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Stromal features are predictive of disease mortality in oral cancer patients

Stromal features are predictive of disease mortality in oral cancer patients
Stromal features are predictive of disease mortality in oral cancer patients
Worldwide, approximately 405 000 cases of oral cancer (OSCC) are diagnosed each year, with a rising incidence in many countries. Despite advances in surgery and radiotherapy, which remain the standard treatment options, the mortality rate has remained largely unchanged for decades, with a 5-year survival rate of around 50%. OSCC is a heterogeneous disease, staged currently using the TNM classification, supplemented with pathological information from the primary tumour and loco-regional lymph nodes. Although patients with advanced disease show reduced survival, there is no single pathological or molecular feature that identifies aggressive, early-stage tumours. We retrospectively analysed 282 OSCC patients for disease mortality, related to clinical, pathological, and molecular features based on our previous functional studies [EGFR, ?v?6 integrin, smooth muscle actin (SMA), p53, p16, EP4]. We found that the strongest independent risk factor of early OSCC death was a feature of stroma rather than tumour cells. After adjusting for all factors, high stromal SMA expression, indicating myofibroblast transdifferentiation, produced the highest hazard ratio (3.06, 95% CI 1.65–5.66) and likelihood ratio (3.6; detection rate: false positive rate) of any feature examined, and was strongly associated with mortality, regardless of disease stage. Functional assays showed that OSCC cells can modulate myofibroblast transdifferentiation through ?v?6-dependent TGF-?1 activation and that myofibroblasts promote OSCC invasion. Finally, we developed a prognostic model using Cox regression with backward elimination; only SMA expression, metastasis, cohesion, and age were significant. This model was independently validated on a patient subset (detection rate 70%; false positive rate 20%; ROC analysis 77%, p < 0.001). Our study highlights the limited prognostic value of TNM staging and suggests that an SMA-positive, myofibroblastic stroma is the strongest predictor of OSCC mortality. Whether used independently or as part of a prognostic model, SMA identifies a significant group of patients with aggressive tumours, regardless of disease stage
1096-9896
470-481
Marsh, Daniel
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Suchak, Krishna
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Moutasim, Karwan A
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Vallath, Sabarinath
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Hopper, Colin
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Jerjes, Waseem
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Upile, Tahwinder
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Kalavrezos, Nicholas
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Violette, Shelia M
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Weinreb, Paul H
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Chester, Kerry A
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Chana, Jagdeep S
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Marshall, John F
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Hart, Ian R
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Hackshaw, Allan K
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Piper, Kim
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Thomas, Gareth J
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Marsh, Daniel
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Suchak, Krishna
82ef309f-eee4-435e-ba89-0ca1091a014b
Moutasim, Karwan A
e2e0efb1-a637-4252-b28d-be205dc94142
Vallath, Sabarinath
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Hopper, Colin
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Jerjes, Waseem
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Upile, Tahwinder
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Kalavrezos, Nicholas
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Violette, Shelia M
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Weinreb, Paul H
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Chester, Kerry A
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Chana, Jagdeep S
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Marshall, John F
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Hart, Ian R
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Hackshaw, Allan K
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Piper, Kim
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Thomas, Gareth J
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Marsh, Daniel, Suchak, Krishna, Moutasim, Karwan A, Vallath, Sabarinath, Hopper, Colin, Jerjes, Waseem, Upile, Tahwinder, Kalavrezos, Nicholas, Violette, Shelia M, Weinreb, Paul H, Chester, Kerry A, Chana, Jagdeep S, Marshall, John F, Hart, Ian R, Hackshaw, Allan K, Piper, Kim and Thomas, Gareth J (2011) Stromal features are predictive of disease mortality in oral cancer patients. The Journal of Pathology, 223 (4), 470-481. (doi:10.1002/path.2830). (PMID:21294121)

Record type: Article

Abstract

Worldwide, approximately 405 000 cases of oral cancer (OSCC) are diagnosed each year, with a rising incidence in many countries. Despite advances in surgery and radiotherapy, which remain the standard treatment options, the mortality rate has remained largely unchanged for decades, with a 5-year survival rate of around 50%. OSCC is a heterogeneous disease, staged currently using the TNM classification, supplemented with pathological information from the primary tumour and loco-regional lymph nodes. Although patients with advanced disease show reduced survival, there is no single pathological or molecular feature that identifies aggressive, early-stage tumours. We retrospectively analysed 282 OSCC patients for disease mortality, related to clinical, pathological, and molecular features based on our previous functional studies [EGFR, ?v?6 integrin, smooth muscle actin (SMA), p53, p16, EP4]. We found that the strongest independent risk factor of early OSCC death was a feature of stroma rather than tumour cells. After adjusting for all factors, high stromal SMA expression, indicating myofibroblast transdifferentiation, produced the highest hazard ratio (3.06, 95% CI 1.65–5.66) and likelihood ratio (3.6; detection rate: false positive rate) of any feature examined, and was strongly associated with mortality, regardless of disease stage. Functional assays showed that OSCC cells can modulate myofibroblast transdifferentiation through ?v?6-dependent TGF-?1 activation and that myofibroblasts promote OSCC invasion. Finally, we developed a prognostic model using Cox regression with backward elimination; only SMA expression, metastasis, cohesion, and age were significant. This model was independently validated on a patient subset (detection rate 70%; false positive rate 20%; ROC analysis 77%, p < 0.001). Our study highlights the limited prognostic value of TNM staging and suggests that an SMA-positive, myofibroblastic stroma is the strongest predictor of OSCC mortality. Whether used independently or as part of a prognostic model, SMA identifies a significant group of patients with aggressive tumours, regardless of disease stage

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Published date: March 2011
Organisations: Cancer Sciences

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Local EPrints ID: 180361
URI: http://eprints.soton.ac.uk/id/eprint/180361
ISSN: 1096-9896
PURE UUID: 8a388ab9-6f33-4906-aac8-67ef4b4910e1

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Date deposited: 08 Apr 2011 10:22
Last modified: 14 Mar 2024 02:52

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Contributors

Author: Daniel Marsh
Author: Krishna Suchak
Author: Karwan A Moutasim
Author: Sabarinath Vallath
Author: Colin Hopper
Author: Waseem Jerjes
Author: Tahwinder Upile
Author: Nicholas Kalavrezos
Author: Shelia M Violette
Author: Paul H Weinreb
Author: Kerry A Chester
Author: Jagdeep S Chana
Author: John F Marshall
Author: Ian R Hart
Author: Allan K Hackshaw
Author: Kim Piper
Author: Gareth J Thomas

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