Proteomics in Personalized Cancer Therapy: Advances, Applications, and Future Perspectives

Cancer is a highly heterogeneous disease, characterized by genetic, epigenetic, transcriptomic, proteomic, and metabolic variability not only between patients but also within the same tumor microenvironment [1]. This heterogeneity significantly complicates the design of standardized therapies. Traditional treatment modalities such as chemotherapy, radiotherapy, and even some targeted therapies often fail to achieve complete remission due to this diversity, contributing to therapy resistance and disease recurrence. The challenge of intratumor and interpatient variability is compounded by clonal evolution, which further diversifies the tumor cell population during treatment [2]. Subpopulations of cancer cells with distinct molecular profiles may respond differently, leading to partial or ineffective therapeutic outcomes [3]. Thus, a shift from a “one-size-fits-all” approach to a more tailored strategy is urgently needed. Personalized or precision oncology addresses this variability by tailoring treatment strategies to the unique molecular characteristics of an individual’s cancer. By utilizing omics technologies, including genomics, transcriptomics, and increasingly proteomics, it becomes possible to identify biomarkers that predict disease progression, drug response, and resistance [4]. In personalized cancer therapy, treatment decisions are based on the molecular signature of the tumor—such as mutations, protein expression levels, or post-translational modifications (PTMs)—rather than solely on histopathological classification. Clinical applications include selection of targeted drugs (e.g., EGFR inhibitors in lung cancer), immunotherapies guided by PD-L1 expression, or treatment stratification using companion diagnostics [5]. While genomics has been instrumental in uncovering mutations and alterations associated with cancer, proteomics provides complementary and functionally relevant insights by directly measuring the proteins, which are the final effectors of cellular function [6]. Proteomic profiling serves several critical functions in oncology. It helps identify tumor-specific biomarkers, track dynamic changes in the proteome in response to therapy, and detect post-translational modifications such as phosphorylation and acetylation, which are crucial for regulating signaling cascades [7]. Additionally, proteomics enables the stratification of patients for targeted therapies based on their unique protein expression profiles. Recent advancements in mass spectrometry-based proteomics and protein microarrays have made it possible to analyze large sets of proteins from tumor biopsies, plasma, and other biofluids. These technologies facilitate the mapping of intracellular signaling networks, the discovery of resistance mechanisms, and the identification of actionable targets in real-time clinical settings [8]. Furthermore, integrative multi-omics approaches—particularly proteogenomics—combine genomic and proteomic data to provide a more comprehensive understanding of tumor biology. Initiatives like the Clinical Proteomic Tumor Analysis Consortium (CPTAC) have demonstrated how alterations in the proteome correlate with phenotypic heterogeneity and can influence treatment outcomes, reinforcing the role of proteomics in advancing precision medicine [9]. This review aims to provide a comprehensive overview of how proteomics is revolutionizing personalized cancer therapy. It begins by examining recent technological and analytical advancements in the field of cancer proteomics, shedding light on the innovations that are enhancing our ability to study proteins at scale and with greater specificity. The review then explores key clinical applications, including the discovery of biomarkers, the prediction of drug resistance, and the development of personalized therapeutic strategies tailored to individual patient profiles. In addition, it highlights the current challenges and outlines future directions for integrating proteomics into routine clinical oncology practice. By synthesizing the latest findings, this paper underscores the indispensable role of proteomics in fulfilling the promises of precision oncology and transforming cancer treatment paradigms. Figure No.1, illustrates how different sample types (tissue, serum, urine) feed into multi-omics analyses—including proteomics—to yield patient-specific insights that inform diagnostics, prognostics, and therapy selection

Figure No.1: Multi?omics pipeline for precision medicine

2. Basics of Proteomics

2.1 Definition and Scope

Proteomics is the large-scale study of proteins, encompassing their structure, function, modifications, and interactions within biological systems. Unlike the genome, which remains relatively stable, the proteome is highly dynamic and reflects the real-time physiological and pathological conditions of cells [10]. In oncology, proteomics offers essential insights into the processes of tumorigenesis, metastasis, and drug resistance by profiling alterations in protein expression and post-translational modifications (PTMs) [11]. Within personalized medicine, the scope of proteomics is broad and impactful. It plays a key role in identifying cancer-specific biomarkers, uncovering distinct molecular subtypes of tumors, evaluating how individual patients respond to specific treatments, and monitoring disease progression as well as detecting minimal residual disease (MRD) [12]. By providing a detailed and functional understanding of the protein landscape, proteomics complements both genomics and transcriptomics, effectively bridging the gap between genetic information and phenotypic expression, and enhancing precision in cancer diagnosis and therapy.

