) and alcohol (7-point scale ranging from ‘never’ to ‘daily’) during pregnancy and birth weight. Birth weight and gestational age were derived from birth records. Birth weight for gestational age was standardized within gender for each week of gestation using Canadian norms (Kramer et al., 2001).
Parenting measures were obtained by observing the mother-child dyad during home visits at 5 and 17 months using the Home Observation for Measurement of the Environment (HOME)-Infant version (Caldwell & Bradley, 1984). The observations were carried out by trained observers who spent about 3 hours in the home to complete a battery of questionnaires and tests. These observers received a one-week training session and were closely supervised during the data collection. Three scales were obtained: hostile reactive parenting (e.g. “talks negatively about her child,” “shouts at her child,” “hits or physically punishes her child”; α=0.43 at 5 months and 0.73 at 17 months), responsiveness (e.g. “responds verbally to child’s vocalizations or verbalizations,” “tells child name of object or person during the visit,” “spontaneously praises the child at least twice”; α=0.85 at 5 months and 0.83 at 17 months), and involvement (e.g. “provides toys that challenge child to develop new skills,” “structures child’s play periods”; α=0.85 at 5 months and 0.88 at 17 months). Scores for each item on each scale ranged from 1 (never) to 5 (all the time). Only the scale score for hostile reactive parenting at 17 months was used because of the low internal reliability of this scale at 5 months. Mean scores across the 5 and 17 months assessments were used for the responsiveness and involvement scales.
Family functioning (at 5 and 17 months) was assessed with a scale containing 12 items measuring communication, problem resolution, control of disruptive behavior, and showing and receiving affection (Statistics Canada, 1995). Scores per item could be 0 (‘never’), 1 (‘sometimes’), or 2 (‘often’), thus ranging from 0 to 36 on the scale. The Cronbach α’s were 0.86 (5 months) and 0.98 (17 months).
In order to assess history of antisocial behavior, both parents completed a questionnaire at the 5-month assessment. The questionnaire included items related to childhood/adolescence (i.e. the period before the end of high school) and items related to adulthood (Zoccolillo, 2000; see also Tremblay et al., 2004), and was largely derived from the NIMH-Diagnostic Interview Schedule (Robins, Helzer, Croughan, & Radcliff, 1981). Childhood/adolescence items included ‘starting fights,’ theft, involvement with youth protection or police, expulsion or suspension from school, truancy, and running away from home. Adult items included arrests (other than for traffic violations), being fired from a job (excluding layoffs for lack of work), trouble at work, with family, or with the police due to drug or alcohol abuse, ‘starting fights’ (fathers), and ‘hitting or throwing things at the spouse or partner’ (mothers). Adolescent and adult scores (0 = no, 1 = yes) were summed for mothers and for fathers. Internal reliability for mother’s items was 0.54 (Cronbach’s α) and 0.59 for father’s items. Latent class analysis identified 3-class models for both mothers ((1) not antisocial, (2) moderately antisocial, (3) antisocial) and fathers ((1) not antisocial, (2) antisocial as an adolescent but not as an adult, (3) moderately antisocial as an adolescent, antisocial as an adult). The Center for Epidemiological Studies-Depression Scale (CES-D; Radloff, 1977) was used for report of symptoms associated with depression (at 5 and 17 months).
Studies have shown that all these factors are associated with increased risk for physical aggression, hyperactivity, CD- and ADHD-symptoms and with prenatal maternal smoking (e.g. Biederman, Milberger, & Faraone, 1995; Campbell, Shaw, & Gilliom, 2000; Huijbregts et al., 2006; Linnet et al., 2003; Maughan et al., 2004; NICHD ECCRN, 2004; Tremblay et al., 2004; Nagin & Tremblay, 2001; Romano et al., 2006; Shaw et al., 2005). Apart from the dichotomous variables (i.e. gender, presence of siblings, drug use during pregnancy, family status), all scores were standardized for statistical analyses.
Data analyses
Assignment to trajectories
Scores from the three assessment points were analyzed to identify distinctive behavioral trajectories across time (Nagin, 1999, 2005; Nagin & Tremblay, 2001). Rather than to assume that all children follow the same developmental pattern, this methodology identifies different groups of individuals who tend follow similar patterns over time. For example, some children may never show a given problem behavior (intercept model or zero order polynomial), others may show constant high levels (also intercept model), and others may increase or decrease over time (e.g., linear – 1st, quadratic – 2nd, or cubic – 3rd order polynomials). The methodology can also be adapted to accommodate various data distributions (i.e., binary, censored normal, zero-inflated Poisson, and count data). The trajectory methodology uses all available developmental data points and assigns individuals to trajectories on the basis of a posterior probability rule. Resulting groups are meant to represent approximations of an underlying continuous process. In order to identify the model that best represents development of a specific behavior during a given time frame, models with a varying number of trajectories are estimated. Model selection is dependent on a combination of statistical and investigator-guided concerns. Besides a need to determine the best model for the data distribution, key decisions are also based on Bayesian fit indices for model selection in accordance to procedures described by Nagin (2005), e.g. the higher the Bayesian Information Criterion (BIC), the better. The optimal model is thus also determined by adding trajectories to the model until the BIC ceases to improve. The investigator would then have enough information to determine the best model.
A key output of model estimation is the posterior probability of group membership. For each trajectory group this probability measures the likelihood of an individual of belonging to that trajectory group based on observations across assessments. In other words, 100% accuracy in classification is not assumed nor required. For example, in the case of an individual who scores high on hyperactivity at all assessment periods, the posterior probability of membership to the chronic group would be high whereas the probability of membership to the low trajectory group would be near 0. Participants can be assigned to the trajectory group for which they show the highest probability of belonging. Ideally, the posterior membership probability should be near 1 for this trajectory group. Further, when trajectories are joined, conditional and joint probabilities can be used to further describe the relationship between the joint factors. Posterior probabilities will then be applied to weight PA, HI and their combination when they enter further analyses (see Séguin et al., 2004).
Prediction of physical aggression, hyperactivity/impulsivity, and their co-occurrence
Weighted multinomial regressions were conducted with PROC CATMOD in SAS v.8.2. (SAS Institute Inc., 2001). First, weighted multinomial regressions were conducted separately on PA and HI between ages 17 and 42 months for descriptive purposes. The influence of maternal prenatal smoking on these behaviors was investigated in analyses with and without control variables.
For the second set of analyses predicting PA and HI between ages 17 and 42 months, we contrasted, in weighted multinomial regression, those children who were high on both PA and HI (combined group), to those who were only high on PA (PA only group), to those only high on HI (HI only group) and to the remainder of the sample.
