Modern Immunological Approaches to the Search for Biomarkers Predicting Efficacy and Safety of Cancer Immunotherapy
- DOI
- 10.5922/ATB-2026-2-1-5
- Pages
- 69-88
- To cite
- Kravtsov I. V., Syutkina A. S., Nekhaeva T. L. Modern Immunological Approaches to the Search for Biomarkers Predicting Efficacy and Safety of Cancer Immunotherapy. Advanced targets in Biomedicine. 2026;2(1):69—88. https://https://doi.org/10.5922/ATB-2026-2-1-5
Abstract
Immunotherapy is a powerful clinical strategy for the treatment of malignant tumours. The number of approved immuno-oncology drugs is growing, and many new therapeutic ap-proaches are currently at the stages of clinical and preclinical trials. Despite these ad-vances, a key challenge for the widespread use of immunotherapy remains the difficulty of controlled activation of the immune system. Existing approaches may cause serious side effects, including autoimmune reactions and nonspecific inflammation. In this regard, understanding the mechanisms that can increase the proportion of patients who respond to the therapy is a crucial factor both for improving treatment efficacy and for controlling its side effects.
The aim of this research is to examine and summarize data on the prognostic and predictive significance of biomarkers associated with the efficacy and safety of immunotherapy in malignant tumours. The study examines molecular genetic alterations (including circulat-ing tumour DNA), cancer-testis antigens, the PD-1 protein and its ligands, as well as ter-tiary lymphoid structures. Special attention is given to biomarkers of systemic and local responses. It has been shown that the cellular composition of the tumour microenvironment and peripheral blood is associated with disease prognosis, while levels of cytokines and soluble immune factors may predict the effectiveness of immunotherapy and the likelihood of side effects. The article also discusses delayed-type hypersensitivity reactions and modern methods for detecting biomarkers, which are used to evaluate the specific anti-tumor immune response.
The authors conclude that the most promising direction is the simultaneous analysis of multiple types of biomarkers using modern computational methods. This approach may improve the effectiveness and safety of immunotherapy and facilitate a transition to per-sonalized treatment.
Reference
1. Larsson A. M., Nordström O., Johansson A., Rydén L., Leandersson K., Bergen-felz C. Peripheral Blood Mononuclear Cell Populations Correlate with Outcome in Patients with Metastatic Breast Cancer. Cells. 2022, 11(10), 1639, DOI: 10.3390/cells11101639.
2. Liu D., Che X., Wang X., Ma C., Wu G. Tumor Vaccines: Unleashing the Power of the Immune System to Fight Cancer. Pharmaceuticals (Basel). 2023, 16(10), 1384, DOI: 10.3390/ph16101384.
3. Shi G., Scott M., Mangiamele C. G., Heller R. Modification of the Tumor Microenvi-ronment Enhances Anti-PD-1 Immunotherapy in Metastatic Melanoma. Pharmaceutics. 2022, 14(11), 2429, DOI: 10.3390/pharmaceutics14112429.
4. Lanman R. B., Mortimer S. A., Zill O. A., Sebisanovic D., Lopez R., Blau S., 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, DOI: 10.1371/journal.pone.0140712.
5. Elazezy M., Joosse S. A. Techniques of using circulating tumor DNA as a liquid biopsy component in cancer management. Comput. Struct. Biotechnol. J. 2018, 16, 370—378, DOI: 10.1016/j.csbj.2018.10.002.
6. Hitchen N., Shahnam A., Tie J. Circulating Tumor DNA: A Pan-Cancer Bi-omarker in Solid Tumors with Prognostic and Predictive Value. Annual Review of Medicine. 2025, 76(1), 207—223, DOI: 10.1146/annurev-med-100223-090016.
7. Martínez-Vila C., Teixido C., Aya F., Martín R., González-Navarro E. A., Alos L., et al. Detection of Circulating Tumor DNA in Liquid Biopsy: Current Techniques and Poten-tial Applications in Melanoma. Int. J. Mol. Sci. 2025, 26(2), 861, DOI: 10.3390/ijms26020861.
