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. Author manuscript; available in PMC: 2017 Mar 1.
Published in final edited form as: Int J Cancer. 2015 Oct 9;138(5):1290–1297. doi: 10.1002/ijc.29870

Prognostic value of pre-therapy platelet elevation in oropharyngeal cancer patients treated with chemoradiation

Sara Shoultz-Henley 1,5, Adam S Garden 1, Abdallah S R Mohamed 1,7,*, Tommy Sheu 1, Michael H Kroll 2, David I Rosenthal 1, G Brandon Gunn 1, Amos J Hayes 1,8, Chloe French 1,5, Hillary Eichelberger 1,5, Jayashree Kalpathy-Cramer 9, Blaine D Smith 1,5, Jack Phan 1, Zeina Ayoub 10, Stephen Y Lai 3, Brian Pham 11, Merrill Kies 4, Kathryn A Gold 4, Erich Sturgis 3, Clifton D Fuller 1,6,*
PMCID: PMC4779600  NIHMSID: NIHMS763666  PMID: 26414107

Abstract

The purpose of this study is to evaluate potential associations between increased platelets and oncologic outcomes in oropharyngeal cancer patients receiving concurrent chemoradiation. 433 oropharyngeal cancer patients (OPC) treated with intensity modulated radiation therapy (IMRT) with concurrent chemotherapy between 2002 and 2012 were included under an approved IRB protocol. Complete blood count (CBC) data was extracted. Platelet and hemoglobin from the last phlebotomy (PLTpre-chemoRT, Hgbpre-chemoRT) before start of treatment were identified. Patients were risk-stratified using Dahlstrom-Sturgis criteria and were tested for association with survival and disease-control outcomes. Locoregional control (LRC), freedom from distant metastasis (FDM) and overall survival (OS) were decreased (p<0.03, p<0.04, and p<0.0001, respectively) for patients with PLT pre-chemoRT value of ≥350 × 109/L. Actuarial 5-year locoregional control (LRC) and FDM were 83% and 85% for non-thrombcythemic patients while patient with high platelets had 5-year LRC and FDM of 73% and 74%, respectively. Likewise, 5- year OS were better for patients with normal platelet counts by comparison (76% vs. 57%; p<0.0001). Comparison of univariate parametric models demonstrated PLTpre-chemoRT was better among tested models. Multivariate assessment demonstrated improved performance of models which included pre-therapy platelet indices. On Bayesian information criteria analysis, the optimal prognostic model was then used to develop nomograms predicting 3-, 5-, and 10-year OS. In conclusion, pre-treatment platelet elevation is a promising predictor of prognosis, and further work should be done to elucidate the utility of anti-platelets in modifying risk in OPC patients.

Keywords: platelet, oropharyngeal cancer, chemoradiation, prognosis, survival

Introduction

Head and neck cancer (HNC) is diagnosed in approximately 500,000 people per year worldwide1, with increasing incidence of human-papilloma virus positive oropharyngeal cancer2-4. These oropharyngeal cancers carry a comparatively better prognosis5-7, and represent a distinct pathobiological disease process, as compared to traditional tobacco-associated cancers8, 9. Furthermore, data suggest that traditional risk stratification variables, such as T-category or N-category, provide comparatively limited prognostic/predictive value for treatment response or survival5, 10.

Reconsideration of pre-treatment prognostic indicators of therapy response and subsequent survival is of increasing importance11. One potential group of candidate indicators is traditional hematologic parameters, easily measured on a complete blood count (CBC). Hemoglobin indices are known to predict initial response to radiotherapy in HNC12, 13, as well as local control, overall survival, and progression-free survival in the HNC population 12, 14. Furthermore, it is known that anemia has an impact on overall survival (OS)1. In fact, anemia as well as other individual lab parameters has been shown to have a stronger association to OS than the traditional tumor-node-metastasis (TNM) model characteristics1.

Despite the recognized contribution of individual hematologic parameters related to reticulocytes, limited data exist regarding platelet values and outcomes in HNC1, 15. Within the available literature, distinct stratification factors of risk for oropharyngeal cancers, despite their distinct patterns of local control and survival, are yet undescribed. Intriguingly, mixed HNC sub-site registry data demonstrate an association not only between platelet counts, but also antiplatelet therapy, suggesting a potential drug class for interventional studies16.

Preliminary data suggest a relationship between platelet count and survival16-20; however evaluation of the effects of platelet elevation not only upon thrombosis-mediated or cardiovascular sequelae (e.g. stroke) but also upon disease specific endpoints (i.e. locoregional control and distant metastasis rates) in a patient cohort receiving standard of care therapy for oropharyngeal cancer is potentially valuable. In an effort to identify potential prognostic features for risk stratification, as well as identifying sub-cohorts at comparative risk of local failure for possible therapeutic intensification or platelet-modifying agents, we sought to undertake the following specific aims:

  • -Evaluate potential associations, if any, between platelet elevation and oncologic outcomes in oropharyngeal cancer patients receiving concurrent chemoradiation.

