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Keywords:

  • symptom profiles;
  • chemotherapy;
  • latent class analysis;
  • quality of life;
  • precision medicine;
  • symptom clusters

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SUPPORT
  8. CONFLICT OF INTEREST DISCLOSURES
  9. REFERENCES

BACKGROUND

A large amount of interindividual variability exists in the occurrence of symptoms in patients receiving chemotherapy (CTX). The purposes of the current study, which was performed in a sample of 582 oncology outpatients who were receiving CTX, were to identify subgroups of patients based on their distinct experiences with 25 commonly occurring symptoms and to identify demographic and clinical characteristics associated with subgroup membership. In addition, differences in quality of life outcomes were evaluated.

METHODS

Oncology outpatients with breast, gastrointestinal, gynecological, or lung cancer completed the Memorial Symptom Assessment Scale before their next cycle of CTX. Latent class analysis was used to identify subgroups of patients with distinct symptom experiences.

RESULTS

Three distinct subgroups of patients were identified (ie, 36.1% in Low class; 50.0% in Moderate class, and 13.9% in All High class). Patients in the All High class were significantly younger and more likely to be female and nonwhite, and had lower levels of social support, lower socioeconomic status, poorer functional status, and a higher level of comorbidity.

CONCLUSIONS

Findings from the current study support the clinical observation that some oncology patients experience a differentially higher symptom burden during CTX. These high-risk patients experience significant decrements in quality of life. Cancer 2014;120:2371–2378. © 2014 American Cancer Society.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SUPPORT
  8. CONFLICT OF INTEREST DISCLOSURES
  9. REFERENCES

Patients receiving chemotherapy (CTX) experience multiple cooccurring symptoms. On average, these patients report 10 unrelieved symptoms that have a negative impact on their functional status and quality of life (QOL).[1] However, a significant amount of interindividual variability exists, with some patients experiencing a few symptoms whereas others experience every symptom associated with a given CTX regimen. The demographic and clinical characteristics that contribute to this interindividual variability in patients' symptom experiences warrant investigation so that high-risk patients can be identified and preemptive symptom management interventions can be initiated.

Previous work from our research team focused on the identification of these high-risk patients based on an evaluation of their experiences with the 4 most common symptoms associated with cancer and its treatment (ie, pain, fatigue, sleep disturbance, and depression).[2-6] Across 5 separate studies, using either cluster analysis or latent class analysis (LCA), 3 to 5 distinct subgroups of patients were identified. It is interesting to note that across all 5 studies, 1 subgroup of patients was characterized as having low levels of all 4 symptoms and another subgroup was characterized as having high levels of all 4 symptoms. In these studies, compared with patients with low levels of pain, fatigue, sleep disturbance, and depression, patients in the “All High” subgroup were significantly younger and reported lower functional status and decreased QOL.[2-6] In the 2 studies that evaluated for differences in clinical characteristics among the patient subgroups,[2, 6] no differences were identified.

In another group of studies[7, 8] that used symptom occurrence ratings from the Memorial Symptom Assessment Scale (MSAS)[9] to identify high-risk patients, only 2 distinct subgroups were identified, namely patients with low and high symptom occurrence rates. Again, in both of these studies, although clinical characteristics were not associated with subgroup membership, patients in the high symptom subgroup reported decrements in functional status and QOL. The reason for the inconsistent number of subgroups identified across these 7 studies[2-8] may relate to the number of symptoms evaluated, whether symptom occurrence or severity ratings were used to create the patient subgroups, and the statistical procedures used to identify the subgroups, as well as the relatively small sample sizes.

