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Reaching the limits of prognostication in non-small cell lung cancer : an optimized biomarker panel fails to outperform clinical parameters

Grinberg, Marianna; Djureinovic, Dijana; Brunnström, Hans RR LU ; Mattsson, Johanna Sm; Edlund, Karolina; Hengstler, Jan G; La Fleur, Linnea; Ekman, Simon; Koyi, Hirsh and Branden, Eva, et al. (2017) In Modern Pathology 30(7). p.964-977
Abstract

Numerous protein biomarkers have been analyzed to improve prognostication in non-small cell lung cancer, but have not yet demonstrated sufficient value to be introduced into clinical practice. Here, we aimed to develop and validate a prognostic model for surgically resected non-small cell lung cancer. A biomarker panel was selected based on (1) prognostic association in published literature, (2) prognostic association in gene expression data sets, (3) availability of reliable antibodies, and (4) representation of diverse biological processes. The five selected proteins (MKI67, EZH2, SLC2A1, CADM1, and NKX2-1 alias TTF1) were analyzed by immunohistochemistry on tissue microarrays including tissue from 326 non-small cell lung cancer... (More)

Numerous protein biomarkers have been analyzed to improve prognostication in non-small cell lung cancer, but have not yet demonstrated sufficient value to be introduced into clinical practice. Here, we aimed to develop and validate a prognostic model for surgically resected non-small cell lung cancer. A biomarker panel was selected based on (1) prognostic association in published literature, (2) prognostic association in gene expression data sets, (3) availability of reliable antibodies, and (4) representation of diverse biological processes. The five selected proteins (MKI67, EZH2, SLC2A1, CADM1, and NKX2-1 alias TTF1) were analyzed by immunohistochemistry on tissue microarrays including tissue from 326 non-small cell lung cancer patients. One score was obtained for each tumor and each protein. The scores were combined, with or without the inclusion of clinical parameters, and the best prognostic model was defined according to the corresponding concordance index (C-index). The best-performing model was subsequently validated in an independent cohort consisting of tissue from 345 non-small cell lung cancer patients. The model based only on protein expression did not perform better compared to clinicopathological parameters, whereas combining protein expression with clinicopathological data resulted in a slightly better prognostic performance (C-index: all non-small cell lung cancer 0.63 vs 0.64; adenocarcinoma: 0.66 vs 0.70, squamous cell carcinoma: 0.57 vs 0.56). However, this modest effect did not translate into a significantly improved accuracy of survival prediction. The combination of a prognostic biomarker panel with clinicopathological parameters did not improve survival prediction in non-small cell lung cancer, questioning the potential of immunohistochemistry-based assessment of protein biomarkers for prognostication in clinical practice.

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Modern Pathology
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30
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7
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14 pages
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Nature Publishing Group
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  • scopus:85014751746
  • wos:000404718100006
ISSN
1530-0285
DOI
10.1038/modpathol.2017.14
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English
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9577a866-5c0d-4f12-ae86-a94268963442
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2017-07-13 09:49:08
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2018-01-07 12:11:35
@article{9577a866-5c0d-4f12-ae86-a94268963442,
  abstract     = {<p>Numerous protein biomarkers have been analyzed to improve prognostication in non-small cell lung cancer, but have not yet demonstrated sufficient value to be introduced into clinical practice. Here, we aimed to develop and validate a prognostic model for surgically resected non-small cell lung cancer. A biomarker panel was selected based on (1) prognostic association in published literature, (2) prognostic association in gene expression data sets, (3) availability of reliable antibodies, and (4) representation of diverse biological processes. The five selected proteins (MKI67, EZH2, SLC2A1, CADM1, and NKX2-1 alias TTF1) were analyzed by immunohistochemistry on tissue microarrays including tissue from 326 non-small cell lung cancer patients. One score was obtained for each tumor and each protein. The scores were combined, with or without the inclusion of clinical parameters, and the best prognostic model was defined according to the corresponding concordance index (C-index). The best-performing model was subsequently validated in an independent cohort consisting of tissue from 345 non-small cell lung cancer patients. The model based only on protein expression did not perform better compared to clinicopathological parameters, whereas combining protein expression with clinicopathological data resulted in a slightly better prognostic performance (C-index: all non-small cell lung cancer 0.63 vs 0.64; adenocarcinoma: 0.66 vs 0.70, squamous cell carcinoma: 0.57 vs 0.56). However, this modest effect did not translate into a significantly improved accuracy of survival prediction. The combination of a prognostic biomarker panel with clinicopathological parameters did not improve survival prediction in non-small cell lung cancer, questioning the potential of immunohistochemistry-based assessment of protein biomarkers for prognostication in clinical practice.</p>},
  author       = {Grinberg, Marianna and Djureinovic, Dijana and Brunnström, Hans RR and Mattsson, Johanna Sm and Edlund, Karolina and Hengstler, Jan G and La Fleur, Linnea and Ekman, Simon and Koyi, Hirsh and Branden, Eva and Ståhle, Elisabeth and Jirström, Karin and Tracy, Derek K and Pontén, Fredrik and Botling, Johan and Rahnenführer, Jörg and Micke, Patrick},
  issn         = {1530-0285},
  language     = {eng},
  number       = {7},
  pages        = {964--977},
  publisher    = {Nature Publishing Group},
  series       = {Modern Pathology},
  title        = {Reaching the limits of prognostication in non-small cell lung cancer : an optimized biomarker panel fails to outperform clinical parameters},
  url          = {http://dx.doi.org/10.1038/modpathol.2017.14},
  volume       = {30},
  year         = {2017},
}