Targeted therapies in lung cancer: personalizing treatment across the age spectrum

TL;DR

This paper reviews how aging biology impacts lung cancer treatment and argues for integrating biological age into precision oncology.

Contribution

The paper introduces biological age as a novel dimension for personalizing lung cancer therapy, beyond chronological age and genomic markers.

Findings

Immunosenescence contributes to immune-evasive tumor phenotypes and reduced checkpoint blockade responses.

Biological age biomarkers like PhenoAgeAccel and epigenetic clocks better predict treatment outcomes than traditional metrics.

Geroscience strategies like senolytics and immune engineering show promise for age-aware cancer treatment.

Abstract

Lung cancer remains the leading cause of cancer-related mortality, yet current precision oncology approaches remain overwhelmingly tumor-centric, guided by genomic alterations and immune biomarkers, while largely neglecting the profound impact of aging biology on treatment response. While emerging evidence suggests that aging biology can modify therapeutic benefit and toxicity, its clinical integration remains uneven and largely investigational. In this review, we explicitly distinguish the chronological aging from biological aging to clarify how host biology modifies therapeutic benefit and toxicity. We synthesize mechanistic, translational, and early clinical evidence, while explicitly noting areas where prospective validation is lacking, to reframe personalization of lung cancer therapy through an age-conscious lens. We summarize data indicating that immunosenescence is associated with T-cell exhaustion, myeloid dominance, and extracellular matrix stiffening, features that may contribute to immune-evasive tumor phenotypes and attenuated responses to immune checkpoint blockade in subsets of patients, while pediatric cases, though rare, illustrate how global precision initiatives like iTHER and ZERO enable cautious adaptation of adult therapies. Moving beyond chronological age, we discuss biological age biomarkers, including PhenoAgeAccel, epigenetic clocks, telomere length, and frailty indices, which outperform traditional metrics in predicting risk, resistance, and toxicity, and propose integrating these tools into trial design, screening, and care planning which show promise for risk stratification and toxicity prediction but are not yet validated for routine treatment selection. Looking forward, we outline investigational strategies at the intersection of geroscience and oncology, including immune engineering, senolytics, microenvironmental modulation, and AI-driven multi-omic modeling. Overall, this review argues that biological age represents a critical but still underdeveloped dimension of precision oncology, and highlights key evidence gaps that must be addressed before age-aware personalization can be implemented in routine lung cancer care.

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