8. Chen X., Zhang M., Zhou Q., Guo N., Cao B., Zeng H., et al. Circulating tumor DNA as prognostic markers of non-small cell lung cancer (NSCLC): a systematic review and meta-analysis. Translational Lung Cancer Research. 2025, 14(12), 5491—5508, DOI: 10.21037/tlcr-2025-900.
9. Panet F., Parakonstantinou A., Borrell M., Vivancos J., Vivancos A., Oliveira M. Use of ctDNA in early breast cancer: analytical validity and clinical potential. NPJ Breast Cancer. 2024, 10(1), 50, DOI: 10.1038/s41523-024-00653-3.
10. Slusher N., Jones N., Nonaka T. Liquid biopsy for diagnostic and prognostic eval-uation of melanoma. Front. Cell Dev. Biol. 2024, 12, 1420360, DOI :10.3389/fcell.2024.1420360.
11. Sánchez-Herrero E., Serna-Blasco R., Robado de Lope L., González-Rumayor V., Romero A., Provencio M. Circulating Tumor DNA as a Cancer Biomarker: An Overview of Biological Features and Factors That may Impact on ctDNA Analysis. Front. Oncol. 2022, 12, 943253, DOI: 10.3389/fonc.2022.943253.
12. Schroeder C., Gatidis S., Kelemen O., Schütz L., Bonzheim I., Muyas F., et al. Tumour-informed liquid biopsies to monitor advanced melanoma patients under immune checkpoint inhibition. Nat. Commun. 2024, 15(1), 8750, DOI: 10.1038/s41467-024-52923-0.
13. Forghani S., Shahsavand A., Samiee R., Kharaghani M., Kiumarsi A., Sabet F. M., et al. The prognostic ana clinical utility of circulating tumor DNA in diffuse large B-cell Lymphoma: a systematic review and meta-analysis. NPJ Precision Oncology. 2025, 9(1), 385, DOI: 10.1038/s41698-025-01174-3.
14. Avanzini S., Kurtz D. M., Chabon J. J., Moding E. J., Hori S. S., Gambhir S. S. A mathematical model of ctDNA shedding predicts tumor detection size. Sci. Adv. 2020, 6(50), eabc4308, DOI: 10.1126/sciadv.abc4308.
15. Alimardani M., Rahimi H., Ghasemi A., Moghbeli M., Gholamin M., Abbaszadegan M. R. Overexpression of cancer testis antigens in gastric cancer and their correlations with the patients’ clinicopathological characteristics. BMC Med. Genomics. 2025, 19(1), 25, DOI: 10.1186/s12920-025-02295-1.
16. Ren S., Zhang Z., Li M., Wang D., Guo R, Fang X., et al. Cancer testis antigen sub-families: Attractive targets for therapeutic vaccine (Review). Int. J. Oncol. 2023, 62(6), 71, DOI: 10.3892/ijo.2023.5519.
17. Alimardani M., Rahimi H., Ghasemi A., Moghbeli M., Gholamin M., Abbaszadegan M. R. Overexpression of cancer testis antigens in gastric cancer and their correlations with the patients’ clinicopathological characteristics. BMC Medical Genomics. 2026, 19(1), 25, DOI: 10.1186/s12920-025-02295-1.
18. Wang H., Chen D., Wang R., Quan W., Xia D., Mei J. NY-ESO-1 expression in solid tumors pre-dicts prognosis. Medicine. 2019, 98(48), e17990, DOI: 10.1097/MD.0000000000017990.
19. Salmaninejad A., Zamani M. R., Pourvahedi M., Golchehre Z., Bereshneh A. H., Rezaei N. Cancer/Testis Antigens: Expression, Regulation, Tumor Invasion, and Use in Immunotherapy of Cancers. Immunol. Invest. 2016, 45(7), 619—640, DOI: 10.1080/08820139.2016.1197241.
20. Alsalloum A., Shevchenko J., Sennikov S. The Melanoma-Associated Antigen Family A (MAGE-A): A Promising Target for Cancer Immunotherapy? Cancers. 2023, 15(6), 1779, DOI: 10.3390/cancers15061779.
