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Clinical Trials |
Departments of Biostatistics [J. J. L.], Clinical Cancer Prevention [S. M. L., X. C. X., C. P.], Thoracic/Head and Neck Medical Oncology [W. K. H., L. M., R. L., D. M. S., J. S. L., V. M. P., C. G.], Head and Neck Surgery [J. W. M.], Clinical Investigation [W. N. H.], and Pathology [A. K. E.], University of Texas M. D. Anderson Cancer Center, Houston, Texas 77030, and Bristol-Myer Squibb, Wallingford, Connecticut 06492 [S. E. B.]
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ABSTRACT |
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 Top ABSTRACT INTRODUCTIONPATIENTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Between 1988 and 1991, 70 advanced OPL patients were enrolled in a chemoprevention trial of induction with high dose isotretinoin (1.5 mg/kg/day for 3 months) followed by 9 months of maintenance treatment with either low dose isotretinoin (0.5 mg/kg/day) or ß-carotene (30 mg/d; total treatment duration, 1 year). We assessed the relationship between cancer risk factors and time to cancer development by means of exploratory data analysis, logrank test, Cox proportional hazard model, and recursive partitioning.
With a median follow-up of 7 years, 22 of our 70 patients (31.4%) developed cancers in the upper aerodigestive tract following treatment. The overall cancer incidence was 5.7% per year. The most predictive factors of cancer risk are OPL histology, cancer history, and three of the five biomarkers we assessed (chromosomal polysomy, p53 protein expression, and loss of heterozygosity at chromosome 3p or 9p). In the multivariable Cox model, histology (P = 0.0003) and the combined biomarker score of chromosomal polysomy, p53, and loss of heterozygosity (P = 0.0008) are the strongest predictors for cancer development. Retinoic acid receptor ß and micronuclei were not associated with increased cancer risk.
We have demonstrated a successful strategy of comprehensive cancer risk assessment in OPL patients. Combining conventional medical/demographic variables and a panel of three biomarkers can identify high risk patients in our sample. This result will need to be validated by future studies. With the identification of high risk individuals, more efficient chemoprevention trials and molecular targeting studies can be designed.
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INTRODUCTION |
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TopABSTRACTINTRODUCTIONPATIENTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The level of risk for malignant transformation of leukoplakia is associated with lesion histology. The overall malignant transformation rates for dysplastic lesions range from 11 to 36%, depending on the length of follow-up (2 , 5) . A recent report showed that proliferative verrucous leukoplakia has a malignant transformation rate as high as 70.3% (mean follow-up of 11.6 years) (6) .
On the basis of strong data showing retinoid activity in preventing cancers of the upper aerodigestive tract (7, 8, 9) , in 1988 we launched a chemoprevention trial of an induction phase of 3-month high dose isotretinoin (13-cis-retinoic acid) followed by a 9-month maintenance phase of low dose isotretinoin or ß-carotene in subjects with OPLs.3 Seventy patients enrolled in the trial. The efficacy of high dose induction and low dose maintenance with isotretinoin in this trial has been reported (10 , 11) .
During the course of this translational trial, we prospectively collected tissue samples for analysis of biomarkers to characterize the molecular/cellular biology of leukoplakia, to assess correlations between biomarker expression and short term response (p53, RAR-ß, and micronuclei) to evaluate the value of these biomarkers for predicting long term outcome (RAR-ß; LOH at 3p, 9p, and chromosome polysomy) (11, 12, 13, 14, 15, 16, 17, 18, 19) .
With accumulating data and follow-up of this pivotal chemoprevention trial, the main objective of the present report is to provide comprehensive cancer risk assessment tools for patients with oral leukoplakia, taking into account all collected variables, including medical-demographic variables, epidemiological factors, and cellular-molecular biomarkers. Our goal is to construct risk models to facilitate assigning appropriate interventions based on OPL patients’ specific cancer risk or disease process. We also examined whether the short term intervention had an impact on preventing or delaying cancer. By identifying high risk leukoplakia patients, more efficient, better targeted chemoprevention studies can be designed with fewer patients and/or shorter duration. Increased knowledge of biomarkers and the effect of chemopreventive agents in the oral carcinogenesis pathway can help us in designing better mechanism-based prevention studies as well.
