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REVIEW article

Front. Immunol., 15 December 2025

Sec. Inflammation

Volume 16 - 2025 | https://doi.org/10.3389/fimmu.2025.1721638

This article is part of the Research TopicDecoding Chronic Inflammation: The Role of Immune Cell CommunicationView all 13 articles

Frontiers in thoracic oncology: new breakthroughs in molecular targets and immunotherapy

Yujing Yang&#x;Yujing Yang1†Dan Pu&#x;Dan Pu2†Xuehan Li*Xuehan Li1*
  • 1Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China
  • 2Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, China

Thoracic tumors, including lung cancer, breast cancer, and thymoma, usually present poor outcomes. The current treatment methods for thoracic tumors have low efficacy and are associated with severe adverse reactions. Molecular targeted therapy and immunotherapy offer new possibilities for the treatment of thoracic tumors. In this review, we have summarized the latest research on these novel therapeutic strategies, and discussed their clinical applications, challenges, and possible countermeasures. This review offers a theoretical basis for improving the outcomes of thoracic tumor patients, along with future research prospects.

1 Introduction

Thoracic tumors include lung cancer, breast cancer, esophageal cancer, and thymoma, and are associated with high incidence and mortality rates worldwide. In the present review, the term “thoracic tumors” is used in a deliberately broad and clinically oriented sense to encompass malignancies arising in organs within or immediately adjacent to the thoracic cavity, specifically lung cancer, breast cancer, esophageal cancer and thymoma. Although breast cancer is not invariably classified along with other thoracic malignancies, its anatomical location and the substantial overlap in systemic treatment strategies – particularly regarding molecular targeted therapy and immunotherapy – provide a clear rationale for its inclusion within the scope of this article. Surgical resection, chemotherapy, and radiotherapy are effective in the early stages of thoracic tumors (1, 2), and may achieve clinical cure in a small percentage of patients. However, the curative effects are limited for patients with advanced or metastatic disease (35). In addition, the conventional treatments for thoracic tumors are associated with significant adverse reactions (6, 7), which lower the quality of life of patients.

In recent years, novel molecular targeted therapies and immunotherapies have emerged for various thoracic tumors (810). Targeted therapies aim at blocking specific receptors or molecules on tumor cells and inhibiting the downstream signaling pathways (11, 12), which selectively eliminate tumor cells while sparing healthy cells. On the other hand, immunotherapies prevent immune evasion and mobilize the host immune surveillance to target and kill tumor cells (1315). Both therapeutic modalities have significantly improved the curative rate for thoracic tumors and broadened the treatment opportunities for patients (16, 17). In this review, we have discussed the latest progress in molecular targeted therapy and immunotherapy for thoracic tumors, with the aim of providing references for clinical practice and future research (1820).

Several meta-analyses and umbrella reviews recently published in peer-reviewed oncology journals have systematically quantified the clinical efficacy and safety of molecular targeted agents and immune checkpoint inhibitors (ICIs) against thoracic malignancies. However, these quantitative analyses are typically organ-specific or modality-restricted, and provide limited information regarding the mechanistic basis of treatment response, cross-disease comparisons, and the rational design of combination strategies. In contrast, the present narrative review adopts an integrative perspective across major thoracic tumors, including lung cancer, breast cancer, esophageal cancer, and thymoma. We have not only summarized the latest advances in targeted therapies and immunotherapies, but also the impact of these interventions on tumor cell signaling and the tumor immune microenvironment, as well as the convergent principles that can guide the development of combined targeted-immunotherapy regimens. Furthermore, we have discussed practical challenges such as resistance, toxicity, costs, and biomarker-driven patient selection, and outlined future directions involving multi-omics profiling, liquid biopsy, and artificial-intelligence (AI)-assisted decision-making. By positioning thoracic oncology within this broader mechanistic and translational framework, our review complements and extends existing meta-analyses and umbrella reviews, and aims to provide clinicians and researchers with cross-cutting insights for therapeutic optimization.

2 New progress in molecular-targeted therapy

2.1 Lung cancer

Lung cancer is the most prevalent and lethal type of thoracic tumor. According to histological characteristics, lung cancer can be classified as non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), with NSCLC accounting for approximately 85% of all cases (21, 22). The treatment of NSCLC has evolved significantly in recent years with the continuous development of drugs targeting different driver gene mutations (Figure 1) (2325).

Figure 1
Illustration of molecular drug targets for lung cancer, showing pathways involving proteins like ALK, ROS1, EGFR, and others. It highlights proteins commonly mutated in lung cancer, marked by an asterisk. Cropped sections include detailed pathways, with drug names listed in a chart below, targeting each pathway.

Figure 1. Molecular targets of lung cancer and the corresponding drugs.

2.1.1 Drugs targeting epidermal growth factor receptor

EGFR, a member of the receptor tyrosine kinase family, is mutated in various cancers, and the abnormal activation of its downstream signaling pathway plays an important role in tumor cell proliferation, survival, migration, and angiogenesis (26, 27). In fact, EGFR gene mutations are present in almost 30%-50% of the NSCLC patients of Asian ancestry. First-generation EGFR tyrosine kinase inhibitors (TKIs), such as gefitinib and erlotinib, reversibly bind to and inhibit the tyrosine kinase domain of EGFR, thus blocking the downstream RAS-RAF-MEK-ERK and PI3K-AKT-mTOR pathways (2830), and inhibiting tumor cell proliferation (31, 32). Compared to traditional chemotherapy, first-generation EGFR-TKIs can effectively improve the objective response rate (ORR) and progression-free survival (PFS) of NSCLC patients with EGFR mutations, and are also associated with fewer adverse reactions. However, most patients develop drug resistance after 10–12 months of treatment with first-generation EGFR-TKIs (33, 34) due to emergence of new EGFR mutations. For instance, the EGFR-T790M mutation enhances the affinity of its kinase domain for ATP, and lowers the efficacy of TKIs.

Second-generation EGFR-TKIs, such as afatinib, irreversibly bind to EGFR. In addition to inhibiting cells with TKI-sensitive EGFR mutations, second-generation TKIs are effective in some patients who are resistant to first-generation TKIs (35, 36). However, while afatinib shows more persistent inhibitory effect against the EGFR signaling pathway compared to other TKIs, it lacks selectivity and also inhibits the proliferation of normal cells. This off-target action of afatinib results in severe side effects such as diarrhea, rash, and oral mucositis, leading to treatment discontinuation in some patients (37, 38).

The third-generation EGFR-TKI osimertinib was developed to target EGFR-T790M drug-resistant mutation, and has shown good efficacy against drug-sensitive EGFR mutations (3941). It can also penetrate the blood–brain barrier and effectively control brain metastases. Furthermore, osimertinib has better safety and tolerability due to its weak inhibitory effect on wild-type EGFR (42, 43). In the FLAURA study, osimertnib achieved a median PFS of 18.9 months as a first-line treatment for NSCLC patients with drug-sensitive EGFR mutations, and the survival benefit was superior compared to that of first-generation EGFR-TKIs. Recent studies have shown that some new EGFR inhibitors, such as poziotinib, are effective against EGFR with exon 20 insertion mutations, which are recalcitrant to traditional EGFR-TKIs (43, 44). The results of a clinical study showed that poziotinib effectively controlled tumor growth in patients with EGFR exon 20 insertion mutations. The mechanisms underlying the action of EGFR-targeted drugs and the drug-resistance mutations are shown in Figure 2 (4547).

Figure 2
Schematic diagram depicting the action and resistance mechanisms of EGFR-targeted drugs. It shows different pathways and responses to EGFR mutations like T790M, C797S, and others, with arrows indicating treatment options including first, second, and third-generation EGFR TKIs. Additional pathways involve K-RAS, MET, HER2, and AXL amplifications, with recommended treatments such as MEK inhibitors, Brigatinib, Cetuximab, EA045, and Cotuximab. The diagram highlights common mutations in non-small cell lung cancer (NSCLC) among Asian patients, with a mutation rate of thirty to fifty percent.

Figure 2. Schematic diagram of the mechanism of action of EGFR-targeted drugs and the EGFR mutations involved in drug resistance.

EGFR mutations often trigger constitutive activation of the RAS-RAF-MEK-ERK and PI3K-AKT-mTOR pathways, often in concert with parallel receptor tyrosine kinases (RTKs) such as MET and HER2. Under the selective pressure of first- and third-generation EGFR-TKIs, tumor cells can adapt through on-target alterations (for example, T790M or C797S), as well as via bypass-track activation, such as MET amplification, HER2 or AXL upregulation, and reactivation of downstream signaling. These resistance loops provide a clear mechanistic rationale for rational combinatorial approaches. For instance, dual blockade of EGFR and MET may overcome MET-amplified or MET-driven resistance, whereas combining EGFR-TKIs with PD-1/PD-L1 inhibitors can potentially counteract TKI-induced upregulation of PD-L1 and the induction of an immunosuppressive tumor microenvironment (TME). Early-phase studies on EGFR-mutant NSCLC patients suggest that these drug combinations may deepen responses in selected patients, although optimization of patient selection, treatment sequencing, and toxicity management remain key areas for future research.

2.1.2 Drugs targeting anaplastic lymphoma kinase fusion

ALK fusion is one of the most common genetic variations in NSCLC, and accounts for approximately 5% of all mutations. Fusion of the ALK gene leads to the continuous activation of the kinase, thus driving the growth and proliferation of tumor cells (4850). The first-generation ALK inhibitor crizotinib competitively binds to the ATP-binding site of the kinase domain, and inhibits tumor growth by blocking the downstream signaling pathway (51, 52). Compared to traditional chemotherapy, crizotinib significantly improved the ORR and PFS of NSCLC patients harboring ALK fusion (ALK-positive), resulting in better prognosis. However, crizotinib resistance frequently develops during treatment (53, 54), and is mainly attributed with mutations in the kinase domain, increase in ALK gene copy number, and the activation of bypass signaling pathways (5557).

Second-generation ALK inhibitors, including ceritinib, alectinib, and brigatinib, exhibit stronger inhibitory effect against ALK, and can partially overcome the mutations involved in crizotinib resistance. In addition, second-generation ALK inhibitors can effectively treat brain metastases due to increased blood-brain barrier penetration (58, 59). Alectinib has shown excellent intracranial antitumor activity in multiple experimental studies. In the ALEX study, first-line treatment with alectinib prolonged the median PFS of ALK-positive NSCLC patients to 34.8 months compared to 9.3 months in the crizotinib-treated arm. Furthermore, alectinib was also effective in patients with brain metastases. The third-generation ALK inhibitor lorlatinib can target multiple ALK mutations, and has a greater ability to penetrate the blood–brain barrier (26, 60). Lorlatinib has shown good efficacy in ALK-positive NSCLC patients resistant to crizotinib and second-generation ALK inhibitors. In the CROWN study, the median PFS (mPFS) of ALK-positive NSCLC patients treated with crizotinib was 9.3 months, whereas that of the Lorlatinib prolonged the PFS of patients (6165).

2.1.3 KRAS-targeted drugs

Activating KRAS mutations are present in approximately 25-30% of non-squamous NSCLCs – predominantly in lung adenocarcinomas. The frequency of these mutations is higher in Western cohorts compared to East Asian populations. The KRAS protein was considered “undruggable” for a long time due to its unique structure and the lack of suitable drug-binding sites (6668). Since 2016, structural biology and medicinal chemistry have been applied to devise strategies for targeting KRAS mutations. Sotorasib and Adagrasib are KRAS mutant-targeting drugs that have been approved for commercial use (69, 70). Sotorasib covalently binds to the KRASG12C mutant and locks it in an inactivated state, thus blocking the downstream pathway. It has shown good efficacy and safety in NSCLC patients with the KRASG12C mutation. In the CodeBreaK100 study, sotorasib achieved an ORR of 37.1% in NSCLC patients with KRASG12C mutations, and prolonged the mPFS to 6.8 months. Adagrasib also targets the KRASG12C mutant, and has greater affinity and longer half-life compared to sotorasib, potentially resulting in a more sustained therapeutic effect. KRASG12C-positive NSCLC patients treated with adagrasib achieved 43% ORR, with mPFS of 6.5 months (71, 72). KRASG12C inhibitors can be combined with immune checkpoint inhibitors (ICIs), MEK inhibitors, etc. to improve their efficacy (7375).

In summary, targeted therapies in lung cancer exemplify the paradigm of drugging specific oncogenic drivers such as EGFR, ALK, and KRAS, thereby providing substantial clinical benefit for well-defined molecular subgroups. Nevertheless, heterogeneous resistance mechanisms, limited benefit in oncogene-negative disease, and challenges in achieving durable central nervous system control still constrain long-term outcomes. These outcomes underscore the importance of pathway-directed strategies and resistance-aware drug development, themes that also underpin targeted approaches in other thoracic malignancies. In the following subsection, we therefore turn to breast cancer, where HER2 amplification and dysregulation of the PI3K-AKT-mTOR pathway have led to similarly transformative yet still incomplete advances in molecular-targeted therapy.

