Integration of proteomics in the molecular tumor board

Cancer remains one of the most complex and challenging diseases in mankind. To address the need for a personalized treatment approach for particularly complex tumor cases, molecular tumor boards (MTBs) have been initiated. MTBs are interdisciplinary teams that perform in‐depth molecular diagnostics to cooperatively and interdisciplinarily advise on the best therapeutic strategy. Current molecular diagnostics are routinely performed on the transcriptomic and genomic levels, aiming to identify tumor‐driving mutations. However, these approaches can only partially capture the actual phenotype and the molecular key players of tumor growth and progression. Thus, direct investigation of the expressed proteins and activated signaling pathways provide valuable complementary information on the tumor‐driving molecular characteristics of the tissue. Technological advancements in mass spectrometry‐based proteomics enable the robust, rapid, and sensitive detection of thousands of proteins in minimal sample amounts, paving the way for clinical proteomics and the probing of oncogenic signaling activity. Therefore, proteomics is currently being integrated into molecular diagnostics within MTBs and holds promising potential in aiding tumor classification and identifying personalized treatment strategies. This review introduces MTBs and describes current clinical proteomics, its potential in precision oncology, and highlights the benefits of multi‐omic data integration.


The molecular tumor board: Introduction for the proteomic researcher
Over the past few years, there have been tremendous developments and advancements in cancer diagnostics and treatment.MTBs are multidisciplinary committees that perform in-depth molecular diagnostics and subsequently discuss and advise on the medical treatment of particularly challenging tumor cases (Figure 1).Typically, the MTBs comprise experts from different fields, including physicians such as oncologists, pathologists, and radiologists as well as expert scientists in genomics, transcriptomics, and bioinformatics [1].The multidisciplinary panel allows expert-level analysis and interpretation of all the data involved, including large and complex data such as nucleic acid-based next-generation sequencing (NGS) data.Furthermore, the frequent meetings stimulate and encourage scientific and medical exchange between the different scientists and physicians, promoting a thorough assessment of each MTB patient case.One of the earliest examples of successful implementation of an MTB has been shown by the University of California San Diego Moores Cancer Center since the end of 2012 [2].In the following years, the concept and applicability of the initial MTBs have prompted the initiation of further MTBs across the United States and other countries [3][4][5][6][7].In-depth molecular characterization and treatment recommendations by the MTBs have been linked to improved clinical outcomes in various medical centers and studies [1,[8][9][10].

Molecular diagnostics for personalized and precision medicine
Molecular diagnostics enables a comprehensive, sensitive, and accurate diagnosis of various diseases.In molecular diagnostics, biomolecules in a patient-derived sample (tissue or body fluid) are investigated aiming to confidently diagnose or classify a disease.An early and precise diagnosis is crucial for the identification of treatment options in tumor patients.Due to vast inter-and intra-heterogeneity in malignancies, there is an urgent need for specific stratification and classification of tumors.This heterogeneity calls for personalized and precision medicine, increasing the chances of therapy response and minimizing side effects [11].One of the most popular examples and successes of precision medicine is the administration of Herceptin for the treatment of HER2-positive breast cancer patients [12].
