# Genetic and Epigenetic Tumor Suppressor Gene Silencing  are Distinct Molecular Phenotypes Driven by  Growth Promoting Mutations in Nonsmall Cell Lung Cancer

Authors: Carmen J. Marsit; E. Andres Houseman; Heather H. Nelson; Karl T. Kelsey
Journal: Journal of Cancer Epidemiology
 (2008)

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## Abstract

Both  genetic and epigenetic alterations characterize human nonsmall cell lung cancer (NSCLC), but the biological processes that create or select these alterations remain  incompletely investigated. Our hypothesis posits that a roughly reciprocal relationship between the propensity for promoter hypermethylation and a propensity for genetic deletion leads to distinct molecular phenotypes of lung cancer. To test this hypothesis, we examined promoter hypermethylation of 17 tumor suppressor genes, as a marker of epigenetic alteration propensity, and deletion events at the 3p21 region, as a marker of genetic alteration. To model the complex biology between these somatic alterations, we utilized an item response theory model. We demonstrated that tumors exhibiting LOH at greater than 30% of informative alleles in the 3p21 region have a significantly reduced propensity for hypermethylation.  At the same time, tumors with activatingKRAS mutations showed a significantly increased propensity for hypermethylation of the loci examined, a result similar to what has been observed in colon cancer. These data suggest that NSCLCs have distinct epigenetic or genetic alteration phenotypes acting upon tumor suppressor genes and that mutation of oncogenic growth promoting genes, such as KRAS, is associated with the epigenetic phenotype.


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## Body

## 1. Introduction

Lung cancer
remains one of the most incident cancers and the leading cause of cancer death
in both men and women in the United States.
In 2006, there were over 162 000 deaths attributable to lung cancer in the U.S. [1]. The
major cause of lung cancer is tobacco smoking, although environmental tobacco
smoke, asbestos, and other environmental and industrial exposures also
contribute to lung carcinogenesis [2]. Nonsmall cell lung cancer (NSCLC) is
derived from the epithelial cells of the lung and bronchus and is characterized
by a wide variety of molecular alterations. Included among these are activating
mutations in oncogenes (such as cMYC, KRAS, EGFR, CCND1, and BCL2) and inactivating lesions in tumor suppressor genes [3, 4]. The
inactivation of tumor suppressor genes occurs chiefly through allele loss (both
single allele loss and homozygous gene deletion) or epigenetic silencing associated
with promoter hypermethylation of CpG islands in the promoters of many of these
genes [5–7].The relationship
between these various genetic and epigenetic alterations has remained
relatively unexplored, although it is now known that there is a surprisingly
large number of genetic alterations evident in solid tumors [8]. A number of
groups have reported that alterations to TP53, measured both as mutation of the
gene or through altered immunohistochemical staining, are associated with greater prevalence of
LOH at various loci [9–11]. There have
also been links made between lung carcinogen exposures and several genetic
alterations. For example, LOH of the FHIT gene is found more often in smokers
and those exposed to asbestos in an occupational setting [12, 13]. Work from our
group and others has also suggested that carcinogen exposures may drive the
type of alterations observed in specific genes, such as CDKN2A (encoding P16INK4A), which is more often deleted in never
smoking individuals [14] but has an increasing prevalence of hypermethylation
with increasing duration of tobacco smoking [15, 16]. Likewise, mutation of the EGFR gene occurs specifically in
adenocarcinomas, and most often in women and never smoking patients [17–20]. This type of
evidence reveals that distinct molecular phenotypes exist in NSCLC and that
exposure, lifestyle, or a combination of these factors drive these phenotypes. Better definition
of the number and character of these phenotypes may be critical for making
clinical decisions about treatment course for patients, as has been evidenced
in the EGFR mutation case [21–24].
Thus, this study was aimed at better defining the molecular phenotypes in
NSCLC, closely examining the relationship between genetic and epigenetic
alterations in this disease.