2.2 Breast cancer

Breast cancer is one of the most common malignancies in women. According to its molecular characteristics, breast cancer can be divided into the luminal A, luminal B, human epidermal growth factor receptor (HER)2-overexpressing, and triple-negative subtypes (7678) (Figure 3). Several targeted therapies have been developed for breast cancer in recent years (79, 80).

Figure 3
Infographic summarizing breast cancer molecular subtypes: Luminal A, Luminal B, HER2-enriched, and Triple Negative. Includes subtype prevalence, characteristics, targeted therapies, and associated diagrams. Visuals involve UMAP plots, heatmaps, flow cytometry data, cell images, and histology illustrations, highlighting subtype distinctions and research focuses.

Figure 3. Molecular subtypes of breast cancer.

2.2.1 HER2-targeted therapy

HER2, a member of the human epidermal growth factor receptor family, is often overexpressed or amplified in cancer cells, and promotes their proliferation, survival, and metastasis. HER2-positive breast cancer accounts for approximately 15% - 20% of all cases, and is characterized by highly invasive tumors and poor prognosis (81, 82). Trastuzumab is the first monoclonal antibody to be approved for the treatment of HER2-positive breast cancer. It binds to the extracellular domain of HER2, and inhibits its dimerization and autophosphorylation, thus blocking the downstream signaling pathway (8385). In addition, trastuzumab can also eliminate cancer cells through antibody-dependent cell-mediated cytotoxicity (ADCC), and activate natural killer (NK) cells. The combination of trastuzumab and chemotherapy has been shown to improve the survival of HER2-positive breast cancer patients and reduce the risk of recurrence (8688). A combined analysis of the NSABPB-31 and NCCTGN9831 studies revealed that trastuzumab combined with chemotherapy lowered the recurrence risk and mortality in HER2-positive breast cancer patients by 52% and 33% respectively (33, 89).

Pertuzumab binds to the dimerization domain of HER2 and prevents formation of heterodimers with other HER family members, resulting in complete inactivation of the HER2 signaling pathway (9092). It can be used in combination with trastuzumab to further improve the treatment benefits in HER2-positive breast cancer. In the CLEOPATRA trial, HER2-positive advanced breast cancer patients treated with the combination of trastuzumab, pertuzumab, and docetaxel had a median overall survival (mOS) of 56.5 months, compared to 40.8 months reported in patients treated with trastuzumab and docetaxel (93, 94).

Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate (ADC) consisting of trastuzumab and the cytotoxic drug mertansine. T-DM1 selectively delivers mertansine to HER2-positive tumor cells through trastuzumab, and the released drug inhibits tubulin polymerization and induces apoptosis (95, 96). In the EMILIA study involving HER2-positive advanced breast cancer patients, T-DM1 extended the median PFS to 9.6 months and the OS to 30.9 months, and resulted in milder adverse events than that observed with the combination of lapatinib and capecitabine (9799).

2.2.2 Drugs targeting the PI3K-AKT-mTOR pathway

The PI3K-AKT-mTOR pathway plays a key role in the proliferation, survival, and metabolism of breast cancer cells. Abnormal activation of this pathway is closely related to the occurrence, development, and drug resistance of breast cancer (100102). Everolimus is an mTOR inhibitor that forms a complex with the intracellular FKBP12 protein, and blocks the downstream pathway (103, 104). The combination of everolimus and endocrine therapy can improve the PFS of patients with hormone receptor-positive and HER2-negative advanced breast cancer. In the BOLERO-2 study, the combination of everolimus and exemestane increased the ORR in postmenopausal women with hormone receptor-positive and HER2-negative advanced breast cancer to 12.6% compared with exemestane monotherapy (105107), and extended the mPFS from 3.2 months to 7.8 months.

Collectively, targeted therapy in breast cancer illustrates how precise molecular subtyping can be translated into effective HER2-directed and pathway-focused regimens, while also revealing persistent obstacles such as primary and acquired resistance, treatment-related toxicity, and economic burden. These opportunities and limitations closely mirror those observed in lung cancer and highlight the need to extend rational targeted strategies to less common thoracic tumors. The next subsection therefore focuses on thymoma, a rare but immunologically distinctive malignancy in which early exploratory data suggest that EGFR-directed agents, recombinant monoclonal antibodies, and novel compounds such as Sitongzhi may open new avenues for patients who have historically lacked effective targeted options.

2.3 Thymoma

Thymomas are relatively rare thoracic tumors that originate from epithelial cells, and are difficult to treat due to the limited efficacy of conventional therapeutic strategies. Molecular targeted therapy offers a new direction for the treatment of thymoma (108110).

2.3.1 EGFR-targeted drugs

The growth and metastasis of thymoma cells is associated with the activation of the EGFR signaling pathway. Although EGFR-targeted drugs, such as erlotinib, can be effective against thymoma cells (111113), their scope is limited due to the complex molecular and biological characteristics of thymomas (114, 115). Only a small proportion of thymoma patients with high EGFR expression may benefit from EGFR-targeted therapy. In small-scale clinical trials, erlotinib treatment led to tumor shrinkage in some EGFRhigh thymoma patients, although the overall response rate was not high (116118).

2.3.2 Recombinant monoclonal antibody drugs

Recombinant monoclonal antibody drugs, including targeted drugs and ICIs, can inhibit the growth and metastasis of thymoma cells through multiple mechanisms by specifically binding to surface antigens (119, 120). Monoclonal antibodies targeting tumor-specific antigens can trigger ADCC and activate the cytotoxic immune effector cells to kill tumor cells. ICIs targeting programmed death receptor 1 (PD-1) and its ligand (PD-L1) can relieve the inhibitory effects of tumor cells on the immune system and restore anti-tumor immune responses (24, 121, 122). PD-1 inhibitors have shown efficacy in thymoma patients, and achieved stable disease in some patients.

2.3.3 Sitongzhi

Sitongzhi inhibits the growth and metastasis of tumor cells, promotes the proliferation and activation of T cells (123125), and enhances the recognition and killing of thymoma cells by T cells. Furthermore, it can also improve the tumor microenvironment (TME) and inhibit angiogenesis, resulting in further tumor inhibition. Although Sitongzhi has demonstrated anti-tumor activity in animal models of thymoma, its efficacy and safety in humans need to be further verified by larger clinical studies (126, 127). A concise overview of representative targeted therapy trials in thoracic tumors has been provided in Table 1.

Table 1
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Table 1. Selected landmark trials of targeted therapy in thoracic tumors.

3 New progress in immunotherapy

Immunotherapy encompasses several complementary strategies that activate the anti-tumor immune response. In this section, we have summarized the current status of ICIs across major thoracic malignancies, and discussed emerging approaches such as adoptive transfer of tumor-infiltrating lymphocytes (TILs) and individualized cancer vaccines. Together, these modalities illustrate how modulation of immune checkpoints, effector cells, and antigen presentation can be leveraged to improve outcomes in thoracic tumors.

3.1 Immune checkpoint inhibitors

Immune checkpoints like PD-1, PD-L1, and cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) are a group of regulatory molecules that limit the damage caused by immune responses to normal tissues, and are also used by tumor cells to escape immune surveillance (128130). ICIs can relieve tumor-induced immunosuppression by disrupting the interaction between immune checkpoints and their ligands, and restore the ability of T cells to eliminate tumor cells (Figure 4) (131, 132).

Figure 4
Illustration showing mechanisms of a combined strategy of molecular targeting and immunotherapy in thoracic tumors. It includes a thoracic tumor section with pruritus, vascular changes, and tumor growth illustrations. The diagram details the effects of drugs like Cabozantinib, Regorafenib, Sorafenib, and Lenvatinib on cell types such as M1/M2 TAM, CD4+/CD8+ T cells, dendritic cells, and MDSCs. Lower doses focus on vascular normalization and immune activation, while higher doses lead to immune suppression and hypoxia through cytokines like VEGF, HIF-1a, and CXCR4.

Figure 4. Mechanism of action of ICIs.

In addition to the clinically validated PD-1/PD-L1 and CTLA-4 pathways (Figure 4), inhibitory receptors such as T cell immunoreceptor with Ig and ITIM domains (TIGIT), lymphocyte activation gene-3 (LAG-3), and T cell immunoglobulin and mucin-domain containing-3 (TIM-3) have been implicated in T-cell exhaustion in thoracic malignancies. Co-expression of these molecules with PD-1 often marks deeply dysfunctional T-cell populations in NSCLC, breast cancer and esophageal cancer, and preclinical studies suggest that dual or triple blockade can further invigorate immune responses against tumors that are recalcitrant to PD-1/PD-L1 monotherapy. Multiple phase I/II trials are therefore evaluating antibodies against TIGIT, LAG-3 or TIM-3 alone or in combination with PD-1/PD-L1 inhibitors in the treatment of lung and other thoracic tumors. Although most of these agents have not yet been integrated into routine clinical practice, they represent a next wave of ICIs that may expand the population benefiting from immunotherapy, and help overcome acquired resistance.

3.1.1 Lung cancer

Pembrolizumab monotherapy is currently used to treat advanced NSCLC patients with high PD-L1 expression (TPS ≥ 50%). The KEYNOTE-024 study revealed that, under second-line conditions, first-line treatment of PD-L1high patients with pembrolizumab extended the mOS from 22.1 months to 30 months, and the mPFS from 6 months to 10.3 months compared to traditional chemotherapy (133135). Furthermore, the adverse reactions of prembolizumab were relatively mild. The combination of PD-L1/PD-L1 inhibitors and chemotherapy also showed good curative effect in the PD-L1low/- patients (136, 137). In the KEYNOTE-189 study, pembrolizumab combined with pemetrexed and platinum-based chemotherapy increased the ORR of lung cancer patients from 29.4% to 47.6% compared to chemotherapy, and extended the mPFS from 4.9 months to 8.8 months, and the mOS from 12 months to 22 months (138140).

ICIs, particularly PD-1 blockers, have been incorporated into the standard treatment for SCLC. The IMpower133 study showed that atezolizumab combined with etoposide and platinum-based chemotherapy extended the mOS of SCLC patients from 10.3 months to 12.3 months, and the mPFS from 4.3 months to 5.2 months compared with chemotherapy alone. The bispecific antibody drug tarlatamab targets the T cell-specific CD3 and the DLL3 protein, which is often overexpressed on SCLC cells. Thus, simultaneous binding of tarlatamab to CD3 and DLL3 allows T cells to aggregate around the tumor cells, resulting in selective elimination of the latter (115, 141, 142). A preliminary clinical trial showed that tarlatamab can control tumor growth in some SCLC patients.

3.1.2 Breast cancer

Immunotherapy has also proven beneficial for triple-negative breast cancer (TNBC) patients. In a clinical study based on the “Fudan classification”, the combination of albumin-bound paclitaxel, the PD-1 blocker camrelizumab, and the TKI famitinib prolonged the PFS of patients with metastatic TNBC to 15.1 months (143145), an increase of 8.6 months compared to traditional chemotherapy, and achieved an ORR of 80% (146, 147). In the KEYNOTE-355 study conducted on PD-L1-positive (CPS≥10) metastatic TNBC patients, pembrolizumab combined with chemotherapy extended the PFS from 5.6 months to 9.7 months, and the OS from 16.1 months to 23 months (148150).

3.1.3 Other thoracic tumors

The therapeutic effects of ICIs have been demonstrated in large-scale clinical studies involving esophageal cancer and thymoma patients. The combination of the PD-1 blocker nivolumab and chemotherapy is currently among the first-line treatment options for patients with advanced esophageal cancer. The ATTRACTION-5 study confirmed that compared to chemotherapy alone, nivolumab combined with chemotherapy can reduce the mortality risk of patients with advanced esophagogastric junction cancer by 25%, and increase the mOS from 10.9 months to 12.6 months (151153). In addition, pembrolizumab alone or in combination with chemotherapy also offers new possibilities for esophageal cancer patients. In the KEYNOT-590 study, pembrolizumab combined with chemotherapy for first-line treatment of advanced esophageal cancer or esophagogastric junction cancer significantly improved the ORR and OS of patients compared to chemotherapy alone.

ICIs have proven to be an effective therapeutic option for thymomas (154156). Some thymoma patients have experienced tumor remission and stable disease following treatment with PD-1/PD-L1 inhibitors. However, due to the heterogenous immune microenvironment of thymomas, many patients develop primary or secondary resistance to ICIs, resulting in sub-optimal treatment outcomes. Therefore, it is crucial to elucidate the immune escape mechanism of thymomas, screen for effective biomarkers (157), select suitable patients, and devise combination treatment strategies in order to improve the efficacy of immunotherapies.

A non-exhaustive overview of key ICIs that have been approved or are guideline-recommended for major thoracic malignancies is presented in Table 2.

Table 2
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Table 2. Selected ICIs approved or guideline-recommended for thoracic malignancies.

Taken together, clinical experience with PD-1/PD-L1 and CTLA-4 blockade in lung cancer, breast cancer, esophageal cancer, and thymoma has established immune checkpoint inhibition as a cornerstone of systemic therapy for thoracic malignancies. At the same time, primary and acquired resistance, heterogeneous PD-L1 expression, and immune-related adverse events (isAEs) limit the proportion of patients who derive durable benefit. These limitations have stimulated interest in complementary immunotherapeutic strategies, including cellular therapies and vaccines, which may broaden the spectrum of responders or be combined with ICIs to enhance efficacy. The major landmark immunotherapy trials across thoracic tumor types are summarized in Table 3.

