New genomic and proteomic biomarker discovery in cancer: revolutionizing diagnosis and prognostication.

Background

Cancer continues to be a major global health burden, with approximately 20 million new cases and 9.7 million deaths reported worldwide in 2022, according to the GLOBOCAN 2022 publication [ 1 ]. The intricate heterogeneity of cancer has long challenged early detection, accurate diagnosis, and effective treatment. Over the past few decades, the rise of genomic and proteomic biomarker discovery, driven by advancements in bioinformatics and open-access data repositories, has reshaped the oncology landscape, enabling precision medicine.

This review on “New Genomic and Proteomic Biomarker Discovery in Cancer” traces the historical evolution, development, and validation of genomic and proteomic biomarkers, emphasizing how scientific progress, technological innovations, bioinformatics, and open-access data have driven their identification and how these biomarkers have transformed cancer diagnosis and prognostication, improving patient outcomes. We further examine how these biomarkers are transforming cancer diagnosis and prognostication, and explore practical considerations for implementing such technologies in diverse health systems, including low- and middle-income countries (LMICs).

Added value of the study

While recent reviews have explored the latest advances in multi-omics technologies, this article specifically integrates discoveries in genomics, proteomics, and proteogenomics with a focus on their clinical readiness, regulatory status, and global implementation gaps. We pay particular attention to the unique barriers and opportunities in LMICs, where factors like limited infrastructure, higher costs, and workforce gaps affect how new technologies are adopted. This review further compares the strengths and weaknesses of methods such as next-generation sequencing, mass spectrometry, liquid biopsy, and AI-driven biomarker tools. It also covers the most recent findings from major research projects like The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), as well as developments in AI-assisted prediction, to provide a practical, globally relevant perspective on how biomarkers are discovered and validated for clinical use.

Historical evolution of cancer biomarkers

The concept of cancer biomarkers, i.e. molecular indicators of a biological state linked to malignancy, emerged in the mid-20th century. Early biomarkers were serological, such as carcinoembryonic antigen (CEA), identified in 1965 for colorectal cancer [ 2 ]. CEA became vital for monitoring disease progression and treatment response, though its limited specificity constrained its diagnostic utility. Prostate-specific antigen (PSA), introduced in the 1980s, revolutionized prostate cancer screening, contributing to a significant decline in mortality [ 3 ]. However, PSA’s issues with false positives and over diagnosis underscored the need for more precise biomarkers.

The 1970s and 1980s marked a turning point with the discovery of oncogenes and tumor suppressor genes. In 1979, p53 was identified as a key tumor suppressor, mutated in over half of cancers [ 4 ]. The cloning of the retinoblastoma (Rb) gene in 1986 and the identification of HRAS and KRAS oncogenes in the early 1980s laid the foundation for genomic biomarkers [ 5 , 6 ]. These discoveries facilitated targeted therapies, such as the KRASG12C inhibitor sotorasib, approved by the FDA in 2021 for non-small cell lung cancer (NSCLC) [ 7 ].

The 1990s brought hereditary cancer biomarkers, with BRCA1 and BRCA2 mutations linked to elevated risks of breast and ovarian cancers [ 8 , 9 ]. These findings enabled risk assessment and preventive strategies, including prophylactic surgeries and PARP inhibitors. Concurrently, advances in monoclonal antibodies and immunoassays transformed proteomic biomarker research by enabling detection of targetable cancer-associated proteins.

The early 2000s introduced high-throughput technologies, such as DNA microarrays and next-generation sequencing (NGS), which allowed comprehensive gene expression analysis [ 10 ]. Proteomics advanced with mass spectrometry (MS)-based techniques, identifying cancer-related proteins and peptides [ 11 ]. The rise of bioinformatics and open-access data repositories further accelerated biomarker discovery by enabling large-scale data analysis and sharing. These developments set the stage for multi-omics approaches integrating genomics, transcriptomics, proteomics, and metabolomics (Fig. 1 ).

Fig. 1 Development of genomic and proteomic biomarkers. Historical evolution of cancer biomarker discovery and detection techniques. [The figure illustrates significant milestones and technological advancements that have accelerated biomarker research]

Genomic biomarkers

Genomic biomarkers include DNA-based alterations, such as mutations, copy number variations, and epigenetic changes, as well as RNA-based markers like microRNAs (miRNAs) and long non-coding RNAs (lncRNAs). Next-generation sequencing (NGS) has been pivotal in identifying these biomarkers. Whole-exome sequencing (WES) and whole-genome sequencing (WGS) have uncovered driver mutations, such as EGFR mutations in NSCLC and BRAF V600E in melanoma, p53 and RAS mutations in head neck cancer guiding targeted therapies like EGFR and BRAF inhibitors [ 12 – 14 ]. RNA sequencing (RNAseq) has broadened the scope of genomic biomarkers by identifying gene expression profiles and non-coding RNAs [ 15 , 16 ]. The Oncotype DX assay, analyzing 16 genes in breast cancer, predicts recurrence risk and informs chemotherapy decisions [ 17 ]. MammaPrint, a 70-gene signature, stratifies breast cancer patients into low- and high-risk recurrence groups [ 18 ]. These assays, although clinically established, are single-omics transcriptomic tests and do not integrate additional molecular layers.

RNA sequencing has expanded the discovery of transcriptomic biomarkers, including gene expression signatures and non-coding RNAs. In gliomas, prognostic biomarkers such as IDH1/IDH2 mutations are identified through DNA sequencing rather than RNA-seq, and remain among the most clinically relevant genomic alterations associated with improved survival [ 19 ]. Despite significant technological advancements, biomarker development has faced several historical and ongoing challenges. Genomic biomarkers may suffer from tumor heterogeneity and sampling bias, leading to discordant results across tissue and liquid modalities.

Liquid biopsies, analyzing circulating tumor DNA (ctDNA), cell-free DNA (cfDNA), and circulating tumor cells (CTCs) in blood or biofluids, offer a non-invasive approach to genomic biomarker discovery. These tests enable real-time tumor monitoring, minimal residual disease detection, and identification of resistance mutations, such as EGFR T790M in NSCLC, which predicts response to osimertinib [ 20 ]. The FDA approval of liquid biopsy tests like Guardant360 CDx and FoundationOne Liquid CDx highlights their clinical value [ 21 ]. Liquid biopsies help in tumors where getting the tumor tissue is difficult due to anatomic reasons such as lung and gallbladder cancer or where the tumor responds to neoadjuvant therapy like breast cancer, and it is where these techniques has been found to be most useful. However, liquid biopsy also exhibits variable sensitivity due to tumor shedding differences across cancer types. Early-stage cancers, gliomas, and certain indolent tumors often release insufficient ctDNA for reliable detection. Additionally, clonal hematopoiesis of indeterminate potential (CHIP) may introduce false positives, complicating interpretation. While genomic biomarkers remain foundational in oncology, their ability to predict outcomes is increasingly being enhanced or complemented by transcriptomic, proteomic, and epigenomic signatures.

Proteomic biomarkers

Proteomic biomarkers encompass proteins, peptides, and post-translational modifications (PTMs) reflecting cancer cell function. The human proteome, estimated at 500,000 proteins from approximately 35,000 genes, is highly complex due to alternative splicing and PTMs [ 22 ]. Mass spectrometry-based proteomics, including stable isotope labeling by amino acids in cell culture (SILAC), tandem mass tags (TMT), and sequential window acquisition of all theoretical fragment ion spectra (SWATH-MS), has enabled high-throughput protein analysis [ 23 – 25 ]. Emerging areas such as single-cell proteomics provide unprecedented resolution of intra-tumoral heterogeneity, revealing resistant cellular subpopulations and therapy-induced phenotypes.

Proteomic studies have identified clinically relevant biomarkers, such as the OVA1 assay for ovarian cancer, combining five proteins (CA125, prealbumin, apolipoprotein A1, beta-2-microglobulin, and transferrin) to assess malignancy risk [ 26 ]. Proteomic profiling of colorectal cancer tissues has revealed biomarkers like Mucin 5AC, linked to tumor invasiveness [ 27 ], in head neck cancers biomarkers like p53 antibodies, heat shock protein 70 etc., has been identified [ 28 ]. In hepatocellular carcinoma, SWATH-MS identified protein abundance changes as a prognostic biomarker tied to disease progression [ 29 ]. Clinical applications of proteomic biomarkers are expanding. Phospho-proteomic signatures, such as phospho-ERK and phospho-AKT patterns, are being evaluated as predictors of response to MEK and PI3K inhibitors in melanoma and lung cancers. Immunopeptidomics has identified clinically actionable neoantigens that have progressed into early-phase vaccine trials.

Proteogenomic, integrating proteomic and genomic data, has uncovered novel biomarkers. A lung adenocarcinoma study identified MCT1 and GLUT1 as prognostic markers, demonstrating the synergy between genomic and proteomic data [ 30 ]. Proteomic analysis of tumor secretomes and microenvironments has also revealed biomarkers like class III β-tubulin in ovarian cancer, predicting poor survival in neoadjuvant chemotherapy patients [ 31 ].

Despite substantial progress, proteomic biomarker development is constrained by pre-analytical variability, batch effects, protein instability, and differences in MS platforms. The high dynamic range of plasma proteins, with abundant proteins masking low-abundance candidates, presents an ongoing analytical challenge. Standardization of sample processing, MS workflows, and data analysis is critical for reproducible, multi-center proteomic biomarker validation.

Validation of genomic and proteomic biomarkers

The transition from biomarker discovery to clinical use requires rigorous validation to ensure sensitivity, specificity, and reproducibility. Challenges include tumor heterogeneity, biological variability, and technical limitations like sample collection and analytical variability. The Early Detection Research Network (EDRN) at the National Cancer Institute has standardized validation protocols [ 32 ]. The development pipeline is shown in Fig. 2 .

Fig. 2 Multi-omics workflow for biomarker discovery. [This image depicts the steps involved in integrating genomic, transcriptomic, proteomic, and metabolomic data to identify and validate potential biomarkers]

Genomic biomarker validation

Genomic biomarkers are validated through large-scale clinical trials and cohort studies. EGFR mutations, predictive of EGFR tyrosine kinase inhibitor response in NSCLC, were validated through multi-center trials showing improved progression-free survival [ 33 ]. The ClonoSEQ assay, the first FDA-approved NGS-based test for minimal residual disease in leukemia and multiple myeloma, was validated in extensive cohorts [ 34 ]. Challenges include NGS costs, platform standardization, and validating rare mutations due to limited cohorts. Sequential filtering and DNA pooling strategies enhance efficiency [ 35 ]. Open-access repositories like The Cancer Genome Atlas (TCGA) and cBioPortal facilitate validation by providing large genomic datasets [ 36 ].

Proteomic biomarker validation

Proteomic validation is hindered by the complexity of biological samples, such as blood, with a high dynamic range of protein concentrations. Techniques like surface-enhanced laser desorption/ionization (SELDI) and two-dimensional gel electrophoresis (2-DE) aid protein separation, followed by MS identification [ 37 ]. Orthogonal methods, such as enzyme-linked immunosorbent assay (ELISA) and Western blotting, confirm results. Challenges include abundant proteins obscuring low-abundance biomarkers. Multi-center studies with standardized protocols and targeted MS assays are essential [ 38 ].

Technological advancements in biomarker discovery

The convergence of high-throughput molecular technologies and advanced computation has accelerated biomarker discovery. Table 1 below summarizes the key technologies that are driving the discovery of genomic and proteomic biomarkers. It gives a summary of the main platforms, their uses, and their significance in biomarker research.

Table 1 Key technologies driving biomarker discovery Technology Description Impact on Biomarker Discovery Next-Generation Sequencing (NGS) Rapid sequencing of genomes, exomes, or gene panels Identifies somatic mutations and epigenetic alterations in cancers like NSCLC [ 39 ] Mass Spectrometry (MS) High-throughput protein quantification using SILAC, TMT, and SWATH-MS Enables single-cell proteomics and PTM detection [ 40 ] Liquid Biopsies Analysis of ctDNA, cfDNA, and CTCs in biofluids Monitors tumor dynamics non-invasively [ 41 ] Multi-Omics Integration Combines genomics, transcriptomics, proteomics, and metabolomics Uncovers novel biomarkers via holistic analysis [ 42 ] Artificial Intelligence (AI) Analyzes omics data using machine learning Enhances diagnostic accuracy through pattern recognition [ 43 ] CRISPR/Cas9 Gene editing for functional genomics Validates biomarker function and targets [ 44 ]

Multi-omics biomarkers - clinical adoption vs. research-stage technologies

The integration of multiple molecular layers represents a paradigm shift beyond single-omics assays, enabling deeper biological insight and improved predictive power. Multi-omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics, have significantly accelerated biomarker discovery; however, their integration into routine clinical oncology still lags far behind their rapidly growing presence in research.

In contemporary practice, the majority of clinically implemented biomarkers continue to rely on targeted genomic sequencing, immunohistochemistry (PD-L1 expression), microsatellite instability (MSI) testing, and a small number of validated transcriptomic panels such as Oncotype DX and MammaPrint. In contrast, comprehensive multi-omics assays that integrate multiple molecular layers, such as combined transcriptomic–proteomic (proteogenomic) signatures, are still largely confined to exploratory or translational research settings.