  • -Determine the potential added value of platelet assessment as a potential covariate of univariate and multivariate overall survival for pre-therapy risk stratification.

  • -Generate testable hypotheses for future prospective research.

Materials and Methods

Institutional review board approved data extraction was performed from an existing oropharyngeal cancer database6, 7, 21-23. To assess long-term results in a modern standard-of-care series, sequential non-metastatic (i.e. T1-4 N0-3 M0) cases treated with definitive intensity modulated radiation therapy (IMRT) with concurrent chemotherapy between 2002 and 2012 were selected for analysis, and personal and therapeutic demographics were tabulated.

Matched CBC data were extracted via a custom script from the hospital electronic medical record (EMR, ClinicStation, MD Anderson Cancer Center, Houston, TX; http://www.clinfowiki.org/wiki/index.php/ClinicStation). Custom scripts were used to match chemoradiotherapy inception and CBC by medical record number (MRN) and dates of service. All pre-therapy platelet and hemoglobin values were extracted. The maximum recorded platelet count (PLTmax) and the values for platelet and hemoglobin from the last phlebotomy (PLT pre-chemoRT, Hgb pre-chemoRT) before start of concurrent chemoradiation were identified. All patients started their treatment within a 14-day interval from the last CBC results. Any episode of pre-therapy anemia/increased platelets was recorded, with platelet elevation indicated by any PLTmax or PLT pre-chemoRT value of ≥350 × 109/L24. Similarly, pre-chemoradiation anemia was coded, with anemia defined as Hgb pre-chemoRT <13.5 for males, and <12.3 for females25. EMR records were reviewed for evidence of hemorrhage, transfusions, or IV fluid resuscitation. If evidence was found via billing code or review of clinical documents, cases were excluded from analysis. Patients that received induction chemotherapy were also excluded. Additionally, human papilloma virus (HPV) status was determined for all cases with P16 immunohistochemical or HPV in situ hybridization assessment. Patients with P16 and/or HPV positivity were considered HPV-associated disease whereas HPV-ve/p16-ve cases were considered HPV non-associated.

Since previous data suggest that traditional TNM staging has comparatively limited predictive utility for oropharyngeal cancers5, patients were risk-stratified using a previously-published institutional oropharyngeal cancer risk partitioning schema by Dahlstrom et al.5, which overlaps the current oropharynx chemoradiation cohort. Using the post-1994 Dahlstrom-Sturgis risk schema, we stratified patients into four risk groups (Figure 1.A); confirmatory actuarial analysis was performed for verification. For ordinal and nominal data, comparison was performed using χ-square analysis for proportional agreement between cohorts. For all eligible cases, locoregional control (LRC or actuarial freedom from local/nodal recurrence; coding recurrent or persistent disease in the radiotherapy field as an event, with all others as censored), freedom from distant metastasis (FDM or time without distant progression; coding any cancer outside the treatment field as an event, with all others as censored), and overall survival (coding death as an event, with all others censored) was calculated using the Kaplan-Meier method for specified hematologic covariates, with Gehan-Breslow-Wilcoxon testing (owing to predominance of failures in the initial 5-year interval) for differences in actuarial data. Non-parametric competing risk analysis was performed, using locoregional recurrence and distant metastasis as cause of recurrence (i.e. binary failure mode) for relapse free survival (i.e. RFS, with locoregional or distant disease after therapy coded as an event, all others censored).

Figure 1.

Figure 1

Figure 1

Figure 1

Figure 1

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Dahlstrom-Sturgis stratification cohorts (A), with locoregional control LRC (B), freedom from distant metastasis FDM (c), and overall survival OS (d), stratified by said cohorts. Solid lines represent Kaplan-Meier curves and short vertical lines represent censored data. Abbreviations: BOT=base of tongue.

Univariate and multivariate Cox proportional hazards assessments were performed to determine whether the following variables were correlated with OS: age (as a continuous variable), sex, Dahlstrom-Sturgis schema (AJCC TNM Staging, smoking status, age, and site of disease), HPV status, platelet elevation (as a binary variable), PLTmax, and PLT pre-chemoRT (recorded as continuous variables), binary sub-site (tonsil vs. non-tonsil), and anemia (as binary variable). After initial assessment, a second simplified multivariate model specifying dichotomized binary parameters was constructed for the following variables: Dahlstrom-Sturgis schema, platelet elevation (as a binary variable), anemia (as a binary variable), and HPV status. After multivariate analysis, best categorical model selected based on experimental Bayesian information criteria26 (BIC) for model optimization and selection27, 28 (data not shown) were converted into visual nomograms of 3-, 5-, and 10-year overall survival using the open-source statistical software R (http://www.R-project.org) with the aid of Harrell's Regression Modeling Strategies package (http://biostat.mc.vanderbilt.edu/rms)29.