Figure No 2: Overview of a targeted proteomics workflow used in personalized cancer therapy.

The process begins with specimen collection from various biological fluids such as blood, urine, saliva, cerebrospinal fluid, and ascites. Sample preparation involves protein extraction, depletion of high-abundance proteins, enrichment of low-abundance proteins, denaturation, digestion, and peptide cleanup. Stable isotope-labeled standard (SIS) peptides are added to enable accurate quantification. The peptides are then analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS), where precursor and product ions are selected and fragmented. Data analysis includes signal extraction, performance checking, and quantification through calibration curves, ultimately leading to clinical interpretation and decision-making in patient charts. This workflow highlights the critical role of proteomics in identifying clinically relevant biomarkers and informing individualized cancer treatment strategies.

2.2 Techniques in Proteomics

Proteomic analysis relies on several sophisticated technologies that enable the detection, quantification, and functional annotation of proteins in cancer tissues and fluids.

2.2.1 Mass Spectrometry (MS)

Mass spectrometry (MS) is the cornerstone of modern proteomics, providing high sensitivity and precision in the identification and quantification of proteins. This technique functions by ionizing peptides and measuring their mass-to-charge ratios, allowing for detailed analysis of complex protein mixtures. When combined with liquid chromatography (LC-MS/MS), mass spectrometry enables deep coverage of the proteome and supports a wide range of applications in cancer research. These include differential expression analysis to compare protein levels across different conditions, mapping of post-translational modifications such as phosphorylation and ubiquitination, and integrating proteomic data with genomic information in cancer cohort studies [13]. Mass spectrometry has played a pivotal role in enabling landmark studies, such as those conducted by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which have led to the identification of actionable cancer subtypes in malignancies including breast, ovarian, and colorectal cancers [14]. This has significantly advanced the field of precision oncology by linking proteomic alterations to clinical outcomes and therapeutic strategies.

2.2.2 Two-Dimensional Gel Electrophoresis (2D-GE)

Two-dimensional gel electrophoresis (2D-GE) separates proteins based on their isoelectric point (pI) and molecular weight, and it was one of the earliest techniques developed in the field of proteomics. Despite the emergence of more advanced technologies, 2D-GE remains a valuable tool for several applications. It is particularly useful for visualizing protein isoforms, studying post-translational modifications (PTMs) and protein degradation products, and resolving thousands of proteins within a single sample [15]. However, the technique has notable limitations. Its reproducibility can be inconsistent, and it performs poorly when detecting hydrophobic or low-abundance proteins. These constraints have led to a decline in its routine use, especially as more sensitive and high-throughput methods have become available. Nonetheless, 2D-GE continues to play a role in specific proteomic analyses where its resolution capabilities are advantageous.

2.2.3 Protein Microarrays

Protein microarrays enable high-throughput screening of protein-protein interactions, antibody specificity, and protein expression levels, making them a powerful tool in proteomic research. There are two primary types of protein microarrays. Analytical arrays, such as antibody microarrays, are designed to detect specific proteins with high sensitivity and specificity. Functional arrays, on the other hand, are used to study protein interactions, post-translational modifications, and other functional aspects of proteins [16]. These microarrays are particularly valuable in oncology for applications such as biomarker validation and immune profiling. Their ability to simultaneously analyze thousands of proteins in a single experiment makes them an efficient and scalable platform for advancing personalized cancer diagnostics and therapeutic monitoring.