A fundamental approach in molecular diagnostics is the immunostaining of established protein biomarkers, such as HER2, PD-L1, or hormone receptors.This enables clinicians to stratify and classify malignancies, estimate prognosis, and screen for potentially effective treatment options.However, this approach is often limited to established antibodies and staining protocols.Technological advancements have pushed molecular diagnostics towards high-throughput and in-depth nucleic acid-based screening approaches such as NGS [11,13,14].The DNA-based NGS approaches are either targeted on individual genes, including small or large panels thereof, or on a larger scale such as whole exome/genome sequencing (WES/WGS) [15][16][17].The primary focus of genomic approaches in oncology is the detection of mutations associated with pathogenic, potentially pathogenic, or benign tumor development and progression.Further essential and more complex analysis of genomic approaches focuses on tumor mutational burden, copy number variations, and microsatellite stability.Additional methods that present an astonishing sensitivity due to the amplification of the respective analytes are RNA-based NGS approaches.The expression of RNA fusions and quantitative analyses of expressed mRNA levels can yield insights into the dynamic alterations during tumor development and progression.Due to continuous technological and methodological advancement as well as the high sensitivity, there is a current trend towards NGS approaches in in-depth molecular diagnostics.This led to numerous clinical studies and vast knowledge databases that investigate and document the link between certain mutations and the occurrence of RNA fusions to respective clinical outcomes in different malignancies [18,19].Current molecular diagnostics mainly comprise protein stainings (e.g., via immunohistochemistry) in combination with genomic and transcriptomic NGS approaches.The extent of the genomic and transcriptomic analyses depends on the clinical questions as well as the individual patient and the respective malignancy.
The diagnostic, prognostic, and therapeutic implications of somatic variants and molecular biomarkers have been comprehensively analyzed, assessed, and assembled in different evidence classification systems [10,23,24].Internationally recognized classification frameworks, such as the "Joint Consensus Recommendation" (JCR) devised by the American Society of Clinical Oncology (ASCO) and the College of American Pathologists (CAP), along with the "ESMO Scale for Clinical Actionability of Molecular Targets" (ESCAT) introduced by the European Society for Medical Oncology (ESMO), provide robust guidelines [25,26].Moreover, national-level classification systems exist, such as Germany's widely adopted National Center for Tumor Diseases (NCT) and the German Consortium for Cancer Research (DKTK) classifications [27].In the latter, evidence level 1 (m1A-C) recommendations are based on biomarkers and respective therapies described in the same entity; whereas evidence level 2 (m2A-C) recommendations are based on observations/studies in another tumor entity.Less substantial evidence level 3 (m3) implies a predictive value or clinical effectiveness of a biomarker based on preclinical data including in vitro/in vivo models and functional genomics.The weakest evidence level 4 (m4) is used for recommendations based on a biological rationale linking a biomarker to prognostic and therapeutic relevance (Table 1) [24].
Several evidence-based classification systems are implemented within knowledge databases, clinical laboratories, and commercial applications, further augmenting their utility and accessibility.However, the different classification systems provide divergent evidence levels for some therapeutic variants, highlighting the importance of interdisciplinary discussions in the MTBs to assess and estimate the best therapeutic option for each patient.Discrepant classification of the same mutation demonstrates an urgent need for standardization and has caused the initiation of centers for personalized medicine (ZPM) in Germany [28].

The potential of clinical proteomics
The vast majority of malignancies have predominantly been studied on genomic and transcriptomic levels, which can only be used as surrogate approaches for the estimation of protein level infor-mation.The limited correlation between mRNA and actual protein abundance has been shown in multiple studies [29][30][31][32].Other biologi-  2) [35][36][37][38].
Generally, MS-based proteomics can either be explorative, aiming to detect as many proteins and peptides in each measurement as possible, or targeted, aiming for reliable and sensitive identification and quantification of a panel of a priori known proteins.With highly sensitive sample processing and LC-MS instruments, targeted and explorative analysis can be performed using minimal sample amounts.
Peptide and protein quantification in MS-based clinical proteomics can be performed label-free or by using metabolic or chemical labeling such as tandem mass tags (TMT) [37,[54][55][56][57]. Due to their costeffectiveness and simplicity, label-free quantification methods are widely used, especially in large-scale studies.Furthermore, for the investigation of individual patient samples, it might prove beneficial to measure the samples independently, circumventing batch effects and minimizing biases to facilitate relative comparisons between samples [58].