All statistical analyses were executed in JMP Pro 11 (SAS Institute, Cary, NC, USA) statistical analysis software, with an a priori non-Bonferroni-corrected α=0.05 pre-specified as indicating statistical significance in this exploratory dataset.

Results

A total of 433 oropharyngeal cancer patients were identified and had matched EMR data available. Median age was 57 years (range 34-88) and 88% were men. The median follow-up for surviving patients was 69 months. Demographics, disease, and treatment data for the entire cohort are summarized in Table 1.

Table 1.

Patient, disease, and treatment characteristics

Characteristic No. of patients (%)
Sex
Female 53 (12)
Male 380 (88)
Age
Median (range) 57 (34-88)
T stage (T)
T1 53 (12)
T2 115 (27)
T3 141 (32)
T4 124 (29)
Nodal stage (N)
N0 42 (10)
N1 56 (13)
N2a 41 (9)
N2b 177 (41)
N2c 83 (19)
N3 34 (8)
AJCC stage
I 0 (0)
II 6 (2)
III 62 (14)
IVa 307 (71)
IVb 58 (13)
HPV status
Positive 109 (25)
Unknown 293 (68)
Negative 31 (7)
Smoking History at Diagnosis
Never smoker 115 (26)
Former smoker 163 (38)
Current smoker 155 (36)
Disease sub-site
Tonsil 182 (42)
Base of tongue 213 (49)
Soft palate 17 (4)
Pharyngeal wall 19 (5)
Total Radiation Dose (Gy) (mean 70 ± s.d. 2.4)
No. of Fractions Received (mean 36 ± s.d. 4)
Fractionation technique
Conventional 265 (61)
Concomitant boost 168 (39)
Chemotherapy
Cetuximab based 77 (18)
Carboplatin based 61 (14)
Cisplatin based 264 (61)
Others 31 (7)
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Calculated overall LRC, FDM, and OS for the entire sampled cohort are shown in Supplemenary Figure S1. Dahlstrom-Sturgis cohorts are tabulated in Figure 1A, with illustrative survival by ordinal cohort in Figure 1B-D (all p<0.01 by Gehan-Breslow-Wilcoxon). Competing risk plots showed increased cumulative probability of failure for both modes (LRC and DM) as shown in Supplementary Figure S2.

Logrank testing of actuarial curves stratified by clinical platelet elevation demonstrated that, LRC (p=0.03), FDM (p<0.04), and OS (p<0.0001) were significantly decreased for patients with PLT pre-chemoRT value of ≥350 × 109/L (Figure 2a-c). Actuarial 5-year LRC and FDM were 83% and 85% for patients with lower platelet counts, respectively; by comparison patients with elevated platelets had 5-year LRC and FDM of 73% and 74%, respectively. Likewise, 5-year OS were 76% for patients with normal platelet counts, compared to 57% for patients with platelets ≥350 × 109/L. Anemic patients (Supplementary Figure S3a-c) likewise exhibited comparatively decreased LRC (p<0.01), FDM (p<0.009), and OS (p<0.0001); cross-stratification for concurrent platelet elevation/anemia evinced that patients with simultaneous platelet elevation and anemia had substantively worse oncologic outcomes for LRC (p<0.02), FDM (p<0.03), and OS (p<0.0001) than those with anemia or platelet elevation alone or those with no alteration (Figure 3A-C).

Figure 2.

Figure 2

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Locoregional control LRC (A), freedom from distant metastasis FDM (B), and overall survival OS (C) in patients with PLTpre-chemoRT <350 × 109/L versus patients with PLTpre-chemoRT ≥350 × 109/L. Solid lines represent Kaplan-Meier curves and short vertical lines represent censored data. Abbreviations: PLT=platelets.

Figure 3.

Figure 3

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Locoregional control LRC (A), freedom from distant metastasis FDM (B), and overall survival OS (C) in composite anemia-platelet cohorts: non-thrombocythemic/non-anemic; thrombocythemic/non-anemic; non- thrombocythemic/anemic; thrombocythemic/anemic). Thrombocythemia is defined as platelet count ≥350 × 109/L. Solid lines represent Kaplan-Meier curves and short vertical lines represent censored data.

Table 2 shows the hazard ratios (HRs) for all studied variables in the univariate Cox regression analysis. Multivariate analysis showed that platelets ≥350 × 109/L was independent predictor of dismal survival (HR 1.9; 95% CI 1.2-2.9; p<0.006), in addition to anemia, Dahlstrom-Sturgis category, and HPV status. Figure 4A-C shows calculated 3-, 5-, and 10-year nomograms based on the lowest BIC model.

Table 2.

