From cell of origin to biomarker-based therapy: Trends in personalized treatment using lung cancer as a case study

This article explores how biomarker-driven precision medicine has transformed lung cancer treatment, illustrating both its clinical impact and the challenges that must be addressed for broader implementation.

There is a witty saying in medicine: “It is quite as important to know what kind of a patient has got the disease, as to know what kind of a disease the patient has got.”

When applying this same thinking in oncology, one must understand the complexity of how cancer cases can sometimes present. As the 2026 World Cancer Day motto “United by Unique” suggests, we must address the uniqueness behind every cancer diagnosis. In recent years, advances in genomic and proteomic technologies have led to major changes in the cancer treatment paradigm [1]. The traditional treatment paradigm based solely on tumor type and its cellular origin is becoming increasingly inadequate. The “one-size-fits-all” approach has been largely insufficient, and the “personalized medicine” concept has emerged [2]. In oncology, it aims to customize treatment by addressing the unique aspects of tumor characterization and tailoring therapies to each patient’s molecular profile [3].

In clinical practice, the application of personalized medicine and precision oncology relies heavily on biomarkers [4]. Characteristics of the cancer at hand can be understood through biomarkers, ideally with a high degree of accuracy and reliability. Biomarkers not only aid in cancer diagnosis, but can also outline disease prognosis, and/or aid in predicting treatment outcomes [1]. Biomarkers play a central role in guiding targeted treatment based on the unique profile of each patient’s tumor [2]. To date, various biomarkers have been identified in cancer, relating to the tumor immune and microenvironment e.g., programmed cell death protein 1 (PD-1), and its ligand (PD-L1) and genetic aberrations like KRAS and HER2 [5].
Tissue sampling (biopsy), liquid biopsy, and imaging are some of the key modalities used for biomarker detection.

Specifics of lung cancer biomarkers

Using lung cancer as a case study, let’s examine how a new understanding of cancer biology has changed the treatment of malignancy today. Traditionally, lung cancer was categorized by histology and cell type as small cell lung cancer, non-small cell squamous cell carcinoma, or adenocarcinoma. In the late 1980s, researchers discovered that adenocarcinoma could be further subdivided beyond histology, based on specific gene mutations. Today, at least 15 unique candidate oncogenic driver genes have been identified in lung adenocarcinoma. Hence, investigating genetic abnormalities (i.e. biomarkers) has made it possible to treat different subtypes of lung adenocarcinomas [1].

Up to 85% of all lung cancer cases are non-small cell lung cancers (NSCLCs). Current guidelines recommend comprehensive molecular testing for biomarkers for NSCLC [5]. For example, HER2 is a type of cancer biomarker associated with gastric, breast, as well as lung cancer. With a targeted approach and directed therapy, it is now possible to treat patients diagnosed with HER2-mutant advanced NSCLC using, for example, HER2-targeting antibody drug conjugate (ADC) or small molecule drugs (e.g., trastuzumab deruxtecan, or zongertinib). Another example is histopathological examination coupled with immunohistochemistry (IHC), which can reveal the expression level of PD-L1 and directly inform the choice of targeted therapy [2]. Another relevant example in the context of lung cancer and biomarkers is PCR-based testing for EGFR mutations in tumor tissue. Patients with EGFR-positive mutations can be prescribed an EGFR-inhibiting drug, thereby enabling targeted treatment and minimizing toxicity [1,2].
To sum up, the identification and targeting of specific biomarkers has propelled NSCLC to the forefront of precision oncology [5], and a similar approach can be observed for other types of cancers, such as breast cancer [6] and melanoma [7].
The recent ESMO guidelines reflect this shift by expanding the recommendations (e.g., advanced non-squamous NSCLC, advanced breast cancer & rare tumors) for tumor next-generation sequencing (NGS) with the ultimate goal of enhancing the efficacy of precision medicine in cancer [8].

Challenges and limitations of biomarker testing in clinical practice

Implementing biomarker testing in clinical practice is not without limitations and challenges. Some of them include inter-laboratory discordance, cost-effectiveness, and accessibility. Additionally, in the context of NSCLC, a survey of 491 pulmonologists affiliated with the American College of Chest Physicians (ACCP) database suggests that a better interdisciplinary collaboration, particularly with oncology, is needed to efficiently implement biomarker testing in clinical practice [9]. Furthermore, for clinical oncologists, developing a precise treatment plan to provide personalized medicine may prove challenging, since the data generated using genomic and proteomic techniques can be too complex to analyze in clinical practice. Moreover, as tissue biopsies capture only a snapshot of constantly evolving solid tumors, tumor heterogeneity poses a significant barrier for precision oncology [10]. In this vein, the integration of deep learning algorithms combined with radiomics has the potential to support the delivery of precision therapies in lung cancer treatment using non-invasive standard-of-care imaging. Through these innovations in artificial intelligence and data-driven approaches, there is great potential to guide personalized treatment for each patient based on biomarkers [11].

To conclude, precision oncology must also become accessible and interpretable to truly address the unique challenges posed by each tumor type and cancer.

Figure 1: Algorithm for molecular stratified therapy. The figure should be used only for demonstration purposes and is not comprehensive. Please refer to guidelines from ESMO [12] or other relevant organizations for detailed information. ALKi, ALK inhibitor; CT, chemotherapy; EGFRi, EGFR inhibitor; ICT, immunochemotherapy; mut, mutation; transl, translocation

References

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