Various multi-omics related tools that have progressed to clinical adoption include a limited number of validated assays that combine genomic and transcriptomic information for therapeutic decision-making. Most clinically implemented biomarker assays, are based solely on mRNA expression profiling and therefore represent single-omics transcriptomic assays, not multi-omics biomarkers. These include transcriptomic expression panels such as Oncotype DX (21-gene panel) and MammaPrint (70-gene signature), both of which guide adjuvant therapy decisions in early-stage breast cancer [ 45 ]. Similarly, homologous recombination deficiency (HRD) and BRCA deficiency assays integrate measures of genomic instability to help identify patients who may benefit from PARP inhibitor therapy [ 46 ]. In contrast, true multi-omics assays integrate at least two molecular layers - such as DNA + RNA, DNA + methylation, RNA + protein, or proteogenomic profiles. Clinically adopted multi-omics tests remain relatively few, but they represent a critical bridge between discovery-stage multi-omics research and routine precision oncology.

In the liquid biopsy domain, circulating tumor DNA (ctDNA) assays - including Guardant360 CDx and FoundationOne Liquid CDx - combine multiple molecular layers, such as sequence variants, copy-number alterations, and methylation features, to support molecular profiling when tissue biopsies are limited or infeasible [ 47 ]. Furthermore, clinical NGS-based diagnostic panels targeting 30–500 genes are now routinely used across oncology to detect actionable mutations, refine diagnosis, and inform targeted treatment strategies. Collectively, these examples represent the small subset of multi-omics-derived approaches that have achieved integration into routine clinical workflows. The assays listed in Table 2 represent the few platforms that integrate multi-layer molecular data and therefore qualify as true multi-omics biomarkers with emerging or established clinical relevance.

Table 2 Clinically available multi-omics biomarker products and their molecular components S. No. Product Name Molecular Layers (Multi-Omics) Clinical Application Regulatory Status Key Reference 1 FoundationOne ® CDx (Foundation Medicine / Roche) DNA mutations, CNVs, gene fusions, MSI, TMB (Genomics + mutational signatures) Tumor profiling; targeted therapy selection; For all solid tumors FDA-approved CDx [ 48 ] 2 Guardant360 ® CDx (Guardant Health) cfDNA mutations, CNVs, fusion detection (Genomic liquid biopsy + fragmentomics) Non-invasive tumor profiling; For advanced solid tumors FDA-approved CDx [ 49 ] 3 Caris Molecular Intelligence ® (Caris Life Sciences) DNA sequencing + RNA sequencing + protein/IHC + MSI/methylation Comprehensive therapy selection and resistance profiling; For solid tumors CLIA-certified multi-omics platform [ 50 ] 4 Tempus xT (500 + gene panel) DNA sequencing + RNA sequencing + AI-based fusion/expression analysis Actionable mutation profiling; expression-based therapy markers; For solid tumors CLIA/CAP-certified [ 51 ] 5 MSK-IMPACT ® + MSK-Fusion RNA Panel DNA sequencing (IMPACT) + RNA-seq for fusions + MSI/TMB Pan-cancer actionable molecular profiling; For solid tumors FDA-recognized NYS test [ 52 ] 6 Galleri ® MCED test (GRAIL) cfDNA methylation + fragmentation patterns + AI classifier (Epigenomics + computational multi-omics) Multi-cancer early detection and tissue-of-origin prediction; screens for a “cancer signal” across over 50 types of cancer LDT; large clinical trials [ 53 ] 7 ClonoSEQ ® MRD Assay (Adaptive Biotechnologies) IGH/IGK/TRA immune receptor sequencing + Bayesian multi-omic modeling Minimal residual disease (ALL, CLL, Multiple myeloma); lymphoid blood cancers FDA-authorized [ 54 ] 8 Caris GPSai™ (AI Multi-Omics) DNA + RNA + Protein/IHC + clinical features integrated by AI Predicts therapy response; tumor classification; For Cancer of Unknown Primary (CUP). CLIA-certified [ 55 ] 9 ExoDx Prostate IntelliScore™ EV-derived RNA signatures + cfRNA (Transcriptomic + EV proteomic context) Predicts risk of high-grade prostate cancer CLIA assay [ 56 ] 10 ThyroSeq ® v3 (Thyroid cancer) DNA mutations, RNA fusions, copy-number alterations, expression markers Indeterminate thyroid nodule classification CLIA-approved [ 57 ]

A structured search of ClinicalTrials.gov identified a total of 121 cancer-related clinical studies investigating multi-omics biomarkers (Table 3 ). These trials encompass a broad spectrum of applications, including early cancer detection, risk stratification, treatment-response prediction, surgical planning, and molecular or digital pathology - based decision support. Only 10 clinical trials were found to have reached full completion, demonstrating the most mature evidence base within this landscape. This compiled list highlights that, despite increasing interest, truly multi-omics clinical trials represent only a small fraction of ongoing biomarker research. Most registered trials remain in the recruitment or early-phase exploratory stage, underscoring the gap between technological capability and clinical translation.

Table 3 List of clinical trials studies associated with multi-omics biomarkers NCT Number Study Title Study Status Interventions NCT04972201 A Proof-of-Concept Study of Pan-cancer Early Detection by Liquid Biopsy COMPLETED DEVICE: Multi-cancer early detection test NCT06969768 Proteogenomics for Follicular Cell-derived Thyroid Cancer: Development of a Classification and Prognosis Prediction Model COMPLETED NCT05197504 Predictive Biomarkers in Patients With Advanced Hepatocellular Carcinoma Treated With Systemic Therapy COMPLETED DRUG: atezolizumab plus bevacizumab NCT04871321 Biomarker Discovery in Patients With Advanced Biliary Tract Cancer COMPLETED DRUG: nab-paclitaxel plus gemcitabine-cisplatin NCT06573424 Tirellizumab + Anlotinib VS Anlotinib for MSS-type CRC COMPLETED COMBINATION_PRODUCT: Tislelizumab + anlotinib|DRUG: anlotinib NCT05417451 Multi-Omics Study of the Effect and Mechanisms of Acupuncture on Psychoneurological Symptoms Among Breast Cancer Survivors COMPLETED OTHER: Acupuncture NCT05729906 Dynamic Predictions of the Links Between Psychological and Physical Health of Older Patients in Nursing Home COMPLETED OTHER: psychological and physical tests NCT07102069 Pharmacogenomic and Pharmacoepigenomic Studies of Antipsychotic Drugs in First-Episode Schizophrenia COMPLETED NCT06759467 Diagnosis of Peritoneal Exfoliative Cytology-positive Gastric Cancer Based on Artificial Intelligence-driven Virtual Biopsy Technology COMPLETED DIAGNOSTIC_TEST: AI-Driven Virtual Biopsy for Diagnosis of Peritoneal Exfoliative Cytology-Positive Gastric Cance

Several barriers continue to hinder the widespread clinical adoption of multi-omics biomarkers. These include the high cost and specialized expertise required for mass spectrometry–based proteomics, limited standardization of pre-analytical workflows, lack of CLIA-validated protocols, dependency on advanced computational infrastructure, challenges in reimbursement, and the absence of sufficiently powered, multi-center, clinically annotated cohorts for robust validation. Together, these limitations highlight the urgent need for simplified, cost-effective, and clinically deployable multi-omics workflows to enable the broader translation of these technologies into routine oncology practice.

Artificial intelligence (AI)-based biomarker discovery

Artificial intelligence plays a dual role in biomarker science enhancing multi-omics biomarker discovery and supporting clinical diagnostics through automated interpretation. AI- based biomarker discovery has emerged as a powerful approach for identifying clinically relevant molecular signatures from high-dimensional genomic and transcriptomic datasets. Traditional biomarker discovery relies largely on differential expression and hypothesis-driven analyses, which often fail to capture non-linear relationships, population-level heterogeneity, or complex regulatory interactions.

In contrast, AI and machine learning algorithms can simultaneously evaluate thousands of features, integrate multi-omics layers, and identify robust biomarkers that are predictive of disease progression, prognosis, and treatment response. Methods such as random forest, LASSO, gradient boosting, autoencoders, and graph-based deep learning models enable unbiased feature selection and can uncover hidden patterns that may remain undetected using standard statistical approaches. When combined with survival modeling and multi-cohort validation, AI-driven frameworks enhance biomarker reproducibility and translational potential, making them particularly valuable for rare and heterogeneous malignancies such as gallbladder cancer.

It is important to differentiate between AI models developed for biomarker discovery and those that are implemented in clinical practice. AI discovery models extract molecular signatures from omics or imaging data, whereas clinically approved AI tools primarily support diagnostic image interpretation and do not directly generate molecular biomarkers (Table 4 ).

Table 4 Clinically available AI-based biomarker products S. No. Product Name Biomarker Type Disease Area Clinical Purpose & Status Discovery Approach Reference 1 Galleri™ (GRAIL MCED Test) cfDNA methylation deep-learning classifier Multi-cancer early detection Detects > 50 cancers; predicts tissue of origin, Laboratory Developed Test (LDT) Deep neural networks trained on > 15,000 cfDNA methylation profiles from CCGA study [ 53 ] 2 IDx-DR ® (Digital Diagnostics) AI deep-learning retinal image classifier Diabetic retinopathy Assists in detecting prostate adenocarcinoma on whole-slide images, FDA-cleared autonomous AI diagnosis CNN trained on > 900,000 expert-labeled fundus images [ 58 ] 3 Paige Prostate ® Deep-learning histology cancer detector Prostate cancer AI detection of prostate carcinoma on whole-slide imaging, Research-only Weakly supervised CNN trained on > 44,000 WSIs [ 59 ] 4 Ibex Galen™ AI CNN-based AI pathology biomarker Breast & prostate cancer Automated cancer detection & grading, CE-IVDR marked) CNN trained on thousands of annotated pathology slides [ 60 ] 5 CNS Tumor Classifier (DKFZ) DNA methylation Random Forest AI classifier Brain tumors Molecular diagnosis & subtype classification, Research-stage Random forest model trained on 2,801 methylation profiles [ 61 ] 6 DeepGlioma™ Deep-learning molecular classifier Glioma Rapid intraoperative molecular diagnosis, Research-stage Deep neural networks on spectroscopy + sequencing [ 62 ] 7 HRDetect AI-based mutational signature model (LASSO ML classifier) Breast cancer Identifies BRCA1/2 deficiency & HRD, Research-stage LASSO logistic regression on 3,479 genome-wide mutational features [ 63 ] 8 PathAI ® (Research-use AI) Deep-learning histopathology predictor NSCLC Predicts histologic subtype & driver mutations, Research-stage CNN trained on > 4,000 H&E slides [ 64 ] 9 Melanoma Immune Response AI Model ML-based transcriptomic immune classifier Melanoma Predicts response to immunotherapy, Research-stage ML trained on RNA-seq + mutation data for immune-score extraction [ 65 ]

AI and bioinformatics are effectively enabling to uncover genomic and proteomic biomarkers by swiftly analyzing big and complex data. A Random Survival Forest model including clinical and CT-based radiomic characteristics outperformed the conventional Cox model (0.775) in non-small cell lung cancer (NSCLC) with a C-index of 0.841 [ 66 ]. A DeepSurv model that combined radiomic and clinical data produced C-index values of 0.74–0.75 and AUCs of 0.73–0.76 at 8, 12, and 24 months [ 67 ]. An interpretable XGBoost model that included tumor and normal-lung radiomics produced a C-index of 0.76 on external validation in patients with locally advanced lung cancer treated with radiation [ 68 ]. In gastric cancer, a deep-learning radiogenomic model combining contrast-enhanced CT radiomics with clinical data predicted MSI status with an AUC of 0.883 in training and 0.802 in validation, suggesting that it can serve as a non-invasive “virtual biopsy” method [ 69 ]. Despite the high predictive accuracy of these AI models, challenges like low interpretability, overfitting risk, and a lack of prospective multi-center validation still exist.

Role of bioinformatics and open-access data in biomarker discovery

Bioinformatics has been instrumental in managing and analyzing the vast datasets generated by genomic and proteomic studies. Computational tools enable pattern recognition, data integration, and predictive modeling, accelerating biomarker discovery. For instance, machine learning algorithms analyze high-dimensional omics data to identify cancer-specific signatures, such as gene expression profiles in breast cancer predicting recurrence [ 18 ]. Deep learning has enhanced histopathological image analysis, identifying spatial patterns linked to tumor aggressiveness, complementing molecular biomarkers [ 43 ]. Bioinformatics platforms like Bioconductor and Galaxy provide open-source tools for differential gene expression analysis, protein quantification, and pathway enrichment, democratizing access to advanced analytics [ 70 ]. These platforms provide defined settings for preprocessing, standardizing, and annotating high-dimensional omics data, which may then be effortlessly integrated into ML models for biomarker identification. For example, Bioconductor tools like as caret and mlr enable users to develop classification models on processed gene-expression datasets, whereas Galaxy provides automated pipelines that integrate feature selection, clustering, and predictive modeling [ 70 ].

Open-access data repositories have transformed biomarker discovery by enabling global data sharing and validation. The TCGA, launched in 2006, provides genomic, transcriptomic, and proteomic data for over 33 cancer types, facilitating the identification of novel biomarkers like IDH mutations in glioma [ 36 ]. The CPTAC integrates proteomic and genomic data, revealing biomarkers like MCT1 in lung adenocarcinoma [ 30 ]. cBioPortal offers user-friendly access to TCGA and other datasets, enabling researchers to explore genetic alterations and their clinical implications [ 71 ]. The Human Protein Atlas provides proteomic data on protein expression across tissues, aiding biomarker validation [ 72 ]. These repositories have reduced duplication, accelerated hypothesis testing, and fostered collaboration, particularly for rare cancers with limited cohorts.