Results
Assignment to trajectories
Models with between 2 to 5 trajectories and varied shapes for each trajectory were compared using BIC for both PA and HI. Three trajectories were modeled for PA between ages 17 and 42 months using a zero-inflated Poisson distribution: a consistently low, a moderate and rising, and a high and rising trajectory, each representing respectively 25, 50, and 25% of the sample, all of these were best modeled using a linear trend except for the low group which was best represented by a constant term (Fig. 1, left panel). The shape and level of the PA trajectories were very similar to those we identified in Tremblay et al., 2004, in a smaller sample using the same measures. It reveals that most of the children show an increase of physical aggression over time, which is consistent with other longitudinal and cross sectional studies of early childhood.
Four trajectories were modeled for HI using a censored normal distribution: consistently low, low to moderate, moderate to high and chronic HI, representing respectively, 12, 45, 37, and 6% of the sample. All were best modeled by a constant term except for the low to moderate group that was best modeled with a quadratic trend (Fig. 1, right panel).Thus in contrast to PA, HI in early childhood appears to be more stable in level across time, with very few children being atypically high.Fig. 1Observed and predicted means for developmental trajectories of physical aggression (PA) and Hyperactivity-impulsivity (HI) between ages 17 and 42 months. *Physical aggression: H-R = high-rising; M-R = moderate-rising; C-L = consistently low; Hyperactivity-Impulsivity: H-C = high-chronic; M-H = moderate-high; L-M = low-moderate; C-L = consistently low. Data courtesy of the Institut de la Statistique du QuébecTable 2Maternal prenatal smoking and physical aggression (PA), hyperactivity-impulsivity (HI) and joint PA+HI trajectories between ages 17 and 42 monthsMLE 1 (SE)MLE 2 (SE)MLE 3 (SE)
χ
2
P
Physical aggression−.40 (.09)−.24 (.07)NA23.6<.001Physical aggression adjusted for control variables−.29 (.10)−.15 (.08)NA8.4.015Hyperactivity−.56 (.15)−.41 (.11)−.22 (.11)22.8<.001Hyperactivity adjusted for control variables−.32 (.17)−.24 (.13)−.13 (.13)5.8.121Physical aggression and hyperactivity−.65 (.14)−.57 (.22)−.41 (.14)31.2<.001Physical aggression and hyperactivity adjusted for control variables−.46 (.16)−.55 (.25)−.33 (.17)10.5.015Note. MLE = Maximum Likelihood Estimate, Reference categories for PA, HI and PA+HI are high PA, high HI and high PA + high HI, respectively. NA = Not Applicable. (MLE) 1 = consistently-low PA; consistently-low HI; and low PA + low HI; (MLE) 2 = moderate-rising PA; low-moderate HI; and low PA + high HI; (MLE) 3 = moderate-high HI; and high PA + low HI. DF = 2 for PA, DF = 3 for HI and for PA+HI. Data courtesy of the Institut de la Statistique du Québec
In the current models for PA and HI the average posterior probability for the assigned trajectory group ranged between .75 and .83, thereby indicating good fit (Nagin, 1999, 2005). Further, a close match of predicted and observed means shown in Fig. 1 also illustrates this good fit. Models with three trajectories for PA and four trajectories for HI have also produced the best fit in studies using other samples that included children in early childhood (e.g. Tremblay et al., 2004; Côté et al., 2006; Romano et al., 2006; Shaw et al., 2005).
Physical aggression
A first multinomial regression revealed that maternal prenatal smoking significantly predicted PA. In an analysis without control variables, both the odds ratio (OR) contrasts between high and low PA [OR=1.49 (95% CI: 1.25 to 1.75), , p<.001] and between high and moderate PA [OR=1.27 (95% CI: 1.11 to 1.45), , p<.001] were significantly predicted by maternal prenatal smoking (, p<.001; see also Table 2). The ORs represent one categorical increase in maternal prenatal smoking, i.e. from ‘0 cigarettes/day’ to ‘1–9,’ ‘1–9’ to ‘10–19,’ and from ‘10–19’ to ≥20 cigarettes/day.
Maternal prenatal smoking was also a significant predictor of PA in a multinomial regression with control factors [, p=.015]. The ORs were reduced to 1.33 (95% CI: 1.10 to 1.61) [, p=.004] for the contrast between high and low PA, and to 1.16 (95% CI: 0.99 to 1.35) [, p=.057] for the contrast between high and moderate PA. Other significant predictors of PA between ages 17 and 42 months were gender [, p<.001], presence of siblings [, p<.001], and hostile reactive parenting (by the mother) [, p<.001].
Hyperactivity-impulsivity
A first multinomial regression predicting HI without control variables revealed significant contrasts between high and low HI [OR=1.75 (95% CI: 1.32 to 2.33), , p<.001], between high and low-moderate HI [OR=1.49 (95% CI: 1.20 to 1.85), , p<.001] and high and moderate-high HI [OR=1.25 (95% CI: 1.01 to 1.56), , p=.043], resulting in an overall significant effect (, p<.001). In a second multinomial regression with control variables the association of maternal prenatal smoking with HI was strongly attenuated, although the overall effect was still not too far from significance [, p=.12] (see also Table 2). This attenuation can also be seen in the contrasts between high and low HI [OR=1.38 (95% CI: 0.99 to 1.91), , p=.052], between high and low-moderate HI [OR=1.27 (95% CI: 0.98 to 1.63),, p=.070] and high and moderate-high HI [OR=1.13 (95% CI: 0.88 to 1.46), , p=.327]. The remaining significant predictors of HI between ages 17 and 42 months were gender [, p<.001], hostile reactive parenting (by the mother) [, p<.001], maternal depression [, p=.006], and age of the mother when she had her first child [, p=.012].