## 2. Subjects and Methods

### 2.1. Study Population

Eligible cases
consisted of all newly diagnosed patients with resectable lung cancer who
received treatment at the Massachusetts General Hospital Thoracic Surgery,
Oncology, and Pulmonary Services from November 1992 through December 1996 [25].  The patients involved in these studies
provided written informed consent under a protocol approved by the appropriate
Institutional Review Boards. Patients with recurrent disease or nonoperable
tumors were excluded. A random subset of 260 was analyzed for somatic loss
(LOH) at 3p21. A subset of 185 patients of the parent study had fresh lung
tumor tissue obtained for use in hypermethylation analysis. Tumor tissue was
snap frozen in liquid nitrogen and stored at −80°C
until processed. Demographic and epidemiological data, including all of the
data on tobacco use, were gathered by interviewer review of a self-administered
questionnaire completed by patients and reviewed by a single reviewer during
the hospitalization for thoracic surgery.


### 2.2. 3p21 Loh Analysis

Analysis of
allelic loss at the 3p21 region at microsatellite markersD3S1029, D3S3582, D3S3667, D3S3640, D3S1568, and D3S3026, and D3S1478 and the details of the statistical analysis
and construction of the fraction allele loss (FAL) score have been previously
described for this population [9, 26].


### 2.3. Hypermethylation Analysis

DNA was extracted
from fresh frozen tumor tissue using the Gentra Puregene DNA extraction kit
(Gentra, Minneapolis, Minn, USA)
following manufacturer’s protocol.
Sodium bisulfite modification of the resultant DNA was performed as
previously described [27].  Briefly, DNA
was denatured in NaOH and treated with sodium bisulfite (Sigma Chemical Co., St. Louis, Miss, USA) for 16 hours. The DNA
was then purified using a Wizard DNA Clean-Up Kit (Promega, Madison, Wiss, USA), treated again with
NaOH, and ethanol precipitated. DNA was rehydrated in water for subsequent use
in PCR amplification.We have
specifically chosen to utilize traditional methylation specific PCR (MSP) [27] for
the analysis of promoter hypermethylation in these studies. We have previously
examined potential biases in the sensitivity of using this assay against the
relative-quantitative Taqman-based methods [28], and have seen no evidence for
potential bias based on tumor quantity or tumor stage in the samples
analyzed.Sodium bisulfite
modified DNA was used as the template for methylation specific PCR (MSP) as
previously described in [27] using primers specific for the methylated
promoters of CDKN2A [27], RASSF1A [29], APC [30], PYCARD [31], LAMC2 [32], SFRP1, SFRP2, SFRP4, SFRP5 [33], MGMT, DAPK, RARB, CDH1 [34], CDH13 [35], MLH1 [36], CCND2 [37], and PRSS3 [38].  All methylation specific
PCRs are optimized to detect greater than 5% methylated substrate in each
sample. To control for the presence of modified DNA, primers specific to a
modified region of the ACTB gene
containing no CpG sites were used [39]. Modified circulating blood lymphocyte
DNA (obtained from a control subject) and the same lymphocyte DNA completely
methylated using SssI DNA methylase
and modified by treatment with sodium bisulfite were used as the negative and
positive controls, respectively, in each run.These genes were
chosen as promoter hypermethylation detected using this method has been
previously shown to be correlated to transcriptional silencing of these genes,
and their hypermethylation occurs in a tumor-specific pattern. We also wished
to examine the silencing of tumor suppressor genes involved in a variety of
cellular processes and pathways thereby not limiting the analysis to genes
involved in a single pathway targeted for inaction. The genes selected are
known to be involved in processes including cell cycle control (CDKN2A, RASSF1A, APC, and CCND2), apoptosis (DAPK, PYCARD), extracellular interactions (LAMC2, PRSS3), transcriptional regulation (RARB), WNT signaling (SFRP family), cell-cell signaling (CDH1, CDH13), and DNA repair (MGMT, MLH1).