As translational chemoprevention study advances, with more and more marker information (e.g., from chip technology) being gathered from smaller and smaller tissue specimens (biopsies and brushings) (20) , the field urgently needs systematic statistical approaches to be able to analyze and integrate the explosion in technology and biomarker data. These approaches are needed for chemopreventive biomarker analyses and must be able to incorporate the real life issue of missing or expended tissue samples or noninformative results (e.g., with LOH in the present study). Our study illustrates the statistical complexity of translational data and provides an example of a systematic framework-model for analyzing them.
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PATIENTS AND METHODS |
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TopABSTRACTINTRODUCTIONPATIENTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The study consisted of two phases. In the first phase, eligible and consenting patients were treated with a high dose isotretinoin induction regimen (1.5 mg/kg/day) for 3 months. Patients with lesion progression during the induction phase were removed from study and offered alternative treatments. Patients with nonprogressing or responding lesions entered the second phase of the study, in which they were randomized to receive a 9-month maintenance therapy with either low dose isotretinoin (0.5 mg/kg/day) or ß-carotene (30 mg/day). A more detailed discussion on patient eligibility and study design can be found in our prior report (10) .
Biomarker Measurements.
The study design called for analysis of five biomarkers: p53; RAR-ß;
CP; LOH; and micronuclei. Detailed descriptions of laboratory
procedures for these biomarker assessments were reported in our
previous papers (12, 13, 14, 15, 16, 17, 18)
. As specified in the protocol,
biopsies were taken from patients’ primary index OPLs during scheduled
clinic visits at baseline, 3 months, and 12 months. The biomarker
measurements included in the present study were performed on all
available and evaluable tissue samples. Sample evaluability was
affected by several factors (e.g., dropout, loss to
follow-up, or patient refusal). Because each biopsy tissue block can be
cut into only 20–30 sections of 4 µm, the need for histological
evaluation and planned and unplanned biomarker analyses had exhausted
certain tissue samples over the years. Inevaluable samples typically
resulted from tangential cut of specimen, which produced insufficient
epithelium cells in the basal and/or parabasal layers for analysis. To
our knowledge, there was no systematic cause behind the absence of
certain data. The majority of available samples are considered
evaluable for biomarker analysis.
Statistical Analysis.
The primary end point of the study and analysis we report here is time
to cancer development. The Kaplan-Meier estimate was computed to
estimate the probability of cancer-free survival. The logrank and Cox
proportional hazards model was applied to analyze the effect of single
and multiple covariates in predicting cancer development
(21)
. A composite score of biomarkers was formed to
evaluate the prognostic effect of multiple biomarkers. Exploratory data
analysis using event charts (22)
and scatter plot matrix
was used to assist in the visualization of the association of multiple
covariates and in model building (23)
. Recursive
partitioning using RPART with exponential scaling for survival data
were applied to provide an alternative method for classifying patients
according to their cancer risk (24)
. All reported
Ps are two sided.
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RESULTS |
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TopABSTRACTINTRODUCTIONPATIENTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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summarizes patients’
demographic, medical, and epidemiological variables and their
prognostic value for cancer development. Older patients had a higher
risk of developing cancer (P = 0.05), with a risk ratio
of 1.61 and a 95% CI of (1.01, 2.58). New cancers developed in
7 of 11 patients (63.6%) with a prior history of cancer compared with
only 15 of 59 patients (25.4%) with no prior cancer history
(P = 0.009; Tables 1
and 2
). The cancer risk of OPLs with moderate
or severe dysplasia was 2.3 times higher than the cancer risk of OPLs
with hyperplasia or mild dysplasia (P = 0.001). In our
sample, however, smoking status, alcohol use, and chewing tobacco did
not predict cancer risk.
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shows the cancer-free survival of the low dose
isotretinoin and ß-carotene groups. Although the overall time to
cancer was not statistically significant between the two groups
(P = 0.70), it appeared that cancer onset was
relatively delayed in the low dose isotretinoin between years 1 and 3.
With a smoothing band width of 0.8 year, the estimated cancer rates at
years 1, 2, and 3 were 2.5%, 5.7%, and 9.6%, respectively, for the
low dose isotretinoin group and 6.2%, 7.1%, and 10.4%, respectively,
for the ß-carotene group. As a reminder, patients in this study
received only 1 year of treatment after registration. Clinical response at 12 months, but not at 3 months, was statistically significantly predictive of cancer. Subjects showing continued response to the maintenance therapy developed fewer cancers than did nonresponders (risk ratio, 0.58; P = 0.04). Note that the status of 3-month and 12-month clinical response was not available in 5 and 19 patients, respectively.