Table 3
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Table 3. Selected landmark immunotherapy trials in thoracic tumors.

3.2 Adoptive transfer of tumor-infiltrating lymphocytes

TILs persist in the TME for a long time, and can recognize and kill tumor cells. The TILs isolated from tumors can be expanded in vitro and reinfused into the patient to initiate an anti-tumor immune response. The adoptive transfer of TILs presents a promising approach for the treatment of lung cancer. A significant proportion of lung cancer patients have experienced tumor shrinkage and prolonged survival after receiving TIL infusion. In a clinical trial conducted by the National Cancer Institute of the United States, TIL infusion achieved a significant ORR in NSCLC patients, with acceptable safety (158160). Nevertheless, there are several challenges associated with the clinical translation of TILs. The extraction and in vitro expansion of TILs are technically demanding, and the treatment is highly complex and expensive at present. Moreover, the sub-optimal infiltration and activity of TILs in tumor tissues, and their potential adverse reactions are other challenges that need to be overcome.

Beyond TIL products, other cell-based immunotherapies are being actively explored in thoracic oncology. NK cell-based strategies, including autologous and allogeneic infusions, as well as genetically modified chimeric antigen receptor (CAR)-NK products, offer the potential for potent cytotoxicity with a lower risk of graft-versus-host disease and cytokine release syndrome. Early-phase studies in NSCL and SCLC patients have demonstrated acceptable safety profiles and preliminary signals of anti-tumor activity. CAR-T cell therapies directed against HER2, mesothelin and EGFR are also under investigation in thoracic tumors; however, on-target/off-tumor toxicities, limited trafficking into solid tumor sites, and the highly immunosuppressive TME remain major obstacles. As manufacturing platforms and safety-engineering strategies continue to improve, these cellular therapies may gradually move from proof-of-concept toward broader clinical application in carefully selected patients with advanced thoracic malignancies.

3.3 Cancer vaccines

Tumor vaccines induce specific immunity against tumor cells, and can be further divided into tumor-preventive and anti-tumor vaccines. The combined application of some individualized anti-tumor vaccines and ICIs has reduced the recurrence risk in lung cancer patients (161, 162). Individualized vaccines are designed on the basis of tumor mutations, and may induce an immune response against tumor-specific antigens. In one study, an individualized preventive lung cancer vaccine was successfully used in combination with ICIs. Furthermore, vaccination of patients with metastatic lung cancer after tumor surgery reduced the risk of recurrence and extended the recurrence-free survival. However, most tumor vaccines are currently in the clinical trial stage, and their clinical translation will depend on overcoming challenges such as immunogenicity (163165), safety, and tumor immune escape. In addition, the production of vaccines is technically complex and costly, which hinder their large-scale clinical application. Thus, the future directions for the development of individualized tumor vaccines would be optimization of design and production process, and cost-control (77, 166, 167).

Overall, advances in immune checkpoint blockade, TIL therapy, and personalized cancer vaccines demonstrate the versatility of immunotherapy platforms for thoracic tumors, but also reveal practical constraints related to manufacturing complexity, cost, and patient selection. The next step is to integrate these immune-based approaches with molecular-targeted agents that reshape oncogenic signaling and the TME. In the following section, we therefore focus on rational combination strategies that couple targeted therapy with immunotherapy to achieve more durable and synergistic clinical benefits.

4 Combined strategies of molecular targeted therapy and immunotherapy

Immunotherapy and molecular targeted therapy can achieve effective tumor control through synergistic action. While molecular targeted therapy can specifically inhibit tumor growth and proliferation (168170), remodel the TME, and increase the immunogenicity of tumor cells, immunotherapy can activate the immune system and kill tumor cells (Figure 5).

Figure 5
Mechanism of action of immune checkpoint inhibitors is illustrated. Left side shows natural binding between proteins on tumor and T cells. Right side shows inhibition by antibodies: Anti-PD-L1, Anti-PD-1, and Anti-CTLA-4, preventing the tumor cell from evading the immune response.

Figure 5. Synergistic action of molecular targeting and immunotherapy against thoracic tumors.

4.1 Lung cancer

The combination of EGFR-TKIs and PD-1 inhibitors offers unique advantages in the treatment of lung cancer. The cellular stress induced by the EGFR-TKIs in EGFR-positive lung tumors triggers the release of tumor-associated antigens (171173). These antigens are captured and presented to T cells by antigen-presenting cells, thereby increasing tumor immunogenicity and augmenting the effect of PD-1 inhibitors. However, some patients experience a relatively high incidence of adverse reactions after receiving this combination treatment (174176). This may be due to the complex effects of EGFR-TKIs on the immune microenvironment, along with increased immune activation through PD-1 blockade. It will be a topic of future research to achieve satisfactory efficacy with this combination therapy while reducing adverse reactions. The combination of ALK inhibitors and ICIs has also shown therapeutic effects in preliminary clinical trials (177179). ALK inhibitors can enhance the immunogenicity of ALK-positive lung cancer cells by altering the surface antigens, which can potentially improve the efficacy of ICIs when used in combination.

However, most EGFR–PD-1/PD-L1 and ALK–ICI combinations have been evaluated only in early-phase, non-randomized trials, or in small retrospective cohorts involving limited numbers of patients. Although these studies provide preliminary indications of antitumor activity and feasibility, they are not sufficient to demonstrate superiority over established sequential targeted therapy or immunotherapy alone. Consequently, such regimens should be regarded as exploratory and preferably implemented within the context of clinical trials until more robust randomized data become available.

4.2 Breast cancer

HER2-positive breast tumors are highly immunogenic, and HER2-targeted drugs (such as trastuzumab) can not only inhibit the growth of tumor cells but also activate immune cells through the ADCC effect (180182). Therefore, combining HER2 inhibitors with ICIs can potentially trigger a strong immune response against HER2-positive breast cancer cells. For instance, preliminary data shows that the combination of trastuzumab and pembrolizumab can improve the ORR and PFS in patients with HER2-positive advanced breast cancer compared to either monotherapy. In addition, the combination of PI3K-AKT-mTOR pathway inhibitors and immunotherapy has shown therapeutic efficacy against TNBC (183185). Abnormal activation of the PI3K-AKT-mTOR pathway in the tumor cells aids in immune escape. The mTOR inhibitors like everolimus can reverse this immune escape, increase the infiltration and cytotoxic function of anti-tumor immune cells, and synergize with ICIs. It should be emphasized that evidence supporting HER2-ICI and PI3K-AKT-mTOR-ICI combinations in breast cancer is derived predominantly from phase I/II studies and small single-arm cohorts, and no large randomized trial has yet established a clear survival advantage of these regimens over current standard-of-care treatments.

4.3 Thymoma

The combination of molecular targeted therapy and immunotherapy is a promising approach for treating thymomas owing to their unique immune microenvironment (186188). EGFR-targeted drugs and PD-1 inhibitors can not only reduce the proliferation of thymoma cells by inhibiting the EGFR signaling pathway but also relieve the immunosuppressive microenvironment and improve anti-tumor immune responses. The combination of new targeted drugs such as Sitongzhi and immunotherapy can also increase the ability of T cells to kill thymoma cells through the remodeling of the TME, thereby improving clinical efficacy. Currently, most of these combined treatments are in the clinical testing stage, and their efficacy and safety still need to be verified by large-scale, multicenter trials. The clinical evidence for targeted therapy-immunotherapy combinations in thymoma is limited to case series and early-phase studies with relatively small sample sizes, often involving only a few dozen patients. These results should therefore be interpreted as exploratory, and no firm conclusions regarding superiority over conventional approaches can yet be drawn. Second (189191), the optimal combination drugs, drug dosages, and treatment sequences will also have to be standardized.

There are certain challenges in combining molecular targeted drugs and immunotherapy in the treatment of thoracic tumors. First, due to individual differences in the sensitivity to combination treatment, it will be crucial to screen for patients who can benefit the most from the treatment, and devise individualized treatments (192194). Second, combination treatment can lead to an increase in adverse reactions, which will have to be minimized while improving therapeutic efficacy in order to improve the quality of life of patients (195197). Third, the high costs of combined treatments limit their clinical application, thereby warranting strategies to reduce costs and improve accessibility.

5 Challenges and prospects

5.1 Drug resistance

A small fraction of patients either do not respond to targeted drugs or immunotherapy, or develop drug resistance. The mechanisms of drug resistance usually involve molecular-level adaptive changes in tumor cells under selection pressure. In addition to the definitive lung cancer-associated T790M mutation in the EGFR gene, new drug-resistant mutations such as C797S can lead to the failure of existing targeted drugs. Increased infiltration of immunosuppressive cells such as regulatory T cells and myeloid-derived suppressor cells in the TME can help tumor cells escape immune surveillance, and render them insensitive to immunotherapy. The heterogeneity of tumor cells is also a driving factor for drug resistance. Different tumor cell subsets differ in their drug sensitivities, and the expansion of drug-resistant clones during treatment leads to treatment failure. Therefore, a greater understanding of the mechanisms of drug resistance, development of new targeted drugs and immunotherapy strategies, and novel combination treatment plans will be key to overcoming drug resistance.

In clinical practice, several actionable strategies are being developed to translate mechanistic insights on resistance into dynamic management. Liquid biopsy-based monitoring of circulating tumor DNA (ctDNA) allows serial detection of emergent resistance alterations, such as secondary EGFR or ALK mutations and MET amplification, often before radiographic progression. Timely identification of these molecular events can guide therapeutic decisions, including switching to next-generation TKIs, adding targeted agents against bypass pathways, or redesigning combination regimens. For patients receiving immunotherapy, longitudinal ctDNA profiling and minimal residual disease assessment may help distinguish true progression from pseudo-progression and identify early molecular escape, thereby supporting risk-adapted continuation, intensification or de-escalation of treatment. Integrating these liquid biopsy approaches with tumor re-biopsy, advanced imaging and multi-omics profiling is expected to form a more precise and proactive framework for resistance surveillance in thoracic tumors.

5.2 Immune-related adverse reactions

Immunotherapies can cause immune-related adverse reactions in the skin, gastrointestinal tract, liver, endocrine system, and other organs, and seriously affect the quality of life of patients and may even be lethal. Therefore, a key challenge in this field is to better predict, monitor, and manage adverse reactions, and improve patient tolerance and quality of life. In the context of clinical application, it is necessary to establish a good adverse reaction monitoring system, identify biomarkers to predict the risk of adverse reactions, implement precise individualized treatment, adjust the treatment plan in a timely manner, and reduce the severity of adverse reactions. In addition, strengthening health education and improving patients’ compliance with self-monitoring of adverse reactions are also helpful for timely detection and treatment of adverse reactions.

Looking forward, a key direction is the development of predictive models that can stratify the risk of irAEs before and during treatment. Emerging evidence suggests that baseline clinical characteristics, serum biomarkers (such as cytokines, autoantibodies and organ-specific enzymes), host genomic factors (for example, HLA genotypes) and gut microbiota may collectively influence susceptibility to severe toxicity during immune checkpoint blockade. Machine learning-based approaches are increasingly being used to integrate these heterogeneous data and generate individualized risk scores for irAEs. In parallel, standardized algorithms for real-time toxicity monitoring, such as incorporating electronic patient-reported outcomes, regular laboratory testing and imaging where appropriate, can support earlier detection, graded intervention and coordinated multidisciplinary management. The implementation and prospective validation of such predictive and monitoring tools will be essential to maximize the therapeutic window of immunotherapy in thoracic oncology.

5.3 Prospects

Molecular typing of tumors through multiomics can help screen more patients who are likely to benefit from molecular targeted therapy and immunotherapy, and aid in devising individualized treatment. In-depth analysis of tumor cell heterogeneity via single-cell sequencing technology will provide more accurate targets for precision treatment. In addition, liquid biopsy can dynamically monitor the molecular changes in tumors, thus allowing clinicians to adjust the treatment plan. Moreover, continuous development of targeted therapy drugs and immunotherapy drugs, more effective combination treatment methods, and in-depth research on tumor immune escape mechanisms will lead to more breakthroughs in the treatment of thoracic tumors. The application of AI and big data will further accelerate drug development, and improve clinical decision-making and treatment efficacy. Large-scale clinical trials and international collaboration will be crucial to the progression of thoracic tumor treatment.

5.4 Multi-omics and AI for biomarker discovery and response prediction

The rapid advances in high-throughput technologies have enabled profiling of thoracic tumors at multiple molecular layers, such as the genome, transcriptome, epigenome, proteome, metabolome, and even radiome, which can detect clinically relevant heterogeneity that is not captured by single biomarkers. By integrating these multi-omics datasets, recent studies have identified composite signatures of NSCLC that predict clinical outcomes of immunotherapy more accurately than individual markers such as PD-L1 or tumor mutational burden. For example, Mei et al. combined genomic, transcriptomic, proteomic and ctDNA-derived features to build AI-driven risk models that stratify NSCLC patients receiving ICIs into distinct prognostic groups (198). Furthermore, multi-omics-driven machine learning can also delineate immune-inflamed versus immune-excluded phenotypes, identify therapy-responsive TME sub-cohorts, and suggest rational combinations of targeted agents and immunotherapies across solid tumors (199). In parallel, multi-modal models that fuse molecular, imaging and clinicopathological data are being developed to predict EGFR genotype and screen patients who can benefit from targeted therapy, further blurring the boundary between “molecular” and “imaging” biomarkers in thoracic oncology (200).

