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PERSPECTIVE article

Front. Immunol., 16 February 2026

Sec. Cancer Immunity and Immunotherapy

Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1777437

This article is part of the Research TopicThe Role of Immunotherapy in Cancer Therapy and Its ChallengesView all 27 articles

Chrono-immunotherapy’s current and future optimization strategies: immunotherapy timing in line with the circadian rhythm brings longer survival benefits

Daiwei Liu,Daiwei Liu1,2Zhanlin Li*Zhanlin Li1*Huijuan CuiHuijuan Cui3Hua ZhangHua Zhang4Hai LiHai Li5Xiaoyuan WuXiaoyuan Wu1
  • 1Department of Traditional Chinese Medicine, Organization The First Affiliated Hospital of Hebei North University, Zhangjiakou, China
  • 2Department of Hebei Key Laboratory of Pathogenic Mechanisms and Diagnosis & Treatment Technologies for Lung Microbiome, Organization The First Affiliated Hospital of Hebei North University, Zhangjiakou, China
  • 3Department of Integrated Traditional Chinese and Western Medicine Oncology, China-Japan Friendship Hospital, Beijing, China
  • 4Department of Respiratory Internal Medicine III, Zhangjiakou First Hospital, Zhangjiakou, China
  • 5Department of Oncology, Zhangjiakou First Hospital, Zhangjiakou, China

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment landscape for malignant tumors such as advanced lung cancer, but their efficacy is limited. In recent years, the circadian rhythm has provided a brand-new optimization strategy for immunotherapy. This perspective article aims to explore the potential mechanisms of the interaction between immunotherapy and circadian rhythms, reviews clinical evidence supporting that “time-of-day receipt of ICIs brings better therapeutic effects”, and conduct an in-depth analysis of the current practical challenges of chrono-immunotherapy. And emphasize the potential of “chrono-immunotherapy” as a valuable therapeutic approach.

1 Introduction

We are currently witnessing a transformation in cancer treatment due to the advent of immunotherapy (1). Global cancer statistics indicate that the incidence and mortality rates of lung cancer remain alarmingly high, presenting a substantial public health challenge (2). Immune checkpoint inhibitors (ICIs), particularly those targeting the PD-1/PD-L1 pathway,have emerged as the standard first-line treatment for lung cancer patients without driver mutations.Nevertheless, clinicians and researchers must confront a pressing issue:despite the promise of immunotherapy for long-term survival, response rates remain limited, and both primary and secondary resistance are common (3). Furthermore, the efficacy of these treatments appears to have reached a plateau. We urgently need to precisely screen out the beneficiaries and maximize their therapeutic effects.

In this context, we have focused that the circadian rhythm system serves as the body’s endogenous time regulator. From microorganisms to humans, almost all living beings have evolved biological clocks with a cycle of approximately 24 hours, enabling them to predict and adapt to environmental changes between day and night (4). Increasing evidence suggests that nearly every facet of the immune system (5), including lymphocyte development, migration, and effector function, is intricately regulated by core clock genes (6). The administration time may be an important factor in determining the efficacy and toxicity of anti-cancer drugs (7). According to the latest assessment from CANCERS discovery (8), aligning the timing of ICIs administration with the body’s inherent circadian rhythm may be the key to breaking through the current efficacy limitations (9). Therefore, we propose the new research direction of chrono-immunotherapy, which focus the role of chronotherapy in ICIs efficacy.

2 Mechanism

The exploration of pathophysiological basis of the immune circadian rhythm can initially clarify the mechanism by which the timing of drug administration affects the efficacy of immunotherapy (10). This is not a simple summary that “immunity is stronger in the morning”, but rather involves a complex and coordinated process of immune cell recruitment and activation regulated by the circadian rhythm (11).

At the molecular level, core clock genes (CLOCK, BMAL1, Per, Cry) function independently within nearly all immune cells via transcription-translation feedback loops (12, 13). These clock genes regulate cellular metabolism and functional status (14), while also directly overseeing essential molecules involved in the immune response (15). At the same time, immune cells themselves can also express core clock genes such as BMAL1 (16). Research conducted by Xinyue Guo et al. demonstrated that morning administration could upregulate the expression of BMAL1 and PER2 in mice while inhibiting PD-L1, which significantly reduced tumor burden (9).

Studies have demonstrated that the migration, activation and differentiation of T cells (17, 18), along with the expression of the costimulatory molecule CD28, exhibit significant diurnal variations (19). The uptake, processing and presentation of antigens by dendritic cells(DCs) (20) are regulated by the biological clock (21). The timing of DCs migration to lymph nodes aligns with the peak period of T cell response (22), thereby enhancing the immune response (23). Administering ICIs when the immune system is most “alert” can alleviate T cell inhibition and elicit a more robust and sustained anti-tumor response (24).

Moreover, the circadian rhythm of the tumor microenvironment plays a significant role. Tumor-associated macrophages(TAM) (25) and myeloid-derived suppressor cells(MDSCs)that express PD-L1 exhibit alignment with the circadian rhythm (26). Additionally,tumor vascular permeability (10), cytokines levels (27), gut microbiota composition (28), and the expression of immunosuppressive receptors (29) demonstrate periodic fluctuations. These dynamics result in increased immune cell infiltration during the daytime period (30), facilitating a transition of the tumor microenvironment from an immunosuppressed state to one that permits immune activity (30), thereby enhancing the therapeutic efficacy of ICIs.

Cutting-edge research has elucidated the mechanisms underlying the influence of administration timing.During the sleep period of mice (31), which corresponds to nighttime in humans,macrophage activity in the sinus cavity of lymphoid organs is at its peak for phagocytosing and clearing antibodies.Consequently, ICIs administered in the afternoon or evening are more likely to be non-specifically eliminated by these cells (32), resulting in a diminished effective drug concentration at the tumor site and reduced binding to T cells (33). In contrast, early morning infusion of ICIs occurs when the inhibitory function of regulatory T cells (Tregs) in peripheral blood is relatively low. This timing enhances the T-cell-mediated immune response and promotes T-cell receptor (TCR) production, thereby strengthening the anti-tumor effect.

Collectively, nearly all aspects of the immune response, encompassing both innate and adaptive mechanisms, exhibit circadian rhythms (34). The efficacy of immunotherapy relies on the precise orchestration of a series of events: antigen presentation (35), T cell activation, migration, infiltration, and cytotoxic activity (36). Each component of this sequence adheres to a circadian rhythm. Therefore, the intervention of ICIs therapy at the appropriate circadian time could theoretically enhance both the intensity and quality of the immune response (37).

3 Current situation

We included the evidence from retrospective studies and randomized controlled trials (RCTs) on the relationship between the efficacy of ICIs and the duration of administration.