Targeted approaches such as selected reaction monitoring (SRM), multiple reaction monitoring (MRM), and parallel reaction monitoring (PRM) enable the highly sensitive, accurate, and reproducible identification and quantification of a predefined set of target peptides [59].In clinical proteomics, they are widely used for biomarker studies as well as in a few diagnostic assays [60][61][62][63][64].In these targeted approaches the mass spectrometer is set to selectively isolate a predefined precursor ion, fragment it, and measure the resulting product ions.Major limitations of targeted LC-MS/MS assays are the need for a priori knowledge of target proteins and peptides as well as the number of potential proteins/peptides that can be targeted during a single measurement.In contrast, explorative LC-MS/MS methods are unbiased TA B L E 1 Overview of the NCT evidence classification system, which is applied within the molecular tumor boards in Germany (adapted from Horak et al. [24]).

Sample preparation
Automation and miniaturization of sample preparation including enrichment strategies for PTMs and immunopeptidomics [39][40][41][42][43][44][45] Mass spectrometers Higher resolution and sensitivity of new mass spectrometers using ion mobility to further separate peptides [46,47] Measurement methods Establishment of DIA as a reproducible and high-coverage method to analyze clinical samples [48][49][50] Using targeted measurement methods for precise and sensitive quantitation of proteins abundance and PTMs in clinical specimen [51,52] Data analysis New software developments for fast and reproducible analysis of different LC-MS/MS approaches [53] and enable the detection of thousands of proteins in individual measurements.There are two main acquisition modes for explorative label-free measurements, namely data-dependent acquisition (DDA) and data-independent acquisition (DIA).In DDA mode, only a few selected precursors with the highest intensity are fragmented [65].
This approach introduces stochastic effects and leads to a significant number of missing values, where proteins are not detected consistently over multiple measurements.Conversely, DIA mode provides a more robust and reproducible approach for the explorative analysis of multiple samples [66,67].In DIA, predefined fragmentation windows (m/z windows) are used for simultaneous fragmentation of multiple precursors and consistent data acquisition across samples.
The assignment of spectra to peptides and proteins is performed by comparing the measured spectra with a reference spectral library.
By utilizing DIA, researchers can achieve more reliable and comprehensive quantification results compared to DDA, thereby enhancing the accuracy and reproducibility of proteomic analyses (Table 2) [66,68].
To improve reproducibility and efficiency, different sample preparation workflows including automation using liquid handling platforms have been established over the last years (Table 2) [39,[42][43][44].These

Proteomics in precision oncology
In-depth clinical proteomics is widely used to analyze large cohorts of patients, providing further insights into the molecular pathology of var-ious malignancies [83,101,127,128].However, an important question arises: How can proteomics prove beneficial in the analysis of the proteome of an individual patient with a clinically challenging and complex tumor disease?
In the past 5 years, there has been a rapid emergence of clinical proteomic data through initiatives and consortia such as CPTAC.
This data facilitates the implementation of proteomics into precision oncology and serves as a valuable reference resource.However, the number of proteomic datasets with detailed clinical annotation in these databases remains limited.Furthermore, the lack of standardized preprocessing of quantitative proteomic data renders direct quantitative comparisons challenging.Therefore, the implementation of proteomics for individual patients remains a challenge, which is addressed by only a few current studies [118,129].The integration of proteomics into the MTB is an emerging and highly relevant topic, as illustrated by the More recently, the Reverse Phase Protein Array (RPPA) approach has been implemented for precision oncology and supported clinical decision-making in MTBs [119,129,135,136].In RPPA, protein samples are arrayed as microspots on a solid phase and subsequently probed with a specific antibody to detect protein abundances and PTMs [137].RPPA is a robust and sensitive method for the detection of low-abundant phosphopeptides [138,139].Consequently, RPPA profiling has been integrated by The Cancer Genome Atlas Project and is included in a variety of ongoing clinical trials (Table 3) [140,141].