Combining physical aggression and hyperactivity-impulsivity
Although distinct, the trajectories of PA and HI were moderately but significantly related (, p<.001; Spearman r=.245, p<.001). The conditional probabilities for PA and HI in Table 3 show that, with higher PA or HI, there is also a higher probability of HI and PA, respectively. The conditional probabilities further reveal an asymmetry in the association in that most high HI children were likely to be high PA, but that most high PA children are not likely to be high HI. Table 3 also shows the joint probabilities of displaying particular levels of PA and HI simultaneously. There are 12 cells (3 PA × 4 HI). In order to test the contrasts relevant to our research question, i.e. that maternal prenatal smoking might specifically predict co-occurring PA and HI as opposed to PA only or HI only, these 12 groups were collapsed into 4 groups representing the combined group (PA3HI4), the high PA only group (PA3HI1 + PA3HI2 + PA3HI3), the high HI only group (PA1HI4 + PA2HI4), and the remainder of the sample (PA1HI1 + PA1HI2 + PA1HI3 + PA2HI1 + PA2HI2 +PA2HI3) (Table 4). For the next analysis, these four groups will constitute our dependent variable.Table 3Co-occurrence of physical aggression (PA) and hyperactivity-impulsivity (HI)Probability of PA conditional on HISumLow PAModerate PAHigh PALow HI.40.49.111.0Low-Moderate (L-M) HI.28.54.181.0Moderate-High (M-H) HI.18.49.331.0Chronic-High (C-H) HI.12.39.491.0Probability of HI conditional on PALow HIL-M HIM-H HIC-H HILow PA.19.51.27.031.0Moderate PA.12.48.36.041.0High PA.05.34.50.111.0Joint probability of PA and HILow HIL-M HIM-H HIC-H HISum/PALow PA.05.13.07.01.25Moderate PA.06.24.18.02.51High PA.01.08.12.03.24Sum/HI.12.45.37.061.0Data courtesy of the Institut de la Statistique du QuébecTable 4Co-occurrence of physical aggression (PA) (2 levels) and hyperactivity-impulsivity (HI) (2 levels)Probability of PA conditional on HISumLow PAHigh PALow HI.77.231.0High HI.51.491.0Probability of HI conditional on PALow HIHigh HILow PA.96.041.0High PA.89.111.0Joint probability of PA and HILow HIHigh HISum/PALow PA.73.03.76High PA.22.03.24Sum/HI.95.051.0Data courtesy of the Institut de la Statistique du Québec
In order to be able to conclude that prenatal smoking specifically predicts co-occurring PA and HI the a priori contrasts of all groups to the combined group should be significant. A multinomial regression with the control variables that had featured in the previous separate analyses of PA and HI showed that this was indeed the case. The overall effect of maternal prenatal smoking [, p=.015], and the predictions by prenatal smoking of the contrasts of the combined group with the PA only group [OR=1.39, 95% CI to 1.01 to 1.92, , p=.047], with HI only group [OR=1.72, 95% CI 1.06 to 2.86, , p=.029], and with the remainder of the sample [OR=1.59, 95% CI 1.16 to 2.17, , p=.004], were all significant (see also Table 2). Although additional contrasts were not significant, the contrast between the PA-only group and ‘the remainder of the sample’- group approached significance [, p=.08]. Other significant and independent predictors of the combined group were gender [, p<.001], presence of siblings [, p<.001], and hostile reactive parenting (by the mother) [, p<.001].
Discussion
The results of this study of early childhood show that maternal prenatal smoking was associated with PA but not with HI in covariate regression analyses when PA and HI were examined separately. When PA and HI were combined, a contrast between the PA-only group and the remainder of the sample, which was neither high on PA or HI, fell short of significance. However, maternal prenatal smoking predicted co-occurring elevated levels of PA and HI. Therefore the key finding of this study is that maternal prenatal smoking predicted high PA, but largely when it is co-occurring with high HI. Had we just examined PA and HI separately, as has often been done in studies of specificity, we would have missed the fact that HI is also sensitive to maternal prenatal smoking, even after controlling for several confounding variables, but only when it is combined with PA.
The first implication of these findings is that maternal prenatal smoking may be associated with the most severe forms of PA and HI, i.e. their combination, in early childhood. Second, the fact that maternal prenatal smoking predicts co-occurring PA and HI may be considered an important finding in light of evidence indicating that multiple co-occurring behavior problems at an early age are associated with a higher risk of persistent antisocial problems (Brame et al., 2001; Broidy et al., 2003; Nagin & Tremblay, 1999; Lacourse et al., 2006; Lahey et al., 2000; Moffitt, 1993; Moffitt, Caspi, Dickson, Silva, & Stanton, 1996), as well as with several other functional impairments (Waschbusch, 2002; Séguin et al., 2004). Finally, the risk factors identified for these combined behavior problems, are largely modifiable.
Our study replicates the reports from Orlebeke et al. (1997), Williams et al. (1998), Day et al. (2000), and Tremblay et al. (2004), based largely on early childhood behavior problems. However, we extend these findings because none of those early childhood studies had tested for the prediction of specific behavior problems or for the specificity of their combination. Tremblay et al. (2004) covered the same developmental period and used the exact same PA scale as we did here on another sample. We both found that the association between maternal prenatal smoking and PA was not explained by control variables. However, our results for HI appear to contrast slightly with those of Romano et al. (2006) who reported that the association between maternal prenatal smoking and hyperactivity was not explained by control variables. There are a number of key differences between the current study and Romano et al. (2006). Besides using a different sample, we included impulsivity along with hyperactivity items, and focused exclusively on behavior during early childhood. Thus, in the current study the effects of maternal prenatal smoking on HI were largely explained by control variables. It is possible that control variables have a greater effect on the impulsivity component of HI than on the hyperactivity component. We note that our results appear to be consistent with those of Wakschlag, Leventhal et al. (2006), who also failed to find a relation of maternal prenatal smoking but with ADHD, which includes impulsivity. Because of our goal for a strict test for specificity of effects on PA, it was more conservative to have a combination of impulsivity and hyperactivity items in our scale. Alternately, by having information extending beyond the early childhood period to estimate trajectories Romano et al. (2006) may have more accurately identified children for whom maternal prenatal smoking matters beyond the effects of control variables. Despite this limitation to early childhood, and despite the fact that we cannot claim to have accounted for all possible control variables (examples include exposure to environmental smoking postnatally and forms of parental psychopathology other than maternal depression and parental history of antisocial behavior such as parental ADHD), this study replicates and extends all previous studies by providing an empirical basis for how the association of prenatal smoking to both PA and HI may manifest itself. It is now much more clear that studies need to examine co-occurrence of behavior problems.
There is a leap in drawing parallels between early childhood symptoms to DSM-based disorder, i.e., from PA to CD and from HI to ADHD. CD is characterized by aggression to people and/or animals, deceitfulness or theft, vandalism and serious rule violations whereas ADHD is characterized by hyperactivity, behavior disinhibition (or impulsivity), and inattention and distractibility (American Psychiatric Association, 1994). It is not yet clear to what extent behavior problems during the early childhood years map onto ADHD and CD symptoms during later childhood and (antisocial) behavior problems during adolescence and adulthood, although we have shown that childhood PA trajectories and hyperactivity trajectories were respectively related to CD and ADHD measured in adolescence (Séguin et al., 2004). But manifestation of PA and HI symptoms are often not sufficient to warrant diagnoses. We also note that behavior problems during the early childhood years tend to be more common and typically decline over time (Côté et al., 2006; Romano et al., 2006; Bongers, Koot, Van Der Ende, & Verhulst, 2003).
