Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
9.0
4.8
Journals
Cancers
Special Issues
Lung Cancer—Advances in Therapy and Prognostic Prediction
cancers-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Cancers Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Lung Cancer—Advances in Therapy and Prognostic Prediction

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Biomarkers".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 4637

Editors

Dr. Chinmay T. Jani

E-Mail Website
Guest Editor
Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
Interests: lung cancer; immunotherapy resistance; novel therapeutics; biomarkers
Dr. Estelamari Rodríguez

E-Mail Website
Guest Editor
Division of Medical Oncology, Department of Medicine, University of Miami Sylvester Comprehensive Cancer Center, Miami, FL 33136, USA
Interests: lung cancer; cancer disparities research; early phase clinical trials

Special Issue Information

Dear Colleagues,

We are pleased to announce an upcoming Special Issue dedicated to the rapidly evolving landscape of lung cancer, with a focus on therapeutic advancements and prognostic prediction. This Issue will highlight innovative, biomarker-driven treatment strategies—including targeted therapies, immunotherapy combinations, and precision medicine approaches that integrate molecular profiling to guide individualized care.

Emphasis will be placed on studies that advance our understanding of how genomic, transcriptomic, and liquid biopsy-based biomarkers can inform treatment selection, monitor disease progression, and refine prognostic models. As lung cancer care becomes increasingly personalized, this Special Issue aims to feature multidisciplinary research that reflects the shift toward precision oncology.

We invite researchers, clinicians, and thought leaders to contribute original research articles, reviews, and commentaries that will define the next chapter in therapeutic advancements in lung cancer.

Dr. Chinmay T. Jani
Dr. Estelamari Rodríguez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • lung cancer
  • biomarkers
  • targeted therapies
  • immunotherapy
  • precision medicine

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

10 pages, 675 KB  
Article
Tarlatamab in Previously Treated Small Cell Lung Cancer: A Real-World Experience in a Predominantly Hispanic Population with CNS Metastases
by Santiago Sucre, Chinmay Jani, Dan Morgenstern-Kaplan, Zuniga Nelsy, Rakhi Modak, Kyle Edwards, Brandon Rose, Subul Malik, Kyle Rowley, Asad Rauf, Gilberto Lopes, Estelamari Rodriguez and Aman Chauhan
Cancers 2026, 18(11), 1806; https://doi.org/10.3390/cancers18111806 - 1 Jun 2026
Viewed by 580
Abstract
Background: SCLC remains an aggressive malignancy with limited therapeutic options after progression. Tarlatamab, a DLL3-directed bispecific T-cell engager, has a demonstrated survival benefit in clinical trials, but real-world data remain limited. We evaluated the safety, radiographic response, and treatment durability of Tarlatamab in [...] Read more.
Background: SCLC remains an aggressive malignancy with limited therapeutic options after progression. Tarlatamab, a DLL3-directed bispecific T-cell engager, has a demonstrated survival benefit in clinical trials, but real-world data remain limited. We evaluated the safety, radiographic response, and treatment durability of Tarlatamab in a real-world cohort treated at a single academic center. Methods: We performed a retrospective review of patients with extensive-stage SCLC who received Tarlatamab at the University of Miami Sylvester Comprehensive Cancer Center between 2024 and 31 October 2025. Demographic, clinical, radiographic, and toxicity data were abstracted from electronic medical records. Radiographic response was defined as partial response or stable disease on follow-up imaging. PFS and overall survival OS were estimated using the Kaplan–Meier method. Results: Twenty-three patients were included (median age 72 years), of whom 61% were Hispanic and 61% had brain metastases prior to treatment. Forty-three percent had received two or more prior lines of therapy. CRS and ICANS occurred primarily during Cycle 1 and were limited to grade 1–2 events, with no grade ≥3 toxicities. Median time on treatment was 92 days with a median of four cycles. Among 18 evaluable patients, partial response was observed in 27.7% and stable disease in 16.7%, yielding a disease control rate of 44.4%. Median PFS was 139 days and median OS was 323 days (95% CI, 31–614). Conclusions: In a predominantly Hispanic, heavily pre-treated real-world population with high CNS disease burden, Tarlatamab demonstrated feasible administration, manageable immune-mediated toxicity, and clinically meaningful antitumor activity. Full article
(This article belongs to the Special Issue Lung Cancer—Advances in Therapy and Prognostic Prediction)
►▼ Show Figures