Multiple retrospective studies have provided preliminary butevidence yet that remains to be validated.The earliest investigation demonstrated that the administration of immune checkpoint inhibitors at specific time points significantly influenced the overall survival rate of patients with advanced melanoma (38), indicating the potential of chronotherapy. Multiple analyses of advanced NSCLC patients undergoing ICIs monotherapy consistently revealed that those treated in the morning (typically defined as before 11:30) exhibited statistically significant improvements in objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) compared to patients treated in the afternoon (39). This correlation has been observed across various types of ICIs (anti-PD-1/PD-L1) and different cancer types, including metastatic melanoma (40), lung cancer (41), kidney cancer (42), urothelial cancer (43), Head and neck cancer (44), esophageal cancer (45), gastric cancer (46), and liver cancer (47), suggesting the possibility of a universal phenomenon (37). These real-world research studys have, for the first time, closely linked the circadian rhythm theory in the laboratory to the long-term survival outcomes of patients.

Retrospective studies have limitations due to potential influences on the timing of administration,such as clinical workflow, highlighting the necessity for prospective randomized controlled trials (RCTs) to confirm this hypothesis. Currently, several phase I and II RCTs are underway to systematically compare the efficacy of ICIs when administered in the morning versus the afternoon in Table 1. These trials have implemented a standardized dosing schedule and incorporated analysis of biomarkers and core clock genes.They initially explored and found that the CD8+T cell activity was better in the early-stage dosing group (48), aiming to elucidate differences in efficacy and potential molecular mechanisms.While the results of these prospective studies are pending, the commencement of such investigations signifies a transition in cancer treatment emphasis from focusing solely on “what to administer” to also considering “when to administer.

Table 1
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Table 1. Multiple phase I and II RCTs are ongoing to systematically compare the impact of morning versus afternoon administration on ICIs efficacy.

Significant differences exist in the time threshold (ToD) settings across the aforementioned studies. Various studies have employed distinct time thresholds to delineate “morning” and “afternoon,” ranging from 11:30 a.m. to 4:30 p.m. The fundamental methodological issue arises from the application of a uniform social clock time to segment the administration period, thereby neglecting individual variations in biological clock times (49). This discrepancy in the definition of TOD represents a systematic measurement error (50). In epidemiological studies, it often results in the dilution of effect estimates, complicating the replication and direct comparison of results (51). This issue significantly impedes meta-analysis and the exploration of underlying mechanisms. Consequently, future research should meticulously document administration times and gather individual circadian covariates to facilitate the transition from descriptive phenomena to precise immunotherapy informed by the biological clock.

4 Challenge

Although clinical trials have provided strong evidence, the translation of “Chrono-immunotherapy” into clinical practice faces many significant challenges.

Medical institutions typically manage a substantial volume of patients seeking treatment (52) and the infusion appointment system is inherently complex (53). Requiring all patients to receive treatment within the morning time window would disrupt the existing framework for medical resource allocation and could result in delayed care for some individuals. Although patients may be admitted to the hospital one day in advance, scheduling treatment for the following day would inevitably impose a substantial burden on those living in remote areas or those with limited mobility (8).

Many workflows of hospitals and clinics has been formed through decades of practice. Modifying administration times necessitates the reorganization of various components, including nurse scheduling, pharmacy drug preparation, and outpatient procedures (54). This process demands coordinated collaboration hroughout the entire medical system. The costs associated with such changes are substantial, and they may encounter resistance due to systemic inertia.

“Morning” represents a social time, construct rather than a biological one (55). A clear distinction exists between “morning-type individuals” and “night-type individuals” within the population (56). For instance, a “night-type individual” who retires at 2 a.m. and awakens at 10 a.m. may experience peak immune system activity at a time that does not align with the socially defined “morning” (57). Studies have shown that the circadian rhythms of shift workers and individuals who cross multiple time zones are disrupted, leading to imbalances in endocrine factors such as growth hormone, prolactin, thyroid hormone, cortisol, and gonadal steroids (58). This can result in the loss of rhythmic fluctuations in IL-12 and IL-10, thereby affecting the occurrence, progression, and treatment of cancer (59). Sleep dysregulation may elevate levels of pro-inflammatory cytokines (e.g., TNF-α), hindering the effectiveness of ICIs (60) and triggering excessive production of Th1 and Th2 cell-derived cytokines, causing immune system dysfunction (61). These challenges complicate the determination of the optimal universal treatment timing. Temporal variation and circadian rhythm disorders in cancer patients complicate the determination of optimal treatment timing. Research indicates that cancer patients exhibit distinct characteristics, including reduced amplitude of melatonin secretion (62), cancer patients loss of cortisol rhythm (63), and phase disorders in the oscillations of core clock gene expression (64). Furthermore, circadian rhythm disorders are prevalent among patients with advanced cancer and are closely associated with symptom burden, quality of life, disease progression, and survival rates (65, 66). Consequently, the biological clock state of cancer patients markedly differs from that of healthy individuals.This poses significant difficulties for us in defining a universally applicable “optimal dosing time”.

Circadian rhythms not only influence therapeutic efficacy but may also impact the incidence and severity of immune-related adverse events (irAEs). Currently, several studies indicate that the overall incidence of toxicity in the early treatment group is relatively high (67, 68), including skin toxicity and fatigue (69), while no cases of discontinuation of ICIs due to irAEs have been reported (40). Research indicates a gender disparity in the incidence of toxic events, with women experiencing higher rates than men (67). Conversely, some reports suggest that early treatment enhances therapeutic efficacy without leading to a difference in adverse reactions (38, 44). Consequently, it remains uncertain whether administering treatment in morning will concurrently improve efficacy and toxicity.

The relationship between the efficacy and toxicity of ICIs remains an active area of research. Studies have demonstrated a degree of synchrony between these two factors in the context of circadian rhythms (67, 68); however, adverse reactions do not consistently correlate with therapeutic outcomes (38, 44). Current investigations indicate that irAEs are linked to inflammatory responses (70), often characterized by abnormal elevations in cytokines such as IL-6 and TNF-α (71). Inflammation can inhibit the expression of core clock genes (72), upregulate the levels of cytokines, cause circadian rhythm disorders, further aggravate the inflammatory response, and thereby exacerbate irAEs (73). These findings initially suggest that precisely adjusting the administration time of ICIs may enhance the anti-tumor immune response while reducing the risk of irAEs. It is necessary to further verify these hypotheses through large-scale RCTs.