In the era of precision medicine,[10] the specialty of oncology has led efforts to identify distinct tumor subtypes for several cancers (eg, breast cancer[11, 12] and lung cancer[13]) based on tumor-specific characteristics and molecular profiles. The goal of these efforts is to develop mechanistically based cancer treatments.[14] Despite some limitations, the emerging evidence cited above suggests that similar studies need to be performed to identify distinct subgroups of patients who will require more targeted symptom management interventions while undergoing cancer treatment.[2-8] The purposes of the current study, performed in a sample of 582 oncology outpatients who were receiving CTX were to identify subgroups of patients based on their distinct experiences with 25 commonly occurring symptoms and to identify demographic and clinical characteristics associated with subgroup membership. In addition, differences in QOL outcomes were evaluated.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SUPPORT
  8. CONFLICT OF INTEREST DISCLOSURES
  9. REFERENCES

Patients and Settings

The current study is part of an ongoing longitudinal study of the symptom experience of oncology outpatients receiving CTX. Eligible patients were aged ≥ 18 years; had a diagnosis of breast, gastrointestinal, gynecological, or lung cancer; had received CTX within the preceding 4 weeks; were scheduled to receive at least 2 additional cycles of CTX; were able to read, write, and understand English; and provided written informed consent. Patients were recruited from 2 comprehensive cancer centers, 1 Veterans Affairs hospital, and 4 community-based oncology programs. A total of 969 patients were approached and 582 consented to participate (60.1% response rate). The major reason for refusal was being overwhelmed with their cancer treatment.

Instruments

A demographic questionnaire obtained information regarding age, sex, ethnicity, marital status, living arrangements, education, employment status, and income. The Karnofsky performance status (KPS) scale[15] was used to evaluate patients' functional status. The Self-administered Comorbidity Questionnaire[16] evaluated the occurrence, treatment, and functional impact of comorbid conditions (eg, diabetes, arthritis).

The MSAS was used to evaluate the occurrence, severity, frequency, and distress of 32 symptoms commonly associated with cancer and its treatment. The MSAS is a self-report questionnaire designed to measure the multidimensional experience of symptoms. Patients were asked to indicate whether they had experienced each symptom within the past week (ie, symptom occurrence). If they had experienced the symptom, they were asked to rate its frequency of occurrence, severity, and distress. The reliability and validity of the MSAS is well established in studies of oncology inpatients and outpatients.[9, 17]

Quality of life (QOL) was evaluated using generic (ie, Medical Outcomes Study-Short Form-12 [SF-12])[18] and disease-specific (ie, Quality of Life Scale-Patient Version [QOL-PV]) measures.[19-21] Both measures have well-established validity and reliability. Higher scores on both measures indicate a better QOL.

Study Procedures

The study was approved by the Committee on Human Research at the University of California at San Francisco and by the Institutional Review Board at each of the study sites. Eligible patients were approached by a research staff member in the infusion unit to discuss participation in the study. Written informed consent was obtained from all patients. Depending on the length of their CTX cycles, patients completed questionnaires in their homes, a total of 6 times over 2 cycles of CTX (ie, before CTX administration [ie, recovery from previous CTX cycle], approximately 1 week after CTX administration [ie, acute symptoms], and approximately 2 weeks after CTX administration [ie, potential nadir]). For this analysis, symptom occurrence data from the enrollment assessment that asked patients to report on their symptom experience for the week before the administration of the next cycle of CTX were analyzed (ie, recovery from previous CTX cycle). Medical records were reviewed for disease and treatment information.

Statistical Analysis

Data were analyzed using SPSS statistical software (version 20; IBM, Armonk, NY). Descriptive statistics and frequency distributions were calculated for demographic and clinical characteristics.

LCA was used to identify subgroups of patients (ie, latent classes) with similar symptom experiences.[22, 23] Although the MSAS evaluates the occurrence, severity, and distress associated with 32 symptoms, for this analysis and consistent with previous studies,[7, 8] the LCA was performed based on patients' ratings of symptom occurrence. Because this analysis was somewhat exploratory, only data from the enrollment assessment were used in the LCA.

LCA identifies latent classes based on an observed response pattern.[24, 25] To have a sufficient number of patients with each symptom to perform the LCA, the MSAS symptoms that occurred in ≥ 40% of the patients were used to identify the distinct latent classes. A total of 25 of 32 symptoms from the MSAS occurred in ≥ 40% of the patients.