Figure 1

17 pages, 1383 KB  
Article
Exploratory Immunohistochemical Profiling of FOXP3, PD-1 and CD32B in Resectable Lung Adenocarcinoma
by Long-Wei Lin, Hong-Jing Chuang, Kuan-Hsun Lian, Yu-Ting Tseng and Chung-Yu Chen
Cancers 2025, 17(23), 3886; https://doi.org/10.3390/cancers17233886 - 4 Dec 2025
Cited by 1 | Viewed by 752
Abstract
Background: Regulatory T cells (FOXP3+), checkpoint signaling (PD-1), and inhibitory B-cell signaling (CD32B/FCGR2B) may shape recurrence risk after resection of lung adenocarcinoma, but small, stage-heterogeneous cohorts complicate inference. Methods: We profiled 21 resected lung adenocarcinomas by immunohistochemistry (IHC) for CD3, CD8, [...] Read more.
Background: Regulatory T cells (FOXP3+), checkpoint signaling (PD-1), and inhibitory B-cell signaling (CD32B/FCGR2B) may shape recurrence risk after resection of lung adenocarcinoma, but small, stage-heterogeneous cohorts complicate inference. Methods: We profiled 21 resected lung adenocarcinomas by immunohistochemistry (IHC) for CD3, CD8, FOXP3, PD-1, CD19, and CD32B. Five systematically sampled 200× fields per stain were quantified in ImageJ to derive continuous percentages and prespecified ratios: FOXP3/CD8 and CD32B/CD19 (primary), and PD-1/CD8 (exploratory). Analyses emphasized effect sizes with exact non-parametric tests for clinicopathologic associations and Cox time-to-event models for disease-free survival (DFS). Kaplan–Meier plots used median splits for visualization only. Results: Higher immunosuppressive balance associated with adverse features and shorter DFS. Patients with higher FOXP3/CD8 and CD32B/CD19 had markedly shorter DFS on K-M displays (FOXP3/CD8: 18.9 vs. 45.6 months; CD32B/CD19: 25.0 vs. 72.8 months). In Cox models, each ratio was associated with increased hazard of recurrence (FOXP3+PD-1/CD8, HR 2.03, 95% CI 1.26–3.29; CD32B/CD19, HR 1.98, 95% CI 1.16–3.37). Conclusions: In this hypothesis-generating pilot, an immunosuppressive tumor microenvironment, indexed by higher FOXP3 (relative to CD8) and higher CD32B (relative to CD19), portends earlier recurrence after surgery. These results support external validation in larger, stage-balanced cohorts and motivate incorporation of quantitative IHC ratios into postoperative risk stratification. Full article
(This article belongs to the Special Issue Lung Cancer—Advances in Therapy and Prognostic Prediction)
►▼ Show Figures

Graphical abstract

23 pages, 1835 KB  
Article
TILDA-X: Transcriptome-Informed Lung Cancer Disparities via Explainable AI
by Masrur Sobhan, Md Mezbahul Islam, Mary Jo Trepka, Gregory E. Holt, Charles J. Dimitroff and Ananda M. Mondal
Cancers 2025, 17(21), 3454; https://doi.org/10.3390/cancers17213454 - 28 Oct 2025
Viewed by 1326
Abstract
Background: Lung cancer is a leading cause of cancer-related mortality, with disparities in incidence and outcomes observed across different racial and sex groups. Identifying both patient-specific and cohort-specific disparity biomarkers is critical for developing targeted treatments. The lung cancer dataset is highly imbalanced [...] Read more.
Background: Lung cancer is a leading cause of cancer-related mortality, with disparities in incidence and outcomes observed across different racial and sex groups. Identifying both patient-specific and cohort-specific disparity biomarkers is critical for developing targeted treatments. The lung cancer dataset is highly imbalanced across races, leading to biased results in disparity information if classification is based on race. Method: This study developed an explainable artificial intelligence-based framework, TILDA-X, which designs classification models based on disease conditions instead of races to mitigate racial imbalance in the dataset and applies explainable AI to delineate patient-specific disparity information. A lung cancer transcriptome dataset with three disease conditions—lung adenocarcinoma, lung squamous cell carcinoma, and healthy samples—was used to develop classification models. Applying a bottom-up approach from patient-specific disparity information, the cohort-specific disparity information is discovered for different racial and sex groups, African American males, European American males, African American females, and European American females. Results: Classification based on disease conditions achieved accuracy between 88% and 100% for minority groups (African American males and females), whereas it was only between 0% and 16% for race-based classification, which underscores the significance of the proposed approach. Functional analysis of sub-cohort-specific biomarker genes revealed unique pathways associated with lung cancers in different races and sexes. Among the significant pathways identified, over ~63% overlapped with previously reported lung cancer-related studies, supporting the biological validity of our findings. Overall, combining disease conditions-based classification with explainable AI, this study provides a robust, interpretable framework for characterizing race- and sex-specific disparities in lung cancer, offering a foundation for precision oncology and equitable therapeutic development based on transcriptome profile only. Full article
(This article belongs to the Special Issue Lung Cancer—Advances in Therapy and Prognostic Prediction)
►▼ Show Figures