A total of 26 clinical trials that were AI–based biomarkers or computational decision-support tools, found to have reached full completion, demonstrating that AI-driven tools are increasingly being evaluated in real-world clinical research settings (Table 5 ). These trials span diagnostics, prognostication, treatment-response prediction, surgical planning, imaging biomarkers, and digital pathology, reflecting the broad applicability of AI in translational oncology. In addition to these completed trials, the dataset also contained 41 studies that were actively recruiting, demonstrating a rapidly expanding pipeline of AI-assisted clinical research. These ongoing trials focus on next-generation applications such as multi-analyte AI blood tests, advanced radiogenomic signatures, AI-enabled clinical trial matching, automated surgical planning, early cancer detection tools, and immune-response prediction algorithms. The large number of recruiting studies underscores significant global interest and investment in the clinical deployment of AI-based biomarker technologies.

Table 5 List of clinical trials studies associated with AI-based biomarkers NCT Number Study Title Study Status Interventions NCT06759467 Diagnosis of Peritoneal Exfoliative Cytology-positive Gastric Cancer Based on Artificial Intelligence-driven Virtual Biopsy Technology COMPLETED DIAGNOSTIC_TEST: AI-Driven Virtual Biopsy for Diagnosis of Peritoneal Exfoliative Cytology-Positive Gastric Cance NCT06888089 Deep Clinical Trajectory Modeling to Optimize Accrual to Cancer Clinical Trials COMPLETED OTHER: AI-assisted MatchMiner Platform NCT03955627 REJOIN Trial for Older Breast Cancer Survivors COMPLETED BEHAVIORAL: Relieving Joint Pain and Improving AI Adherence in Older Breast Cancer Survivors (REJOIN) NCT05396807 REVERT - taRgeted thErapy for adVanced colorEctal canceR paTients COMPLETED DEVICE: AI NCT07049627 AI-Based Prediction of Pathological Response in Rectal Cancer Patients Receiving Total Neoadjuvant Therapy COMPLETED OTHER: Total Neoadjuvant Therapy (TNT) NCT06526754 Feasibility Analysis of LCD-SLA 3D Printing Technology for Overall Surgical Planning of Liver Malignant Tumors COMPLETED DEVICE: 3D-Printed Liver Model|PROCEDURE: Digital Simulation-Based Surgical Planning NCT01564368 DWI in Assessing Treatment Response in Patients With Breast Cancer Receiving Neoadjuvant Chemotherapy COMPLETED PROCEDURE: diffusion-weighted magnetic resonance imaging NCT06648512 Association of Ex Vivo Drug Response ( EVDR) and Clinical Outcome in Acute Myeloid Leukaemia (EXCYTE-2) COMPLETED OTHER: No Interventions NCT03688906 AI-EMERGE: Development and Validation of a Multi-analyte, Blood-based Colorectal Cancer Screening Test COMPLETED NCT01322802 Vaccine Therapy in Treating Patients With Stage III-IV or Recurrent Ovarian Cancer COMPLETED BIOLOGICAL: pUMVC3-hIGFBP-2 multi-epitope plasmid DNA vaccine|OTHER: laboratory biomarker analysis NCT01928186 FLT PET in Measuring Treatment Response in Patients With Newly Diagnosed Estrogen Receptor-Positive, HER2-Negative Stage I-III Breast Cancer COMPLETED DRUG: Fluorothymidine F-18|PROCEDURE: Positron Emission Tomography|OTHER: Laboratory Biomarker Analysis|DRUG: Run-in (short pre-surgery course) of endocrine-targeted therapy NCT02737475 An Investigational Immuno-Therapy Study of Experimental Medication BMS-986,178 by Itself or in Combination With Nivolumab and/or Ipilimumab in Participants With Solid Cancers That Are Advanced or Have Spread COMPLETED DRUG: BMS-986,178|DRUG: Nivolumab|DRUG: Ipilimumab|BIOLOGICAL: Tetanus vaccine|BIOLOGICAL: DPV-001 vaccine|DRUG: Cyclophosphamide NCT02206984 Endocrine Response in Women With Invasive Lobular Breast Cancer COMPLETED DRUG: Tamoxifen|DRUG: Anastrozole|DRUG: Fulvestrant NCT01478477 Omega-3 Fatty Acids in Preventing Joint Symptoms in Patients With Stage I-III Breast Cancer Receiving Anastrozole, Exemestane, or Letrozole COMPLETED DIETARY_SUPPLEMENT: omega-3 fatty acid supplement|OTHER: Placebo|OTHER: Clinical assessments|OTHER: Assessment of therapy complications|PROCEDURE: Magnetic Resonance Imaging|PROCEDURE: Correlative/special studies NCT02448420 Study of Palbociclib and Trastuzumab With Endocrine Therapy in HER2-positive Metastatic Breast Cancer COMPLETED DRUG: Palbociclib|DRUG: Trastuzumab|DRUG: Endocrine therapy|DRUG: Chemotherapy|DRUG: Antibody-Drug Conjugates NCT05369156 The Effects of Chiropractic Care in Adults With Subclinical Spinal Pain COMPLETED OTHER: Chiropractic Care|OTHER: Control Group NCT02730546 Pembrolizumab, Combination Chemotherapy, and Radiation Therapy Before Surgery in Treating Adult Patients With Locally Advanced Gastroesophageal Junction or Gastric Cardia Cancer That Can Be Removed by Surgery COMPLETED DRUG: Carboplatin|PROCEDURE: Computed Tomography|DRUG: Fluorouracil|OTHER: Laboratory Biomarker Analysis|DRUG: Leucovorin Calcium|DRUG: Oxaliplatin|DRUG: Paclitaxel|BIOLOGICAL: Pembrolizumab|PROCEDURE: Positron Emission Tomography|RADIATION: Radiation Therapy|PROCEDURE: Therapeutic Conventional Surgery NCT04164069 Dasatinib for the Prevention of Oxaliplatin-Induced Neuropathy in Patients With Metastatic Gastrointestinal Cancer Receiving FOLFOX Chemotherapy With or Without Bevacizumab COMPLETED BIOLOGICAL: Bevacizumab|DRUG: Dasatinib|DRUG: Fluorouracil|DRUG: Leucovorin|DRUG: Leucovorin Calcium|DRUG: Oxaliplatin|OTHER: Quality-of-Life Assessment NCT02833350 Safety and Efficacy Study of GDC-0853 Compared With Placebo and Adalimumab in Participants With Rheumatoid Arthritis (RA) COMPLETED DRUG: GDC-0853|DRUG: Adalimumab|DRUG: Folic Acid|DRUG: MTX|DRUG: Placebo NCT02983227 A Study to Evaluate the Long-Term Safety and Efficacy of GDC-0853 in Participants With Moderate to Severe Rheumatoid Arthritis Enrolled in Study GA29350 COMPLETED DRUG: GDC-0853

In addition, AI-based biomarker discovery pipelines are vulnerable to biases arising from underrepresentation of specific ethnic or geographic populations in training datasets. Such biases can lead to reduced diagnostic accuracy and unequal clinical performance. To make results fairer and more reliable, researchers now combine data from diverse populations and use methods that check for and correct these biases. Testing models on different groups and including information from large, international datasets helps make sure any new biomarkers discovered work well for everyone.

Bioinformatics and open-access data have also addressed tumor heterogeneity by enabling single-cell omics analysis, revealing subpopulations driving resistance [ 73 ]. Multi-omics integration, supported by bioinformatics, uncovers complex interactions between genomic and proteomic alterations, as seen in ovarian cancer studies identifying diagnostic panels [ 74 ]. However, challenges remain, including data standardization, privacy concerns, and computational demands, necessitating robust bioinformatics infrastructure.

Although AI has transformed biomarker discovery research, the clinical implementation of AI-based biomarkers remains limited. Most radiomic, radiogenomic, and deep-survival models described in the literature-including those for NSCLC, gastric cancer, and lung radiotherapy-represent research-stage tools that have not undergone regulatory validation or multicenter harmonization. These models perform well in retrospective cohorts but are vulnerable to dataset bias, overfitting, limited ethnic diversity, and lack of external validation, which restricts their diagnostic deployment.

In contrast, only a small number of AI tools have achieved FDA clearance or CE marking, and these predominantly address diagnostic image analysis rather than molecular biomarker discovery. The IDx-DR system for diabetic retinopathy screening and digital pathology algorithms such as Paige Prostate and Ibex Galen, which support histopathological detection and grading. Importantly, these systems do not replace pathologists but enhance detection consistency, reduce oversight errors, and serve as decision-support tools.

A clear gap therefore exists between AI models used to discover biomarkers (transcriptomic feature selection, mutational classifiers, immune-score prediction) and AI tools used clinically. Translational barriers include the need for prospective trials, explainability requirements, uncertainty quantification, regulatory validation pathways, and challenges integrating AI into existing laboratory information systems. These limitations underscore that AI-based biomarker identification is a promising but still evolving field whose real-world deployment is emerging only gradually. It is important to distinguish between AI models used for biomarker discovery and AI tools that are clinically deployed. While the former extract molecular signatures from omics data, the latter such as IDx-DR, Paige Prostate, and Ibex Galen support diagnostic decision-making but do not directly generate multi-omics biomarkers. This distinction is critical for interpreting their clinical applicability. Therefore, while AI has revolutionized biomarker discovery, its integration into routine molecular diagnostics is still emerging and requires multi-center validation, transparency in model interpretability, and standardized evaluation frameworks.

Impact on cancer diagnosis and prognostication

Diagnosis

Biomarkers have improved diagnostic accuracy by distinguishing malignant from benign lesions and identifying subtypes. HER-2 amplification, detected by fluorescence in situ hybridization or immunohistochemistry, guides anti-HER2 therapy in breast and gastric cancers [ 75 ]. PSA testing, combined with multiparametric MRI, enhances prostate cancer diagnosis [ 76 ]. Liquid biopsies detect early cancer-specific alterations, such as hypermethylated cfDNA in cancer or ncRNA [ 77 , 78 ]. Multi-omics panels, leveraging bioinformatics, offer high sensitivity for early-stage cancers, as shown in ovarian cancer [ 74 ].

Prognostication

Biomarkers predict recurrence, treatment response, and survival. Oncotype DX and MammaPrint stratify breast cancer patients by recurrence risk, reducing unnecessary chemotherapy [ 17 , 18 ]. KRAS mutations in colorectal cancer, present in 30–40% of cases, predict anti-EGFR therapy resistance [ 79 ]. Proteomic biomarkers like class III β-tubulin in ovarian cancer predict survival [ 31 ]. The RAF-MEK-ERK pathway, altered in half of cancers, is prognostic in breast cancer, with BRAF mutations linked to worse survival [ 80 ]. Liquid biopsies monitor minimal residual disease, detecting early recurrence in NSCLC [ 81 ].

Comparative assessment of multi-omics platforms

The Table 6 below address a new comparative summary.

Table 6 Global comparison S. No. Platform Strengths Limitations 1. NSG Broad mutation detection Cost, bioinformatics complexity, VUS issues 2. MS Proteomics PTM profiling, pathway insights Reproducibility, dynamic range issues 3. Liquid Biopsy Non-invasive, longitudinal Low ctDNA in early tumors, CHIP interference 4. AI Pattern recognition Bias, overfitting, limited multi-ethnic data

Biomarker implementation in LMICs - opportunities and practical considerations

The adoption of genomic, proteomic, and multi-omics biomarkers in LMICs is limited by systemic barriers, including high equipment and consumable costs, lack of molecular pathology expertise, absence of reimbursement mechanisms, inconsistent electricity and cold-chain infrastructure, and limited access to high-performance computing [ 82 , 83 ]. These constraints restrict deployment of NGS, MS-based proteomics, and liquid biopsy assays in routine oncology practice. The absence of reimbursement mechanisms for molecular testing and limited integration of precision oncology into national cancer control strategies further hinder adoption [ 84 ]. Within this setting, biomarker deployment must prioritize appropriate, scalable, and cost-effective technologies.

Several LMIC-adapted solutions have demonstrated feasibility. Targeted sequencing panels of 25–50 genes offer clinically relevant substantially lower cost than whole-exome or whole-genome assays and have been validated in resource-constrained settings [ 85 ] Low-cost methylation assays, including methylation-sensitive restriction enzyme (MSRE)–based qPCR, enable early cancer detection where array- or sequencing-based methylation profiling is impractical [ 86 ]. Portable nanopore sequencing platforms, such as the Oxford Nanopore MinION, allow near–real-time mutation detection without specialized infrastructure and have been deployed successfully in LMIC molecular diagnostic programs [ 87 ]. Cloud-based bioinformatics resources—including Galaxy, Terra, and DNAnexus—further circumvent the need for local high-performance computing, reducing hardware barriers to molecular interpretation [ 88 ].