21. D’Angelo S. P., Araujo D. M., Razak A. R. A., Agulnik M., Attia S., Blay J.-Y., et al. Afamitresgene autoleucel for advanced synovial sarcoma and myxoid round cell liposarcoma (SPREARHEAD-1): an inter-national, open-label, phase 2 trial. Lancet. 2024, 403(10435), 1460—1471, DOI: 10.1016/S0140-6736(24)00319-2.
22. Wood G. E., Meyer C., Petitprez F., D’Angelo S. P. Immunotherapy in Sarcoma: Current Data and Promising Strategies. ASCO Educ. Book. 2024, 44(3), e432234, DOI: 10.1200/EDBK_432234.
23. Dendrou C. A., Petersen J., Rossjohn J., Fugger L. HLA variation and disease. Nat. Rev. Immunol. 2018, 18, 325—339, DOI: 10.1038/nri.2017.143.
24. Abualrous E. T., Stricht J., Freund C. Major histocompatibility complrx (MHC) class I and class II proteins: impact of polymorphism on antigen presentation. Current Opinion in Immunology. 2021, 70, 95—104, DOI: 10.1016/j.coi.2021.04.009.
25. Ivanova M., Shivarov V. HLA genotyping meets response to immune checkpoint inhibitors prediction: A story just started. Int. J. Immunogenet. 2021, 48(2), 193—200, DOI: 10.1111/iji.12517.
26. Correale P., Saladino R. E., Giannarelli D., Giannicola R., Agostino R., Staropoli N., et al. Distinctive germline expression of class I human leukocyte antigen (HLA) alleles and DRB1 heterozygosis predict the outcome of patients with non-small cell lung cancer receiv-ing PD-1/PD-L1 immune checkpoint blockade. J. Immunother. Cancer. 2020, 8(1), e000733, DOI: 10.1136/jitc-2020-000733.
27. Tanegashima T., Shiota M., Fujiyama N., Narita S., Habuchi T., Fukuchi G., et al. Effect of HLA Genotype on Anti-PD-1 Antibody Treatment for Advanced Renal Cell Carci-noma in the SNiP-RCC Study. J. Immunol. 2024, 213(1), 23—28, DOI: 10.4049/jimmunol.2300308.
28. Nekhaeva T. L., Novik A. V., Girdyuk D. V., Danilova A. B., Savchenko P. A., Grigoryevskaya A. V., et al. Prognostic value of HLA class I expression in patients with cutaneous melanoma and soft tissue sarcomas treated with cancer-testis antigens-based vaccine. Explor Med. 2025, 6, 1001287, DOI: 10.37349/emed.2025.1001287.
29. Naranbhai V., Viard M., Dean M., Groha S., Braun D. A., Labaki C., et al. HLA-A*03 and response to immune checkpoint blockade in cancer: an epidemiological biomarker study. Lancet Oncol. 2022, 23(1), 172—184, DOI: 10.1016/S1470-2045(21)00582-9.
30. Mandal K., Barik G. K., Santra M. K. Overcoming resistance to anti-PD-L1 immuno-therapy: mechanisms, combination strategies, and future directions. Mol. Cancer. 2025, 24(1), 246, DOI: 10.1186/s12943-025-02400-z.
31. Neil V., Lee S. W. Advancing Cancer Treatment: A Review of Immune Check-point Inhibitors and Combination Strategies. Cancers (Basel). 2025, 17(9), 1408, DOI: 10.3390/cancers17091408.
32. Topalian S. L., Hodi F. S., Brahmer J. R., Gettinger S. N., Smith D. C., McDermott D. F., et al. Five-Year Survival and Correlates Among Patients With Advanced Melanoma, Renal Cell Carcinoma, or Non-Small Cell Lung Cancer Treated With Nivolumab. JAMA Oncol. 2019, 5(10), 1411—1420, DOI: 10.1001/jamaoncol.2019.2187.
33. Zhang J., Song Z., Zhang Y., Zhang C., Xue Q., Zhang G., et al. Recent advances in biomarkers for predicting the efficacy of immunotherapy in non-small cell lung cancer. Front. Immunol. 2025, 16, 1554871, DOI: 10.3389/fimmu.2025.1554871.
