Proteomics Clin. Appl. 2015, 9, 885–888
885
DOI 10.1002/prca.201400144
Can proteomics-based diagnostics aid clinical psychiatry?
Michaela D. Filiou Max Planck Institute of Psychiatry, Munich, Germany Despite major advances in infrastructure and instrumentation, proteomics-driven translational applications have not yet yielded the results that the scientific community has envisaged. In this viewpoint, the perspective of proteomics-based diagnostics in the field of clinical psychiatry is explored. The challenges that proteomics faces in the context of translational approaches are outlined and directions toward a successful clinical implementation are provided. Additional challenges that psychiatric disorders pose for clinical proteomics are highlighted and the potential of proteomics-based, blood tests for psychiatric disorders is being assessed. Proteomics offers a valuable toolkit for clinical translation that needs to be handled in a pragmatic manner and with realistic expectations.
Received: September 29, 2014 Revised: November 14, 2014 Accepted: January 21, 2015
Keywords: Blood tests / Clinical proteomics / Diagnostics / Implementation / Psychiatric disorders
1
Introduction
Almost 20 years after the first publications using the term “proteome” [1, 2], the field of proteomics has rapidly evolved with remarkable advancements in MS instrumentation, automation, data analysis, accuracy, and precision [3]. Initiatives such as the Proteomics Standards Initiative by the Human Proteome Organization (HUPO) have been launched, aiming at standardizing proteomics experiments and devising guidelines on reporting and presenting proteomics data [4, 5]. Is it also now clear that to understand brain dysfunction, a thorough characterization of normal brain function needs to be carried out so that disease-related changes can be interpreted with confidence [6]. Despite this progress, there is skepticism concerning the translational outcome and the implementation of proteomics approaches in health care [7], a topic recently addressed in the “Diagnostic Proteomics” special issue of Proteomics Clinical Applications [8]. To investigate the potential of proteomics technologies as a reliable diagnostic tool for psychiatric disorders in clinical settings, two major issues will be discussed: (1) Can proteomics-based diagnostics be used routinely in health care and what needs to be done toward this direction? (2) How can proteomics-based diagnostics be beneficial for clinical psychiatry? Correspondence: Dr. Michaela D. Filiou, Max Planck Institute of Psychiatry, Kraepelinstr. 2, 80804 Munich, Germany E-mail: [email protected] Fax: +49 8930622 200 C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
2
Toward proteomics-based diagnostics in health care
Although proteomics offers a highly sensitive toolbox for translational applications, the implementation to the clinic faces a series of inherent challenges. A proteomics approach in the context of a clinically oriented question typically includes a quantitative proteome comparison of disease versus nondisease or treated versus untreated states. As a readout, a list of differentially expressed proteins between the compared states is eventually generated. Unfortunately, a detailed interpretation of these results followed by validation experiments has been rare. This can be attributed to the lack of interdisciplinary approaches and collaborative efforts preventing a conventional MS laboratory from performing follow-up experiments with complementary methods to functionally validate proteomics data. As a consequence, the acquired knowledge from proteomics experiments remains unexploited and potential clinical applications fail to be explored. Besides pitfalls in the conceptual framework, it is unclear whether the identified protein expression differences reflect causal diseaseand treatment-derived changes or represent nonspecific organismal responses. HSPs as well as oxidative stress protein markers and enzymes constitute sensitive sensors of an organism in response to a stimulus and their expression levels may change without them being necessarily involved in the underlying disease or treatment molecular mechanisms [9]. Other methodological concerns include the wide dynamic range of protein abundances in the examined specimens [10] www.clinical.proteomics-journal.com
886
M. D. Filiou
Correspondence concerning this and other Viewpoint articles can be accessed on the journals’ home page at: http://viewpoint.clinical.proteomicsjournal.com Correspondence for posting on these pages is welcome and can also be submitted at this site.
which contributes to the misrepresentation of protein detection limits [11], the masking of low-abundant proteins by high-abundant proteins that may result in a repeated identification/quantification of the same set of proteins irrespective of the condition studied [12] and the bias of computational tools for pathway analysis that may hamper accurate data interpretation [13]. For the successful implementation of the proteomics toolbox into clinical practice, it is essential to address the aforementioned points. In this context, several steps need to be considered from the scientific community, health care professionals, stakeholders, and policy makers: (i) Sample preparation: Acquisition, storage, and handling of samples largely affect subsequent proteomics analysis [14, 15]. Therefore, standard operating procedures for each of these steps should be followed to ensure meaningful data comparisons across different clinical settings. (ii) Workflow performance: Technical characteristics including accuracy and precision of measurements should be determined to avoid overinterpretation of the acquired data [16]. (iii) Biological variation: Variability of protein amounts across individuals within the normal physiological range should be a priori defined to allow identification of disease-relevant changes only [17]. (iv) Data reporting: Uniform guidelines for all steps toward clinical translation as well as for data reporting are critical [18, 19]. (v) Personnel: Key players for a successful clinical implementation are a bioinformatician to address the computational requirements of MS data and skilled personnel for maintenance and troubleshooting of mass spectrometers. Adequate training in sample preparation, MS-based proteomics, and data analysis should be additionally provided. (vi) Funding: In addition to biomarker discovery studies, sufficient funding for biomarker implementation projects and initiatives to support this cause are of major importance [20]. (vii) Cost: The ultimate goal for proteomics-based clinical analyses is a cost-effective service, which will be affordable for patients and will be covered by their health insurance. C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Proteomics Clin. Appl. 2015, 9, 885–888
3
How can proteomics-based diagnostics be used in clinical psychiatry?
