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Automated quantitative multiplex immunofluorescence in situ imaging identifies phospho-S6 and phospho-PRAS40 as predictive protein biomarkers for prostate cancer lethality.

Shipitsin M, Small C, Giladi E, Siddiqui S, Choudhury S, Hussain S, Huang YE, Chang H, Rimm DL, Berman DM, Nifong TP, Blume-Jensen P - Proteome Sci (2014)

Bottom Line: Most current, gene expression-based platforms require tissue lysis resulting in loss of structural and molecular information, and hence are blind to tumor heterogeneity and morphological features.Using this approach, we confirm that four previously reported prognostic markers, PTEN, SMAD4, CCND1 and SPP1, can predict lethal outcome of human prostate cancer.The ability to substitute PTEN with phospho-markers demonstrates the potential of quantitative protein activity state measurements on intact tissue.

View Article: PubMed Central - HTML - PubMed

Affiliation: Metamark Genetics Inc, Cambridge, MA, USA.

ABSTRACT

Background: We have witnessed significant progress in gene-based approaches to cancer prognostication, promising early intervention for high-risk patients and avoidance of overtreatment for low-risk patients. However, there has been less advancement in protein-based approaches, even though perturbed protein levels and post-translational modifications are more directly linked with phenotype. Most current, gene expression-based platforms require tissue lysis resulting in loss of structural and molecular information, and hence are blind to tumor heterogeneity and morphological features.

Results: Here we report an automated, integrated multiplex immunofluorescence in situ imaging approach that quantitatively measures protein biomarker levels and activity states in defined intact tissue regions where the biomarkers of interest exert their phenotype. Using this approach, we confirm that four previously reported prognostic markers, PTEN, SMAD4, CCND1 and SPP1, can predict lethal outcome of human prostate cancer. Furthermore, we show that two PI3K pathway-regulated protein activities, pS6 (RPS6-phosphoserines 235/236) and pPRAS40 (AKT1S1-phosphothreonine 246), correlate with prostate cancer lethal outcome as well (individual marker hazard ratios of 2.04 and 2.03, respectively). Finally, we incorporate these 2 markers into a novel 5-marker protein signature, SMAD4, CCND1, SPP1, pS6, and pPRAS40, which is highly predictive for prostate cancer-specific death. The ability to substitute PTEN with phospho-markers demonstrates the potential of quantitative protein activity state measurements on intact tissue.

Conclusions: In summary, our approach can reproducibly and simultaneously quantify and assess multiple protein levels and functional activities on intact tissue specimens. We believe it is broadly applicable to not only cancer but other diseases, and propose that it should be well suited for prognostication at early stages of pathogenesis where key signaling protein levels and activities are perturbed.

No MeSH data available.


Related in: MedlinePlus

Outline of experimental approach for automated, quantitative multiplex immunofluorescence and biomarker measurements in defined regions of interest of prostatectomy tissue. A) Spectral profiles of each fluorophore in the spectral library used in the assay and profiles for tissue autofluorescence signals (AFL) and bright autofluorescence (BAFL) signals, respectively. B) A general outline of staining procedure for quantitative multiplex immunofluorescent biomarker measurements in tissue region of interest. SPP1 and SMAD4 were used as an example. Region of interest marker antibodies (KRT8 (CK8) and KRT18 (CK18) for total epithelium and KRT5 (CK5) and TRIM29 for basal epithelium) were directly conjugated to Alexa488 and Alexa 555, respectively. Biomarker antibodies were detected with a sequence of secondary and tertiary antibodies, as described. Colors in the table illustrate unique spectral positions of emission peaks for the indicated Alexa fluorophore dyes. C) A composite multispectral image (i) is unmixed into separate channels corresponding to AFL and BAFL, region of interest markers, and biomarkers, as indicated (ii). D) Definiens script-based tissue segmentation and biomarker quantitation. Moving through panels 1-6, from the composite image (1), first total epithelial regions are identified (2), followed by nuclear areas (3). The epithelial regions are further segmented into tumor shown in red, benign in green, and undetermined in yellow (4). Gray color denotes non-epithelial regions, e.g. stroma and vessels (4). Finally, biomarkers are quantified from tumor epithelium areas only, outlined in red (5 and 6).
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Figure 1: Outline of experimental approach for automated, quantitative multiplex immunofluorescence and biomarker measurements in defined regions of interest of prostatectomy tissue. A) Spectral profiles of each fluorophore in the spectral library used in the assay and profiles for tissue autofluorescence signals (AFL) and bright autofluorescence (BAFL) signals, respectively. B) A general outline of staining procedure for quantitative multiplex immunofluorescent biomarker measurements in tissue region of interest. SPP1 and SMAD4 were used as an example. Region of interest marker antibodies (KRT8 (CK8) and KRT18 (CK18) for total epithelium and KRT5 (CK5) and TRIM29 for basal epithelium) were directly conjugated to Alexa488 and Alexa 555, respectively. Biomarker antibodies were detected with a sequence of secondary and tertiary antibodies, as described. Colors in the table illustrate unique spectral positions of emission peaks for the indicated Alexa fluorophore dyes. C) A composite multispectral image (i) is unmixed into separate channels corresponding to AFL and BAFL, region of interest markers, and biomarkers, as indicated (ii). D) Definiens script-based tissue segmentation and biomarker quantitation. Moving through panels 1-6, from the composite image (1), first total epithelial regions are identified (2), followed by nuclear areas (3). The epithelial regions are further segmented into tumor shown in red, benign in green, and undetermined in yellow (4). Gray color denotes non-epithelial regions, e.g. stroma and vessels (4). Finally, biomarkers are quantified from tumor epithelium areas only, outlined in red (5 and 6).