The final number of latent classes was identified by evaluating the Bayesian information criterion (BIC) and entropy. The model that fits the data best had the lowest BIC.[26] In addition, well-fitting models produce entropy values of ≥ .80.[27] Finally, well-fitting models “make sense” conceptually and the estimated classes differ as might be expected on variables not used in the generation of the model.[26]

The LCA was performed using Mplus Version 7.[28, 29] Estimation was performed with robust maximum-likelihood and the expectation-maximization algorithms.[22] Differences in demographic and clinical characteristics and QOL outcomes among the latent classes were evaluated using analyses of variance, Kruskal-Wallis, and chi-square analyses. A P value of < .05 was considered to be statistically significant. As was done in our previous studies,[5, 30-32] based on the recommendations of Rothman[33] and the large sample size, adjustments were not made for multiple comparisons.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SUPPORT
  8. CONFLICT OF INTEREST DISCLOSURES
  9. REFERENCES

Latent Class Analysis

A total of 25 symptoms from the MSAS occurred in ≥ 40% of the patients (Fig. 1) Using LCA, 3 distinct latent classes of patients were identified based on their ratings of the occurrence of these 25 MSAS symptoms. Fit indices for the candidate models are shown in Table 1. The 3-class solution was selected because its BIC was lower than the BIC for both the 2-class and 4-class solutions. As summarized in Table 2 and illustrated in Figure 1, the largest percentage of patients (291 patients; 50.0%) was classified in the “Moderate” class. The probability of occurrence for the majority of the MSAS symptoms for this class was between 0.4 and 0.6. A second group, which comprised 13.9% of patients (81 patients) was classified as the “All High” class. The probability of occurrence for the majority of the MSAS symptoms for this class was between 0.7 and 1.0. The third class, comprised of 36.1% of the sample (210 patients), was classified as the “Low” class. The probability of occurrence for the majority of the MSAS symptoms for this class was between 0.1 and 0.4.

Table 1. Latent Class Solutions and Fit Indices for 2-Class Through 4-Class Solutions
ModelLLAICBICEntropy
  1. Abbreviations: AIC, Akaike information criterion; BIC, Bayesian information criterion; LL, log-likelihood.

  2. a

    The 3-class solution was selected because the BIC for that solution was lower than the BIC for both the 2-class and 4-class solutions.

2 class−8592.3017286.5917509.28.83
3 classa−8404.4816962.9617299.17.85
4 class−8329.8316865.6617315.40.87
Table 2. Differences in Demographic and Clinical Characteristics Among the Latent Classes (n = 582)
CharacteristicLow (1) n = 210; 36.1%Moderate (2) n = 291; 50.0%All High (3) n = 81; 13.9%Statistics
 Mean (SD)Mean (SD)Mean (SD) 
  1. Abbreviations: BMI, body mass index; CTX, chemotherapy; KW, Kruskal-Wallis test; LN, lymph node; MSAS, Memorial Symptom Assessment Scale; RT, radiotherapy; SD, standard deviation.

  2. a

    The total number of metastatic sites evaluated was 9.