Psychiatric disorders pose additional challenges to the implementation of proteomics-based diagnostics into clinical routine. The highly complex interplay between genetic and environmental factors, which is responsible for the manifestation of psychiatric phenotypes, results in disease heterogeneity. Diagnosis is entirely based on a verbal interview between the doctor and the patient using questionnaires. Psychiatric disorders often co-exist with other confounding pathologies whereas the boundaries between different psychiatric phenotypes are hard to define [21, 22]. At the level of treatment, existing drugs do not act rapidly and are accompanied by severe side effects that compromise life quality of patients and their families. Not all patients respond to the same treatment, remission rates are low and a considerable percentage of patients are treatment resistant [23, 24]. The major underlying cause of all these problems is the lack of molecular understanding of disease pathobiology [25]. Although hope has been placed on the identification of molecular biomarkers for psychiatric disorders with the development of high-throughput technologies, the vision of biomarker discovery has not yet fulfilled the expectations of the scientific community [26]. Recently, pharmaceutical companies have announced budget cuts for psychiatric research [27–29] whereas a paradigm shift on how psychiatric phenotypes are being assessed in animal models appears to be necessary [30]. Therefore, the need for identification and implementation of measurable molecular correlates to aid prognosis, diagnosis, and treatment for psychiatric disorders is imperative. Proteomics has several required features to provide viable diagnostic tools for psychiatric disorders. MS-based proteomics approaches are high-throughput and hypothesis-free, thus not requiring prior molecular knowledge. Furthermore, they focus on the proteome which constitutes from a molecular perspective an endpoint that can be correlated with the behavioral phenotype and disease symptoms. Quantitative proteomics is able to generate molecular signatures characteristic for disease predisposition, accurate diagnosis, patient stratification, choice of the optimal treatment, and monitoring of treatment response. Toward this direction, the establishment of diagnostic tests for psychiatric disorders is the next step for clinical proteomics. Such tests are inevitably restricted to peripheral material because of the inaccessibility of brain tissue of living subjects. Ideally, screening tests will be blood-based due to the noninvasive and straightforward sample acquisition. Despite the fact that psychiatric disorders have been traditionally viewed as brain disorders, it is now established that alterations initiated in the periphery affect the course of brain disorders, highlighting the validity of a whole body approach [31]. Blood has been studied for more than 150 years to investigate differences between patients suffering from psychiatric disorders and healthy controls [32]. Interestingly, characteristic protein www.clinical.proteomics-journal.com
887
Proteomics Clin. Appl. 2015, 9, 885–888
(as well as metabolite) signatures for psychiatric disorders have been reported in blood or blood-derived samples from disease versus control states [33–37], prior to disease manifestation [38] or as predictors of treatment response and relapse time [39, 40]. A blood-based test for schizophrenia based on a multiplex immunoassay to aid the diagnosis of schizophrenia was recently introduced. This test assesses the levels of 51 proteins which were chosen based on the findings of previous proteomics studies with a sensitivity and a specificity of 83% [41]. The test is now withdrawn [42] for further optimization to include the capacity of distinguishing schizophrenia from other psychiatric disorders [43]. Surprisingly, quantitative proteomics studies in blood/plasma of human cohorts are markedly less compared to those performed in postmortem human brain tissue or animal models. To advance proteomics-based diagnostics for psychiatric disorders, substantially more blood and blood-derived specimens are crucial as this is the material of choice for translational applications. These studies should interrogate the same protein populations and/or human cohorts with multiple, complementary proteomics approaches for an in-depth characterization of molecular signatures, overcoming limitations and bias of individual gel-based or labeling-based proteomics methods [44]. To this end, all methodological considerations when working with blood are to be taken into account [45]. For serum/plasma analyses in particular, a highly reproducible depletion method of high-abundant proteins should be established as nontargeted proteins have been shown to be co-depleted as well [46], affecting the composition of the studied proteome and the accuracy of the quantification. It is important to not have unrealistic expectations for the applications of these tests. These blood-based molecular tests do not aim to replace the communication between psychiatrists and patients, but facilitate accurate diagnosis and effective treatment. When this has been achieved, it is essential to create a legal framework of optimal implementation of these tests that would not lead to misuse of results and financial exploitation of patients. In summary, proteomics has to overcome a series of conceptual and technical pitfalls for a fruitful implementation to clinical practice in psychiatric disorders. To unravel the molecular underpinnings of these conditions, multilevel approaches combining -omics and imaging technologies together with accurate patient stratification will have the greatest impact. Care should be taken to avoid a paradigm shift where the ultimate goal is merely to establish screening tools for diagnosis or treatment response instead of understanding the molecular disease mechanisms [47]. There is still a long way to go for clinical proteomics in order to achieve reliability, reproducibility, time- and cost-effectiveness. However, encouraging news has already come from the field of clinical microbiology in which the US Food and Drug Administration approved the first MS-based diagnostic application in 2013 [48]. The availability of sophisticated software for data analysis, reliable instrumentation, and interdisciplinary training of C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
scientists hold great potential to accelerate clinical translation of proteomics technologies. The author would like to thank Chris Turck and all past and current members of the Proteomics and Biomarkers Group at the Max Planck Institute of Psychiatry. This work was funded by a DFG Forschungsgemeinschaft grant to M.D.F and the Max Planck Society. The author declares no conflict of interest.