AI methods, including classical machine learning and deep learning, are central to extracting actionable information from these complex datasets and translating it into clinically usable tools. Deep learning radiomics models based on CT or PET/CT have been shown to infer PD-L1 status in a non-invasive manner, and predict durable benefit from immunotherapy in advanced NSCLC, thus supporting treatment decision-making tissues are limited or repeated biopsies are impractical (201). At the same time, longitudinal liquid-biopsy studies demonstrate that dynamic changes in circulating tumor DNA (ctDNA) can anticipate response, resistance and even hyper-progression under immune checkpoint blockade, and AI-enhanced analysis of serial ctDNA profiles is emerging as a powerful approach for real-time response monitoring (202). Systematic evaluations indicate that AI models for biomarker prediction in lung cancer can achieve robust sensitivity and specificity across heterogeneous cohorts, but also highlight key challenges, including data quality and harmonization, external validation, model interpretability and regulatory and ethical considerations. Overall, the convergence of multi-omics profiling, liquid biopsy and AI-assisted analytics is expected to become a major driving force behind biomarker discovery, individualized selection of targeted and immune therapies, and adaptive response prediction in thoracic tumors.

6 Conclusion

Molecular targeted therapy and immunotherapy have led to significant improvement in the prognosis and quality of life of patients with thoracic tumors. New targeted drugs and immunotherapy strategies have been developed in recent years for lung cancer, breast cancer, and thymoma, which also offer the possibility of combined application. However, despite their advantages, emergence of drug resistance and adverse reactions are pressing challenges. Nevertheless, molecular targeted therapy and immunotherapy will likely play a greater role in the treatment of thoracic tumors in the foreseeable future.

Author contributions

YY: Data curation, Methodology, Visualization, Conceptualization, Writing – original draft, Supervision, Resources, Writing – review & editing, Investigation, Formal Analysis, Software. DP: Writing – original draft, Resources, Formal Analysis, Visualization, Project administration, Software, Methodology, Supervision, Writing – review & editing, Conceptualization, Validation. XL: Software, Conceptualization, Investigation, Supervision, Writing – original draft, Writing – review & editing, Resources, Validation, Data curation, Visualization, Project administration, Formal Analysis, Methodology.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported and funded by The National Natural Science Fund of Sichuan (No. 2022NSFSC0604) and "Qimingxing" Research Fund for Young Talents (No. HXQMX0042).

Conflict of interest

The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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References

1. Zhou R, Chen S, Wu Q, Liu L, Wang Y, Mo Y, et al. CD155 and its receptors in cancer immune escape and immunotherapy. Cancer Lett. (2023) 573:12. doi: 10.1016/j.canlet.2023.216381

PubMed Abstract | Crossref Full Text | Google Scholar

2. Zhang R, Jiang Q, Zhuang Z, Zeng H, and Li Y. A bibliometric analysis of drug resistance in immunotherapy for breast cancer: trends, themes, and research focus. Front Immunol. (2024) 15:1452303. doi: 10.3389/fimmu.2024.1452303

PubMed Abstract | Crossref Full Text | Google Scholar

3. Zimmermann A, Hofer SJ, and Madeo F. Molecular targets of spermidine: implications for cancer suppression. Cell Stress. (2023) 7:9. doi: 10.15698/cst2023.07.281

PubMed Abstract | Crossref Full Text | Google Scholar

4. Zhu HH, Feng Y, and Hu XS. Emerging immunotherapy targets in lung cancer. Chin Med J. (2020) 133:10. doi: 10.1097/CM9.0000000000001082

PubMed Abstract | Crossref Full Text | Google Scholar

5. Zheng M, Kumar A, Sharma V, Behl T, Sehgal A, Wal P, et al. Revolutionizing pediatric neuroblastoma treatment: unraveling new molecular targets for precision interventions. Front Cell Dev Biol. (2024) 12:16. doi: 10.3389/fcell.2024.1353860

PubMed Abstract | Crossref Full Text | Google Scholar

6. Zeng Y, Lv X, and Du J. Natural killer cell‑based immunotherapy for lung cancer: Challenges and perspectives (Review). Oncol Rep. (2021) 46:232. doi: 10.3892/or.2021.8183

PubMed Abstract | Crossref Full Text | Google Scholar

7. Yu J, Li M, Ren B, Cheng L, Wang X, Ma Z, et al. Unleashing the efficacy of immune checkpoint inhibitors for advanced hepatocellular carcinoma: factors, strategies, and ongoing trials. Front Pharmacol. (2023) 14:24. doi: 10.3389/fphar.2023.1261575

PubMed Abstract | Crossref Full Text | Google Scholar

8. Zhao W, Fernando LD, Kirui A, Deligey F, and Wang T. Solid-state NMR of plant and fungal cell walls: A critical review. Solid State Nucl Magnetic Resonance. (2020) 107:101660. doi: 10.1016/j.ssnmr.2020.101660

PubMed Abstract | Crossref Full Text | Google Scholar

9. Zhang H, Liu Y, Hu D, and Liu S. Identification of novel molecular therapeutic targets and their potential prognostic biomarkers based on cytolytic activity in skin cutaneous melanoma. Front Oncol. (2022) 12. doi: 10.3389/fonc.2022.844666

PubMed Abstract | Crossref Full Text | Google Scholar

10. Zeng J, Wu Z, Luo M, Chen Z, Xu X, Xie G, et al. Identification of a long non-coding RNA signature associated with cuproptosis for prognosis and immunotherapy response prediction in patients with lung adenocarcinoma. Discover Oncol. (2025) 16:1–17. doi: 10.1007/s12672-025-02092-3

PubMed Abstract | Crossref Full Text | Google Scholar

11. Ying C, Hui L, Liang Z, Jing-Jing L, Chang-Liang Y, and Shuang Z. Current and future drug combination strategies based on programmed death-1/programmed death-ligand 1 inhibitors in non-small cell lung cancer. Chin Med J. (2021) 134:9. doi: 10.1097/CM9.0000000000001560

PubMed Abstract | Crossref Full Text | Google Scholar

12. Yi-Xuan W, Fang L, Cong-Hui P, Xiao-Na YU, and Lu G. Research progress of immune checkpoint molecules in pregnancy. J Hainan Med Univ. (2023) 29:72–78.

Google Scholar

13. van der Zanden SY, Luimstra JJ, Neefjes J, Borst J, and Ovaa H. Opportunities for small molecules in cancer immunotherapy. Trends Immunol. (2020) 41:493–511. doi: 10.1016/j.it.2020.04.004

PubMed Abstract | Crossref Full Text | Google Scholar

14. Yadav M, Kumar N, Kumar A, Jindal DK, and Dahiya M. Possible biomarkers and contributing factors of psychosis: a review. Curr Pharmacol Rep. (2021) 7:123–34. doi: 10.1007/s40495-021-00260-x

Crossref Full Text | Google Scholar

15. Xue W and Zhang M. Updating targets for natural killer/T-cell lymphoma immunotherapy. Cancer Biol Med. (2021) 018:52–62. doi: 10.20892/j.issn.2095-3941.2020.0400

PubMed Abstract | Crossref Full Text | Google Scholar

16. Yang S, Li L, Duan D, Hou Z, and Tan J. Homing barriers and solutions for CAR-T cells in the treatment of solid tumors. Chin J Clin Oncol. (2019) 755–9.

Google Scholar

17. Yang H, Li C, and Xie Q. Advances in the use of nanomaterials in tumour\nTherapy: challenges and prospects. Cancer Insight. (2023) 2:37–48. doi: 10.58567/ci02010006

Crossref Full Text | Google Scholar

18. Xu M, Lu J, Zhong Y, Jiang J, Shen Y, Su J, et al. Immunogenic cell death-relevant damage-associated molecular patterns and sensing receptors in triple-negative breast cancer molecular subtypes and implications for immunotherapy. Front Oncol. (2022) 12. doi: 10.3389/fonc.2022.870914

PubMed Abstract | Crossref Full Text | Google Scholar

19. Xu B, Tseng I, Zhang X, Chen H, Campo ERD, Deng J, et al. Immune characteristics and genetic markers of esophageal cancer by single-cell analysis: implications for immunotherapy. J Thorac Dis. (2023) 15:18. doi: 10.21037/jtd-23-317

PubMed Abstract | Crossref Full Text | Google Scholar

20. Xiong W, Cheng Z, Chen H, Liang H, Wang M, Chen Y, et al. Biomimetic tumor cell membrane-encapsulated nanoparticles combine NIR-II photothermal therapy and chemotherapy for enhanced immunotherapy in triple-negative breast cancer. Advanced Funct Materials. (2024) 34:2410841. doi: 10.1002/adfm.202410841

Crossref Full Text | Google Scholar

21. Yan T, Yu L, Zhang N, Peng C, Su G, Jing Y, et al. The advanced development of molecular targeted therapy for hepatocellular carcinoma. Cancer Biol Med. (2022) 19:802–17. doi: 10.20892/j.issn.2095-3941.2021.0661

PubMed Abstract | Crossref Full Text | Google Scholar

22. Xu S, Wang C, Yang L, Wu J, Li M, Xiao P, et al. Targeting immune checkpoints on tumor-associated macrophages in tumor immunotherapy. Front Immunol. (2023) 14:22. doi: 10.3389/fimmu.2023.1199631

PubMed Abstract | Crossref Full Text | Google Scholar

23. Xiang D, Fu G, Chen Y, and Chu X. Case report: POLE (P286R) mutation in a case of recurrent intestinal leakage and its treatment. Front Oncol. (2023) 13. doi: 10.3389/fonc.2023.1028179

PubMed Abstract | Crossref Full Text | Google Scholar

24. Hu XC. Progress in research on tumor metastasis inhibitors. Curr medicinal Chem. (2020) 27:5758–5772. doi: 10.2174/0929867326666190927120847

PubMed Abstract | Crossref Full Text | Google Scholar

25. Wong B, Birtch R, Rezaei R, Jamieson T, Crupi MJF, Diallo JS, et al. Optimal delivery of RNA interference by viral vectors for cancer therapy. Mol Ther. (2023) 31:19. doi: 10.1016/j.ymthe.2023.09.012

PubMed Abstract | Crossref Full Text | Google Scholar

26. Pu X, Wu L, Su D, Mao W, and Fang B. Immunotherapy for non-small cell lung cancers: biomarkers for predicting responses and strategies to overcome resistance. BMC Cancer. (2018) 18:1082. doi: 10.1186/s12885-018-4990-5

PubMed Abstract | Crossref Full Text | Google Scholar

27. Xie Q, Zhang P, Wang Y, Mei W, and Zeng C. Overcoming resistance to immune checkpoint inhibitors in hepatocellular carcinoma: Challenges and opportunities. Front Oncol. (2022) 12. doi: 10.3389/fonc.2022.958720

PubMed Abstract | Crossref Full Text | Google Scholar

28. Wang L, Li C, and Luo K. Biosynthesis and metabolic engineering of isoflavonoids in model plants and crops: a review. Front Plant Sci. (2024) 15:17. doi: 10.3389/fpls.2024.1384091

PubMed Abstract | Crossref Full Text | Google Scholar

29. Wan W, Qian X, Zhou B, Gao J, Deng J, and Zhao D. Integrative analysis and validation of necroptosis-related molecular signature for evaluating diagnosis and immune features in Rheumatoid arthritis. Int Immunopharmacol. (2024) 131:111809. doi: 10.1016/j.intimp.2024.111809

PubMed Abstract | Crossref Full Text | Google Scholar

30. Vilela T, Valente S, Correia J, and Ferreira F. Advances in immunotherapy for breast cancer and feline mammary carcinoma: From molecular basis to novel therapeutic targets. Biochim Biophys Acta (BBA) - Rev Cancer. (2024) 1879:18. doi: 10.1016/j.bbcan.2024.189144

PubMed Abstract | Crossref Full Text | Google Scholar

31. Xiaoling J, Bofei W, Chenxi T, Mei L, and Yinhong S. New progress in immunotherapy for pancreatic cancer. Front Immunol. (2019) 15:1383978. doi: 10.3389/fimmu.2024.1383978

PubMed Abstract | Crossref Full Text | Google Scholar

32. Wu M, Bai J, Ma C, Wei J, and Du X. The role of gut microbiota in tumor immunotherapy. J Immunol Res. (2021) 2021:5061570. doi: 10.1155/2021/5061570

PubMed Abstract | Crossref Full Text | Google Scholar

33. Heng WS, Gosens R, and Kruyt FAE. Lung cancer stem cells: origin, features, maintenance mechanisms and therapeutic targeting. Biochem Pharmacol. (2018) 160:121–133. doi: 10.1016/j.bcp.2018.12.010

