Emerging Topics in Precision Medicine: Non-Invasive Innovations Shaping Cancer and Immunotherapy Progress

1. Introduction

Cancer is a heterogeneous disease, resulting from genetic, epigenetic, signaling, and metabolic alterations, which are supported by a highly organized tumor microenvironment (TME) [1]. The dynamic interplay between cancer and TME allows tumors to adapt, evade therapy, and develop resistance, making treatment a persistent challenge [2]. While next-generation sequencing has yielded critical genetic insights, it has become increasingly evident that single-marker analyses and one-size-fits-all treatment approaches are insufficient to capture the multiscale complexity of cancer biology [3].

Conventional diagnostic approaches often rely on invasive and temporally limited tissue sampling, which restricts longitudinal monitoring and increases patient burden [4]. For instance, tissue biopsies offer only a static snapshot of a highly dynamic disease, are vulnerable to sampling bias driven by intratumoral heterogeneity, and are frequently impractical to repeat throughout the course of treatment [5]. Consequently, key biological shifts associated with tumor evolution, therapeutic pressure, and the emergence of resistance may go undetected until overt clinical progression occurs. These constraints limit real-time clinical decision-making and hinder the timely adaptation of therapeutic strategies to the evolving state of tumors. Due to these limitations, the oncology landscape has evolved to accelerate the development of minimally invasive and non-invasive methods [6]. The patient-derived data are increasingly leveraged through the integration of multimodal biomarkers, state-of-the-art imaging, and mechanistic as well as AI-driven computational models to uncover tumor complexity and guide precision therapies [7].

The first edition of this research topic, entitled “Emerging Topics in Precision Medicine: Non-invasive Innovations Shaping Cancer and Immunotherapy Progress,” collectively illustrates the ongoing shift toward integrative and non-invasive strategies in precision oncology. Rather than addressing isolated molecular events, the studies focus on the need to understand cancer as a dynamic system shaped by tumor-intrinsic programs, immune and stromal interactions, metabolic states, and therapeutic pressures.

2. An Overview of Published Articles

2.1. Non-Invasive Approaches

Cancer biomarkers have expanded beyond static genetic alterations to encompass functional, spatial, and systemic indicators that better reflect tumor dynamics and adaptability. Biomarkers are grouped as diagnostic, prognostic, predictive, and pharmacodynamic categories, reflecting their clinical utility rather than the underlying biology [8]. Diagnostic biomarkers enable disease detection and classification, prognostic biomarkers provide information on disease outcome independent of treatment, predictive biomarkers identify patients most likely to benefit from a specific therapy, and pharmacodynamic biomarkers monitor treatment response and target engagement [9]. However, this functional classification oversimplifies tumor biology, as many biomarkers participate in interconnected signaling, metabolic, and microenvironmental processes and may simultaneously fulfill multiple roles depending on context [10]. Three reviews in the present collection highlight how immune cell plasticity, stromal support, hypoxia, metabolism, and inflammatory signaling collectively shape tumor progression and therapeutic resistance. By emphasizing tumor–microenvironment crosstalk, these contributions reinforce the notion that durable clinical benefit will require strategies that disrupt not only malignant cells but also the supportive niches that sustain them [11].

To address this, the growing availability of large-scale data repositories enables proof-of-concept identification of novel biomarkers across spatial and temporal scales [12]. This is particularly important in accelerating early discovery, as the path for clinical approval remains long and complex [13]. Complementing this, in vitro experiments using 2D and 3D models support the mechanistic validation of candidate biomarkers, interrogation of signaling pathways, and assessment of drug sensitivity under defined conditions [14]. Together, in silico and in vitro strategies create an iterative pipeline improving the identification of promising biomarkers for further development [8,15].

Moreover, advanced imaging with AI further expanded the biomarker landscape beyond classical molecular assays [16]. Radiomics extracts high-dimensional quantitative features from medical imaging, capturing spatial heterogeneity, tumor shape, and texture that are not discernible by visual inspection [17]. In the present collection, combining artificial intelligence with PET/CT radiomics predicts immunotherapy response and tumor grade in advanced cutaneous squamous cell carcinoma. Additional molecular layers can also be integrated into imaging; for example, radiogenomics combines imaging-derived features with genomic and transcriptomic data, enabling non-invasive inference of molecular phenotypes and tumor evolution [18]. Longitudinal imaging monitoring is a powerful tool for patient stratification and predicting therapeutic response, capturing temporal changes in tumor size, shape, texture, and functional parameters [19].

Collectively, the integration of molecular biomarkers with radiomic, in silico, and in vitro approaches supports a more holistic, multiscale view of tumor biology. This convergence moves biomarker research from single-layer checkpoints toward dynamic indicators of disease state and therapeutic vulnerability, laying the groundwork for more precise and adaptive precision oncology strategies.

2.2. Minimally Invasive Approaches

Traditional diagnostic approaches, such as biopsies, are the gold standard for initial diagnosis, offering deeper tissue context [20]. However, these methods are invasive, risky, and provide a static snapshot of the tumor site, with results often influencing initial treatment decisions. While liquid biopsies (blood/fluid test) are minimally invasive, providing real-time monitoring of tumors, but they can miss rare mutations. While both can be complementary, liquid biopsies are increasingly transitioning from experimental tools to clinically relevant technologies, offering real-time, high-resolution insights into tumor burden, immune dynamics, and disease evolution [21]. Together, these innovations are reshaping cancer management toward more adaptive and patient-centered care [22].

A prominent theme across the collected works is the increasing relevance of minimally invasive biomarkers. Blood-based analytes, immune cell dynamics, and circulating tumor-derived signals are highlighted as powerful tools for assessing tumor burden, biological aggressiveness, and treatment response. For example, analyses of circulating cell-free DNA and immune cell populations were used to monitor tumor size and advanced pathological stages, while also providing actionable insights into immunotherapy response [23,24]. These approaches highlight the additional framework of liquid biopsy to complement or, in selected contexts, partially substitute tissue-based diagnostics, particularly for longitudinal disease monitoring.

3. Conclusions and Future Perspectives

The articles in this Special Issue reflect a maturation of the precision oncology field, moving beyond reductionist models toward systems-level perspectives that integrate molecular, cellular, and environmental dimensions of cancer biology. To translate these advances into durable clinical benefit, future research must prioritize longitudinal monitoring, integrative analytical frameworks, and rational combination strategies that account for tumor heterogeneity and adaptive resistance. By embracing complexity rather than attempting to oversimplify it, the field is better positioned to deliver truly personalized and effective cancer care.