4
References
[1] Wasinger, V. C., Cordwell, S. J., Cerpa-Poljak, A., Yan, J. X. et al., Progress with gene-product mapping of the Mollicutes: Mycoplasma genitalium. Electrophoresis 1995, 16, 1090–1094. [2] Wilkins, M. R., Sanchez, J. C., Gooley, A. A., Appel, R. D. et al., Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it. Biotechnol. Genet. Eng. Rev. 1996, 13, 19–50. [3] Khan, M. F., Bennett, M. J., Jumper, C. C., Percy, A. J. et al., Proteomics by mass spectrometry-go big or go home? J. Pharm. Biomed. Anal. 2011, 55, 832–841. [4] Orchard, S., Hermjakob, H., Apweiler, R., The proteomics standards initiative. Proteomics 2003, 3, 1374–1376. [5] Taylor, C. F., Paton, N. W., Lilley, K. S., Binz, P. A. et al., The minimum information about a proteomics experiment (MIAPE). Nat. Biotechnol. 2007, 25, 887–893. [6] Veenstra, T. D., Marcus, K., Multidimensional advancement of neuroproteomics. Expert. Rev. Proteomics 2008, 5, 149– 151. [7] Poste, G., Bring on the biomarkers. Nature 2011, 469, 156– 157. [8] Sauer, S., Diagnostic proteomics: will this impact the clinic? Proteomics Clin. Appl. 2013, 7, 725–726. [9] Wang, P., Bouwman, F. G., Mariman, E. C., Generally detected proteins in comparative proteomics-a matter of cellular stress response? Proteomics 2009, 9, 2955–2966. [10] Anderson, N. L., Anderson, N. G., The human plasma proteome—History, character, and diagnostic prospects. Mol. Cell. Proteomics 2002, 1, 845–867. [11] Hortin, G. L., Sviridov, D., The dynamic range problem in the analysis of the plasma proteome. J. Proteomics 2010, 73, 629–636. [12] Petrak, J., Ivanek, R., Toman, O., Cmejla, R. et al., Deja vu in proteomics. A hit parade of repeatedly identified differentially expressed proteins. Proteomics 2008, 8, 1744– 1749. [13] Mrowka, R., Patzak, A., Herzel, H., Is there a bias in proteome research? Genome. Res. 2001, 11, 1971–1973. [14] Hebels, D. G. A. J., Georgiadis, P., Keun, H. C., Athersuch, T. J. et al., Performance in omics analyses of blood samples in long-term storage: opportunities for the exploitation of existing biobanks in environmental health research. Environ. Health Perspect. 2013, 121, 480–487. www.clinical.proteomics-journal.com
888
M. D. Filiou
Proteomics Clin. Appl. 2015, 9, 885–888
[15] Lee, D. H., Kim, J. W., Jeon, S. Y., Park, B. K., Han, B. G., Proteomic analysis of the effect of storage temperature on human serum. Ann. Clin. Lab. Sci. 2010, 40, 61–70.
[34] Schwarz, E., Guest, P. C., Rahmoune, H., Harris, L. W. et al., Identification of a biological signature for schizophrenia in serum. Mol. Psychiatry 2012, 17, 494–502.
[16] Vitzthum, F., Behrens, F., Anderson, N. L., Shaw, J. H., Proteomics: from basic research to diagnostic application. A review of requirements & needs. J. Proteome Res. 2005, 4, 1086–1097.
[35] Domenici, E., Wille, D. R., Tozzi, F., Prokopenko, I. et al., Plasma protein biomarkers for depression and schizophrenia by multi analyte profiling of case-control collections. PLoS One 2010, 5, e9166.
[17] O’Neil, S. E., Palviainen, M. J., Ten Have, S., Filiou, M. et al., Clinical proteomics stretch goals: EuPA 2012 roundtable report. J. Proteomics 2013, 88, 37–40.
[36] Zhan, Y., Yang, Y. T., You, H. M., Cao, D. et al., Plasmabased proteomics reveals lipid metabolic and immunoregulatory dysregulation in post-stroke depression. Eur. Psychiatry 2014, 29, 307–315.
