You are currently viewing a new version of our website. To view the old version click .
MDPIMDPI
Search all articles
Cancers
Download PDF
  • Editorial
  • Open Access

31 December 2025

Liquid Biopsy in Cancer: The Importance of Integrating Bioinformatics Approaches Towards the Development of a Personalized Molecular Profile

,
and
1
Department of Biological Chemistry, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
2
First University Department of Respiratory Medicine, ‘Sotiria’ Chest Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
*
Authors to whom correspondence should be addressed.
Cancers2026, 18(1), 140;https://doi.org/10.3390/cancers18010140 
(registering DOI)
This article belongs to the Special Issue Circulating Tumor Cells (CTCs) and the Implementation of Liquid Biopsy (2nd Edition)
Version Notes
Order Reprints
Liquid biopsy is now a valuable complementary tool that oncologists use to obtain a more complete picture of their patients’ condition in real time [1]. This enables them to monitor patients or intervene as needed to further investigate or adjust treatment [2]. In lung cancer, liquid biopsy has been used in numerous studies analyzing various biomarkers in blood, bronchoalveolar lavage (BAL), cerebrospinal fluid (CSF), and other fluid samples [3]. However, this information is complex and can be simplified with bioinformatics tools [4]. Starting from molecular/minimal residual disease (MRD) analysis, research can advance to multi-omic, fragmentomic, and transcriptomic analyses, making bioinformatics essential for translating liquid biopsy data into clinically actionable biomarkers for early detection, prognosis, and therapeutic decision-making in precision oncology [5].
Bioinformatics has been used to identify the molecular mechanisms underpinning relevant networks, pathways, and potential biomarkers related to tumor metastasis [6]. The TRAcking Cancer Evolution through therapy/Rx (TRACERx) program exemplifies integrative bioinformatics in large-scale longitudinal studies by leveraging high-dimensional genomic data to track early metastatic spread and subclonal dynamics [7]. The bioinformatic tool ECLIPSE is specifically designed for non-invasive tracking of subclonal architecture at low circulating tumor DNA (ctDNA) levels. ECLIPSE identified patients with polyclonal metastatic dissemination, which was associated with poor clinical outcomes. Complementary analyses have validated clonal expression-based biomarkers, such as Outcome Risk Associated Clonal Lung Expression (ORACLE), which correlate with survival and predict chemotherapy sensitivity, underlining the clinical relevance of integrating evolutionary and transcriptional information across solid tumors [8].
Recent advances in liquid biopsy and computational modeling have enabled the development of dynamic biomarkers that outperform traditional static clinicopathologic features. Patient-specific pROgnostic and Potential tHErapeutic marker Tracking (PROPHET) uses deep sequencing of patient-specific somatic variants to achieve ultra-sensitive MRD detection and earlier relapse prediction than fixed-panel assays [9]. A novel serial quantitative tumor fraction algorithm, xM, was applied to data from the Tempus de-identified clinical genomic database, which includes 14 solid cancer types in patients who received longitudinal liquid-based next-generation sequencing (NGS). The results show that xM can be used clinically to evaluate immune checkpoint inhibitor (ICI) therapy efficacy [10]. Similarly, the integration of immune profiling and pre-treatment ctDNA status could predict which patients will achieve durable clinical benefit (DCB) from immune checkpoint inhibition [11].
A novel machine-learning approach capable of exploring the full landscape of mutations and combinations could identify new predictive biomarkers for chemoimmunotherapy, underscoring the importance of modeling combinatorial mutation patterns rather than single-gene effects [12]. In parallel, integrative frameworks that combine longitudinal ctDNA kinetics with peripheral immune biomarkers such as neutrophil-to-lymphocyte ratio or eosinophil dynamics, capture evolving tumor–immune interactions that correlate with clinical outcomes following immune checkpoint blockade across multiple cancer types [13].
Multi-level classification frameworks that integrate cell-free DNA (cfDNA)/ctDNA genomic features with circulating protein biomarkers illustrate how staged binary classifiers, ensemble learning, and consensus feature selection can achieve high diagnostic accuracy across diverse malignancies, including small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC), colorectal cancer (CRC), breast cancer, and gastrointestinal (GI) cancers [14]. Another major advancement has been the use of non-mutational cfDNA signals and transcriptomic diversity to address sensitivity and cost limitations [15]. Complementary transcriptome-based liquid biopsy approaches including circulating microRNAs (miRNAs), exosomal RNAs (exRNAs) [16], and platelet-derived mRNA and circular RNA (circRNA) signatures [17] show that computational feature extraction and combinatorial modeling can reveal cancer-specific signals missed by single-analyte assays. Population-scale analyses of longitudinal circulating RNA profiles further suggest that molecular dysregulation may precede clinical cancer diagnosis by years or even decades, highlighting the potential for ultra-early detection through large-scale bioinformatic data mining [18].