PubMed Abstract | Crossref Full Text | Google Scholar

34. Wang W, Ye L, Li H, Chen W, Hong W, Mao W, et al. A narrative review on advances in neoadjuvant immunotherapy for esophageal cancer: Molecular biomarkers and future directions. Int J Cancer. (2025) 156:20–33. doi: 10.1002/ijc.35153

PubMed Abstract | Crossref Full Text | Google Scholar

35. Wang F, Huang Q, Guo S, Traverso A, Teng F, and Liu C. Editorial: Novel immune markers and predictive models for immunotherapy and prognosis in breast and gynecological cancers. Front Immunol. (2024) 15:1431245. doi: 10.3389/fimmu.2024.1431245

PubMed Abstract | Crossref Full Text | Google Scholar

36. Vandoni G, D’Amico F, Fabbrini M, Mariani L, Sieri S, Casirati A, et al. Gut microbiota, metabolome, and body composition signatures of response to therapy in patients with advanced melanoma. Int J Mol Sci. (2023) 24:11611. doi: 10.3390/ijms241411611

PubMed Abstract | Crossref Full Text | Google Scholar

37. Thais BCM, Farooq AR, Wang X, and Elimova E. Gastric cancer: molecular mechanisms, novel targets, and immunotherapies: from bench to clinical therapeutics. Cancers. (2023) 15:5075. doi: 10.3390/cancers15205075

PubMed Abstract | Crossref Full Text | Google Scholar

38. Tahmasebi S, Elahi R, Khosh E, and Esmaeilzadeh A. Programmable and multi-targeted CARs: a new breakthrough in cancer CAR-T cell therapy. Clin Trans Oncol. (2020) 22:1003–19. doi: 10.1007/s12094-020-02490-9

PubMed Abstract | Crossref Full Text | Google Scholar

39. Underwood PW, Ruff SM, and Pawlik TM. Update on targeted therapy and immunotherapy for metastatic colorectal cancer. Cells. (2024) 13:16. doi: 10.3390/cells13030245

PubMed Abstract | Crossref Full Text | Google Scholar

40. Tran K, Buchanan C, and Shepherd P. Evolution of molecular targets in melanoma treatment. Curr Pharm Design. (2020) 26:396–414. doi: 10.2174/1381612826666200130091318

PubMed Abstract | Crossref Full Text | Google Scholar

41. Titeux M, Izmiryan A, and Hovnanian A. The molecular revolution in cutaneous biology: emerging landscape in genomic dermatology: new mechanistic ideas, gene editing, and therapeutic breakthroughs. J Invest Dermatol. (2017) 137:e123. doi: 10.1016/j.jid.2016.08.038

PubMed Abstract | Crossref Full Text | Google Scholar

42. Sun L, Xu G, Li F, Yan P, Guo G, Chen Y, et al. Endoplasmic reticulum targeting nanoparticle for efficient anti-tumor immunotherapy. ACS Appl Nano Materials. (2025) 8:459–68. doi: 10.1021/acsanm.4c05728

Crossref Full Text | Google Scholar

43. Su H and Bu Z. Research progress of minimally invasive surgery for gastric cancer. China Cancer Res. (2023) 35:343–53. doi: 10.21147/j.issn.1000-9604.2023.04.02

PubMed Abstract | Crossref Full Text | Google Scholar

44. Shields CW, Wang LLW, Evans MA, and Mitragotri S. Materials for immunotherapy. Advanced Materials. (2020) 32:e1901633. doi: 10.1002/adma.201901633

PubMed Abstract | Crossref Full Text | Google Scholar

45. Tao Y, Li P, Feng C, and Cao Y. New insights into immune cells and immunotherapy for thyroid cancer. Immunol Investigations. (2023) 52:1039–64. doi: 10.1080/08820139.2023.2268656

PubMed Abstract | Crossref Full Text | Google Scholar

46. Tanriverdi O and Yildiz A. Current molecular and therapeutic advances in liposarcoma, rhabdomyosarcoma, leiomyosarcoma, synovial sarcoma, and angiosarcoma. J Oncol Pharm Pract. (2022) 28:635–45. doi: 10.1177/10781552211073139

PubMed Abstract | Crossref Full Text | Google Scholar

47. Sun H, Ren J, and Qu X. Carbon nanomaterials and DNA: from molecular recognition to applications. Acc Chem Res. (2016) 49:461–70. doi: 10.1021/acs.accounts.5b00515

PubMed Abstract | Crossref Full Text | Google Scholar

48. Shum E, Wang F, Kim S, Perez-Soler R, and Cheng H. Investigational therapies for squamous cell lung cancer: from animal studies to phase II trials. Expert Opin Investigational Drugs. (2017) 26:1. doi: 10.1080/13543784.2017.1302425

PubMed Abstract | Crossref Full Text | Google Scholar

49. Shin J, Park JW, Kim SY, Lee JH, Choi WS, and Kim HS. Strategies for overcoming immune evasion in bladder cancer. Int J Mol Sci. (2024) 25:20. doi: 10.3390/ijms25063105

PubMed Abstract | Crossref Full Text | Google Scholar

50. Shende S, Rathored J, and Budhbaware T. Role of metabolic transformation in cancer immunotherapy resistance: molecular mechanisms and therapeutic implications. Discover Oncol. (2025) 16:453. doi: 10.1007/s12672-025-02238-3

PubMed Abstract | Crossref Full Text | Google Scholar

51. Shi J, Wang K, Xiong Z, Yuan C, and Zhang X. Impact of inflammation and immunotherapy in renal cell carcinoma (Review). Oncol Lett. (2020) 20:1–1. doi: 10.3892/ol.2020.12135

PubMed Abstract | Crossref Full Text | Google Scholar

52. Shi J, Wang K, Xiong Z, Yuan C, Wang C, Cao Q, et al. Impact of inflammation and immunotherapy in renal cell carcinoma. Oncol Lett. (2020) 20:272. Review. doi: 10.3892/ol.2020.12135

PubMed Abstract | Crossref Full Text | Google Scholar

53. Shams M, Abdallah S, Alsadoun L, Hamid YH, Gasim R, and Hassan A. Oncological horizons: the synergy of medical and surgical innovations in cancer treatment. Cureus. (2023) 15:7. doi: 10.7759/cureus.49249

PubMed Abstract | Crossref Full Text | Google Scholar

54. Schvartsman G, Pinto NA, Bell D, and Ferrarotto R. Salivary gland tumors: Molecular characterization and therapeutic advances for metastatic disease. Head Neck. (2019) 41:239–47. doi: 10.1002/hed.25468

PubMed Abstract | Crossref Full Text | Google Scholar

55. Sham NO, Zhao L, Zhu Z, Roy TM, Xiao H, Bai Q, et al. Immunotherapy for non-small cell lung cancer: current agents and potential molecular targets. Anticancer Res. (2022) 42:3275–84. doi: 10.21873/anticanres.15816

PubMed Abstract | Crossref Full Text | Google Scholar

56. Shaikh S, Yadav DK, Bhadresha K, and Rawal RM. Integrated computational screening and liquid biopsy approach to uncover the role of biomarkers for oral cancer lymph node metastasis. Sci Rep. (2023) 13:14033. doi: 10.1038/s41598-023-41348-2

PubMed Abstract | Crossref Full Text | Google Scholar

57. Rouce RH and Rau RE. Chapter 67 - clinical manifestations and treatment of childhood acute lymphoblastic leukemia. In: Hematology Elsevier (2023) p. 1020–8. Available online at: https://linkinghub.elsevier.com/retrieve/pii/B9780323357623000652.

Google Scholar

58. Rosell R, Jain A, Codony-Servat J, Jantus-Lewintre E, Morrison B, Ginesta JB, et al. Biological insights in non-small cell lung cancer. Cancer Biol Med. (2023) 20:500–18. doi: 10.20892/j.issn.2095-3941.2023.0108

PubMed Abstract | Crossref Full Text | Google Scholar

59. Ricker CA, Meli K, and Allen EMV. Historical perspective and future directions: computational science in immuno-oncology. J Immunotherapy Cancer. (2024) 12:18. doi: 10.1136/jitc-2023-008306

PubMed Abstract | Crossref Full Text | Google Scholar

60. Matthias J and Zielinski CE. Shaping the diversity of Th2 cell responses in epithelial tissues and its potential for allergy treatment. Eur J Immunol. (2019) 49:1321–33. doi: 10.1002/eji.201848011

PubMed Abstract | Crossref Full Text | Google Scholar

61. Majd N, Dasgupta P, and Groot JD. Immunotherapy for neuro-oncology. Adv Exp Med Biol. (2020) 1342:233–258. doi: 10.1007/978-3-030-79308-1_7

PubMed Abstract | Crossref Full Text | Google Scholar

62. Maccagno M, Tapparo M, Saccu G, Rumiano L, Kholia S, Silengo L, et al. Emerging cancer immunotherapies: cutting-edge advances and innovations in development. Med Sci. (2024) 12:43. doi: 10.3390/medsci12030043

PubMed Abstract | Crossref Full Text | Google Scholar

63. Rogers J and Dasari A. Pharmacotherapy for unresectable metastatic colorectal cancer. Expert Opin pharmacotherapy. (2021) 23:211–220. doi: 10.1080/14656566.2021.1982895

PubMed Abstract | Crossref Full Text | Google Scholar

64. Reyners AKL, Broekman KE, Glaudemans AWJM, Brouwers AH, Arts HJG, van der Zee AGJ, et al. Molecular imaging in ovarian cancer. Ann Oncol Off J Eur Soc Med Oncol. (2016) 27 Suppl 1:i23. doi: 10.1093/annonc/mdw091

PubMed Abstract | Crossref Full Text | Google Scholar

65. Reschke R, Enk AH, and Hassel JC. Chemokines and cytokines in immunotherapy of melanoma and other tumors: from biomarkers to therapeutic targets. Int J Mol Sci. (2024) 25:17. doi: 10.3390/ijms25126532

PubMed Abstract | Crossref Full Text | Google Scholar

66. Rebuzzi SE, Zullo L, Rossi G, Grassi M, and Genova C. Novel emerging molecular targets in non-small cell lung cancer. Int J Mol Sci. (2021) 22:2625. doi: 10.3390/ijms22052625

PubMed Abstract | Crossref Full Text | Google Scholar

67. Kumari P, Ghosh E, and Shukla AK. Emerging approaches to GPCR ligand screening for drug discovery. Trends Mol Med. (2015) 21:687–701. doi: 10.1016/j.molmed.2015.09.002

PubMed Abstract | Crossref Full Text | Google Scholar

68. Prakash V, Wu S, Farrell A, Gao L, Darabi S, Perry C, et al. 559Molecular characterization of Merkel cell carcinoma and association with Merkel cell polyomavirus. J Immunotherapy Cancer. (2023) 11:1. doi: 10.1136/jitc-2023-SITC2023.0559

Crossref Full Text | Google Scholar

69. Ma YS, Liu JB, Wu TM, and Fu D. New therapeutic options for advanced hepatocellular carcinoma. Cancer control. (2020) 27:107327482094597. doi: 10.1177/1073274820945975

PubMed Abstract | Crossref Full Text | Google Scholar

70. Lindsay CR, Shaw EC, Moore DA, Rassl D, Jamal-Hanjani M, Steele N, et al. Large cell neuroendocrine lung carcinoma: consensus statement from The British Thoracic Oncology Group and the Association of Pulmonary Pathologists. Br J Cancer. (2021) 125:1210–1216. doi: 10.1038/s41416-021-01407-9

PubMed Abstract | Crossref Full Text | Google Scholar

71. Lin-Lin D and Dong-Dong LI. Progress in clinical research of small molecule tumor immunotherapy drugs. Chin J New Drugs Clin Remedies. (2019) 38:455–63. doi: 10.14109/j.cnki.xyylc.2019.08.002

Crossref Full Text | Google Scholar

72. Kou F and Liu W. Advances in the diagnosis and treatment of cancer of unknown primary in the genomic era. Chin J Clin Oncol. (2018) 45:427–32. doi: 10.3969/j.issn.1000-8179.2018.08.051

Crossref Full Text | Google Scholar

73. Peng X, Cai Z, Chen D, Ye F, and Hong L. Prognostic significance and immune characteristics of APOE in gastric cancer. Aging. (2023) 15:14. doi: 10.18632/aging.205265

PubMed Abstract | Crossref Full Text | Google Scholar

74. Noreng S, Li T, and Payandeh J. Structural pharmacology of voltage-gated sodium channels. J Mol Biol. (2021) 433:166967. doi: 10.1016/j.jmb.2021.166967

PubMed Abstract | Crossref Full Text | Google Scholar

75. Muzyka L, Goff NK, Choudhary N, and Koltz MT. Systematic review of molecular targeted therapies for adult-type diffuse glioma: an analysis of clinical and laboratory studies. Int J Mol Sci. (2023) 24:10456. doi: 10.3390/ijms241310456

PubMed Abstract | Crossref Full Text | Google Scholar

76. Moshrefiravasjani R, Kamrani A, Nazari N, Jafari F, Nasiri H, Jahanban-Esfahlan R, et al. Exosome-mediated tumor metastasis: Biology, molecular targets and immuno-therapeutic options. Pathol Res Pract. (2024) 254:155083. doi: 10.1016/j.prp.2023.155083

PubMed Abstract | Crossref Full Text | Google Scholar

77. Mortezaee K and Najafi M. Immune system in cancer radiotherapy: Resistance mechanisms and therapy perspectives. Crit Rev Oncology/Hematology. (2021) 157:103180. doi: 10.1016/j.critrevonc.2020.103180

PubMed Abstract | Crossref Full Text | Google Scholar

78. Montal R, Sia D, Montironi C, Leow WQ, and Llovet JM. Molecular classification and therapeutic targets in extrahepatic cholangiocarcinoma. J Hepatol. (2020) 73:315–27. doi: 10.1016/j.jhep.2020.03.008

PubMed Abstract | Crossref Full Text | Google Scholar

79. Jun W, Yan S, Yue LI, and Ya Z. Recent progress in immune checkpoint molecules in Plasmodium infection and immunity. Chin J Parasitol Parasitic Dis. (2019) 37:472–80.