Univariate analysis

Covariate(s) Strata Hazard ratio 95% CI p-value
1 PLTpre-chemoRT (continuous) 13.6 4.3-39.7 <0.0001
2 Platelet count (binary) PLTpre-chemoRT <350 × 109/L -
PLTpre-chemoRT ≥350 × 109/L 2.1 1.4-3.1 0.0013
3 Dahlstrom-Sturgis cohort Group 1 -
Group 2 1.9 1.04-4.1 0.036
Group 3 3.9 21-7.8 <0.0001
Group 4 11.1 5.4-23.7 <0.0001
4 Anemia Non-anemic -
Anemic 2.4 1.7-3.4 <0.0001
5 Age (continuous) 14.2 5.5-37.2 <0.0001
6 Oropharynx subsite Tonsil -
Non-tonsil 1.8 1.2-2.6 <.0013
7 Smoking status Never -
Former 1.8 1.2-2.9 0.007
Current 3.2 2.1-5.1 <0.0001
8 PLTmax (continuous) 16.9 5.4-50 <0.0001
10 HPV status HPV+ -
Unknown 1.9 1.2-3.3 0.006
HPV- 2.6 1.2-5.6 0.002
11 Sex Female -
Male 3.9 1.8-11.2 <0.0001
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Figure 4.

Figure 4

Figure 4

Figure 4

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3-year (a), 5-year (b), and 10-year (c) nomograms. A vertical line is drawn from each of the covariates to the “Points” line. The sum of the points from all covariates determines the “Total Points” score. Another vertical line is drawn from the “Total Points” line to the bottommost line to give the probability of survival.

Post hoc analysis of OS stratified by HPV status for binary pre-therapy platelets levels (platelets ≥350 × 109/L vs. platelets <350 × 109/L) showed that thromothycemic group had significantly worse OS in both the HPV negative and HPV unknown cohorts (p=0.02, and p=0.002, respectively) while it didn't reach statistical significance in HPV positive cohort, noting that very few thrombocythemic cases were encountered in the HPV positive cohort (11 out of 109 patients) as shown in Supplementary figure S4A-C.

Discussion

There is evidence that increased platelet counts, even at “normal levels” are associated with decrements in cancer-related survival. In a large-scale registry study of 1,051 cases, Rachidi et al. demonstrated stratification of low-normal, mid-normal, high-normal, and frank thrombocytosis on survival in a mixed head and neck cohort16. Post hoc application of the same criteria to our dataset showed a similar logrank differential between cohorts (p<0.0001, Supplementary Figure S5); however, on post hoc model comparison, a resultant univariate BIC was inferior to presented models for the current dataset. In our estimation, this is likely a feature of the fact that, in contrast to the Rachidi et al. registry dataset, which was comprised of a mélange of head and neck subsites (i.e. grouping “oral cavity” and “larynx”, typically non-HPV-associated, with “pharynx” cases) and therapeutic regimens (e.g. “chemotherapy, surgery, radiation and/or other”)16, our dataset is anatomically uniform (i.e. comprised of only oropharyngeal cancers) with comparatively homogenous therapy (concurrent chemoradiation). Additionally, we used a platelet elevation threshold of 350×109 cells/Liter (as reported by Stravodimou and Voutsadakis24); admittedly, a variety of platelet thresholds have been used by various authors15, 16.

Platelet elevation is associated with many solid tumors, and its presence is often indicative of unfavorable prognosis30. Recent data clarify that thrombocytosis is not a pathophysiologically irrelevant epiphenomenon, but that platelets direct tumor cell growth, vascular invasion, hematogenous dissemination, immune system evasion and establishment of a metastatic niche31-35. These data provide a mechanistic basis for our observation that platelet elevation is associated with both locoregional recurrence and metastatic progression, and highlight the symbiotic relationship between tumor cells and platelets that is key to a future mechanistic understanding of platelet elevation as an independent prognostic indicator. The association we observed between locoregional and distant disease progression supports the assertion by Sharma that platelet-mediated processes may be associated with “promotion of tumor cell migration & invasion” in oropharyngeal cancers32.

The association between platelet elevation and cancer cell growth and spread provides an opportunity to evaluate the role of anti-platelet agents in treating cancer. Aspirin is demonstrated to have a significant effect on primary and secondary prevention of many cancers, including malignancies of the upper aerodigestive track, and may favorably affect survival in head and neck cancers16, 36, 37.The mechanism of these effects is unknown, and it is important to determine if platelet inhibition is the molecular mechanism by which aspirin that is chronically used for other indications protects against cancer incidence, metastasis and mortality.