Age, y59.5 (11.4)56.5 (12.2)54.7 (11.2)F = 6.07; P = .002 1>2 and 3
Education, y16.5 (3.0)16.5 (3.0)15.4 (2.5)F = 5.00; P = .007 1 and 2 >3
BMI, kg/m226.0 (5.6)26.5 (6.2)26.9 (5.3)F = 0.88; P = .417
Karnofsky performance status85.5 (10.3)79.2 (11.6)72.6 (11.8)F = 38.73; P <.0001 1>2>3
Self-administered Comorbidity Questionnaire score5.2 (3.0)6.7 (3.5)9.2 (4.6)F = 38.99; P <.0001 1<2<3
Time since diagnosis, y2.1 (3.4)2.8 (4.9)2.4 (4.7)F = 1.33; P = .266
No. of prior cancer treatments1.8 (1.6)2.0 (1.6)2.0 (1.5)F = 1.71; P = .311
No. of metastatic sites including LN involvementa1.4 (1.3)1.4 (1.4)1.2 (1.2)F = 1.00; P = .370
No. of metastatic sites excluding LN involvement0.9 (1.1)1.0 (1.2)0.7 (1.0)F = 1.89; P = .152
Mean no. of MSAS symptoms (out of 25)5.7 (2.3)12.9 (2.6)20.3 (2.7)F = 1106.36; P <.0001 1<2<3
 % (No.)% (No.)% (No.) 
Sex (% female)69.5 (146)83.5 (243)92.6 (75)Chi-square = 24.39; P <.0001 1<2 and 3
Self-reported ethnicity    
White75.4 (156)74.8 (211)59.0 (46)Chi-square = 8.81; P = .012
Nonwhite24.6 (51)25.2 (71)41.0 (28)1 and 2 >3
Married or partnered (% yes)74.5 (155)65.6 (189)55.0 (44)Chi-square = 10.80; P = .005 1>3
Lives alone (% yes)16.0 (33)22.6 (65)22.2 (18)Chi-square = 3.45; P = .179
Currently employed (% yes)39.0 (82)34.6 (100)23.8 (19)Chi-square = 5.99; P = .050
Annual household income    
<$30,00015.8 (29)15.2 (40)40.5 (30) 
$30,000 to $70,00016.9 (31)22.7 (60)14.9 (11)KW = 22.81; P <.0001
$70,000 to $100,00016.9 (31)15.2 (40)21.6 (16)1 and 2 >3
>$100,00050.3 (92)47.0 (124)23.0 (17) 
Child care responsibilities (% yes)18.5 (38)23.9 (68)31.2 (25)Chi-square = 5.52; P = .063
Elder care responsibilities (% yes)5.2 (10)11.7 (31)6.6 (5)Chi-square = 6.56; P = .038 1<2
Cancer diagnosis    
Breast cancer38.6 (81)42.3 (123)54.3 (44) 
Gastrointestinal cancer32.4 (68)25.4 (74)17.3 (14)Chi-square = 11.17; P = .083
Gynecological cancer18.1 (38)21.6 (63)22.2 (18) 
Lung cancer11.0 (23)10.7 (31)6.2 (5) 
Prior cancer treatment    
No prior treatment23.2 (48)15.3 (44)12.3 (10) 
Only surgery, CTX, or RT35.7 (74)44.3 (127)44.4 (36)Chi-square = 9.56; P = .144
Surgery and CTX, or surgery and RT, or CTX and RT25.1 (52)22.0 (63)22.2 (18) 
Surgery and CTX and RT15.9 (33)18.5 (53)21.0 (17) 
Reason for current treatment    
Curative intent76.1 (159)70.6 (202)77.8 (63)Chi-square = 2.678, P = .261
Noncurative intent23.9 (50)29.4 (84)22.2 (18) 
Metastatic sites    
No metastasis31.1 (65)31.7 (92)37.5 (30) 
Only LN metastasis22.0 (46)19.3 (56)21.2 (17)Chi-square = 3.94; P = .685
Only metastatic disease in other sites21.5 (45)24.5 (71)15.0 (12) 
Metastatic disease in LNs and other sites25.4 (53)24.5 (71)26.2 (21) 
image

Figure 1. The probability of symptom occurrence for the total sample (ie, sample percentage) and each of the latent classes for the 25 symptoms on the Memorial Symptom Assessment Scale that occurred in ≥ 40% of the total sample (n = 582) is shown.

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Differences in Patient Characteristics Among the Latent Classes

Table 2 summarizes the differences in demographic and clinical characteristics among the latent classes. Compared with the Low class, patients in the Moderate and All High classes were more likely to be female and significantly younger, reported a lower KPS score, and had a higher comorbidity score. With the exception of the KPS and comorbidity scores, none of the clinical characteristics (ie, time since diagnosis, cancer diagnosis, types and number of prior treatments, reason for current therapy, presence or number of metastatic sites) was found to differ among the latent classes. Patients in the All High class reported the occurrence of a significantly higher number of symptoms (20.3 ± 2.7) compared with patients in the Moderate class (12.9 ± 2.6). Patients in the Moderate class reported a significantly higher number of symptoms than patients in the Low class (5.7 ± 2.3).