[18] Omenn, G. S., On best practices: the Institute of Medicine scheme for developing, validating, and demonstrating clinical utility of omics-based diagnostic and predictive tests. Proteomics Clin. Appl. 2013, 7, 748–755. [19] Mischak, H., Apweiler, R., Banks, R. E., Conaway, M. et al., Clinical proteomics: a need to define the field and to begin to set adequate standards. Proteomics Clin. Appl. 2007, 1, 148–156. [20] Mischak, H., Ioannidis, J. P., Argiles, A., Attwood, T. K. et al., Implementation of proteomic biomarkers: making it work. Eur. J. Clin. Invest. 2012, 42, 1027–1036. [21] Cosgrove, V. E., Suppes, T., Informing DSM-5: biological boundaries between bipolar I disorder, schizoaffective disorder, and schizophrenia. BMC Med. 2013, 11, 127. [22] Filiou, M. D., Turck, C. W., Martins-de-Souza, D., Quantitative proteomics for investigating psychiatric disorders. Proteomics Clin. Appl. 2011, 5, 38–49. [23] Bystritsky, A., Treatment-resistant anxiety disorders. Mol. Psychiatry 2006, 11, 805–814. [24] Filiou, M. D., Moy, J., Wang, M., Guillermier, C. et al., Effect of an anti-depressant on mouse hippocampus protein turnover using MIMS. Surf. Interface Anal. 2014, 46, S144–S146. [25] Filiou, M. D., Turck, C. W., General overview: biomarkers in neuroscience research. Int. Rev. Neurobiol. 2011, 101, 1–17. [26] Kobeissy, F., Alawieh, A., Mondello, S., Boustany, R. M., Gold, M. S., Biomarkers in psychiatry: how close are we? Front. Psychiatry 2013, 3, 114. [27] Abbott, A., Novartis to shut brain research facility. Nature 2011, 480, 161–162. [28] Miller, G., Is pharma running out of brainy ideas? Science 2010, 329, 502–504. [29] Wegener, G., Rujescu, D., The current development of CNS drug research. Int. J. Neuropsychopharmacol. 16, 1687– 1693. [30] Hyman, S. E., Revolution stalled. Sci. Transl. Med. 2012, 4, 155cm111. [31] Chan, M. K., Gottschalk, M. G., Haenisch, F., Tomasik, J. et al., Applications of blood-based protein biomarker strategies in the study of psychiatric disorders. Prog. Neurobiol. 2014, 122, 45–72. [32] Noll, R., The blood of the insane. Hist. Psychiatry 2006, 17, 395–418. [33] He, Y., Yu, Z., Giegling, I., Xie, L. et al., Schizophrenia shows a unique metabolomics signature in plasma. Transl. Psychiatry 2012, 2, e149.
C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
[37] Li, Y., Zhou, K. J., Zhang, Z., Sun, L. Y. et al., Label-free quantitative proteomic analysis reveals dysfunction of complement pathway in peripheral blood of schizophrenia patients: evidence for the immune hypothesis of schizophrenia. Mol. Biosyst. 2012, 8, 2664–2671. [38] Schwarz, E., Guest, P. C., Rahmoune, H., Martins-de-Souza, D. et al., Identification of a blood-based biological signature in subjects with psychiatric disorders prior to clinical manifestation. World J. Biol. Psychiatry 2012, 13, 627– 632. [39] Schwarz, E., Guest, P. C., Steiner, J., Bogerts, B., Bahn, S., Identification of blood-based molecular signatures for prediction of response and relapse in schizophrenia patients. Transl. Psychiatry 2012, 2, e82. [40] Sussulini, A., Dihazi, H., Banzato, C. E. M., Arruda, M. A. Z. et al., Apolipoprotein A-I as a candidate serum marker for the response to lithium treatment in bipolar disorder. Proteomics 2011, 11, 261–269. [41] Schwarz, E., Izmailov, R., Spain, M., Barnes, A. et al., Validation of a blood-based laboratory test to aid in the confirmation of a diagnosis of schizophrenia. Biomark. Insights 2010, 5, 39–47. [42] Kingsmore, S. F., Comment on “validation of a blood-based laboratory test to aid in the confirmation of a diagnosis of schizophrenia”. Biomark. Insights 2014, 9, 7. [43] Bahn, S., Schwarz, E., Harris, L. W., Martins-de-Souza, D. et al., Biomarker blood tests for diagnosis and management of mental disorders: focus on schizophrenia. Rev. Psiquiatr. Cl´ın. 2013, 40, 2–9. [44] Filiou, M. D., Martins-de-Souza, D., Guest, P. C., Bahn, S., Turck, C. W., To label or not to label: applications of quantitative proteomics in neuroscience research. Proteomics 2012, 12, 736–747. [45] Lista, S., Faltraco, F., Hampel, H., Biological and methodical challenges of blood-based proteomics in the field of neurological research. Prog. Neurobiol. 2013, 101, 18– 34. [46] Koutroukides, T. A., Guest, P. C., Leweke, F. M., Bailey, D. M. D. et al., Characterization of the human serum depletome by label-free shotgun proteomics. J. Sep. Sci. 2011, 34, 1621– 1626. [47] Singh, I., Rose, N., Biomarkers in psychiatry. Nature 2009, 460, 202–207. [48] Rohn, J., First mass spectrometry diagnostic approved in US. Nat. Biotechnol. 2013, 31, 862.
www.clinical.proteomics-journal.com
885
DOI 10.1002/prca.201400144
Can proteomics-based diagnostics aid clinical psychiatry?
