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Commentary

Cancer Characteristics and Immunotherapy in Older Adults: Treatment Approaches, Immune-Related Adverse Events, and Management Considerations

by
Graham Pawelec
1,2,*,
Suzanne Ostrand-Rosenberg
3,4,
Tamas Fülöp
5,
Flore Van Leemput
2,6 and
Chris P. Verschoor
2,6,7
1
Institute of Immunology, University of Tübingen, 72076 Tübingen, Germany
2
Health Sciences North Research Institute, Sudbury, ON P3E 5J1, Canada
3
Department of Pathology, University of Utah, Salt Lake City, UT 84112, USA
4
Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
5
Research Center on Aging, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
6
NOSM, Sudbury, ON P3E 2C6, Canada
7
Department of Medicine, McMaster University, Hamilton, ON L8P 1A2, Canada
*
Author to whom correspondence should be addressed.
Onco 2026, 6(1), 7; https://doi.org/10.3390/onco6010007
Submission received: 8 December 2025 / Revised: 9 January 2026 / Accepted: 14 January 2026 / Published: 16 January 2026
Versions Notes

Simple Summary

Cancer therapies rarely consider the effect of patient age on clinical outcomes. However, it is becoming increasingly clear that cancers in older adults differ from those in younger patients in their biology and response to treatment. Both tolerance and efficacy of different treatment regimens may differ due to age-associated physiological and immunological alterations, as well as the prevalence of frailty and comorbidities. Given the general lack of awareness of the impact of ageing as a determinant of cancer treatment outcomes, this Commentary briefly reviews the state of the art of cancer research and treatment in the older patient, emphasizing the need for a better understanding of how older adults respond to anti-cancer treatments, particularly immunotherapies.

Abstract

Most clinical cancer therapy trials do not specifically consider the effect of patient age on treatment outcomes, and many even exclude older individuals. This is despite the fact that solid cancers are age-associated diseases and that there are many shared hallmarks between biological ageing and cancer. Thus, there is an increasing awareness of the serious gaps remaining in our knowledge of how older adults respond to cancer treatments, particularly immunotherapies. Emerging evidence suggests that it is not only the physiological and immunological changes associated with chronological ageing that impacts cancer treatment, but also those heterogeneous differences that impact treatment outcomes, such as frailty, comorbidities, and more generally, biological ageing. Importantly, it remains unclear which of these factors are negative or positive contributors, as has been illuminated by recent evidence pertaining to the incidence and severity of immune-related adverse events and survival. Much of our information on older patients in this context is essentially anecdotal, mostly deriving from the treatment of older adults in real-world practice or clinical trials that happened to include some older patients. Given the lack of comprehensive articles on the heterogeneity of ageing as a core determinant of cancer treatment outcomes, we briefly consider the state of the art of cancer research and treatment in the older patient, with an emphasis on immunotherapy and geriatric oncology.

1. Introduction

Few cancer therapy clinical trials have specifically considered the impact of patient age on outcome and sometimes have even excluded older individuals, despite the fact that solid cancers are age-associated diseases [1,2] and that there are many shared hallmarks between biological ageing and cancer [3]. Nonetheless, at the extremes of age (>90 years), some ageing processes may also reduce carcinogenesis [4]. This clearly reinforces the notion that the ageing process per se may contribute to carcinogenesis but is not necessarily the main driver. Hence, there remains a serious gap in evidence-based knowledge of how carcinogenesis differs in older and younger adults, and in how response to cancer treatments, particularly immunotherapies, may also differ. This presents a growing challenge not only in industrialized countries but also increasingly in low- and middle-income countries, due to advances in healthcare and increases in life expectancy worldwide [5]. Cancer treatments commonly trialled in younger patients may need to be optimized for older adults because of age-related physiological changes [6] and immunological changes [7], frailty and comorbidities [8], and different access to health care resources [9]. Much of our information on older patients in this context is essentially anecdotal, mostly deriving from the treatment of older adults in real-world practice or clinical trials that happened to include some older patients [10,11]. Hence, geriatric oncology struggles to provide evidence-based recommendations for treatment of older patients [12]. Currently, it appears that oncologists in general do not have significant training in geriatrics, so they are not able to use or interpret geriatric screening tools or are not aware of them. Training oncologists to assess older patients, to use screening and to refer to geriatricians only patients who do not perform well on these tests would be of great practical importance. Additionally, employing advanced care geriatric nurses to see the patients first, evaluate them, and if needed refer them for a clinical geriatric assessment would help to increase the efficacy of geriatric oncology to benefit the vulnerable older patient. There is an increasing awareness of the importance of this challenge [13]; to this end, and to draw further attention to this increasingly important problem, the present Commentary briefly discusses the state of the art of cancer research and treatment in older adults.