34. Oisakede E. O., Akinro O., Bello O. J., Analikwu C. C., Egbon E., Olawade D. B. Predictive models for immune checkpoint inhibitor response in cancer: A review of current ap-proaches and future directions. Crit. Rev. Oncol. Hematol. 2025, 216, 104980, DOI: 10.1016/j.critrevonc.2025.104980.
35. Li A., Luo L., Du W., Yu Z., He L., Fu S., et al. Deciphering transcriptomic determinants of the divergent link between PD-L1 and immunotherapy efficacy. NPJ Precis. Oncol. 2023, 7(1), 87, DOI: 10.1038/s41698-023-00443-3.
36. Hernández-Verdin I., Dimberg A., Thommen D. S., Engblom C., Vanhersecke L., Silina K., et al. Tertiary lymphoid structures in the era of cancer therapy. J. Exp. Clin. Cancer Res. 2026, 45(1), 48, DOI: 10.1186/s13046-026-03644-3.
37. Su G.-L., Zhang M.-J., Li H., Sun Z.-J. Dissecting Tertiary Lymphoid Structures in Cancer: Maturation, Localization and Density. Theranostics. 2025, 15(18), 9459—9485, DOI: 10.7150/thno.113940.
38. Liu Y.-M., Zhao K., Xie M., Bao X.-Y., Cai W.-H., Jin J.-M., et al. Density and maturity ratio of tertiary lymphoid structures in stage II-III non-small cell lung cancer predict postop-erative recurrence risk. Transl. Lung Cancer Res. 2025, 14(9), 3349—3362, DOI: 10.21037/tlcr-2025-477.
39. Hayashi Y., Makino T., Doki Y. Tertiary Lymphoid Structures as Predictive Bi-omarkers for Immune Checkpoint Inhibitor Therapy in Esophageal Cancer. Gan To Kagaku Ryoho. 2025, 52(9), 618—623, PMID: 41047768.
40. Italiano A., Bessede A., Pulido M., Bompas E., Piperno-Neumann S., Chevreau C., et al. Pembrolizumab in soft-tissue sarcomas with tertiary lymphoid structures: a phase 2 PEMBROSARC trial cohort. Nat. Med. 2022, 28(6), 1199—1206, DOI: 10.1038/s41591-022-01821-3.
41. Luis M.-E., Rhodes J. L., Marion V. C., Kemp R. A. Tertiary lymphoid structures in cancer — considerations for patient prognosis. Cell Mol. Immunol. 2020, 17(6), 570—575, DOI: 10.1038/s41423-020-0457-0.
42. van Rijthoven M., Obahor S., Pagliarulo F., van den Broek M., Schraml P., Moch H., et al. Multi-resolution deep learning characterizes tertiary lymphoid structures and their prognostic relevance in solid tumors. Commun. Med. (Lond). 2024, 4(1), 5, DOI: 10.1038/s43856-023-00421-7.
43. Le Rochais M., Brahim I., Zeghlache R., Redoulez G., Guillard M., Le Noac’h P., et al. Automated classification of tertiary lymphoid structures in colorectal cancer using TLS-PAT artificial intelligence tool. Sci. Rep. 2025, 15(1), 9845, DOI: 10.1038/s41598-025-94664-0.
44. Tang Z., Bai Y., Fang Q., Yuan Y., Zeng Q., Chen S., et al. Spatial transcriptomics reveals tryptophan metabolism restricting maturation of intratumoral tertiary lymphoid structures. Cancer Cell. 2025, 43(6), 1025—1044.e14, DOI: 10.1016/j.ccell.2025.03.011.
45. Zhao Y., Ge X., He J., Cheng Y., Wang Z., Wang J., et al. The prognostic value of tumor-infiltrating lymphocytes in colorectal cancer differs by anatomical subsite: a systematic review and meta-analysis. World J. Surg. Oncol. 2019, 17(1), 85, DOI: 10.1186/s12957-019-1621-9.