However, one major limitation of the RPPA and other classical immunobased assays remains the intrinsic dependency on validated and highly specific antibodies.Typically, those assays do not directly detect the target protein but rather calculate the target quantitation based on a reporter signal [142].Hence, complementary and orthogonal methods such as targeted LC-MS/MS-based assays have been implemented [57,61,63,143,144].An immuno-MRM assay enables robust measurement of the HER2 protein abundance in patients with low expression of HER2 and no detectable ERBB2 amplification, illustrating the potential of clinical mass spectrometry [144].Targeted approaches are limited to the predefined lists of measured proteins, whereas explorative LC-MS/MS-based proteomics enables time-shifted and personalized TA B L E 3 Overview of clinical/biomarker advancements in clinical proteomics.

Biomarker discovery studies
Proteomic studies proposing panels of predictive biomarkers/prognostic proteomic signatures Identification of a prognostic signature in oral cancer including COL6A1, CSTB, NDRG1, LTA4H, ITGAV, PGK1, and MB [98].Identification of FKBP4 and S100A9 as potential prediction markers of therapeutic response in patients with breast cancer [99].Identification of a prognostic signature for clinical response including KRT19, KRT4, ACTN4, RANBP1, IGLL5, and TPMT in ovarian cancer [91].

Drug target discovery studies Identification of possible drug targets and mechanism of actions
Analysis of the target landscape of kinase drugs [89].
reanalysis.Thus, one key potential of unbiased and comprehensive clinical proteomics lies within the detailed inquiry of therapy-relevant proteins within the extensive lists of identified and quantified proteins.LC-MS/MS approaches enable the detection of direct drug targets, such as PD-L1, which is necessary for successful immunotherapeutic treatment [145,146].Furthermore, frequently amplified targets and biomarkers such as HER2 and EGFR can be identified in the proteomic data and could be subsequently corroborated in a histology-conserving approach such as immunohistochemistry [147,148].Other druggable targets that can be observed using LC-MS/MS include MAP2K1, MAP2K2, CDK4, and CDK6 [149,150].The loss of CDKN2A/B is associated with loss of p16 and a loss of cell cycle control which is a possible indication for CDK4/CDK6 inhibitors, such as Ribociclib, Abemaciclib, and Palbociclib [151,152].Hence, detection of CDK4 and CDK6 in patients with a loss of function mutation in CDKN2A might support the rationale for effective treatment with CDK4/6 inhibitors.
The development of antibody-drug conjugate (ADC) therapy, where monoclonal antibodies are covalently linked to cytotoxic agents, has immensely broadened the selection of potential targets in personalized cancer therapy [21,[153][154][155].This has led to novel therapeutic targets such as Trop-2 and Nectin-4 for which the proteomic data can be systematically screened, followed by corroboration using immunostaining [156].The comprehensive proteomic data can be further screened for the absence or presence of tumor suppressors such as TP53 and mismatch repair proteins including MLH1, MSH2, MSH3, MSH6, and PMS2, which indicates microsatellite-stability [157].Advanced bioinformatic search strategies emerged to not only account for the canonical human proteome but to also enable the detection of protein isoforms as well as proteins from other species, for example, viral or bacterial proteins.One example is the protein isoform claudin-18.2, a novel target in esophagus carcinoma and gastric cancer [160].Metaproteomic investigations enable the detection of viral antigens in the context of virus-related tumors, such as cervical cancer, and intra-tumoral fungi and bacteria in different cancer types [161,162].

Data integration and proteogenomics
One of the most crucial developments in clinical proteomics is the potential to detect thousands of proteins from minute sample material.The highly sensitive protocols and LC-MS instruments enable the integration of proteomics into the current molecular diagnostic routine within the MTB without the need for excessive additional samples.
Consequently, proteomics provides complementary biological information which enables the integration of multiple layers of molecular information (Figure 2).
By integrating proteomics, several questions regarding the correlation of these data can be studied.How well do copy number variations correlate with proteomics?Do proteomic results correlate with findings in immunohistochemistry? Can a loss or gain of function mutation of a protein be detected in the downstream pathway activity?