Differences in QOL Scores Among the Latent Classes

As shown in Figure 2A, for all of the scales on the SF-12 as well as the Physical Component Summary (PCS) and Mental Component Summary (MCS) scores, patients in the All High class reported significantly lower scores compared with patients in the Moderate class. Except for the General Health score on the SF-12, patients in the Moderate class reported significantly lower scores than patients in the Low class.

image

Figure 2. (A) Differences among the latent classes in the subscale and summary scores for the Medical Outcomes Study-Short Form 12 (SF-12) are shown. All values were plotted as the means ± the standard deviations. Significant differences were as follows: for Physical Functioning (PF): Low>Moderate (P < 0001) and >All High (P = .002). For Role Physical (RP), Bodily Pain (BP), Social Functioning (SF), Role Emotional (RE), Mental Health (MH), and Mental Component Summary (MCS): Low>Moderate>All High (both P < .0001). For General Health (GH): Low>Moderate and All High (P < .0001). For Vitality (VT): Low>Moderate (P < .0001) and >All High (P = .037). For Physical Component Summary (PCS): Low > Moderate (P < .0001) and >All High (P = .006). (B) Differences among the latent classes in the subscale and total quality of life (QOL) scores are shown for the Quality of Life Scale-Patient Version (QOL-PV). All values are plotted as means ± the standard deviations. Significant differences: Physical, Psychological, and Social Functioning subscales and total QOL scores: Low>Moderate>All High (both P < .0001).

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As shown in Figure 2B, with the exception of the Spiritual Well-Being subscale, patients in the All High class reported significantly lower scores on the QOL-PV subscale and total scores than patients in the Moderate class. Patients in the Moderate class reported significantly lower QOL-PV scores than patients in the Low class.

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SUPPORT
  8. CONFLICT OF INTEREST DISCLOSURES
  9. REFERENCES

To our knowledge, the current study is the first to use LCA to identify 3 distinct subgroups of patients based on their reports of the occurrence of 25 common symptoms before their next cycle of CTX. Consistent with our previous studies,[2, 5, 6] approximately 14% of the patients reported relatively high occurrence rates for all 25 symptoms. The mean number of symptoms reported by patients in the All High class (ie, 20.3) is higher than the mean of 10 symptoms reported in cross-sectional studies that did not use specific analytic techniques to identify interindividual variability in patients' symptom experiences.[1] Equally important, patients in the Low class reported an average of 6 symptoms, which constitutes a fairly high symptom burden. Findings from the current study suggest that rather than simply reporting the mean number of symptoms, future studies should use the types of statistical approaches used in this and other studies[2, 5-8] to be able to identify oncology patients at higher risk of increased symptom burden. The reliance on mean values for the total number of symptoms will overestimate and underestimate symptom burden and not allow for the identification of patients who require more intensive symptom management interventions.

Although none of the clinical characteristics, except KPS and comorbidity, was found to be associated with class membership, several demographic characteristics differentiated among the 3 latent classes. Consistent with previous reports,[5, 34, 35] younger patients were more likely to be in the All High class. Several potential explanations may account for this finding: older patients may receive lower doses of CTX[36, 37]; age-related changes may occur in the hypothalamic-adrenal-pituitary axis that mediate the occurrence of cancer-related symptoms[38]; or older patients may experience a “response shift” in their perception of symptoms.[39, 40] Other characteristics that differentiated among the classes were sex and ethnicity, with a higher percentage of women and nonwhite individuals being in the All High class. Additional research is warranted because findings regarding sex[41-45] and ethnic[46, 47] differences in the occurrence and severity of symptoms in oncology patients are inconsistent.

Equally important, several socioeconomic characteristics were found to be associated with a higher symptom burden. The finding that marital status distinguished among the 3 latent classes may be related to perceived levels of social support. In several studies, oncology patients who reported higher levels of social support reported lower levels of depressive symptoms.[48-50] Although social support was not measured in the current study, this hypothesis is supported by the finding that patients in the Moderate and All High classes reported significantly lower social functioning scores on both the generic and disease specific measures of QOL. Consistent with previous reports,[51-53] patients in the All High class were more likely to report a lower annual household income. The reason for this disparity warrants investigation in future studies.