Prior Cancer Sites, Leukoplakia Sites, and New Cancer Sites
Table 2
lists the prior cancer site, primary leukoplakia site, and
new cancer site for the 22 study patients who developed new cancer. Of
the seven patients with prior cancer history, only one had a new cancer
that developed in the same location as the prior cancer and leukoplakia
(tongue-FOM). Fifteen patients with no prior cancer history developed
cancer. In nine of these patients, new cancer occurred in the
leukoplakia site. The remaining patients had cancers develop in new
sites. Among the 22 patients who developed cancers in the upper
aerodigestive tract, 13 (59%) had new cancer in the same site as prior
cancer or leukoplakia. However, the remaining nine patients (41%)
developed new cancer in new sites away from the prior lesions,
suggesting that field cancerization may play an important role in
cancer development in leukoplakia patients.
Combined Predictive Effect of Histology and Prior Cancer History on
Cancer Development
Table 1
shows that histology and prior cancer history are the two
most important clinical-epidemiological predictors for cancer. Fig. 2
provides a graphic assessment of their
effect on time to cancer using the interval event chart
(22)
. Starting from the bottom of the figure, six of nine
patients with moderate or severe dysplasia developed cancer. One of
these six patients had a prior cancer (solid circle) which occurred
10 years before registration. The events of 59 patients with
hyperplasia or mild dysplasia were plotted in the upper part of the
figure. Six of 10 patients with prior cancer history developed new
cancer. Although patients with prior cancer were more likely to develop
new cancers, it appears that time since the prior cancer did not
correlate with time to new cancer. Furthermore, the relatively long
time from prior cancer to new cancer diagnosis suggests that the new
cancer is unlikely the result of locoregional recurrence (range,
6.0–26.6 years; median, 9.2 years; n = 7).
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shows that six patients developed cancer within 5 years
but had no prior cancer or moderate/severe dysplasia. On the other
hand, one patient with moderate/severe dysplasia and three patients
with a history of prior cancer did not develop cancer with >5 years of
follow-up. These findings indicate that prior cancer history and
histology combined could not completely explain the cancer risk. The
results call for a need to investigate the predictive effect of the
molecular and cellular biomarkers to enhance the accuracy of cancer
risk assessment.
Biomarkers as Predictors for Cancer, One Covariate Case
CP.
Baseline tissue samples were available from 40 patients for the
analysis. Due to the skewed distribution (range, 0.2–39.5; mean, 6.6;
median, 3.0), we chose 3 as the cutoff for dichotomizing the CP into
the low and high groups. Table 3
shows
that 13 of 20 (65%) patients with high polysomy developed cancer
whereas only 5 of 20 (25%) patients had cancer in the low polysomy
group. The risk ratio was 1.85 with a 95% CI of 1.05–3.25
(P = 0.03).
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). Analysis based on the principal component analysis on multiple p53
measurements gave similar results (data not shown).
LOH at 3p or 9p.
In 37 evaluable patients, 8 of 19 (42%) patients with 3p or 9p LOH and
2 of 18 (11%) patients without LOH developed cancer. Our initial
report showed that 3p or 9p LOH was associated with higher cancer risk
(17)
. However, with longer follow-up, this significant
association was somewhat weakened (P = 0.09; Table 3
).
RAR-ß Expression.
RAR-ß expression was measured at baseline, 3 months, and 12
months after registration. Due to the change of RAR-ß status
(up-regulated to 90% expression at 3 months) (13)
in our
retinoid-based trial, we used the last measured RAR-ß status as the
covariate for modeling cancer development. Consistent with our prior
report (19)
, we found that RAR-ß expression measured at
the last follow-up in the trial was not a predictor for long term
cancer risk (P = 0.88; Table 3
).
Micronuclei.
Similar to the RAR-ß expression, micronuclei were also measured
at baseline, 3 months, and 12 months after registration. Because the
baseline micronuclei were reduced after the induction of isotretinoin
treatment (16)
, we have chosen the last measured
micronuclei as the predictor for cancer. Table 3
showed that there was
no difference in cancer risk between the low and high micronuclei
groups (P = 0.70).