At the system level, regional biobanking and sequencing hubs operating under shared-resource models can reduce duplication of costs and expand access to standardized molecular testing [ 89 ]. Telepathology and AI-assisted triage platforms have improved diagnostic turnaround times and mitigated shortages of pathology expertise in several LMICs [ 90 ]. Furthermore, pooled DNA sequencing strategies have emerged as a cost-saving approach for high-volume public hospitals conducting population-scale mutation screening [ 91 ]. Collectively, these context-appropriate approaches provide a practical roadmap for advancing biomarker-driven oncology in LMICs and support more equitable global access to precision cancer care.

Policy-level recommendations for LMIC implementation

To accelerate equitable biomarker integration in LMICs, policy frameworks must prioritize (i) national cancer genomic programs with shared sequencing hubs, (ii) government-supported reimbursement for essential biomarker tests, (iii) international public–private partnerships for subsidized reagents and instruments, (iv) workforce development programs in molecular diagnostics and bioinformatics, and (v) harmonized quality-control standards for sample handling, NGS workflows, and data reporting. These measures ensure scalability, sustainability, and long-term integration of biomarker-driven oncology in resource-constrained settings. Strategic investment in scalable technologies, cloud-based computation, and regional resource-sharing models can enable LMICs to leapfrog traditional infrastructure constraints and rapidly adopt biomarker-driven precision oncology.

Challenges and future directions

Although multi-omics technologies have transformed biomarker discovery, still there are lot of challenges that need to be resolved. Challenges include tumor heterogeneity, biological variability. NGS provides high-resolution detection of somatic mutations and gene fusions but is limited by sequencing costs, bioinformatics complexity, and challenges in interpreting variants of uncertain significance [ 92 ]. Although, targeted NGS panels and focused proteomic assays provide cost-effective alternatives to whole-genome or deep proteomic profiling. Mass spectrometry enables deep protein and PTM profiling but suffers from batch effects, limited sensitivity for low-abundance proteins, and reproducibility issues across laboratories. Integrating these platforms introduces additional challenges, including multi-layer data harmonization, analytical noise, and computational demands.

Bioinformatics faces issues with data standardization and computational demands. Open-access repositories require improved interoperability and privacy safeguards. Cloud-based bioinformatics pipelines lower computational barriers by enabling remote analysis without local servers. Establishing regional testing hubs and biobanks can support multi-institutional sample processing and validation at scale. Moreover, open-access datasets enable researchers in LMICs to contribute to biomarker discovery without incurring high experimental costs. Public–private partnerships, government-supported screening initiatives, and capacity-building programs can further facilitate the integration of genomics and proteomics into cancer care in resource-limited settings.

A challenge in biomarker research is ensuring reproducible results, which is difficult due to differences in data collection, processing, and reporting between studies. To address these concerns, numerous international guidelines have been developed to aid with data standardization. MIAME (Minimum Information About a Microarray Experiment) guidelines provide a standardized framework for describing experimental design, sample preparation, array protocols, and data processing processes in transcriptomic studies, allowing researchers to generate transparent and reproducible datasets. In proteomics, the HUPO Proteomics Standards Initiative (HUPO-PSI) provides standardized formats and reporting criteria for mass spectrometry processes, protein identification, and quantification.

There are additional factors that influence the real-world implementation of these biomarkers, including cost-effectiveness, regulatory hurdles, and challenges in integrating them into clinical policy. Another concern is bringing a cancer biomarker test from research to clinical use, which necessitates significant validation and regulatory approval. Tests like Guardant360 CDx, FoundationOne Liquid CDx, and Oncotype DX demonstrate the level of evidence required to demonstrate a biomarker’s accuracy, clinical utility, and safety prior to its adoption in standard healthcare. Future directions include standardizing protocols to enhance reproducibility, integrating multi-omics with AI for novel biomarkers, and advancing single-cell omics to address heterogeneity [ 73 ]. Despite cost and infrastructure constraints, LMICs can benefit substantially from emerging biomarker technologies through strategic adaptation.

Conclusion

Genomic and proteomic biomarker discovery, powered by bioinformatics and open-access data, has revolutionized cancer care. From CEA and PSA to NGS, MS, and liquid biopsies, scientific and technological progress has enhanced our understanding of cancer, improving diagnosis and prognostication.

Bioinformatics and repositories like TCGA and CPTAC have accelerated discovery by enabling data integration and collaboration. Despite challenges, multi-omics, AI, and global efforts promise to advance precision oncology, delivering personalized, effective care to patients worldwide.

Collectively, advances in multi-omics biomarker development, AI-assisted analysis, and scalable technologies provide a roadmap toward equitable precision oncology. Continued integration of omics data, machine learning, clinical validation, and health system reforms will be essential to narrow the global gap in cancer diagnosis and treatment.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Clinical trial number

Not applicable.

Authors’ contributions

MR and RD wrote the manuscript. MR and RD prepared figures. MP reviewed and edited the manuscript. All authors reviewed the manuscript.

Funding

No funding.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors agree that the work will be published in a journal and understand and agree to the full terms and conditions of its publication.

Competing interests

The authors declare no competing interests.

References

1. Bray F Laversanne M Sung H Ferlay J Siegel RL Soerjomataram I Jemal A Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries CA Cancer J Clin 2024 74 3 229 63 10.3322/caac.21834 38572751 Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. 10.3322/caac.21834 Epub 2024 Apr 4. PMID: 38572751. 38572751 10.3322/caac.21834

2. Gold P Freedman SO Demonstration of tumor-specific antigens in human colonic carcinomata by immunological tolerance and absorption techniques J Exp Med 1965 121 3 439 62 10.1084/jem.121.3.439 14270243 PMC2137957 Gold P, Freedman SO. Demonstration of tumor-specific antigens in human colonic carcinomata by immunological tolerance and absorption techniques. J Exp Med. 1965;121(3):439–62. 10.1084/jem.121.3.439. PMID: 14270243; PMCID: PMC2137957. 14270243 10.1084/jem.121.3.439 PMC2137957

3. Schröder FH Hugosson J Roobol MJ Tammela TL Ciatto S Nelen V Kwiatkowski M Lujan M Lilja H Zappa M Denis LJ Recker F Berenguer A Määttänen L Bangma CH Aus G Villers A Rebillard X van der Kwast T Blijenberg BG Moss SM de Koning HJ Auvinen A ERSPC Investigators. Screening and prostate-cancer mortality in a randomized European study N Engl J Med 2009 360 13 1320 8 10.1056/NEJMoa0810084 19297566 Schröder FH, Hugosson J, Roobol MJ, Tammela TL, Ciatto S, Nelen V, Kwiatkowski M, Lujan M, Lilja H, Zappa M, Denis LJ, Recker F, Berenguer A, Määttänen L, Bangma CH, Aus G, Villers A, Rebillard X, van der Kwast T, Blijenberg BG, Moss SM, de Koning HJ, Auvinen A. ERSPC Investigators. Screening and prostate-cancer mortality in a randomized European study. N Engl J Med. 2009;360(13):1320–8. 10.1056/NEJMoa0810084. Epub 2009 Mar 18. PMID: 19297566. 19297566 10.1056/NEJMoa0810084

4. Lane DP, Crawford LV. T antigen is bound to a host protein in SV40-transformed cells. Nature. 1979;278(5701):261-3. 10.1038/278261a0. PMID: 218111. 10.1038/278261a0 218111

5. Friend SH, Bernards R, Rogelj S, Weinberg RA, Rapaport JM, Albert DM, Dryja TP. A human DNA segment with properties of the gene that predisposes to retinoblastoma and osteosarcoma. Nature. 1986 Oct 16–22;323(6089):643-6. 10.1038/323643a0. PMID: 2877398. 10.1038/323643a0 2877398

6. Barbacid M. ras genes. Annu Rev Biochem. 1987;56:779–827. 10.1146/annurev.bi.56.070187.004023. PMID: 3304147. 10.1146/annurev.bi.56.070187.004023 3304147

7. Skoulidis F Li BT Dy GK Price TJ Falchook GS Wolf J Italiano A Schuler M Borghaei H Barlesi F Kato T Curioni-Fontecedro A Sacher A Spira A Ramalingam SS Takahashi T Besse B Anderson A Ang A Tran Q Mather O Henary H Ngarmchamnanrith G Friberg G Velcheti V Govindan R Sotorasib for lung cancers with KRAS p.G12C mutation N Engl J Med 2021 384 25 2371 81 10.1056/NEJMoa2103695 34096690 PMC9116274 Skoulidis F, Li BT, Dy GK, Price TJ, Falchook GS, Wolf J, Italiano A, Schuler M, Borghaei H, Barlesi F, Kato T, Curioni-Fontecedro A, Sacher A, Spira A, Ramalingam SS, Takahashi T, Besse B, Anderson A, Ang A, Tran Q, Mather O, Henary H, Ngarmchamnanrith G, Friberg G, Velcheti V, Govindan R. Sotorasib for lung cancers with KRAS p.G12C mutation. N Engl J Med. 2021;384(25):2371–81. 10.1056/NEJMoa2103695. Epub 2021 Jun 4. PMID: 34096690; PMCID: PMC9116274. 34096690 10.1056/NEJMoa2103695 PMC9116274

8. Miki Y, Swensen J, Shattuck-Eidens D, Futreal PA, Harshman K, Tavtigian S, Liu Q, Cochran C, Bennett LM, Ding W et al. A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science. 1994;266(5182):66–71. 10.1126/science.7545954. PMID: 7545954. 10.1126/science.7545954 7545954

9. Wooster R, Bignell G, Lancaster J, Swift S, Seal S, Mangion J, Collins N, Gregory S, Gumbs C, Micklem G. Identification of the breast cancer susceptibility gene BRCA2. Nature. 1995 Dec 21–28;378(6559):789 – 92. 10.1038/378789a0. Erratum in: Nature 1996;379(6567):749. PMID: 8524414. 10.1038/378789a0 8524414

10. Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995;270(5235):467 – 70. 10.1126/science.270.5235.467. PMID: 7569999. 10.1126/science.270.5235.467 7569999

11. Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature. 2003;422(6928):198–207. 10.1038/nature01511. PMID: 12634793. 10.1038/nature01511 12634793

12. Lynch TJ Bell DW Sordella R Gurubhagavatula S Okimoto RA Brannigan BW Harris PL Haserlat SM Supko JG Haluska FG Louis DN Christiani DC Settleman J Haber DA Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib N Engl J Med 2004 350 21 2129 39 10.1056/NEJMoa040938 15118073 Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, Harris PL, Haserlat SM, Supko JG, Haluska FG, Louis DN, Christiani DC, Settleman J, Haber DA. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med. 2004;350(21):2129–39. 10.1056/NEJMoa040938. Epub 2004 Apr 29. PMID: 15118073. 15118073 10.1056/NEJMoa040938

13. Flaherty KT Puzanov I Kim KB Ribas A McArthur GA Sosman JA O’Dwyer PJ Lee RJ Grippo JF Nolop K Chapman PB Inhibition of mutated, activated BRAF in metastatic melanoma N Engl J Med 2010 363 9 809 19 10.1056/NEJMoa1002011 20818844 PMC3724529 Flaherty KT, Puzanov I, Kim KB, Ribas A, McArthur GA, Sosman JA, O’Dwyer PJ, Lee RJ, Grippo JF, Nolop K, Chapman PB. Inhibition of mutated, activated BRAF in metastatic melanoma. N Engl J Med. 2010;363(9):809–19. 10.1056/NEJMoa1002011. PMID: 20818844; PMCID: PMC3724529. 20818844 10.1056/NEJMoa1002011 PMC3724529

14. Batta N Pandey M Mutational spectrum of tobacco associated oral squamous carcinoma and its therapeutic significance World J Surg Oncol 2019 17 1 2 10.1186/s12957-019-1741-2 31775759 PMC6882338 Batta N, Pandey M. Mutational spectrum of tobacco associated oral squamous carcinoma and its therapeutic significance. World J Surg Oncol. 2019;17:1–2. 31775759 10.1186/s12957-019-1741-2 PMC6882338

15. Dixit R Pandey M Shukla VK Bioprospecting potential genetic biomarkers of gallbladder cancer Mol Biol Rep 2025 52 1 1 4 10.1007/s11033-025-10607-y 40439781 Dixit R, Pandey M, Shukla VK. Bioprospecting potential genetic biomarkers of gallbladder cancer. Mol Biol Rep. 2025;52(1):1–4. 10.1007/s11033-025-10607-y 40439781

16. Dixit R Pandey M Rajput M Shukla VK Unravelling of the comparative transcriptomic profile of gallbladder cancer using mRNA sequencing Mol Biol Rep 2022 49 7 6395 403 10.1007/s11033-022-07448-4 35469389 Dixit R, Pandey M, Rajput M, Shukla VK. Unravelling of the comparative transcriptomic profile of gallbladder cancer using mRNA sequencing. Mol Biol Rep. 2022;49(7):6395–403. 35469389 10.1007/s11033-022-07448-4

17. Paik S Shak S Tang G Kim C Baker J Cronin M Baehner FL Walker MG Watson D Park T Hiller W Fisher ER Wickerham DL Bryant J Wolmark N A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer N Engl J Med 2004 351 27 2817 26 10.1056/NEJMoa041588 15591335 Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T, Hiller W, Fisher ER, Wickerham DL, Bryant J, Wolmark N. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351(27):2817–26. 10.1056/NEJMoa041588. Epub 2004 Dec 10. PMID: 15591335. 15591335 10.1056/NEJMoa041588