Michaela D. Filiou Max Planck Institute of Psychiatry, Munich, Germany Despite major advances in infrastructure and instrumentation, proteomics-driven translational applications have not yet yielded the results that the scientific community has envisaged. In this viewpoint, the perspective of proteomics-based diagnostics in the field of clinical psychiatry is explored. The challenges that proteomics faces in the context of translational approaches are outlined and directions toward a successful clinical implementation are provided. Additional challenges that psychiatric disorders pose for clinical proteomics are highlighted and the potential of proteomics-based, blood tests for psychiatric disorders is being assessed. Proteomics offers a valuable toolkit for clinical translation that needs to be handled in a pragmatic manner and with realistic expectations.
Received: September 29, 2014 Revised: November 14, 2014 Accepted: January 21, 2015
Keywords: Blood tests / Clinical proteomics / Diagnostics / Implementation / Psychiatric disorders
1
Introduction
Almost 20 years after the first publications using the term “proteome” [1, 2], the field of proteomics has rapidly evolved with remarkable advancements in MS instrumentation, automation, data analysis, accuracy, and precision [3]. Initiatives such as the Proteomics Standards Initiative by the Human Proteome Organization (HUPO) have been launched, aiming at standardizing proteomics experiments and devising guidelines on reporting and presenting proteomics data [4, 5]. Is it also now clear that to understand brain dysfunction, a thorough characterization of normal brain function needs to be carried out so that disease-related changes can be interpreted with confidence [6]. Despite this progress, there is skepticism concerning the translational outcome and the implementation of proteomics approaches in health care [7], a topic recently addressed in the “Diagnostic Proteomics” special issue of Proteomics Clinical Applications [8]. To investigate the potential of proteomics technologies as a reliable diagnostic tool for psychiatric disorders in clinical settings, two major issues will be discussed: (1) Can proteomics-based diagnostics be used routinely in health care and what needs to be done toward this direction? (2) How can proteomics-based diagnostics be beneficial for clinical psychiatry? Correspondence: Dr. Michaela D. Filiou, Max Planck Institute of Psychiatry, Kraepelinstr. 2, 80804 Munich, Germany E-mail: [email protected] Fax: +49 8930622 200 C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
2
Toward proteomics-based diagnostics in health care
Although proteomics offers a highly sensitive toolbox for translational applications, the implementation to the clinic faces a series of inherent challenges. A proteomics approach in the context of a clinically oriented question typically includes a quantitative proteome comparison of disease versus nondisease or treated versus untreated states. As a readout, a list of differentially expressed proteins between the compared states is eventually generated. Unfortunately, a detailed interpretation of these results followed by validation experiments has been rare. This can be attributed to the lack of interdisciplinary approaches and collaborative efforts preventing a conventional MS laboratory from performing follow-up experiments with complementary methods to functionally validate proteomics data. As a consequence, the acquired knowledge from proteomics experiments remains unexploited and potential clinical applications fail to be explored. Besides pitfalls in the conceptual framework, it is unclear whether the identified protein expression differences reflect causal diseaseand treatment-derived changes or represent nonspecific organismal responses. HSPs as well as oxidative stress protein markers and enzymes constitute sensitive sensors of an organism in response to a stimulus and their expression levels may change without them being necessarily involved in the underlying disease or treatment molecular mechanisms [9]. Other methodological concerns include the wide dynamic range of protein abundances in the examined specimens [10] www.clinical.proteomics-journal.com
886
M. D. Filiou
Correspondence concerning this and other Viewpoint articles can be accessed on the journals’ home page at: http://viewpoint.clinical.proteomicsjournal.com Correspondence for posting on these pages is welcome and can also be submitted at this site.
which contributes to the misrepresentation of protein detection limits [11], the masking of low-abundant proteins by high-abundant proteins that may result in a repeated identification/quantification of the same set of proteins irrespective of the condition studied [12] and the bias of computational tools for pathway analysis that may hamper accurate data interpretation [13]. For the successful implementation of the proteomics toolbox into clinical practice, it is essential to address the aforementioned points. In this context, several steps need to be considered from the scientific community, health care professionals, stakeholders, and policy makers: (i) Sample preparation: Acquisition, storage, and handling of samples largely affect subsequent proteomics analysis [14, 15]. Therefore, standard operating procedures for each of these steps should be followed to ensure meaningful data comparisons across different clinical settings. (ii) Workflow performance: Technical characteristics including accuracy and precision of measurements should be determined to avoid overinterpretation of the acquired data [16]. (iii) Biological variation: Variability of protein amounts across individuals within the normal physiological range should be a priori defined to allow identification of disease-relevant changes only [17]. (iv) Data reporting: Uniform guidelines for all steps toward clinical translation as well as for data reporting are critical [18, 19]. (v) Personnel: Key players for a successful clinical implementation are a bioinformatician to address the computational requirements of MS data and skilled personnel for maintenance and troubleshooting of mass spectrometers. Adequate training in sample preparation, MS-based proteomics, and data analysis should be additionally provided. (vi) Funding: In addition to biomarker discovery studies, sufficient funding for biomarker implementation projects and initiatives to support this cause are of major importance [20]. (vii) Cost: The ultimate goal for proteomics-based clinical analyses is a cost-effective service, which will be affordable for patients and will be covered by their health insurance. C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Proteomics Clin. Appl. 2015, 9, 885–888
3
How can proteomics-based diagnostics be used in clinical psychiatry?