Advanced feature selection, class imbalance correction, and ensemble modeling improve predictive performance in high-dimensional proteomic and transcriptomic datasets [19], while decision-support algorithms that integrate ctDNA with circulating protein markers demonstrate clear clinical utility when tissue biopsies are inconclusive or infeasible [20]. Thus, a key issue is that bioinformatics contributes not only predictive accuracy but also interpretability and translational relevance. Additionally, tumor-educated platelet (TEP)-derived spliced RNAs selected by optimized machine learning algorithms may serve as biomarkers for minimally invasive clinical blood tests [21].
Recent progress in liquid biopsy-based NGS for NSCLC highlights a critical transition from variant detection toward quantitative and reliability-aware interpretation of molecular data. According to Serna-Blasco et al. [22], the R-score represents an important methodological contribution to this shift, addressing a long-standing gap in objective quality metrics for NGS variant calls in plasma [22]. By integrating variant allele fraction (VAF) and the Median of the Absolute values of all Pairwise Differences (MAPD) into a single parameter that correlates strongly with positive percent agreement across platforms, the R-score provides a practical, data-driven framework for reducing false-positives and false-negative variant calls in ctDNA analysis. This concept aligns well with broader efforts in the field to improve analytical robustness, such as ultra-deep sequencing strategies for early detection [23], algorithmic integration of clinical and molecular variables [24], and emerging approaches that combine DNA and RNA profiling to maximize actionable insight from limited material [25].
In conclusion, the rapid evolution of liquid biopsy technologies, along with advances in bioinformatics and machine learning, has transformed cancer research and clinical management across various tumor types. These developments illustrate a paradigm shift in liquid biopsy-based precision oncology, where bioinformatics-driven integration, modeling, and quality control are as critical as sequencing technology itself. By linking molecular dynamics with clinical and immune context, these approaches move the field beyond static, single-marker assays toward scalable, reproducible, and clinically deployable decision-support systems applicable across a broad spectrum of solid cancer types.

Author Contributions

Conceptualization, A.S. and A.G.P.; writing—original draft preparation, A.S. and K.A.P.; literature search and preparation of all references, A.S. and K.A.P.; supervision, A.S. and A.G.P.; writing—review and editing, A.S. and A.G.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Lianidou, E.; Pantel, K. Liquid Biopsies. Genes Chromosomes Cancer 2019, 58, 219–232. [Google Scholar]
  2. Strati, A.; Economopoulou, P.; Lianidou, E.; Psyrri, A. Clinical Significance of PD-L1 Status in Circulating Tumor Cells for Cancer Management during Immunotherapy. Biomedicines 2023, 11, 1768. [Google Scholar] [CrossRef]
  3. Strati, A.; Zavridou, M.; Papavassiliou, K.A.; Papavassiliou, A.G. Comprehensive Liquid Biopsy Approaches for the Clinical Management of Lung Cancer Using Multiple Biological Matrices. Int. J. Mol. Sci. 2025, 26, 11304. [Google Scholar] [CrossRef]