One powerful example of data integration is the rising field of proteogenomics which links genomics and transcriptomics to proteomics [33,34,87,100,105,108,[163][164][165].The CPTAC aims to systematically identify cancer-relevant proteins that derive from alterations in cancer genomes and the related biological processes in large-scale studies [166].
However, proteogenomics can also be applied to individual patients in a personalized approach.Typically, in an endogenous bottom-up proteomic search, proteins are cleaved into peptides during tryptic digestion and subsequently measured and analyzed using a canonical protein database.Genetic mutations can lead to single amino acid variants (SAAVs), that reflect the mutated DNA in an altered peptide sequence.Thus, by using the patient-specific genomic/transcriptomic information, the endogenous protein database can be expanded to contain tryptic SAAV peptide sequences.
Proteogenomic analyses address the penetrance of genomic alterations and provide complementary biological information.Mutated proteins can be direct targets of drugs such as the KRAS G12C variant or propose a therapeutic direction [167,168].
We detected the KRAS G12D variant peptide (LVVVGADGVGK) by applying an in-house patient-matched proteogenomic analysis workflow combining genomic (TruSight Oncology 500 Assay) and LC-MS/MS proteomic data.The same variant peptide was found in several CPTAC cases from 5 different tumor entities [169].Thus, proteomic data corroborates genomic findings and provides direct evidence of the actual presence of the mutated gene.The KRAS G12D variant is a potential therapeutic target for selective inhibition and the identification of the KRAS G12D mutation provides a rationale for the inclusion of the respective patients into ongoing clinical studies [170][171][172][173][174][175].Until now, only a few mass spectrometry laboratories performing targeted measurements have been CLIA-certified, such as the Paulovich and the Hoofnagle laboratories [97,176].

Limitations and upcoming challenges
The integration of large-scale proteomic studies into clinical research is an emerging field.Current clinical studies mainly investigate genomic alterations in context with clinical phenotypes and treatment responses, which promotes the lack of proteomic data and the correlation thereof with clinical presentations.Accordingly, decision support systems for MTBs presently involve exclusively NGS data [114][115][116].However, further integration of proteomics is a crucial aspect of the molecular understanding of tumor biology.Therefore, proteomics should be included in clinical studies on all levels, complementing genomic stratification and deepening the molecular understanding of individual disease progression and therapy response.This requires a fundamental rethinking process and shift on multiple layers, most of all an openness to this new multi-omics approach.

CONCLUSION
Advancements on all levels of proteomics make it possible to integrate proteomics into clinical applications and thereby complement the missing piece of the actual phenotype in molecular diagnostics.Integrating proteomics into the MTB can aid in tumor stratification and the identification of personalized therapeutic strategies.
We emphasize the merits of data integration which enables more detailed stratification, classification, and diagnosis based on multiple layers of molecular information.Data integration promotes a more precise and comprehensive understanding of the underlying molecular pathomechanisms and potential therapeutic targets for complex tumor cases.
The frequent discussions within the MTB further promote this integrative approach and drive both research advancements and clinical applications paving the way toward precision medicine.
Continuous research and technological progress allow early detection and targeted therapies for various malignancies.This has led to detailed clinical guidelines that entail treatment recommendations based on each patient's tumor entity, mutational pattern, F I G U R E 1 Schematic workflow of the molecular tumor board.Patients are integrated into the molecular tumor board by registration from the treating physicians.Molecular diagnostics are performed on either already existing samples or newly acquired biopsies.Results are discussed in a biweekly interdisciplinary discussion with physicians and scientists.A treatment recommendation/decision is made either for a targeted or generalized therapeutic strategy or more diagnostic follow-up.and other tumor characteristics.However, despite all efforts, there are still many patients suffering from malignancies that are either non-treatable or non-responsive to the available and recommended therapeutic options.Examples of these challenging cases include tumors that show particularly aggressive growth and progression that are atypical for the respective entities, rendering the recommended treatments inadequate or ineffective.Other challenging cases are malignancies in young patients, which may indicate particularly aggressive tumor-promoting factors.Importantly, there are numerous rare malignancies, for which clinical and molecular data is lacking, resulting in a limited understanding of the underlying molecular pathomechanisms and treatment guidelines.To address the urgent need for a more comprehensive molecular understanding and effective medical treatment of such challenging tumor cases, molecular tumor boards (MTBs) were initiated.