### 2.4. Statistical Analysis

We have previously
demonstrated that there are no discreet groupings of tumors by the number of
genes undergoing hypermethylation [5] and we have observed that
there is a great deal of correlation between methylation of the individual
loci, such that simply counting the number of methylated loci in an individual
tumor is statistically inappropriate. Therefore, we employed an item response
theory (IRT) model [40] which has been shown to be the most appropriate latent
trait technique [41] for examining this type of discrete data [42] and which
allows the modeling of the propensity for methylation to be treated as a
continuous variable in a regression framework.To construct our
IRT model, we used a stepwise selection combined with domain knowledge
examining the effect of exposures on the latent methylation variable. Age and
gender are included in the model as there have been reports of age-related
methylation [43, 44], and there is a well-established difference in the
prevalence of bladder cancer by gender. Histology (adenocarcinoma, squamous
cell carcinoma, and others)
was initially included in the model. We examined the effects of exposures which have been
demonstrated to be associated with lung cancer incidence including cigarette
smoking and occupational asbestos exposure. Cigarette smoking was examined as a
variable comparing never, former, and current smokers as well as using measures
of duration and intensity. We also examined the effect of mutation of the KRAS gene which has been previously
reported in this population of tumors [25, 45], as mutation of this pathway is
associated with a methylator phenotype in colon cancer [46]. As a marker of
genetic alteration, we included the FAL score of 3p21 LOH. Variables were
excluded from the model by using the AIC to determine the most parsimonious
model. The final model included gender, KRAS mutation, and 3p21 FAL (low ≤0.3 versus high >0.3) as covariates in addition
to the promoter hypermethylation status of the 17 genes was examined.We also examined
the associations between propensity for hypermethylation, again characterized
by the methylation latent trait, and patient survival.  In a second stage analysis, we used the
predictions of the methylation latent trait and employed a Cox proportional
hazards model to examine the logarithm of the hazard of death as a linear
function of covariates, including the methylation latent trait. All analyses
were conducted in R version 2.4.1 [47], including custom software for the IRT
model, available upon request.


## 2.1. Study Population

Eligible cases
consisted of all newly diagnosed patients with resectable lung cancer who
received treatment at the Massachusetts General Hospital Thoracic Surgery,
Oncology, and Pulmonary Services from November 1992 through December 1996 [25].  The patients involved in these studies
provided written informed consent under a protocol approved by the appropriate
Institutional Review Boards. Patients with recurrent disease or nonoperable
tumors were excluded. A random subset of 260 was analyzed for somatic loss
(LOH) at 3p21. A subset of 185 patients of the parent study had fresh lung
tumor tissue obtained for use in hypermethylation analysis. Tumor tissue was
snap frozen in liquid nitrogen and stored at −80°C
until processed. Demographic and epidemiological data, including all of the
data on tobacco use, were gathered by interviewer review of a self-administered
questionnaire completed by patients and reviewed by a single reviewer during
the hospitalization for thoracic surgery.


## 2.2. 3p21 Loh Analysis

Analysis of
allelic loss at the 3p21 region at microsatellite markersD3S1029, D3S3582, D3S3667, D3S3640, D3S1568, and D3S3026, and D3S1478 and the details of the statistical analysis
and construction of the fraction allele loss (FAL) score have been previously
described for this population [9, 26].