In addition to potential targeted pharmacologic interventions, the strong model contributions observed for patients with frank thrombocytosis, even with correction for demographic-, exposure- and tumor/node associated risk through the Dahlstrom-Sturgis schema, suggest that patients with pre-therapy platelet elevation/thrombocytosis warrant either more rigorous surveillance post-therapy, or potentially, therapeutic modification. Easily acquired hematologic profiles might be used to select patients at higher risk for locoregional recurrence of distant failure for therapeutic intensification by induction chemotherapy. Alternatively, those with excellent survival probability might be selected for careful and judicious therapeutic de-escalation by use of definitive radiotherapy alone7, 23, especially when current AJCC staging provides limited guidance regarding risk-assessment for OPC 5, 6, 21, 38.

Risk assessment tools implementing hematologic parameters have already emerged in the radiation oncology literature. Rios-Velasquez et al. reported a prognostic model which included TNM components, moderate to severe comorbidity, HPV status, hemoglobin count, and male gender, which, like our model outperformed AJCC staging10. Our model, however, added pre-therapy platelet count in addition to hemoglobin, HPV status and Dahlstrom-Sturgis categories which includes age, smoking staus, T stage, N stage, and subsite which led to the development of a predictive nomogram with superior performance to the listed covariables alone or when combined without platelet count.

Our data, as presented, have several limitations. As a retrospective study, the standard caveats apply. In order to ensure sufficient follow-up interval (>5 years for all patients), we utilized data preceding institutional standard HPV/p16-status assessment; consequently, the proportion of HPV/P16 tested cases in the cohort is <35%. In order to define a “standard of care” population and maximize extramural interpretability of our results, we selected only cases receiving definitive concurrent chemoradiation; however, institutionally, we routinely utilize radiotherapy-alone and/or induction chemotherapy for selected patients with excellent historical results5-7, 21-23, 39. Consequently, there is distinct possibility of treatment selection bias in the current cohort. We utilized an a priori definition of platelet elevation (as per Stravodimou and Voutsadakis24), which might skew results if another threshold were used instead. Finally, we were unable to extract granular data regarding several unspecified variables (e.g. pre-therapy use of thrombolytic agents, NSAIDs, or other comorbid conditions which might alter platelet counts pre-therapy).

Nonetheless, our data represents, to our knowledge, the single largest domestic series ever to evaluate the relationship between pre-therapy platelet levels and oncologic outcomes in oropharyngeal cancer patients receiving definitive chemoradiation, and thus serves as a benchmark for outcome estimation, as well as a steppingstone to further efforts. The analyses performed show pre-therapy platelet count provides a distinct risk stratification indicator for oropharyngeal cancer cases in a curated, uniform series with extensive follow-up, critical for assessment of late FDM and OS alterations, which might be obscured in series with limited (<5-year) follow-up. As we seek to elucidate modifiable cofactors associated with disease progression, it is conceivable that observational or exploratory prospective investigation of prophylactic thrombolytic agents such as NSAIDs for oropharyngeal cancer patients at highest risk of platelet elevation-associated disease progression may be warranted. We are continuing efforts to assess systemic risk factors (including inflammatory processes associated with altered oncologic outcomes, as part of our larger comprehensive oropharyngeal cancer and survivorship program.

In conclusion, our data suggest that platelet elevation carries a demonstrable association with poorer oncologic and survival outcomes. Further study is needed into the mechanism of this observed relationship, as well as potential strategies to ameliorate these effects. In the interim, we recommend continued routine evaluation by care providers of platelet counts pre-therapy, and increased surveillance as appropriate for these patients who may be at substantively increased risk of locoregional failure, distant metastasis and overall mortality, and judicious risk assessment using provided nomograms as an exploratory tool for validation in other datasets.

Supplementary Material

Supporting data

Novelty & Impact Statements.

This study investigated pre-chemoradiation platelet count as an independent prognostic factor for oncologic and survival outcomes in oropharyngeal cancer. Univariate and multivariate assessments demonstrated improved model performance when including pre-therapy platelet indices compared to traditional prognostic schema. On Bayesian information criteria analysis, the optimal prognostic model was then used to develop nomograms predicting 3-, 5-, and 10-year OS. These data highlight potentially actionable relationships between platelet count and tumor-related outcomes, and may potentiate more accurate pre-therapy risk stratification and/or trials investigating the potential oncologic role of anti-platelet therapy.

Acknowledgments

Dr. Fuller received/receives grant support from the National Science Foundation Division of Mathematical Sciences Proposals for Quantitative Approaches to Biomedical Big Data (QuBBD) Grant (Award 1557559), National Institutes of Health/National Cancer Institute's Paul Calabresi Clinical Oncology Award Program (K12 CA088084-06) and Clinician Scientist Loan Repayment Program (L30 CA136381-02); the SWOG/Hope Foundation Dr. Charles A. Coltman, Jr., Fellowship in Clinical Trials; a General Electric Healthcare/MD Anderson Center for Advanced Biomedical Imaging In-Kind Award; an Elekta AB/MD Anderson Department of Radiation Oncology Seed Grant; the Center for Radiation Oncology Research at MD Anderson Cancer Center; and the MD Anderson Institutional Research Grant. Dr. Mohamed received salary support from the Union for International Cancer Control /American Cancer Society International Fellowships for Beginning Investigators (UICC/ACSBI) mechanism. These listed funders/supporters played no role in the study design, collection, analysis, interpretation of data, manuscript writing, or decision to submit the report for publication.

