Advances in molecular pathology and therapy of non-small cell lung cancer

Abstract

Over the past two decades, non-small cell lung cancer (NSCLC) has witnessed encouraging advancements in basic and clinical research. However, substantial unmet needs remain for patients worldwide, as drug resistance persists as an inevitable reality. Meanwhile, the journey towards amplifying the breadth and depth of the therapeutic effect requires comprehending and integrating diverse and profound progress. In this review, therefore, we aim to comprehensively present such progress that spans the various aspects of molecular pathology, encompassing elucidations of metastatic mechanisms, identification of therapeutic targets, and dissection of spatial omics. Additionally, we also highlight the numerous small molecule and antibody drugs, encompassing their application alone or in combination, across later-line, frontline, neoadjuvant or adjuvant settings. Then, we elaborate on drug resistance mechanisms, mainly involving targeted therapies and immunotherapies, revealed by our proposed theoretical models to clarify interactions between cancer cells and a variety of non-malignant cells, as well as almost all the biological regulatory pathways. Finally, we outline mechanistic perspectives to pursue innovative treatments of NSCLC, through leveraging artificial intelligence to incorporate the latest insights into the design of finely-tuned, biomarker-driven combination strategies. This review not only provides an overview of the various strategies of how to reshape available armamentarium, but also illustrates an example of clinical translation of how to develop novel targeted drugs, to revolutionize therapeutic landscape for NSCLC.

Fig. 1

Key milestone events in lung cancer research. The illustration provides 11 significant advances in the diagnosis and treatment of lung cancer, highlighting the most rapid developments over the past 20 years and projecting future directions

Fig. 2

Known carcinogenic causes of lung cancer (cited from the World Cancer Report, Cancer research for cancer prevention, 2020, with modifications and updates). The carcinogenic causes of lung cancer vary by region, race, and gender. Collectively, they, when combined, contribute to the development of lung cancer, including environmental pollution, unhealthy lifestyle habits, and genetic predisposition, though the extent may vary among individuals. Over time, with social progress, the nature of these carcinogenic factors has evolved; for example, infections have increasingly been linked to environmental pollution, and traditional tobacco use has shifted toward electronic cigarettes

Fig. 3

Mechanisms involved in cancer cell metastasis in NSCLC. This schematic diagram summarizes the mechanisms involved in cancer cell metastasis in NSCLC. From an evolutionary perspective, metastasis represents a systemic adaptive response to stress within the survival microenvironment. The process begins with several changes in cancer cell characteristics, at least including the activation of oncogenes (e.g., EGFR, ALK, and RAS, etc.), upregulation of cytokines (e.g., IL-6, TGF, IGF, and HIF-1, etc.), chemokines, and their receptors (e.g., CXCL9, CXCL10, CXCL11, CXCR3, CXCR4, and CCR5, etc.), as well as metabolic reprogramming, as detailed in the box. This is followed by phenotypic transformation involving EMT, primarily driven by miRNAs, ZEB1/2, and EZH2. Subsequently, through various soluble molecules and exosomes, the microenvironment of distant organs is altered to support the survival of cancer cells(direct effect). Cancer cells also reeducate bone marrow-derived immune cells, creating an immunosuppressive microenvironment (indirect effect). Additionally, when circulating in the bloodstream, cancer cells co-opt neutrophils and platelets to resist anoikis, and subsequently upregulate adhesion molecules on endothelial cells, facilitating their entry into tissues. In the pre-metastatic niche of distant organs, restoration of MET, ECM remodeling (by MMP2, 9, and 10), induction of angiogenesis (via VEGF, VEGFR, TIE2, and angiopoietin 1/2), and recruitment of CAFs and neuronal cells (via GABA) occur, alongside the engagement of immunosuppressive cells (MDSCs, T_regs, tumor-promoting TANs and TAMs). These cells assist in inducing T cell exhaustion (via PD-(L)1, CTLA-4, LAG3, and TIM3, etc.) and metabolic competition (through upregulation of glutaminases GLS1 and GLS2, and accumulation of toxic cancerous metabolites), enabling the evasion of surveillance by TILs and tissue-resident T cells (αβ or γδ T cells), NK cells, macrophages, and B cells. This complex interplay allows for survival, subclonal evolution, and therapeutic resistance. Without these mechanisms, the tumor may remain dormant for over 10 years before manifesting clinically in distant organs. Note that the pathways and molecules listed do not cover the entire spectrum of the metastatic process, nor do they fully explain the organ-specific nature of metastasis or provide insights into how these contribute to therapeutic resistance

Fig. 4

Alternations in oncogenic and anti-oncogenic pathways involving drug targets or biomarkers in NSCLC. In NSCLC, numerous activated or upregulated intracellular oncogenic and non-oncogenic protein kinase signaling pathways have been identified. However, we primarily focus on targets that are or will become druggable, highlighting their mutation frequencies. Although mutation frequencies reported in various literatures or databases may vary, the overall differences are not significant. The illustration may not fully capture the diversity and functional complexity of these signaling pathways and their interactions in vivo. Additionally, due to space limitations, extracellular or microenvironment features such as the dependence of cancer cells on VEGF/VEGFR signaling, other metabolic pathways (beyond glucose), and hypoxia are not depicted. LCC large cell carcinoma, LCNC large cell neuroendocrine carcinoma, PSC pulmonary sarcomatoid carcinoma, ASC adenosquamous carcinoma, ACC adenoid cystic carcinoma, PMEC pulmonary mucoepidermoid carcinoma, PPC pulmonary pleomorphic carcinoma