Google Scholar

80. Jiao R, Lin X, Zhang Q, Zhang Y, Qin W, Yang Q, et al. Anti-tumor immune potentiation targets-engineered nanobiotechnologies: Design principles and applications. Prog Materials Sci. (2024) 142:31. doi: 10.1016/j.pmatsci.2023.101230

Crossref Full Text | Google Scholar

81. Jiang A, He W, and Yao Y. Editorial: Overcoming obstacles of cancer immunotherapy: the important role of emerging nanomedicine. Front Oncol. (2024) 14:1406244. doi: 10.3389/fonc.2024.1406244

PubMed Abstract | Crossref Full Text | Google Scholar

82. Hussein S, Qurbani K, Hamzah H, Ali S, and Ahmed SK. Biotechnology breakthroughs: Revolutionizing oral cancer treatment. Oral Oncol Rep. (2024) 10:100404. doi: 10.1016/j.oor.2024.100404

Crossref Full Text | Google Scholar

83. Mcnally L, Wu S, Hodges K, Oberley M, Wallbillich JJ, Jones NL, et al. Molecular profiling of gestational trophoblastic neoplasia: Identifying therapeutic targets. Gynecologic Oncol. (2024) 184:111–6. doi: 10.1016/j.ygyno.2024.01.033

PubMed Abstract | Crossref Full Text | Google Scholar

84. Mao XWM. Progress in the understanding of the immune microenvironment and immunotherapy in Malignant pleural mesothelioma. Curr Drug targets-The Int J timely in-depth Rev Drug Targets. (2020) 21:1606–12. Bentham Science. doi: 10.2174/1389450121666200719011234

PubMed Abstract | Crossref Full Text | Google Scholar

85. Lu X, Li Y, Li Y, Zhang X, Shi J, Feng H, et al. Advances of multi-omics applications in hepatic precancerous lesions and hepatocellular carcinoma: The role of extracellular vesicles. Front Mol Biosci. (2023) 10:17. doi: 10.3389/fmolb.2023.1114594

PubMed Abstract | Crossref Full Text | Google Scholar

86. Llovet JM, Montal R, Sia D, and Finn RS. Molecular therapies and precision medicine for hepatocellular carcinoma. Nat Rev Clin Oncol. (2018) 15:599–616. doi: 10.1038/s41571-018-0073-4

PubMed Abstract | Crossref Full Text | Google Scholar

87. Liu Y. Advances in molecular pathology of obstructive sleep apnea. Molecules. (2022) 27:8422. doi: 10.3390/molecules27238422

PubMed Abstract | Crossref Full Text | Google Scholar

88. Liu J and Shu J. Immunotherapy and targeted therapy for cholangiocarcinoma: Artificial intelligence research in imaging. Crit Rev Oncol Hematol. (2024) 194:9. doi: 10.1016/j.critrevonc.2023.104235

PubMed Abstract | Crossref Full Text | Google Scholar

89. Hua-Li L, Bin XU, Guang H, Hospital R, and University W. Progress of EGFR-TKIs in non-small cell lung cancer. China Cancer. (2018) 27:285–93. doi: 10.11735/j.issn.1004-0242.2018.04.A008

Crossref Full Text | Google Scholar

90. Lin CY, Tsai CL, Chao A, Lee LY, Chen WC, Tang YH, et al. Nucleophosmin/B23 promotes endometrial cancer cell escape from macrophage phagocytosis by increasing CD24 expression. J Mol Med. (2021) 99:1125–37. doi: 10.1007/s00109-021-02079-x

PubMed Abstract | Crossref Full Text | Google Scholar

91. Lim EA, Drake CG, and Mintz A. Molecular imaging for cancer immunotherapy. Immuno-Oncology Technol. (2020) 5:10–21. doi: 10.1016/j.iotech.2020.03.001

PubMed Abstract | Crossref Full Text | Google Scholar

92. Liberini V, Laudicella R, Capozza M, Huellner MW, Burger IA, Baldari S, et al. The Future of Cancer Diagnosis, Treatment and Surveillance: A Systemic Review on Immunotherapy and Immuno-PET Radiotracers. Molecules. (2021) 26:2201. doi: 10.3390/molecules26082201

PubMed Abstract | Crossref Full Text | Google Scholar

93. He D, Bai R, Chen N, and Cui J. Immune status and combined immunotherapy progression in Kirsten rat sarcoma viral oncogene homolog(KRAS)-mutant tumors. Chin J Cancer Res. (2024) 36:421–41. doi: 10.21147/j.issn.1000-9604.2024.04.06

PubMed Abstract | Crossref Full Text | Google Scholar

94. Haiying B, Jing W, Dan W, Weihua Z, University SM, and Hematology DO. Progress of dendritic cell vaccine in immunotherapy of acute myeloid leukemia. Med Recapitulate. (2019) 25:1900–4.

Google Scholar

95. Fenton OS, Olafson KN, Pillai PS, Mitchell MJ, and Langer R. Advances in biomaterials for drug delivery. Advanced Materials. (2018) 30:1705328. doi: 10.1002/adma.201705328

PubMed Abstract | Crossref Full Text | Google Scholar

96. Fan J, Gao Q, and Huang R. Research frontiers in precision therapy for liver cancer. Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chin J Hepatol. (2020) 28:897–900. doi: 10.3760/cma.j.cn501113-20201103-00596

PubMed Abstract | Crossref Full Text | Google Scholar

97. Liao C, Hu L, and Zhang Q. Von Hippel–Lindau protein signalling in clear cell renal cell carcinoma. Nat Rev Urol. (2024) 21:14. doi: 10.1038/s41585-024-00876-w

PubMed Abstract | Crossref Full Text | Google Scholar

98. Liang Z, Khawar M, Liang J, and Sun H. Bio-conjugated quantum dots for cancer research: detection and imaging. Front Oncol. (2021) 11:749970. doi: 10.3389/fonc.2021.749970

PubMed Abstract | Crossref Full Text | Google Scholar

99. Liang Y, Zhang H, Song X, and Yang Q. Metastatic heterogeneity of breast cancer: Molecular mechanism and potential therapeutic targets. Semin Cancer Biol. (2020) 60:14–27. doi: 10.1016/j.semcancer.2019.08.012

PubMed Abstract | Crossref Full Text | Google Scholar

100. Liang R, Abudurexiti N, Wu J, Ling J, Peng Z, Yuan H, et al. Exosomes and miRNAs in cardiovascular diseases and transcatheter pulmonary valve replacement: advancements, gaps and perspectives. Int J Mol Sci. (2024) 25. doi: 10.3390/ijms252413686

PubMed Abstract | Crossref Full Text | Google Scholar

101. Li Y, Jin H, Li Q, Shi L, Mao Y, and Zhao L. The role of RNA methylation in tumor immunity and its potential in immunotherapy. Mol Cancer. (2024) 23. doi: 10.1186/s12943-024-02041-8

PubMed Abstract | Crossref Full Text | Google Scholar

102. Leng X, Nivelo L, Buchness L, Ristin S, Rafie C, Villarino A, et al. Reinvigorated exhausted T cells exhibit distinct features within 24 hours after PD-1 therapy. J Immunol. (2023) 210:1. doi: 10.4049/jimmunol.210.Supp.68.16

Crossref Full Text | Google Scholar

103. El Demerdash N, Kedda J, Ram N, Brem H, and Tyler B. Novel therapeutics for brain tumors: current practice and future prospects. Expert Opin Drug delivery. (2020) 17. doi: 10.1080/17425247.2019.1676227

PubMed Abstract | Crossref Full Text | Google Scholar

104. Dai Z, Zhang J, Wu Q, Fang H, and Wang D. Intestinal microbiota: A new force in cancer immunotherapy. Cell Communication Signaling. (2020) 18. doi: 10.1186/s12964-020-00599-6

PubMed Abstract | Crossref Full Text | Google Scholar

105. Lee CY, Kim H, Lee H, Randall T, Ly A, Lee H, et al. Abstract 6082: Rapid on-site nucleic acid detection using CRISPR and digital signal processing for portable and integrated cervical cancer screening in low resource settings. Cancer Res. (2024) 84:4. doi: 10.1158/1538-7445.AM2024-6082

Crossref Full Text | Google Scholar

106. Krfer J, Lordick F, and Hacker U. Molecular targets for gastric cancer treatment and future perspectives from a clinical and translational point of view. Cancers. (2021) 13. doi: 10.3390/cancers13205216

PubMed Abstract | Crossref Full Text | Google Scholar

107. Kong X, Zhang J, Chen S, Wang X, Xi Q, Shen H, et al. Immune checkpoint inhibitors:breakthroughs in cancer treatment. Cancer Biol Med. (2024) 21:451–72. doi: 10.20892/j.issn.2095-3941.2024.0055

PubMed Abstract | Crossref Full Text | Google Scholar

108. Kilmister EJ and Tan ST. Cancer stem cells and the renin-angiotensin system in the tumor microenvironment of melanoma: implications on current therapies. Int J Mol Sci. (2025) 26. doi: 10.3390/ijms26031389

PubMed Abstract | Crossref Full Text | Google Scholar

109. Khadela A, Postwala H, Rana D, Dave H, Ranch K, and Boddu SHS. A review of recent advances in the novel therapeutic targets and immunotherapy for lung cancer. Med Oncol. (2023) 40. doi: 10.1007/s12032-023-02005-w

PubMed Abstract | Crossref Full Text | Google Scholar

110. Kelley MR. DNA Repair in Cancer Therapy: Molecular Targets and Clinical Applications (Second edition.). Academic Press (2016). https://www.sciencedirect.com/science/book/9780128035825

Google Scholar

111. Kadian LK, Verma D, Lohani N, Yadav R, Ranga S, Gulshan G, et al. Long non-coding RNAs in cancer: multifaceted roles and potential targets for immunotherapy. Mol Cell Biochem. (2024) 479:26. doi: 10.1007/s11010-024-04933-1

PubMed Abstract | Crossref Full Text | Google Scholar

112. Johnson AG, Grosely R, Petrov AN, and Puglisi JD. Dynamics of IRES-mediated translation. Philos Trans R Soc Lond B Biol. (2017) 372:20160177. doi: 10.1098/rstb.2016.0177

PubMed Abstract | Crossref Full Text | Google Scholar

113. Jie Z. Rote of ubiquitin signaling in modulating dendritic cell function. Adv Exp Med Biol. (2024) 1466:101–11. doi: 10.1007/978-981-97-7288-9_7

PubMed Abstract | Crossref Full Text | Google Scholar

114. Cheng W, Zhu N, Wang J, and Yang R. A role of gut microbiota metabolites in HLA-E and NKG2 blockage immunotherapy against tumors: new insights for clinical application. Front Immunol. (2024) 15:1331518. doi: 10.3389/fimmu.2024.1331518

PubMed Abstract | Crossref Full Text | Google Scholar

115. Cheng H, Chen W, Lin YB, Zhang J, Song X, and Zhang D. Signaling pathways involved in the biological functions of dendritic cells and their implications for disease treatment. Mol Biomedicine. (2023) 4. doi: 10.1186/s43556-023-00125-3

PubMed Abstract | Crossref Full Text | Google Scholar

116. Jagat K, Kislay R, and Rupinder K. Molecular targets in arthritis and recent trends in nanotherapy. Int J Nanomedicine. (2015) 10. doi: 10.2147/IJN.S89156

PubMed Abstract | Crossref Full Text | Google Scholar

117. Hutton D. Endeavouring to improve glioblastoma multiforme patient prognosis – a literature review of biomarkers and novel therapeutic approaches. Cambridge Med J. (2021). doi: 10.7244/cmj.2021.02.001

Crossref Full Text | Google Scholar

118. Huang F, Yaermaimaiti D, Ding G, Zhao L, Zhou J, and Wu S. A PTEN-autophagy risk model for the prediction of prognosis and immune microenvironment in hepatocellular carcinoma. J Oncol. (2023) 2023:2973480. doi: 10.1155/2023/2973480

PubMed Abstract | Crossref Full Text | Google Scholar

119. Bockman JM. Biotech and breakthroughs in immuno-oncology. Curr Problems Cancer. (2020) 47. doi: 10.1016/j.ucl.2020.07.006

PubMed Abstract | Crossref Full Text | Google Scholar

120. Barden MM and Omuro AM. Top advances of the year: Neuro-oncology. Cancer. (2023) 129:1467–72. doi: 10.1002/cncr.34711

PubMed Abstract | Crossref Full Text | Google Scholar

121. He C. Activating invasion and metastasis in small cell lung cancer: role of the tumour immune microenvironment and mechanisms of vasculogenesis, epithelial-mesenchymal transition, cell migration, and organ tropism. Cancer Rep. (2024) 7. doi: 10.1002/cnr2.70018

PubMed Abstract | Crossref Full Text | Google Scholar

122. Gu T, Qi H, Wang J, Sun L, and Hu H. Identification of T cell dysfunction molecular subtypes and exploration of potential immunotherapy targets in BRAF V600E-mutant colorectal cancer. Discover Oncol. (2025) 16. doi: 10.1007/s12672-025-01930-8

PubMed Abstract | Crossref Full Text | Google Scholar

123. Gouda NA, Elkamhawy A, and Cho J. Emerging therapeutic strategies for parkinson’s disease and future prospects: A 2021 update. Biomedicines. (2022) 10. doi: 10.3390/biomedicines10020371

PubMed Abstract | Crossref Full Text | Google Scholar

124. Gay CM, Owonikoko TK, Byers LA, Choudhury NJ, Ahmed S, Cain Z, et al. Multidimensional analysis of B7 homolog 3 (B7-H3) RNA expression in small-cell lung cancer (SCLC) molecular subtypes. J Clin Oncol. (2024) 42:8088–8. doi: 10.1200/JCO.2024.42.16_suppl.8088

PubMed Abstract | Crossref Full Text | Google Scholar

125. Galambus J and Tsai K. Molecular and immune targets in cutaneous squamous cell carcinoma. Mol Carcinogenesis. (2022) 62:38–51. doi: 10.1002/mc.23451

PubMed Abstract | Crossref Full Text | Google Scholar

126. Bai RL, Wang NY, Zhao LL, Zhang YF, and Cui JW. Diverse and precision therapies open new horizons for patients with advanced pancreatic ductal adenocarcinoma. Int J Hepatobiliary Pancreatic Diseases. (2022) 21:15. doi: 10.1016/j.hbpd.2021.08.012

PubMed Abstract | Crossref Full Text | Google Scholar

127. Bai RL, Chen NF, Li LY, and Cui JW. A brand new era of cancer immunotherapy: breakthroughs and challenges. Chin Med J. (2021) 134. doi: 10.1097/CM9.0000000000001490

PubMed Abstract | Crossref Full Text | Google Scholar

128. Fontaine F and Overman J. Fran?Ois M: Pharmacological manipulation of transcription factor protein-protein interactions: opportunities and obstacles. Cell Regeneration. (2015) 4:1–12. doi: 10.1186/s13619-015-0015-x

PubMed Abstract | Crossref Full Text | Google Scholar

129. Felix S, Luca B, Sebastian B, David C, Roland G, Jenkinson MD, et al. European Association of Neuro-Oncology guideline on molecular testing of meningiomas for targeted therapy selection. Neuro-Oncology. (2024) 27:869–83. doi: 10.1093/neuonc/noae253

PubMed Abstract | Crossref Full Text | Google Scholar

130. Famakinde DO. Treading the path towards genetic control of snail resistance to schistosome infection. Trop Med Infect Dis. (2018) 3:86. doi: 10.3390/tropicalmed3030086

PubMed Abstract | Crossref Full Text | Google Scholar

131. Orlando E, Aebersold DM, Medová M, and Zimmer Y. Oncogene addiction as a foundation of targeted cancer therapy: The paradigm of the MET receptor tyrosine kinase - ScienceDirect. Cancer Lett. (2019) 443:189–202. doi: 10.1016/j.canlet.2018.12.001

PubMed Abstract | Crossref Full Text | Google Scholar

132. Araghi M, Mannani R, Maleki AH, Hamidi A, Rostami S, Safa SH, et al. Recent advances in non-small cell lung cancer targeted therapy; an update review. Cancer Cell Int. (2023) 23. doi: 10.1186/s12935-023-02990-y

PubMed Abstract | Crossref Full Text | Google Scholar

133. Effer B, Perez I, Ulloa D, Mayer C, Muoz F, Bustos D, et al. Therapeutic targets of monoclonal antibodies used in the treatment of cancer: current and emerging. Biomedicines. (2023) 11. doi: 10.3390/biomedicines11072086

PubMed Abstract | Crossref Full Text | Google Scholar

134. Dhanasopon AP. New molecular and immunologic targets of therapy for esophageal cancer and the prospects for ongoing and future clinical trials. J Surg Oncol. (2023) 127:239–43. doi: 10.1002/jso.27194

PubMed Abstract | Crossref Full Text | Google Scholar

135. Reichstein D. New concepts in the molecular understanding of uveal melanoma. Curr Opin Ophthalmol. (2017) 28:219–27. doi: 10.1097/ICU.0000000000000366

PubMed Abstract | Crossref Full Text | Google Scholar

136. Ali A. Advances in non-small cell lung cancer (NSCLC) treatment—A paradigm shift in oncology. Pharmaceuticals. (2024) 17:4. doi: 10.3390/ph17020246

PubMed Abstract | Crossref Full Text | Google Scholar

137. Ahmad MZ, Ahmad J, Umar A, Abdel-Wahab BA, Lahiq AA, Khan NH, et al. Nanomaterials in cancer immunotherapy: A spotlight on breast cancer. Sci advanced materials. (2023) 15:285–318. doi: 10.1166/sam.2023.4438

Crossref Full Text | Google Scholar

138. Crispen PL and Kusmartsev S. Mechanisms of immune evasion in bladder cancer. Cancer Immunology Immunotherapy. (2020) 69:3–14. doi: 10.1007/s00262-019-02443-4

PubMed Abstract | Crossref Full Text | Google Scholar

139. Crian A, Semenescu LE, Miretean CC, Mitrea A, Iancu IR, and Teodor Iancu DP. FOLFIRINOX in adjuvant and metastatic settings for pancreatic cancer in the era of precision oncology. Oncolog-Hematolog. (2021).

Google Scholar

140. Cooksey LC, Friesen DC, Mangan ED, and Mathew PA. Prospective molecular targets for natural killer cell immunotherapy against glioblastoma multiforme. Cells (2073-4409). (2024) 13. doi: 10.3390/cells13181567

PubMed Abstract | Crossref Full Text | Google Scholar

141. Cho C, Woodard G, Lopez-Giraldez F, Badri T, Vesely M, and Chen L. 167Comprehensive profiling of cancer-associated fibroblasts in CD8+T cell-exclusive non-small cell lung cancer tumor microenvironments using the Nanostring GeoMX digital spatial profiler. J Immunotherapy Cancer. (2022) 10:1.

Google Scholar

142. Chen Y, Han K, Liu Y, Wang Q, Wu Y, Chen S, et al. Identification of effective diagnostic genes and immune cell infiltration characteristics in small cell lung cancer by integrating bioinformatics analysis and machine learning algorithms. Saudi Med J. (2024) 45. doi: 10.15537/smj.2024.45.8.20240170

PubMed Abstract | Crossref Full Text | Google Scholar

143. Chalif J, Kistenfeger Q, Fulton J, Morton M, Devengencie I, Weldemichael W, et al. Diagnosis and management of gastric-type endocervical adenocarcinoma: A case report and review of the literature. Gynecologic Oncol. (2024) 185:8. doi: 10.1016/j.ygyno.2024.02.024

PubMed Abstract | Crossref Full Text | Google Scholar

144. Castro JVD, Gonalves CS, Hormigo A, and Costa BM. Exploiting the complexities of glioblastoma stem cells: insights for cancer initiation and therapeutic targeting. Int J Mol Sci. (2020) 21:5278. doi: 10.3390/ijms21155278

PubMed Abstract | Crossref Full Text | Google Scholar

145. Bouché M, Hognon C, Grandemange S, Monari A, and Gros PC. Recent advances in iron-complexes as drug candidates for cancer therapy: reactivity, mechanism of action and metabolites. Dalton Trans. (2020) 49:11451–11466. doi: 10.1039/d0dt02135k

PubMed Abstract | Crossref Full Text | Google Scholar

146. Heng WS, Gosens R, and Kruyt FAE. Lung cancer stem cells: origin, features, maintenance mechanisms and therapeutic targeting - ScienceDirect. Biochem Pharmacol. (2019) 160:121–33. doi: 10.1016/j.bcp.2018.12.010

PubMed Abstract | Crossref Full Text | Google Scholar

147. Yuan, Zhang, Huan, and Chen. Advances in the application of bacteria in tumor therapy. Hans J Biomedicine. (2024) 14:221–8. doi: 10.12677/hjbm.2024.142024

Crossref Full Text | Google Scholar

148. Boilève A, Faron M, Fodil-Cherif S, Bayle A, Lamartina L, Planchard D, et al. Molecular profiling and target actionability for precision medicine in neuroendocrine neoplasms: real-world data. Eur J Cancer. (2023) 186:11. doi: 10.1016/j.ejca.2023.03.024

PubMed Abstract | Crossref Full Text | Google Scholar

149. Bartoletti M, Musacchio L, Giannone G, Tuninetti V, Bergamini A, Scambia G, et al. Emerging molecular alterations leading to histology-specific targeted therapies in ovarian cancer beyond PARP inhibitors. Cancer Treat Rev. (2021) 101:102298. doi: 10.1016/j.ctrv.2021.102298

PubMed Abstract | Crossref Full Text | Google Scholar

150. Bai L, Xu J, Zeng L, Zhang L, and Zhou F. A review of HSV pathogenesis, vaccine development, and advanced applications. Mol Biomedicine. (2024) 5. doi: 10.1186/s43556-024-00199-7

PubMed Abstract | Crossref Full Text | Google Scholar

151. Baghy K, Ladányi A, Reszegi A, and Kovalszky I. Insights into the tumor microenvironment—Components, functions and therapeutics. Int J Mol Sci. (2023) 24. doi: 10.3390/ijms242417536

PubMed Abstract | Crossref Full Text | Google Scholar

152. Babu S, Krishnan M, and Bari ABA. (2024). The Role of Artificial Intelligence in Enhancing Small Molecule-Based Cancer Treatments.

Google Scholar

153. Neupane SP. Psychoneuroimmunology: the new frontier in suicide research. Brain Behavior Immun - Health. (2021) 17:100344. doi: 10.1016/j.bbih.2021.100344

PubMed Abstract | Crossref Full Text | Google Scholar

154. Ashaq MS, Zhou Q, Li Z, and Zhao B. Novel targeted therapies in chronic myeloid leukemia. Microelectronics J. (2024) 2. doi: 10.1016/j.pscia.2024.100052

Crossref Full Text | Google Scholar

155. Anel A, Pardo J, and Villalba M. Editorial: the natural killer cell interactome in the tumor microenvironment: basic concepts and clinical application. Front Immunol. (2020) 11:872. doi: 10.3389/fimmu.2020.00872

PubMed Abstract | Crossref Full Text | Google Scholar

156. An J and Zhang X. Crbn-based molecular Glues: Breakthroughs and perspectives. Bioorganic Medicinal Chem. (2024) 104:11. doi: 10.1016/j.bmc.2024.117683

PubMed Abstract | Crossref Full Text | Google Scholar

157. Ferdous N, Reza MN, Islam MS, Hossain Emon MT, Mohiuddin AKM, and Hossain MU. Newly designed analogues from SARS-CoV inhibitors mimicking the druggable properties against SARS-CoV-2 and its novel variants. RSC Adv. (2021) 11:31460–31476. doi: 10.1039/d1ra04107j

PubMed Abstract | Crossref Full Text | Google Scholar

158. Xiao Z and Puré E. Imaging of T-cell responses in the context of cancer immunotherapy. Cancer Immunol Res. (2021) 9:490–502. doi: 10.1158/2326-6066.CIR-20-0678

PubMed Abstract | Crossref Full Text | Google Scholar

159. Wondergem NE, Nijenhuis DNLM, Poell JB, RenéLeemans C, Brakenhoff RH, and Ven RVD. At the crossroads of molecular biology and immunology: molecular pathways for immunological targeting of head and neck squamous cell carcinoma. Front Oral Health. (2021) 2:647980. doi: 10.3389/froh.2021.647980

PubMed Abstract | Crossref Full Text | Google Scholar

160. Wang Z, Kim J, Zhang P, Achi JMG, Jiang Y, and Rong L. Current therapy and development of therapeutic agents for lung cancer. Cell Insight. (2022) 1. doi: 10.1016/j.cellin.2022.100015

PubMed Abstract | Crossref Full Text | Google Scholar

161. Wang Y, Zhu T, Shi Q, Zhu G, Zhu S, and Hou F. Tumor-draining lymph nodes: opportunities, challenges, and future directions in colorectal cancer immunotherapy. J Immunotherapy Cancer. (2024) 12:11. doi: 10.1136/jitc-2023-008026

PubMed Abstract | Crossref Full Text | Google Scholar

162. Wang S. Construction of T-cell-related prognostic risk models and prediction of tumor immune microenvironment regulation in pancreatic adenocarcinoma via integrated analysis of single-cell RNA-seq and bulk RNA-seq. Int J Mol Sci. (2025) 26.