Figure 1

Review

Jump to: Research

22 pages, 719 KB  
Review
The Evolving Role of Bispecific Antibodies in Oncogene-Driven NSCLC
by Jun Chih Wang, Daniel Rosas and Luis E. Raez
Cancers 2026, 18(14), 2197; https://doi.org/10.3390/cancers18142197 - 8 Jul 2026
Viewed by 195
Abstract
Bispecific antibodies (bsAbs) have emerged as a novel therapeutic class in oncogene-driven non-small-cell lung cancer (NSCLC), designed to simultaneously target multiple signaling pathways and overcome resistance mechanisms associated with tyrosine kinase inhibitors (TKIs). Unlike small-molecule TKIs, bsAbs enable dual receptor blockade and immune [...] Read more.
Bispecific antibodies (bsAbs) have emerged as a novel therapeutic class in oncogene-driven non-small-cell lung cancer (NSCLC), designed to simultaneously target multiple signaling pathways and overcome resistance mechanisms associated with tyrosine kinase inhibitors (TKIs). Unlike small-molecule TKIs, bsAbs enable dual receptor blockade and immune effector engagement, offering a mechanistically distinct advantage in the context of tumor heterogeneity and bypass signaling. This review summarizes the structural and biological principles underlying bsAb design, with a focus on clinically approved agents such as amivantamab (EGFR/MET) and zenocutuzumab (HER2/HER3) and a growing pipeline of investigational agents. We evaluate key clinical evidence from Phase I-III trials including CHRYSALIS, PAPILLON, MARIPOSA, MARIPOSA-2 and eNRGy, and compare the efficacy, toxicity, and CNS penetration profile of bsAbs relative to TKIs and antibody–drug conjugates (ADCs). While bsAbs demonstrate meaningful clinical activity, particularly in TKI-resistant disease and molecularly defined subsets such as EGFR exon 20 insertions and NRG1 fusions, their limitations, including intravenous administration, increased immune-mediated and thromboembolic toxicity, currently preclude replacement of TKIs in most settings. Collectively, available evidence supports a complementary role for bsAbs within evolving multimodal treatment paradigms, particularly in combination strategies. Future directions include biomarker-driven patient selection, improved drug engineering and integration into adaptive therapeutic sequencing frameworks. Full article
(This article belongs to the Special Issue Lung Cancer—Advances in Therapy and Prognostic Prediction)
►▼ Show Figures