2.2.4 Label-Free and Labeled Quantification Methods

Quantification in proteomics can be achieved through both label-free and labeled methods, each offering distinct advantages depending on the experimental context. Label-free approaches involve comparing peptide intensities or spectral counts across multiple mass spectrometry runs. These methods are cost-effective and well-suited for analyzing large sample cohorts, making them ideal for population-scale studies. In contrast, labeled methods utilize isotopic or isobaric tags—such as iTRAQ, TMT, or SILAC—that enable the simultaneous analysis of multiple samples within a single mass spectrometry run [17]. These techniques provide greater accuracy and sensitivity, particularly for detecting subtle changes in protein expression levels. As a result, labeled approaches are often preferred in studies where precise quantification is critical, while label-free methods remain a practical choice for broad, exploratory proteomic analyses.

2.3 Bioinformatics in Proteomic Data Analysis

Proteomic datasets are inherently large, complex, and multi-dimensional, requiring sophisticated bioinformatics tools for meaningful interpretation. These tools play a vital role in various stages of data analysis. For instance, software such as Mascot and MaxQuant is used to identify peptides and proteins from mass spectrometry (MS) spectra. Functional enrichment analysis, using databases like Gene Ontology (GO) and KEGG, helps elucidate the biological significance of identified proteins. Integration of proteomic data with genomic and transcriptomic profiles—an approach known as proteogenomics—provides a more comprehensive view of cancer biology and enhances biomarker and target discovery [18]. Machine learning and network analysis techniques are increasingly employed to predict therapeutic targets and stratify patients based on their molecular profiles. Visualization platforms such as Cytoscape are essential for mapping protein-protein interaction networks, enabling researchers to better understand signaling pathways and cellular mechanisms. Additionally, cloud-based platforms like cBioPortal, ProteomicsDB, and PRIDE Archive facilitate public access to annotated cancer proteomics datasets, supporting data sharing and collaborative research across the global scientific community [19].

3. Personalized Cancer Therapy: A Paradigm Shift

3.1 Concept and Evolution

Personalized cancer therapy—also referred to as precision oncology—marks a transformative shift in the way cancer is both understood and treated. Rather than relying on standardized treatment protocols for all patients with a particular type of cancer, this approach customizes interventions based on the unique molecular, genetic, and proteomic characteristics of an individual’s tumor [20]. The foundation of this paradigm began with the sequencing of the human genome and was further advanced through large-scale initiatives like The Cancer Genome Atlas (TCGA). These efforts enabled the identification of oncogenic mutations, gene fusions, and copy number alterations across a wide range of cancers. As a result, targeted therapies were developed to address specific molecular alterations—for example, imatinib for BCR-ABL–positive chronic myeloid leukemia (CML), trastuzumab for HER2-positive breast cancer, and erlotinib for non-small cell lung cancer (NSCLC) harboring EGFR mutations [21]. However, despite these important genomic breakthroughs, it became evident that genetic alterations do not always correlate with protein expression levels or with a patient’s actual response to therapy. This realization highlighted the critical need to integrate proteomic data into clinical decision-making, as proteomics offers a more direct and functional readout of cellular activity and treatment response.

3.2 Genomics vs. Proteomics in Personalization

While genomics provides a blueprint of potential cancer drivers, proteomics reveals the functional state of the tumor, offering dynamic insights into pathways that are actively involved in cancer progression and therapeutic resistance [22]. The table 1 is highlights the key differences between genomics and proteomics in terms of focus, temporal resolution, detection methods, clinical utility, and limitations.

Table 1: Comparative Analysis of Genomics and Proteomics in Cancer Research

For instance, a mutation in PIK3CA may not result in pathway activation unless the downstream proteins are phosphorylated—something only proteomic profiling can determine [23]. Moreover, some therapeutic responses are governed not by DNA mutations but by post-translational modifications such as phosphorylation, acetylation, or ubiquitination—crucial aspects measurable by proteomics [24]. The integration of both omics has been demonstrated by studies like CPTAC, which combine proteogenomic approaches to better subtype tumors and stratify treatment [25].

