SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer

Dale McLerran; William E. Grizzle; Ziding Feng; Ian M. Thompson; William L. Bigbee; Lisa H. Cazares; Daniel W. Chan; Jackie Dahlgren; Jose Diaz; Jacob Kagan; Daniel W. Lin; Gunjan Malik; Denise Oelschlager; Alan Partin; Timothy W. Randolph; Lori Sokoll; Shiv Srivastava; Sudhir Srivastava; Mark Thornquist; Dean Troyer; George L. Wright; Zhen Zhang; Liu Zhu; O. John Semmes

doi:10.1373/clinchem.2007.091496

SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer

Dale McLerran, William E. Grizzle, Ziding Feng, Ian M. Thompson, William L. Bigbee, Lisa H. Cazares, Daniel W. Chan, Jackie Dahlgren, Jose Diaz, Jacob Kagan, Daniel W. Lin, Gunjan Malik, Denise Oelschlager, Alan Partin, Timothy W. Randolph, Lori Sokoll, Shiv Srivastava, Sudhir Srivastava, Mark Thornquist, Dean TroyerGeorge L. Wright, Zhen Zhang, Liu Zhu, O. John Semmes

School of Medicine

Research output: Contribution to journal › Article › peer-review

114 Scopus citations

Abstract

BACKGROUND: The analysis of bodily fluids using SELDI-TOF MS has been reported to identify signatures of spectral peaks that can be used to differentiate patients with a specific disease from normal or control patients. This report is the 2nd of 2 companion articles describing a validation study of a SELDI-TOF MS approach with IMAC surface sample processing to identify prostatic adenocarcinoma. METHODS: We sought to derive a decision algorithm for classification of prostate cancer from SELDI-TOF MS spectral data from a new retrospective sample cohort of 400 specimens. This new cohort was selected to minimize possible confounders identified in the previous study described in the companion paper. RESULTS: The resulting new classifier failed to separate patients with prostate cancer from biopsy-negative controls; nor did it separate patients with prostate cancer with Gleason scores <7 from those with Gleason scores ≥7. CONCLUSIONS: In this, the 2nd stage of our planned validation process, the SELDI-TOF MS-based protein expression profiling approach did not perform well enough to advance to the 3rd (prospective study) stage. We conclude that the results from our previous studies - in which differentiation between prostate cancer and noncancer was demonstrated - are not generalizable. Earlier study samples likely had biases in sample selection that upon removal, as in the present study, resulted in inability of the technique to discriminate cancer from noncancer cases.

Original language	English (US)
Pages (from-to)	53-60
Number of pages	8
Journal	Clinical chemistry
Volume	54
Issue number	1
DOIs	https://doi.org/10.1373/clinchem.2007.091496
State	Published - Jan 1 2008

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Medicine(all)

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McLerran, D., Grizzle, W. E., Feng, Z., Thompson, I. M., Bigbee, W. L., Cazares, L. H., Chan, D. W., Dahlgren, J., Diaz, J., Kagan, J., Lin, D. W., Malik, G., Oelschlager, D., Partin, A., Randolph, T. W., Sokoll, L., Srivastava, S., Srivastava, S., Thornquist, M., ... Semmes, O. J. (2008). SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer. Clinical chemistry, 54(1), 53-60. https://doi.org/10.1373/clinchem.2007.091496

McLerran, D, Grizzle, WE, Feng, Z, Thompson, IM, Bigbee, WL, Cazares, LH, Chan, DW, Dahlgren, J, Diaz, J, Kagan, J, Lin, DW, Malik, G, Oelschlager, D, Partin, A, Randolph, TW, Sokoll, L, Srivastava, S, Srivastava, S, Thornquist, M, Troyer, D, Wright, GL, Zhang, Z, Zhu, L & Semmes, OJ 2008, 'SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer', Clinical chemistry, vol. 54, no. 1, pp. 53-60. https://doi.org/10.1373/clinchem.2007.091496

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title = "SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer",

abstract = "BACKGROUND: The analysis of bodily fluids using SELDI-TOF MS has been reported to identify signatures of spectral peaks that can be used to differentiate patients with a specific disease from normal or control patients. This report is the 2nd of 2 companion articles describing a validation study of a SELDI-TOF MS approach with IMAC surface sample processing to identify prostatic adenocarcinoma. METHODS: We sought to derive a decision algorithm for classification of prostate cancer from SELDI-TOF MS spectral data from a new retrospective sample cohort of 400 specimens. This new cohort was selected to minimize possible confounders identified in the previous study described in the companion paper. RESULTS: The resulting new classifier failed to separate patients with prostate cancer from biopsy-negative controls; nor did it separate patients with prostate cancer with Gleason scores <7 from those with Gleason scores ≥7. CONCLUSIONS: In this, the 2nd stage of our planned validation process, the SELDI-TOF MS-based protein expression profiling approach did not perform well enough to advance to the 3rd (prospective study) stage. We conclude that the results from our previous studies - in which differentiation between prostate cancer and noncancer was demonstrated - are not generalizable. Earlier study samples likely had biases in sample selection that upon removal, as in the present study, resulted in inability of the technique to discriminate cancer from noncancer cases.",

