Margarita M. Sazhina
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Margarita M. Sazhina has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Alena Gavrina
Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603005 Nizhny Novgorod, Russia
1 shared publication - Anastasia I. Kononova
Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603005 Nizhny Novgorod, Russia
1 shared publication - Anna V. Izosimova
Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603005 Nizhny Novgorod, Russia
1 shared publication - Diana Vladimirovna Yuzhakova
Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603005 Nizhny Novgorod, Russia
1 shared publication - Dmitry A. Kuzmin
Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603005 Nizhny Novgorod, Russia
1 shared publication - Irina S. Shumskaya
Research Institute of Clinical Oncology «Nizhny Novgorod Regional Clinical Oncological Dispensary
1 shared publication
Latest Publications
- Open Access
- 5 May 2026
- Published by MDPI AG in Biomedicines
- Vol. 14 (5) , 1049
Prediction of Patients’ Response to Immune Checkpoint Inhibitors Using Fluorescence Lifetime Imaging of Lymphocytes
Background/Objectives: Although immune checkpoint inhibitors (ICIs) have dramatically transformed the landscape of cancer treatment, a significant percentage of patients are resistant to therapy. Identifying biomarkers to accurately predict patient response prior to treatment remains one of the key challenges in this field. Our study aimed to develop a platform for personalized prediction of immunotherapy efficacy for melanoma using isolated lymphocytes and fluorescence lifetime imaging (FLIM) of their autofluorescence. Methods: At the first stage, the ability of FLIM of the autofluorescent coenzyme NAD(P)H to resolve cellular response to anti-CTLA-4 was tested on lymphocytes isolated from lymphatic nodes (LNs) of mice with the B16 melanoma model. Then, cellular metabolic shifts in response to treatment with anti-PD-1 or its combination with anti-CTLA-4 were assessed via FLIM of patients’ blood lymphocytes. Activation of T-cells and alterations in the expression of metabolic genes after the treatment in vitro were additionally evaluated. Results: Fluorescence decay parameters α1 (free NAD(P)H fraction) and τ2 (protein-bound NAD(P)H lifetime) were identified as sensitive markers of lymphocyte activation after the treatment with immune checkpoint inhibitors. Responsive samples exhibited an increase in these parameters, which was associated with metabolic reprogramming toward more active glycolysis, the TCA cycle, fatty acid oxidation (FAO) and synthesis, the pentose phosphate pathway (PPP) and mitochondrial biogenesis. Importantly, the in vitro response of primary lymphocytes isolated prior to treatment correlated well with subsequent clinical outcomes. Conclusions: The proposed platform represents a promising solution for patient-specific immunotherapeutic drug screening and personalized treatment optimization.
- 5 May 2026
- Published by MDPI AG in Biomedicines
- Vol. 14 (5) , 1049
Open AccessPrediction of Patients’ Response to Immune Checkpoint Inhibitors Using Fluorescence Lifetime Imaging of Lymphocytes
Background/Objectives: Although immune checkpoint inhibitors (ICIs) have dramatically transformed the landscape of cancer treatment, a significant percentage of patients are resistant to therapy. Identifying biomarkers to accurately predict patient response prior to treatment remains one of the key challenges in this field. Our study aimed to develop a platform for personalized prediction of immunotherapy efficacy for melanoma using isolated lymphocytes and fluorescence lifetime imaging (FLIM) of their autofluorescence. Methods: At the first stage, the ability of FLIM of the autofluorescent coenzyme NAD(P)H to resolve cellular response to anti-CTLA-4 was tested on lymphocytes isolated from lymphatic nodes (LNs) of mice with the B16 melanoma model. Then, cellular metabolic shifts in response to treatment with anti-PD-1 or its combination with anti-CTLA-4 were assessed via FLIM of patients’ blood lymphocytes. Activation of T-cells and alterations in the expression of metabolic genes after the treatment in vitro were additionally evaluated. Results: Fluorescence decay parameters α1 (free NAD(P)H fraction) and τ2 (protein-bound NAD(P)H lifetime) were identified as sensitive markers of lymphocyte activation after the treatment with immune checkpoint inhibitors. Responsive samples exhibited an increase in these parameters, which was associated with metabolic reprogramming toward more active glycolysis, the TCA cycle, fatty acid oxidation (FAO) and synthesis, the pentose phosphate pathway (PPP) and mitochondrial biogenesis. Importantly, the in vitro response of primary lymphocytes isolated prior to treatment correlated well with subsequent clinical outcomes. Conclusions: The proposed platform represents a promising solution for patient-specific immunotherapeutic drug screening and personalized treatment optimization.