3.3 Role of Biomarkers in Therapy Selection

Biomarkers are measurable indicators of biological processes or pharmacologic responses and play a critical role in personalized cancer therapy. Protein-based biomarkers, in particular, are essential for guiding various clinical decisions. They assist in therapy selection, as seen with PD-L1 expression used to determine eligibility for immunotherapy. They also have predictive value; for instance, the expression of estrogen receptor (ER), progesterone receptor (PR), and HER2 informs the use of hormone therapy or anti-HER2 treatment in breast cancer. Additionally, protein biomarkers are used to monitor therapeutic response, such as prostate-specific antigen (PSA) levels in prostate cancer, and to detect emerging resistance, exemplified by the detection of the EGFR T790M mutation following resistance to first-generation EGFR inhibitors [26]. Proteomics has significantly advanced the discovery of both prognostic and predictive biomarkers through techniques like quantitative mass spectrometry profiling, reverse-phase protein arrays (RPPA), and artificial intelligence (AI)-driven biomarker panels [27]. For example, in patients with non-small cell lung cancer (NSCLC), plasma proteomics has been used to predict responses to PD-1/PD-L1 inhibitors by employing an immune-proteome–based scoring system known as I-SCORE [28]. Furthermore, the integration of artificial intelligence and machine learning has transformed biomarker interpretation. These technologies analyze vast and complex proteomic datasets to identify signature biomarker panels that are predictive of survival outcomes, treatment-related toxicity, and therapeutic efficacy, thereby enhancing the precision and effectiveness of personalized oncology care [29].

4. Proteomics in Cancer Diagnosis And Prognosis

4.1 Discovery of Novel Protein Biomarkers

The discovery of protein biomarkers plays a pivotal role in improving diagnostic accuracy and guiding therapeutic decision-making in oncology. Traditional diagnostic approaches, which often rely on histopathological grading and genetic testing, may fail to fully capture the functional abnormalities occurring within tumors. Proteomics has addressed this limitation through advanced technologies such as mass spectrometry and protein microarrays, which allow for the identification of cancer-specific proteins and post-translational modifications that more accurately reflect the tumor’s real-time phenotype [30]. Proteomic profiling enables the detection of tumor-associated proteins in biological specimens such as serum, plasma, and tissue. These proteins can serve various clinical purposes: as diagnostic biomarkers—such as the overexpression of HER2 in breast cancer—as predictive biomarkers, exemplified by EGFR expression guiding the use of tyrosine kinase inhibitors, or as theranostic markers that inform both diagnosis and treatment strategies [31]. The development and application of cutting-edge proteomic tools, including iTRAQ, TMT, and label-free mass spectrometry platforms, have further accelerated the identification of low-abundance and functionally significant proteins across different cancer types [32]. These advancements continue to enhance the utility of proteomics in personalizing oncology care by providing more precise molecular insights into tumor biology.

4.2 Early Detection Through Proteomic Profiling

Early detection of cancer is one of the most effective strategies for improving patient survival, and proteomics plays a crucial role in achieving this goal. By enabling the detection of subtle changes in protein expression during the earliest stages of tumorigenesis—well before any morphological or radiological abnormalities are evident—proteomics provides a powerful tool for identifying cancer at its inception [33]. In ovarian cancer, for example, proteomic analyses have led to the discovery of serum proteins such as osteopontin and mesothelin, which demonstrate greater specificity than the commonly used biomarker CA-125 [34]. Similarly, in pancreatic cancer, plasma proteomic signatures involving galectin-3-binding protein and thrombospondin-1 (THBS1) have been associated with early-stage disease, offering a promising avenue for earlier intervention [35]. The integration of high-throughput proteomic techniques with machine learning algorithms has facilitated the development of multiplexed biomarker panels. These panels are capable of distinguishing between cancer subtypes with over 90% accuracy across several cancer types [36]. As a result, they are increasingly being incorporated into liquid biopsy platforms, enabling non-invasive early screening and enhancing the potential for timely and targeted treatment.

4.3 Prognostic Markers for Disease Progression

Prognostic biomarkers offer valuable insight into the likely course and aggressiveness of a disease, independent of the specific treatment administered. Proteomic studies contribute significantly to this area by enabling the classification of tumors into molecular subtypes that are associated with distinct clinical outcomes. This stratification helps guide therapeutic decisions, particularly in determining the appropriate intensity of treatment based on the biological behavior of the tumor [37]. In the case of triple-negative breast cancer (TNBC), proteomic profiling has revealed the existence of distinct subgroups characterized by varying levels of immune activation and cellular proliferation, which correlate closely with patient prognosis [38]. Similarly, in non-small cell lung cancer (NSCLC), specific proteins such as KRT6A, SERPINA1, and SPP1 have been identified as markers linked to poor prognosis and an increased risk of metastasis [39]. Proteomic research has also highlighted the prognostic significance of the tumor microenvironment. For instance, in colorectal cancer, certain proteomic patterns reflecting stromal activation have been associated with a higher risk of recurrence. These findings underscore the importance of the surrounding tumor stroma in influencing disease progression and patient outcomes [40].