Psychiatric disorders pose additional challenges to the implementation of proteomics-based diagnostics into clinical routine. The highly complex interplay between genetic and environmental factors, which is responsible for the manifestation of psychiatric phenotypes, results in disease heterogeneity. Diagnosis is entirely based on a verbal interview between the doctor and the patient using questionnaires. Psychiatric disorders often co-exist with other confounding pathologies whereas the boundaries between different psychiatric phenotypes are hard to define [21, 22]. At the level of treatment, existing drugs do not act rapidly and are accompanied by severe side effects that compromise life quality of patients and their families. Not all patients respond to the same treatment, remission rates are low and a considerable percentage of patients are treatment resistant [23, 24]. The major underlying cause of all these problems is the lack of molecular understanding of disease pathobiology [25]. Although hope has been placed on the identification of molecular biomarkers for psychiatric disorders with the development of high-throughput technologies, the vision of biomarker discovery has not yet fulfilled the expectations of the scientific community [26]. Recently, pharmaceutical companies have announced budget cuts for psychiatric research [27–29] whereas a paradigm shift on how psychiatric phenotypes are being assessed in animal models appears to be necessary [30]. Therefore, the need for identification and implementation of measurable molecular correlates to aid prognosis, diagnosis, and treatment for psychiatric disorders is imperative. Proteomics has several required features to provide viable diagnostic tools for psychiatric disorders. MS-based proteomics approaches are high-throughput and hypothesis-free, thus not requiring prior molecular knowledge. Furthermore, they focus on the proteome which constitutes from a molecular perspective an endpoint that can be correlated with the behavioral phenotype and disease symptoms. Quantitative proteomics is able to generate molecular signatures characteristic for disease predisposition, accurate diagnosis, patient stratification, choice of the optimal treatment, and monitoring of treatment response. Toward this direction, the establishment of diagnostic tests for psychiatric disorders is the next step for clinical proteomics. Such tests are inevitably restricted to peripheral material because of the inaccessibility of brain tissue of living subjects. Ideally, screening tests will be blood-based due to the noninvasive and straightforward sample acquisition. Despite the fact that psychiatric disorders have been traditionally viewed as brain disorders, it is now established that alterations initiated in the periphery affect the course of brain disorders, highlighting the validity of a whole body approach [31]. Blood has been studied for more than 150 years to investigate differences between patients suffering from psychiatric disorders and healthy controls [32]. Interestingly, characteristic protein www.clinical.proteomics-journal.com
887
Proteomics Clin. Appl. 2015, 9, 885–888
(as well as metabolite) signatures for psychiatric disorders have been reported in blood or blood-derived samples from disease versus control states [33–37], prior to disease manifestation [38] or as predictors of treatment response and relapse time [39, 40]. A blood-based test for schizophrenia based on a multiplex immunoassay to aid the diagnosis of schizophrenia was recently introduced. This test assesses the levels of 51 proteins which were chosen based on the findings of previous proteomics studies with a sensitivity and a specificity of 83% [41]. The test is now withdrawn [42] for further optimization to include the capacity of distinguishing schizophrenia from other psychiatric disorders [43]. Surprisingly, quantitative proteomics studies in blood/plasma of human cohorts are markedly less compared to those performed in postmortem human brain tissue or animal models. To advance proteomics-based diagnostics for psychiatric disorders, substantially more blood and blood-derived specimens are crucial as this is the material of choice for translational applications. These studies should interrogate the same protein populations and/or human cohorts with multiple, complementary proteomics approaches for an in-depth characterization of molecular signatures, overcoming limitations and bias of individual gel-based or labeling-based proteomics methods [44]. To this end, all methodological considerations when working with blood are to be taken into account [45]. For serum/plasma analyses in particular, a highly reproducible depletion method of high-abundant proteins should be established as nontargeted proteins have been shown to be co-depleted as well [46], affecting the composition of the studied proteome and the accuracy of the quantification. It is important to not have unrealistic expectations for the applications of these tests. These blood-based molecular tests do not aim to replace the communication between psychiatrists and patients, but facilitate accurate diagnosis and effective treatment. When this has been achieved, it is essential to create a legal framework of optimal implementation of these tests that would not lead to misuse of results and financial exploitation of patients. In summary, proteomics has to overcome a series of conceptual and technical pitfalls for a fruitful implementation to clinical practice in psychiatric disorders. To unravel the molecular underpinnings of these conditions, multilevel approaches combining -omics and imaging technologies together with accurate patient stratification will have the greatest impact. Care should be taken to avoid a paradigm shift where the ultimate goal is merely to establish screening tools for diagnosis or treatment response instead of understanding the molecular disease mechanisms [47]. There is still a long way to go for clinical proteomics in order to achieve reliability, reproducibility, time- and cost-effectiveness. However, encouraging news has already come from the field of clinical microbiology in which the US Food and Drug Administration approved the first MS-based diagnostic application in 2013 [48]. The availability of sophisticated software for data analysis, reliable instrumentation, and interdisciplinary training of C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
scientists hold great potential to accelerate clinical translation of proteomics technologies. The author would like to thank Chris Turck and all past and current members of the Proteomics and Biomarkers Group at the Max Planck Institute of Psychiatry. This work was funded by a DFG Forschungsgemeinschaft grant to M.D.F and the Max Planck Society. The author declares no conflict of interest.