author = "Dale McLerran and Grizzle, {William E.} and Ziding Feng and Thompson, {Ian M.} and Bigbee, {William L.} and Cazares, {Lisa H.} and Chan, {Daniel W.} and Jackie Dahlgren and Jose Diaz and Jacob Kagan and Lin, {Daniel W.} and Gunjan Malik and Denise Oelschlager and Alan Partin and Randolph, {Timothy W.} and Lori Sokoll and Shiv Srivastava and Sudhir Srivastava and Mark Thornquist and Dean Troyer and Wright, {George L.} and Zhen Zhang and Liu Zhu and Semmes, {O. John}",

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doi = "10.1373/clinchem.2007.091496",

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T1 - SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer

AU - McLerran, Dale

AU - Grizzle, William E.

AU - Feng, Ziding

AU - Thompson, Ian M.

AU - Bigbee, William L.

AU - Cazares, Lisa H.

AU - Chan, Daniel W.

AU - Dahlgren, Jackie

AU - Diaz, Jose

AU - Kagan, Jacob

AU - Lin, Daniel W.

AU - Malik, Gunjan

AU - Oelschlager, Denise

AU - Partin, Alan

AU - Randolph, Timothy W.

AU - Sokoll, Lori

AU - Srivastava, Shiv

AU - Srivastava, Sudhir

AU - Thornquist, Mark

AU - Troyer, Dean

AU - Wright, George L.

AU - Zhang, Zhen

AU - Zhu, Liu

AU - Semmes, O. John

PY - 2008/1/1

Y1 - 2008/1/1

N2 - BACKGROUND: The analysis of bodily fluids using SELDI-TOF MS has been reported to identify signatures of spectral peaks that can be used to differentiate patients with a specific disease from normal or control patients. This report is the 2nd of 2 companion articles describing a validation study of a SELDI-TOF MS approach with IMAC surface sample processing to identify prostatic adenocarcinoma. METHODS: We sought to derive a decision algorithm for classification of prostate cancer from SELDI-TOF MS spectral data from a new retrospective sample cohort of 400 specimens. This new cohort was selected to minimize possible confounders identified in the previous study described in the companion paper. RESULTS: The resulting new classifier failed to separate patients with prostate cancer from biopsy-negative controls; nor did it separate patients with prostate cancer with Gleason scores <7 from those with Gleason scores ≥7. CONCLUSIONS: In this, the 2nd stage of our planned validation process, the SELDI-TOF MS-based protein expression profiling approach did not perform well enough to advance to the 3rd (prospective study) stage. We conclude that the results from our previous studies - in which differentiation between prostate cancer and noncancer was demonstrated - are not generalizable. Earlier study samples likely had biases in sample selection that upon removal, as in the present study, resulted in inability of the technique to discriminate cancer from noncancer cases.

AB - BACKGROUND: The analysis of bodily fluids using SELDI-TOF MS has been reported to identify signatures of spectral peaks that can be used to differentiate patients with a specific disease from normal or control patients. This report is the 2nd of 2 companion articles describing a validation study of a SELDI-TOF MS approach with IMAC surface sample processing to identify prostatic adenocarcinoma. METHODS: We sought to derive a decision algorithm for classification of prostate cancer from SELDI-TOF MS spectral data from a new retrospective sample cohort of 400 specimens. This new cohort was selected to minimize possible confounders identified in the previous study described in the companion paper. RESULTS: The resulting new classifier failed to separate patients with prostate cancer from biopsy-negative controls; nor did it separate patients with prostate cancer with Gleason scores <7 from those with Gleason scores ≥7. CONCLUSIONS: In this, the 2nd stage of our planned validation process, the SELDI-TOF MS-based protein expression profiling approach did not perform well enough to advance to the 3rd (prospective study) stage. We conclude that the results from our previous studies - in which differentiation between prostate cancer and noncancer was demonstrated - are not generalizable. Earlier study samples likely had biases in sample selection that upon removal, as in the present study, resulted in inability of the technique to discriminate cancer from noncancer cases.

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JF - Clinical chemistry

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ER -

SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer

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