18. van ‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415(6871):530-6. 10.1038/415530a. PMID: 11823860. 10.1038/415530a 11823860

19. Parsons DW Jones S Zhang X Lin JC Leary RJ Angenendt P Mankoo P Carter H Siu IM Gallia GL Olivi A McLendon R Rasheed BA Keir S Nikolskaya T Nikolsky Y Busam DA Tekleab H Diaz LA Jr Hartigan J Smith DR Strausberg RL Marie SK Shinjo SM Yan H Riggins GJ Bigner DD Karchin R Papadopoulos N Parmigiani G Vogelstein B Velculescu VE Kinzler KW An integrated genomic analysis of human glioblastoma multiforme Science 2008 321 5897 1807 12 10.1126/science.1164382 18772396 PMC2820389 Parsons DW, Jones S, Zhang X, Lin JC, Leary RJ, Angenendt P, Mankoo P, Carter H, Siu IM, Gallia GL, Olivi A, McLendon R, Rasheed BA, Keir S, Nikolskaya T, Nikolsky Y, Busam DA, Tekleab H, Diaz LA Jr, Hartigan J, Smith DR, Strausberg RL, Marie SK, Shinjo SM, Yan H, Riggins GJ, Bigner DD, Karchin R, Papadopoulos N, Parmigiani G, Vogelstein B, Velculescu VE, Kinzler KW. An integrated genomic analysis of human glioblastoma multiforme. Science. 2008;321(5897):1807–12. 10.1126/science.1164382. Epub 2008 Sep 4. PMID: 18772396; PMCID: PMC2820389. 18772396 10.1126/science.1164382 PMC2820389

20. Mok TS Wu Y-L Ahn M-J Garassino MC Kim HR Ramalingam SS Shepherd FA He Y Akamatsu H Theelen WS Lee CK Sebastian M Templeton A Mann H Marotti M Ghiorghiu S Papadimitrakopoulou VA AURA3 Investigators. Osimertinib or Platinum-Pemetrexed in EGFR T790M-Positive lung cancer N Engl J Med 2017 376 7 629 40 10.1056/NEJMoa1612674 27959700 PMC6762027 Mok TS, Wu Y-L, Ahn M-J, Garassino MC, Kim HR, Ramalingam SS, Shepherd FA, He Y, Akamatsu H, Theelen WS, Lee CK, Sebastian M, Templeton A, Mann H, Marotti M, Ghiorghiu S, Papadimitrakopoulou VA. AURA3 Investigators. Osimertinib or Platinum-Pemetrexed in EGFR T790M-Positive lung cancer. N Engl J Med. 2017;376(7):629–40. 10.1056/NEJMoa1612674. Epub 2016 Dec 6. PMID: 27959700; PMCID: PMC6762027. 27959700 10.1056/NEJMoa1612674 PMC6762027

21. FDA. (2020). FDA approves first liquid biopsy next-generation sequencing companion diagnostic test. FDA News Release. https://www.fda.gov/drugs/drug-approvals-and-databases/fda-approves-liquid-biopsy-next-generation-sequencing-companion-diagnostic-test [Accessed 10 Jul 2025].

22. Anderson NL, Anderson NG. The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics. 2002;1(11):845 – 67. 10.1074/mcp.r200007-mcp200. Erratum in: Mol Cell Proteomics. 2003;2(1):50. PMID: 12488461. 10.1074/mcp.r200007-mcp200 12488461

23. Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics. 2002;1(5):376 – 86. 10.1074/mcp.m200025-mcp200. PMID: 12118079. 10.1074/mcp.m200025-mcp200 12118079

24. Thompson A, Schäfer J, Kuhn K, Kienle S, Schwarz J, Schmidt G, Neumann T, Johnstone R, Mohammed AK, Hamon C. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem. 2003;75(8):1895 – 904. 10.1021/ac0262560 Erratum in: Anal Chem. 2003;75(18):4942. Johnstone, R . Erratum in: Anal Chem. 2006;78(12):4235. Mohammed, A Karim A. PMID: 12713048. 10.1021/ac0262560 12713048

25. Gillet LC Navarro P Tate S Röst H Selevsek N Reiter L Bonner R Aebersold R Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis Mol Cell Proteom 2012 11 6 O111016717 10.1074/mcp.O111.016717 PMC3433915 22261725 Gillet LC, Navarro P, Tate S, Röst H, Selevsek N, Reiter L, Bonner R, Aebersold R Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteom. 2012;11(6):O111016717. 10.1074/mcp.O111.016717. Epub 2012 Jan 18. PMID: 22261725; PMCID: PMC3433915. 10.1074/mcp.O111.016717 PMC3433915 22261725

26. Ueland FR Desimone CP Seamon LG Miller RA Goodrich S Podzielinski I Sokoll L Smith A van Nagell JR Jr Zhang Z Effectiveness of a multivariate index assay in the preoperative assessment of ovarian tumors Obstet Gynecol 2011 117 6 1289 97 10.1097/AOG.0b013e31821b5118 21606739 Ueland FR, Desimone CP, Seamon LG, Miller RA, Goodrich S, Podzielinski I, Sokoll L, Smith A, van Nagell JR Jr, Zhang Z. Effectiveness of a multivariate index assay in the preoperative assessment of ovarian tumors. Obstet Gynecol. 2011;117(6):1289–97. 10.1097/AOG.0b013e31821b5118. PMID: 21606739. 21606739 10.1097/AOG.0b013e31821b5118

27. Ludvigsen M Thorlacius-Ussing L Vorum H Stender MT Thorlacius-Ussing O Honoré B Proteomic characterization of colorectal cancer tissue from patients identifies novel putative protein biomarkers Curr Issues Mol Biol 2021 43 2 1043 56 10.3390/cimb43020074 34563043 PMC8929084 Ludvigsen M, Thorlacius-Ussing L, Vorum H, Stender MT, Thorlacius-Ussing O, Honoré B. Proteomic characterization of colorectal cancer tissue from patients identifies novel putative protein biomarkers. Curr Issues Mol Biol. 2021;43(2):1043–56. 10.3390/cimb43020074. PMID: 34563043; PMCID: PMC8929084. 34563043 10.3390/cimb43020074 PMC8929084

28. Pillai J Chincholkar T Dixit R A systematic review of proteomic biomarkers in oral squamous cell cancer World J Surg Onc 2021 19 315 10.1186/s12957-021-02423-y PMC8555221 34711249 Pillai J, Chincholkar T, Dixit R, et al. A systematic review of proteomic biomarkers in oral squamous cell cancer. World J Surg Onc. 2021;19:315. 10.1186/s12957-021-02423-y. 10.1186/s12957-021-02423-y PMC8555221 34711249

29. Zhu Y Zhu J Lu C Zhang Q Xie W Sun P Dong X Yue L Sun Y Yi X Zhu T Ruan G Aebersold R Huang S Guo T Identification of protein abundance changes in hepatocellular carcinoma tissues using PCT-SWATH Proteom Clin Appl 2019 13 1 e1700179 10.1002/prca.201700179 30365225 Zhu Y, Zhu J, Lu C, Zhang Q, Xie W, Sun P, Dong X, Yue L, Sun Y, Yi X, Zhu T, Ruan G, Aebersold R, Huang S, Guo T. Identification of protein abundance changes in hepatocellular carcinoma tissues using PCT-SWATH. Proteom Clin Appl. 2019;13(1):e1700179. 10.1002/prca.201700179. Epub 2018 Nov 19. PMID: 30365225. 10.1002/prca.201700179 30365225

30. Gillette MA Satpathy S Cao S Dhanasekaran SM Vasaikar SV Krug K Petralia F Li Y Liang WW Reva B Krek A Ji J Song X Liu W Hong R Yao L Blumenberg L Savage SR Wendl MC Wen B Li K Tang LC MacMullan MA Avanessian SC Kane MH Newton CJ Cornwell M Kothadia RB Ma W Yoo S Mannan R Vats P Kumar-Sinha C Kawaler EA Omelchenko T Colaprico A Geffen Y Maruvka YE da Veiga Leprevost F Wiznerowicz M Gümüş ZH Veluswamy RR Hostetter G Heiman DI Wyczalkowski MA Hiltke T Mesri M Kinsinger CR Boja ES Omenn GS Chinnaiyan AM Rodriguez H Li QK Jewell SD Thiagarajan M Getz G Zhang B Fenyö D Ruggles KV Cieslik MP Robles AI Clauser KR Govindan R Wang P Nesvizhskii AI Ding L Mani DR Carr SA Clinical proteomic tumor analysis Consortium. Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma Cell 2020 182 1 200 e22535 10.1016/j.cell.2020.06.013 32649874 PMC7373300 Gillette MA, Satpathy S, Cao S, Dhanasekaran SM, Vasaikar SV, Krug K, Petralia F, Li Y, Liang WW, Reva B, Krek A, Ji J, Song X, Liu W, Hong R, Yao L, Blumenberg L, Savage SR, Wendl MC, Wen B, Li K, Tang LC, MacMullan MA, Avanessian SC, Kane MH, Newton CJ, Cornwell M, Kothadia RB, Ma W, Yoo S, Mannan R, Vats P, Kumar-Sinha C, Kawaler EA, Omelchenko T, Colaprico A, Geffen Y, Maruvka YE, da Veiga Leprevost F, Wiznerowicz M, Gümüş ZH, Veluswamy RR, Hostetter G, Heiman DI, Wyczalkowski MA, Hiltke T, Mesri M, Kinsinger CR, Boja ES, Omenn GS, Chinnaiyan AM, Rodriguez H, Li QK, Jewell SD, Thiagarajan M, Getz G, Zhang B, Fenyö D, Ruggles KV, Cieslik MP, Robles AI, Clauser KR, Govindan R, Wang P, Nesvizhskii AI, Ding L, Mani DR, Carr SA. Clinical proteomic tumor analysis Consortium. Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma. Cell. 2020;182(1):200–e22535. 10.1016/j.cell.2020.06.013. PMID: 32649874; PMCID: PMC7373300. 32649874 10.1016/j.cell.2020.06.013 PMC7373300

31. Roque DM Buza N Glasgow M Bellone S Bortolomai I Gasparrini S Cocco E Ratner E Silasi DA Azodi M Rutherford TJ Schwartz PE Santin AD Class III β-tubulin overexpression within the tumor microenvironment is a prognostic biomarker for poor overall survival in ovarian cancer patients treated with neoadjuvant carboplatin/paclitaxel Clin Exp Metastasis 2014 31 1 101 10 10.1007/s10585-013-9614-5 24005572 PMC3947146 Roque DM, Buza N, Glasgow M, Bellone S, Bortolomai I, Gasparrini S, Cocco E, Ratner E, Silasi DA, Azodi M, Rutherford TJ, Schwartz PE, Santin AD. Class III β-tubulin overexpression within the tumor microenvironment is a prognostic biomarker for poor overall survival in ovarian cancer patients treated with neoadjuvant carboplatin/paclitaxel. Clin Exp Metastasis. 2014;31(1):101–10. Epub 2013 Sep 5. PMID: 24005572; PMCID: PMC3947146. 24005572 10.1007/s10585-013-9614-5 PMC3947146

32. Pepe MS, Etzioni R, Feng Z, Potter JD, Thompson ML, Thornquist M, Winget M, Yasui Y. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst. 2001;93(14):1054-61. 10.1093/jnci/93.14.1054. PMID: 11459866. 10.1093/jnci/93.14.1054 11459866

33. Mok TS Wu YL Thongprasert S Yang CH Chu DT Saijo N Sunpaweravong P Han B Margono B Ichinose Y Nishiwaki Y Ohe Y Yang JJ Chewaskulyong B Jiang H Duffield EL Watkins CL Armour AA Fukuoka M Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma N Engl J Med 2009 361 10 947 57 10.1056/NEJMoa0810699 19692680 Mok TS, Wu YL, Thongprasert S, Yang CH, Chu DT, Saijo N, Sunpaweravong P, Han B, Margono B, Ichinose Y, Nishiwaki Y, Ohe Y, Yang JJ, Chewaskulyong B, Jiang H, Duffield EL, Watkins CL, Armour AA, Fukuoka M. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Engl J Med. 2009;361(10):947–57. 10.1056/NEJMoa0810699. Epub 2009 Aug 19. PMID: 19692680. 19692680 10.1056/NEJMoa0810699

34. FDA. FDA authorizes first next generation sequencing-based test to detect very low levels of remaining cancer cells in patients with acute lymphoblastic leukemia or multiple myeloma. (2018). FDA News Release. https://www.fda.gov/news-events/press-announcements/fda-authorizes-first-next-generation-sequencing-based-test-detect-very-low-levels-remaining-cancer [Accessed 10 Jul 2025].