A number of pioneering studies showed that behavior problems later in life (ranging from school-age to adulthood) could already be identified during early childhood (e.g. Campbell et al., 1986; Keenan & Wakschlag, 2000), but only recently a number of studies have started to show, particularly with respect to HI, a significant consistency in the likelihood to display (very) high levels from age 1 1/2 years onwards (e.g. Romano et al., 2006; Shaw et al., 2005). With respect to PA, there is a larger group of very young children showing rather high levels, but most of them will use less PA from early school age onwards (e.g. Côté et al., 2006; NICHD Early Child Care Research Network, 2004; Shaw et al., 2005). Such general trends might also underlie differences in number of groups identified by the trajectory methodology in samples with different age ranges. Whereas our three PA-groups were consistent with the three groups identified by some authors (e.g. Côté et al., 2006) covering early and middle childhood, other authors identified more than three groups for this period (NICHD Early Child Care Research Network, 2004; Shaw et al., 2005). However, most children will not yet have finished learning alternative strategies (i.e. have undergone ‘socialization of aggression,’ Tremblay, 2003) between 17 and 42 months, so the expected ‘desisting’ patterns of PA will not yet be evident. One thing the abovementioned studies have in common is that they show a degree of consistency between early and middle childhood conduct problems, i.e. children with chronic PA during middle childhood generally had high PA levels during early childhood as well. This, in turn, emphasizes the importance of searching for predictors of co-occurring early childhood behavior problems: children most at risk of subsequent behavior problems might be those who display both high PA and high HI at this stage.
The combination of conduct problems and hyperactivity-impulsivity-attention problems has been distinguished from their pure forms at a number of levels (Waschbusch, 2002). Although research has again mainly focused on older children with diagnosed disorders rather than the combination of behaviors such as PA and HI (but see Séguin, Arseneault, Boulerice, Harden, & Tremblay, 2002; Séguin et al., 2004), evidence has been provided for a number of etiological and developmental differences between the comorbid and individual conditions. For example, greater behavioral and autonomic nervous system reactivity in response to provocation has been shown in the comorbid compared to the separate conditions (Waschbusch, 2002). Children with CD+ADHD were also shown to have lower baseline sympathetic arousal than children with the separate disorders (specifically those with ADHD only; Herpertz et al., 2001). There is also evidence for differences in patterns of brain activity between CD+ADHD and the separate conditions (Banaschewski et al., 2003) and for specific heritable risk of CD+ADHD (Dick, Viken, Kaprio, Pulkkinen, & Rose, 2005; Thapar, Harrington, & McGuffin, 2001). Several studies have shown that specific family psychosocial characteristics (e.g. parent-child conflict; parental psychopathology) specifically predict CD+ADHD or predict CD+ADHD more strongly than the separate conditions (e.g. Burt, Krueger, McGue, & Iacono, 2003; Pfiffner, McBurnett, Rathouz, & Judice, 2005). Thus, although extrapolation to CD and ADHD must be done with caution, the results of the present study support an etiological basis for the combined behavior problems in an early childhood sample.
Finally, in addition to prenatal maternal smoking, we found that maternal hostile- reactive parenting was also associated with co-occurring PA and HI beyond its effects on the separate behaviors. Hostile-reactive parenting and similar ‘negative’ parenting behaviors have been associated with children’s PA and HI levels in earlier studies (e.g. Côté et al., 2006; Romano et al., 2006; Shaw et al., 2005; Tremblay et al., 2004), Negative parenting has also been related to the failure of training programs aimed at improving child conduct problems (Webster-Stratton, Reid, & Hammond, 2001). This association could be seen as resulting from the impact of maternal behavior on their child’s behavior. However, they may also reflect, at least in part, parent’s reactions to their child’s behavior. For instance, when faced with conduct problems, mothers of preschoolers at risk for ADHD and Oppositional Defiant Disorder tend to resort to more negative parenting strategies (Cunningham & Boyle, 2002). Furthermore, mothers hostile-reactive parenting toward their 5 month-old infants has been found to be partly driven by their infant’s difficult temperament (Boivin et al., 2005).
Our finding that maternal hostile-reactive parenting is associated with an increased risk of co-occurring PA and HI may thus reflect the increased challenge of parenting when a child displays multiple behavior problems (cf. Seipp & Johnston, 2005). This pattern could lead to the establishment of a family coercive process and the further learning of anti-social behaviors by the child. However, the state of evidence precludes any definite conclusions at this point, and there is a clear need to better document the early dynamics of negative parenting and externalizing problems in early childhood.
To the extent that they may be involved in causation, the good news is that prenatal maternal smoking and hostile-reactive parenting could be significantly attenuated in prevention and intervention programs. The fact that the effects of smoking during pregnancy on both PA and PA+HI were robust, added to the evidence that PA+HI increases the risk for continued serious behavior problems, emphasizes the importance of smoking cessation during pregnancy (for a review on effectiveness of cessation programs, see Lumley, Oliver, Chamberlain, & Oakley, 2004). Considering the high prevalence of prenatal smoking (around 25% in Western countries), it will be important to further refine the identification of families most at risk of having children with a prepotency to develop co-occurring PA and HI. Our results further confirm that hostile-reactive parenting should be targeted in preventive intervention aimed at children with early multiple behavior problems. However, because parenting interventions often are the least effective in multiple-risk families (for a review, see Hutchings & Lane, 2005), including those characterized by prenatal smoking (Vuijk, van Lier, Huizink, Verhulst, & Crijnen, 2006), cessation programs targeting the most vulnerable families should be emphasized, intensified, and given appropriate means to succeed.

prenatal smoking
physical aggression
hyperactivity
impulsivity
comorbidity


Femara® and the future: tailoring treatment and combination therapies with Femara

Long-term estrogen deprivation treatment for breast cancer can, in some patients, lead to the activation of alternate cellular pathways, resulting in the re-emergence of the disease. This is a distressing scenario for oncologists and patients, but recent intensive molecular and biochemical studies are beginning to unravel these pathways, revealing opportunities for new targeted treatments. Far from making present therapies redundant, these new discoveries open the door to novel combination therapies that promise to provide enhanced efficacy or overcome treatment resistance. Letrozole, one of the most potent aromatase inhibitors, is the ideal candidate for combination therapy; indeed, it is one of the most intensively studied aromatase inhibitors in the evolving combinatorial setting. Complementary to the use of combination therapy is the development of molecular tools to identify patients who will benefit the most from these new treatments. Microarray gene profiling studies, designed to detect letrozole-responsive targets, are currently under way to understand how the use of the drug can be tailored more efficiently to specific patient needs.