46. Nersesian S., Schwartz S. L., Grantham S. R., MacLean L. K., Lee S. N., Pugh-Toole M., et al. NK cell infiltration is associated with improved overall survival in solid cancers: A systematic review and meta-analysis. Transl. Oncol. 2021, 14(1), 100930, DOI: 10.1016/j.tranon.2020.100930.
47. Speiser D. E., Chijioke O., Schaeuble K., Münz C. CD4+ T cells in cancer. Nat Cancer. 2023, 4(3), 317—329, DOI: 10.1038/s43018-023-00521-2.
48. Киселева Е. П., Кудрявцев И. В., Рубинштейн А. А., Старикова Э. А., Маммедова Д. Т., Нехаева Т. Л., и др. Особенности дифференцировки и поляризации Т-хелперов периферической крови у пациентов с первичной меланомой кожи. Вопросы онкологии. 2025, 71(4), 799—809, DOI: 10.37469/0507-3758-2025-71-4-OF-2436.
49. Dobrowolska-Szumowska A., Kamocki Z. K., Mierzejewska Ż. A. Integrating Blood-Based Immune-Inflammation Biomarkers into Artificial Intelligence—Driven Prog-nostic Models in Oncology. Int. J. Mol. Sci. 2026, 27(7), 3192, DOI: 10.3390/ijms27073192.
50. Gooden M. J. M., de Bock G. H., Leffers N., Nijman H. W. The prognostic influence of tumour-infiltrating lymphocytes in cancer: a systematic review with meta-analysis. Br. J. Cancer. 2011, 105(1), 93—103, DOI: 10.1038/bjc.2011.189.
51. Carrión-Barberà I., Lood C. Performance of the neutrophil-to-lymphocyte ratio as a prognostic tool for survival in solid cancers. Front. Oncol. 2025, 15, 1616477, DOI: 10.3389/fonc.2025.1616477.
52. Hamid H. K. S., Emile S. H., Davis G. N. Prognostic Significance of Lymphocyte-to-Monocyte and Platelet-to-Lymphocyte Ratio in Rectal Cancer: A Systematic Review, Meta-analysis, and Meta-regression. Dis. Colon. Rectum. 2022, 65(2), 178—187, DOI: 10.1097/DCR.0000000000002291.
53. Jin J., Yang L., Liu D., Li W. M. Prognostic Value of Pretreatment Lymphocyte-to-Monocyte Ratio in Lung Cancer: A Systematic Review and Meta-Analysis. Technol. Cancer Res. Treat. 2021, 20, 1533033820983085, DOI: 10.1177/1533033820983085.
54. Novik A. V., Emelianova N. V., Nekhaeva T. L., Pipia N. P., Zozulya A. Yu., Avdonkina N. A., et al. Prevalence of deviations of immunological parameters from reference values in patients with solid tumors. Proceedings of the 6th St. Petersburg International Oncology Forum “White Nights 2020”. St. Petersburg, 2020. 129.
55. Berraondo P., Sanmamed M. F., Ochoa M. C., Etxeberria I., Aznar M. A., Perez-Gracia J. L., et al. Cytokines in clinical cancer immunotherapy. Br. J. Cancer. 2019, 120(1), 6—15, DOI: 10.1038/S41416-018-0328-Y.
56. Wang M., Zhai X., Li J., Guan J., Xu S., Li Y., et al. The Role of Cytokines in Predict-ing the Response and Adverse Events Related to Immune Checkpoint Inhibitors. Front. Immunol. 2021, 12, 670391, DOI: 10.3389/fimmu.2021.670391.
57. Chen J., Tarantino G., Severgnini M., Baginska J., Giobbie-Hurder A., Weirather J. L. et al. Circulating cytokine associations with clinical outcomes in melanoma patients treated with combination nivolumab plus ipilimumab. Oncoimmunology. 2024, 14(1), 2432723, DOI: 10.1080/2162402X.2024.2432723.
58. Schalper K. A., Carleton M., Zhou M., Chen T., Feng Y., Huang S.-P., et al. Elevated serum inter-leukin-8 is associated with enhanced intratumor neutrophils and reduced clinical benefit of immune-checkpoint inhibitors. Nat. Med. 2020, 26(5), 688—692, DOI: 10.1038/s41591-020-0856-x.
