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\begin{document}

\title{Integration of proteomics in the molecular tumor board}



\author[1]{Johanna Thiery}%
\author[1]{Matthias Fahrner}%
\affil[1]{Institute for Surgical Pathology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Germany}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\selectlanguage{english}
\begin{abstract}
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 routine molecular diagnostics are
routinely performed on the transcriptomic and genomic levels, aiming for
the identification of tumor-driving mutations. However, these approaches
can only partially capture the actual phenotype as well as the molecular
key players of tumor growth and progression. Thus, direct investigation
of the expressed proteins and activated signaling pathways provide
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 gives an introduction to MTBs and describes current
state-of-the-art clinical proteomics, its potential in precision
oncology, and highlights the benefits of multi-omic data integration.%
\end{abstract}%



\sloppy


\section*{Introduction}

{\label{introduction}}

\subsection*{The molecular tumor board: Introduction for the proteomic
researcher}

{\label{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. 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, 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 based on clinical guidelines
inadequate or ineffective. Other challenging cases are malignancies in
young patients, which may indicate particularly aggressive
tumor-promoting factors. Importantly, there are numerous examples of
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 entities, molecular tumor boards
were initiated.

Molecular tumor boards (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 the field of genomics, transcriptomics,
and bioinformatics\textsuperscript{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 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\textsuperscript{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\textsuperscript{3--7}. In-depth molecular
characterization and treatment recommendations by the MTBs have been
linked to improved clinical outcomes in various medical centers and
studies\textsuperscript{1,8--10}.

\subsection*{Molecular diagnostics for personalized and precision
medicine}

{\label{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\textsuperscript{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\textsuperscript{12}.

A fundamental approach in molecular diagnostics is the immunostaining of
established protein biomarkers, such as HER2, PD-L1, and different types
of keratins, or hormone receptors. This enables pathologists and
clinicians to stratify and classify malignancies, estimate prognosis,
and in some cases 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 next-generation sequencing
(NGS)\textsuperscript{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)\textsuperscript{15--17}. The primary focus of genomic
approaches in oncology is the detection of mutations that can be
associated with pathogenic, potentially pathogenic, or benign tumor
development and progression. Further essential parameters of genomic
approaches are the analysis of the tumor mutational burden, copy number
variations as well as 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\textsuperscript{18,19}. Current routine 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.

Molecular diagnostics paved the way towards personalized and
individualized diagnostics and in consequence, targeted
therapies\textsuperscript{20--22}. The diagnostic, prognostic, and
therapeutic implications of somatic variants and molecular biomarkers
have been comprehensively analyzed and assessed, and assembled in
different evidence classification systems\textsuperscript{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\textsuperscript{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\textsuperscript{27}. In the latter,
evidence level 1 (m1A-C) recommendations are based on biomarkers and
respective therapies that were 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 \emph{in vitro} / \emph{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)\textsuperscript{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 individual patient. The divergent
variant classifications demonstrate an urgent need for standardization
and have caused the initiation of centers for personalized medicine
(ZPM) in Germany\textsuperscript{28}.

\section*{Integrating Proteomics into the
MTB}

{\label{integrating-proteomics-into-the-mtb}}

\subsection*{The potential of clinical
proteomics}

{\label{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 information. However, the
limited correlation between mRNA and actual protein abundance has been
shown in multiple studies\textsuperscript{29--32}. Other biologically
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 in the context of malignancies 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. In-depth large-scale and
unbiased proteomic analyses are typically performed using liquid
chromatography coupled with tandem mass spectrometry (LC-MS/MS).

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\textsuperscript{33,34}.

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, allowing the
identification and quantification of over 5000 proteins using minute
input/sample material\textsuperscript{35--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 \emph{a priori} known proteins. With highly
sensitive sample processing and LC-MS instruments, targeted and
explorative analysis can be performed using minimal sample amounts.
During bottom-up proteomic approaches the proteins are enzymatically
digested into peptides, which are ionized and measured in the mass
spectrometer.

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)\textsuperscript{36,39--42}. Due to their
cost-effectiveness 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
minimize biases to facilitate relative comparisons between
samples\textsuperscript{43}. In data-dependent acquisition (DDA) mode,
only a few selected precursors with the highest intensity are
fragmented\textsuperscript{44}. This approach introduces stochastic
effects and leads to a significant number of missing values, where
proteins are not detected consistently over multiple samples.
Conversely, the data-independent acquisition (DIA) mode provides a more
robust and reproducible approach for the explorative analysis of
multiple samples\textsuperscript{45,46}. 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\textsuperscript{46,47}.