## 2.3. Hypermethylation Analysis

DNA was extracted
from fresh frozen tumor tissue using the Gentra Puregene DNA extraction kit
(Gentra, Minneapolis, Minn, USA)
following manufacturer’s protocol.
Sodium bisulfite modification of the resultant DNA was performed as
previously described [27].  Briefly, DNA
was denatured in NaOH and treated with sodium bisulfite (Sigma Chemical Co., St. Louis, Miss, USA) for 16 hours. The DNA
was then purified using a Wizard DNA Clean-Up Kit (Promega, Madison, Wiss, USA), treated again with
NaOH, and ethanol precipitated. DNA was rehydrated in water for subsequent use
in PCR amplification.We have
specifically chosen to utilize traditional methylation specific PCR (MSP) [27] for
the analysis of promoter hypermethylation in these studies. We have previously
examined potential biases in the sensitivity of using this assay against the
relative-quantitative Taqman-based methods [28], and have seen no evidence for
potential bias based on tumor quantity or tumor stage in the samples
analyzed.Sodium bisulfite
modified DNA was used as the template for methylation specific PCR (MSP) as
previously described in [27] using primers specific for the methylated
promoters of CDKN2A [27], RASSF1A [29], APC [30], PYCARD [31], LAMC2 [32], SFRP1, SFRP2, SFRP4, SFRP5 [33], MGMT, DAPK, RARB, CDH1 [34], CDH13 [35], MLH1 [36], CCND2 [37], and PRSS3 [38].  All methylation specific
PCRs are optimized to detect greater than 5% methylated substrate in each
sample. To control for the presence of modified DNA, primers specific to a
modified region of the ACTB gene
containing no CpG sites were used [39]. Modified circulating blood lymphocyte
DNA (obtained from a control subject) and the same lymphocyte DNA completely
methylated using SssI DNA methylase
and modified by treatment with sodium bisulfite were used as the negative and
positive controls, respectively, in each run.These genes were
chosen as promoter hypermethylation detected using this method has been
previously shown to be correlated to transcriptional silencing of these genes,
and their hypermethylation occurs in a tumor-specific pattern. We also wished
to examine the silencing of tumor suppressor genes involved in a variety of
cellular processes and pathways thereby not limiting the analysis to genes
involved in a single pathway targeted for inaction. The genes selected are
known to be involved in processes including cell cycle control (CDKN2A, RASSF1A, APC, and CCND2), apoptosis (DAPK, PYCARD), extracellular interactions (LAMC2, PRSS3), transcriptional regulation (RARB), WNT signaling (SFRP family), cell-cell signaling (CDH1, CDH13), and DNA repair (MGMT, MLH1).


## 2.4. Statistical Analysis

We have previously
demonstrated that there are no discreet groupings of tumors by the number of
genes undergoing hypermethylation [5] and we have observed that
there is a great deal of correlation between methylation of the individual
loci, such that simply counting the number of methylated loci in an individual
tumor is statistically inappropriate. Therefore, we employed an item response
theory (IRT) model [40] which has been shown to be the most appropriate latent
trait technique [41] for examining this type of discrete data [42] and which
allows the modeling of the propensity for methylation to be treated as a
continuous variable in a regression framework.To construct our
IRT model, we used a stepwise selection combined with domain knowledge
examining the effect of exposures on the latent methylation variable. Age and
gender are included in the model as there have been reports of age-related
methylation [43, 44], and there is a well-established difference in the
prevalence of bladder cancer by gender. Histology (adenocarcinoma, squamous
cell carcinoma, and others)
was initially included in the model. We examined the effects of exposures which have been
demonstrated to be associated with lung cancer incidence including cigarette
smoking and occupational asbestos exposure. Cigarette smoking was examined as a
variable comparing never, former, and current smokers as well as using measures
of duration and intensity. We also examined the effect of mutation of the KRAS gene which has been previously
reported in this population of tumors [25, 45], as mutation of this pathway is
associated with a methylator phenotype in colon cancer [46]. As a marker of
genetic alteration, we included the FAL score of 3p21 LOH. Variables were
excluded from the model by using the AIC to determine the most parsimonious
model. The final model included gender, KRAS mutation, and 3p21 FAL (low ≤0.3 versus high >0.3) as covariates in addition
to the promoter hypermethylation status of the 17 genes was examined.We also examined
the associations between propensity for hypermethylation, again characterized
by the methylation latent trait, and patient survival.  In a second stage analysis, we used the
predictions of the methylation latent trait and employed a Cox proportional
hazards model to examine the logarithm of the hazard of death as a linear
function of covariates, including the methylation latent trait. All analyses
were conducted in R version 2.4.1 [47], including custom software for the IRT
model, available upon request.