4.4 Case Studies: Breast Cancer, Lung Cancer, etc.

Breast Cancer

Breast cancer is one of the most extensively studied malignancies in the field of proteomics, leading to several important discoveries that have shaped clinical practice. A major milestone was the identification of HER2 overexpression, which became a key therapeutic target for the monoclonal antibody trastuzumab, significantly improving outcomes for HER2-positive patients. In addition to HER2, proteomic analyses have identified other proteins such as Annexin A1 and members of the S100 family, which are associated with more aggressive tumor phenotypes and poorer prognoses. Furthermore, proteomic subtyping has expanded the traditional classification of breast cancer beyond the established luminal and basal categories. These novel subgroups, defined by distinct protein expression patterns, have provided deeper insights into tumor biology and have proven useful in predicting treatment response, paving the way for more personalized therapeutic strategies [41].

Figure No. 3. Progression, microenvironment, and lineage differentiation in breast cancer.

The figure No.1 illustrates the multistep progression of breast cancer from normal terminal ductal lobular units (TDLU) to metastatic disease (MET) through premalignant (ADH, DCIS) and malignant (IBC) stages (a). It also highlights the role of tumor–stromal interactions during invasion and metastasis (b), and the differentiation of breast tumors from distinct epithelial progenitor cell lineages, leading to luminal, basal, or mixed-lineage subtypes (c). These mechanisms underpin breast cancer heterogeneity and therapeutic response.

Lung Cancer

Proteomic advances have helped stratify NSCLC patients for immunotherapy. A notable example is the I-SCORE system, a plasma-based proteomic signature predictive of PD-1/PD-L1 inhibitor response [42]. Mass spectrometry has also uncovered markers such as periostin, MMP9, and ENO1 in bronchoalveolar lavage fluid and plasma, contributing to early diagnosis and prognosis of lung cancer [43].

Figure No. 4. Proteomic stratification of NSCLC subtypes and their clinical, molecular, and immunological characteristics.

This figure No. 4, presents a large-scale proteomic analysis of 141 NSCLC tumor samples using TMT-based mass spectrometry. Consensus clustering based on protein expression identified six distinct molecular subtypes with varying histological, genomic, immune, and pathway features (a–c). Subtype-specific proteins were further mapped via UMAP dimensionality reduction and network modules (d), showing differences in cell types and signaling pathways. Box plots show subtype-specific oncogene expression levels (e), while correlation plots (f–h) reveal quantitative discrepancies between mRNA and protein levels, influenced by transcriptional, post-transcriptional, and methylation regulation mechanisms.

Other Cancers

In ovarian cancer, proteomic profiling of high-grade serous ovarian carcinoma (HGSOC) has uncovered critical molecular pathways such as epithelial-mesenchymal transition (EMT) and the PI3K-Akt signaling cascade, both of which have emerged as potential targets for therapeutic intervention [44]. In prostate cancer, urinary proteomics has advanced risk stratification beyond the conventional prostate-specific antigen (PSA) test. Proteins such as alpha-methylacyl-CoA racemase (AMACR) and prostate cancer antigen 3 (PCA3) have been identified as more specific biomarkers, offering improved diagnostic accuracy and aiding in clinical decision-making [45].