In clinical practice, immune checkpoint inhibitors are frequently administered in conjunction with chemotherapy (74), anti-vascular targeted agents (75), and radiotherapy (76). Circadian rhythms have different effects on each treatment (7779). Therefore, the selection the optimal timing for administering combine treatments presents significant challenges, highlighting the need for more rigorous trials to validate these approaches.

5 Future

In addressing current challenges, our future research direction should not be simply and rigidly scheduling all patients’ treatments in the morning (8). Rather, we should utilize new technologies to precisely screen advantageous patients, provide individualized treatment advantageous times through wearable sensors and intelligent systems, and take multiple measures to actively regulate patients’ circadian rhythms to enhance ICIs efficacy.Further promote the entry of immunotherapy into a highly personalized “chrono-immunotherapy” era (Figure 1).

Figure 1
Circular infographic with four quadrants describing strategies for circadian rhythm intervention: sleep-wake cycles, feeding regimen, and melatonin for optimization; biomarker molecular typing; wearable rhythm sensors like ECG, pulse oximetry, and body temperature during exercise; and intelligent drug delivery systems with automated, wireless technology and machine learning for adaptive clinical trials, illustrated with related icons and diagrams.

Figure 1. Analyze the characteristic molecular typing and biomarkers of the circadian rhythm of immune cells through single-cell sequencing; Monitor the individualized immune circadian rhythm of patients using wearable rhythm sensors; Develop an intelligent drug delivery systems and adaptive clinical trials to output personalized immune administration times.;By taking multiple measures to intervene and optimize the circadian rhythm of patients, the therapeutic effect of ICIs treatment can ultimately be improved.

It is essential to identify biomarkers that accurately reflect an individual’s immune rhythmic state. This process involves detecting core clock genes (80)、immune cell subsets (81) and cytokine levels (82) in peripheral blood of patients receiving ICIs treatment at designated time points, identifying key immune cell subsets that are positively or negatively correlated with the efficacy of ICIs,as well as analyzing the rhythmic gene expression profiles of immune cells through single-cell sequencing (83, 84). By combining tumor genomics for fine classification, we may be able to define “circadian rhythmically sensitive” and “circadian rhythmically insensitive” tumors (83), achieving more precise classification at a deeper level.

Based on the above molecular typing, by using utilize genomic (85) and physiological data, including melatonin levels (38), combined with the molecular typing information related to circadian rhythms obtained from single-cell sequencing (86), and through machine learning or deep learning models (87), it can identify the individualized circadian rhythm characteristics of different patients and predict their optimal response time window to ICIs treatment.In the future, key instruments may include smartwatches worn on patients’ wrists or multisensory wearable technology (45, 88). These devices are capable of continuously and non-invasively monitoring rhythm-related physiological parameters, such as rest and activity cycles, heart rate variability, and skin temperature, thereby creating a unique and dynamic “intrinsic biological clock map” for each individual (45). Consequently, treatment decisions will transition from reliance on standard clock time to alignment with the patient’s specific biological rhythm phase (89).

Based on the data from wearable devices (90), intelligent clinical decision support systems can be developed (91). These systems can calculate the specific circadian rhythm phase with the highest immune system activity or the greatest sensitivity of tumor cells to ICIs for each patient (89), and recommend personalized optimal drug delivery Windows (69, 92). Future clinical trial designs are expected to exhibit greater flexibility by adopting “basket” or “umbrella” frameworks (93), which incorporate rhythm states as stratification factors, and allow for dynamic adjustments to treatment plans.

In addition to adjusting the administration times, proactive interventions can optimize the patient’s circadian rhythm to enhance the therapeutic efficacy of ICIs. Research shows that This approach involves intervention measures such as timed light therapy (94), melatonin supplementation (95), time-restricted eating (TRE) (96) and cognitive behavioral therapy (95, 97). These measures can rectify circadian rhythm disorders, improve overall physiological conditions and enhance immune function.By fostering a more favorable internal environment for ICIs treatment, these strategies may ultimately improve therapeutic outcomes for cancer patients undergoing immunotherapy (73).

6 Conclusion

Emerging evidence has begun to illuminate the scientific rationale and clinical promise of aligning ICIs administration with circadian rhythm to augment therapeutic efficacy. Our analysis further suggests that morningdosing—,when immune surveillance mechanisms naturally peak —may confer survival benefits in patients with advanced malignancies. This finding that converges with well-documented diurnal oscillations in immune cell dynamics and effector function. A key innovation of this chronotherapeutic approach lies in its paradigm shift from a static, lesion-centric focus to a dynamic, time-aware strategy, thereby establishing “chrono-immunotherapy”as a novel frontier in precision oncology.

While challenges remain,including healthcare system adaptability, interindividual variability in circadian profiles, and the temporal coordination of combination regimens, the “chrono-immunotherapy” represents a compelling avenue to advance toward more dynamic and precise immune-based cancer care. Future RCT studies will be critical to validate optimal timing windows, technological innovations will help critical to validate optimal timing windows, dissect the biological underpinnings of immune circadian rhythm, and targeted circadian interventions will be explored to further enhance immunotherapy outcomes. Ultimately, by synchronizing ICIs delivery with the patient’s endogenous biological clock, we aim to realize a new era of dynamic,personalized cancer immunotherapy.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

Author contributions

DL: Conceptualization, Writing – original draft, Writing – review & editing. ZL: Conceptualization, Writing – review & editing. HC: Supervision, Writing – review & editing. HZ: Methodology, Writing – original draft. HL: Methodology, Writing – original draft. XW: Resources, Writing – original draft.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This research was supported by Noncommunicable Chronic Diseases-National Science and 252 Technology Major Project : Study on Intervention Strategies of Traditional Chinese Medicine 253 Combined with PD-1/PD-L1 Inhibitors to Enhance Clinical Efficacy in Advanced Lung 254 Cancer (NO.2023ZD0502500) and Hebei Province Clinical Excellent Talents Project (ZF2025273) 255 and Hebei Administration of Traditional Chinese Medicine (2021186).

Acknowledgments

This is a short text to acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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The author(s) declared that generative AI was not used in the creation of this manuscript.

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References

1. Ielpo S, Barberini F, Dabbagh Moghaddam F, Pesce S, Cencioni C, Spallotta F, et al. Crosstalk and communication of cancer-associated fibroblasts with natural killer and dendritic cells: New frontiers and unveiled opportunities for cancer immunotherapy. Cancer Treat Rev. (2024) 131:102843. doi: 10.1016/j.ctrv.2024.102843

PubMed Abstract | Crossref Full Text | Google Scholar

2. Hallquist V. The american cancer society’s facts & Figures: 2020 edition. JADPRO. (2020) 11. doi: 10.6004/jadpro.2020.11.2.1. Rn, Ms, Cns, Anp P.