Biomarkers as Predictors for Cancer, Multiple Covariates Case
Fig. 3
shows the scatter plot matrix
of the follow-up time (time to cancer or lost to follow-up),
cancer status, CP, p53, and LOH. The scatter plot matrix revealed that
there was a weak to moderate correlation between CP and p53
expression (Pearson’s r = 0.61, Spearman’s 
=
0.23) but no correlation between polysomy and LOH (Spearman’s = -0.07) or p53 and LOH (Spearman’s = 0.07). The
second row of the plots indicates that patients with cancer were
inclined to have higher CP, higher p53, and LOH, consistent with the
result presented in Table 3
. Fig. 3
also shows that patients
having two or three of the high risk factors were more likely to
develop cancer. (Note: Better visualization can be achieved by
"brushing" or highlighting points with certain features
interactively on a computer monitor.) Because of the limited sample
size and missing biomarker values, however, only 24 patients had
complete data on these 3 variables, restricting the use of conventional
regression analysis with multiple covariates. To overcome this
limitation, we devised a combined score, denoted as CP.p53.LOH, to
capture the collective information contained in these three markers.
CP.p53.LOH was computed as the sum of the three indicators, one for
each of the three biomarkers. Each indicator was assigned a value of
either 0 or 1, denoting either low or high risk marker value,
respectively. If a biomarker value was missing, the indicator was set
as 0. Nine patients with missing CP, p53, and LOH information were
removed from the analysis. With this combined score, CP.p53.LOH has a
risk ratio of 2.27 (95% CI 1.41–3.66) and was highly significant in
predicting cancer (P = 0.0008; Table 4
).
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showed that
CP.p53.LOH remained highly significant in predicting cancer. Cancer
history remained significant (P = 0.046) for cancer
risk but was not as significant as it was when used as a single
predictor (P = 0.009; Table 1
). The result indicated
that after incorporating the biomarker information, the predictive
value of cancer history was not as important as before. In other words,
a major part of the information contained in cancer history for
predicting new cancer was associated with high CP, high p53 expression,
and LOH. Model 3 in Table 4
shows the joint predictive effect of
CP.p53.LOH and histology. Both variables remained highly significant
(CP.p53.LOH, P = 0.0008; histology, P =
0.0003), suggesting that the histology and the combined biomarker score
jointly were important in predicting cancer. Fig. 4
plots the probability of cancer
incidence by histology and CP.p53.LOH, showing that among patients with
hyperplasia-mild dysplasia lesions, the cancer incidence rates were 1
of 22 (4.5%), 6 of 22 (27.3%), 4 of 7 (57.1%), and 2 of 2 (100%)
for the combined biomarker score of 0, 1, 2, and 3, respectively. The
cancer incidence rates for patients with moderate-severe dysplasia were
1 of 3 (33.3%) if the combined biomarker score was 0 and 5 of 5
(100%) if patients had at least 1 biomarker in the high risk range.
The same trend held in 24 patients with complete biomarker information
(data not shown). Although the sample size in several subgroups was
small, the trend shown in Fig. 4
illustrates the value of using both
the histology and biomarker information in modeling the cancer risk.
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. The likelihood ratio test showed that
model 4 did not provide significant improvement over model 3 by adding
prior cancer history (P = 0.13). Therefore, the best
model for predicting cancer risk in our data contains the combined
biomarker score and histology presented in model 3.
Sensitivity Analysis on Patients with Missing Biomarker Information
We also performed the sensitivity analysis by assigning missing
biomarkers a value of 0.5. The results are consistent with models 1–4
in Table 4
. Specifically, if 0.5 was assigned to missing biomarkers,
the combined biomarker score was still significant in predicting cancer
development as a single covariate (P = 0.002) and along
with histology (combined biomarker score, P = 0.002;
histology, P = 0.001).
Recursive Partitioning for the Classification of Cancer Risk,
Multiple Covariates Case
In the above analyses, we determined that histology, history of
cancer, CP, p53, and LOH are important predictors for cancer
development. We have also applied recursive partitioning to construct
an alternative classification model for cancer risk using these five
covariates (Fig. 5)
. Except for p53, four
of the five covariates were chosen in the model. The classification
tree started with node 1 at the top where 22 of 70 total patients
developed cancer. The standardized event rate, which is a special case
of the event rate in the Poisson model for censored data with
exponential scaling, was set to 1 for the entire sample in node 1. The
recursive partitioning model chose histology for the first split.