PubMed Abstract | Google Scholar

163. Siegel PM, Nystrm H, and Brodt P. Chapter 2 - The tumor microenvironment of colorectal cancer liver metastases: Molecular mediators and future therapeutic targets. In: Contemporary Management of Metastatic Colorectal Cancer Ejaz A and Pawlik TM, Eds. Academic Press (2022) pp. 17–44.

Google Scholar

164. Shen M, Jiang X, Peng Q, Oyang L, Ren Z, Wang J, et al. The cGASSTING pathway in cancer immunity: mechanisms, challenges, and therapeutic implications. J Hematol Oncol. (2025) 18:1–19.

Google Scholar

165. Rozenberg JM, Filkov GI, Trofimenko AV, Karpulevich EA, Parshin VD, Royuk VV, et al. Biomedical applications of non-small cell lung cancer spheroids. Front Oncol. (2021) 11:791069. doi: 10.3389/fonc.2021.791069

PubMed Abstract | Crossref Full Text | Google Scholar

166. Rosell R, Karachaliou N, and Arrieta O. Novel molecular targets for the treatment of lung cancer. Curr Opin Oncol. (2020) 32:37–43. doi: 10.1097/CCO.0000000000000590

PubMed Abstract | Crossref Full Text | Google Scholar

167. Nelson B, Hong A, and Jana B. Elucidation of novel molecular targets for therapeutic strategies in urothelial carcinoma: A literature review. Front Oncol. (2021) 11:705294. doi: 10.3389/fonc.2021.705294

PubMed Abstract | Crossref Full Text | Google Scholar

168. Makarova AO, Svirshchevskaya EV, Titov MM, Deyev SM, and Kholodenko RV. Prospects for the use of antibody-drug conjugates in cancer therapy. Russian J Bioorganic Chem. (2025) 51:556–73. doi: 10.1134/S1068162024605597

Crossref Full Text | Google Scholar

169. Ma TZ, Liu LY, Zeng YL, Ding K, Liu W, Xiong X, et al. G-quadruplex-guided bifunctional platinum complexes induce multiple pyroptosis pathways for antitumor therapy. Inorganic Chem Front. (2025) 12. doi: 10.1039/D4QI02098G

Crossref Full Text | Google Scholar

170. Lyu G, Dai L, Deng X, Liu X, Guo Y, Zhang Y, et al. Integrative analysis of cuproptosis-related mitochondrial depolarisation genes for prognostic prediction in non-small cell lung cancer. J Cell Mol Med. (2025) 29. doi: 10.1111/jcmm.70438

PubMed Abstract | Crossref Full Text | Google Scholar

171. Lv K, Li X, Yu H, Chen X, Zhang M, and Wu X. Selection of new immunotherapy targets for NK/T cell lymphoma. Am J Trans Res. (2020) 12:7034–47.

PubMed Abstract | Google Scholar

172. Lin X and Lin X. Regulate PD-L1’s membrane orientation thermodynamics with hydrophobic nanoparticles. Biomaterials Sci. (2025) 13. doi: 10.1039/D4BM01469C

PubMed Abstract | Crossref Full Text | Google Scholar

173. Liberini V, Laudicella R, Capozza M, Huellner MW, and Deandreis D. The future of cancer diagnosis, treatment and surveillance: A systemic review on immunotherapy and immuno-PET radiotracers. Molecules. (2021) 26:2201. doi: 10.3390/molecules26082201

PubMed Abstract | Crossref Full Text | Google Scholar

174. Li Y, Sharma A, Hoffmann MJ, Skowasch D, Essler M, Weiher H, et al. Discovering single cannabidiol or synergistic antitumor effects of cannabidiol and cytokine-induced killer cells on non-small cell lung cancer cells. Front Immunol. (2024) 15:1268652. doi: 10.3389/fimmu.2024.1268652

PubMed Abstract | Crossref Full Text | Google Scholar

175. Li S, Yu Y, Xu Y, Zhou Y, Huang J, and Jia J. Clinicopathological characteristics and the relationship of PD-L1 status, tumor mutation burden, and microsatellite instability in patients with esophageal carcinoma. BMC Cancer. (2025) 25:1–9. doi: 10.1186/s12885-025-13938-y

PubMed Abstract | Crossref Full Text | Google Scholar

176. Lam M, Lum C, Latham S, Smith ST, and Segelov E. Refractory metastatic colorectal cancer: current challenges and future prospects. Cancer Manage Res. (2020) 12:5819–30. doi: 10.2147/CMAR.S213236

PubMed Abstract | Crossref Full Text | Google Scholar

177. Kwon S, Iba M, Kim C, and Masliah E. Immunotherapies for aging-related neurodegenerative diseases—Emerging perspectives and new targets. Neurotherapeutics. (2020) 17:935–54. doi: 10.1007/s13311-020-00853-2

PubMed Abstract | Crossref Full Text | Google Scholar

178. Kumar M, Nanga R, and Chawla S. Editorial: structural, metabolic, and physiologic MR imaging to study glioblastomas. Front Neurol. (2022) 13. doi: 10.3389/fneur.2022.887027

PubMed Abstract | Crossref Full Text | Google Scholar

179. Ivey A, Pratt H, and Boone BA. Molecular pathogenesis and emerging targets of gastric adenocarcinoma. J Surg Oncol. (2022) 125:1079–95. doi: 10.1002/jso.26874

PubMed Abstract | Crossref Full Text | Google Scholar

180. Huang S, Shi J, Shen J, and Fan X. Metabolic reprogramming of neutrophils in the tumor microenvironment: Emerging therapeutic targets. Cancer Lett. (2025) 612. doi: 10.1016/j.canlet.2025.217466

PubMed Abstract | Crossref Full Text | Google Scholar

181. Huang BJ, Smith JL, Shaw TI, Furlan SN, Ries RE, Leonti AR, et al. Integrated transcriptomics and proteomics identifies therapeutic targets in pediatric acute myeloid leukemia. Blood. (2021) 138:1296. doi: 10.1182/blood-2021-153970

Crossref Full Text | Google Scholar

182. Han J and Poma A. Molecular targets for antibody-based anti-biofilm therapy in infective endocarditis. Polymers. (2022) 14:26. doi: 10.3390/polym14153198

PubMed Abstract | Crossref Full Text | Google Scholar

183. Gambardella V, Castillo J, Tarazona N, Gimeno-Valiente F, Martínez-Ciarpaglini C, Cabeza-Segura M, et al. The role of tumor-associated macrophages in gastric cancer development and their potential as a therapeutic target. Cancer Treat Rev. (2020) 86:102015. doi: 10.1016/j.ctrv.2020.102015

PubMed Abstract | Crossref Full Text | Google Scholar

184. Frassineti GL. Molecular targets and emerging therapies for advanced gallbladder cancer. Cancers. (2021) 13.

PubMed Abstract | Google Scholar

185. Ferini G, Palmisciano P, Forte S, Viola A, Martorana E, Parisi S, et al. Advanced or metastatic cutaneous squamous cell carcinoma: the current and future role of radiation therapy in the era of immunotherapy. Cancers. (2022) 14. doi: 10.3390/cancers14081871

PubMed Abstract | Crossref Full Text | Google Scholar

186. Falcone I, Conciatori F, Bazzichetto C, Ferretti G, and Milella M. Tumor microenvironment: implications in melanoma resistance to targeted therapy and immunotherapy. Cancers. (2020) 12:2870. doi: 10.3390/cancers12102870

PubMed Abstract | Crossref Full Text | Google Scholar

187. Elkrief A and Alcindor T. Molecular targets and novel therapeutic avenues in soft-tissue sarcoma. Curr Oncol. (2020) 27.

Google Scholar

188. Dey DK, Krause D, Rai R, Choudhary S, Dockery LE, and Chandra V. The role and participation of immune cells in the endometrial tumor microenvironment. Pharmacol Ther. (2023) 251:16. doi: 10.1016/j.pharmthera.2023.108526

PubMed Abstract | Crossref Full Text | Google Scholar

189. Demory A and Nault JC. Molecular perspectives for the treatment of hepatocellular carcinoma. Acta Gastro-Enterologica Belgica. (2020) 83:309–12.

PubMed Abstract | Google Scholar

190. Cocorocchio E. Novel biomarkers and druggable targets in advanced melanoma. Cancers. (2021) 14.

PubMed Abstract | Google Scholar

191. Cherian A, Vadivel V, Thiruganasambandham S, and Madhavankutty S. Phytocompounds and their molecular targets in immunomodulation: a review. J basic Clin Physiol Pharmacol. (2021) 34:577–90. doi: 10.1515/jbcpp-2021-0172

PubMed Abstract | Crossref Full Text | Google Scholar

192. Cavalluzzo B, Mauriello A, Ragone C, Manolio C, Tornesello ML, Buonaguro FM, et al. Novel molecular targets for hepatocellular carcinoma. Cancers. (2021) 14. doi: 10.3390/cancers14010140

PubMed Abstract | Crossref Full Text | Google Scholar

193. Cascante-Estepa N, Mayrhofer S, and Enzmann H. P04.03Cancer immunotherapies, companion diagnostics and precision medicine. J Immunotherapy Cancer. (2022) 10:2.

Google Scholar

194. Anderson D and Cooke DT. Metastatic Primary Lung Cancer. In: Randall RL (eds) Metastatic Bone Disease. Cham: Springer. (2024). doi: 10.1007/978-3-031-52001-3_7

Crossref Full Text | Google Scholar

195. Akinsulie OC, Shahzad S, Ogunleye SC, Oladapo IP, Joshi M, Ugwu CE, et al. Crosstalk between hypoxic cellular micro-environment and the immune system: a potential therapeutic target for infectious diseases. Front Immunol. (2023) 14:1224102. doi: 10.3389/fimmu.2023.1224102

PubMed Abstract | Crossref Full Text | Google Scholar

196. Abdelmoneim M, Aboalela MA, Naoe Y, Matsumura S, Eissa IR, Bustos-Villalobos I, et al. The impact of metformin on tumor-infiltrated immune cells: preclinical and clinical studies. Int J Mol Sci. (2023) 24. doi: 10.3390/ijms241713353

PubMed Abstract | Crossref Full Text | Google Scholar

197. MZO A, PPS B, DM B, and ML B. C NSM: new prospects for molecular targets for chordomas. Neurosurg Clinics North America. (2020) 31:289–300. doi: 10.1016/j.nec.2019.11.004

PubMed Abstract | Crossref Full Text | Google Scholar

198. Mei T, Wang T, and Zhou Q. Multi-omics and artificial intelligence predict clinical outcomes of immunotherapy in non-small cell lung cancer patients. Clin Exp Med. (2024) 24:60. doi: 10.1007/s10238-024-01324-0

PubMed Abstract | Crossref Full Text | Google Scholar

199. Li Y, Wu X, Fang D, and Luo Y. Informing immunotherapy with multi-omics driven machine learning. NPJ Digit Med. (2024) 7:67. doi: 10.1038/s41746-024-01043-6

PubMed Abstract | Crossref Full Text | Google Scholar

200. AlOsaimi HM, Alshilash AM, Al-Saif LK, Bosbait JM, Albeladi RS, Almutairi DR, et al. AI models for the identification of prognostic and predictive biomarkers in lung cancer: a systematic review and meta-analysis. Front Oncol. (2025) 15:1424647. doi: 10.3389/fonc.2025.1424647

PubMed Abstract | Crossref Full Text | Google Scholar

201. Li B, Su J, Liu K, and Hu C. Deep learning radiomics model based on PET/CT predicts PD-L1 expression in non-small cell lung cancer. Eur J Radiol Open. (2024) 12:100549. doi: 10.1016/j.ejro.2024.100549

PubMed Abstract | Crossref Full Text | Google Scholar

202. Boscolo Bragadin A, Del Bianco P, Zulato E, Attili I, Pavan A, Carlet J, et al. Longitudinal liquid biopsy predicts clinical benefit from immunotherapy in advanced non-small cell lung cancer. NPJ Precis Oncol. (2024) 8:234. doi: 10.1038/s41698-024-00704-9

PubMed Abstract | Crossref Full Text | Google Scholar

Keywords: thoracic tumors, molecular targeted therapy, immunotherapy, treatment progress, immune cell communication

Citation: Yang Y, Pu D and Li X (2025) Frontiers in thoracic oncology: new breakthroughs in molecular targets and immunotherapy. Front. Immunol. 16:1721638. doi: 10.3389/fimmu.2025.1721638

Received: 09 October 2025; Accepted: 30 November 2025; Revised: 09 November 2025;
Published: 15 December 2025.

Edited by:

Qi Wang, Shanghai Jiao Tong University, China

Reviewed by:

Adil Maqbool, Health and Disease Research Center for Rural Peoples, Bangladesh
Yuanyin Teng, Zhejiang University, China

Copyright © 2025 Yang, Pu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Xuehan Li, bHhoaGFuQGdtYWlsLmNvbQ==

These authors have contributed equally to this work and share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.