References

  • 1.Peter F, Wittekindt C, Finkensieper M, Kiehntopf M, Guntinas-Lichius O. Prognostic impact of pretherapeutic laboratory values in head and neck cancer patients. J Cancer Res Clin Oncol. 2013;139:171–8. doi: 10.1007/s00432-012-1320-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Habbous S, Chu KP, Qiu X, La Delfa A, Harland LT, Fadhel E, Hui A, Perez-Ordonez B, Weinreb I, Liu FF, Waldron J, O'Sullivan B, et al. The changing incidence of human papillomavirus-associated oropharyngeal cancer using multiple imputation from 2000 to 2010 at a Comprehensive Cancer Centre. Cancer Epidemiol. 2013;37:820–9. doi: 10.1016/j.canep.2013.09.011. [DOI] [PubMed] [Google Scholar]
  • 3.Chaturvedi AK, Engels EA, Pfeiffer RM, Hernandez BY, Xiao W, Kim E, Jiang B, Goodman MT, Sibug-Saber M, Cozen W, Liu L, Lynch CF, et al. Human papillomavirus and rising oropharyngeal cancer incidence in the United States. J Clin Oncol. 2011;29:4294–301. doi: 10.1200/JCO.2011.36.4596. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Ernster JA, Sciotto CG, O'Brien MM, Finch JL, Robinson LJ, Willson T, Mathews M. Rising incidence of oropharyngeal cancer and the role of oncogenic human papilloma virus. Laryngoscope. 2007;117:2115–28. doi: 10.1097/MLG.0b013e31813e5fbb. [DOI] [PubMed] [Google Scholar]
  • 5.Dahlstrom KR, Calzada G, Hanby JD, Garden AS, Glisson BS, Li G, Roberts DB, Weber RS, Sturgis EM. An evolution in demographics, treatment, and outcomes of oropharyngeal cancer at a major cancer center: a staging system in need of repair. Cancer. 2013;119:81–9. doi: 10.1002/cncr.27727. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Garden AS, Dong L, Morrison WH, Stugis EM, Glisson BS, Frank SJ, Beadle BM, Gunn GB, Schwartz DL, Kies MS, Weber RS, Ang KK, et al. Patterns of disease recurrence following treatment of oropharyngeal cancer with intensity modulated radiation therapy. Int J Radiat Oncol Biol Phys. 2013;85:941–7. doi: 10.1016/j.ijrobp.2012.08.004. [DOI] [PubMed] [Google Scholar]
  • 7.Garden AS, Kies MS, Morrison WH, Weber RS, Frank SJ, Glisson BS, Gunn GB, Beadle BM, Ang KK, Rosenthal DI, Sturgis EM. Outcomes and patterns of care of patients with locally advanced oropharyngeal carcinoma treated in the early 21st century. Radiat Oncol. 2013;8:21. doi: 10.1186/1748-717X-8-21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Setton J, Caria N, Romanyshyn J, Koutcher L, Wolden SL, Zelefsky MJ, Rowan N, Sherman EJ, Fury MG, Pfister DG, Wong RJ, Shah JP, et al. Intensity-Modulated Radiotherapy in the Treatment of Oropharyngeal Cancer: An Update of the Memorial Sloan-Kettering Cancer Center Experience. Int J Radiat Oncol. 2012;82:291–8. doi: 10.1016/j.ijrobp.2010.10.041. [DOI] [PubMed] [Google Scholar]
  • 9.Daly ME, Le QT, Maxim PG, Loo BW, Kaplan MJ, Fischbein NJ, Pinto H, Chang DT. Intensity-Modulated Radiotherapy in the Treatment of Oropharyngeal Cancer: Clinical Outcomes and Patterns of Failure. Int J Radiat Oncol. 2010;76:1339–46. doi: 10.1016/j.ijrobp.2009.04.006. [DOI] [PubMed] [Google Scholar]
  • 10.Rios Velazquez E, Hoebers F, Aerts HJWL, Rietbergen MM, Brakenhoff RH, Leemans RC, Speel E-J, Straetmans J, Kremer B, Lambin P. Externally validated HPV-based prognostic nomogram for oropharyngeal carcinoma patients yields more accurate predictions than TNM staging. Radiotherapy and Oncology. doi: 10.1016/j.radonc.2014.09.005. [DOI] [PubMed] [Google Scholar]
  • 11.Huang SH, Waldron JN, Milosevic M, Shen X, Ringash J, Su J, Tong L, Perez-Ordonez B, Weinreb I, Bayley AJ, Kim J, Hope A, et al. Prognostic value of pretreatment circulating neutrophils, monocytes, and lymphocytes in oropharyngeal cancer stratified by human papillomavirus status. Cancer. 2014 doi: 10.1002/cncr.29100. n/a-n/a. [DOI] [PubMed] [Google Scholar]