5. Proteomic Approaches in Therapeutic Target Identification

5.1 Protein Expression and Post-Translational Modification Profiling

Therapeutic target discovery in oncology is increasingly reliant on proteomics to move beyond static genomic alterations and assess the actual functional molecules—proteins—that drive tumor behavior. By profiling protein expression, researchers gain valuable insights into which signaling pathways are active, which receptors are overexpressed, and how tumors dynamically adapt in response to therapy. Crucially, cancer progression is influenced not only by genetic mutations but also by post-translational modifications (PTMs), which significantly affect protein function and cellular behavior. These modifications include phosphorylation, which plays a central role in intracellular signaling cascades; ubiquitination, which regulates protein stability and degradation; acetylation and methylation, which are involved in epigenetic control; and glycosylation, which can modulate immune evasion and cell adhesion mechanisms [46]. Mass spectrometry-based phosphoproteomics has become a powerful tool for mapping phosphorylation events within tumors, enabling the identification of druggable kinases such as AKT, mTOR, and EGFR. The work of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) has shown that PTM-based proteomic profiling provides both predictive and prognostic information that often surpasses what genomic data alone can offer [47]. A notable example of this is the identification of hyperphosphorylation of HER2 and CDK1 as markers of aggressive breast cancer phenotypes, which are particularly responsive to targeted kinase inhibitors [48]. This highlights the growing importance of proteomics in guiding the development of precision therapies in cancer treatment.

5.2 Drug Target Validation Using Proteomic Technologies

Once potential therapeutic targets are identified, validation is a critical step to confirm their clinical relevance and functional significance. Proteomics provides a range of powerful tools that not only confirm target engagement but also help elucidate the mechanism of action (MoA) of candidate drugs.One widely used method is Thermal Proteome Profiling (TPP), which detects direct drug-protein interactions by measuring changes in protein thermal stability within living cells. This approach helps identify whether a drug binds specifically to its intended target or affects other proteins, revealing potential off-target effects [49]. Another technique, chemical proteomics, employs activity-based probes or affinity tags to isolate and identify interacting proteins, thereby determining binding specificity and functional interactions at a molecular level [50]. Additionally, Reverse Phase Protein Arrays (RPPA) are used to validate target protein expression and activation status across large sample cohorts, often with the aid of phosphorylation-specific antibodies [51]. An illustrative example of TPP's utility is its application in acute myeloid leukemia, where it confirmed BRD4 as a direct target of BET inhibitors. Moreover, the method also uncovered off-target effects that could contribute to drug toxicity or therapeutic resistance [52]. These proteomic strategies collectively ensure that potential targets are not only mechanistically understood but also clinically actionable, advancing the translation of proteomic discoveries into effective cancer therapies.

5.3 Examples of Identified Targets in Various Cancers

Proteomics-driven target discovery has significantly advanced therapeutic strategies across a range of cancers, enabling the development of novel treatments and the repurposing of existing drugs based on functional protein data.In breast cancer, proteomic techniques such as reverse-phase protein arrays (RPPA) and phosphoproteomics have revealed both overexpression and hyperphosphorylation of HER2, directly informing the use of HER2-targeted therapies [53]. Additionally, activation profiles of cyclin-dependent kinases CDK4/6 have been associated with resistance to endocrine therapy, prompting the clinical adoption of CDK inhibitors as a complementary strategy [54]. Lung cancer research, particularly in non-small cell lung cancer (NSCLC), has also benefited from proteomic insights. Proteomic screening identified the expression of ALK fusion proteins in certain NSCLC subtypes, which led to the targeted use of crizotinib. Furthermore, plasma proteomics has uncovered proteins such as ENPP1 and periostin as predictive biomarkers of response to immune checkpoint inhibitors (ICIs), helping refine immunotherapy strategies [55]. In colorectal cancer, proteomic analyses have shown hyperactivation of the EGFR and PI3K-AKT pathways in RAS wild-type tumors, suggesting a potential benefit from dual-targeting therapies. In addition, proteomics has highlighted the role of stromal components, identifying fibronectin and tenascin-C within the tumor microenvironment as potential therapeutic candidates [56]. Ovarian cancer, particularly high-grade serous ovarian carcinoma (HGSOC), has also been a focus of proteomics and phosphoproteomics studies, which have identified SRC-family kinases and the mTOR pathway as key therapeutic vulnerabilities. These findings open up opportunities for targeted intervention in an otherwise challenging cancer type [57]. In prostate cancer, urinary proteomics has led to the identification of alpha-methylacyl-CoA racemase (AMACR), a protein that serves as both a diagnostic and therapeutic biomarker, providing a non-invasive tool for stratification and intervention [58]. Collectively, these examples underscore the power of proteomics not only to identify overexpressed proteins but also to provide functional context. This enhances the precision of drug development pipelines and supports the design of more effective combination therapies tailored to the specific molecular landscape of each cancer type.