Introduction
The proliferative, invasive, and metastatic potential of breast tumors may be largely predetermined at an early stage in the course of disease, whereas genetic alterations that accumulate during progression from in situ to metastatic disease are unpredictable and result in specific phenotypic changes and loss of sensitivity to treatments [1]; for example, although the estrogen receptor-positive (ER+) phenotype may be largely stable over time [2], hormone therapy-sensitive breast tumors may develop resistance and progress to a hormone-independent state [3]. In addition, progression to hormone independence may be associated with alterations in the expression of other regulatory genes, such as human epidermal growth factor receptor 2 (HER2) [4]. In the clinical setting, a quantitative decrease in ER expression was found in over 900 patients with primary breast cancer when HER2 was amplified [5].
Recent advances in translational research studies have highlighted the complexity of ER signaling, including differential roles for the ERα and ERβ subtypes [6], and multiple regulatory interactions between steroid hormone, growth factor, and other tyrosine kinase signaling pathways [7–10]. Greater understanding of tumor biology is beginning to help physicians to individualize treatment selection based on clinical, pathologic, molecular, and genetic profiling, and to rationally design novel combinations to improve efficacy and safety.
This article reviews novel approaches with the more potent third-generation aromatase inhibitor (AI) letrozole (Femara®; Novartis Pharmaceuticals) in combination strategies with agents targeting other growth factor pathways. The review explores the hypothesis that combining letrozole with specifically targeted therapies may delay or overcome endocrine therapy resistance in postmenopausal women with hormone receptor-positive (HR+) breast cancer.
Endocrine therapy resistance
It is well established that breast cancer cells can adapt to low concentrations of estrogen by becoming hypersensitive to estradiol [11]. Long-term exposure to tamoxifen induces hypersensitivity to estradiol [12] and this adaptive change can result in resistance to endocrine therapy [13]. It has been postulated that tamoxifen is more susceptible than AIs to this phenomenon because of its intrinsic agonist properties [12]. Moreover, it has been suggested that highly potent AIs are required to block estrogen synthesis when breast cancer tumors are hypersensitive to small amounts of estradiol [13].
Recent research has provided a compelling explanation for the development of resistance [13, 14]. Experiments using MCF-7 and other breast cancer model systems have identified alternative intracellular signaling pathways used by breast tumors to enhance and activate ER signaling, thus allowing cells to escape from the inhibitory effects of endocrine therapies [15, 16]. It has been shown that long-term estrogen deprivation upregulates ERα and growth factor signaling pathways such as mitogen-activated protein kinase (MAPK), phosphatidylinositol 3-kinase (PI3K), and the mammalian target of rapamycin (mTOR) pathways [14–19]. Of note, Jeng et al. [15] reported that a specific inhibitor of MAPK (PD98059) could block the elevation of activated MAPK observed in MCF-7 cells exposed to long-term estrogen deprivation. Furthermore, studies in both wild-type and long-term estrogen-deprived MCF-7 cells suggested that mTOR has a key role in breast cancer cell proliferation and showed that mTOR inhibition by farnesylthiosalicylic acid (FTS) can reduce proliferation and induce apoptosis [19].
Growth factor pathways
The role of nongenomic pathways has been highlighted in resistance to antiestrogen therapy [20]. Classically, estrogens bind to nuclear ER to enhance transcription of genes important in breast cancer proliferation and survival (genomic pathway) [21]; however, estrogen may also act through ER located in or near the cell membrane [22]. Nongenomic actions include activation of various growth signaling pathways, including MAPK [15]. In addition, ER may indirectly activate epidermal growth factor receptors (EGFR) via coactivators, including src, leading to activation of EGFR [23–25]. Subsequently, dimerization of activated EGFR with other HER family receptors, particularly HER2, activates intracellular signaling pathways, which in turn may enhance nuclear ER signaling [4], thus completing a vicious cycle of events. Cross-talk between ER and HER2 pathways has been implicated in clinical resistance to tamoxifen [4, 7]. Shou et al. [4] reported that tamoxifen behaves as an agonist in MCF-7 breast cancer cells that express high levels of the coactivator AIB1 (src3) and HER2, resulting in de novo resistance. Interestingly, addition of an anti-EGFR tyrsosine kinase inhibitor eliminated cross-talk and restored tamoxifen’s antitumor activity [4].
It has been postulated that AIs may be more effective than selective estrogen-receptor modulators (SERMs) [26] because they can block genomic and nongenomic activities of ER [27]. Elucidation of ER biology and interactions with growth factor signaling pathways will help to identify potential therapeutic targets for HR+ breast cancer [4, 28].
Combination therapy
Several strategies to inhibit growth factor signaling and signal transduction in breast cancer have been tested in the preclinical setting (see Fig. 1). The humanized monoclonal antibody trastuzumab specifically targets the extracellular domain of HER2 [29, 30]. Amplification of HER2 occurs in approximately 25% of breast tumors and is associated with more aggressive disease and a poor prognosis [31]. Trastuzumab has been shown to restore sensitivity to estrogen and tamoxifen in estrogen-independent HER2-transduced MCF-7 cells [32]. Furthermore, trastuzumab blocked HER2 heterodimer formation and phosphorylation, reduced ERK1/2 activity, and strongly inhibited cell growth in MCF-7 cells overexpressing EGFR and HER2 and resistant to tamoxifen [18]. Of note, synergistic inhibition of the BT474 breast cancer cell line was observed with trastuzumab and the HER dimerization inhibitor pertuzumab [33], which targets a different domain of HER2 [34]. The combination of tamoxifen and trastuzumab in ER+, HER2+ BT-474 cells resulted in synergistic growth inhibition due to the enhancement of cell accumulation in the G0–G1 phase of the cell cycle, and a decreased number of cells in S phase [35].