PubMed Abstract | Crossref Full Text | Google Scholar

3. Sun J-Y, Zhang D, Wu S, Xu M, Zhou X, Lu X-J, et al. Resistance to PD-1/PD-L1 blockade cancer immunotherapy: mechanisms, predictive factors, and future perspectives. biomark Res. (2020) 8:35. doi: 10.1186/s40364-020-00212-5

PubMed Abstract | Crossref Full Text | Google Scholar

4. Finger A-M and Kramer A. Peripheral clocks tick independently of their master. Genes Dev. (2021) 35:304–6. doi: 10.1101/gad.348305.121

PubMed Abstract | Crossref Full Text | Google Scholar

5. Scheiermann C, Kunisaki Y, and Frenette PS. Circadian control of the immune system. Nat Rev Immunol. (2013) 13:190–8. doi: 10.1038/nri3386

PubMed Abstract | Crossref Full Text | Google Scholar

6. Alam H, Tang M, Maitituoheti M, Dhar SS, Kumar M, Han CY, et al. KMT2D deficiency impairs super-enhancers to confer a glycolytic vulnerability in lung cancer. Cancer Cell. (2020) 37:599–617.e7. doi: 10.1016/j.ccell.2020.03.005

PubMed Abstract | Crossref Full Text | Google Scholar

7. Prasanna PG, Citrin DE, Hildesheim J, Ahmed MM, Venkatachalam S, Riscuta G, et al. Therapy-induced senescence: opportunities to improve anticancer therapy. JNCI: J Natl Cancer Institute. (2021) 113:1285–98. doi: 10.1093/jnci/djab064

PubMed Abstract | Crossref Full Text | Google Scholar

8. Fey RM, Billo A, Clister T, Doan KL, Berry EG, Tibbitts DC, et al. Personalization of cancer treatment: exploring the role of chronotherapy in immune checkpoint inhibitor efficacy. Cancers. (2025) 17:732. doi: 10.3390/cancers17050732

PubMed Abstract | Crossref Full Text | Google Scholar

9. Guo X, Qin L, Wang X, Geng Q, Li D, Lu Y, et al. Chronological effects of immune checkpoint inhibitors in non-small cell lung cancer. Immunology. (2025) 174:402–10. doi: 10.1111/imm.13897

PubMed Abstract | Crossref Full Text | Google Scholar

10. Wang C, Zeng Q, Gül ZM, Wang S, Pick R, Cheng P, et al. Circadian tumor infiltration and function of CD8+ T cells dictate immunotherapy efficacy. Cell. (2024) 187:2690–2702.e17. doi: 10.1016/j.cell.2024.04.015

PubMed Abstract | Crossref Full Text | Google Scholar

11. Chen Y, Wang J, Li X, Hu N, Voelcker NH, Xie X, et al. Emerging roles of 1D vertical nanostructures in orchestrating immune cell functions. Advanced Materials. (2020) 32:2001668. doi: 10.1002/adma.202001668

PubMed Abstract | Crossref Full Text | Google Scholar

12. Liu X, Fang J, Cheng D, Luan W, Lv Y, Hu W, et al. The circadian transcription factor CLOCK modulates oxidative stress resistance via the ACHL– relish axis in drosophila. Advanced Sci. (2025), e14388. doi: 10.1002/advs.202514388

PubMed Abstract | Crossref Full Text | Google Scholar

13. Takahashi JS. Transcriptional architecture of the mammalian circadian clock. Nat Rev Genet. (2017) 18:164–79. doi: 10.1038/nrg.2016.150

PubMed Abstract | Crossref Full Text | Google Scholar

14. Nosal C, Ehlers A, and Haspel JA. Why lungs keep time: circadian rhythms and lung immunity. Annu Rev Physiol. (2020) 82:391–412. doi: 10.1146/annurev-physiol-021119-034602

PubMed Abstract | Crossref Full Text | Google Scholar

15. Thoeni V, Dimova EY, Kietzmann T, Usselman RJ, and Egg M. Therapeutic nuclear magnetic resonance and intermittent hypoxia trigger time dependent on/off effects in circadian clocks and confirm a central role of superoxide in cellular magnetic field effects. Redox Biol. (2024) 72:103152. doi: 10.1016/j.redox.2024.103152

PubMed Abstract | Crossref Full Text | Google Scholar

16. Scrima R, Cela O, Agriesti F, Piccoli C, Tataranni T, Pacelli C, et al. Mitochondrial calcium drives clock gene-dependent activation of pyruvate dehydrogenase and of oxidative phosphorylation. Biochim Biophys Acta (BBA) - Mol Cell Res. (2020) 1867:118815. doi: 10.1016/j.bbamcr.2020.118815

PubMed Abstract | Crossref Full Text | Google Scholar

17. Bollinger T, Bollinger A, Naujoks J, Lange T, and Solbach W. The influence of regulatory T cells and diurnal hormone rhythms on T helper cell activity. Immunology. (2010) 131:488–500. doi: 10.1111/j.1365-2567.2010.03320.x

PubMed Abstract | Crossref Full Text | Google Scholar

18. Fortier EE, Rooney J, Dardente H, Hardy M-P, Labrecque N, and Cermakian N. Circadian variation of the response of T cells to antigen. J Immunol. (2011) 187:6291–300. doi: 10.4049/jimmunol.1004030

PubMed Abstract | Crossref Full Text | Google Scholar

19. Chen W, Tian M, Shen J, Liu Q, Li R, Liu B, et al. A rapid ex vivo co-culture of memory-like NK and activated T cells enhances anti-tumor response in gastric cancer. Int Immunopharmacol. (2025) 166:115569. doi: 10.1016/j.intimp.2025.115569

PubMed Abstract | Crossref Full Text | Google Scholar

20. Barbet G, Demion M, Moura IC, Serafini N, Léger T, Vrtovsnik F, et al. The calcium-activated nonselective cation channel TRPM4 is essential for the migration but not the maturation of dendritic cells. Nat Immunol. (2008) 9:1148–56. doi: 10.1038/ni.1648

PubMed Abstract | Crossref Full Text | Google Scholar

21. Holtkamp SJ, Ince LM, Barnoud C, Schmitt MT, Sinturel F, Pilorz V, et al. Circadian clocks guide dendritic cells into skin lymphatics. Nat Immunol. (2021) 22:1375–81. doi: 10.1038/s41590-021-01040-x

PubMed Abstract | Crossref Full Text | Google Scholar

22. Druzd D, Matveeva O, Ince L, Harrison U, He W, Schmal C, et al. Lymphocyte circadian clocks control lymph node trafficking and adaptive immune responses. Immunity. (2017) 46:120–32. doi: 10.1016/j.immuni.2016.12.011