It is interesting to note that for both the generic and disease-specific measures of QOL, patients in the All High class reported worse QOL outcomes than patients in the Low and Moderate classes. Compared with the Low class, decrements in functional status reported by the Moderate (d = 0.5) and All High (d = 1.1) classes represent not only statistically significant but clinically meaningful differences in KPS scores.[54, 55] The effect size indicator (ie, d) equals the difference between the 2 group means in standard deviation units. Similar effect sizes were found for the various subscales of both the generic and disease-specific QOL measures. In terms of the SF-12 PCS scores, all 3 classes had scores below the US population mean score of 50. The effect size calculations for differences between the Low and Moderate classes, as well as the Low and All High classes, in PCS scores were d = 0.6 and d = 1.0, respectively.

For the SF-12 MCS scores, the Moderate and All High classes had scores below national norms. The effect size calculations for the MCS scores indicated clinically meaningful differences between the Low and Moderate (d = 0.5) as well as the Low and High (d = 1.3) classes. Finally, the effect size calculations for the total score on the disease-specific measure of QOL (ie, QOL-PV[19-21]) identified clinically meaningful differences when the Low class was compared with the Moderate (d = 0.9) and All High (d = 1.6) classes. Taken together and consistent with previous reports,[2, 5-8] these findings emphasize the significant impact that the cooccurrence of multiple symptoms has on patients' ability to function and their QOL.

Although consistent with previous reports,[2-4, 6-8] a surprising finding from the current study is that with the exception of the decrements in functional status and severity of comorbidities, none of the disease and treatment characteristics was found to be associated with class membership. The relatively small and heterogeneous samples in terms of cancer treatment may explain the lack of associations between disease and treatment characteristics observed in previous studies. However, in the current study, the large sample size as well as the relatively even distribution of cancer diagnoses, reasons for CTX, and extent of metastatic disease across the 3 latent classes suggests that alternative explanations are plausible. One potential explanation for the lack of disease and treatment effects is that patients with a higher disease burden and more severe symptoms declined study participation. Another explanation for the lack of disease and treatment effects is that interindividual variability in patients' symptom experiences may be associated with genetic and epigenetic determinants. This hypothesis is supported by work from our research team and others on the association between several candidate genes and individual symptoms (eg, pain,[32, 56, 57] fatigue,[58-63] depression,[30, 64, 65] and sleep disturbance[31]) in oncology patients. Studies currently are underway in the study laboratory to identify specific biomarkers associated with membership in the All High class identified herein.

Several study limitations need to be acknowledged. Patients were recruited at various points in their CTX. In addition, the types of CTX were not homogeneous. Although we cannot rule out the potential contributions of clinical characteristics to patients' symptom experiences, the relatively similar percentages of cancer diagnoses, reasons for current treatment, time since diagnosis, and evidence of metastatic disease suggest that the classes were relatively similar in terms of disease and treatment characteristics. Although it is possible that patients in the Low class were receiving more aggressive symptom management interventions, the occurrence rates for the 5 most common symptoms (ie, lack of energy, difficulty sleeping, pain, feeling drowsy, and difficulty concentrating) were relatively proportional across the 3 classes. The study used symptom occurrence rates in the LCA and did not evaluate for changes over time in latent class membership. It is possible that using ratings of severity or distress to create the latent classes would provide additional information on interindividual differences in the symptom experience of these patients.

In conclusion, the findings from the current study support the clinical observation that some oncology patients experience a differentially higher symptom burden during CTX. Risk factors identified in this study included younger age, being female, being nonwhite, having lower levels of social support, being of lower socioeconomic status, having poorer functional status, and having a higher level of comorbidity. These high-risk patients experience significant decrements in QOL. Future studies will focus on the identification of molecular mechanisms that contribute to this high-risk phenotype, as well as the identification of latent classes using patients' ratings of symptom severity and distress.

FUNDING SUPPORT

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SUPPORT
  8. CONFLICT OF INTEREST DISCLOSURES
  9. REFERENCES

This study was funded by the National Cancer Institute (grant CA134900).

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SUPPORT
  8. CONFLICT OF INTEREST DISCLOSURES
  9. REFERENCES
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