4
References
[1] Wasinger, V. C., Cordwell, S. J., Cerpa-Poljak, A., Yan, J. X. et al., Progress with gene-product mapping of the Mollicutes: Mycoplasma genitalium. Electrophoresis 1995, 16, 1090–1094. [2] Wilkins, M. R., Sanchez, J. C., Gooley, A. A., Appel, R. D. et al., Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it. Biotechnol. Genet. Eng. Rev. 1996, 13, 19–50. [3] Khan, M. F., Bennett, M. J., Jumper, C. C., Percy, A. J. et al., Proteomics by mass spectrometry-go big or go home? J. Pharm. Biomed. Anal. 2011, 55, 832–841. [4] Orchard, S., Hermjakob, H., Apweiler, R., The proteomics standards initiative. Proteomics 2003, 3, 1374–1376. [5] Taylor, C. F., Paton, N. W., Lilley, K. S., Binz, P. A. et al., The minimum information about a proteomics experiment (MIAPE). Nat. Biotechnol. 2007, 25, 887–893. [6] Veenstra, T. D., Marcus, K., Multidimensional advancement of neuroproteomics. Expert. Rev. Proteomics 2008, 5, 149– 151. [7] Poste, G., Bring on the biomarkers. Nature 2011, 469, 156– 157. [8] Sauer, S., Diagnostic proteomics: will this impact the clinic? Proteomics Clin. Appl. 2013, 7, 725–726. [9] Wang, P., Bouwman, F. G., Mariman, E. C., Generally detected proteins in comparative proteomics-a matter of cellular stress response? Proteomics 2009, 9, 2955–2966. [10] Anderson, N. L., Anderson, N. G., The human plasma proteome—History, character, and diagnostic prospects. Mol. Cell. Proteomics 2002, 1, 845–867. [11] Hortin, G. L., Sviridov, D., The dynamic range problem in the analysis of the plasma proteome. J. Proteomics 2010, 73, 629–636. [12] Petrak, J., Ivanek, R., Toman, O., Cmejla, R. et al., Deja vu in proteomics. A hit parade of repeatedly identified differentially expressed proteins. Proteomics 2008, 8, 1744– 1749. [13] Mrowka, R., Patzak, A., Herzel, H., Is there a bias in proteome research? Genome. Res. 2001, 11, 1971–1973. [14] Hebels, D. G. A. J., Georgiadis, P., Keun, H. C., Athersuch, T. J. et al., Performance in omics analyses of blood samples in long-term storage: opportunities for the exploitation of existing biobanks in environmental health research. Environ. Health Perspect. 2013, 121, 480–487. www.clinical.proteomics-journal.com
888
M. D. Filiou
Proteomics Clin. Appl. 2015, 9, 885–888
[15] Lee, D. H., Kim, J. W., Jeon, S. Y., Park, B. K., Han, B. G., Proteomic analysis of the effect of storage temperature on human serum. Ann. Clin. Lab. Sci. 2010, 40, 61–70.
[34] Schwarz, E., Guest, P. C., Rahmoune, H., Harris, L. W. et al., Identification of a biological signature for schizophrenia in serum. Mol. Psychiatry 2012, 17, 494–502.
[16] Vitzthum, F., Behrens, F., Anderson, N. L., Shaw, J. H., Proteomics: from basic research to diagnostic application. A review of requirements & needs. J. Proteome Res. 2005, 4, 1086–1097.
[35] Domenici, E., Wille, D. R., Tozzi, F., Prokopenko, I. et al., Plasma protein biomarkers for depression and schizophrenia by multi analyte profiling of case-control collections. PLoS One 2010, 5, e9166.
[17] O’Neil, S. E., Palviainen, M. J., Ten Have, S., Filiou, M. et al., Clinical proteomics stretch goals: EuPA 2012 roundtable report. J. Proteomics 2013, 88, 37–40.
[36] Zhan, Y., Yang, Y. T., You, H. M., Cao, D. et al., Plasmabased proteomics reveals lipid metabolic and immunoregulatory dysregulation in post-stroke depression. Eur. Psychiatry 2014, 29, 307–315.