PubMed Abstract | Crossref Full Text | Google Scholar

23. Haus E and Smolensky MH. Biologic rhythms in the immune system. Chronobiology Int. (1999) 16:581–622. doi: 10.3109/07420529908998730

PubMed Abstract | Crossref Full Text | Google Scholar

24. Erfani A, Diaz AE, and Doyle PS. Hydrogel-enabled, local administration and combinatorial delivery of immunotherapies for cancer treatment. Materials Today. (2023) 65:227–43. doi: 10.1016/j.mattod.2023.03.006

Crossref Full Text | Google Scholar

25. Blacher E, Tsai C, Litichevskiy L, Shipony Z, Iweka CA, Schneider KM, et al. Aging disrupts circadian gene regulation and function in macrophages. Nat Immunol. (2022) 23:229–36. doi: 10.1038/s41590-021-01083-0

PubMed Abstract | Crossref Full Text | Google Scholar

26. Fortin BM, Pfeiffer SM, Insua-Rodríguez J, Alshetaiwi H, Moshensky A, Song WA, et al. Circadian control of tumor immunosuppression affects efficacy of immune checkpoint blockade. Nat Immunol. (2024) 25:1257–69. doi: 10.1038/s41590-024-01859-0

PubMed Abstract | Crossref Full Text | Google Scholar

27. Man K, Loudon A, and Chawla A. Immunity around the clock. Science. (2016) 354:999–1003. doi: 10.1126/science.aah4966

PubMed Abstract | Crossref Full Text | Google Scholar

28. Liu J-L, Xu X, Rixiati Y, Wang C-Y, Ni H-L, Chen W-S, et al. Dysfunctional circadian clock accelerates cancer metastasis by intestinal microbiota triggering accumulation of myeloid-derived suppressor cells. Cell Metab. (2024) 36:1320–1334.e9. doi: 10.1016/j.cmet.2024.04.019

PubMed Abstract | Crossref Full Text | Google Scholar

29. Wu G, Ren H, Hu Q, Ma H, Chen H, Zhou L, et al. The circadian rhythm key gene ARNTL2: a novel prognostic biomarker for immunosuppressive tumor microenvironment identification and immunotherapy outcome prediction in human cancers. Front Immunol. (2023) 14:1115809. doi: 10.3389/fimmu.2023.1115809

PubMed Abstract | Crossref Full Text | Google Scholar

30. Guo M, Li S, Peng W, Wang S, Zhang X-D, Han T, et al. NIR-triggered engineered photosynthetic micro–nanodevice for reversing the hypoxic tumor immunosuppressive microenvironment. Mater Chem Front. (2021) 5:2234–46. doi: 10.1039/D0QM01001D

Crossref Full Text | Google Scholar

31. Tsuruta A, Shiiba Y, Matsunaga N, Fujimoto M, Yoshida Y, Koyanagi S, et al. Diurnal expression of PD-1 on tumor-associated macrophages underlies the dosing time-dependent antitumor effects of the PD-1/PD-L1 inhibitor BMS-1 in B16/BL6 melanoma-bearing mice. Mol Cancer Res. (2022) 20:972–82. doi: 10.1158/1541-7786.MCR-21-0786

PubMed Abstract | Crossref Full Text | Google Scholar

32. Munteanu C, Achim L, Muresan R, Souca M, Prifti E, Mârza SM, et al. The relationship between circadian rhythm and cancer disease. IJMS. (2024) 25:5846. doi: 10.3390/ijms25115846

PubMed Abstract | Crossref Full Text | Google Scholar

33. Xia A, Zhang Y, Xu J, Yin T, and Lu XJ. T cell dysfunction in cancer immunity and immunotherapy. Front Immunol. (2019) 10:1719. doi: 10.3389/fimmu.2019.01719

PubMed Abstract | Crossref Full Text | Google Scholar

34. Shimba A and Ikuta K. Glucocorticoids regulate circadian rhythm of innate and adaptive immunity. Front Immunol. (2020) 11:2143. doi: 10.3389/fimmu.2020.02143

PubMed Abstract | Crossref Full Text | Google Scholar

35. Wang Z and Wu X. Study and analysis of antitumor resistance mechanism of PD1/PD-L1 immune checkpoint blocker. Cancer Med. (2020) 9:8086–121. doi: 10.1002/cam4.3410

PubMed Abstract | Crossref Full Text | Google Scholar

36. Chen C, Wang Z, Ding Y, and Qin Y. Tumor microenvironment-mediated immune evasion in hepatocellular carcinoma. Front Immunol. (2023) 14:1133308. doi: 10.3389/fimmu.2023.1133308

PubMed Abstract | Crossref Full Text | Google Scholar

37. Karaboué A, Innominato PF, Wreglesworth NI, Duchemann B, Adam R, and Lévi FA. Why does circadian timing of administration matter for immune checkpoint inhibitors’ efficacy? Br J Cancer. (2024) 131:783–96. doi: 10.1038/s41416-024-02704-9

PubMed Abstract | Crossref Full Text | Google Scholar

38. Qian DC, Kleber T, Brammer B, Xu KM, Switchenko JM, Janopaul-Naylor JR, et al. Effect of immunotherapy time-of-day infusion on overall survival among patients with advanced melanoma in the USA (MEMOIR): a propensity score-matched analysis of a single-centre, longitudinal study. Lancet Oncol. (2021) 22:1777–86. doi: 10.1016/S1470-2045(21)00546-5

PubMed Abstract | Crossref Full Text | Google Scholar

39. Huang Z, Karaboué A, Zeng L, Lecoeuvre A, Zhang L, Li X-M, et al. Overall survival according to time-of-day of combined immuno-chemotherapy for advanced non-small cell lung cancer: a bicentric bicontinental study. eBioMedicine. (2025) 113:105607. doi: 10.1016/j.ebiom.2025.105607

PubMed Abstract | Crossref Full Text | Google Scholar

40. Gonçalves L, Gonçalves D, Esteban-Casanelles T, Barroso T, Soares De Pinho I, Lopes-Brás R, et al. Immunotherapy around the clock: impact of infusion timing on stage IV melanoma outcomes. Cells. (2023) 12:2068. doi: 10.3390/cells12162068

PubMed Abstract | Crossref Full Text | Google Scholar

41. Barrios CH, Montella TC, Ferreira CGM, De Marchi P, Coutinho LF, Lemos Duarte I, et al. Time-of-day infusion of immunotherapy may impact outcomes in advanced non-small cell lung cancer patients (NSCLC). JCO. (2022) 40:e21126–6. doi: 10.1200/JCO.2022.40.16_suppl.e21126