  • 12.Suzuki G, Ogo E, Abe T, Hayabuchi N, Umeno H, Nakashima T, Yamazaki H, Nakamura S, Yoshida K. Non-surgical multimodality treatment for locally advanced (T3-4) hypopharyngeal cancer: the impact of pre-treatment hemoglobin level. Anticancer Res. 2013;33:5561–5. [PubMed] [Google Scholar]
  • 13.Hoff CM, Hansen HS, Overgaard M, Grau C, Johansen J, Bentzen J, Overgaard J. The importance of haemoglobin level and effect of transfusion in HNSCC patients treated with radiotherapy--results from the randomized DAHANCA 5 study. Radiother Oncol. 2011;98:28–33. doi: 10.1016/j.radonc.2010.09.024. [DOI] [PubMed] [Google Scholar]
  • 14.Prosnitz RG, Yao B, Farrell CL, Clough R, Brizel DM. Pretreatment anemia is correlated with the reduced effectiveness of radiation and concurrent chemotherapy in advanced head and neck cancer. Int J Radiat Oncol Biol Phys. 2005;61:1087–95. doi: 10.1016/j.ijrobp.2004.07.710. [DOI] [PubMed] [Google Scholar]
  • 15.Chen MH, Chang PM, Chen PM, Tzeng CH, Chu PY, Chang SY, Yang MH. Prognostic significance of a pretreatment hematologic profile in patients with head and neck cancer. J Cancer Res Clin Oncol. 2009;135:1783–90. doi: 10.1007/s00432-009-0625-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Rachidi S, Wallace K, Day TA, Alberg AJ, Li Z. Lower circulating platelet count and antiplatelet therapy independently predict better outcomes in patients with head and neck squamous cell carcinoma. J Hematol Oncol. 2014;7:65. doi: 10.1186/s13045-014-0065-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Ikeda M, Furukawa H, Imamura H, Shimizu J, Ishida H, Masutani S, Tatsuta M, Satomi T. Poor prognosis associated with thrombocytosis in patients with gastric cancer. Ann Surg Oncol. 2002;9:287–91. doi: 10.1007/BF02573067. [DOI] [PubMed] [Google Scholar]
  • 18.Shimada H, Oohira G, Okazumi S, Matsubara H, Nabeya Y, Hayashi H, Takeda A, Gunji Y, Ochiai T. Thrombocytosis associated with poor prognosis in patients with esophageal carcinoma. J Am Coll Surg. 2004;198:737–41. doi: 10.1016/j.jamcollsurg.2004.01.022. [DOI] [PubMed] [Google Scholar]
  • 19.Symbas NP, Townsend MF, El-Galley R, Keane TE, Graham SD, Petros JA. Poor prognosis associated with thrombocytosis in patients with renal cell carcinoma. BJU international. 2000;86:203–7. doi: 10.1046/j.1464-410x.2000.00792.x. [DOI] [PubMed] [Google Scholar]
  • 20.Heng DY, Xie W, Regan MM, Warren MA, Golshayan AR, Sahi C, Eigl BJ, Ruether JD, Cheng T, North S, Venner P, Knox JJ, et al. Prognostic factors for overall survival in patients with metastatic renal cell carcinoma treated with vascular endothelial growth factor-targeted agents: results from a large, multicenter study. J Clin Oncol. 2009;27:5794–9. doi: 10.1200/JCO.2008.21.4809. [DOI] [PubMed] [Google Scholar]
  • 21.Garden AS, Asper JA, Morrison WH, Schechter NR, Glisson BS, Kies MS, Myers JN, Ang KK. Is concurrent chemoradiation the treatment of choice for all patients with Stage III or IV head and neck carcinoma? Cancer. 2004;100:1171–8. doi: 10.1002/cncr.20069. [DOI] [PubMed] [Google Scholar]
  • 22.Garden AS, Gunn GB, Hessel A, Beadle BM, Ahmed S, El-Naggar AK, Fuller CD, Byers LA, Phan J, Frank SJ, Morrison WH, Kies MS, et al. Management of the lymph node-positive neck in the patient with human papillomavirus-associated oropharyngeal cancer. Cancer. 2014;120:3082–8. doi: 10.1002/cncr.28831. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Garden AS, Morrison WH, Wong PF, Tung SS, Rosenthal DI, Dong L, Mason B, Perkins GH, Ang KK. Disease-control rates following intensity-modulated radiation therapy for small primary oropharyngeal carcinoma. Int J Radiat Oncol Biol Phys. 2007;67:438–44. doi: 10.1016/j.ijrobp.2006.08.078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Stravodimou A, Voutsadakis IA. Pretreatment thrombocytosis as a prognostic factor in metastatic breast cancer. Int J Breast Cancer. 2013;2013:289563. doi: 10.1155/2013/289563. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Dubray B, Mosseri V, Brunin F, Jaulerry C, Poncet P, Rodriguez J, Brugere J, Point D, Giraud P, Cosset JM. Anemia is associated with lower local-regional control and survival after radiation therapy for head and neck cancer: A prospective study. Radiology. 1996;201:553–8. doi: 10.1148/radiology.201.2.8888257. [DOI] [PubMed] [Google Scholar]