Figure 1

20 pages, 3998 KB  
Review
Decoding Small Cell Lung Cancer: Molecular Subtypes, Surface Antigens, and the Target-Modality Problem
by Mijail I. Zambrano Iglesias, Daniel Rosas, Salih Akgun, Ines C. Padron Cubillan, Fedor Wadi Richani Meinhardt, Atif Hussein and Luis E. Raez
Cancers 2026, 18(13), 2173; https://doi.org/10.3390/cancers18132173 - 7 Jul 2026
Viewed by 270
Abstract
Small cell lung cancer (SCLC) has historically been treated as a single, uniformly aggressive disease defined by neuroendocrine differentiation, near-universal loss of TP53 and RB1, and the absence of classical druggable oncogene addictions. Two converging lines of evidence are now reshaping that view. [...] Read more.
Small cell lung cancer (SCLC) has historically been treated as a single, uniformly aggressive disease defined by neuroendocrine differentiation, near-universal loss of TP53 and RB1, and the absence of classical druggable oncogene addictions. Two converging lines of evidence are now reshaping that view. First, transcriptomic profiling has resolved SCLC into molecular subtypes—SCLC-A (ASCL1-driven), SCLC-N (NEUROD1-driven), SCLC-P (POU2F3-driven), and SCLC-I (inflamed)—with distinct immune microenvironments, surface-antigen expression patterns, and emerging therapeutic vulnerabilities, although intratumoral heterogeneity and phenotypic plasticity complicate clean subtype assignment. Second, the development of delta-like ligand 3 (DLL3)-directed therapies provides a natural experiment: the same validated surface antigen failed as an antibody–drug conjugate (rovalpituzumab tesirine, three negative randomized trials) yet succeeded as a bispecific T-cell engager (tarlatamab, which received FDA accelerated approval in 2024 and subsequent traditional FDA approval in 2025 following positive confirmatory phase 3 data). In this review, we integrate the current first-line standard of care—chemoimmunotherapy with atezolizumab- or durvalumab-based regimens followed by maintenance intensification with lurbinectedin–atezolizumab (IMforte)—with the molecular framework of subtypes and biomarkers, and we use DLL3 as a case study to propose that delivery modality is an important determinant of therapeutic success in SCLC and should be considered alongside target biology and tumor heterogeneity. Rapid proliferation, antigen heterogeneity, subtype plasticity, and a relatively less immunogenic microenvironment systematically penalize modalities dependent on payload accumulation and cell-cycle progression and reward modalities that recruit catalytic, cell-cycle-independent cytotoxic effectors. The emerging B7-H3 and SEZ6 programs—including ifinatamab deruxtecan and ABBV-706—are the next test of this framework. We discuss implications for biomarker development, trial design, and the operational challenges of subtype-guided precision oncology in a disease where tissue is scarce and biology shifts under therapy. Full article
(This article belongs to the Special Issue Lung Cancer—Advances in Therapy and Prognostic Prediction)
►▼ Show Figures

Figure 1

16 pages, 303 KB  
Review
Advances in Multi-Modal Biomarkers for Immunotherapy Response in Non-Small Cell Lung Cancer: ctDNA, Microbiome, and Radiomics
by Turja Chakrabarti and Matthew Lee
Cancers 2026, 18(8), 1281; https://doi.org/10.3390/cancers18081281 - 17 Apr 2026
Viewed by 858
Abstract
Lung cancer remains the leading cause of cancer-related mortality worldwide, and although immunotherapy has transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC), durable benefit is limited to a subset of patients. PD-L1 immunohistochemistry and tumor mutational burden, while clinically utilized, [...] Read more.
Lung cancer remains the leading cause of cancer-related mortality worldwide, and although immunotherapy has transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC), durable benefit is limited to a subset of patients. PD-L1 immunohistochemistry and tumor mutational burden, while clinically utilized, demonstrate imperfect predictive capacity, underscoring the need for more robust biomarkers. This review highlights emerging multimodal biomarkers—including circulating tumor DNA (ctDNA), the gut microbiome, and artificial intelligence (AI)-driven radiomics—as promising tools to enhance the prediction of immunotherapy response. Longitudinal ctDNA monitoring offers a minimally invasive method to assess tumor burden dynamics, detect early molecular response, distinguish pseudo-progression from true progression, and stratify risk, with ctDNA clearance correlating with improved survival outcomes. The gut microbiome has also been associated with ICI efficacy, as specific bacterial taxa and composite scoring systems correlate with treatment response, though methodological heterogeneity limits clinical translation. Radiomic analyses leveraging CT and PET imaging extract quantitative tumor features that, when integrated with clinical and molecular data, demonstrate improved predictive performance compared to single-modality approaches. Despite promising advances, challenges including assay standardization, external validation, data harmonization, interpretability of AI models, and infrastructure requirements remain barriers to widespread adoption. Multimodal integration of genomic, microbiome, and imaging biomarkers represents a critical step toward precision immuno-oncology, with prospective validation needed to translate these approaches into improved outcomes for patients with advanced NSCLC. Full article
(This article belongs to the Special Issue Lung Cancer—Advances in Therapy and Prognostic Prediction)
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop