Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient ...response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including post-translational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics' integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice. Keywords: Clinical proteomics, Mass spectrometry, Cancer, Biomarker discovery, Targeted assay, Proteogenomics
Intratumoral heterogeneity is a critical frontier in understanding how the tumor microenvironment (TME) propels malignant progression. Here, we deconvolute the human pancreatic TME through ...large-scale integration of histology-guided regional multiOMICs with clinical data and patient-derived preclinical models. We discover “subTMEs,” histologically definable tissue states anchored in fibroblast plasticity, with regional relationships to tumor immunity, subtypes, differentiation, and treatment response. “Reactive” subTMEs rich in complex but functionally coordinated fibroblast communities were immune hot and inhabited by aggressive tumor cell phenotypes. The matrix-rich “deserted” subTMEs harbored fewer activated fibroblasts and tumor-suppressive features yet were markedly chemoprotective and enriched upon chemotherapy. SubTMEs originated in fibroblast differentiation trajectories, and transitory states were notable both in single-cell transcriptomics and in situ. The intratumoral co-occurrence of subTMEs produced patient-specific phenotypic and computationally predictable heterogeneity tightly linked to malignant biology. Therefore, heterogeneity within the plentiful, notorious pancreatic TME is not random but marks fundamental tissue organizational units.
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•PDAC regional heterogeneity stems from sub-tumor microenvironments (subTMEs)•SubTMEs exhibit distinct immune phenotypes and CAF differentiation states•SubTMEs execute distinct tumor-promoting and chemoprotective functions•Intratumoral subTME co-occurrence links stromal heterogeneity to patient outcome
Intratumoral heterogeneity in the human pancreatic tumor microenvironment is not random but originates in well-definable regional tissue states. The underlying sub-tumor microenvironments shape regional epithelial and immune phenotypes and influence key clinical metrics of disease progression.
Cancer is the second most common cause of death worldwide and its heterogeneity complicates therapy. Standard cytotoxic regiments disrupt rapidly dividing cells, regardless of their neoplastic ...status. The introduction of less toxic targeted therapies has partially contributed to the observed decrease in cancer-related mortality. Cell-surface proteins represent attractive targets for therapy, due to their easily-accessible localization and their involvement in essential signaling pathways, often dysregulated in cancer. Despite their clinical appeal, cell-surface proteins are often underrepresented in standard proteomic data sets, due to their poor solubility and lower expression levels compared to intracellular proteins. Areas covered: This review will summarize some of the available techniques for enriching the cell-surface proteome, and discuss their advantages, limitations and applicability to clinical sample-testing. Moreover, we discuss currently available strategies for the development of novel targeted therapies in cancer. Expert commentary: The interest in elucidating the cancer-associated surfaceome is growing and will likely benefit from recent advancements in instrument sensitivity, method development, and a growing body of high-quality proteomics databases. Multiomics studies, in combination with functional validations (e.g. dropout screens), and evaluation of the healthy surfaceome, will likely aid in the selection of relevant targets for future therapy development.
DNA sequencing has identified recurrent mutations that drive the aggressiveness of prostate cancers. Surprisingly, the influence of genomic, epigenomic, and transcriptomic dysregulation on the tumor ...proteome remains poorly understood. We profiled the genomes, epigenomes, transcriptomes, and proteomes of 76 localized, intermediate-risk prostate cancers. We discovered that the genomic subtypes of prostate cancer converge on five proteomic subtypes, with distinct clinical trajectories. ETS fusions, the most common alteration in prostate tumors, affect different genes and pathways in the proteome and transcriptome. Globally, mRNA abundance changes explain only ∼10% of protein abundance variability. As a result, prognostic biomarkers combining genomic or epigenomic features with proteomic ones significantly outperform biomarkers comprised of a single data type.
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•A comprehensive proteomic analyses of localized prostate cancers•Integration of all levels of the central dogma (DNA → RNA → protein)•ETS fusions have divergent effects on transcriptome and proteome•Combining genomics and proteomics improves biomarker performance
Sinha et al. determine the proteogenomic landscape of localized, intermediate-risk prostate cancers and show that the presence of ETS gene fusions has one of the strongest effects on the proteome. Prognostic biomarkers that integrate multi-omics significantly outperform those comprised of a single data type.
High-grade serous carcinoma (HGSC) is the most prevalent and aggressive subtype of ovarian cancer. The large degree of clinical heterogeneity within HGSC has justified deviations from the traditional ...one-size-fits-all clinical management approach. However, the majority of HGSC patients still relapse with chemo-resistant cancer and eventually succumb to their disease, evidence that further work is needed to improve patient outcomes. Advancements in high-throughput technologies have enabled novel insights into biological complexity, offering a large potential for informing precision medicine efforts. Here, we review the current landscape of clinical management for HGSC and highlight applications of high-throughput biological approaches for molecular subtyping and the discovery of putative blood-based biomarkers and novel therapeutic targets. Additionally, we present recent improvements in model systems and discuss how their intersection with high-throughput platforms and technological advancements is positioned to accelerate the realization of precision medicine in HGSC.
The knowledge gained from comprehensive profiling projects that aim to define the complex genomic alterations present within cancers will undoubtedly improve our ability to detect and treat those ...diseases, but the influence of these resources on our understanding of basic cancer biology is still to be demonstrated. Extracellular vesicles have gained considerable attention in past years, both as mediators of intercellular signalling and as potential sources for the discovery of novel cancer biomarkers. In general, research on extracellular vesicles investigates either the basic mechanism of vesicle formation and cargo incorporation, or the isolation of vesicles from available body fluids for biomarker discovery. A deeper understanding of the cargo molecules present in extracellular vesicles obtained from patients with urogenital cancers, through high-throughput proteomics or genomics approaches, will aid in the identification of novel diagnostic and prognostic biomarkers, and can potentially lead to the discovery of new therapeutic targets.
Transitions of the cancer cell phenotype between epithelial and mesenchymal states (EMT) are likely to alter the patterns of intercellular communication. In this regard we have previously documented ...that EMT-like changes trigger quantitative rearrangements in exosomal vesicle emission in A431 cancer cells driven by oncogenic epidermal growth factor receptor (EGFR). Here we report that extracellular vesicles (EVs) produced by these cancer cells in their epithelial and mesenchymal states exhibit profound qualitative differences in their proteome. Thus, induction of the EMT-like state through blockade of E-cadherin and EGFR stimulation provoked a mesenchymal shift in cellular morphology and enrichment in the CD44-high/CD24-low immunophenotype, often linked to cellular stemness. This change also resulted in reprogramming of the EV-related proteome (distinct from that of corresponding cells), which contained 30 unique protein signals, and revealed enrichment in pathways related to cellular growth, cell-to-cell signaling, and cell movement. Some of the most prominent EV-related proteins were validated, including integrin α2 and tetraspanin CD9. We propose that changes in cellular differentiation status translate into unique qualitative rearrangements in the cargo of EVs, a process that may have implications for intercellular communication and could serve as source of new biomarkers to detect EMT-like processes in cancer.
•Cancer cells can be induced to exhibit mesenchymal and stem cell-like phenotype (EMT).•EGFR stimulation coupled with blockade of E-cadherin triggers EMT in epithelial cancer cells.•We surveyed proteomic changes associated with EMT using mass spectrometry.•EMT influenced the proteome of both cells and their extracellular vesicles (EVs).•EV proteome may reflect pathways of EMT, but also exhibits features specific to vesiculation.
Cancer-associated fibroblasts (CAFs) drive tumour progression, but the emergence of this cell state is poorly understood. A broad spectrum of metalloproteinases, controlled by the Timp gene family, ...influence the tumour microenvironment in human cancers. Here, we generate quadruple TIMP knockout (TIMPless) fibroblasts to unleash metalloproteinase activity within the tumour-stromal compartment and show that complete Timp loss is sufficient for the acquisition of hallmark CAF functions. Exosomes produced by TIMPless fibroblasts induce cancer cell motility and cancer stem cell markers. The proteome of these exosomes is enriched in extracellular matrix proteins and the metalloproteinase ADAM10. Exosomal ADAM10 increases aldehyde dehydrogenase expression in breast cancer cells through Notch receptor activation and enhances motility through the GTPase RhoA. Moreover, ADAM10 knockdown in TIMPless fibroblasts abrogates their CAF function. Importantly, human CAFs secrete ADAM10-rich exosomes that promote cell motility and activate RhoA and Notch signalling in cancer cells. Thus, Timps suppress cancer stroma where activated-fibroblast-secreted exosomes impact tumour progression.
Bidirectional communication between cells and their microenvironment is crucial for both normal tissue homeostasis and tumor growth. During the development of oral tongue squamous cell carcinoma ...(OTSCC), cancer-associated fibroblasts (CAFs) create a supporting niche by maintaining a bidirectional crosstalk with cancer cells, mediated by classically secreted factors and various nanometer-sized vesicles, termed as extracellular vesicles (EVs). To better understand the role of CAFs within the tumor stroma and elucidate the mechanism by which secreted proteins contribute to OTSCC progression, we isolated and characterized patient-derived CAFs from resected tumors with matched adjacent tissue fibroblasts (AFs). Our strategy employed shotgun proteomics to comprehensively characterize the proteomes of these matched fibroblast populations. Our goals were to identify CAF-secreted factors (EVs and soluble) that can functionally modulate OTSCC cells in vitro and to identify novel CAF-associated biomarkers. Comprehensive proteomic analysis identified 4247 proteins, the most detailed description of a pro-tumorigenic stroma to date. We demonstrated functional effects of CAF secretomes (EVs and conditioned media) on OTSCC cell growth and migration. Comparative proteomics identified novel proteins associated with a CAF-like state. Specifically, MFAP5, a protein component of extracellular microfibrils, was enriched in CAF secretomes. Using in vitro assays, we demonstrated that MFAP5 activated OTSCC cell growth and migration via activation of MAPK and AKT pathways. Using a tissue microarray of richly annotated primary human OTSCCs, we demonstrated an association of MFAP5 expression with patient survival. In summary, our proteomics data of patient-derived stromal fibroblasts provide a useful resource for future mechanistic and biomarker studies.
Biomarkers are rapidly gaining importance in personalized medicine. Although numerous molecular signatures have been developed over the past decade, there is a lack of overlap and many biomarkers ...fail to validate in independent patient cohorts and hence are not useful for clinical application. For these reasons, identification of novel and robust biomarkers remains a formidable challenge. We combine targeted proteomics with computational biology to discover robust proteomic signatures for prostate cancer. Quantitative proteomics conducted in expressed prostatic secretions from men with extraprostatic and organ-confined prostate cancers identified 133 differentially expressed proteins. Using synthetic peptides, we evaluate them by targeted proteomics in a 74-patient cohort of expressed prostatic secretions in urine. We quantify a panel of 34 candidates in an independent 207-patient cohort. We apply machine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prognosis. Our results demonstrate that computationally guided proteomics can discover highly accurate non-invasive biomarkers.
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