Fig. 1Cross-talk between signal transduction pathways and ER signaling in endocrine resistant breast cancer, with opportunities for targeted intervention. Estrogen (E2)-liganded ER activates E2-regulated genes in classical pathway (thick black arrow), but following long-term tamoxifen therapy resistance can develop with bidirectional cross-talk (gray arrows) between ER and growth factor receptors, with association of membrane bound ER with growth factor receptors, and/or IGFR or EGFR/HER2 activation of ER phosphorylation. Stars show various targeted therapies. AI aromatase inhibitor, SERD selective estrogen receptor down-regulator, MoAb monoclonal antibodies, TKI tyrosine kinase inhibitor, FTI farnesyltransferase inhibitor, CCI cell cycle inhibitor. Reprinted from [28] with permission from the American Association for Cancer Research
HER signaling pathways can also be targeted by inhibiting receptor phosphorylation by intracellular tyrosine kinases [36]. Gefitinib and erlotinib both specifically inhibit the EGFR tyrosine kinase and have demonstrated inhibitory activity in models of hormone-resistant breast cancer [37, 38]. Interestingly, pretreatment of HER2+, hormone-resistant MCF-7 cells with gefitinib eliminated the agonist effects of tamoxifen and restored its antitumor activity [4]. In ER+ breast tumors, targeted therapy with single-agent gefitinib was found to be less effective than endocrine therapy [39]; however, tamoxifen plus gefitinib had greater activity than tamoxifen alone in vivo in hormone-sensitive cells [39]. Data also support the use of EGFR tyrosine kinase inhibitors in combination with HER2 antibodies, such as trastuzumab, against breast tumors that express EGFR and high levels of HER2 [37]. Alternatively, the tyrosine kinase inhibitor lapatinib provides single-agent targeting of both EGFR and HER2 [40, 41]. Using a panel of 31 human breast tumors, Konecny et al. [41] showed that response to lapatinib was significantly correlated with HER2 expression and its ability to inhibit HER2, Raf, Akt, and ERK phosphorylation. In vivo studies showed that lapatinib had a sustained antitumor effect that was further prolonged by combination with trastuzumab [41]. Another study showed that the combination of AEE788 (an EGFR/HER2 tyrosine kinase inhibitor) with letrozole enhanced antiproliferative effects of these agents by 20–30% in MCF-7 and ZR75.1 cell lines and by 60–70% in the BT474 cell line [42]. In a model system of acquired resistance to letrozole, AEE788 partially restored sensitivity to letrozole, whereas rapamycin was not effective, suggesting that letrozole resistance and mTOR activation may not be connected in this model [43]. The authors concluded that inhibition of both HER2-mediated signaling and mTOR-dependent translation may restore responsiveness to letrozole in breast cancer refractory to this AI [43].
Strategies to inhibit downstream signal pathways have also been developed. Farnesyl transferase inhibitors block the first and most important step in the activation of Ras signaling pathways [44]. Aberrant function of the Ras signal transduction pathway is common in breast cancer as a result of upstream activation via HER2 or EGFR [45]. The farnesyl transferase inhibitor R11577 (tipifarnib) was found to have antitumor activity against MCF-7 xenografts [44]. Another interesting therapeutic target is mTOR, a central regulator of G1 cell cycle protein synthesis, that precedes commitment to normal cellular replication [46]. Treatment of MCF-7 Arom-1 cells with letrozole and the mTOR inhibitor RAD001 resulted in a further 50% reduction in proliferation compared with letrozole alone [47]. Another set of experiments, developed to test the hypothesis that Akt kinase confers resistance to endocrine therapy through suppression of ASK1/JNK pathway, showed that combining RAD001 with letrozole restored activation of the ASK/JNK pathway and increased the sensitivity of MCF-7 cells with constitutively active Akt to endocrine therapy [48]. Studies have also targeted the selective estrogen receptor down-regulator (SERD) fulvestrant, which indirectly inhibits growth factor pathways by down-regulating ER [3]. In MCF-7Ca xenografts, combined treatment with fulvestrant and letrozole prevented increases in HER2 and activation of MAPK and inhibited tumor growth [49].
These preclinical models (see Table 1) suggest that treatments to reduce growth factor signaling pathways may be useful in the treatment of human breast cancer [52]. Specifically, novel combination strategies may be developed to prevent or delay the development of endocrine therapy resistance [3, 39], to restore sensitivity to endocrine therapy [53], and to treat hormone-resistant tumors [18, 46]. Anti-vascular endothelial growth factor (VEGF) therapy with bevacizumab may be able to overcome resistance to endocrine therapy and improve efficacy in HR+ metastatic breast cancer [54], and preclinical models have shown that the estrogen-induced increase in VEGF expression may be counteracted by aromatase inhibition. Inhibition of growth factor signaling and angiogenesis pathways may be rationally combined with conventional endocrine strategies for breast cancer [4, 35, 50, 55, 56].
Table 1Summary of letrozole in combination with growth factor signaling inhibitors in preclinical modelsTarget for growth factor inhibitorCombination regimenSummary of key findingsReferencesEGFR/HER2Letrozole + AEE78Combination enhanced antiproliferative effects in MCF-7 (ER+ HER2−), ZR75.1 (ER+ HER2+), and BT474 (ER+ HER2+) cell lines[42]Partial restoration of growth inhibitory effects of letrozole in refractory cell lines (LTLT-Ca; long-term letrozole treated)[43]mTORLetrozole + RAD001 (everolimus)Letrozole + RAD001 significantly increased apoptosis compared with either agent alone[50]Co-treatment increased sensitivity to letrozole in resistant MCF-7 cells with constitutively active Akt[48]RAD001 increased antiproliferative effects of letrozole in MCF-7 Arom 1 cell line[47]IGFBPLetrozole + rhIGFBP-3rhIGFBP-3 Enhanced letrozole activity in MCF-7-Ca cells in vitro and in vivo[51]rhIGFBP recombinant human insulin-like growth factor binding protein
The activity of inhibitors of growth factor signaling depends on the presence of specific cellular aberrations, such as overexpression of HER2 [57] or mutations of EGFR [58]. Consequently, targeted therapies may have limitations as single agents because the target is active in a restricted subset of patients, and breast tumors may undergo adaptive changes to render the target redundant. Since tamoxifen exhibits agonist effects on breast cancer cells exposed to long-term estrogen deprivation [12], it may be better to combine AIs with inhibitors of growth factor signaling. Letrozole is one of the most potent AIs and is one of the most extensively studied AIs in combination with new agents (see Table 1).
Clinical trials of letrozole in combination with inhibitors of growth factor signaling pathways
Based on results from preclinical studies, several clinical trials of novel combinations are under way, with the aim of improving efficacy and safety of endocrine therapy with letrozole (see Table 2). Many of these trials are being conducted in patients with locally advanced or metastatic breast cancer who have failed prior tamoxifen or have a suboptimal response to letrozole. This represents a high-risk, difficult-to-treat population who are candidates for cytotoxic chemotherapy. Preliminary results have shown that letrozole can be safely combined with trastuzumab, lapatinib, everolimus, tipifarnib, bevacizumab, and imatinib. It is too early, however, to make definitive conclusions about efficacy and clinical benefits with these novel combinations.