Mentions: To address the first, we optimized long-pass diamidino-2-phenylindole (DAPI), fluorescein isothiocyanate (FITC), tetramethylrhodamine isothiocyanate (TRITC) and indodicarbocyanine (Cy5) filter sets to have sufficient excitation energy and emission bandpass with minimal interference between channels. We further separated biomarker signals from endogenous autofluorescence through spectral unmixing of images (Figure 1A [14]). In order to measure biomarkers in tumor epithelium only, we needed to achieve “tissue segmentation”, distinguishing tumor from benign areas. Segmentation was achieved using a combination of feature extraction and protein co-localization algorithms. Total epithelium was stained using Alexa488 conjugated anti-KRT8 and KRT18 antibodies, while Alexa555 conjugated anti-KRT5 and TRIM29 antibodies stained basal epithelium (Figure 1B) [15,16]. Using automated Definiens (Munich, Germany) image analysis, epithelial structures with an outer layer of basal cells were considered benign, while those lacking basal cells were considered cancer [16]. Non-epithelial areas were considered stroma. Ultimately, quantitative biomarker values that correlated with accessible protein were extracted only from cancer epithelium (the ‘region of interest’; Figure 1B-D).


Automated quantitative multiplex immunofluorescence in situ imaging identifies phospho-S6 and phospho-PRAS40 as predictive protein biomarkers for prostate cancer lethality.

Shipitsin M, Small C, Giladi E, Siddiqui S, Choudhury S, Hussain S, Huang YE, Chang H, Rimm DL, Berman DM, Nifong TP, Blume-Jensen P - Proteome Sci (2014)

Outline of experimental approach for automated, quantitative multiplex immunofluorescence and biomarker measurements in defined regions of interest of prostatectomy tissue. A) Spectral profiles of each fluorophore in the spectral library used in the assay and profiles for tissue autofluorescence signals (AFL) and bright autofluorescence (BAFL) signals, respectively. B) A general outline of staining procedure for quantitative multiplex immunofluorescent biomarker measurements in tissue region of interest. SPP1 and SMAD4 were used as an example. Region of interest marker antibodies (KRT8 (CK8) and KRT18 (CK18) for total epithelium and KRT5 (CK5) and TRIM29 for basal epithelium) were directly conjugated to Alexa488 and Alexa 555, respectively. Biomarker antibodies were detected with a sequence of secondary and tertiary antibodies, as described. Colors in the table illustrate unique spectral positions of emission peaks for the indicated Alexa fluorophore dyes. C) A composite multispectral image (i) is unmixed into separate channels corresponding to AFL and BAFL, region of interest markers, and biomarkers, as indicated (ii). D) Definiens script-based tissue segmentation and biomarker quantitation. Moving through panels 1-6, from the composite image (1), first total epithelial regions are identified (2), followed by nuclear areas (3). The epithelial regions are further segmented into tumor shown in red, benign in green, and undetermined in yellow (4). Gray color denotes non-epithelial regions, e.g. stroma and vessels (4). Finally, biomarkers are quantified from tumor epithelium areas only, outlined in red (5 and 6).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4114438&req=5