6. Proteomics in Drug Resistance And Sensitivity

6.1 Mechanisms of Resistance Revealed by Proteomics

One of the major challenges in cancer therapy is the development of drug resistance, which often leads to treatment failure and disease relapse. While genomic analyses can identify resistance-associated mutations, it is proteomics that offers a deeper understanding of the functional changes at the protein level that drive adaptive resistance mechanisms. Proteomic investigations have revealed several critical pathways and processes involved in resistance. One such mechanism is the activation of alternative or bypass signaling pathways; for instance, in lung cancer, activation of MET or IGF1R has been observed following inhibition of EGFR, enabling the tumor to circumvent the therapeutic blockade. Another mechanism involves post-translational modifications, such as phosphorylation events on downstream effectors, which can restore signaling activity despite receptor inhibition. In addition, proteomics has uncovered resistance mechanisms linked to epigenetic reprogramming and the expression of protein isoforms—changes that are often missed by transcriptomic analyses [59]. A concrete example comes from phosphoproteomic studies of non-small cell lung cancer (NSCLC), where resistance to EGFR inhibitors was found to be driven by compensatory signaling through SRC and AXL kinases. These kinases bypass the EGFR blockade and sustain proliferative signals [60]. Similarly, in breast cancer, mass spectrometry-based profiling showed that resistance to trastuzumab was associated with increased phosphorylation of HER3 and reactivation of the PI3K pathway, both of which undermine the effectiveness of HER2-targeted therapy [61]. Proteomic studies have also highlighted the role of the tumor microenvironment in mediating drug resistance. Proteins such as cytokines and components of the extracellular matrix have been shown to affect drug uptake and promote immune evasion, further complicating therapeutic outcomes [62]. These insights emphasize the importance of proteomics in uncovering complex, dynamic resistance mechanisms and guiding the development of more effective, adaptive cancer treatment strategies.

6.2 Predictive Proteomic Markers for Chemotherapy Response

Predicting individual patient responses to chemotherapy remains a significant clinical challenge, but proteomic technologies are emerging as powerful tools to address this issue. By identifying predictive biomarkers associated with drug sensitivity or resistance, proteomics enables more precise patient stratification and personalized treatment planning. Several studies have demonstrated the utility of proteomic profiling in uncovering protein markers linked to specific chemotherapy responses. For instance, elevated expression of ERCC1 has been associated with resistance to platinum-based chemotherapy in both lung and ovarian cancers, while high levels of TOP2A have been linked to a better response to anthracyclines in breast cancer. Similarly, the presence of glutathione S-transferase P1 (GSTP1) has been correlated with resistance to alkylating agents in colon cancer, indicating that protein expression can serve as a valuable predictor of treatment efficacy [63]. In colorectal cancer, quantitative proteomic analysis has identified thymidylate synthase (TS) and dihydropyrimidine dehydrogenase (DPD) as strong predictors of response to 5-fluorouracil (5-FU)–based chemotherapy regimens [64]. Likewise, in ovarian cancer, high expression levels of heat shock protein 90 (HSP90) have been linked to poor response to cisplatin, suggesting potential avenues for treatment optimization based on proteomic markers [65]. Beyond individual biomarkers, advanced machine learning techniques are now being integrated with proteomic datasets to enhance predictive accuracy. Approaches such as SHAP (SHapley Additive exPlanations)-based biomarker interpretation are being used to generate clinically relevant predictive signatures, enabling oncologists to anticipate therapeutic outcomes with greater confidence and tailor treatments accordingly [66].