Table 2Clinical studies of letrozole and inhibitors of growth factor signaling pathwaysTarget for growth factor inhibitorCombination regimenStudy type and patient populationSummary of key findingsReferencesHER2Letrozole + trastuzumabPhase IIMetastatic BC, postmenopausal, ER+ and/or PR+, HER2+ (n = 31)ORR 26%; median TTP 5.8 months[59]EGFR/HER2Letrozole + lapatinibPhase IAdvanced BC (ER+ or PR+) or other tumors (n = 36)Letrozole + lapatinib safely combined at recommended single agent doses[60]Phase IIIAdvanced/metastatic BC (n = 1,200 target accrual)Ongoing trial; primary end point TTP[61]mTORLetrozole + RAD-001 (everolimus)Phase IbAdvanced BC pts with suboptimal response to letrozole (n = 6)RAD001 pharmacokinetics not altered by letrozole[62]Phase IIPresurgical therapy in patients with newly diagnosed ER+ BC (n = 255 planned)Ongoing trial of efficacy and biomarkers[63]Letrozole + CCI-779 (temsirolimus)Phase IIAdvanced or metastatic BC (n = 92)No difference in ORR, but trend to longer PFS with letrozole + temsirolimus (30 mg)[64]Phase IIIAdvanced or metastatic breast cancer (n = 1,236 planned)Terminated[65]Farnesyl transferaseLetrozole + tipifarnib Randomized phase IIAdvanced or metastatic BC that has progressed on tamoxifen (n = 121)ORR 38% for letrozole and 26% for letrozole + tipifarnib (NS)[66]Randomized, placebo-controlled phase IIAdvanced or metastatic BC that has progressed on antiestrogen therapy (n = 120)No longer recruiting[67]VEGFLetrozole + bevacizumab (anti-VEGF monoclonal antibody)Phase IIMetastatic BC, postmenopausal, candidates for AI (n = 28)Letrozole + bevacizumab is well-tolerated[54]Endocrine therapy (tamoxifen or aromatase inhibitor) + bevacizumabPhase III placebo-controlledFirst-line therapy in ER+/PR+ Metastatic BC (n = TBC)Planned trialPlanned CALGB trial [54]Bcr-ablLetrozole + imatinibPhase IIMetastatic BC, postmenopausal ER+ and/or PR+ (n = 15)Letrozole + imatinib is feasible[68]BC breast cancer, ORR overall response rate, TTP time to progression, PFS progression-free survival, AI aromatase inhibitor, TBC to be confirmed
In a clinical trial [69] designed to test whether combination therapy with letrozole and bevacizumab was possible, patients with ER+ or progesterone receptor-positive (PR+) metastatic or locally advanced breast cancer were treated with letrozole (2.5 mg daily) and bevacizumab (15 mg/kg IV every 3 weeks) [54]. The majority of patients had received prior therapy with a nonsteroidal AI. The combination of bevacizumab and letrozole was found to be well-tolerated. Common drug-related toxicities reported were hypertension, fatigue, headache, and joint pain. Median progression-free survival was reported to be 10 months, and this compares favorably with the published data on median time to progression with first-line letrozole (9.4 months) [70]. However, analysis of efficacy and biomarker data was confounded by the long duration of prestudy aromatase inhibition [54, 71]. Nevertheless, when the data were corrected for duration of previous AI therapy, the study did determine that changes in circulating endothelial cell (CEC) levels may be a biomarker of response or progression on anti-angiogenic therapy [71]. Based on these findings, a randomized, double-blind, placebo-controlled trial of bevacizumab combined with endocrine therapy in patients with ER+ or PR+ metastatic breast cancer has been initiated by the Cancer and Leukemia Group B (CALGB) (see Fig. 2) [54]. The primary end point of the trial is progression-free survival.
Fig. 2Planned CALGB trial of first-line endocrine therapy (tamoxifen or aromatase inhibitor) with or without bevacizumab
Letrozole plus trastuzumab produced durable responses in about one in four patients with ER/PR+, HER2+ metastatic breast cancer, but early progression occurred in one in two patients [59]. This suggests that common resistance pathways may be responsible for relapse [59]. Targeting multiple pathways may reduce the risk of resistance. The combination of letrozole and the dual EGFR/HER2 inhibitor lapatinib was found to be feasible and well-tolerated in a phase I study [60], and this regimen is currently being compared with letrozole plus placebo in a phase III study in women with ER/PR+ advanced or metastatic breast cancer [61] (see Fig. 3). Future studies will focus on finding the right combination or sequence of agents for different patients in specific treatment settings. Furthermore, studies in the neoadjuvant setting in patients with locally advanced breast cancer will allow correlative biomarker assessment, such as the proliferation marker Ki-67, to determine the efficacy of combination therapies. For example, everolimus and letrozole are being studied as preoperative therapy of primary breast cancer in postmenopausal women [63]. In this phase II trial, patients are randomized to receive letrozole in combination with everolimus or placebo, an adaptive design strategy, so that identification of biomarkers can be used to optimize patient selection for a future phase III trial of first-line combination therapy in patients with advanced breast cancer. Even if a biomarker is not identified, the trial is adequately powered to demonstrate a statistically significant difference in treatment effect of the combination in the overall population.
Fig. 3Letrozole and lapatinib phase III trial design. Target recruitment: 1,280 patients
Combination strategies may also change the conventional approach for treatment selection based on HR status. Interestingly, it has been suggested that treatment of HER2+, ER− metastatic breast cancer with trastuzumab may transform the tumor phenotype to become hormone responsive [72]. The hypothesis-generating study found that three of ten patients demonstrated ER overexpression at 9, 12, and 37 weeks, respectively, following the initiation of trastuzumab. Two of these patients were subsequently treated with endocrine therapy alone and one received letrozole for 3 years without evidence of progression. Further trials of letrozole used in combination with trastuzumab or sequentially in patients with HER2+, ER− tumors appear warranted.
Microarray/gene profiling studies and optimization of treatment with letrozole
The growing importance of biomarkers in oncology has been reflected in the US Food and Drug Administration (FDA), the National Cancer Institute (NCI), and the Centers for Medicare and Medicaid Services (CMS) Oncology Biomarker Qualification Initiative (OBQI), designed to improve the development of cancer therapies and the outcomes for cancer patients through biomarker development and evaluation [73]. Not only are biomarkers potentially useful as prognostic and predictive factors but they also serve as surrogate end points for long-term outcomes. For example, Dowsett et al. found that changes in Ki-67 in the neoadjuvant setting may be used to predict likely benefit (improved disease-free and overall survival) of AIs in the adjuvant setting [74], potentially expediting clinical development. Therefore, identifying biomarkers that predict endocrine resistance is essential for the optimal use of letrozole. In addition, biomarker studies that help to delineate the oncogenic pathways may be particularly useful for the development of novel combination therapies with letrozole.