Patients with hyperplasia or mild dysplasia were placed in node 2 where
16 of 61 patients had cancer. The remaining patients with moderate or
severe dysplasia formed node 3 where 6 of 9 patients had cancer. The
standardized event rates were 0.8 and 2.6 in nodes 2 and 3,
respectively. Patients in node 2 were further split into two groups
according to their prior cancer history to nodes 4 and 5. For patients
with hyperplasia or mild dysplasia and no history of cancer (node 4),
LOH was the next variable chosen for classification. Finally, CP with a
cutoff value of 4.25 was selected to split patients in node 7 to nodes
8 and 9. Nodes 6, 8, 9, 5, and 3 are terminal nodes with increasing
standardized event rates. The observed cancer incidence rates in these
groups were 5.3, 13.3, 33.3, 60, and 66.7%, respectively. Note that
eight patients were not shown in the terminal nodes due to missing
biomarker information. The recursive partitioning algorithm uses all
available information at each split. Therefore, variables with higher
predictive power and less missing values are more likely to be chosen
in earlier steps. The results show that patients with either
moderate/severe dysplasia or history of cancer had a high risk for new
cancer. In the remaining patients, i.e., patients with
hyperplasia-mild dysplasia and no histology of cancer, LOH and CP can
provide additional information to classify patients according to their
cancer risk.
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DISCUSSION |
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TopABSTRACTINTRODUCTIONPATIENTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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After 10 years of translational assessments, we have identified clinical and molecular-cellular factors that appear to indicate leukoplakia patients at higher risk of malignant transformation. Although smoking and alcohol consumption are risk factors for OPLs, they were not associated with increased cancer risk in our sample. In our patients, the factors of prior cancer history and/or moderate-to-severe dysplastic OPLs apparently were associated with higher risk of cancer development, results agreeing with medical knowledge common in the field and many published reports (2 , 5 , 6) . These cancer history and pathological risk factors, however, are not very sensitive cancer predictors. Therefore, we assessed the predictive effect of five cellular or molecular biomarkers that may complement the medical history and pathological factors and provide a more accurate risk profile.
Our OPL data indicate that expressions within three of these five biomarkers were associated with higher cancer risk: high CP; high p53 protein accumulation in the parabasal layer; and LOH at 3p or 9p. The combined score of these three markers was more predictive of cancer risk than was the independent score of any single marker. Moreover, combining both phenotypic histopathology and genotypic biomarker information can provide better power in predicting cancer development. Therefore, the accuracy of cancer risk assessment apparently can be improved by using collective information derived from a panel of biomarkers combined with histological evaluation of the OPL. Histology and combined biomarker score also were independent predictors for cancer risk. The more abnormalities found in either histology or relevant biomarker expressions, the higher was the cancer risk. The same conclusions were substantiated by the multivariable Cox regression analysis and the recursive partitioning method. These results support the concept of multistep carcinogenesis and warrant further investigation to understand the underlying process.
Our biomarker model analysis led to a "statistically" diagnosed
cancer in one of our patients (a 65-year-old male former smoker and
current alcohol drinker). He was generally low risk [hyperplastic
histology (tongue OPL) and no prior cancer history]. Nevertheless, our
risk model indicated increased cancer risk (CP of 24%, parabasal p53
labeling index of 0.71, and LOH at both 3p and 9p). At the scheduled
phone contact (September 1998), the patient (still asymptomatic) agreed
to come in for an unscheduled clinic visit to have a
biopsy-pathological procedure (December 1998), which revealed an
invasive squamous cell carcinoma in the tongue. This cancer diagnosis
(9.7 years after trial enrollment) resulted exclusively from our
predictive model finding. The long delay of malignant transformation in
some leukoplakia patients provides a window for treatment intervention,
such as with chemoprevention. To avoid the possible bias introduced by
the unscheduled follow-up clinic visit of this particular patient, we
also considered that the patient was cancer free in September 1998 (the
date of our systematic/scheduled phone contact/follow-up) and redid the
analysis. The P values in Table 4
changed slightly, but all
of the major findings remained the same.
In our study, the clinical study design allowed collection of tissue samples only during the 1-year treatment period. There were no prespecified follow-up visits or scheduled biopsies during the follow-up period. Therefore, we were limited in assessing longer term biomarker changes and in mapping out the carcinogenesis pathway. This limitation may explain in part why RAR-ß and micronuclei expressions did not help predict cancer risk. Possibly, loss of RAR-ß or increase in micronuclei precedes cancer development, but our study did not provide enough follow-up information to support or refute either possibility. Future chemoprevention trial designs should include scheduled follow-ups and biopsies during and after the treatment period. This will allow uniform follow-up and assessments of all patients to document patterns of biomarker changes over time. Long term follow-up of chemoprevention study patients will be essential to gain a full understanding of multistep carcinogenesis.