Crossref Full Text | Google Scholar

42. Patel JS, Woo Y, Draper A, Jansen CS, Carlisle JW, Innominato PF, et al. Impact of immunotherapy time-of-day infusion on survival and immunologic correlates in patients with metastatic renal cell carcinoma: a multicenter cohort analysis. J Immunother Cancer. (2024) 12:e008011. doi: 10.1136/jitc-2023-008011

PubMed Abstract | Crossref Full Text | Google Scholar

43. Ortego I, Molina-Cerrillo J, Pinto A, Santoni M, Alonso-Gordoa T, Lopez Criado MP, et al. Time-of-day infusion of immunotherapy in metastatic urothelial cancer (mUC): Should it be considered to improve survival outcomes? JCO. (2022) 40:e16541–1. doi: 10.1200/JCO.2022.40.16_suppl.e16541

Crossref Full Text | Google Scholar

44. Ruiz-Torres DA, Naegele S, Podury A, Wirth L, Shalhout SZ, and Faden DL. Immunotherapy time of infusion impacts survival in head and neck cancer: A propensity score matched analysis. Oral Oncol. (2024) 151:106761. doi: 10.1016/j.oraloncology.2024.106761

PubMed Abstract | Crossref Full Text | Google Scholar

45. Nomura M, Hosokai T, Tamaoki M, Yokoyama A, Matsumoto S, and Muto M. Timing of the infusion of nivolumab for patients with recurrent or metastatic squamous cell carcinoma of the esophagus influences its efficacy. Esophagus. (2023) 20:722–31. doi: 10.1007/s10388-023-01006-y

PubMed Abstract | Crossref Full Text | Google Scholar

46. Tanaka T, Suzuki H, Yamaguchi S, Shimotsuura Y, Nagasu S, Murotani K, et al. Efficacy of timing-dependent infusion of nivolumab in patients with advanced gastric cancer. Oncol Lett. (2024) 28:463. doi: 10.3892/ol.2024.14596

PubMed Abstract | Crossref Full Text | Google Scholar

47. Ishizuka Y, Narita Y, Sakakida T, Wakabayashi M, Kodama H, Honda K, et al. Impact of time-of-day on nivolumab monotherapy infusion in patients with metastatic gastric cancer. JCO. (2024) 42:268–8. doi: 10.1200/JCO.2024.42.3_suppl.268

Crossref Full Text | Google Scholar

48. Huang Z, Zeng L, Ruan Z, Zeng Q, Yan H, Jiang W, et al. Time-of-day immunochemotherapy in non-small cell lung cancer: a randomized phase 3 trial. Nat Med. (2026). doi: 10.1038/s41591-025-04181-w

PubMed Abstract | Crossref Full Text | Google Scholar

49. Tassino B, Horta S, Santana N, Levandovski R, and Silva A. Extreme late chronotypes and social jetlag challenged by Antarctic conditions in a population of university students from Uruguay. Sleep Sci. (2016) 9:20–8. doi: 10.1016/j.slsci.2016.01.002

PubMed Abstract | Crossref Full Text | Google Scholar

50. Cappelleri JC, Jason Lundy J, and Hays RD. Overview of classical test theory and item response theory for the quantitative assessment of items in developing patient-reported outcomes measures. Clin Ther. (2014) 36:648–62. doi: 10.1016/j.clinthera.2014.04.006

PubMed Abstract | Crossref Full Text | Google Scholar

51. Erdman JW, Balentine D, Arab L, Beecher G, Dwyer JT, Folts J, et al. Flavonoids and heart health: proceedings of the ILSI north america flavonoids workshop, may 31–june 1, 2005, washington, DC1. J Nutr. (2007) 137:718S–37S. doi: 10.1093/jn/137.3.718S

PubMed Abstract | Crossref Full Text | Google Scholar

52. Mahmoud K, Jaramillo C, and Barteit S. Telemedicine in low- and middle-income countries during the COVID-19 pandemic: A scoping review. Front Public Health. (2022) 10:914423. doi: 10.3389/fpubh.2022.914423

PubMed Abstract | Crossref Full Text | Google Scholar

53. Jusufi H and Boivin N. Navigating access and optimizing medication infusions in an academic medical center: A quality improvement study. Pharmacy. (2023) 11:111. doi: 10.3390/pharmacy11040111

PubMed Abstract | Crossref Full Text | Google Scholar

54. Kim K, Park YR, Lee JB, Kim HR, Lyu Y, Kim J-E, et al. Evaluating waiting time with real-world health information in a high-volume cancer center. Medicine. (2020) 99:e21796. doi: 10.1097/MD.0000000000021796

PubMed Abstract | Crossref Full Text | Google Scholar

55. Poggiogalle E, Jamshed H, and Peterson CM. Circadian regulation of glucose, lipid, and energy metabolism in humans. Metabolism. (2018) 84:11–27. doi: 10.1016/j.metabol.2017.11.017

PubMed Abstract | Crossref Full Text | Google Scholar

56. Ribas-Latre A, Fernández-Veledo S, and Vendrell J. Time-restricted eating, the clock ticking behind the scenes. Front Pharmacol. (2024) 15:1428601. doi: 10.3389/fphar.2024.1428601

PubMed Abstract | Crossref Full Text | Google Scholar

57. Hori K, Konishi K, Tani M, Tomioka H, Akita R, Kitajima Y, et al. Serum anticholinergic activity: A possible peripheral marker of the anticholinergic burden in the central nervous system in alzheimer’s disease. Dis Markers. (2014) 2014:1–7. doi: 10.1155/2014/459013

PubMed Abstract | Crossref Full Text | Google Scholar

58. Roshanmehr F, Hayashi K, Tahara Y, Suiko T, Nagamori Y, Iwai T, et al. Association between breakfast meal categories and timing of physical activity of Japanese workers. Foods. (2022) 11:2609. doi: 10.3390/foods11172609

PubMed Abstract | Crossref Full Text | Google Scholar

59. Lange T. Shift of monocyte function toward cellular immunity during sleep. Arch Intern Med. (2006) 166:1695. doi: 10.1001/archinte.166.16.1695

PubMed Abstract | Crossref Full Text | Google Scholar

60. Chen AY, Wolchok JD, and Bass AR. TNF in the era of immune checkpoint inhibitors: friend or foe? Nat Rev Rheumatol. (2021) 17:213–23. doi: 10.1038/s41584-021-00584-4

PubMed Abstract | Crossref Full Text | Google Scholar

61. Dimitrov S, Lange T, Fehm HL, and Born J. A regulatory role of prolactin, growth hormone, and corticosteroids for human T-cell production of cytokines. Brain Behavior Immun. (2004) 18:368–74. doi: 10.1016/j.bbi.2003.09.014

PubMed Abstract | Crossref Full Text | Google Scholar

62. Zaki NF, Sabri YM, Farouk O, Abdelfatah A, Spence DW, Bahammam AS, et al. Depressive symptoms, sleep profiles and serum melatonin levels in a sample of breast cancer patients. NSS. (2020) 12:135–49. doi: 10.2147/NSS.S206768

PubMed Abstract | Crossref Full Text | Google Scholar

63. Bunimovich YL, Keskinov AA, Shurin GV, and Shurin MR. Schwann cells: a new player in the tumor microenvironment. Cancer Immunol Immunother. (2017) 66:959–68. doi: 10.1007/s00262-016-1929-z

PubMed Abstract | Crossref Full Text | Google Scholar

64. Lee Y. Roles of circadian clocks in cancer pathogenesis and treatment. Exp Mol Med. (2021) 53:1529–38. doi: 10.1038/s12276-021-00681-0

PubMed Abstract | Crossref Full Text | Google Scholar

65. Gouldthorpe C, Power J, and Davies A. Circadian rhythm disorders in patients with advanced cancer: a scoping review. Front Oncol. (2023) 13:1240284. doi: 10.3389/fonc.2023.1240284

PubMed Abstract | Crossref Full Text | Google Scholar

66. Gouldthorpe C and Davies AN. Circadian rest-activity rhythm disorders in advanced cancer: assessment, diagnosis and clinical correlates. BMJ Support Palliat Care. (2026) 16:162–9. doi: 10.1136/spcare-2025-005410

PubMed Abstract | Crossref Full Text | Google Scholar

67. Catozzi S, Assaad S, Delrieu L, Favier B, Dumas E, Hamy A-S, et al. Early morning immune checkpoint blockade and overall survival of patients with metastatic cancer: An In-depth chronotherapeutic study. Eur J Cancer. (2024) 199:113571. doi: 10.1016/j.ejca.2024.113571

PubMed Abstract | Crossref Full Text | Google Scholar

68. Yeung C, Kartolo A, Tong J, Hopman W, and Baetz T. Association of circadian timing of initial infusions of immune checkpoint inhibitors with survival in advanced melanoma. Immunotherapy. (2023) 15:819–26. doi: 10.2217/imt-2022-0139

PubMed Abstract | Crossref Full Text | Google Scholar

69. Karaboué A, Collon T, Pavese I, Bodiguel V, Cucherousset J, Zakine E, et al. Time-dependent efficacy of checkpoint inhibitor nivolumab: results from a pilot study in patients with metastatic non-small-cell lung cancer. Cancers. (2022) 14:896. doi: 10.3390/cancers14040896

PubMed Abstract | Crossref Full Text | Google Scholar

70. Myers JS, Parks AC, Mahnken JD, Young KJ, Pathak HB, Puri RV, et al. First-line immunotherapy with check-point inhibitors: prospective assessment of cognitive function. Cancers. (2023) 15:1615. doi: 10.3390/cancers15051615

PubMed Abstract | Crossref Full Text | Google Scholar

71. Ma H, Zhang S, Jiao P, Ding H, Wang F, Zhao Y, et al. Serum IL-6 predicts immunotherapy-related adverse and outcome in advanced gastric and esophageal cancer patients with Anti-PD-1 treatment. Front Immunol. (2025) 16:1553882. doi: 10.3389/fimmu.2025.1553882

PubMed Abstract | Crossref Full Text | Google Scholar

72. Haimovich B, Calvano J, Haimovich AD, Calvano SE, Coyle SM, and Lowry SF. In vivo endotoxin synchronizes and suppresses clock gene expression in human peripheral blood leukocytes. Crit Care Med. (2010) 38::51–758. doi: 10.1097/CCM.0b013e3181cd131c

PubMed Abstract | Crossref Full Text | Google Scholar

73. Hughes BR, Shanaz S, Ismail-Sutton S, Wreglesworth NI, Subbe CP, and Innominato PF. Circadian lifestyle determinants of immune checkpoint inhibitor efficacy. Front Oncol. (2023) 13:1284089. doi: 10.3389/fonc.2023.1284089

PubMed Abstract | Crossref Full Text | Google Scholar

74. Wu L, Chen B, Wang J, Pu X, Li J, Wang Q, et al. EP08.01–093 ICI in combination with chemotherapy or anti-angiogenic agents as second-line orbeyondtreatment for advanced non-small cell lung cancer. J Thorac Oncol. (2022) 17:S387. doi: 10.1016/j.jtho.2022.07.664

Crossref Full Text | Google Scholar

75. Zhou B, Gao Y, Zhang P, and Chu Q. Acquired resistance to immune checkpoint blockades: the underlying mechanisms and potential strategies. Front Immunol. (2021) 12:693609. doi: 10.3389/fimmu.2021.693609

PubMed Abstract | Crossref Full Text | Google Scholar

76. Jassem J and Dziadziuszko R. Radiotherapy in thoracic Malignancies: the preface. Transl Lung Cancer Res. (2021) 10:1928–9. doi: 10.21037/tlcr-21-335

PubMed Abstract | Crossref Full Text | Google Scholar

77. Gonzalez-Aponte MF, Huang Y, Leidig WA, Simon T, Butt OH, Ruben MD, et al. Circadian variation in MGMT promoter methylation and expression predicts sensitivity to temozolomide in glioblastoma. J Neurooncol. (2026) 176:36. doi: 10.1007/s11060-025-05242-3

PubMed Abstract | Crossref Full Text | Google Scholar

78. Chan S, Rowbottom L, McDonald R, Bjarnason GA, Tsao M, Danjoux C, et al. Does the time of radiotherapy affect treatment outcomes? A review of the literature. Clin Oncol. (2017) 29:231–8. doi: 10.1016/j.clon.2016.12.005

PubMed Abstract | Crossref Full Text | Google Scholar

79. Hesse J, Martinelli J, Aboumanify O, Ballesta A, and Relógio A. A mathematical model of the circadian clock and drug pharmacology to optimize irinotecan administration timing in colorectal cancer. Comput Struct Biotechnol J. (2021) 19:5170–83. doi: 10.1016/j.csbj.2021.08.051

PubMed Abstract | Crossref Full Text | Google Scholar

80. Shen Z, Zhao Y, Xu X, Yang H, He S, Ma J, et al. Single-cell RNA sequencing integrated with bulk RNA sequencing analysis of clock circadian regulator with prognostic and immune microenvironment in thyroid cancer. Trans Oncol. (2025) 53:102299. doi: 10.1016/j.tranon.2025.102299