Gene expression analysis of tumors represents a novel approach for biomarker development that promises to increase the understanding of breast cancer heterogeneity and facilitate the development of individualized treatment strategies. Microarray analysis, such as the Affymetrix Human Genome Gene Chips, is an exciting development in breast cancer diagnostics that allows the expression of genes in tumors to be quantified using RNA retrieved from breast cancer biopsies. Using cDNA microarray and unsupervised clustering analysis, ER+ breast cancer can be subdivided into at least two subtypes, luminal A and luminal B, with distinct gene expression patterns and clinical outcomes [75–77]. Supervised analysis, comparing gene expression patterns between tumors that relapsed and those that did not, generated a “gene signature” as an independent prognostic parameter for lymph-node negative disease [78, 79]. The National Surgical Adjuvant Breast and Bowel Project (NSABP) has developed a multigene profiling assay, using reverse transcriptase polymerase chain reaction (RT-PCR) to quantify the expression of 21 genes [80]. The 21-gene assay (Oncotype DX, Genomic Health) can predict risk of recurrence in patients with HR+ early breast cancer receiving tamoxifen [80, 81] or chemotherapy [82]. Naderi et al. [83] have also reported validation of gene expression signatures that may have predictive value in the clinic. Furthermore, newly identified interactions between ER and other signaling pathways have been studied using microarray analysis [84, 85]. For example, Bex1 and Bex2 genes have been identified as novel breast cancer-related genes and identify a subtype of ER+ tumors associated with estrogen response and nuclear factor kappa B (NF-κB) pathways [84]. Advances in gene profiling will provide further insights into tumor biology and improve prediction of likely response to specific therapies in the clinic.
Microarray analysis has shown to be valuable in predicting response to neoadjuvant endocrine therapy and was used in a study of neoadjuvant letrozole 2.5 mg/day administered to postmenopausal women with large operable or locally advanced breast cancers for 3 months [86, 87]. Changes in patterns of gene expression were assessed from tissue samples taken at diagnosis, 14 days, and 3 months (N = 58). Changes in gene expression level with treatment were identified as early as 14 days and involved classical markers of estrogen action (trefoil factors 1 and 3, LIV-1, KIAA0101) as well as tumor proliferation (cyclin D1, cyclin B2, CSK2, cell division cycle 2). The objective clinical response to neoadjuvant letrozole was 71% (N = 52 assessable). Of note, the observed changes in gene expression, when clustered, were predictive of response in all cases except one, whereas classical markers of estrogen action were not predictive [87]. In another study, postmenopausal women with primary operable breast cancer were randomized to 2 weeks of presurgical treatment with letrozole 2.5 mg/day or anastrozole 1 mg/day [88]. Microarray gene expression profiling (Breakthrough Centre cDNA chips) of biopsies taken before and during treatment identified differences in gene expression patterns between the two AIs, although the clinical significance of these preliminary findings remains to be clarified. Itoh et al. [89] also found differences between letrozole, anastrozole, and tamoxifen using microarray analysis on MCF-7 cells stably transfected with the aromatase gene (MCF-7Aro). Gene expression patterns revealed a high correlation between the AIs (letrozole and anastrozole) and a clear difference between AIs and tamoxifen [89].
Emerging data therefore suggest that estrogen-responsive genes are candidate biomarkers [90] and may be useful in the clinic as predictive factors of benefit from AI treatment [89]. The value of gene expression profiling is now being evaluated in prospective studies. MINDACT (Microarray In Node negative Disease may Avoid ChemoTherapy), an ongoing three-part randomized trial of 6,000 patients with node-negative breast cancer, is comparing the efficacy of selection of breast cancer patients for adjuvant chemotherapy based on either clinical criteria or the 70-gene microarray prognosis profile [91]. In one part of the trial, 3,500 node-negative, HR+ positive patients will be randomized to receive either 7 years of letrozole or 2 years of tamoxifen followed by 5 years of letrozole. In the US, the TAILORx Trial [92] is comparing hormone therapy with or without combination chemotherapy as adjuvant therapy for node-negative, ER+ breast cancer. The objective of this randomized phase III trial is to determine the best individual therapy using Oncotype DX gene profiling.
One of the more recent advances in transcriptional profiling addresses its potential application as an identification tool for “oncogenic pathway signatures” that could be used to guide targeted therapy. Oncogenic pathway signatures, developed in cell-line models, have been shown to predict sensitivity to therapeutic agents in vitro [93]. This offers an opportunity to identify pathway-specific drugs in endocrine therapy-resistant tumors and creates an opportunity for the rational design of combination therapy with letrozole. In addition to the RNA-based gene expression profiling approach, high throughput analysis at the DNA level, such as genome-wide microarray comparative genomic hybridization (aCGH) and DNA sequencing, has made it possible to decipher the genetic anomalies that drive a particular tumor phenotype. The use of these molecular approaches for biomarker development is still in its infancy but is now feasible with recent advances in genomic technologies.
Conclusions
Hormone-sensitive breast cancer can be regarded as a chronic disease with a persistent risk of escape from effective endocrine control. Activation of growth factor signaling pathways has been implicated in progression of HR+ breast cancer to an estrogen-independent phenotype and the development of resistance to endocrine therapy, particularly tamoxifen. It has been hypothesized that combining endocrine therapy with targeted signal transduction inhibitors may circumvent hormone-independent signaling pathways, so that patients may experience prolonged disease control.
Letrozole is one of the most potent AIs. As such, it may be more effective than tamoxifen for patients with tumor profiles associated with a high risk of developing hormone resistance (e.g., tumors with HER2 gene amplification) and represents an ideal combination partner for agents that inhibit growth signaling pathways implicated in hormone resistance. The efficacy of letrozole is currently being investigated with a variety of signal transduction inhibitors with different mechanisms of action, including monoclonal antibodies against HER family receptors, receptor tyrosine kinase inhibitors, and downstream signaling pathway inhibitors.
Gene expression profiling has been validated as a useful new tool to predict risk of relapse in patients treated with hormone therapy and chemotherapy. This approach will help physicians to identify which patient will likely benefit from specific therapies, such as letrozole. Tailoring therapy to individual patient profiles (clinical, histologic, pathologic, and genetic) will become more sophisticated in the future, helping to maximize the benefits of endocrine therapy throughout the breast cancer continuum; letrozole will undoubtedly become an integral part of the next generation of tailored combination regimens for the treatment of breast cancer.

combination therapy
breast cancer
letrozole
aromatase inhibitors
postmenopausal
adjuvant therapy
mechanism of resistance