Differentiation of tumour-stage mycosis fungoides, psoriasis vulgaris and normal controls in a pilot study using serum proteomic analysis

E. W. Cowen, C. W. Liu, S. M. Steinberg, S. Kang, E. C. Vonderheid, H. S. Kwak, S. Booher, E. F. Petricoin, L. A. Liotta, G. Whiteley, Samuel T Hwang

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Background: Serum proteomic analysis is an analytical technique utilizing high-throughput mass spectrometry (MS) in order to assay thousands of serum proteins simultaneously. The resultant 'proteomic signature' has been used to differentiate benign and malignant diseases, enable disease prognosis, and monitor response to therapy. Objectives: This pilot study was designed to determine if serum protein patterns could be used to distinguish patients with tumour-stage mycosis fungoides (MF) from patients with a benign inflammatory skin condition (psoriasis) and/or subjects with healthy skin. Methods: Serum was analysed from 45 patients with tumour-stage MF, 56 patients with psoriasis, and 47 controls using two MS platforms of differing resolution. An artificial intelligence-based classification model was constructed to predict the presence of the disease state based on the serum proteomic signature. Results: Based on data from an independent testing set (14-16 subjects in each group), MF was distinguished from psoriasis with 78·6% (or 78·6%) sensitivity and 86·7% (or 93·8%) specificity, while sera from patients with psoriasis were distinguished from those of nonaffected controls with 86·7% (or 93·8%) sensitivity and 75·0% (or 76·9%) specificity (depending on the MS platform used). MF was distinguished from unaffected controls with 61·5% (or 71·4%) sensitivity and 91·7% (or 92·9%) specificity. In addition, a secondary survival analysis using 11 MS peaks identified significant survival differences between two MF groups (all P-values <0·05). Conclusions: Serum proteomics should be further investigated for its potential to identify patients with neoplastic skin disease and its ability to determine disease prognosis.

Original languageEnglish (US)
Pages (from-to)946-953
Number of pages8
JournalBritish Journal of Dermatology
Volume157
Issue number5
DOIs
StatePublished - Nov 2007
Externally publishedYes

Fingerprint

Mycosis Fungoides
Psoriasis
Proteomics
Mass Spectrometry
Serum
Neoplasms
Blood Proteins
Skin
Artificial Intelligence
Survival Analysis
Skin Diseases
Healthy Volunteers
Survival

Keywords

  • Mycosis fungoides
  • Proteomics
  • Psoriasis

ASJC Scopus subject areas

  • Dermatology

Cite this

Differentiation of tumour-stage mycosis fungoides, psoriasis vulgaris and normal controls in a pilot study using serum proteomic analysis. / Cowen, E. W.; Liu, C. W.; Steinberg, S. M.; Kang, S.; Vonderheid, E. C.; Kwak, H. S.; Booher, S.; Petricoin, E. F.; Liotta, L. A.; Whiteley, G.; Hwang, Samuel T.

In: British Journal of Dermatology, Vol. 157, No. 5, 11.2007, p. 946-953.

Research output: Contribution to journalArticle

Cowen, EW, Liu, CW, Steinberg, SM, Kang, S, Vonderheid, EC, Kwak, HS, Booher, S, Petricoin, EF, Liotta, LA, Whiteley, G & Hwang, ST 2007, 'Differentiation of tumour-stage mycosis fungoides, psoriasis vulgaris and normal controls in a pilot study using serum proteomic analysis', British Journal of Dermatology, vol. 157, no. 5, pp. 946-953. https://doi.org/10.1111/j.1365-2133.2007.08185.x
Cowen, E. W. ; Liu, C. W. ; Steinberg, S. M. ; Kang, S. ; Vonderheid, E. C. ; Kwak, H. S. ; Booher, S. ; Petricoin, E. F. ; Liotta, L. A. ; Whiteley, G. ; Hwang, Samuel T. / Differentiation of tumour-stage mycosis fungoides, psoriasis vulgaris and normal controls in a pilot study using serum proteomic analysis. In: British Journal of Dermatology. 2007 ; Vol. 157, No. 5. pp. 946-953.
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abstract = "Background: Serum proteomic analysis is an analytical technique utilizing high-throughput mass spectrometry (MS) in order to assay thousands of serum proteins simultaneously. The resultant 'proteomic signature' has been used to differentiate benign and malignant diseases, enable disease prognosis, and monitor response to therapy. Objectives: This pilot study was designed to determine if serum protein patterns could be used to distinguish patients with tumour-stage mycosis fungoides (MF) from patients with a benign inflammatory skin condition (psoriasis) and/or subjects with healthy skin. Methods: Serum was analysed from 45 patients with tumour-stage MF, 56 patients with psoriasis, and 47 controls using two MS platforms of differing resolution. An artificial intelligence-based classification model was constructed to predict the presence of the disease state based on the serum proteomic signature. Results: Based on data from an independent testing set (14-16 subjects in each group), MF was distinguished from psoriasis with 78·6{\%} (or 78·6{\%}) sensitivity and 86·7{\%} (or 93·8{\%}) specificity, while sera from patients with psoriasis were distinguished from those of nonaffected controls with 86·7{\%} (or 93·8{\%}) sensitivity and 75·0{\%} (or 76·9{\%}) specificity (depending on the MS platform used). MF was distinguished from unaffected controls with 61·5{\%} (or 71·4{\%}) sensitivity and 91·7{\%} (or 92·9{\%}) specificity. In addition, a secondary survival analysis using 11 MS peaks identified significant survival differences between two MF groups (all P-values <0·05). Conclusions: Serum proteomics should be further investigated for its potential to identify patients with neoplastic skin disease and its ability to determine disease prognosis.",
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T1 - Differentiation of tumour-stage mycosis fungoides, psoriasis vulgaris and normal controls in a pilot study using serum proteomic analysis