35. Druley TE Vallania FL Wegner DJ Varley KE Knowles OL Bonds JA Robison SW Doniger SW Hamvas A Cole FS Fay JC Mitra RD Quantification of rare allelic variants from pooled genomic DNA Nat Methods 2009 6 4 263 5 10.1038/nmeth.1307 19252504 PMC2776647 Druley TE, Vallania FL, Wegner DJ, Varley KE, Knowles OL, Bonds JA, Robison SW, Doniger SW, Hamvas A, Cole FS, Fay JC, Mitra RD. Quantification of rare allelic variants from pooled genomic DNA. Nat Methods. 2009;6(4):263–5. 10.1038/nmeth.1307. Epub 2009 Mar 1. PMID: 19252504; PMCID: PMC2776647. 19252504 10.1038/nmeth.1307 PMC2776647

36. Cancer Genome Atlas Research Network Weinstein JN Collisson EA Mills GB Shaw KR Ozenberger BA Ellrott K Shmulevich I Sander C Stuart JM The cancer genome atlas Pan-Cancer analysis project Nat Genet 2013 45 10 1113 20 10.1038/ng.2764 24071849 PMC3919969 Cancer Genome Atlas Research Network, Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM. The cancer genome atlas Pan-Cancer analysis project. Nat Genet. 2013;45(10):1113–20. 10.1038/ng.2764. PMID: 24071849; PMCID: PMC3919969. 24071849 10.1038/ng.2764 PMC3919969

37. Petricoin EF, Ardekani AM, Hitt BA, Levine PJ, Fusaro VA, Steinberg SM, Mills GB, Simone C, Fishman DA, Kohn EC, Liotta LA. Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002;359(9306):572-7. 10.1016/S0140-6736(02)07746-2. PMID: 11867112. 10.1016/S0140-6736(02)07746-2 11867112

38. Addona TA, Abbatiello SE, Schilling B, Skates SJ, Mani DR, Bunk DM, Spiegelman CH, Zimmerman LJ, Ham AJ, Keshishian H, Hall SC, Allen S, Blackman RK, Borchers CH, Buck C, Cardasis HL, Cusack MP, Dodder NG, Gibson BW, Held JM, Hiltke T, Jackson A, Johansen EB, Kinsinger CR, Li J, Mesri M, Neubert TA, Niles RK, Pulsipher TC, Ransohoff D, Rodriguez H, Rudnick PA, Smith D, Tabb DL, Tegeler TJ, Variyath AM, Vega-Montoto LJ, Wahlander A, Waldemarson S, Wang M, Whiteaker JR, Zhao L, Anderson NL, Fisher SJ, Liebler DC, Paulovich AG, Regnier FE, Tempst P, Carr SA. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat Biotechnol. 2009;27(7):633 – 41. 10.1038/nbt.1546. Epub 2009 Jun 28. Erratum in: Nat Biotechnol. 2009;27(9):864. PMID: 19561596; PMCID: PMC2855883. 10.1038/nbt.1546 PMC2855883 19561596

39. Metzker ML Sequencing technologies - the next generation Nat Rev Genet 2010 11 1 31 46 10.1038/nrg2626 19997069 Metzker ML. Sequencing technologies - the next generation. Nat Rev Genet. 2010;11(1):31–46. 10.1038/nrg2626. Epub 2009 Dec 8. PMID: 19997069. 19997069 10.1038/nrg2626

40. Aebersold R, Mann M. Mass-spectrometric exploration of proteome structure and function. Nature. 2016;537(7620):347 – 55. 10.1038/nature19949. PMID: 27629641. 10.1038/nature19949 27629641

41. Wan JCM Massie C Garcia-Corbacho J Mouliere F Brenton JD Caldas C Pacey S Baird R Rosenfeld N Liquid biopsies come of age: towards implementation of Circulating tumour DNA Nat Rev Cancer 2017 17 4 223 38 10.1038/nrc.2017.7 28233803 Wan JCM, Massie C, Garcia-Corbacho J, Mouliere F, Brenton JD, Caldas C, Pacey S, Baird R, Rosenfeld N. Liquid biopsies come of age: towards implementation of Circulating tumour DNA. Nat Rev Cancer. 2017;17(4):223–38. 10.1038/nrc.2017.7. Epub 2017 Feb 24. PMID: 28233803. 28233803 10.1038/nrc.2017.7

42. Drendel V Heckelmann B Schell C Proteomic distinction of renal Oncocytomas and chromophobe renal cell carcinomas Clin Proteom 2018 15 25 10.1186/s12014-018-9200-6 PMC6074034 30087584 Drendel V, Heckelmann B, Schell C, et al. Proteomic distinction of renal Oncocytomas and chromophobe renal cell carcinomas. Clin Proteom. 2018;15:25. 10.1186/s12014-018-9200-6. 10.1186/s12014-018-9200-6 PMC6074034 30087584

43. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118. 10.1038/nature21056. Epub 2017 Jan 25. Erratum in: Nature. 2017;546(7660):686. doi: 10.1038/nature22985. PMID: 28117445; PMCID: PMC8382232. 10.1038/nature21056 PMC8382232 28117445

44. Cong L Ran FA Cox D Lin S Barretto R Habib N Hsu PD Wu X Jiang W Marraffini LA Zhang F Multiplex genome engineering using CRISPR/Cas systems Science 2013 339 6121 819 23 10.1126/science.1231143 23287718 PMC3795411 Cong L, Ran FA, Cox D, Lin S, Barretto R, Habib N, Hsu PD, Wu X, Jiang W, Marraffini LA, Zhang F. Multiplex genome engineering using CRISPR/Cas systems. Science. 2013;339(6121):819–23. 10.1126/science.1231143. Epub 2013 Jan 3. PMID: 23287718; PMCID: PMC3795411. 23287718 10.1126/science.1231143 PMC3795411

45. van ‘t Paik S van de Shak S Tang G Veer LJ Dai H Vijver MJ A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer N Engl J Med 2004 351 27 2817 26 10.1056/NEJMoa041588 15591335 Paik S, Shak S, Tang G, van ‘t Veer LJ, Dai H, van de Vijver MJ, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351(27):2817–26. 15591335 10.1056/NEJMoa041588

46. Davies H Glodzik D Morganella S HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures Nat Med 2017 23 4 517 25 10.1038/nm.4292 28288110 PMC5833945 Davies H, Glodzik D, Morganella S, et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat Med. 2017;23(4):517–25. 28288110 10.1038/nm.4292 PMC5833945

47. Lanman RB Mortimer SA Zill OA Analytical and clinical validation of a digital sequencing panel for detection of circulating tumor DNA PLoS Med 2015 12 10 e1001880 26474073 10.1371/journal.pone.0140712 PMC4608804 Lanman RB, Mortimer SA, Zill OA, et al. Analytical and clinical validation of a digital sequencing panel for detection of circulating tumor DNA. PLoS Med. 2015;12(10):e1001880. 26474073 10.1371/journal.pone.0140712 PMC4608804

48. Frampton GM Fichtenholtz A Otto GA Wang K Downing SR He J Schnall-Levin M White J Sanford EM An P Sun J Juhn F Brennan K Iwanik K Maillet A Buell J White E Zhao M Balasubramanian S Terzic S Richards T Banning V Garcia L Mahoney K Zwirko Z Donahue A Beltran H Mosquera JM Rubin MA Dogan S Hedvat CV Berger MF Pusztai L Lechner M Boshoff C Jarosz M Vietz C Parker A Miller VA Ross JS Curran J Cronin MT Stephens PJ Lipson D Yelensky R Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing Nat Biotechnol 2013 31 11 1023 31 10.1038/nbt.2696 24142049 PMC5710001 Frampton GM, Fichtenholtz A, Otto GA, Wang K, Downing SR, He J, Schnall-Levin M, White J, Sanford EM, An P, Sun J, Juhn F, Brennan K, Iwanik K, Maillet A, Buell J, White E, Zhao M, Balasubramanian S, Terzic S, Richards T, Banning V, Garcia L, Mahoney K, Zwirko Z, Donahue A, Beltran H, Mosquera JM, Rubin MA, Dogan S, Hedvat CV, Berger MF, Pusztai L, Lechner M, Boshoff C, Jarosz M, Vietz C, Parker A, Miller VA, Ross JS, Curran J, Cronin MT, Stephens PJ, Lipson D, Yelensky R. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnol. 2013;31(11):1023–31. 10.1038/nbt.2696. Epub 2013 Oct 20. PMID: 24142049; PMCID: PMC5710001. 24142049 10.1038/nbt.2696 PMC5710001

49. Lanman RB Mortimer SA Zill OA Analytical and clinical validation of a digital sequencing panel for quantitative, highly accurate evaluation of cell-free Circulating tumor DNA PLoS ONE 2015 10 10 e0140712 10.1371/journal.pone.0140712 26474073 PMC4608804 Lanman RB, Mortimer SA, Zill OA, et al. Analytical and clinical validation of a digital sequencing panel for quantitative, highly accurate evaluation of cell-free Circulating tumor DNA. PLoS ONE. 2015;10(10):e0140712. 26474073 10.1371/journal.pone.0140712 PMC4608804

50. Carter P et al. Jan. Does molecular profiling of tumors using the Caris molecular intelligence platform improve outcomes for cancer patients? Oncotarget vol. 9,10 9456–9467. 16 2018, 10.18632/oncotarget.24258 10.18632/oncotarget.24258 PMC5823623 29507702

51. Beaubier N Tell R Lau D Clinical validation of the tempus xT next-generation targeted oncology sequencing assay Oncotarget 2019 10 24 2384 96 10.18632/oncotarget.26797 31040929 PMC6481324 Beaubier N, Tell R, Lau D. Clinical validation of the tempus xT next-generation targeted oncology sequencing assay. Oncotarget. 2019;10(24):2384–96. 31040929 10.18632/oncotarget.26797 PMC6481324

52. Zehir A, Benayed R, Shah RH, Syed A, Middha S, Kim HR, Srinivasan P, Gao J, Chakravarty D, Devlin SM, Hellmann MD, Barron DA, Schram AM, Hameed M, Dogan S, Ross DS, Hechtman JF, DeLair DF, Yao J, Mandelker DL, Cheng DT, Chandramohan R, Mohanty AS, Ptashkin RN, Jayakumaran G, Prasad M, Syed MH, Rema AB, Liu ZY, Nafa K, Borsu L, Sadowska J, Casanova J, Bacares R, Kiecka IJ, Razumova A, Son JB, Stewart L, Baldi T, Mullaney KA, Al-Ahmadie H, Vakiani E, Abeshouse AA, Penson AV, Jonsson P, Camacho N, Chang MT, Won HH, Gross BE, Kundra R, Heins ZJ, Chen HW, Phillips S, Zhang H, Wang J, Ochoa A, Wills J, Eubank M, Thomas SB, Gardos SM, Reales DN, Galle J, Durany R, Cambria R, Abida W, Cercek A, Feldman DR, Gounder MM, Hakimi AA, Harding JJ, Iyer G, Janjigian YY, Jordan EJ, Kelly CM, Lowery MA, Morris LGT, Omuro AM, Raj N, Razavi P, Shoushtari AN, Shukla N, Soumerai TE, Varghese AM, Yaeger R, Coleman J, Bochner B, Riely GJ, Saltz LB, Scher HI, Sabbatini PJ, Robson ME, Klimstra DS, Taylor BS, Baselga J, Schultz N, Hyman DM, Arcila ME, Solit DB, Ladanyi M, Berger MF. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med. 2017;23(6):703–713. 10.1038/nm.4333. Epub 2017 May 8. Erratum in: Nat Med. 2017;23(8):1004. doi: 10.1038/nm0817-1004c. PMID: 28481359; PMCID: PMC5461196. 10.1038/nm.4333 PMC5461196 28481359

53. Liu MC Oxnard GR Klein EA Swanton C Seiden MV CCGA Consortium Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA Ann Oncol 2020 31 6 745 59 10.1016/j.annonc.2020.02.011 33506766 PMC8274402 Liu MC, Oxnard GR, Klein EA, Swanton C, Seiden MV, CCGA Consortium. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann Oncol. 2020;31(6):745–59. 10.1016/j.annonc.2020.02.011. Epub 2020 Mar 30. PMID: 33506766; PMCID: PMC8274402. 33506766 10.1016/j.annonc.2020.02.011 PMC8274402

54. Faham M Zheng J Moorhead M Carlton VE Stow P Coustan-Smith E Pui CH Campana D Deep-sequencing approach for minimal residual disease detection in acute lymphoblastic leukemia Blood 2012 120 26 5173 80 10.1182/blood-2012-07-444042 23074282 PMC3537310 Faham M, Zheng J, Moorhead M, Carlton VE, Stow P, Coustan-Smith E, Pui CH, Campana D. Deep-sequencing approach for minimal residual disease detection in acute lymphoblastic leukemia. Blood. 2012;120(26):5173–80. 10.1182/blood-2012-07-444042. Epub 2012 Oct 16. PMID: 23074282; PMCID: PMC3537310. 23074282 10.1182/blood-2012-07-444042 PMC3537310

55. Ghani H GPSai: A clinically validated AI tool for tissue of origin prediction during routine tumor profiling Cancer Res Commun Vol 2025 5 9 1477 89 10.1158/2767-9764.CRC-25-0171 PMC12399951 40741943 Ghani H, et al. GPSai: A clinically validated AI tool for tissue of origin prediction during routine tumor profiling. Cancer Res Commun Vol. 2025;5(9):1477–89. 10.1158/2767-9764.CRC-25-0171. 10.1158/2767-9764.CRC-25-0171 PMC12399951 40741943

56. McKiernan J et al. A novel urine exosome gene expression assay to predict High-grade prostate cancer at initial biopsy. JAMA Oncol 2,7 (2016): 882–9. 10.1001/jamaoncol.2016.0097 10.1001/jamaoncol.2016.0097 27032035