Fig. 5

An algorithm of evidence-based management for stage I-III NSCLC. This flowchart comprehensively illustrates the treatment options for early-stage lung cancer, maintaining flexibility through the use of dashed lines to indicate alternative choices. However, this illustration focuses primarily on multi-level drug options and does not detail more technical treatment options such as radiotherapy and surgery, which may be more crucial for certain patients. For neoadjuvant regimens, particularly the combination of chemotherapy with ICIs, the recommendation level remains consistent across different options

Fig. 6

Timeline of the research history and milestone events in targeted therapy for NSCLC. The annotation times in this illustration refer to the initial FDA or NMPA approval dates for each drug, whether under accelerated or regular approval. This timeline showcases the significant advancements in NSCLC treatments over the last five decades, featuring nine targetable biomarkers and over 30 drugs. These developments represent some of the most impressive progress across all cancer types. While this growth underscores the advancement of personalized medicine, the abundance of data also presents challenges in treatment selection, especially when our knowledge is limited. It is important to note that our data sources, the FDA and NMPA, do not include drugs approved in other regions. Progress in drug iterations has been particularly notable for EGFR and ALK TKIs, while advancements in other targets have been less prominent, highlighting the challenges in developing treatments for less common targets, which continue to represent unmet clinical needs

Fig. 7

Timeline of the research history and milestone events in immunotherapy for NSCLC. The annotation times refer to the initial FDA or NMPA approval dates, whether under accelerated or regular approval. Many drugs are approved by regional regulatory agencies, and thus their availability may be limited. The illustration shows that immunotherapy has rapidly evolved from a later-line to a front-line treatment for advanced NSCLC and has expanded into both neoadjuvant and adjuvant settings, compared to targeted therapies

Fig. 8

Recommended therapy algorithm for oncogene addicted metastatic NSCLC. The flowchart provides a comprehensive view of clinical decision-making for targeted therapies in metastatic NSCLC. It highlights the importance of flexibility in treatment decisions, such as choosing between combining osimertinib with chemotherapy or using osimertinib as a single agent, based on tumor burden and patient preferences. For EGFR and ALK mutations, third-generation TKIs are generally the preferred choice. The decision to combine ICIs with chemotherapy should consider the specific characteristics of the tumor per se, similar to non-mutated tumors (as shown in Fig. 9), although the efficacy may be compromised

Fig. 9

Recommended therapy algorithm for non-oncogene addicted metastatic NSCLC. This flowchart categorizes immunotherapy options primarily based on the characteristics of the drugs and the features of the tumor, which are currently numerous and should be selected based on availability. While standard regimens exist for chemotherapy combined with immunotherapy, special considerations, such as the neurotoxicity associated with nab-paclitaxel, may necessitate alternative treatment strategies. Anti-angiogenic biosimilars can serve as parallel substitutes

Fig. 10

Resistance mechanisms underlying targeted therapy in NSCLC. The disappearance of target sites can be explained by two scenarios: the loss of target sites due to genetic mutations, or the loss of affinity between the drug and the target site due to genetic or epigenetic modifications. More importantly, the distinction between substitution and decentralization is blurred, as partial substitution can also lead to multiple pathways that provide survival signals to cancer cells – a strategy akin to how humans diversify investments to mitigate risks. The illustration lists several factors contributing to resistance, but it is crucial to acknowledge that many unknown mechanisms of resistance against targeted therapy remain

Fig. 11

Resistance mechanisms underlying immunotherapy in NSCLC. Cancer immunotherapy may involve some of the most complex resistance mechanisms known, affecting nearly all cells, molecules, and pathways. The vast genetic and epigenetic abnormalities, estimated to involve approximately 1000 genes – including various cytokines, chemokines, protein kinases, and metabolic enzymes – result in T cells tolerating or coexisting with cancer cells. Therefore, we propose a new theoretical framework for understanding the mechanism of immunotherapy resistance, characterized by a dynamic and cyclical process: an equilibrium/balance favoring cancer cells (primary resistance), followed by treatment shifting the balance towards favoring immune cells, and eventually a re-establishment of equilibrium favoring cancer cells (secondary resistance) – which repeats over time

Fig. 12

Established or developing strategies to overcome drug resistance in NSCLC. This diagram categorizes treatment drugs and strategies to facilitate logical understanding, though it may not be entirely accurate. A single treatment can overcome resistance through multiple mechanisms, and conversely, a single resistance mechanism may require multiple drugs for optimal combination therapy. Future developments should focus on combining multiple treatment regimens, particularly in immunotherapy, to overcome resistance. However, determining how to combine these treatments with other therapeutic approaches not shown in the diagram, such as surgery, radiotherapy, and local ablation, remains a challenge. It is important to note that even if some drug effects are not highly pronounced, if side effects are minimal, the cumulative effect can still be considerable. ICD includes apoptosis (in some cases), necroptosis, ferroptosis, and pyroptosis

Fig. 13

Flowchart for attaining optimal drug treatment efficacy in NSCLC. This flowchart represents an ideal scenario, and not all drugs or treatments will necessarily follow this development process. The exploration of mechanisms is the most critical and fundamental step. Without this, subsequent steps might merely be illusory. The battle against NSCLC requires close collaboration among various teams, which involves searching for precise internal markers within seemingly random surface events and strategically allocating dominant and cooperative roles in combination therapy to ultimately conquer this formidable disease