cally relevant aspects such as post-translational modifications (PTMs) and protein isoforms, can only be studied by directly investigating the proteome.Proteins are the effector molecules within cells and tissues and promote or inhibit tumor progression and development.Proteomics provides direct evidence on a) protein abundances and b) pathway activity, for example by the detection of activity-associated phosphorylation events.The National Cancer Institute (NCI) has recognized the huge potential of clinical proteomics and launched an initiative, the Clinical Proteomic Tumor Analysis Consortium (CPTAC), to accelerate the understanding of tumor biology in ways not possible through genomics alone.By applying rigorous standards to proteomic measurements, CPTAC investigators perform large-scale reproducible proteomic studies.CPTAC's research over the last years has shown the power of proteomics and phosphoproteomics, enabling the reclassification of molecular tumor subtypes and the identification of pathways related to clinical outcomes [33, 34].In-depth large-scale and unbiased proteomic analyses are typically performed using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS).In bottom-up proteomic approaches the proteins are enzymatically digested into peptides, which are ionized and measured in the mass spectrometer.Technical and methodological development on multiple fronts of MS-based proteomics, from sample preparation to data analysis, have led to increased throughput, sensitivity, and accuracy (Table developments paved the way for robust proteomics while allowing for high throughput and minimizing human errors and variability during sample preparation.Proteomic workflows can handle a wide range of different sample types including solid tissue samples, serum, and urine.Dedicated workflows have been developed for the processing of formalin-fixed and paraffin-embedded (FFPE) samples which constitute the most common storage modality for resected tissue and the standard modality for routine diagnostics.Multiple studies showed that proteins and PTMs are generally more stable than RNA, allowing for in-depth analysis even after decades of storage time[69][70][71][72].Consequently, proteomic workflows can be seamlessly integrated into routine sample processing in pathology institutes.State-of-the-art protocols and instrumentation enable proteomic processing and analysis of FFPE samples within less than a week, meeting the clinical need for timely treatment recommendations[73].Furthermore, these protocols routinely yield more than 5000 identified and quantified proteins even from minimal FFPE input material such as biopsies.Proteomics further allows the large-scale detection and quantification of PTMs such as phosphorylation, glycosylation, acetylation, and ubiquitination[40,41,45,74,75].These modifications are an important mechanism to adapt and alter protein activity, stability, and localization.The modified peptides are most often substoichiometric, which necessitates enrichment during sample processing.Dedicated and partially automated workflows have been developed for this over the last few years, allowing stable detection of modified proteins (Table2)[76][77][78][79][80].Another proteomic field called immunopeptidomics focuses on all peptides presented by human leukocyte antigen (HLA) proteins on the cell surface.HLA proteins present a wide range of different antigen peptides originating from the proteasomal degradation of endogenous or exogenous proteins to T-cells.In the tumor context, the identification of tumorspecific antigens is essential for the development of epitope-specific cancer immunotherapies[81,82].The vast potential of clinical proteomics can be seen by multiple studies identifying single and/or panels of biomarkers and potential therapeutic targets in various diseases (Table3)[54,55,[83][84][85][86][87][88].Furthermore, clinical proteomics holds the potential for therapy response prediction and longitudinal treatment characterization, such as in the context of tumor development and progression[89][90][91].