Increasing our ability to predict cancer development is prerequisite to the next step of cancer control, i.e., to develop more effective and/or longer-term chemopreventive interventions within the carcinogenic pathway of individuals at increased cancer risk. Our molecular-cellular risk modeling approach dovetails with the most exciting new developments in chemoprevention, which involve molecular targeting approaches of agent development. Molecular targeting study is advancing rapidly in chemoprevention in general (25 , 26) and in OPLs specifically, illustrated, e.g., by research targeting p53 (27 , 28) .
This report does not attempt to present a validated, generalizable, definitive risk assessment model. Rather, it attempts to illustrate the utility of various statistical modeling approaches for gaining insight into cancer development under the constraints of the design, patient population, missing data, and so on, of a study. Given these limitations, the data set (collected by an experienced group of investigators from a prospective National Cancer Institute randomized trial) for our risk modeling still represents the most comprehensive and mature (10 years of collection) translational data of which we are aware. The use of event chart, scatter plot matrix, and recursive partitioning, along with the Cox model, illustrates rational steps for modeling the risks associated with cancer development. We recognize, however, that this relatively comprehensive data set still is very small, and so the present findings with respect to specifically predicting head and neck cancer must be validated by future studies. Notwithstanding these caveats, we believe that the current methodological and analytical approach contributes substantially to the future development of predictors of cancer, not only in the head and neck, but in other sites as well. Our statistical modeling approach also can help address the growing translational chemoprevention problem of analyzing ever more biomarkers and biomarker data gathered (via chip technology, and the like) from much smaller tissue specimens (20 , 29) .
In summary, our report presents a successful strategy of comprehensive cancer risk assessment in OPL patients under the constraints of a translational chemoprevention trial. We demonstrated that both medical-demographic variables and a panel of three biomarkers can identify high risk patients. Our biomarker risk modeling approach currently is being tested for validation in an ongoing large scale long term National Cancer Institute-sponsored chemoprevention trial in patients with OPLs. Use of biomarkers to increase the sensitivity of histology in predicting cancer development will help in identifying high risk patients, including those with lower risk histology. With quantified cancer risk assessments, investigators and clinicians can offer the most appropriate, tailored cancer prevention strategies to each individual.
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FOOTNOTES |
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1 Supported in part by grants from the National
Institutes of Health (CA94026, CA16672, CA52051, DE11906), funds from
the M. D. Anderson Cancer Center’s Tobacco Initiative Research
Program, the Margaret and Ben Love Professorship (S. M. L.), and the
Stanley S. Schor Visiting Scholar program of Merck Research
Laboratories (J. J. L.). Dr. Waun Ki Hong is an American Cancer
Society Clinical Research Professor. 
2 To whom requests for reprints should be
addressed, at the Department of Biostatistics, Box 213, University of
Texas M. D. Anderson Cancer Center, 1515 Holcombe Boulevard,
Houston, TX 77030. Phone: (713) 794-4943; Fax: (713) 745-4940;
E-mail: jjlee{at}mdanderson.org
3 The abbreviations used are: OPLs, oral
premalignant lesions; RAR-ß, retinoic acid receptor ß; LOH, loss of
heterozygosity; FOM, floor of mouth; CI, confidence interval; CP,
chromosomal polysomy.
Received 9/10/99; revised 2/ 4/00; accepted 2/ 7/00.
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TopABSTRACTINTRODUCTIONPATIENTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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L. Zhang, M. Williams, C. F. Poh, D. Laronde, J. B. Epstein, S. Durham, H. Nakamura, K. Berean, A. Hovan, N. D. Le, et al. Toluidine Blue Staining Identifies High-Risk Primary Oral Premalignant Lesions with Poor Outcome Cancer Res., September 1, 2005; 65(17): 8017 - 8021. [Abstract] [Full Text] [PDF]
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S. M. Lippman, J. Sudbo, and W. K. Hong Oral Cancer Prevention and the Evolution of Molecular-Targeted Drug Development J. Clin. Oncol., January 10, 2005; 23(2): 346 - 356. [Abstract] [Full Text] [PDF]
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V. Stearns, A. Coop, B. Singh, A. Gallagher, H. Yamauchi, R. Lieberman, M. Pennanen, B. Trock, D. F. Hayes, and M. J. Ellis A Pilot Surrogate End Point Biomarker Trial of Perillyl Alcohol in Breast Neoplasia Clin. Cancer Res., November 15, 2004; 10(22): 7583 - 7591. [Abstract] [Full Text] [PDF]
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