2. Differences in Carcinogenesis in Older vs. Younger Hosts

Most obviously, older hosts have been exposed to extrinsic and intrinsic carcinogens for longer periods of time [14], which is hypothesized to lead to a greater accumulation of genetic mutations over time [15] not only in the nuclear but also the mitochondrial genome [16]. However, while older adults do indeed exhibit increased levels of mutations associated with carcinogenesis, these do not necessarily lead to overt clinical cancer [17]. In fact, recent data suggest that driver mutations [18] may often be acquired quite early in life but mutant clones require decades to escape homeostatic control [19]. Moreover, new data suggest that old stem cells may exhibit intrinsic enhanced resistance to carcinogenesis [20] or that some non-malignant mutant clones in normal epithelium may actually outcompete and eliminate nascent tumours [21]. Nonetheless, the increased mutational load may generally contribute to the higher incidence of cancer in older individuals. Partly as a result of such insults, DNA damage in all nucleated cells and telomere shortening in mitotic cells may result in cellular senescence, hypothesized to be a mechanism of tumour suppression [22], but also contributing to inflammation and potentially to carcinogenesis if the senescent cells are not removed by the immune system [23]. There is some evidence that this process of immune-mediated clearance may be compromised in older adults due to immunosenescence [24,25,26]. Moreover, DNA damage itself may degrade immune function, leading to a negative feedback loop and slippage of tumour suppression control [27]. Adding to the complexity of understanding the sequelae of the ageing process, hormonal changes [28], fibrotic alterations and changing tissue microenvironments [29], and a plethora of other differences including the gut microbiota [30] may all contribute to a more pro-cancerogenic state, the individual influences on which remain challenging to dissect out [31]. Currently, most of these age-associated processes have been studied in isolation, but a holistic approach is required for a full understanding of carcinogenesis in older hosts. To this end, as in many other research fields, the wider application of machine learning (ML) is likely to improve our understanding of the multi-faceted manner in which ageing affects cancer in the older adult. One early attempt to integrate ML into geriatric oncology sought to simplify the identification of parameters predicting mortality [32], but currently the application of ML in actual diagnosis [33] and treatment [34] of cancer in older adults remains in its early stages. It is anticipated that in the coming years, ML will facilitate more insight into the critical differences between carcinogenesis in and treatment of younger vs. older hosts [34]. An example of how things may develop is provided by a more recent publication updating the work of Sena et al. [32] by Audureau et al. [35] on prediction of mortality by ML in older cancer patients by means of a Geriatric Cancer Scoring System (GCSC) [35]. In particular, the construction of so-called “digital twins” (i.e., a virtual, real-time model of a patient that uses inputted data to reflect the changing status of its real-world counterpart) may offer a personalized dynamic basis for predicting and improving clinical outcome in older patients [36]. This rapidly developing field will undoubtedly continue to offer new and more refined tools for understanding and treating cancer in the older patient. However, the contribution of frailty, comorbidities or polypharmacy to the treatment of older cancer patients is currently very difficult to assess because of a lack of sufficient evidence on the role of ageing-related intrinsic capacity and changes, making it challenging to develop individualized treatment regimens.
Much of the cancer biology information that we currently possess is derived from animal models, usually young mice, which therefore requires investigation also in older animals and validation in humans [37]. In this context, the notion is emerging that a comprehensive understanding of whether tumour-intrinsic mechanisms play the more important role or whether tumour-extrinsic factors are crucial to cancer development [38] will be required to make any progress. In the latter case, it is essential to determine whether the immune system, which intimately influences the maintenance of systemic organismal integrity, is primarily responsible for determining the age-associated pro-tumorigenic properties of the tumour microenvironment. For example, recent work has shown that an aged immune system promotes tumour growth as a result of skewing of haematopoiesis towards the generation of myeloid cells that produce excessive amounts of Interleukin-1 due to decreased DNA methyltransferase 3A activity [39]. This results in increased levels of myeloid-derived suppressor cells (MDSCs), which are key players in inhibiting anti-tumour immunity and contribute to many other disease states [40]. There is clear evidence that both the induction and function of MDSCs is exacerbated in older adults, potentially contributing to impaired T cell responses, at least in vitro [41]. However, as considered in the next section, it is incontrovertible that MDSCs also play a major role in vivo not only in predicting the outcome of cancer immunotherapy at baseline, as shown in many studies [42], but also that dynamic changes in blood levels early during treatment may allow patient segregation into clinical responders and non-responders [43].

3. Differences in Treatments in Older vs. Younger Patients

The efficacy of and tolerance of anti-cancer treatments in older adults has been viewed as especially problematic for the application of chemotherapies, which remain the mainstay of most systemic treatments for metastatic solid tumours. Older patients commonly exhibit reduced organ function, which may be further exacerbated by the effect of chemotherapeutic drugs, and which also influences drug metabolism and elimination [44]. Hence, received wisdom has been that different treatment protocols are required for geriatric patients [45]. However, a recent meta-analysis of such studies concluded that only a small fraction of published papers yielded reliable data in this context [46]. Nonetheless, in clinical practice, dose reductions are commonly applied in the belief that older adults cannot tolerate treatment as well as younger patients, hence reducing efficacy [47]. However, some more recent re-analyses of trial data suggest that dose reduction may not always be necessary (e.g., in the Geriatric Assessment Intervention for Reducing Toxicity in Older Patients With Advanced Cancer (GAP70+) Trial) [48]. Findings such as these emphasize the importance of geriatric screening resulting in a personalized approach, as we have also published in an earlier position paper on geriatric assessment and biological markers of ageing [49].
Be that as it may, the perceived treatment toxicity and patient resilience issues are considered less critical with immunotherapies [50,51] but functionality of the older immune system may be compromised by immunosenescence [52,53]. The latter may be important not only for checkpoint blockade immunotherapy (CBI) but also for recovery of immune function, especially T-cell-mediated immunity after aggressive chemotherapy [54]. Thus, immmunosupportive therapies may become increasingly important, for example, by probiotic supplementation [55]. However, despite the assumption that immunosenescence contributes to poorer responses to CBI in older adults, there is little evidence for this. Thus, a recent meta-analysis including a total of 17,476 patients, of whom 10,119 were <65 years old and 7357 were over 65, concluded that despite some subtle differences between tumour types, there was no significant difference in OS (P = 0.954) or PFS (P = 0.555) between the two age groups [56]. At least for NSCLC, accumulated real-world data on 24,136 patients with stage IV disease aged > 75 years and 62,037 under 75 showed unequivocally that older and younger patients exhibit similar responses [57]. Hence, these and several other studies imply that any age-associated impairments of immune function apparently do not greatly affect the efficacy of CBI in older patients. In fact, one study found that older patients had similar clinical outcomes as younger patients despite exhibiting “immunosenescent” phenotypes at baseline which persisted during CBI, including lower cytokine responses, fewer naïve T cells, and higher relative expression of immune checkpoint markers [58]. Interestingly, some of these age-related immune characteristics, such as elevated CD57 expression on NK- and T-cells and frequencies of terminally differentiated CD8 T-cells, have been found to be associated with the incidence of immune-related adverse events (irAEs), a serious treatment-related outcome commonly experienced by cancer patients [59], as discussed in the next section.

4. IrAEs Are More Common in Older Patients Undergoing CBI

IrAEs can occur in up to 70% of patients receiving CBI and may impact multiple organ systems throughout the body, often exhibiting variability in their severity and time of onset [60,61]. A recent systematic review of 121 studies spanning a range of different cancer types found that gastrointestinal (19%), endocrine (24%), and skin (29%) irAEs were most common [62]. Although the efficacy of CBI appears similar in older and younger patients, the prevalence of irAEs seems to be different. For example, in a study of patients with advanced solid tumours, the incidence of experiencing two or more irAEs among older patients was nearly twice that of younger patients (18% vs. 32%) [63]. Similarly, findings from a study based on the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) showed that the overall risk of developing irAEs is higher among patients aged 65–84 years compared to those aged 65 years and younger [64]. However, those in the 85+ age group experienced a lower overall irAE risk (possibly attributable to survivorship bias [64]). Across organ systems, the prevalence of irAEs also appears to differ by age, with the FAERS study demonstrating that older adults experience a decrease in endocrine, ocular, gastrointestinal, and hepatobiliary irAEs, while the occurrence of renal, musculoskeletal, and cardiovascular irAEs increases relative to those aged 64 years and younger [64]. Other studies have also explored the relationship between the severity of irAEs and therapeutic efficacy, although it remains unclear whether severe events in older adults are significantly associated with clinical outcomes such as survival and treatment discontinuation [65,66].
Beyond age-related trends in the frequency and severity of irAEs, it is also important to consider the heterogeneity of ageing and its role in shaping the risk of treatment-related toxicities. For example, a retrospective analysis of 596 patients found that the risk of developing irAEs of any grade was higher among frail patients compared to those who were not frail (59% vs. 43%) [67]. Another recent study of 138 patients with advanced NSCLC investigated the utility of “biological age” in predicting patients’ susceptibility to irAEs [68]. Here, biological age was calculated based on chronological age together with a series of clinical biomarkers, such as creatinine, albumin, C-reactive protein, and systolic blood pressure [68]. Although the overall risk of developing irAEs was not significantly associated with biological age, the risk of developing pneumonitis was nearly three times higher for those patients determined to be biologically older [68]. Thus, this complexity suggests that additional studies across multiple cancer types are needed before definite conclusions can be made regarding the role of biological age in irAE incidence. Studies such as the above also highlight the potential relationship between the risk of irAE development and biological processes underlying frailty and biological ageing, such as systemic inflammation and impairments of immune function. Notably, increases in biomarkers of the inflammatory response, including C-reactive protein and interleukin-6, have been significantly associated with a higher incidence of irAEs [69,70]. This may be related to the upregulated activity of inflammatory and TNF and NF-KB signalling pathways, as has been shown in patients with advanced NSCLC who experienced irAEs [71]. Specifically, researchers demonstrated an increase in myeloid cells, including monocytes and macrophages, expressing elevated cytokines and chemokines such as CXCL8, IL-1β, and TNF in patients who experienced severe irAEs relative to patients with no irAEs or mild/moderate irAEs [71]. Finally, cytomegalovirus (CMV) seropositivity, a prominent correlate of immune remodelling, particularly in the T-cell compartment [72], has been associated with a lower cumulative incidence of high-grade irAEs in patients with metastatic melanoma [73]. With previous research demonstrating that T-cell receptor diversity is associated with a higher risk of irAEs, a reduction in the number of naïve T-cells as a result of latent CMV infection has been hypothesized to be protective against irAEs [73].

5. IrAEs in Older Patients Impact Therapeutic Efficacy

Even at low grades, irAEs can significantly affect health-related quality of life, impact treatment tolerance, and increase the risk of hospitalization and even mortality [74,75]. However, the incidence of these treatment-related consequences can differ between older and younger patients. For example, the rate of early treatment discontinuation because of irAE incidence in patients aged 90 years and older was reported to be more than double that of patients aged 80–89 years (31% vs. 15%). Similarly, among patients with melanoma or NSCLC, CBI interruptions or discontinuations were significantly higher in patients aged 75 years and older relative to those aged 65–75 years or 65 years and younger (39% vs. 18% or 14%, respectively) [76]. Further, a study of advanced melanoma patients observed an increase in the length of hospitalization and risk of death due to irAEs among older patients [77]. Hospitalizations related to irAEs have also been shown to be significantly more common among patients who were frail compared to non-frail patients (54% vs. 29%) [78].
As mentioned above, it appears that survival may actually be improved in patients who develop irAEs, a finding that has been confirmed across numerous cancer types, leading some to hypothesize that irAEs are in fact a product of immune activation by CBI, which ultimately leads to improved therapeutic efficacy [79]. However, this observation is contextual, as a systematic review and meta-analysis of 30 studies including 4971 patients with different cancer types found that while low-grade irAEs improved CBI efficacy and overall survival, high-grade irAEs did not [79]. Amongst other explanations, this may be due to the common use of glucocorticoids or other immunosuppressive agents for the management of high-grade irAEs, which can compromise the efficacy of CBI therapy.

6. Managing Treatment Complications in Older vs. Younger Hosts

In addition to the effects of the ageing process per se, most older cancer patients suffer from comorbidities often compounded by polypharmacy [80]. Indeed, a prospective analysis of 140 patients with metastatic NSCLC or melanoma found that the burden of high-grade comorbidities and polypharmacy was significantly higher in older patients relative to their younger counterparts (77% vs. 56% and 61.4% vs. 37.1%, respectively) [81]. Importantly, multiple comorbidities and polypharmacy can give rise to more severe irAEs and a subsequent increase in related consequences, including the use of immunosuppressive agents and interruptions in CBI therapy [76]. For example, in a study of older adults with melanoma or NSCLC, in patients with five or more comorbidities who were taking five or more concomitant medications, high-grade irAEs (25% vs. 9%), administration of glucocorticoids (37% vs. 20%), and CBI therapy interruptions (37% vs. 16%) occurred significantly more often compared to those with five or less comorbidities and five or less comedications [76]. Given that the effects of comorbidities and polypharmacy on patients’ responses to treatment can be difficult to predict, conducting comprehensive geriatric assessments [82] and determining patients’ frailty status [83] can help to take chronic diseases and conditions other than cancer into account. However, translating results into successful personalized treatment regimens remains challenging [84]. Common comorbidities such as cardiovascular disease, diabetes, and chronic respiratory conditions have a disproportionate impact on the older cancer patient and complicate cancer treatments [85]. The complexity and heterogeneity of cancer presentation in older patients suggests that the knowledge and attitudes of healthcare providers may also have a disproportionate influence on the treatment decision-making process [86,87].
The increasing application of new ML models is expected to assist in identifying crucial parameters for improving the treatment of older patients, although a very recent overview concluded that “Successful integration of ML in oncology decision-making requires standardized data and methodologies, larger sample sizes, greater transparency, and robust validation and clinical utility assessment” [88]. Thus, all treatment decisions, be they surgery, radiotherapy, chemotherapy, targeted molecular therapy, or immunotherapy, need to be personalized for older patients, for which ML assistance is still being developed. While important for younger patients as well, it is particularly crucial in older adults, where quality-of-life issues may take precedence over overall survival. Nevertheless, the relative importance of these outcomes will depend on the circumstances, and efficacy may still remain the overriding aim [89]. Even when considering surgical options, risks may increase with age, and the same is true for radiotherapies where side effects are often more serious and harder to manage in older patients [84,90]. More recent chemotherapeutic approaches targeting specific molecules necessary for cancer cell survival are generally much better tolerated than older less-specific chemotherapies, but they are also not without issues of toxicity and tolerability [91]. The same can be said for the application of immunotherapies in older patients, mostly CBI first with anti-CTLA-4 antibodies and nowadays more-or-less routinely with anti-PD-1 or anti-PD-L1 antibodies for many solid tumours [92], and CAR-T cells for hematological malignancies [93,94].
Importantly, CTLA-4, PD-1, and PD-L1 inhibitors are active in different phases of the anti-tumour immune response and differ in the cells that they target. Although both CTLA-4 and PD-1 serve as negative regulators of T-cell activation and proliferation, CTLA-4 acts during the priming phase of T-cell activation, while PD-1 operates during the effector phase. Furthermore, CTLA-4 primarily targets T cells, whereas PD-1 acts more broadly on T cells, B cells, and myeloid cells [95]. In contrast, PD-L1 specifically targets tumour cells and antigen-presenting cells with high PD-L1 expression. Given these different mechanisms of action and the heterogeneous nature of age-related immune dysregulation, it is not surprising that the efficacy of different immunotherapies varies between older and younger patients. For example, a systematic review and meta-analysis of 25 randomized controlled trials suggests that treatment efficacy with CTLA-4 antibody monotherapy is lower in patients aged 65 years or older compared to younger patients [96]. However, CTLA-4 monotherapy is no longer state-of-the-art, and the same study indicated that survival benefit was similar in both age groups for PD-1 and PD-L1 treatment [96].

7. Obesity Impacts Cancer Incidence and Immunotherapy Efficacy

Ageing is frequently, although not always, associated with significant weight gain [97]. The “obesity paradox” is a controversial medical hypothesis supporting the concept that higher body mass index (BMI) facilitates survival of patients with cancer and certain other diseases [98]. However, high BMI has been shown in clinical studies to result in multiple health complications including increased risk of developing breast, endometrial, colorectal, esophageal, kidney, gallbladder, uterine, pancreatic, and liver cancers [99,100]. Animal studies support a detrimental role for obesity and have established that elevated BMI causes increased tumour progression and more rapid morbidity, the latter being due to the low-grade inflammation in adipocyte deposits that increases the levels of MDSCs [101,102,103] and promotes T cell dysfunction [40].
Nonetheless, obesity may play a beneficial role in CBI for some cancer patients, since obese melanoma and patients with other types of cancers respond better to PD-1-therapy than non-obese patients do [104,105]. However, obesity is not universally beneficial since patients with renal cancer are poorer responders to CBI as compared to non-obese patients [106].
Any benefit of obesity in CBI has been proposed to be due to restoration of T cell function. T cell dysfunction and exhaustion as measured by increased expression of PD-1, low expression of T-bet, and high expression of the transcription factor Eomes are common in obese mice. Similar observations for exhaustion markers in older patients with melanoma showed similar T cell dysfunction markers including elevated levels of PD-1 [107]. These findings have led to the hypothesis that the enhanced efficacy of CBI in obese individuals may be due to high T cell levels of PD-1-mediated immune suppression so that CBI has a broader target cell population upon which to act and thereby restore antitumour immunity [107]. Leptin, which is elevated in both obese mice and obese patients, may contribute to T cell exhaustion since its level correlates with PD-1 levels on CD8+ T cells [107]. Interestingly, leptin also drives PD-L1+ MDSCs in obese mice [101], suggesting that the efficacy of CBI in obese individuals may be due to restoring T cell function by simultaneously un-exhausting T cells and blocking MDSC-mediated immune suppression.

8. Hormonal Changes in Older Women May Impact Immunotherapy Efficacy

The ageing process in women is characterized by specific hormonal and metabolic changes which are not present in men and are presumably due to women undergoing menopause. For example, blood levels of polyunsaturated fatty acids (PUFAs) are elevated post-menopause [108]. Although PUFAs are protective in ageing-associated cardiovascular disease, animal studies have demonstrated that PUFAs are a driver of MDSCs and immune suppression [109]. As discussed above, clinical studies have demonstrated that immune suppressive MDSCs accompany poorer immunotherapy prognosis and may be predictive of CBI efficacy [42,43].

9. Implications for Older Cancer Patients from the Geriatrician’s Perspective

The issues discussed above should make practitioners seriously reconsider the commonly “ageist” attitude towards the way that the treatment of most older cancer patients is approached [110]. Often, the assumption is that because individuals are old they will not be able to tolerate the same treatment as younger patients [111]. Fortunately, most oncologists treating older patients under real-life conditions know that it is not the chronological age which will determine how a treatment will be tolerated, but mostly the comorbidities and functionality of older patients. However, it can be challenging to differentiate between adverse events that are genuinely related to treatment and comorbidity or functional decline that are age-related [64]. This can give rise to the underreporting of AEs. It is therefore critical to consider the overall biological effects of cancer before considering whether all that is observed clinically, like frailty, is due to ageing or to the effect of the cancer.
To guide the best practice to treat cancer, the stage of the individual cancer and how it can be treated should be clearly defined. Next, the oncology team should inquire about what the perspectives, the wishes, and the life course of the patient are. It should be determined what is due to the cancer itself as a disease, which may cause many different symptoms and signs, which most of the time unfortunately are believed to be due to the so-called frailty or age, such as loss of weight, fatigue, etc. If it is possible to identify the conditions resulting from cancer, then other contributing factors may be considered such as the co-morbidities and the nutritional and/or the physical state of the patient. These considerations should lead to the resolution of these uncertainties using the Comprehensive Geriatric Assessment (CGA) or its short form [112,113,114,115]. Only at this stage is it possible to decide with the patient what would be the best treatment for that individual. These considerations will guide the clinical decision in a personalized manner as to what treatment should be used, how it should be administered, and at which dosage, while considering the complex nature of the individual. In this setting, we can be optimistic in anticipating that ML will help to better determine which patient will and which will not tolerate the proposed treatment. However, because many aspects of the patient will not be taken into consideration, better training of oncologists in oncogeriatrics would probably be more helpful.
Geriatricians are focused on age and to some extent believe that older patients need protection from harsh therapies. Age is a consideration; however, the older patient’s overall condition must also be considered in a much more pro-active perspective. This would need a paradigm shift from asking what not to do to what can be done given the patient’s individual condition. This is the point where oncologists, medical oncologists, and geriatricians should come together and speak the same language for the benefit of the older patient. Unfortunately, in many cases, this can result in an antagonistic attitude which harms the older patient. Nonetheless, because of patient heterogeneity, no prediction can reliably determine how an individual will respond to a specific treatment, even when the frailty index or biological age is cited as decisive.
As discussed above, many treatment results are anecdotal. It is clear that rare descriptions of successful treatments in older patients are outnumbered by those reporting failures and complications. Many older patients with cancer who had chemotherapy, radiotherapy or immunotherapy have tolerated the treatment well, and their cancer is in remission. Certainly, from those who have multiple comorbidities such as hypertension, diabetes, CKD, cognitive issues, etc., some may die, but younger patients also die. OS is not the only way to measure success, but PFS or even functionality are the real measures for older subjects. It is not uncommon that younger patients who go through all treatments without any problems die, whereas some nonagenarians with cognitive problems, in wheelchairs, etc., return home after treatment. An overemphasis on biological age does not mean fitness; the functional considerations of the specific patient are much better indicators, which is why a CGA should be performed on all patients [116]. The main priorities for change regarding older patients will be by considering their biological and physiological age, their own will, and their inclusion in clinical trials. Oncologists need to be educated with respect to these conditions and geriatricians integrated into treatment regimens for older patients.