57. Steward DL Carty SE Sippel RS Yang SP Sosa JA Sipos JA Figge JJ Mandel S Haugen BR Burman KD Baloch ZW Lloyd RV Seethala RR Gooding WE Chiosea SI Gomes-Lima C Ferris RL Folek JM Khawaja RA Kundra P Loh KS Marshall CB Mayson S McCoy KL Nga ME Ngiam KY Nikiforova MN Poehls JL Ringel MD Yang H Yip L Nikiforov YE Performance of a Multigene Genomic Classifier in Thyroid Nodules With Indeterminate Cytology: A Prospective Blinded Multicenter Study JAMA Oncol 2019 5 2 204 12 10.1001/jamaoncol.2018.4616 30419129 PMC6439562 StewardDL, Carty SE, Sippel RS, Yang SP, Sosa JA, Sipos JA, Figge JJ, Mandel S, Haugen BR, Burman KD, Baloch ZW, Lloyd RV, Seethala RR, Gooding WE, Chiosea SI, Gomes-Lima C, Ferris RL, Folek JM, Khawaja RA, Kundra P, Loh KS, Marshall CB, Mayson S, McCoy KL, Nga ME, Ngiam KY, Nikiforova MN, Poehls JL, Ringel MD, Yang H, Yip L, Nikiforov YE. Performance of a Multigene Genomic Classifier in Thyroid Nodules With Indeterminate Cytology: A Prospective Blinded Multicenter Study. JAMA Oncol. 2019;5(2):204–12. 10.1001/jamaoncol.2018.4616 Erratum in:JAMA Oncol. 2019 Feb 1;5(2):271. doi: 10.1001/jamaoncol.2018.6413. PMID: 30419129; PMCID: PMC6439562. 30419129 10.1001/jamaoncol.2018.4616 PMC6439562

58. Abràmoff MD Lavin PT Birch M Shah N Folk JC Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices NPJ Digit Med 2018 1 39 10.1038/s41746-018-0040-6 31304320 PMC6550188 Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1:39. 10.1038/s41746-018-0040-6. PMID: 31304320; PMCID: PMC6550188. 31304320 10.1038/s41746-018-0040-6 PMC6550188

59. Campanella G Hanna MG Geneslaw L Miraflor A Werneck Krauss Silva V Busam KJ Brogi E Reuter VE Klimstra DS Fuchs TJ Clinical-grade computational pathology using weakly supervised deep learning on whole slide images Nat Med 2019 25 8 1301 9 10.1038/s41591-019-0508-1 31308507 PMC7418463 Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ, Brogi E, Reuter VE, Klimstra DS, Fuchs TJ. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019;25(8):1301–9. 10.1038/s41591-019-0508-1. Epub 2019 Jul 15. PMID: 31308507; PMCID: PMC7418463. 31308507 10.1038/s41591-019-0508-1 PMC7418463

60. Lami K Yoon HS Parwani AV Pham HH Tachibana Y Linhart C Grinwald M Vecsler M Fukuoka J Validation of prostate and breast cancer detection artificial intelligence algorithms for accurate histopathological diagnosis and grading: a retrospective study with a Japanese cohort Pathology 2024 56 5 633 42 10.1016/j.pathol.2024.02.009 38719771 Lami K, Yoon HS, Parwani AV, Pham HH, Tachibana Y, Linhart C, Grinwald M, Vecsler M, Fukuoka J. Validation of prostate and breast cancer detection artificial intelligence algorithms for accurate histopathological diagnosis and grading: a retrospective study with a Japanese cohort. Pathology. 2024;56(5):633–42. 38719771 10.1016/j.pathol.2024.02.009

61. von Capper D von Jones DTW Sill M Nature., Capper D Jones DTW Sill M Hovestadt V Schrimpf D Sturm D Koelsche C Sahm F Chavez L Reuss DE Kratz A Wefers AK Huang K Pajtler KW Schweizer L Stichel D Olar A Engel NW Lindenberg K Harter PN Braczynski AK Plate KH Dohmen H Garvalov BK Coras R Hölsken A Hewer E Bewerunge-Hudler M Schick M Fischer R Beschorner R Schittenhelm J Staszewski O Wani K Varlet P Pages M Temming P Lohmann D Selt F Witt H Milde T Witt O Aronica E Giangaspero F Rushing E Scheurlen W Geisenberger C Rodriguez FJ Becker A Preusser M Haberler C Bjerkvig R Cryan J Farrell M Deckert M Hench J Frank S Serrano J Kannan K Tsirigos A Brück W Hofer S Brehmer S Seiz-Rosenhagen M Hänggi D Hans V Rozsnoki S Hansford JR Kohlhof P Kristensen BW Lechner M Lopes B Mawrin C Ketter R Kulozik A Khatib Z Heppner F Koch A Jouvet A Keohane C Mühleisen H Mueller W Pohl U Prinz M Benner A Zapatka M Gottardo NG Driever PH Kramm CM Müller HL Rutkowski S Hoff K Frühwald MC Gnekow A Fleischhack G Tippelt S Calaminus G Monoranu CM Perry A Jones C Jacques TS Radlwimmer B Gessi M Pietsch T Schramm J Schackert G Westphal M Reifenberger G Wesseling P Weller M Collins VP Blümcke I Bendszus M Debus J Huang A Jabado N Northcott PA Paulus W Gajjar A Robinson GW Taylor MD Jaunmuktane Z Ryzhova M Platten M Unterberg A Wick W Karajannis MA Mittelbronn M Acker T Hartmann C Aldape K Schüller U Buslei R Lichter P Kool M Herold-Mende C Ellison DW Hasselblatt M Snuderl M Brandner S Korshunov A Deimling A Pfister SM DNA methylation-based classification of central nervous system tumours Nature 2018 555 7697 469 74 10.1038/nature26000 29539639 PMC6093218 Capper D, Jones DTW, Sill M, Nature., Capper D, Jones DTW, Sill M, Hovestadt V, Schrimpf D, Sturm D, Koelsche C, Sahm F, Chavez L, Reuss DE, Kratz A, Wefers AK, Huang K, Pajtler KW, Schweizer L, Stichel D, Olar A, Engel NW, Lindenberg K, Harter PN, Braczynski AK, Plate KH, Dohmen H, Garvalov BK, Coras R, Hölsken A, Hewer E, Bewerunge-Hudler M, Schick M, Fischer R, Beschorner R, Schittenhelm J, Staszewski O, Wani K, Varlet P, Pages M, Temming P, Lohmann D, Selt F, Witt H, Milde T, Witt O, Aronica E, Giangaspero F, Rushing E, Scheurlen W, Geisenberger C, Rodriguez FJ, Becker A, Preusser M, Haberler C, Bjerkvig R, Cryan J, Farrell M, Deckert M, Hench J, Frank S, Serrano J, Kannan K, Tsirigos A, Brück W, Hofer S, Brehmer S, Seiz-Rosenhagen M, Hänggi D, Hans V, Rozsnoki S, Hansford JR, Kohlhof P, Kristensen BW, Lechner M, Lopes B, Mawrin C, Ketter R, Kulozik A, Khatib Z, Heppner F, Koch A, Jouvet A, Keohane C, Mühleisen H, Mueller W, Pohl U, Prinz M, Benner A, Zapatka M, Gottardo NG, Driever PH, Kramm CM, Müller HL, Rutkowski S, von Hoff K, Frühwald MC, Gnekow A, Fleischhack G, Tippelt S, Calaminus G, Monoranu CM, Perry A, Jones C, Jacques TS, Radlwimmer B, Gessi M, Pietsch T, Schramm J, Schackert G, Westphal M, Reifenberger G, Wesseling P, Weller M, Collins VP, Blümcke I, Bendszus M, Debus J, Huang A, Jabado N, Northcott PA, Paulus W, Gajjar A, Robinson GW, Taylor MD, Jaunmuktane Z, Ryzhova M, Platten M, Unterberg A, Wick W, Karajannis MA, Mittelbronn M, Acker T, Hartmann C, Aldape K, Schüller U, Buslei R, Lichter P, Kool M, Herold-Mende C, Ellison DW, Hasselblatt M, Snuderl M, Brandner S, Korshunov A, von Deimling A, Pfister SM, et al. DNA methylation-based classification of central nervous system tumours. Nature. 2018;555(7697):469–74. 10.1038/nature26000 doi:10.1038/nature26000. Epub 2018 Mar 14. PMID: 29539639; PMCID: PMC6093218. 29539639 10.1038/nature26000 PMC6093218

62. Hollon T Jiang C Chowdury A Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging Nat Med 2023 29 828 32 10.1038/s41591-023-02252-4 36959422 PMC10445531 Hollon T, Jiang C, Chowdury A, et al. Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging. Nat Med. 2023;29:828–32. 10.1038/s41591-023-02252-4. 36959422 10.1038/s41591-023-02252-4 PMC10445531

63. Davies H Glodzik D Morganella S Yates LR Staaf J Zou X Ramakrishna M Martin S Boyault S Sieuwerts AM Simpson PT King TA Raine K Eyfjord JE Kong G Borg Å Birney E Stunnenberg HG van de Vijver MJ Børresen-Dale AL Martens JW Span PN Lakhani SR Vincent-Salomon A Sotiriou C Tutt A Thompson AM Van Laere S Richardson AL Viari A Campbell PJ Stratton MR Nik-Zainal S HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures Nat Med 2017 23 4 517 25 10.1038/nm.4292 28288110 PMC5833945 Davies H, Glodzik D, Morganella S, Yates LR, Staaf J, Zou X, Ramakrishna M, Martin S, Boyault S, Sieuwerts AM, Simpson PT, King TA, Raine K, Eyfjord JE, Kong G, Borg Å, Birney E, Stunnenberg HG, van de Vijver MJ, Børresen-Dale AL, Martens JW, Span PN, Lakhani SR, Vincent-Salomon A, Sotiriou C, Tutt A, Thompson AM, Van Laere S, Richardson AL, Viari A, Campbell PJ, Stratton MR, Nik-Zainal S. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat Med. 2017;23(4):517–25. 10.1038/nm.4292. Epub 2017 Mar 13. PMID: 28288110; PMCID: PMC5833945. 28288110 10.1038/nm.4292 PMC5833945

64. Coudray N Ocampo PS Sakellaropoulos T Narula N Snuderl M Fenyö D Moreira AL Razavian N Tsirigos A Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning Nat Med 2018 24 10 1559 67 10.1038/s41591-018-0177-5 30224757 PMC9847512 Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, Moreira AL, Razavian N, Tsirigos A. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med. 2018;24(10):1559–67. 10.1038/s41591-018-0177-5. Epub 2018 Sep 17. PMID: 30224757; PMCID: PMC9847512. 30224757 10.1038/s41591-018-0177-5 PMC9847512

65. Lauss M, Donia M, Harbst K, Andersen R, Mitra S, Rosengren F, Salim M, Vallon-Christersson J, Törngren T, Kvist A, Ringnér M, Svane IM, Jönsson G. Mutational and putative neoantigen load predict clinical benefit of adoptive T cell therapy in melanoma. Nat Commun. 2017;8(1):1738. 10.1038/s41467-017-01460-0. Erratum in: Nat Commun. 2020;11(1):1714. doi: 10.1038/s41467-020-15531-2. PMID: 29170503; PMCID: PMC5701046. 10.1038/s41467-017-01460-0 PMC5701046 29170503

66. Koyama J Morise M Furukawa T Oyama S Matsuzawa R Tanaka I Wakahara K Yokota H Kimura T Shiratori Y Kondoh Y Hashimoto N Ishii M Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer BMC Cancer 2024 24 1 1417 10.1186/s12885-024-13190-w 39558311 PMC11572056 Koyama J, Morise M, Furukawa T, Oyama S, Matsuzawa R, Tanaka I, Wakahara K, Yokota H, Kimura T, Shiratori Y, Kondoh Y, Hashimoto N, Ishii M. Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer. BMC Cancer. 2024;24(1):1417. 10.1186/s12885-024-13190-w. PMID: 39558311; PMCID: PMC11572056. 39558311 10.1186/s12885-024-13190-w PMC11572056

67. Hou KY Chen JR Wang YC Chiu MH Lin SP Mo YH Peng SC Lu CF Radiomics-Based deep learning prediction of overall survival in Non-Small-Cell lung cancer using Contrast-Enhanced computed tomography Cancers (Basel) 2022 14 15 3798 10.3390/cancers14153798 35954461 PMC9367244 Hou KY, Chen JR, Wang YC, Chiu MH, Lin SP, Mo YH, Peng SC, Lu CF. Radiomics-Based deep learning prediction of overall survival in Non-Small-Cell lung cancer using Contrast-Enhanced computed tomography. Cancers (Basel). 2022;14(15):3798. 10.3390/cancers14153798. PMID: 35954461; PMCID: PMC9367244. 35954461 10.3390/cancers14153798 PMC9367244

68. Luo T Yan M Zhou M Dekker A Appelt AL Ji Y Zhu J de Ruysscher D Wee L Zhao L Zhang Z Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters Radiol Med 2025 130 1 96 109 10.1007/s11547-024-01919-3 39542968 Luo T, Yan M, Zhou M, Dekker A, Appelt AL, Ji Y, Zhu J, de Ruysscher D, Wee L, Zhao L, Zhang Z. Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters. Radiol Med. 2025;130(1):96–109. 10.1007/s11547-024-01919-3. Epub 2024 Nov 14. PMID: 39542968. 39542968 10.1007/s11547-024-01919-3