59. Tobin R. P., Jordan K. R., Kapoor P., Spongberg E., Davis D., Vorwald V. M., et al. IL-6 and IL-8 Are Linked With Myeloid-Derived Suppressor Cell Accumulation and Correlate With Poor Clinical Outcomes in Melanoma Patients. Front. Oncol. 2019, 9, 1223, DOI: 10.3389/fonc.2019.01223.
60. Reschke R., Enk A. H., Hassel J. C. Chemokines and Cytokines in Immunotherapy of Melanoma and Other Tumors: From Biomarkers to Therapeutic Targets. Int. J. Mol. Sci. 2024, 25(12), 6532, DOI: 10.3390/ijms25126532.
61. de Vries I. J. M., Bernsen M. R., Lesterhuis W. J., Scharenborg N. M., Strijk S. P., Gerritsen M. J. P., et al. Immunomonitoring tumor-specific T cells in delayed-type hypersensitivity skin biopsies after dendritic cell vaccination correlates with clinical outcome. J. Clin. Oncol. 2005, 23(24), 5779—5787, DOI: 10.1200/JCO.2005.06.478.
62. Wong C. E., Chang Y., Chen P.-W., Huang Y.-T., Chang Y.-C., Chiang C.-H., et al. Dendritic cell vaccine for glioblastoma: an updated meta-analysis and trial sequential analysis. J. Neurooncol. 2024, 170(2), 253—263, DOI: 10.1007/s11060-024-04798-w.
63. van ‘t Land F. R., Willemsen M., Bezemer K., van der Burg S. H., van den Bosch T. P. P., Doukas M., et al. Dendritic Cell-Based Immunotherapy in Patients With Resected Pancreatic Cancer. J. Clin. Oncol. 2024, 42(26), 3083—3093, DOI: 10.1200/JCO.23.02585.
64. McCune C. S., O’Donnell R. W., Marquis D. M., Sahasrabudhe D. M. Renal cell carcinoma treated by vaccines for active specific immunotherapy: Correlation of survival with skin testing by autologous tumor cells. Cancer Immunol. Immunother. 1990, 32(1), 62—66, DOI: 10.1007/BF01741726.
65. Нехаева Т. Л., Савченко П. А., Новик А. В., Ефремова Н. А., Балдуева И. А. Оценка прогностического значения реакции гиперчувствительности замедленного типа (ГЗТ) при применении аутологичной дендритно-клеточной вакцины у больных меланомой и саркомами мягких тканей. Вопросы онкологии. 2024, 70(6), 1077—1085, DOI: 10.37469/0507-3758-2024-70-6-1077-1085.
66. Kotsakis A., Vetsika E.-K., Christou S., Hatzidaki D., Vardakis N., Aggouraki D., et al. Clinical outcome of patients with various advanced cancer types vaccinated with an optimized cryptic human telomerase reverse transcriptase (TERT) pep-tide: results of an expanded phase II study. Ann. Oncol. 2012, 23(2), 442—449, DOI: 10.1093/annonc/mdr396.
67. Ogasawara M. Wilms’ tumor 1 -targeting cancer vaccine: Recent advancements and future perspectives. Hum. Vaccin Immunother. 2024, 20(1), 2296735, DOI: 10.1080/21645515.2023.2296735.
68. Bol K. F., Schreibelt G., Rabold K., Wculek S. K., Schwarze J. K., Dzionek A., et al. The clinical application of cancer immunotherapy based on naturally circulating dentritic cells. J. for ImmunoTherapy of Cancer. 2019, 7, 109, DOI: 10.1186/s40425-019-0580-6.
69. Marino F. Z., Brunelli M., Rossi G., Calabrese G., Caliò A., Nardiello P., et al. Multitarget fluorescence in situ hybridization diagnostic applications in solid and hematological tumors. Expert Rev. Mol. Diagn. 2021, 21(2), 161—173, DOI: 10.1080/14737159.2021.1887733.