Figure 1: Outline of experimental approach for automated, quantitative multiplex immunofluorescence and biomarker measurements in defined regions of interest of prostatectomy tissue. A) Spectral profiles of each fluorophore in the spectral library used in the assay and profiles for tissue autofluorescence signals (AFL) and bright autofluorescence (BAFL) signals, respectively. B) A general outline of staining procedure for quantitative multiplex immunofluorescent biomarker measurements in tissue region of interest. SPP1 and SMAD4 were used as an example. Region of interest marker antibodies (KRT8 (CK8) and KRT18 (CK18) for total epithelium and KRT5 (CK5) and TRIM29 for basal epithelium) were directly conjugated to Alexa488 and Alexa 555, respectively. Biomarker antibodies were detected with a sequence of secondary and tertiary antibodies, as described. Colors in the table illustrate unique spectral positions of emission peaks for the indicated Alexa fluorophore dyes. C) A composite multispectral image (i) is unmixed into separate channels corresponding to AFL and BAFL, region of interest markers, and biomarkers, as indicated (ii). D) Definiens script-based tissue segmentation and biomarker quantitation. Moving through panels 1-6, from the composite image (1), first total epithelial regions are identified (2), followed by nuclear areas (3). The epithelial regions are further segmented into tumor shown in red, benign in green, and undetermined in yellow (4). Gray color denotes non-epithelial regions, e.g. stroma and vessels (4). Finally, biomarkers are quantified from tumor epithelium areas only, outlined in red (5 and 6).
Mentions: To address the first, we optimized long-pass diamidino-2-phenylindole (DAPI), fluorescein isothiocyanate (FITC), tetramethylrhodamine isothiocyanate (TRITC) and indodicarbocyanine (Cy5) filter sets to have sufficient excitation energy and emission bandpass with minimal interference between channels. We further separated biomarker signals from endogenous autofluorescence through spectral unmixing of images (Figure 1A [14]). In order to measure biomarkers in tumor epithelium only, we needed to achieve “tissue segmentation”, distinguishing tumor from benign areas. Segmentation was achieved using a combination of feature extraction and protein co-localization algorithms. Total epithelium was stained using Alexa488 conjugated anti-KRT8 and KRT18 antibodies, while Alexa555 conjugated anti-KRT5 and TRIM29 antibodies stained basal epithelium (Figure 1B) [15,16]. Using automated Definiens (Munich, Germany) image analysis, epithelial structures with an outer layer of basal cells were considered benign, while those lacking basal cells were considered cancer [16]. Non-epithelial areas were considered stroma. Ultimately, quantitative biomarker values that correlated with accessible protein were extracted only from cancer epithelium (the ‘region of interest’; Figure 1B-D).

Bottom Line: Most current, gene expression-based platforms require tissue lysis resulting in loss of structural and molecular information, and hence are blind to tumor heterogeneity and morphological features.Using this approach, we confirm that four previously reported prognostic markers, PTEN, SMAD4, CCND1 and SPP1, can predict lethal outcome of human prostate cancer.The ability to substitute PTEN with phospho-markers demonstrates the potential of quantitative protein activity state measurements on intact tissue.

View Article: PubMed Central - HTML - PubMed

Affiliation: Metamark Genetics Inc, Cambridge, MA, USA.

ABSTRACT

Background: We have witnessed significant progress in gene-based approaches to cancer prognostication, promising early intervention for high-risk patients and avoidance of overtreatment for low-risk patients. However, there has been less advancement in protein-based approaches, even though perturbed protein levels and post-translational modifications are more directly linked with phenotype. Most current, gene expression-based platforms require tissue lysis resulting in loss of structural and molecular information, and hence are blind to tumor heterogeneity and morphological features.

Results: Here we report an automated, integrated multiplex immunofluorescence in situ imaging approach that quantitatively measures protein biomarker levels and activity states in defined intact tissue regions where the biomarkers of interest exert their phenotype. Using this approach, we confirm that four previously reported prognostic markers, PTEN, SMAD4, CCND1 and SPP1, can predict lethal outcome of human prostate cancer. Furthermore, we show that two PI3K pathway-regulated protein activities, pS6 (RPS6-phosphoserines 235/236) and pPRAS40 (AKT1S1-phosphothreonine 246), correlate with prostate cancer lethal outcome as well (individual marker hazard ratios of 2.04 and 2.03, respectively). Finally, we incorporate these 2 markers into a novel 5-marker protein signature, SMAD4, CCND1, SPP1, pS6, and pPRAS40, which is highly predictive for prostate cancer-specific death. The ability to substitute PTEN with phospho-markers demonstrates the potential of quantitative protein activity state measurements on intact tissue.

Conclusions: In summary, our approach can reproducibly and simultaneously quantify and assess multiple protein levels and functional activities on intact tissue specimens. We believe it is broadly applicable to not only cancer but other diseases, and propose that it should be well suited for prognostication at early stages of pathogenesis where key signaling protein levels and activities are perturbed.

No MeSH data available.


Related in: MedlinePlus