6.3 Proteomic-Guided Therapy Adjustment

Proteomic profiling serves not only to guide the initial selection of cancer therapies but also to facilitate real-time adaptation of treatment strategies by monitoring changes in protein expression and signaling dynamics throughout the course of therapy. This dynamic capability allows clinicians to make timely therapeutic adjustments in response to molecular shifts observed during treatment. One notable application involves modifying kinase inhibitor regimens based on evolving phosphoproteomic landscapes. For example, in non-small cell lung cancer (NSCLC) patients who develop resistance to EGFR inhibitors, proteomic analyses have revealed ALK upregulation, prompting a switch to ALK-targeted therapies. Similarly, the integration of immune-proteomic data has proven valuable in assessing eligibility for immune checkpoint inhibitors (ICIs) and in fine-tuning dosage to improve therapeutic response while reducing adverse effects. Additionally, the monitoring of minimal residual disease (MRD) through liquid biopsies has been enhanced by detecting circulating tumor-derived proteins in plasma or urine, offering a non-invasive means of tracking tumor dynamics [67]. In clinical practice, dynamic proteomic assays such as longitudinal plasma proteomics and treatment response proteomics (TRP) are increasingly being used to predict early relapse, assess tumor adaptation to therapy, and identify emerging mechanisms of resistance [68]. These proteomic strategies have shown significant promise in cancers such as pancreatic, lung, and melanoma, where they help refine treatment plans in a personalized manner. By continuously evaluating the tumor’s molecular profile, clinicians can maximize the therapeutic window—delivering the most effective treatment while minimizing unnecessary toxicity and improving overall patient outcomes.

7. Clinical Applications and Trials

7.1 Proteomics-Based Diagnostics in Clinical Practice

The integration of proteomics into clinical oncology is redefining how cancer is diagnosed and treated by offering real-time insights into the molecular behavior of tumors. Proteomics-based diagnostic tools are already being utilized or are approaching clinical implementation, providing valuable capabilities in cancer subtype classification, drug-response prediction, minimal residual disease (MRD) monitoring, and treatment stratification. Techniques such as mass spectrometry (MS), reverse-phase protein arrays (RPPA), and immunoassays are employed to measure protein expression in clinical specimens, including plasma, tumor biopsies, and urine. These tools have been effectively applied in a range of cancers. For instance, in breast cancer, proteomic assays are used to assess HER2 expression; in prostate cancer, proteins such as PCA3 and TMPRSS2 fusion products are used for diagnosis; and in lung cancer, PD-L1 levels are quantified through immunohistochemistry and MS-based approaches to guide immunotherapy decisions [69]. Furthermore, artificial intelligence–driven proteomic platforms have emerged as powerful aids for clinicians, offering data interpretation through decision-support systems. Classifiers trained on proteomic data can distinguish between patients likely to respond to specific treatments, such as immunotherapies, and those who are not, thereby enhancing the precision and personalization of cancer care [70].

7.2 Current Clinical Trials Using Proteomic Strategies

An increasing number of clinical trials are incorporating proteomic profiling to enhance patient stratification, guide treatment decisions, and validate biomarkers. These trials span a broad spectrum, from early-phase studies focused on biomarker discovery to advanced-phase trials evaluating therapeutic efficacy. One ongoing example is clinical trial NCT04335006, which is examining plasma proteomic signatures in patients with metastatic non-small cell lung cancer (NSCLC) undergoing treatment with immune checkpoint inhibitors. Similarly, trials linked to the Clinical Proteomic Tumor Analysis Consortium (CPTAC) are bridging the gap between research and clinical application by correlating genomic and proteomic alterations with treatment responses in ovarian, breast, and colorectal cancers [71]. Another notable trial, NCT03895684, is evaluating the predictive potential of urine proteomics in determining response to androgen deprivation therapy in prostate cancer. Additionally, the APOLLO network (Applied Proteogenomics Organizational Learning and Outcomes), part of the NCI-MATCH trial extension, is a nationwide U.S. initiative aimed at integrating proteomic data into routine oncology practice [72]. These clinical studies employ a range of advanced proteomic platforms. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is used for global proteomic profiling, while methods such as SWATH-MS and data-independent acquisition (DIA-MS) provide reproducible, clinical-grade data. For quantitative biomarker validation, targeted proteomics techniques like multiple reaction monitoring (MRM) and selected reaction monitoring (SRM) are applied, supporting robust and scalable clinical translation.

7.3 FDA-Approved Proteomic Biomarkers and Companion Diagnostics

Although genomic biomarkers dominate FDA approvals, proteomic biomarkers have also gained ground—particularly in areas like early detection, treatment monitoring, and immunotherapy response. Some notable FDA-approved proteomic-based diagnostics and companion diagnostics include: