Lung cancer remains the leading cause of cancer-related mortality worldwide, causing approximately 15,000 deaths annually in individuals under 45 years, over 600,000 deaths among those aged 45–64 years, and nearly 1.2 million deaths in people aged 65 years and older, together exceeding 1.8 million deaths per year (1, 2). Despite advances in screening, imaging, and early detection, most cases are still diagnosed at advanced stages, contributing to persistently high lethality. Over the past two decades, however, the therapeutic landscape has shifted dramatically. The identification of oncogenic drivers such as EGFR, ALK, ROS1, RET, HER2, MET, and KRAS, alongside the clinical success of immune checkpoint inhibitors (ICIs) targeting PD-1, PD-L1, and CTLA-4, has ushered in an era of precision oncology (3). For biomarker-selected patients, these therapies have achieved survival gains once considered unattainable, underscoring the transformative power of targeted and immune-based approaches.

Yet, these breakthroughs also expose the boundaries of current personalization strategies. Precision oncology in lung cancer remains largely tumor-centric, guided by genomic alterations, PD-L1 expression, or tumor mutational burden. While invaluable, this framework often neglects host-level determinants that critically shape therapeutic efficacy and safety (4–8). Among these, age emerges as one of the most underrecognized, yet biologically decisive, modifiers of cancer therapy. Age influences treatment response through diverse biological and clinical mechanisms, spanning pharmacokinetics, immune competence, comorbidity burden, and survivorship considerations. These age-related dynamics help explain why outcomes diverge so markedly across the lifespan and why frameworks that treat age merely as a chronological cutoff fall short of true personalization (9–13). Importantly, “age” denotes chronological time, whereas “aging” refers to biological processes, including immunosenescence, inflammaging, and tissue/cellular senescence, that reprogram host defenses and thereby alter lung cancer behavior and treatment outcomes. These distinctions help explain why patients with similar tumors may experience markedly different responses across the lifespan and why reliance on chronological age alone falls short of true personalization. At the same time, it remains uncertain which aging-related features are sufficiently robust to guide real-world treatment decisions, underscoring the need for cautious interpretation of existing data.

Against this backdrop, a central challenge emerges, which is how to reconcile tumor-centric precision oncology with the biological heterogeneity introduced by aging. Rather than treating age as a statistical covariate or eligibility criterion, it may need to be considered an integral dimension of personalized care. In this review, we examine how age intersects with targeted therapies and immunotherapies in lung cancer across pediatric, adult, and elderly populations, highlighting current gaps in trial design, biomarker development, and clinical evidence. We further discuss emerging strategies to integrate age-conscious principles into translational research and clinical practice, to advance more biologically informed, equitable, and durable precision oncology across the lifespan.

The prevailing framework of precision oncology in lung cancer is predominantly tumor-centered, anchored in actionable genomic alterations and immune biomarkers such as PD-L1 expression or tumor mutational burden. While this paradigm has yielded transformative therapies, it underestimates the profound influence of host biology on therapeutic efficacy and safety. Among host-related variables, age represents one of the most underappreciated and complex modifiers of cancer therapy. It is not merely a demographic characteristic but a biological determinant that shapes pharmacokinetics, immune dynamics, comorbidity burden, and survivorship.

Chronological age is an imprecise proxy for fitness, while biological age captures cumulative functional decline across tissues and immune compartments, explaining why patients of the same chronological age can have divergent pharmacokinetics, toxicity profiles, and responses to targeted therapy and immunotherapy (14). A key limitation in current practice is the reliance on chronological age as a surrogate for patient fitness. Clinical trial eligibility and treatment algorithms frequently apply arbitrary cutoffs (e.g., ≥65 or ≥70 years, or defining “early-onset” as <50 years), yet these thresholds fail to capture biological heterogeneity (15). Evidence from large cohort studies highlights this limitation. In one analysis of more than 25,000 cancer patients, using 50 years as a universal cutoff for hereditary cancer testing would have failed to detect nearly 400 clinically significant germline mutations (16). Likewise, patients with early-onset cancers frequently carry a higher burden of pathogenic germline variants, emphasizing that cancer risk and treatment decisions should be guided by tumor biology rather than arbitrary age thresholds (17). From a molecular standpoint, precision oncology is fundamentally “age-agnostic” (18). Genomics-driven approaches have revealed actionable alterations across the lifespan, from pediatric to geriatric patients. Tumor-agnostic approvals for alterations such as NTRK fusions and high microsatellite instability exemplify how targeted therapies transcend age categories (19). Large-scale efforts like the GENIE Consortium further demonstrate that ~30% of tumors harbor clinically actionable drivers in both younger and older patients, with sequencing-matched therapy consistently improving outcomes regardless of age (20). These data reinforce the principle that tumor biology, not patient age, should guide therapeutic choice.

Despite this, structural age bias persists. Older adults comprise more than half of all cancer cases, yet account for only about one-third of clinical trial participants. Protocols often exclude patients with comorbidities or organ dysfunction, disproportionately limiting access for those over 65 and especially over 75. This underrepresentation hampers accurate assessment of dosing, safety, and efficacy in the very populations most affected by lung cancer (21, 22). Recent regulatory and research initiatives call for greater inclusion of elderly patients in trials, with adapted designs that account for frailty, organ reserve, and polypharmacy rather than blanket exclusions.

Looking ahead, a redefinition of age in precision oncology is urgently needed. Instead of relying on fixed chronological cutoffs, age classification should be specific to tumor type and incorporate molecular and germline testing strategies to better identify high-risk or targetable subgroups. Advances in biological age biomarkers, such as epigenetic clocks, immune repertoire profiling, and senescence-associated signatures, provide tools to improve stratification and customize therapy beyond basic demographics. Multi-omics and longitudinal studies are beginning to reveal how cumulative mutational load, immunosenescence, and metabolic reprogramming influence therapeutic vulnerabilities across the lifespan. In this context, rethinking age is not about abandoning demographic categories but about incorporating functional, molecular, and geroscience-based measures of aging into precision oncology. This transition promises more equitable and effective personalization, ensuring therapies are optimized not only for tumors but also for the patients who have them. Ultimately, since most lung cancers occur after the sixth decade, any attempt to adjust standards of care based on age must be grounded in age- and stage-matched evidence rather than demographics alone, a limitation we will address in the following sections.

Targeted therapies have transformed cancer treatment by enabling personalized interventions based on defined molecular drivers. These strategies now extend across the lifespan, from pediatric to elderly populations, yet their efficacy, safety, and application vary according to tumor biology, comorbidities, and age-related physiology.

Immune aging, also termed immunosenescence, is an age-associated immune remodeling that is not merely immune “decline” but a systemic reprogramming of innate and adaptive immunity characterized by reduced naïve lymphocyte production and repertoire diversity, accumulation of dysfunctional/exhausted effector subsets, myeloid skewing, and a chronically inflamed baseline state called “inflammaging” (58). These age-linked changes profoundly affect lung cancer immunotherapy by altering systemic immune competence and reshaping the TME, thereby modifying both the efficacy and toxicity of ICIs. Importantly, these effects are heterogeneous across patients and tumor contexts, and not all older individuals exhibit clinically meaningful immunosenescence. Rather than a uniform loss of function, immune aging is dynamic and heterogeneous, involving chronic inflammatory signaling, immune exhaustion, myeloid dominance, and stromal remodeling processes that collectively determine whether an antitumor response can be initiated and sustained (58). An overview of age-associated changes in immune, stromal, and metabolic landscapes shaping lung cancer biology and therapy response is illustrated in Figure 1. Understanding these mechanisms is key to optimizing treatment for the growing population of older adults with lung cancer, who remain underrepresented in clinical trials but comprise the majority of cases.

Chronological age has long been used as a proxy for fitness in lung cancer management, yet it fails to capture the wide heterogeneity of aging biology, particularly in predicting therapy tolerance, response, and long-term outcomes. Recent advances in biological age biomarkers offer a more precise understanding of patient vulnerability, integrating genetic, environmental, metabolic, and immune factors that collectively shape cancer risk and therapeutic resilience. These tools are emerging as a cornerstone of age-conscious oncology, allowing for treatment planning that reflects physiological rather than chronological age.

Among the most promising measures is PhenoAgeAccel, a composite index derived from standard clinical blood biomarkers, including CRP, glucose, albumin, alkaline phosphatase, and blood cell counts (85). Large cohort studies, such as those from the UK Biobank, demonstrate that PhenoAgeAccel is independently associated with increased lung cancer risk, even after adjusting for polygenic risk scores (PRS) and chronological age (86). Individuals classified as biologically older by PhenoAgeAccel display significantly higher five-year absolute risk of lung cancer than their chronologically matched peers, while Mendelian randomization studies suggest a causal link between accelerated biological aging and lung cancer susceptibility. Importantly, combining PhenoAgeAccel with PRS improves risk stratification, offering additive predictive power that reclassifies some individuals into higher or lower risk categories than genetic data alone would indicate. This integration has significant implications for refining screening eligibility and early detection strategies, potentially enabling earlier interventions for biologically older patients who would otherwise be overlooked by PRS-based models. Other biomarkers of biological aging are equally promising (87). Epigenetic clocks, including DNA methylation age and GrimAge, estimate biological age from methylation patterns that integrate cumulative exposures and cellular stress. GrimAge further incorporates smoking and protein-related methylation signals, allowing it to outperform chronological age in predicting lung cancer risk and therapy outcomes (88). Shortened leukocyte telomere length, which reflects cumulative replicative stress and cellular aging, is another established marker of accelerated aging and has been associated with poorer outcomes and increased lung cancer risk, further supporting its role in prognostication (89). Circulating inflammatory markers such as IL-6, TNF-α, CRP, and p16^INK4a capture systemic inflammaging, immune senescence, and therapy vulnerability. It correlates with frailty, reduced ICI responsiveness, and higher complication rates (90). Organ-specific measures like “lung age,” calculated from spirometry indices such as FEV1, capture pulmonary reserve and are associated with postoperative risk and treatment tolerance. When integrated with systemic biological age scores, pulmonary metrics contribute to more accurate risk models for surgical and treatment planning (91). Similarly, comprehensive geriatric assessments and frailty indices combine clinical measures of functional reserve with emerging molecular biomarkers to guide therapy personalization in elderly and comorbid patients (92). A summary of key biological aging biomarkers with mechanistic underpinnings and implications for lung cancer precision therapy is presented in Table 2.

Collectively, these tools shift precision oncology toward biological age-guided care, enabling treatment decisions based on physiological resilience rather than chronological age, with potential benefits for screening, therapy sequencing, and toxicity reduction. While large-scale prospective trials are needed, current evidence strongly supports a future where biological age biomarkers guide precision medicine, ensuring that patients across the age spectrum, from rare pediatric cases to the very elderly, receive appropriately tailored, effective, and less toxic care. Despite growing interest, biological aging biomarkers remain outside current lung cancer guidelines due to limited prospective validation, lack of standardized thresholds, cohort heterogeneity, and uncertain clinical integration. At present, they are best applied for research and risk stratification rather than treatment selection.

Lung cancer therapy is evolving beyond tumor genomics toward integrative models that incorporate host aging biology. While many such approaches remain investigational, the convergence of geroscience, precision oncology, and artificial intelligence is enabling more adaptive, patient-centered strategies that account for immune aging, tumor microenvironment dynamics, and physiological resilience.

A major frontier in this evolution is immune rejuvenation, with emerging technologies aiming to reverse the effects of immune senescence rather than bypass them (93). CRISPR/Cas9-based genome engineering enables precise enhancement of T and natural killer cells to improve tumor recognition, checkpoint resistance, and cytokine expression. These advances have accelerated the development of CAR-T and CAR-NK therapies capable of functioning within the immunosuppressive microenvironments of aged lungs (94). iPSC-derived NK and T cells represent another breakthrough, offering robust cytotoxicity, improved persistence, and reduced toxicity compared with traditional donor-derived therapies. These engineered cells are not only highly effective in preclinical lung cancer models but also demonstrate the ability to sensitize resistant tumors to PD-1 inhibitors by promoting T-cell infiltration, providing a potent combination approach for elderly or immunocompromised patients (95). Clinical trials are now validating the safety and efficacy of iPSC-based therapies in advanced lung cancer, offering scalable, standardized solutions that overcome the limitations of aging immune systems.

Innovations in cellular reprogramming are also extending into in vivo applications, with strategies to rejuvenate exhausted immune cells directly within the patient. Partial cellular reprogramming through transient expression of Yamanaka factors and targeted epigenetic editing shows promise in resetting senescence-associated dysfunction without inducing full pluripotency, restoring immune function without increasing tumorigenic risk (96). In parallel, senolytic therapies that selectively clear senescent immune and stromal cells, as well as drugs targeting inflammatory circuits like the NLRP3 inflammasome or IL-1 signaling, are under investigation to dismantle the chronic inflammatory feedback loops that blunt immunotherapy responses in older patients (97). Together, these advances aim to create a new therapeutic paradigm where the immune system is actively rebuilt to sustain durable anti-tumor immunity. Remodeling the TME is another crucial pillar of next-generation strategies. Age-related changes in lung tissue architecture, such as ECM stiffening and fibroblast activation, create physical and biochemical barriers that exclude immune cells and limit drug penetration. Novel therapeutics targeting LOX-mediated collagen crosslinking, integrin signaling, and fibroblast activation protein (FAP) are being developed to soften these fibrotic barriers and reprogram the TME into an immune-permissive state (98). Combination approaches pairing these ECM-targeting therapies with immune checkpoint blockade hold particular promise for elderly patients, whose tumors are often characterized by fibrosis, immune exclusion, and resistance to standard immunotherapies. Oncolytic viruses, in situ vaccines, and bispecific antibodies are also being tested to ignite immune responses in these otherwise “cold” tumors, demonstrating that therapy can be designed not only to target cancer cells but also to reshape the ecosystem in which they thrive (99).

The integration of AI and multi-omics is accelerating these advances by enabling more precise patient stratification and therapy design. Multi-layered datasets, including single-cell transcriptomics, spatial proteomics, metabolomics, and epigenomic profiling, are being analyzed through AI-driven algorithms to predict resistance mechanisms, toxicity risk, and optimal treatment sequencing. These models are evolving into patient “digital twins,” virtual simulations that allow clinicians to test therapeutic strategies in silico before implementation (100). A clinically useful digital twin is not a static risk score but a continuously updated, patient-specific computational model that integrates tumor evolution with host resilience. In age-aware lung cancer care, constructing such a model requires three core components. First, it must incorporate multimodal inputs, including tumor genomics, longitudinal imaging, treatment history, laboratory trajectories (such as inflammatory markers), comorbidity and frailty measures, and, when feasible, immune profiling (101). Second, it should employ hybrid modeling approaches that combine mechanistic components, such as tumor growth and response dynamics, pharmacokinetics/pharmacodynamics, and toxicity constraints, with machine-learning layers capable of capturing complex, non-linear interactions. Third, the model must be continuously updated as new imaging data, clinical symptoms, and adverse events accrue during therapy. Validation of digital twin models should proceed in a stepwise manner, beginning with internal calibration of predicted versus observed response and toxicity, followed by external validation across institutions and temporal validation in later cohorts to ensure robustness as standards of care evolve (102). Importantly, model performance should be evaluated using clinically actionable endpoints, including the probability of durable response, risk of pneumonitis or interstitial lung disease, hospitalization, functional decline, and competing-risk mortality, rather than relying on discrimination metrics alone. In practice, digital twins can function as a decision sandbox, allowing comparison of plausible strategies such as targeted monotherapy versus targeted therapy followed by immunotherapy, or the addition of microenvironment-modifying agents in biologically older patients (103).

Combining these tools with frailty metrics, wearable health monitors, and advanced imaging will create continuously updated treatment plans that respond dynamically to tumor evolution and changes in patient physiology. Realizing this vision will require a redesign of clinical trials to reflect the complexity of both lung cancer and aging biology. Adaptive trial frameworks that stratify participants by biological age, immune function, and molecular signatures, rather than rigid chronological cutoffs, will allow more representative data collection and equitable access to novel therapies. Lessons from pediatric oncology initiatives like iTHER and ZERO Childhood Cancer demonstrate how global collaboration and precision-based trial design can improve outcomes in rare cancers, and similar approaches are now emerging in geriatric oncology consortia to better serve older patients who have historically been underrepresented in clinical research.

Collectively, these strategies support a shift toward age-aware lung cancer care that integrates biological profiling, AI modeling, and cellular engineering. Although they hold promise for improving survival and quality of life across the lifespan, although many of them, including senolytics and immune rejuvenation, remain investigational and require lung cancer-specific prospective validation.

The consequences of host aging, including immunosenescence, systemic inflammaging, and tissue senescence, represent biologically meaningful variables that can influence lung cancer evolution and treatment response, although their relative contribution varies across patients and clinical contexts. While tumor-centric precision oncology has transformed outcomes in molecularly selected subsets, its translation across the age spectrum remains inconsistent. Current evidence supports an association between aging biology and therapy outcomes, but definitive prospective validation remains limited. Immunosenescence, inflammaging, and lung tissue remodeling should therefore be viewed not as universally deterministic factors, but as interacting modifiers that may constrain therapeutic durability and increase toxicity risk in susceptible individuals.

One central hypothesis is that tumor-centric personalization without concurrent host-directed adaptation is inherently self-limiting. Targeted therapies may successfully engage their molecular targets, but their outcomes are capped by the capacity of an aged immune system and a fibrotic, immunologically restrictive microenvironment to sustain antitumor pressure. The logical extension of this idea is that future regimens must pair tumor-directed interventions with host-targeted co-therapies. Senolytic drugs, inflammasome inhibitors, mitochondrial fitness restorers, and stromal remodeling agents represent not adjunctive strategies but rational partners for targeted therapy and immunotherapy, particularly in biologically older patients. Clinical trials designed with these considerations should move beyond radiographic endpoints to include measures of immune reinvigoration, stromal remodeling, and functional preservation, thereby aligning therapeutic success with both tumor and host adaptation. Pediatric and geriatric lung cancer patients, though often seen as occupying opposite ends of the spectrum, face a strikingly parallel challenge: systemic neglect in clinical research. Pediatric cases are sidelined due to rarity, while older patients are routinely excluded based on comorbidities and organ function thresholds. Both scenarios lead to a deficit of high-quality evidence precisely where it is most needed. This observation suggests that trial infrastructures designed to accommodate low-prevalence cohorts, such as adaptive basket and umbrella studies, should serve both pediatric and geriatric populations. By building shared platforms that unify rare molecular subsets with vulnerable age-defined groups, oncology could transcend the dichotomy between children and the elderly, establishing a continuum of care that is truly inclusive and biology-driven. Biological age emerges as the critical stratifier to replace crude chronological thresholds in both trial design and clinical practice. Tools such as PhenoAgeAccel, epigenetic clocks, telomere length, and frailty indices capture dimensions of aging that more accurately predict therapy tolerance, toxicity, and benefit. A biologically “young” 75-year-old may derive substantial benefit from aggressive targeted and immune combinations, while a biologically “old” 60-year-old might require a more conservative approach emphasizing microenvironmental conditioning and reduced-toxicity regimens. Integrating biological age into randomization schemas and treatment sequencing not only enhances scientific rigor but also promises more equitable outcomes by aligning intervention intensity with physiological resilience rather than demographic categories. The remodeling of the tumor microenvironment represents another axis where age-conscious adaptation can shift outcomes. In elderly patients, ECM stiffening, fibroblast activation, and aberrant integrin and YAP-TAZ signaling create mechanical and biochemical barriers that exclude immune cells and blunt drug penetration. Reframing these processes as therapeutic targets positions microenvironmental remodeling as a geriatric-specific precision strategy. Agents that modulate collagen crosslinking, inhibit integrins, or reprogram activated fibroblasts could act as sequencing primers, administered to restore immune permissiveness before immunotherapy or targeted therapy. The incorporation of spatial-omics and imaging-based biomarkers of tissue stiffness and fibrosis into clinical research could accelerate translation of such strategies into practice, providing tangible tools for tailoring care in elderly populations.

Advances in cellular engineering offer yet another route to counteract the constraints of immune aging. CRISPR-edited and iPSC-derived immune products optimized for checkpoint resistance, metabolic resilience, and persistence in fibrotic tissues may transform immunologically “cold” tumors into responsive ones. Beyond ex vivo engineering, in vivo rejuvenation of immune cells through partial reprogramming or targeted epigenetic editing represents a bold but increasingly feasible strategy to restore T-cell diversity and function without increasing oncogenic risk. These innovations illustrate that age-induced immune dysfunction need not be passively accepted but can itself become a therapeutic target, opening new frontiers for durable immunotherapy responses in older adults. The operationalization of such complex, age-aware personalization will likely depend on artificial intelligence and digital twin models. These systems, capable of integrating tumor genomics, biological age biomarkers, wearable-derived data, and comorbidity profiles, can simulate therapeutic strategies and predict both efficacy and toxicity in near real time. By continuously updating predictions as tumors evolve and patients’ physiological states shift, digital twins could transform personalization from a reactive to a predictive and adaptive enterprise. Their integration into prospective trials, with endpoints including not only survival but also quality-adjusted life years and preservation of functional independence, would mark a decisive step toward holistic age-conscious oncology.

Another underexplored dimension of age-informed precision oncology is the biology of early-onset cancers, including the rising incidence of lung and colorectal cancers in adults under 50. While these cases are often linked to hereditary syndromes, emerging data suggest that accelerated biological aging, manifested through early epigenetic drift, telomere attrition, and chronic inflammation, may contribute even in the absence of germline predisposition. Comparing immune and stromal profiles between early- and late-onset lung adenocarcinoma could reveal whether “premature inflammaging” or subclinical immune senescence predisposes younger individuals to disease. Such age-matched comparative research remains logistically challenging but conceptually vital, as it could clarify how biological aging processes begin decoupled from chronological time and reshape risk, progression, and therapy response across the lifespan.

Taken together, these arguments converge on a critical conclusion: age is not a barrier to personalization but a variable to be leveraged. True precision in lung cancer therapy requires a dual focus, targeting tumor biology while simultaneously adapting to the evolving biology of the host. By embedding biological age into trial eligibility, stratification, and therapeutic sequencing, and by developing host-directed co-therapies, cellular engineering approaches, and AI-driven decision support, oncology can transcend the current ceiling of tumor-centric personalization. The future of lung cancer care will depend on dismantling the artificial divide between tumor and host, reframing therapy as a dynamic co-optimization of both systems. Such a transformation promises not only to extend survival but also to safeguard function and quality of life, delivering genuinely durable and equitable outcomes across the lifespan.

Despite growing interest, the clinical application of biological aging in lung cancer remains constrained by several unresolved challenges. Most data linking immunosenescence or aging biomarkers to treatment outcomes are retrospective or correlative. Few trials stratify patients by biological age, and no randomized studies have demonstrated that age-guided treatment modification improves survival or quality of life. Additionally, biomarker thresholds lack standardization, and the interplay between tumor genomics and host aging biology remains incompletely defined. These gaps highlight the need for prospective, biomarker-integrated trial designs before age-aware precision oncology can be implemented at scale.