## 3. Results

The demographics
of the study population are shown in Table1. As expected, the majority of
patients were either current or former smokers, with a mean age of
approximately 67 years. Greater than 50% of the tumors were adenocarcinomas,
with about 34% squamous cell carcinoma, and the remaining of the rarer
histologies such as large cell. As previously noted, approximately 17% of the
cases had mutation of KRAS at codon
12 [45], the hotspot for lung cancer, and about 34% had loss of heterozygosity
(LOH) at greater than 30% of informative alleles in the 3p21 region [9].Table 1
Demographics of the NSCLC case series.
CharacteristicAge, mean (SD)66.9(10.6)Gender, n (prevalence)Female80(0.45)Male97(0.55)Histology, n (prevalence)Adenocarcinoma95(0.54)Adeno-squamous5(0.03)Bronchoalveolar2(0.01)Large cell14(0.08)Squamous cell61(0.34)Tumor TNM stage, n (prevalence)Stage 199(0.56)Stage 234(0.19)Stage 339(0.22)Stage 45(0.03)Smoking status, n (prevalence)Never13(0.07)Former93(0.52)Current71(0.40)KRAScodon 12 mutation, n (prevalence)No147(0.83)Yes30(0.17)3p21 LOH fraction allelic loss score, n (prevalence)*0–0.3100(0.66)>0.352(0.34)3p21* LOH data not available for 25 samples.Figure1(a) presents the prevalence of hypermethylation of the 17 gene promoters
examined in this study. The prevalence of this alteration appears highly
variable by the gene examined, with some genes, such as SFRP1 and SFRP2 found to
be hypermethylated in nearly 80% of cases, while other genes, such as MLH1 or APC, rarely exhibiting hypermethylation in this series. There was
moderate correlation between methylation at the individual loci, as depicted in
Figure 1(b), where lighter shades represent correlation coefficients
approaching 1. As we observed, this relatively high correlation between
methylation events, and the broad range of prevalence of these alterations at
different genes, rather than treat these events as independent in the analysis,
we employed an item response theory model to investigate the propensity for
hypermethylation in these tumors. This approach takes into account this
correlation and has been shown to be appropriate for similar data examined in
bladder cancer [42].Prevalence and
correlation between promoter hypermethylation at 17 genes in NSCLC. (a) Bar
graph representing the prevalence of promoter hypermethylation at various gene
promoters examined in 177 NSCLC tumors. (b) Heat map depicting the correlation
coefficient (as depicted in the gradient bar) between individual promoter
hypermethylation of 17 genes examined in NSCLC.
(a)(b)The
results of the item response model are listed in Table2, which provides the coefficient
values of the slopes and intercepts for each of the genes examined in this
series. Although the values of the intercepts are not directly interpretable,
they are provided for completeness as they are used in plotting the
relationship between the latent trait and the probability of methylation. The
slope, on the other hand, indicates the strength of the relationship between
the latent trait and probability of methylation of the gene. The range of
values for the slope terms of these genes suggests that genes contribute
differentially to the modeled underlying propensity for methylation.  Comparisons with the overall prevalence demonstrate that the
model is not solely driven by prevalence, as genes with relative high prevalence,
such as RASSF1A, which are methylated in
approximately 50% of cases, in fact have a nonsignificant and negative item response slope, while CCND2 (encoding Cyclin-D2), with a
prevalence of methylation of approximately 30%, has a highly significant slope
of 1.3.Table 2
Item response model for NSCLC promoter hypermethylation and covariates.
EstSEPItem response interceptsDAPK−1.100.22<.0001RASSF1A−0.200.17.24CDKN2A−0.840.18<.0001MGMT−0.660.27.02RARB0.820.28.004SFRP12.240.42<.0001SFRP22.800.51<.0001SFRP4−0.860.28.002SFRP51.670.42.0001CDH10.880.36.01APC−1.830.25<.0001PYCARD−2.990.42<.0001LAMAC2−0.380.40.35CDH130.030.24.90MLH1−3.770.57<.0001CCND2−0.900.32.006PRSS30.200.19.30Item response slopesDAPK0.500.24.04RASSF1−0.160.17.33CDKN2A0.110.19.56MGMT1.020.26.0001RARB0.980.24<.0001SFRP11.700.29<.0001SFRP21.680.35<.0001SFRP40.990.26.0002SFRP51.670.27<.0001CDH11.430.28<.0001APC0.150.22.50PYCARD0.610.25.01LAMAC21.820.27<.0001CDH130.840.24.0005MLH10.140.40.72CCND21.320.28<.0001PRSS30.430.19.02Latent trait mean effectsGenderMaleReferentFemale−0.340.22.11KRAS codon 12 mutationNo mutationReferentMutation0.540.28.053p21 LOH FAL score0–0.3Referent>0.3−0.560.24.02This
model also examined the impact of exposures, demographics, tumor
characteristics, and other molecular alterations on the underlying propensity
for methylation. We found that onlyKRAS mutation status and fraction allelic loss score of LOH at 3p21 were
significantly associated with the propensity for hypermethylation.  Gender was kept in the model as it is a known
confounder of KRAS mutation status
[25].  Having mutation of KRAS leads to an approximately 0.5 unit increase in latent trait mean (where
latent trait is scaled to have unit standard deviation) (P<.05). On
the other hand, having a 3p21 LOH fraction allele loss score of >0.3,
indicating that greater than 30% of informative loci examined in the region
demonstrated LOH, was associated with a statistically significant reduction by 0.5 unit in the methylation
latent trait mean (P<.02).  We
found no significant association between overall patient survival and the
methylation latent trait in a second stage analysis.


## 4. Discussion

The existence of a
CpG island methylator phenotype (CIMP) has been demonstrated in a number of
tumor types, most conclusively in colorectal, and gastric cancers [44, 48–50].  In
colon cancer, the CIMP phenotype is associated with genetic mutation of the BRAF gene, and it has also been
suggested that this phenotype, particularly through its associated methylation
of the MLH1 gene, is responsible for
mismatch repair deficiency and thus the microsatellite instability observed in
a subset of colorectal cancers [46]. Mutation of the KRAS gene has also been associated with CIMP in colorectal cancer
[48, 50]. Our recent work has also suggested that tumors from a number of other
sites, including the lung, may exhibit differences in their underlying
propensity for hypermethylation [5]. Assessment of the methylator phenotype in
lung cancer is complicated by a lack of understanding as to which genes should
be assessed to determine this phenotype, as those used in colon cancers may not
be appropriate. Thus, we have sought to more thoroughly examine what factors
may be driving this propensity, and if there are specific molecular phenotypes
extant in NSCLC.As
we previously demonstrated, there are no discreet groupings of tumors by the
number of genes undergoing hypermethylation [5], a finding that has led some to
question the existence of a methylator phenotype [51]. We have also shown (Figure 1(b)) that there is a great deal of correlation between methylation of the
individual loci, such that simply counting the number of methylated loci in an
individual tumor is statistically inappropriate. Therefore, in order to employ
appropriate statistical methodologies as well as avoid biased separation of
tumors into classes based on arbitrary counts, we employed an item response
theory model which has been shown to be the most appropriate latent trait
technique for examining this type of discrete data [42] and which allows the
propensity for methylation to be treated as a continuous variable in a
regression framework.Using
this approach, we observe differences in the contribution to the underlying
methylation latent trait by the different loci examined, an observation that
would be lost if the methylation events were simply counted and thus considered
equal. This suggests that some genes, such asSFRP2, SFRP5, and CCND2, may be more informative (due to
their significant and greater item response slopes) than genes with
nonsignificant or small item response slopes, such as RASSF1A or CDKN2A. This
is of interest as CDKN2A (encoding
P16INK4A) is often considered as a marker of CIMP in colorectal cancer [49],
but in NSCLC, silencing of this gene appears less informative for predicting the
overall propensity for methylation. CDKN2A has also been shown to be one of the earliest genes identified to become
hypermethylated during lung carcinogenesis, with detection possible in
epithelial cells from smokers prior to lung cancer diagnosis [52]. Thus, alteration of this gene may be more
important in the early stages of carcinogenesis and may be common in tumors
irrespective of their overall propensity for methylation.We observed that
tumors exhibiting more extensive LOH at the 3p21 region showed a lower
propensity for methylation. This is particularly interesting, as it is often
thought that LOH and methylation may be occurring together since 2 hits are
needed to inactivate tumor suppressors. Further, the tumor suppressorRASSF1A, which is located at 3p21, was
included in the panel of genes in our model, suggesting that the deletion
phenotype and methylation phenotype are perhaps even less correlated than we
report here. Our results, from this examination, our previous examination of
LOH at 3p21 [9], and our work showing p16 deletion and methylation to be
reciprocal [14], suggest that allele loss in the 3p21 region, beyond
identifying the site of a specific NSCLC-specific tumor suppressor gene, is a
more general marker of a propensity for genetic inactivation of tumor
suppressor genes via allelic imbalance or loss. Our data demonstrates that
tumors predisposed to this type of genetic alteration are less likely to
undergo epigenetic alterations and vice versa. That is, our result suggests
that tumor suppressor gene silencing in NSCLCs arises either through a
preponderance of allele loss events or epigenetic silencing events, occurring
in a roughly dichotomous fashion.Our
model also demonstrates thatKRAS mutation is associated with a greater propensity for methylation, a result
similar to that reported in colon cancer [48, 50].  Figure 2 presents a model describing this
overall relationship. The mechanism that underlies the connection between KRAS mutation and increased methylation
propensity remains unclear; indeed, the same can be said for the established connection
between BRAF mutation and CIMP, although
it is known that these two oncogenes operate in the same cell signaling pathway
[48, 53]. Our study as well as previous studies cannot absolutely discern which
event is occurring first, mutation of these genes or establishment of greater
promoter methylation, but one can speculate that the oncogenic activation of
KRAS may drive cell division, thus increasing the possibility for alteration of
promoter methylation profiles. At the same time, silencing of genes involved in
DNA damage or repair through promoter hypermethylation may allow for the
propagation of mutation of these oncogenes. Approaches employing model systems
with oncogenic mutation of these growth promoting genes may help to shed light
on the timing of these events and help to answer this question.Figure 2
Model of the molecular
phenotypes of NSCLC driven by promoter hypermethylation propensity. The model
depicts the inverse relationship between epigenetic and genetic alterations  and its
positive association between epigenetic propensity and growth promoting
oncogene activation.A limitation of
this study was the use of a panel of selected loci and a limited regional
examination of deletion. Thus, our results may be indicative of gene-specific
selection events and not of the broader phenotypes that we have proposed. To more
conclusively examine, this requires genomic-level approaches be applied for
examination of the epigenetic and genetic character within the same tumor, and
new technologies are becoming available which can allow for this type of
examination on clinical specimens.  As
these technologies develop, it will be critical to employ appropriate and
rigorous statistical methods, such as those used here, to analyze the data to
allow for an understanding of the biology driving these alterations. Our
results, though, do provide an impetus to test these hypotheses using these
more genome-wide approaches.More broadly,
these results suggest the existence of distinct molecular pathways of tumor
suppressor gene inactivation in sporadic tumors. Classifying tumors based on
these pathways may lead to improved understanding of the etiology of these
diseases, as it may improve the overall classification of disease for genetic
association studies. For example, one may posit that individuals with
polymorphisms in, perhaps, genes involved in DNA repair (particularly
recombinational repair) that lead to reduced repair capacity, may be at higher
risk for the genetically altered subclass of tumors. This improved
subclassification also holds tremendous clinical utility as it may help to
define patients’ response to specific chemotherapeutic regimens. In this
series, we did not observe any relationship between the methylation latent
trait and overall patient survival, but we could not examine specific subgroups
based on treatment, which could be critical for understanding this
relationship.



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*Source: 215809-2009-01-28.xml*