  4. Ramalingam, N.; Jeffrey, S.S. Future of Liquid Biopsies with Growing Technological and Bioinformatics Studies: Opportunities and Challenges in Discovering Tumor Heterogeneity with Single Cell Level Analysis. Cancer J. 2018, 24, 104. [Google Scholar] [CrossRef]
  5. Amelio, I.; Bertolo, R.; Bove, P.; Buonomo, O.C.; Candi, E.; Chiocchi, M.; Cipriani, C.; Di Daniele, N.; Ganini, C.; Juhl, H.; et al. Liquid Biopsies and Cancer Omics. Cell Death Discov. 2020, 6, 131. [Google Scholar] [CrossRef] [PubMed]
  6. Peng, H.; Tan, H.; Zhao, W.; Jin, G.; Sharma, S.; Xing, F.; Watabe, K.; Zhou, X. Computational Systems Biology in Cancer Brain Metastasis. Front. Biosci. Sch. Ed. 2016, 8, 169–186. [Google Scholar] [CrossRef]
  7. Abbosh, C.; Frankell, A.M.; Harrison, T.; Kisistok, J.; Garnett, A.; Johnson, L.; Veeriah, S.; Moreau, M.; Chesh, A.; Chaunzwa, T.L.; et al. Tracking Early Lung Cancer Metastatic Dissemination in TRACERx Using CtDNA. Nature 2023, 616, 553. [Google Scholar] [CrossRef]
  8. Biswas, D.; Liu, Y.H.; Herrero, J.; Wu, Y.; Moore, D.A.; Karasaki, T.; Grigoriadis, K.; Lu, W.T.; Veeriah, S.; Naceur-Lombardelli, C.; et al. Prospective Validation of ORACLE, a Clonal Expression Biomarker Associated with Survival of Patients with Lung Adenocarcinoma. Nat. Cancer 2025, 6, 86–101. [Google Scholar] [CrossRef] [PubMed]
  9. Chen, K.; Yang, F.; Shen, H.; Wang, C.; Li, X.; Chervova, O.; Wu, S.; Qiu, F.; Peng, D.; Zhu, X.; et al. Individualized Tumor-Informed Circulating Tumor DNA Analysis for Postoperative Monitoring of Non-Small Cell Lung Cancer. Cancer Cell 2023, 41, 1749–1762.e6. [Google Scholar] [CrossRef] [PubMed]
  10. Gentzler, R.D.; Guittar, J.; Mitra, A.; Iams, W.T.; Driessen, T.; Schwind, R.; Stein, M.M.; Kaneva, K.; Hyun, S.W.; Liu, Y.; et al. Dynamic Changes in Circulating Tumor Fraction as a Predictor of Real-World Clinical Outcomes in Solid Tumor Malignancy Patients Treated with Immunotherapy. Oncol. Ther. 2024, 12, 509–524. [Google Scholar] [CrossRef]
  11. Nabet, B.Y.; Esfahani, M.S.; Moding, E.J.; Hamilton, E.G.; Chabon, J.J.; Rizvi, H.; Steen, C.B.; Chaudhuri, A.A.; Liu, C.L.; Hui, A.B.; et al. Noninvasive Early Identification of Therapeutic Benefit from Immune Checkpoint Inhibition. Cell 2020, 183, 363–376.e13. [Google Scholar] [CrossRef]
  12. Yuan, M.; Feng, W.; Ding, H.; Yang, Y.; Xu, X.S. Discovery of Mutations Predictive of Survival Benefit from Immunotherapy in First-Line NSCLC: A Retrospective Machine Learning Study of IMpower150 Liquid Biopsy Data. Comput. Biol. Med. 2025, 189, 109964. [Google Scholar] [CrossRef]
  13. Hwang, M.; Canzoniero, J.V.; Rosner, S.; Zhang, G.; White, J.R.; Belcaid, Z.; Cherry, C.; Balan, A.; Pereira, G.; Curry, A.; et al. Peripheral Blood Immune Cell Dynamics Reflect Antitumor Immune Responses and Predict Clinical Response to Immunotherapy. J. Immunother. Cancer 2022, 10, e004688. [Google Scholar] [CrossRef]
  14. Eledkawy, A.; Hamza, T.; El-Metwally, S. Towards Precision Oncology: A Multi-Level Cancer Classification System Integrating Liquid Biopsy and Machine Learning. BioData Min. 2025, 18, 29. [Google Scholar] [CrossRef]
  15. Nguyen, V.T.C.; Vo, D.H.; Tran, T.T.; Tran, T.T.; Nguyen, T.H.H.; Vo, T.D.H.; Van, T.T.V.; Vu, T.L.; Lam, M.Q.; Nguyen, G.T.H.; et al. Cost-Effective Shallow Genome-Wide Sequencing for Profiling Plasma CfDNA Signatures to Enhance Lung Cancer Detection. Future Oncol. 2025, 21, 1391–1402. [Google Scholar] [CrossRef] [PubMed]