10. Conclusions

As the number of older cancer patients continues to increase globally, there is an urgent need for more research into treatments that recognize multiple levels of age-associated issues to facilitate onco-geriatric teams’ abilities to formulate therapies specifically for these patients. Clinical trials that purposefully include older patients will be essential for determining how treatments differ in younger and older patients [117]. Hence, requirements for future progress must include clinical trials to ensure that older patients are adequately represented and for whom personalized treatment protocols can be developed, taking into account the heterogeneous outcomes of the ageing process and the individual patient’s overall health and circumstances. In the absence of validated prognostic tools for AEs, the geroscience concept of assessing biological age rather than chronological age [118] in combination with geriatric assessments and frailty measures may offer a holistic way forward to improving outcomes for the older cancer patient. Better education and communication between oncologists and geriatricians are also essential for developing sound personalized treatments for older cancer patients.

Author Contributions

All the authors contributed to the writing and revision of the text and are entirely responsible for the content of this article and the opinions expressed therein. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Acknowledgments

This paper was created using several editorial tools, including AI, as part of the process. None of the written text was generated by AI.

Conflicts of Interest

SOR and GP are members of the Onco Editorial Board. None of the other authors declare any conflicts of interest.

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MDPI and ACS Style

Pawelec, G.; Ostrand-Rosenberg, S.; Fülöp, T.; Van Leemput, F.; Verschoor, C.P. Cancer Characteristics and Immunotherapy in Older Adults: Treatment Approaches, Immune-Related Adverse Events, and Management Considerations. Onco 2026, 6, 7. https://doi.org/10.3390/onco6010007

AMA Style

Pawelec G, Ostrand-Rosenberg S, Fülöp T, Van Leemput F, Verschoor CP. Cancer Characteristics and Immunotherapy in Older Adults: Treatment Approaches, Immune-Related Adverse Events, and Management Considerations. Onco. 2026; 6(1):7. https://doi.org/10.3390/onco6010007

Chicago/Turabian Style

Pawelec, Graham, Suzanne Ostrand-Rosenberg, Tamas Fülöp, Flore Van Leemput, and Chris P. Verschoor. 2026. "Cancer Characteristics and Immunotherapy in Older Adults: Treatment Approaches, Immune-Related Adverse Events, and Management Considerations" Onco 6, no. 1: 7. https://doi.org/10.3390/onco6010007

APA Style

Pawelec, G., Ostrand-Rosenberg, S., Fülöp, T., Van Leemput, F., & Verschoor, C. P. (2026). Cancer Characteristics and Immunotherapy in Older Adults: Treatment Approaches, Immune-Related Adverse Events, and Management Considerations. Onco, 6(1), 7. https://doi.org/10.3390/onco6010007

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