[18] Omenn, G. S., On best practices: the Institute of Medicine scheme for developing, validating, and demonstrating clinical utility of omics-based diagnostic and predictive tests. Proteomics Clin. Appl. 2013, 7, 748–755. [19] Mischak, H., Apweiler, R., Banks, R. E., Conaway, M. et al., Clinical proteomics: a need to define the field and to begin to set adequate standards. Proteomics Clin. Appl. 2007, 1, 148–156. [20] Mischak, H., Ioannidis, J. P., Argiles, A., Attwood, T. K. et al., Implementation of proteomic biomarkers: making it work. Eur. J. Clin. Invest. 2012, 42, 1027–1036. [21] Cosgrove, V. E., Suppes, T., Informing DSM-5: biological boundaries between bipolar I disorder, schizoaffective disorder, and schizophrenia. BMC Med. 2013, 11, 127. [22] Filiou, M. D., Turck, C. W., Martins-de-Souza, D., Quantitative proteomics for investigating psychiatric disorders. Proteomics Clin. Appl. 2011, 5, 38–49. [23] Bystritsky, A., Treatment-resistant anxiety disorders. Mol. Psychiatry 2006, 11, 805–814. [24] Filiou, M. D., Moy, J., Wang, M., Guillermier, C. et al., Effect of an anti-depressant on mouse hippocampus protein turnover using MIMS. Surf. Interface Anal. 2014, 46, S144–S146. [25] Filiou, M. D., Turck, C. W., General overview: biomarkers in neuroscience research. Int. Rev. Neurobiol. 2011, 101, 1–17. [26] Kobeissy, F., Alawieh, A., Mondello, S., Boustany, R. M., Gold, M. S., Biomarkers in psychiatry: how close are we? Front. Psychiatry 2013, 3, 114. [27] Abbott, A., Novartis to shut brain research facility. Nature 2011, 480, 161–162. [28] Miller, G., Is pharma running out of brainy ideas? Science 2010, 329, 502–504. [29] Wegener, G., Rujescu, D., The current development of CNS drug research. Int. J. Neuropsychopharmacol. 16, 1687– 1693. [30] Hyman, S. E., Revolution stalled. Sci. Transl. Med. 2012, 4, 155cm111. [31] Chan, M. K., Gottschalk, M. G., Haenisch, F., Tomasik, J. et al., Applications of blood-based protein biomarker strategies in the study of psychiatric disorders. Prog. Neurobiol. 2014, 122, 45–72. [32] Noll, R., The blood of the insane. Hist. Psychiatry 2006, 17, 395–418. [33] He, Y., Yu, Z., Giegling, I., Xie, L. et al., Schizophrenia shows a unique metabolomics signature in plasma. Transl. Psychiatry 2012, 2, e149.
C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
[37] Li, Y., Zhou, K. J., Zhang, Z., Sun, L. Y. et al., Label-free quantitative proteomic analysis reveals dysfunction of complement pathway in peripheral blood of schizophrenia patients: evidence for the immune hypothesis of schizophrenia. Mol. Biosyst. 2012, 8, 2664–2671. [38] Schwarz, E., Guest, P. C., Rahmoune, H., Martins-de-Souza, D. et al., Identification of a blood-based biological signature in subjects with psychiatric disorders prior to clinical manifestation. World J. Biol. Psychiatry 2012, 13, 627– 632. [39] Schwarz, E., Guest, P. C., Steiner, J., Bogerts, B., Bahn, S., Identification of blood-based molecular signatures for prediction of response and relapse in schizophrenia patients. Transl. Psychiatry 2012, 2, e82. [40] Sussulini, A., Dihazi, H., Banzato, C. E. M., Arruda, M. A. Z. et al., Apolipoprotein A-I as a candidate serum marker for the response to lithium treatment in bipolar disorder. Proteomics 2011, 11, 261–269. [41] Schwarz, E., Izmailov, R., Spain, M., Barnes, A. et al., Validation of a blood-based laboratory test to aid in the confirmation of a diagnosis of schizophrenia. Biomark. Insights 2010, 5, 39–47. [42] Kingsmore, S. F., Comment on “validation of a blood-based laboratory test to aid in the confirmation of a diagnosis of schizophrenia”. Biomark. Insights 2014, 9, 7. [43] Bahn, S., Schwarz, E., Harris, L. W., Martins-de-Souza, D. et al., Biomarker blood tests for diagnosis and management of mental disorders: focus on schizophrenia. Rev. Psiquiatr. Cl´ın. 2013, 40, 2–9. [44] Filiou, M. D., Martins-de-Souza, D., Guest, P. C., Bahn, S., Turck, C. W., To label or not to label: applications of quantitative proteomics in neuroscience research. Proteomics 2012, 12, 736–747. [45] Lista, S., Faltraco, F., Hampel, H., Biological and methodical challenges of blood-based proteomics in the field of neurological research. Prog. Neurobiol. 2013, 101, 18– 34. [46] Koutroukides, T. A., Guest, P. C., Leweke, F. M., Bailey, D. M. D. et al., Characterization of the human serum depletome by label-free shotgun proteomics. J. Sep. Sci. 2011, 34, 1621– 1626. [47] Singh, I., Rose, N., Biomarkers in psychiatry. Nature 2009, 460, 202–207. [48] Rohn, J., First mass spectrometry diagnostic approved in US. Nat. Biotechnol. 2013, 31, 862.
www.clinical.proteomics-journal.com