70. Mishra H. K. The Applications of ELISpot in the Identification and Treat-ment of Various Forms of Tuberculosis and in the Cancer Immunotherapies. Methods in molecular biology. 2024, 2768, 51—58, DOI: 10.1007/978-1-0716-3690-9_4.
71. Huang J., Ehrnfelt C., Paulie S., Zuber B., Ahlborg N. ELISpot and ELISA analyses of human IL-21-secreting cells: Impact of blocking IL-21 interaction with cellular receptors. J. Immunol. Methods. 2015, 417, 60—66, DOI: 10.1016/j.jim.2014.12.007.
72. Slota M., Lim J. B., Dang Y., Disis M. L. ELISpot for measuring human immune re-sponses to vaccines. Expert Rev Vaccines. 2011, 10(3), 299—306, DOI: 10.1586/erv.10.169.
73. Lecoester B., Xie Y., Marguier A., Boulerot L., Malfroy M., Adotévi O., et al. Enzyme-linked ImmunoSpot (ELISpot) assay to quantify peptide-specific IFN-γ production by splenocytes in a mouse tumor model after radiation therapy. Methods Cell Biol. 2024, 189, 41—54, DOI: 10.1016/bs.mcb.2024.07.001.
74. Mauthe A., Cedrone E., Villar-Hernández R., Rusch E., Springer M., Schuster M., et al. IFN-γ/IL-2 Double-Color FluoroSpot Assay for Monitoring Human Primary T Cell Activation: Validation, Inter-Laboratory Comparison, and Recommendations for Clinical Studies. AAPS J. 2025, 27, 81, DOI: 10.1208/s12248-025-01072-3.
75. Rydzewski N. R., Peterson E., Lang J. M., Yu M., Chang S. L., Sjöström M., et al. Predicting Cancer Drug TARGETS-TreAtment Response Generalized Elastic-NeT Signatures. NPJ Genom. Med. 2021, 6(1), 76, DOI: 10.1038/s41525-021-00239-z.
76. Jou A. F.-J., Lu C.-H., Ou Y.-C., Wang S.-S., Hsu S.-L., Willner I., et al. Diagnosing the MiR-141 Prostate Cancer Biomarker Using Nucleic Acid-Functionalized CdSe/ZnS QDs and Te-lomerase. Chem. Sci. 2015, 6(1), 659—665, DOI: 10.1039/c4sc02104e.
77. Sina A. A. I., Carrascosa L. G., Liang Z., Grewal Y. S., Wardiana A., Shiddiky M. J. A., et al. Epigenetically Reprogrammed Methylation Landscape Drives the DNA Self-Assembly and Serves as a Universal Cancer Biomarker. Nat. Commun. 2018, 9(1), 4915, DOI: 10.1038/s41467-018-07214-w.
78. Chu Y., Gao Y., Tang W., Qiang L., Han Y., Gao J., et al. Attomolar-Level Ultrasensi-tive and Multiplex MicroRNA Detection Enabled by a Nanomaterial Locally Assembled Microfluidic Biochip for Cancer Diagnosis. Anal. Chem. 2021, 93(12), 5129—5136, DOI: 10.1021/acs.analchem.0c04896.
79. Zhang Q., Liu H., Xu Q., Liu H., Han Y., Li D.-L., et al. Construction of a 3D Quantum Dot Nanoassembly with Two-Step FRET for One-Step Sensing of Human Telomerase RNA in Breast Cancer Cells and Tissues. Anal. Chem. 2024, 96(19), 7738—7746, DOI: 10.1021/acs.analchem.4c01042.
80. Li C., He W., Wang N., Xi Z., Deng R., Liu X., et al. Application of Microfluidics in Detection of Circulating Tumor Cells. Front. Bioeng. Biotechnol. 2022, 10, 907232, DOI: 10.3389/fbioe.2022.907232.
81. Malhotra R., Patel V., Chikkaveeraiah B. V., Munge B. S., Cheong S. C., Zain R. B., et al. Ultrasensitive Detection of Cancer Biomarkers in the Clinic by Use of a Nanostructured Microfluidic Array. Anal. Chem. 2012, 84(14), 6249—6255, DOI: 10.1021/ac301392g.