PubMed Abstract | Crossref Full Text | Google Scholar

81. He H, Yang Y, Wang L, Guo Z, Ye L, Ou-Yang W, et al. Combined analysis of single-cell and bulk RNA sequencing reveals the expression patterns of circadian rhythm disruption in the immune microenvironment of Alzheimer’s disease. Front Immunol. (2023) 14:1182307. doi: 10.3389/fimmu.2023.1182307

PubMed Abstract | Crossref Full Text | Google Scholar

82. Hergenhan S, Holtkamp S, and Scheiermann C. Molecular interactions between components of the circadian clock and the immune system. J Mol Biol. (2020) 432:3700–13. doi: 10.1016/j.jmb.2019.12.044

PubMed Abstract | Crossref Full Text | Google Scholar

83. Liu G, Luo Y, Liu J, Yang T, Ma Z, Sun J, et al. Identification of a novel circadian rhythm-related signature for predicting prognosis and therapies in hepatocellular carcinoma based on bulk and single-cell RNA sequencing. Eur J Cancer Care. (2024) 2024:1–15. doi: 10.1155/2024/1834636

Crossref Full Text | Google Scholar

84. Tao Y, Li J, Pan J, Wang Q, Ke R, Yuan D, et al. Integration of scRNA-seq and bulk RNA-seq identifies circadian rhythm disruption-related genes associated with prognosis and drug resistance in colorectal cancer patients. ITT. (2025) 14:475–89. doi: 10.2147/ITT.S499806

PubMed Abstract | Crossref Full Text | Google Scholar

85. Liao J, Duan Y, Xu X, Liu Y, Zhan C, and Xiao G. Circadian rhythm related genes signature in glioma for drug resistance prediction: a comprehensive analysis integrating transcriptomics and machine learning. Discov Onc. (2025) 16:119. doi: 10.1007/s12672-025-01863-2

PubMed Abstract | Crossref Full Text | Google Scholar

86. Mu Q, Zhang H, Wang K, Tan L, Li X, and Sun D. Application of single-cell sequencing and machine learning in prognosis and immune profiling of lung adenocarcinoma: exploring disease mechanisms and treatment strategies based on circadian rhythm gene signatures. Cancers. (2025) 17:2911. doi: 10.3390/cancers17172911

PubMed Abstract | Crossref Full Text | Google Scholar

87. Xu Z, Huang W, Zou X, and Liu S. Integrated machine learning constructed a circadian-rhythm-related model to assess clinical outcomes and therapeutic advantages in hepatocellular carcinoma. Transl Cancer Res. (2025) 14:1799–823. doi: 10.21037/tcr-24-1155

PubMed Abstract | Crossref Full Text | Google Scholar

88. Martin JL and Hakim AD. Wrist actigraphy. Chest. (2011) 139:1514–27. doi: 10.1378/chest.10-1872

PubMed Abstract | Crossref Full Text | Google Scholar

89. Saleem M, Watson AE, Anwaar A, Jasser AO, and Yusuf N. Optimizing immunotherapy: the synergy of immune checkpoint inhibitors with artificial intelligence in melanoma treatment. Biomolecules. (2025) 15:589. doi: 10.3390/biom15040589

PubMed Abstract | Crossref Full Text | Google Scholar

90. Hua Z, Dai C, Yang Y, and Song Y. Wearable bioelectronics for cancer theranostics. Microsyst Nanoeng. (2025) 11:180. doi: 10.1038/s41378-025-01048-5

PubMed Abstract | Crossref Full Text | Google Scholar

91. Xue H, Jin J, Huang X, Tan Z, Zeng Y, Lu G, et al. Wearable flexible ultrasound microneedle patch for cancer immunotherapy. Nat Commun. (2025) 16:2650. doi: 10.1038/s41467-025-58075-z

PubMed Abstract | Crossref Full Text | Google Scholar

92. Zarić M, Čanović P, Živković Zarić R, Protrka S, and Glišić M. The three musketeers in cancer therapy: pharmacokinetics, pharmacodynamics and personalised approach. JPM. (2025) 15:516. doi: 10.3390/jpm15110516

PubMed Abstract | Crossref Full Text | Google Scholar

93. Yi C, Bian D, Wang J, Hu S, Sun L, Yan Y, et al. Anti-PD1 based precision induction therapy in unresecta ble stage III non-small cell lung cancer: a phase II umbrella clinical trial. Nat Commun. (2025) 16:1932. doi: 10.1038/s41467-025-57184-z

PubMed Abstract | Crossref Full Text | Google Scholar

94. Keir LHM and Breen DP. New awakenings: current understanding of sleep dysfunction and its treatment in Parkinson’s disease. J Neurol. (2020) 267:288–94. doi: 10.1007/s00415-019-09651-z

PubMed Abstract | Crossref Full Text | Google Scholar

95. Gradisar M, Kahn M, Micic G, Short M, Reynolds C, Orchard F, et al. Sleep’s role in the development and resolution of adolescent depression. Nat Rev Psychol. (2022) 1:512–23. doi: 10.1038/s44159-022-00074-8

PubMed Abstract | Crossref Full Text | Google Scholar

96. Adafer R, Messaadi W, Meddahi M, Patey A, Haderbache A, Bayen S, et al. Food timing, circadian rhythm and chrononutrition: A systematic review of time-restricted eating’s effects on human health. Nutrients. (2020) 12:3770. doi: 10.3390/nu12123770

PubMed Abstract | Crossref Full Text | Google Scholar

97. McLay L, Hunter J, Ballam K, Marie Emerson L, Day AS, Vandeleur M, et al. An evaluation of psychosocial sleep interventions for children with chronic health conditions: A systematic review. Sleep Med Rev. (2024) 77:101962. doi: 10.1016/j.smrv.2024.101962

PubMed Abstract | Crossref Full Text | Google Scholar

Keywords: chrono-immunotherapy, efficacy, immunotherapy, the circadian rhythm, tumors

Citation: Liu D, Li Z, Cui H, Zhang H, Li H and Wu X (2026) Chrono-immunotherapy’s current and future optimization strategies: immunotherapy timing in line with the circadian rhythm brings longer survival benefits. Front. Immunol. 17:1777437. doi: 10.3389/fimmu.2026.1777437

Received: 29 December 2025; Accepted: 27 January 2026; Revised: 23 January 2026;
Published: 16 February 2026.

Edited by:

Jeffrey J. Pu, Tufts University, United States

Reviewed by:

Thierry Landre, Hôpitaux universitaires Paris Seine-Saint-Denis, France

Copyright © 2026 Liu, Li, Cui, Zhang, Li and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zhanlin Li, emprbHpsQHNvaHUuY29t

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.