number of PubMed entries (as of 07/23): The search for "molecular tumor board*" genomic*, yields about 190 entries, whereas searching for "molecular tumor board*" proteomic* results in mere 9 entries.This underlines the novelty and the untapped potential of implementing proteomics into MTBs and highlights the urgent need for further investigation and adoption of proteomic approaches in personalized oncology.In this endeavor, several aspects need to be considered.Given that the patients' samples often include non-tumorous areas, macrodissection of tumorous tissue is required to focus on the actual tumor proteomes.Techniques such as laser capture microdissection offer the ability to selectively isolate specific tumor regions and individual cell populations, providing a focused analysis and enabling the representation of tumor heterogeneity at a spatial resolution[130][131][132][133][134].Further insight into proteome alterations during tumor development and progression can be gained by additionally processing healthy tissue and in case of metastasis formation the metastatic and primary tumor tissues.
Druggable oncogenic signaling pathways are of particular interest in tumor treatment.Pathway activity can be assessed by a thorough investigation of PTMs.Protein phosphorylation is one of the most crucial PTMs and has been associated with promoting tumor development and progression highlighting the relevance for a detailed analysis of the phosphoproteome.Phosphoproteomics provides particular deep insights into pathway activity.Several kinase-related pathways are connected to tumor growth and progression, which has promoted the development of multiple therapeutic drugs targeting these kinases.The individual analyses of activated pathways and highly active kinases hold promising potential in the precise identification of therapeutic targets.The ongoing TOPAS study explores the benefit of integrating phosphoproteomics for individual patients [118].We and others detected multiple phosphorylated peptides of ERK1/2 connected to MAPK-ERK pathway activity in patients with a BRAF fusion, highlighting an activation of BRAF by this fusion event [158, 159].Further molecular insights can be gained by combining proteins into baskets, for example, therapeutic targets, morphologic/histologic markers, and proteins related to the same pathway.Thus, proteomic F I G U R E 2 Schematic overview of molecular and patho-clinical data integration towards personalized medicine.Proteomics and phosphoproteomics provide complementary biological information to current state-of-the-art molecular diagnostics within the molecular tumor boards.Integration of genomic and proteomic data promotes comprehensive molecular characterizations and paves the way toward personalized medicine and individualized treatment.analyses significantly contribute to the understanding of the underlying cancer type and propose potential treatment options.In a retrospective study, targeted proteomics (RPPA) partially supported genomic-driven therapeutic strategies but additionally proposed alternative treatment options and provided possible explanations for treatment failure [129].

First
installation of LC-MS/MS instrumentation, methods, and standard operating protocol development as well as system maintenance remains costly and often requires specialized personnel, dampening the broad applicability in a clinical setting.Other major limitations of clinical state-of-the-art proteomics remain the intrinsic complexity, the dynamic range of protein abundances as well as the lack of an amplification step during the sample preparation.Hence, proteomic measurements are limited to detectable proteins and peptides and require sensitive and thoroughly maintained instrumentation.The adequate interpretation of proteomic data derived from individual patients remains challenging, particularly for non-identified proteins, since those could be below the limit of detection or indeed absent in the sample.Thus, combinatorial approaches including explorative and targeted measurements might provide a compromise for deep proteome coverage as well as reliable and robust detection of proteins of interest such as established biomarkers.Generally, rigorous quality control (QC) must be included throughout the proteomic processing to ensure equal and high-quality proteomic results.For LC-MS/MS-based proteomics, the QC could include parameters such as the addition of synthetic indexed retention time peptides and/or measurement of control samples such as commercial peptide standards.For these standards, certain analysis results such as retention times, number of peptide and protein IDs as well as intensities should be within an expected range and consequently could be reported in conjunction with the individual MS-based patient data.These quality standards and prerequisites are partially defined by governmental regulations.In the US, the Clinical Laboratory Improvement Amendments (CLIA) establishes quality standards for all laboratory testing, except clinical trials and basic research.In adherence to the CLIA program, clinical laboratories must obtain the relevant certificate before they are authorized to process human samples for testing.