AU - Cowen, E. W.

AU - Liu, C. W.

AU - Steinberg, S. M.

AU - Kang, S.

AU - Vonderheid, E. C.

AU - Kwak, H. S.

AU - Booher, S.

AU - Petricoin, E. F.

AU - Liotta, L. A.

AU - Whiteley, G.

AU - Hwang, Samuel T

PY - 2007/11

Y1 - 2007/11

N2 - Background: Serum proteomic analysis is an analytical technique utilizing high-throughput mass spectrometry (MS) in order to assay thousands of serum proteins simultaneously. The resultant 'proteomic signature' has been used to differentiate benign and malignant diseases, enable disease prognosis, and monitor response to therapy. Objectives: This pilot study was designed to determine if serum protein patterns could be used to distinguish patients with tumour-stage mycosis fungoides (MF) from patients with a benign inflammatory skin condition (psoriasis) and/or subjects with healthy skin. Methods: Serum was analysed from 45 patients with tumour-stage MF, 56 patients with psoriasis, and 47 controls using two MS platforms of differing resolution. An artificial intelligence-based classification model was constructed to predict the presence of the disease state based on the serum proteomic signature. Results: Based on data from an independent testing set (14-16 subjects in each group), MF was distinguished from psoriasis with 78·6% (or 78·6%) sensitivity and 86·7% (or 93·8%) specificity, while sera from patients with psoriasis were distinguished from those of nonaffected controls with 86·7% (or 93·8%) sensitivity and 75·0% (or 76·9%) specificity (depending on the MS platform used). MF was distinguished from unaffected controls with 61·5% (or 71·4%) sensitivity and 91·7% (or 92·9%) specificity. In addition, a secondary survival analysis using 11 MS peaks identified significant survival differences between two MF groups (all P-values <0·05). Conclusions: Serum proteomics should be further investigated for its potential to identify patients with neoplastic skin disease and its ability to determine disease prognosis.

AB - Background: Serum proteomic analysis is an analytical technique utilizing high-throughput mass spectrometry (MS) in order to assay thousands of serum proteins simultaneously. The resultant 'proteomic signature' has been used to differentiate benign and malignant diseases, enable disease prognosis, and monitor response to therapy. Objectives: This pilot study was designed to determine if serum protein patterns could be used to distinguish patients with tumour-stage mycosis fungoides (MF) from patients with a benign inflammatory skin condition (psoriasis) and/or subjects with healthy skin. Methods: Serum was analysed from 45 patients with tumour-stage MF, 56 patients with psoriasis, and 47 controls using two MS platforms of differing resolution. An artificial intelligence-based classification model was constructed to predict the presence of the disease state based on the serum proteomic signature. Results: Based on data from an independent testing set (14-16 subjects in each group), MF was distinguished from psoriasis with 78·6% (or 78·6%) sensitivity and 86·7% (or 93·8%) specificity, while sera from patients with psoriasis were distinguished from those of nonaffected controls with 86·7% (or 93·8%) sensitivity and 75·0% (or 76·9%) specificity (depending on the MS platform used). MF was distinguished from unaffected controls with 61·5% (or 71·4%) sensitivity and 91·7% (or 92·9%) specificity. In addition, a secondary survival analysis using 11 MS peaks identified significant survival differences between two MF groups (all P-values <0·05). Conclusions: Serum proteomics should be further investigated for its potential to identify patients with neoplastic skin disease and its ability to determine disease prognosis.

KW - Mycosis fungoides

KW - Proteomics

KW - Psoriasis

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