69. Jiang Z Xie W Zhou X Pan W Jiang S Zhang X Zhang M Zhang Z Lu Y Wang D A virtual biopsy study of microsatellite instability in gastric cancer based on deep learning radiomics Insights Imaging 2023 14 1 104 10.1186/s13244-023-01438-1 37286810 PMC10247640 Jiang Z, Xie W, Zhou X, Pan W, Jiang S, Zhang X, Zhang M, Zhang Z, Lu Y, Wang D. A virtual biopsy study of microsatellite instability in gastric cancer based on deep learning radiomics. Insights Imaging. 2023;14(1):104. 10.1186/s13244-023-01438-1. PMID: 37286810; PMCID: PMC10247640. 37286810 10.1186/s13244-023-01438-1 PMC10247640

70. Huber W Carey VJ Gentleman R Anders S Carlson M Carvalho BS Bravo HC Davis S Gatto L Girke T Gottardo R Hahne F Hansen KD Irizarry RA Lawrence M Love MI MacDonald J Obenchain V Oleś AK Pagès H Reyes A Shannon P Smyth GK Tenenbaum D Waldron L Morgan M Orchestrating high-throughput genomic analysis with bioconductor Nat Methods 2015 12 2 115 21 10.1038/nmeth.3252 25633503 PMC4509590 Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M. Orchestrating high-throughput genomic analysis with bioconductor. Nat Methods. 2015;12(2):115–21. 10.1038/nmeth.3252. PMID: 25633503; PMCID: PMC4509590. 25633503 10.1038/nmeth.3252 PMC4509590

71. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, Antipin Y, Reva B, Goldberg AP, Sander C, Schultz N. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401-4. 10.1158/2159-8290.CD-12-0095. Erratum in: Cancer Discov. 2012;2(10):960. PMID: 22588877; PMCID: PMC3956037. 10.1158/2159-8290.CD-12-0095 PMC3956037 22588877

72. Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, Olsson I, Edlund K, Lundberg E, Navani S, Szigyarto CA, Odeberg J, Djureinovic D, Takanen JO, Hober S, Alm T, Edqvist PH, Berling H, Tegel H, Mulder J, Rockberg J, Nilsson P, Schwenk JM, Hamsten M, von Feilitzen K, Forsberg M, Persson L, Johansson F, Zwahlen M, von Heijne G, Nielsen J, Pontén F. Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419. 10.1126/science.1260419. PMID: 25613900. 10.1126/science.1260419 25613900

73. Navin N Hicks J Future medical applications of single-cell sequencing in cancer Genome Med 2011 3 5 31 10.1186/gm247 21631906 PMC3219072 Navin N, Hicks J. Future medical applications of single-cell sequencing in cancer. Genome Med. 2011;3(5):31. 10.1186/gm247. PMID: 21631906; PMCID: PMC3219072. 21631906 10.1186/gm247 PMC3219072

74. Zhang, Zheng M, Hu Y, Gou R, Wang J, Nie X, Li X, Liu Q, Liu J, Lin B et al. Integrated multi-omics analysis of genomics, epigenomics, and transcriptomics in ovarian carcinoma. Aging (Albany NY). 2019;11(12):4198–4215. 10.18632/aging.102047. PMID: 31257224; PMCID: PMC6629004. 10.18632/aging.102047 PMC6629004 31257224

75. Slamon DJ, Godolphin W, Jones LA et al. Studies of the HER-2/neu proto-oncogene in human breast and ovarian cancer. Science (New York, N.Y.). 1989;244(4905):707–712. 10.1126/science.2470152. PMID: 2470152. 10.1126/science.2470152 2470152

76. Ahmed HU El-Shater Bosaily A Brown LC Gabe R Kaplan R Parmar MK Collaco-Moraes Y Ward K Hindley RG Freeman A Kirkham AP Oldroyd R Parker C Emberton M PROMIS study group Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study Lancet 2017 389 10071 815 22 10.1016/S0140-6736(16)32401-1 28110982 Ahmed HU, El-Shater Bosaily A, Brown LC, Gabe R, Kaplan R, Parmar MK, Collaco-Moraes Y, Ward K, Hindley RG, Freeman A, Kirkham AP, Oldroyd R, Parker C, Emberton M, PROMIS study group. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet. 2017;389(10071):815–22. 10.1016/S0140-6736(16)32401-1. Epub 2017 Jan 20. PMID: 28110982. 28110982 10.1016/S0140-6736(16)32401-1

77. Li L, Sun Y. Circulating tumor DNA methylation detection as biomarker and its application in tumor liquid biopsy: advances and challenges. MedComm (2020). 2024;5(11):e766. 10.1002/mco2.766. PMID: 39525954; PMCID: PMC11550092. 10.1002/mco2.766 PMC11550092 39525954

78. Das D Singh P Pal M Pandey M Dixit R Global perspective on the genetic, epigenetic, and transcriptomic basis of gallbladder cancer Crit Rev Oncol/Hematol 2025 214 104830 10.1016/j.critrevonc.2025.104830 40617327 Das D, Singh P, Pal M, Pandey M, Dixit R. Global perspective on the genetic, epigenetic, and transcriptomic basis of gallbladder cancer. Crit Rev Oncol/Hematol. 2025;214:104830. 10.1016/j.critrevonc.2025.104830. 40617327 10.1016/j.critrevonc.2025.104830

79. Karapetis CS, Khambata-Ford S, Jonker DJ, O’Callaghan CJ, Tu D, Tebbutt NC, Simes RJ, Chalchal H, Shapiro JD, Robitaille S, Price TJ, Shepherd L, Au HJ, Langer C, Moore MJ, Zalcberg JR. K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med. 2008;359(17):1757-65. 10.1056/NEJMoa0804385. PMID: 18946061. 10.1056/NEJMoa0804385 18946061

80. Davies H Bignell GR Cox C Stephens P Edkins S Clegg S Teague J Woffendin H Garnett MJ Bottomley W Davis N Dicks E Ewing R Floyd Y Gray K Hall S Hawes R Hughes J Kosmidou V Menzies A Mould C Parker A Stevens C Watt S Hooper S Wilson R Jayatilake H Gusterson BA Cooper C Shipley J Hargrave D Pritchard-Jones K Maitland N Chenevix-Trench G Riggins GJ Bigner DD Palmieri G Cossu A Flanagan A Nicholson A Ho JW Leung SY Yuen ST Weber BL Seigler HF Darrow TL Paterson H Marais R Marshall CJ Wooster R Stratton MR Futreal PA Mutations of the BRAF gene in human cancer Nature 2002 417 6892 949 54 10.1038/nature00766 12068308 Davies H, Bignell GR, Cox C, Stephens P, Edkins S, Clegg S, Teague J, Woffendin H, Garnett MJ, Bottomley W, Davis N, Dicks E, Ewing R, Floyd Y, Gray K, Hall S, Hawes R, Hughes J, Kosmidou V, Menzies A, Mould C, Parker A, Stevens C, Watt S, Hooper S, Wilson R, Jayatilake H, Gusterson BA, Cooper C, Shipley J, Hargrave D, Pritchard-Jones K, Maitland N, Chenevix-Trench G, Riggins GJ, Bigner DD, Palmieri G, Cossu A, Flanagan A, Nicholson A, Ho JW, Leung SY, Yuen ST, Weber BL, Seigler HF, Darrow TL, Paterson H, Marais R, Marshall CJ, Wooster R, Stratton MR, Futreal PA. Mutations of the BRAF gene in human cancer. Nature. 2002;417(6892):949–54. 10.1038/nature00766. Epub 2002 Jun 9. PMID: 12068308. 12068308 10.1038/nature00766

81. Chaudhuri AA Chabon JJ Lovejoy AF Newman AM Stehr H Azad TD Khodadoust MS Esfahani MS Liu CL Zhou L Scherer F Kurtz DM Say C Carter JN Merriott DJ Dudley JC Binkley MS Modlin L Padda SK Gensheimer MF West RB Shrager JB Neal JW Wakelee HA Loo BW Jr Alizadeh AA Diehn M Early detection of molecular residual disease in localized lung cancer by Circulating tumor DNA profiling Cancer Discov 2017 7 12 1394 403 10.1158/2159-8290.CD-17-0716 28899864 PMC5895851 Chaudhuri AA, Chabon JJ, Lovejoy AF, Newman AM, Stehr H, Azad TD, Khodadoust MS, Esfahani MS, Liu CL, Zhou L, Scherer F, Kurtz DM, Say C, Carter JN, Merriott DJ, Dudley JC, Binkley MS, Modlin L, Padda SK, Gensheimer MF, West RB, Shrager JB, Neal JW, Wakelee HA, Loo BW Jr, Alizadeh AA, Diehn M. Early detection of molecular residual disease in localized lung cancer by Circulating tumor DNA profiling. Cancer Discov. 2017;7(12):1394–403. 10.1158/2159-8290.CD-17-0716. Epub 2017 Sep 24. PMID: 28899864; PMCID: PMC5895851. 28899864 10.1158/2159-8290.CD-17-0716 PMC5895851

82. Farmer P Frenk J Knaul FM Expansion of cancer care and control in countries of low and middle income: a call to action Lancet 2010 376 9747 1186 93 10.1016/S0140-6736(10)61152-X 20709386 Farmer P, Frenk J, Knaul FM, et al. Expansion of cancer care and control in countries of low and middle income: a call to action. Lancet. 2010;376(9747):1186–93. 20709386 10.1016/S0140-6736(10)61152-X

83. Sullivan R Alatise OI Anderson BO Global cancer surgery: delivering safe, affordable, and timely cancer surgery Lancet Oncol 2015 16 11 1193 224 10.1016/S1470-2045(15)00223-5 26427363 Sullivan R, Alatise OI, Anderson BO, et al. Global cancer surgery: delivering safe, affordable, and timely cancer surgery. Lancet Oncol. 2015;16(11):1193–224. 26427363 10.1016/S1470-2045(15)00223-5

84. Fojo T Mailankody S Lo A Unintended consequences of expensive cancer therapeutics-the pursuit of marginal indications and a me-too mentality that stifles innovation and creativity: the John Conley lecture JAMA Otolaryngol Head Neck Surg 2014 140 12 1225 36 10.1001/jamaoto.2014.1570 25068501 Fojo T, Mailankody S, Lo A. Unintended consequences of expensive cancer therapeutics-the pursuit of marginal indications and a me-too mentality that stifles innovation and creativity: the John Conley lecture. JAMA Otolaryngol Head Neck Surg. 2014;140(12):1225–36. 25068501 10.1001/jamaoto.2014.1570

85. Rivera-Colón G Chapman JS White J Implementation of targeted next-generation sequencing panels in developing countries: opportunities and challenges J Glob Oncol 2017 3 3 256 63 Rivera-Colón G, Chapman JS, White J, et al. Implementation of targeted next-generation sequencing panels in developing countries: opportunities and challenges. J Glob Oncol. 2017;3(3):256–63.

86. Koch A Joosten SC Feng Z Analysis of DNA methylation in cancer: location revisited Nat Rev Clin Oncol 2018 15 7 459 66 10.1038/s41571-018-0004-4 29666440 Koch A, Joosten SC, Feng Z, et al. Analysis of DNA methylation in cancer: location revisited. Nat Rev Clin Oncol. 2018;15(7):459–66. 29666440 10.1038/s41571-018-0004-4

87. Quick J Loman NJ Duraffour S Real-time, portable genome sequencing for Ebola surveillance Nature 2016 530 7589 228 32 10.1038/nature16996 26840485 PMC4817224 Quick J, Loman NJ, Duraffour S, et al. Real-time, portable genome sequencing for Ebola surveillance. Nature. 2016;530(7589):228–32. 26840485 10.1038/nature16996 PMC4817224

88. Afgan E Baker D Batut B The galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update Nucleic Acids Res 2018 46 W1 W537 44 10.1093/nar/gky379 29790989 PMC6030816 Afgan E, Baker D, Batut B, et al. The galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 2018;46(W1):W537–44. 29790989 10.1093/nar/gky379 PMC6030816

89. Lin D Alpert A Chen K Establishing regional biobanks to advance genomic medicine: challenges and opportunities Genome Med 2020 12 51 Lin D, Alpert A, Chen K. Establishing regional biobanks to advance genomic medicine: challenges and opportunities. Genome Med. 2020;12:51.32471518

90. Williams BJ DaCosta P Goacher E A systematic review of the benefits and challenges of digital pathology and telepathology J Clin Pathol 2018 71 6 463 9 10.1136/jclinpath-2017-204808 29317516 PMC5916098 Williams BJ, DaCosta P, Goacher E, et al. A systematic review of the benefits and challenges of digital pathology and telepathology. J Clin Pathol. 2018;71(6):463–9. 29317516 10.1136/jclinpath-2017-204808 PMC5916098

91. Schmitt MW Loeb LA Salk JJ The potential of DNA sequencing-based detection of low-frequency mutations in cancer Genome Med 2016 8 89 Schmitt MW, Loeb LA, Salk JJ. The potential of DNA sequencing-based detection of low-frequency mutations in cancer. Genome Med. 2016;8:89.27562343

92. Marusyk A, Almendro V, Polyak K. Intra-tumour heterogeneity: a looking glass for cancer? Nat Rev Cancer. 2012;12(5):323 – 34. 10.1038/nrc3261. PMID: 22513401. 10.1038/nrc3261 22513401