  • 26.Volinsky CT, Raftery AE. Bayesian information criterion for censored survival models. Biometrics. 2000;56:256–62. doi: 10.1111/j.0006-341x.2000.00256.x. [DOI] [PubMed] [Google Scholar]
  • 27.Kass RE, Raftery AE. Bayes Factors. J Am Stat Assoc. 1995;90:773–95. [Google Scholar]
  • 28.Raftery A. Bayesian Model Selection in Social Research. Sociological Methodology. 1995;25:111–63. [Google Scholar]
  • 29.Harrell FE. Package 'rms', ed. 3.6-3. 2013 [Google Scholar]
  • 30.Zhu Z, Liu W, Gillin M, Gomez DR, Komaki R, Cox JD, Mohan R, Chang JY. Assessing the robustness of passive scattering proton therapy with regard to local recurrence in stage III non-small cell lung cancer: a secondary analysis of a phase II trial. Radiat Oncol. 2014;9:108. doi: 10.1186/1748-717X-9-108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.van Es N, Sturk A, Middeldorp S, Nieuwland R. Effects of cancer on platelets. Seminars in oncology. 2014;41:311–8. doi: 10.1053/j.seminoncol.2014.04.015. [DOI] [PubMed] [Google Scholar]
  • 32.Sharma D, Brummel-Ziedins KE, Bouchard BA, Holmes CE. Platelets in Tumor Progression: A Host Factor That Offers Multiple Potential Targets in the Treatment of Cancer. J Cell Physiol. 2014;229:1005–15. doi: 10.1002/jcp.24539. [DOI] [PubMed] [Google Scholar]
  • 33.Stone RL, Nick AM, McNeish IA, Balkwill F, Han HD, Bottsford-Miller J, Rupairmoole R, Armaiz-Pena GN, Pecot CV, Coward J, Deavers MT, Vasquez HG, et al. Paraneoplastic thrombocytosis in ovarian cancer. The New England journal of medicine. 2012;366:610–8. doi: 10.1056/NEJMoa1110352. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Labelle M, Begum S, Hynes RO. Platelets guide the formation of early metastatic niches. Proceedings of the National Academy of Sciences of the United States of America. 2014;111:E3053–61. doi: 10.1073/pnas.1411082111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Polyak K, Weinberg RA. Transitions between epithelial and mesenchymal states: acquisition of malignant and stem cell traits. Nat Rev Cancer. 2009;9:265–73. doi: 10.1038/nrc2620. [DOI] [PubMed] [Google Scholar]
  • 36.Rothwell PM, Wilson M, Price JF, Belch JF, Meade TW, Mehta Z. Effect of daily aspirin on risk of cancer metastasis: a study of incident cancers during randomised controlled trials. Lancet. 2012;379:1591–601. doi: 10.1016/S0140-6736(12)60209-8. [DOI] [PubMed] [Google Scholar]
  • 37.Macfarlane TV, Lefevre K, Watson MC. Aspirin and non-steroidal anti-inflammatory drug use and the risk of upper aerodigestive tract cancer. British journal of cancer. 2014;111:1852–9. doi: 10.1038/bjc.2014.473. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Fakhry C, Zhang Q, Nguyen-Tan PF, Rosenthal D, El-Naggar A, Garden AS, Soulieres D, Trotti A, Avizonis V, Ridge JA, Harris J, Le QT, et al. Human papillomavirus and overall survival after progression of oropharyngeal squamous cell carcinoma. J Clin Oncol. 2014;32:3365–73. doi: 10.1200/JCO.2014.55.1937. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Kies MS, Holsinger FC, Lee JJ, William WN, Jr., Glisson BS, Lin HY, Lewin JS, Ginsberg LE, Gillaspy KA, Massarelli E, Byers L, Lippman SM, et al. Induction chemotherapy and cetuximab for locally advanced squamous cell carcinoma of the head and neck: results from a phase II prospective trial. J Clin Oncol. 2010;28:8–14. doi: 10.1200/JCO.2009.23.0425. [DOI] [PMC free article] [PubMed] [Google Scholar]

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