  16. Sun, Z.; Du, T.; Yang, G.; Sun, Y.; Xiao, X. Identification of Exosome-Related Genes in NSCLC via Integrated Bioinformatics and Machine Learning Analysis. Sci. Rep. 2025, 15, 22962. [Google Scholar] [CrossRef]
  17. D’Ambrosi, S.; Giannoukakos, S.; Antunes-Ferreira, M.; Pedraz-Valdunciel, C.; Bracht, J.W.P.; Potie, N.; Gimenez-Capitan, A.; Hackenberg, M.; Fernandez Hilario, A.; Molina-Vila, M.A.; et al. Combinatorial Blood Platelets-Derived CircRNA and MRNA Signature for Early-Stage Lung Cancer Detection. Int. J. Mol. Sci. 2023, 24, 4881. [Google Scholar] [CrossRef] [PubMed]
  18. Keller, A.; Fehlmann, T.; Backes, C.; Kern, F.; Gislefoss, R.; Langseth, H.; Rounge, T.B.; Ludwig, N.; Meese, E. Competitive Learning Suggests Circulating MiRNA Profiles for Cancers Decades Prior to Diagnosis. RNA Biol. 2020, 17, 1416–1426. [Google Scholar] [CrossRef] [PubMed]
  19. Yao, N.; Pan, J.; Chen, X.; Li, P.; Li, Y.; Wang, Z.; Yao, T.; Qian, L.; Yi, D.; Wu, Y. Discovery of Potential Biomarkers for Lung Cancer Classification Based on Human Proteome Microarrays Using Stochastic Gradient Boosting Approach. J. Cancer Res. Clin. Oncol. 2023, 149, 6803–6812. [Google Scholar] [CrossRef]
  20. Visser, E.; Genet, S.A.A.M.; de Kock, R.P.P.A.; van den Borne, B.E.E.M.; Youssef-El Soud, M.; Belderbos, H.N.A.; Stege, G.; de Saegher, M.E.A.; van ’t Westeinde, S.C.; Brunsveld, L.; et al. Liquid Biopsy-Based Decision Support Algorithms for Diagnosis and Subtyping of Lung Cancer. Lung Cancer 2023, 178, 28–36. [Google Scholar] [CrossRef]
  21. Antunes-Ferreira, M.; D’Ambrosi, S.; Arkani, M.; Post, E.; In ‘t Veld, S.G.J.G.; Ramaker, J.; Zwaan, K.; Kucukguzel, E.D.; Wedekind, L.E.; Griffioen, A.W.; et al. Tumor-Educated Platelet Blood Tests for Non-Small Cell Lung Cancer Detection and Management. Sci. Rep. 2023, 13, 9359. [Google Scholar] [CrossRef]
  22. Serna-Blasco, R.; Sánchez-Herrero, E.; Renedo, M.B.; Calabuig-Fariñas, S.; Molina-Vila, M.Á.; Provencio, M.; Romero, A. R-Score: A New Parameter to Assess the Quality of Variants’ Calls Assessed by NGS Using Liquid Biopsies. Biology 2021, 10, 954. [Google Scholar] [CrossRef]
  23. Peng, M.; Xie, Y.; Li, X.; Qian, Y.; Tu, X.; Yao, X.; Cheng, F.; Xu, F.; Kong, D.; He, B.; et al. Resectable Lung Lesions Malignancy Assessment and Cancer Detection by Ultra-Deep Sequencing of Targeted Gene Mutations in Plasma Cell-Free DNA. J. Med. Genet. 2019, 56, 647–653. [Google Scholar] [CrossRef] [PubMed]
  24. Liao, M.Y.; Hao, Y.J.; Luo, C.S.; Chen, C.M.; Feng, P.H.; Yang, H.Y.; Yao, D.J.; Lee, K.Y.; Tseng, F.G. Development and Validation of a Novel Combinational Index of Liquid Biopsy Biomarker for Longitudinal Lung Cancer Patient Management. J. Liq. Biopsy 2024, 6, 100167. [Google Scholar] [CrossRef] [PubMed]
  25. Tran, N.H.B.; Nguyen, T.P.H.; Bui, V.Q.; Le, V.T.; Mai, T.K.; Nguyen Hoang, V.A.; Nguyen, T.A.; Nguyen, M.D.; Pham, H.H.; Vo, T.T.L.; et al. Tumor Genomic and Transcriptomic Analysis Integrated with Liquid Biopsy CtDNA Monitoring: Analytical Validation and Clinical Insights. Cancer Med. 2025, 14, e71465. [Google Scholar] [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Article metric data becomes available approximately 24 hours after publication online.
Download PDF
Chemotherapy for Gastric Cancer Is Not Solely the Domain of the Oncologist
Previous
A Review of ARID1A’s Role in Breast Cancer Progression: Context-Dependent Mechanisms and Therapeutic Implications
Next