To improve reproducibility and efficiency, different sample preparation
workflows including automation using liquid handling platforms have been
established over the last years\textsuperscript{48--51}. These
developments paved the way for reproducible and robust proteomics while
allowing for high-throughput and minimizing human errors and
variability. 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 (e.g. immunostainings and genomics). Multiple
studies showed that proteins and PTMs are generally more stable than
RNA, allowing for in-depth analysis even after decades of storage
time\textsuperscript{52--55}. Consequently, proteomic workflows can be
easily integrated into routine molecular diagnostic sample processing in
pathology institutes. Proteomics further allows the large-scale
detection and quantification of PTMs such as phosphorylation,
glycosylation, acetylation, and ubiquitination\textsuperscript{56--60}.
These modifications are an important mechanism to adapt and alter
protein activity, stability, and localization. The modified peptides are
most often substoichiometric, caused by regulatory processes, which
necessitates enrichment during sample processing. Dedicated (partially
automated) workflows have been developed for this over the last few
years, allowing stable detection of modified
proteins\textsuperscript{61--64}. 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 tumor-specific antigens is essential for
the development of epitope-specific cancer
immunotherapies\textsuperscript{65}. Presented antigens do not correlate
with transcript or protein abundance and until now, mass
spectrometry-based immunopeptidomics is the only available method for
providing direct evidence of antigen presentation.

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\textsuperscript{39,42,66--71}.
Furthermore, clinical proteomics holds the potential for therapy
response prediction and longitudinal treatment characterization, such as
in the context of tumor development and
progression\textsuperscript{72--74}.

\subsection*{Proteomics in precision
oncology}

{\label{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
various malignancies\textsuperscript{67,75--77}. 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 five years, there has been a rapid emergence of clinical
proteomic data through initiatives like CPTAC and other projects. 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 still remains a challenge, which is addressed by
only a few current studies\textsuperscript{78,79}. The integration of
proteomics into the MTB is an emerging and highly relevant topic, as
illustrated by the 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\textsuperscript{80--82}. 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.

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\textsuperscript{83}. Furthermore, these protocols
routinely yield more than 5000 identified and quantified proteins even
from minimal FFPE input material such as biopsies. One of the key
potentials of comprehensive MS-based 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
a successful immunotherapeutic treatment\textsuperscript{84,85}.
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\textsuperscript{86,87}. Other druggable targets
that can be observed using LC-MS/MS include MAP2K1, MAP2K2, CDK4, and
CDK6. 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\textsuperscript{88,89}. In our own work, we were able to
detect CDK4 and CDK6 in a patient with a loss of function mutation in
\emph{CDKN2A} , supporting the rationale for CDK4/6 inhibition.

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\textsuperscript{22,90--92}. 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\textsuperscript{93}. 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, PMS2, which indicates
microsatellite-stability\textsuperscript{94}.

Druggable oncogenic signaling pathways are of particular interest in
tumor treatment. Pathway activity can be assessed by thorough
investigation of PTMs. Protein phosphorylation is one of the most
crucial PTMs and has been associated to promote 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\textsuperscript{78}. In our
own work, we detected multiple phosphorylated peptides of ERK1/2
connected to MAPK-ERK pathway activity in a patient with a BRAF fusion.
This fits findings from other studies, highlighting an activation of
BRAF by this fusion event\textsuperscript{95}.

Further molecular insights can be gained by combining proteins into
baskets, e.g. therapeutic targets, morphologic/histologic markers, and
proteins related to the same pathway. Thus, proteomic analyses
significantly contribute to the understanding of the underlying cancer
type and propose potential treatment options. In a retrospective study,
it could be shown that targeted proteomics partially support
genomic-driven therapeutic strategies but also propose alternative
treatment options and provide possible explanations for treatment
failure\textsuperscript{79}.

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, e.g. viral or bacterial
proteins. One example is the protein isoform claudin-18.2, a novel
target in esophagus carcinoma and gastric cancer\textsuperscript{96}.
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\textsuperscript{97,98}.

\subsection*{Data Integration and
Proteogenomics}

{\label{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\textsuperscript{33,34,66,99--104}. 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\textsuperscript{105}.

However, proteogenomics can also be applied for 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.
In the tumor context oncogenic 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.

These proteogenomic analyses are especially valuable as they report on
the penetrance of genomic alterations and give complementary biological
information provided by actual effector molecules. Mutated proteins can
be either a direct target of drugs such as the KRAS G12C variant and
also propose a therapeutic direction\textsuperscript{106,107}.

Examples from our own work include the detection of the KRAS G12D
variant peptide (LVVVGA\emph{D} GVGK) by applying an in-house
patient-matched proteogenomic analysis workflow combining genomic
(TruSight Oncology 500 Assay) and LC-MS/MS proteomic data. Thus,
proteomic data corroborates genomic findings and provides direct
evidence of the actual presence of the mutated gene. The KRAS G12D
variant has been shown to be a potential therapeutic target for
selective inhibition and the identification of the KRAS G12D mutation
provides a rationale for the inclusion in ongoing clinical
studies\textsuperscript{108--113}.

\subsection*{Limitations and upcoming
challenges}

{\label{limitations-and-upcoming-challenges}}

The adequate interpretation of proteomic data derived from individual
patients remains challenging. One of the major limitations of clinical
state-of-the-art proteomics remains that due to the intrinsic
complexity, the dynamic range of protein abundances as well as the lack
of amplification during the sample preparation the complete proteome
cannot be measured. Consequently, the reported non-identification of a
protein does not necessarily mean that it was not present in the
original sample. Thus, combinatorial procedures including explorative
and targeted measurements might provide a compromise for vast 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 of the 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.

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 the clinical presentations. Accordingly, decision support
systems for molecular tumor boards presently involve exclusively NGS
data\textsuperscript{114--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.

\section*{Conclusion}

{\label{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.

\section*{Acknowledgments}

{\label{acknowledgments}}

The authors would like to thank Prof. Dr. Oliver Schilling, Dr. Niko
Pinter, Dr. Melanie F\selectlanguage{ngerman}öll as well as the whole Schilling group for the
continuous support, helpful discussions, and constructive feedback that
have greatly improved this review article. The authors acknowledge the
support and valuable discussions held at the Institute for Surgical
Pathology, with Prof Dr. Martin Werner, Prof. Dr. Silke Laßmann, Dr. Uta
Matysiak, Stepan Cysar, and Dr. Luise Bergmann. Furthermore, the authors
acknowledge the continuous scientific and clinical knowledge exchange
during the frequent MTB meetings with all participating experts at the
Medical Center -- University of Freiburg.

\section*{Conflict of interest}

{\label{conflict-of-interest}}

The authors have declared no conflict of interest.

\section*{References}

{\label{references}}

1. Luchini, C., Lawlor, R. T., Milella, M. \& Scarpa, A. Molecular Tumor
Boards in Clinical Practice. \emph{Trends Cancer} \textbf{6}, 738--744
(2020).

2. Schwaederle, M.\emph{et al.} Molecular Tumor Board: The University of
California San Diego Moores Cancer Center Experience. \emph{The
Oncologist}\textbf{19}, 631--636 (2014).

3. Hoefflin, R.\emph{et al.} Personalized Clinical Decision Making
Through Implementation of a Molecular Tumor Board: A German
Single-Center Experience. \emph{JCO Precis. Oncol.} 1--16 (2018)
doi:10.1200/PO.18.00105.

4. Ortiz, M. V.\emph{et al.} Integrating Genomics Into Clinical
Pediatric Oncology Using the Molecular Tumor Board at the Memorial Sloan
Kettering Cancer Center: Pediatric Molecular Tumor Board in Clinical
Oncology.\emph{Pediatr. Blood Cancer} \textbf{63}, 1368--1374 (2016).

5. Dalton, W. B.\emph{et al.} Personalized Medicine in the Oncology
Clinic: Implementation and Outcomes of the Johns Hopkins Molecular Tumor
Board.\emph{JCO Precis. Oncol.} 1--19 (2017) doi:10.1200/PO.16.00046.

6. Ree, A. H. \emph{et al.} Implementing precision cancer medicine in
the public health services of Norway: the diagnostic infrastructure and
a cost estimate.\emph{ESMO Open} \textbf{2}, e000158 (2017).

7. Tafe, L. J.\emph{et al.} Implementation of a Molecular Tumor Board:
The Impact on Treatment Decisions for 35 Patients Evaluated at
Dartmouth-Hitchcock Medical Center. \emph{The Oncologist} \textbf{20},
1011--1018 (2015).

8. Larson, K. L.\emph{et al.} Clinical Outcomes of Molecular Tumor
Boards: A Systematic Review. \emph{JCO Precis. Oncol.} 1122--1132 (2021)
doi:10.1200/PO.20.00495.

9. Behel, V. \emph{et al.} Impact of Molecular Tumor Board on the
Clinical Management of Patients With Cancer. \emph{JCO Glob. Oncol.}
e2200030 (2022) doi:10.1200/GO.22.00030.

10. Liu, A. \emph{et al.} Molecular Tumor Boards: The Next Step towards
Precision Therapy in Cancer Care. \emph{Hematol. Rep.} \textbf{15},
244--255 (2023).

11. Brown, N. A. \& Elenitoba-Johnson, K. S. J. Enabling Precision
Oncology Through Precision Diagnostics. \emph{Annu. Rev. Pathol. Mech.
Dis.} \textbf{15}, 97--121 (2020).

12. Baselga, J., Perez, E. A., Pienkowski, T. \& Bell, R. Adjuvant
Trastuzumab: A Milestone in the Treatment of HER-2-Positive Early Breast
Cancer.\emph{The Oncologist} \textbf{11}, 4--12 (2006).

13. Wakai, T. \emph{et al.} Next-generation sequencing-based clinical
sequencing: toward precision medicine in solid tumors. \emph{Int. J.
Clin. Oncol.}\textbf{24}, 115--122 (2019).

14. Beg, S. \emph{et al.} Integration of whole-exome and anchored
PCR-based next generation sequencing significantly increases detection
of actionable alterations in precision oncology. \emph{Transl. Oncol.}
\textbf{14}, 100944 (2021).

15. Ottestad, A. L.\emph{et al.} Assessment of Two Commercial
Comprehensive Gene Panels for Personalized Cancer Treatment. \emph{J.
Pers. Med.} \textbf{13}, 42 (2022).

16. Wei, B. \emph{et al.} Evaluation of the TruSight Oncology 500 Assay
for Routine Clinical Testing of Tumor Mutational Burden and Clinical
Utility for Predicting Response to Pembrolizumab. \emph{J. Mol. Diagn.}
\textbf{24}, 600--608 (2022).

17. Froyen, G., Geerdens, E., Berden, S., Cruys, B. \& Maes, B.
Diagnostic Validation of a Comprehensive Targeted Panel for Broad
Mutational and Biomarker Analysis in Solid Tumors. \emph{Cancers}
\textbf{14}, 2457 (2022).

18. Landrum, M. J.\emph{et al.} ClinVar: public archive of relationships
among sequence variation and human phenotype. \emph{Nucleic Acids Res.}
\textbf{42}, D980--D985 (2014).

19. Chakravarty, D.\emph{et al.} OncoKB: A Precision Oncology Knowledge
Base. \emph{JCO Precis. Oncol.} 1--16 (2017) doi:10.1200/PO.17.00011.

20. Lee, J., Ledermann, J. A. \& Kohn, E. C. PARP Inhibitors for BRCA1/2
mutation-associated and BRCA-like malignancies. \emph{Ann.
Oncol.}\textbf{25}, 32--40 (2014).

21. Al-Batran, S. E.\emph{et al.} LBA-06 IMAB362: a novel
immunotherapeutic antibody targeting the tight-junction protein
component CLAUDIN18.2 in gastric cancer. \emph{Ann. Oncol.} \textbf{27},
ii141 (2016).

22. Fu, Z., Li, S., Han, S., Shi, C. \& Zhang, Y. Antibody drug
conjugate: the ``biological missile'' for targeted cancer therapy.
\emph{Signal Transduct. Target. Ther.} \textbf{7}, 93 (2022).

23. Hoefflin, R.\emph{et al.} Transitioning the Molecular Tumor Board
from Proof of Concept to Clinical Routine: A German Single-Center
Analysis.\emph{Cancers} \textbf{13}, 1151 (2021).

24. Horak, P. \emph{et al.} Assigning evidence to actionability: An
introduction to variant interpretation in precision cancer medicine.
\emph{Genes. Chromosomes Cancer} \textbf{61}, 303--313 (2022).

25. Mateo, J. \emph{et al.} A framework to rank genomic alterations as
targets for cancer precision medicine: the ESMO Scale for Clinical
Actionability of molecular Targets (ESCAT). \emph{Ann. Oncol.}
\textbf{29}, 1895--1902 (2018).

26. Li, M. M. \emph{et al.} Standards and Guidelines for the
Interpretation and Reporting of Sequence Variants in Cancer. \emph{J.
Mol. Diagn.} \textbf{19}, 4--23 (2017).

27. Leichsenring, J.\emph{et al.} Variant classification in precision
oncology. \emph{Int. J. Cancer} \textbf{145}, 2996--3010 (2019).

28. Illert, A. L.\emph{et al.} The German Network for Personalized
Medicine to enhance patient care and translational research. \emph{Nat.
Med.} \textbf{29}, 1298--1301 (2023).

29. Maier, T., Güell, M. \& Serrano, L. Correlation of mRNA and protein
in complex biological samples. \emph{FEBS Lett.} \textbf{583},
3966--3973 (2009).

30. Greenbaum, D., Colangelo, C., Williams, K. \& Gerstein, M. Comparing
protein abundance and mRNA expression levels on a genomic scale.
\emph{Genome Biol.}(2003).

31. Chen, G. \emph{et al.} Discordant Protein and mRNA Expression in
Lung Adenocarcinomas.\emph{Mol. Cell. Proteomics} \textbf{1}, 304--313
(2002).

32. Koussounadis, A., Langdon, S. P., Um, I. H., Harrison, D. J. \&
Smith, V. A. Relationship between differentially expressed mRNA and
mRNA-protein correlations in a xenograft model system. \emph{Sci. Rep.}
\textbf{5}, 10775 (2015).

33. NCI CPTAC \emph{et al.} Proteogenomic characterization of human
colon and rectal cancer.\emph{Nature} \textbf{513}, 382--387 (2014).

34. NCI CPTAC \emph{et al.} Proteogenomics connects somatic mutations to
signalling in breast cancer. \emph{Nature} \textbf{534}, 55--62 (2016).

35. Muntel, J.\emph{et al.} Surpassing 10\selectlanguage{english} 000 identified and quantified
proteins in a single run by optimizing current LC-MS instrumentation and
data analysis strategy. \emph{Mol. Omics} \textbf{15}, 348--360 (2019).

36. Eckert, M. A.\emph{et al.} Proteomics reveals NNMT as a master
metabolic regulator of cancer-associated fibroblasts. \emph{Nature}
\textbf{569}, 723--728 (2019).

37. Aebersold, R. \& Mann, M. Mass-spectrometric exploration of proteome
structure and function. \emph{Nature} \textbf{537}, (2016).

38. Bache, N. \emph{et al.} A Novel LC System Embeds Analytes in
Pre-formed Gradients for Rapid, Ultra-robust Proteomics. \emph{Mol.
Cell. Proteomics}\textbf{17}, 2284--2296 (2018).

39. Barbieux, C.\emph{et al.} Netherton syndrome subtypes share
IL-17/IL-36 signature with distinct IFN-\selectlanguage{greek}α \selectlanguage{english}and allergic responses.
\emph{J. Allergy Clin. Immunol.} S0091674921013981 (2021)
doi:10.1016/j.jaci.2021.08.024.

40. Fahrner, M., Bronsert, P., Fichtner-Feigl, S., Jud, A. \& Schilling,
O. Proteome biology of primary colorectal carcinoma and corresponding
liver metastases. \emph{Neoplasia} \textbf{23}, 1240--1251 (2021).

41. Werner, J.\emph{et al.} Targeted and explorative profiling of
kallikrein proteases and global proteome biology of pancreatic ductal
adenocarcinoma, chronic pancreatitis, and normal pancreas highlights
disease-specific proteome remodelling. \emph{Neoplasia} \textbf{36},
100871 (2023).

42. Coscia, F.\emph{et al.} Multi-level Proteomics Identifies CT45 as a
Chemosensitivity Mediator and Immunotherapy Target in Ovarian
Cancer.\emph{Cell} \textbf{175}, 159-170.e16 (2018).

43. Brenes, A., Hukelmann, J., Bensaddek, D. \& Lamond, A. I. Multibatch
TMT Reveals False Positives, Batch Effects, and Missing Values. 15
(2019).

44. Kreimer, S.\emph{et al.} Advanced Precursor Ion Selection Algorithms
for Increased Depth of Bottom-Up Proteomic Profiling. \emph{J. Proteome
Res.}\textbf{15}, 3563--3573 (2016).

45. Gillet, L. C.\emph{et al.} Targeted Data Extraction of the MS/MS
Spectra Generated by Data-independent Acquisition: A New Concept for
Consistent and Accurate Proteome Analysis*#S. 17 (2012).

46. Barkovits, K.\emph{et al.} Reproducibility, Specificity and Accuracy
of Relative Quantification Using Spectral Library-based Data-independent
Acquisition. \emph{Mol. Cell. Proteomics} \textbf{19}, 181--197 (2020).

47. Fr\selectlanguage{ngerman}öhlich, K.\emph{et al.} Benchmarking of analysis strategies for
data-independent acquisition proteomics using a large-scale dataset
comprising inter-patient heterogeneity. \emph{Nat. Commun.} \textbf{13},
2622 (2022).

48. Kulak, N. A., Pichler, G., Paron, I., Nagaraj, N. \& Mann, M.
Minimal, encapsulated proteomic-sample processing applied to copy-number
estimation in eukaryotic cells. \emph{Nat. Methods} \textbf{11},
319--324 (2014).

49. Kuras, M. \emph{et al.} Proteomic Workflows for High-Quality
Quantitative Proteome and Post-Translational Modification Analysis of
Clinically Relevant Samples from Formalin-Fixed Paraffin-Embedded
Archives. \emph{J. Proteome Res.}\textbf{20}, 1027--1039 (2021).

50. Müller, T.\emph{et al.} Automated sample preparation with SP3 for
low-input clinical proteomics. \emph{Mol. Syst. Biol.} \textbf{16},
e9111 (2020).

51. Bekker-Jensen, D. B. \emph{et al.} A Compact Quadrupole-Orbitrap
Mass Spectrometer with FAIMS Interface Improves Proteome Coverage in
Short LC Gradients.\emph{Mol. Cell. Proteomics} \textbf{19}, 716--729
(2020).

52. Ostasiewicz, P., Zielinska, D. F., Mann, M. \& Wi\selectlanguage{polish}ś\selectlanguage{english}niewski, J. R.
Proteome, Phosphoproteome, and N-Glycoproteome Are Quantitatively
Preserved in Formalin-Fixed Paraffin-Embedded Tissue and Analyzable by
High-Resolution Mass Spectrometry. \emph{J. Proteome Res.} \textbf{9},
3688--3700 (2010).

53. Warinner, C., Korzow Richter, K. \& Collins, M. J. Paleoproteomics.
\emph{Chem. Rev.}\textbf{122}, 13401--13446 (2022).

54. Jersie-Christensen, R. R. \emph{et al.} Quantitative metaproteomics
of medieval dental calculus reveals individual oral health
status.\emph{Nat. Commun.} \textbf{9}, 4744 (2018).

55. Balgley, B. M.\emph{et al.} Evaluation of Archival Time on Shotgun
Proteomics of Formalin-Fixed and Paraffin-Embedded Tissues. \emph{J.
Proteome Res.}\textbf{8}, 917--925 (2009).

56. Sharma, K.\emph{et al.} Ultradeep Human Phosphoproteome Reveals a
Distinct Regulatory Nature of Tyr and Ser/Thr-Based Signaling.
\emph{Cell Rep.}\textbf{8}, 1583--1594 (2014).

57. Pan, S., Chen, R., Aebersold, R. \& Brentnall, T. A. Mass
Spectrometry Based Glycoproteomics---From a Proteomics Perspective.
\emph{Mol. Cell. Proteomics} \textbf{10}, R110.003251 (2011).

58. Choudhary, C., Weinert, B. T., Nishida, Y., Verdin, E. \& Mann, M.
The growing landscape of lysine acetylation links metabolism and cell
signalling.\emph{Nat. Rev. Mol. Cell Biol.} \textbf{15}, 536--550
(2014).

59. Kim, W. \emph{et al.} Systematic and Quantitative Assessment of the
Ubiquitin-Modified Proteome. \emph{Mol. Cell} \textbf{44}, 325--340
(2011).

60. Choudhary, C.\emph{et al.} Lysine Acetylation Targets Protein
Complexes and Co-Regulates Major Cellular Functions. \emph{Science}
\textbf{325}, 834--840 (2009).

61. F\selectlanguage{ngerman}íla, J. \& Honys, D. Enrichment techniques employed in
phosphoproteomics. \emph{Amino Acids} \textbf{43}, 1025--1047 (2012).

62. Tape, C. J.\emph{et al.} Reproducible Automated Phosphopeptide
Enrichment Using Magnetic TiO \textsubscript{2} and Ti-IMAC. \emph{Anal.
Chem.}\textbf{86}, 10296--10302 (2014).

63. Udeshi, N. D., Mertins, P., Svinkina, T. \& Carr, S. A. Large-scale
identification of ubiquitination sites by mass spectrometry. \emph{Nat.
Protoc.}\textbf{8}, 1950--1960 (2013).

64. Fang, P., Ji, Y., Oellerich, T., Urlaub, H. \& Pan, K.-T. Strategies
for Proteome-Wide Quantification of Glycosylation Macro- and
Micro-Heterogeneity.\emph{Int. J. Mol. Sci.} \textbf{23}, 1609 (2022).

65. Becker, J. P. \& Riemer, A. B. The Importance of Being Presented:
Target Validation by Immunopeptidomics for Epitope-Specific
Immunotherapies. \emph{Front. Immunol.} \textbf{13}, 883989 (2022).

66. Vasaikar, S.\emph{et al.} Proteogenomic Analysis of Human Colon
Cancer Reveals New Therapeutic Opportunities. \emph{Cell} 1035--1049
(2019) doi:10.1016/j.cell.2019.03.030.

67. Chinese Human Proteome Project (CNHPP) Consortium \emph{et al.}
Proteomics identifies new therapeutic targets of early-stage
hepatocellular carcinoma.\emph{Nature} \textbf{567}, 257--261 (2019).

68. Niu, L. \emph{et al.} Noninvasive proteomic biomarkers for
alcohol-related liver disease.\emph{Nat. Med.} \textbf{28}, 1277--1287
(2022).

69. Intasqui, P., Bertolla, R. P. \& Sadi, M. V. Prostate cancer
proteomics: clinically useful protein biomarkers and future
perspectives. \emph{Expert Rev. Proteomics} \textbf{15}, 65--79 (2018).

70. Deeb, S. J.\emph{et al.} Machine Learning-based Classification of
Diffuse Large B-cell Lymphoma Patients by Their Protein Expression
Profiles.\emph{Mol. Cell. Proteomics} \textbf{14}, 2947--2960 (2015).

71. Zhang, Z. \emph{et al.} Three Biomarkers Identified from Serum
Proteomic Analysis for the Detection of Early Stage Ovarian Cancer.

72. Liu, L. \emph{et al.} Novel Mechanism of Lapatinib Resistance in
HER2-Positive Breast Tumor Cells: Activation of AXL. \emph{Cancer Res.}
\textbf{69}, 6871--6878 (2009).

73. Klaeger, S.\emph{et al.} The target landscape of clinical kinase
drugs.\emph{Science} \textbf{358}, eaan4368 (2017).

74. Yu, K.-H. \emph{et al.} Predicting Ovarian Cancer Patients' Clinical
Response to Platinum-Based Chemotherapy by Their Tumor Proteomic
Signatures.\emph{J. Proteome Res.} \textbf{15}, 2455--2465 (2016).

75. Li, C. \emph{et al.} Integrated Omics of Metastatic Colorectal
Cancer. \emph{Cancer Cell} \textbf{38}, 734-747.e9 (2020).

76. Xu, J.-Y. \emph{et al.} Integrative Proteomic Characterization of
Human Lung Adenocarcinoma. \emph{Cell} \textbf{182}, 245-261.e17 (2020).

77. Cao, L. \emph{et al.} Proteogenomic characterization of pancreatic
ductal adenocarcinoma.\emph{Cell} \textbf{184}, 5031-5052.e26 (2021).

78. Exploiting the Tumor Proteome Activity Status for Future Cancer
Therapies \textbar{} TOPAS Project \textbar{} Fact Sheet \textbar{}
H2020. \emph{CORDIS \textbar{} European
Commission}https://cordis.europa.eu/project/id/833710.

79. Wahjudi, L. W.\emph{et al.} Integrating proteomics into precision
oncology. \emph{Int. J. Cancer} \textbf{148}, 1438--1451 (2021).

80. Craven, R. A. \& Banks, R. E. Laser Capture Microdissection for
Proteome Analysis.\emph{Curr. Protoc. Protein Sci.} \textbf{31}, (2003).

81. Longuespee, R.\emph{et al.} A laser microdissection-based workflow
for FFPE tissue microproteomics: important considerations for small
sample processing.\emph{Methods San Diego Calif} (2015)
doi:10.1016/j.ymeth.2015.12.008.

82. Mustafa, D., Kros, J. M. \& Luider, T. Combining Laser Capture
Microdissection and Proteomics Techniques. in \emph{Clinical Proteomics}
(ed. Vlahou, A.) vol. 428 159--178 (Humana Press, 2008).

83. Doll, S. \emph{et al.} Rapid proteomic analysis for solid tumors
reveals LSD 1 as a drug target in an end\selectlanguage{english}-stage cancer patient.
\emph{Mol. Oncol.} \textbf{12}, 1296--1307 (2018).

84. Doroshow, D. B.\emph{et al.} PD-L1 as a biomarker of response to
immune-checkpoint inhibitors. \emph{Nat. Rev. Clin. Oncol.} \textbf{18},
345--362 (2021).

85. Herbst, R. S.\emph{et al.} Pembrolizumab versus docetaxel for
previously treated, PD-L1-positive, advanced non-small-cell lung cancer
(KEYNOTE-010): a randomised controlled trial. \emph{The Lancet}
\textbf{387}, 1540--1550 (2016).

86. Tong, J., Taylor, P. \& Moran, M. F. Proteomic Analysis of the
Epidermal Growth Factor Receptor (EGFR) Interactome and
Post-translational Modifications Associated with Receptor Endocytosis in
Response to EGF and Stress*\selectlanguage{english}#S.

87. Zhang, E. Y.\emph{et al.} Genome Wide Proteomics of ERBB2 and EGFR
and Other Oncogenic Pathways in Inflammatory Breast Cancer. \emph{J.
Proteome Res.} \textbf{12}, 2805--2817 (2013).

88. Knudsen, E. S. \& Witkiewicz, A. K. The Strange Case of CDK4/6
Inhibitors: Mechanisms, Resistance, and Combination Strategies.
\emph{Trends Cancer} \textbf{3}, 39--55 (2017).

89. Goel, S., Bergholz, J. S. \& Zhao, J. J. Targeting CDK4 and CDK6 in
cancer.\emph{Nat. Rev. Cancer} \textbf{22}, 356--372 (2022).

90. Sievers, E. L. \& Senter, P. D. Antibody-Drug Conjugates in Cancer
Therapy. \emph{Annu. Rev. Med.} \textbf{64}, 15--29 (2013).

91. Criscitiello, C., Morganti, S. \& Curigliano, G. Antibody--drug
conjugates in solid tumors: a look into novel targets. \emph{J. Hematol.
Oncol.J Hematol Oncol} \textbf{14}, 20 (2021).

92. Moek, K. L., De Groot, D. J. A., De Vries, E. G. E. \& Fehrmann, R.
S. N. The antibody--drug conjugate target landscape across a broad range
of tumour types. \emph{Ann. Oncol.} \textbf{28}, 3083--3091 (2017).

93. ADC Drug Map \textbar{} ADC Review.
https://www.adcreview.com/adc-drugmap/.

94. Cicek, M. S.\emph{et al.} Quality Assessment and Correlation of
Microsatellite Instability and Immunohistochemical Markers among
Population- and Clinic-Based Colorectal Tumors. \emph{J. Mol. Diagn.}
\textbf{13}, 271--281 (2011).

95. Jones, D. T. W.\emph{et al.} Tandem Duplication Producing a Novel
Oncogenic \emph{BRAF}Fusion Gene Defines the Majority of Pilocytic
Astrocytomas. \emph{Cancer Res.} \textbf{68}, 8673--8677 (2008).

96. Kyuno, D. \emph{et al.} Claudin-18.2 as a therapeutic target in
cancers: cumulative findings from basic research and clinical trials.
\emph{Tissue Barriers}\textbf{10}, 1967080 (2022).

97. Long, S. \emph{et al.} Metaproteomics characterizes human gut
microbiome function in colorectal cancer. \emph{Npj Biofilms
Microbiomes} \textbf{6}, 14 (2020).

98. Narunsky-Haziza, L. \emph{et al.} Pan-cancer analyses reveal
cancer-type-specific fungal ecologies and bacteriome interactions.
\emph{Cell} \textbf{185}, 3789-3806.e17 (2022).

99. Lehtio, J.\emph{et al.} Proteogenomics of non-small cell lung cancer
reveals molecular subtypes associated with specific therapeutic targets
and immune-evasion mechanisms. \emph{Nat. Cancer} \textbf{2}, 1224--1242
(2021).

100. Zhang, H.\emph{et al.} Integrated Proteogenomic Characterization of
Human High-Grade Serous Ovarian Cancer. \emph{Cell} \textbf{166},
755--765 (2016).

101. Blank-Landeshammer, B. \emph{et al.} Proteogenomics of Colorectal
Cancer Liver Metastases: Complementing Precision Oncology with
Phenotypic Data.\emph{Cancers} \textbf{11}, 1907 (2019).

102. Sinha, A.\emph{et al.} The Proteogenomic Landscape of Curable
Prostate Cancer.\emph{Cancer Cell} \textbf{35}, 414-427.e6 (2019).

103. Nesvizhskii, A. I. Proteogenomics: concepts, applications and
computational strategies.\emph{Nat. Methods} \textbf{11}, 1114--1125
(2014).

104. Rodriguez, H., Zenklusen, J. C., Staudt, L. M., Doroshow, J. H. \&
Lowy, D. R. The next horizon in precision oncology: Proteogenomics to
inform cancer diagnosis and treatment. \emph{Cell} \textbf{184},
1661--1670 (2021).

105. Ellis, M. J.\emph{et al.} Connecting Genomic Alterations to Cancer
Biology with Proteomics: The NCI Clinical Proteomic Tumor Analysis
Consortium.\emph{Cancer Discov.} \textbf{3}, 1108--1112 (2013).

106. Nakajima, E. C.\emph{et al.} FDA Approval Summary: Sotorasib for
\emph{KRAS G12C}-Mutated Metastatic NSCLC. \emph{Clin. Cancer Res.}
\textbf{28}, 1482--1486 (2022).

107. Kopetz, S.\emph{et al.} Encorafenib, Binimetinib, and Cetuximab in
\emph{BRAF}V600E--Mutated Colorectal Cancer. \emph{N. Engl. J. Med.}
\textbf{381}, 1632--1643 (2019).

108. Akkapeddi, P.\emph{et al.} Exploring switch II pocket conformation
of KRAS(G12D) with mutant-selective monobody inhibitors. \emph{Proc.
Natl. Acad. Sci.}\textbf{120}, e2302485120 (2023).

109. Feng, J. \emph{et al.} Feedback activation of EGFR/wild-type RAS
signaling axis limits KRASG12D inhibitor efficacy in KRASG12D-mutated
colorectal cancer.\emph{Oncogene} \textbf{42}, 1620--1633 (2023).

110. Tang, D. \& Kang, R. Glimmers of hope for targeting oncogenic
KRAS-G12D. \emph{Cancer Gene Ther.} s41417-022-00561--3 (2022)
doi:10.1038/s41417-022-00561-3.

111. Mirati Therapeutics Inc. \emph{A Phase 1/2 Multiple Expansion
Cohort Trial of MRTX1133 in Patients With Advanced Solid Tumors
Harboring a KRAS G12D Mutation}.
https://clinicaltrials.gov/study/NCT05737706 (2023).

112. National Cancer Institute (NCI). \emph{A Phase I/II Study
Administering Peripheral Blood Lymphocytes Transduced With a Murine
T-Cell Receptor Recognizing the G12D Variant of Mutated RAS in
HLA-A*11:01 Patients}. https://clinicaltrials.gov/study/NCT03745326
(2023).

113. Taiho Oncology, Inc. \emph{A Phase 1 Study of TAS0612 in Patients
With Locally Advanced or Metastatic Solid Tumors}.
https://clinicaltrials.gov/study/NCT04586270 (2023).

114. Buechner, P.\emph{et al.} Requirements Analysis and Specification
for a Molecular Tumor Board Platform Based on cBioPortal.
\emph{Diagnostics}\textbf{10}, 93 (2020).

115. Tamborero, D.\emph{et al.} Support systems to guide clinical
decision-making in precision oncology: The Cancer Core Europe Molecular
Tumor Board Portal.\emph{Nat. Med.} \textbf{26}, 992--994 (2020).

116. Tamborero, D.\emph{et al.} The Molecular Tumor Board Portal
supports clinical decisions and automated reporting for precision
oncology. \emph{Nat. Cancer} \textbf{3}, 251--261 (2022).

\section*{Figure legends}

{\label{figure-legends}}

\textbf{Figure 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.

\textbf{Figure 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.

\section*{Tables}

{\label{tables}}

\textbf{Table 1. Overview of the NCT evidence classification system,
which is applied within the molecular tumor boards in Germany} (adapted
from Horak et al.\textsuperscript{24}).\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Level of evidence} & \textbf{Basis for classification} &
\textbf{Tissue context} & \textbf{Description of
classification}\tabularnewline
m1A & Clinical data & Same histopathologic entity & The biomarker's
predictive value or the drug's clinical effectiveness was shown in a
study or analysis of a molecularly stratified cohort with the same type
of tumor.\tabularnewline
m1B & Clinical data & Same histopathologic entity & The biomarker's
predictive value or the drug's clinical effectiveness was proven in a
retrospective cohort or case--control study of molecularly stratified
patients with the same tumor type.\tabularnewline
m1C & Clinical data & Same histopathologic entity & A case study or
single unusual responder indicates that the biomarker is associated with
response to the corresponding drug in the same tumor
type.\tabularnewline
m2A & Clinical data & Different histopathologic entity & The predictive
value of the biomarker or clinical effectiveness of the corresponding
drug in a molecularly stratified cohort was demonstrated in a
prospective study or meta-analysis in a different tumor
type.\tabularnewline
m2B & Clinical data & Different histopathologic entity & The predictive
value of the biomarker or clinical effectiveness of the drug in a
molecularly stratified cohort was demonstrated in a retrospective cohort
or case--control study in a different tumor type.\tabularnewline
m2C & Clinical data & Different histopathologic entity & A case study or
single unusual responder indicates that the biomarker is associated with
response to the corresponding drug in a different tumor
type.\tabularnewline
m3 & Preclinical data & Not applicable & Preclinical data demonstrate
that the biomarker predicts response to a specific drug, supported by a
scientific rationale.\tabularnewline
m4 & Biological rationale & Not applicable & A biological rationale
exists that associates the biomarker with altered activity of cellular
pathways/processes or drug sensitivity without direct clinical or
preclinical evidence for a response to the drug.\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
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\end{figure}\selectlanguage{english}
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