Molecular phenotyping of small cell lung cancer using targeted cfDNA profiling of transcriptional regulatory regions

Challenges of small cell lung cancer heterogeneity and phenotypic plasticity

Small cell lung cancer (SCLC) is an aggressive neuroendocrine malignancy with ~7% 5-year overall survival reflecting early metastasis and rapid acquired chemoresistance. Immunotherapy briefly extends overall survival in ~15% cases, yet predictive biomarkers are lacking. Targeted therapies are beginning to show promise, with a recently approved delta-like ligand 3 (DLL3)-targeted therapy impacting the treatment landscape. The increased availability of patient-faithful models, accumulating human tumour biobanks and numerous comprehensive molecular profiling studies have collectively facilitated the mapping and understanding of substantial intertumoural and intratumoural heterogeneity. Beyond the almost ubiquitous loss of wild-type p53 and RB1, SCLC is characterized by heterogeneously mis-regulated expression of MYC family members, yes-associated protein 1 (YAP1), NOTCH pathway signalling, anti-apoptotic BCL2 and epigenetic regulators. Molecular subtypes are based on the neurogenic transcription factors achaete-scute homologue 1 (ASCL1) and neurogenic differentiation factor 1 (NEUROD1), the rarer non-neuroendocrine transcription factor POU class 2 homeobox 3 (POU2F3), and immune- and inflammation-related signatures. Furthermore, SCLC shows phenotypic plasticity, including neuroendocrine-to-non-neuroendocrine transition driven by NOTCH signalling, which is associated with disease progression, chemoresistance and immune modulation and, in mouse models, with metastasis. Although these features pose substantial challenges, understanding the molecular vulnerabilities of transcription factor subtypes, the functional relevance of plasticity and cell cooperation offer opportunities for personalized therapies informed by liquid and tissue biomarkers.

Liquid biopsy in diagnosis and monitoring of treatment efficacy in patients with small cell lung cancer

Small-cell lung cancer (SCLC) remains one of the deadliest cancers worldwide. Patients' survival remains poor due to its rapid growth, high metastatic rate and limited possibilities of treatment. For many years, SCLC management has been based mostly on chemo and radiotherapy. However, new therapeutic approaches have been proposed in the past few years, including immunotherapy, which is currently implemented in clinical practice. Unfortunately, in many cases, response to therapy, especially chemotherapy, remains poor, or the patient becomes resistant to initially effective treatment. One of the crucial problems during SCLC patient care is a lack of appropriate predictive biomarkers for various therapeutic approaches. Another critical issue is the scarcity of collected tissue during biopsy, which may be insufficient or of too poor quality for analysis. A liquid biopsy might be the key to solving both of those problems as it is collected in a non-invasive way and enables the measurement of various biomarkers, including circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs). In this review, we discuss various approaches to potentially incorporating liquid biopsy into clinical application - as a companion to imaging during SCLC diagnostics, a new approach to molecular subtyping, and a material enabling predictive or prognostic biomarkers assessment. We also summarize ongoing clinical trials encompassing SCLC patients in which liquid biopsy is collected and examined.

Current advances and future prospects of blood-based techniques for identifying benign and malignant pulmonary nodules

Lung cancer is the leading cause of cancer-related mortality worldwide, highlighting the urgent need for more accurate and minimally invasive diagnostic tools to improve early detection and patient outcomes. While low-dose computed tomography (LDCT) is effective for screening in high-risk individuals, its high false-positive rate necessitates more precise diagnostic strategies. Liquid biopsy, particularly ctDNA methylation analysis, represents a promising alternative for non-invasive classification of indeterminate pulmonary nodules (IPNs). This review highlights the progress and clinical potential of liquid biopsy technologies, including traditional proteins markers, cfDNA, exosomes, metabolomics, circulating tumor cells (CTCs) and platelets, in lung cancer diagnosis. We discuss the integration of ctDNA methylation analysis with traditional imaging and clinical data to enhance the early detection of IPNs, as well as potential solutions to address the challenges of low biomarker concentration and background noise. By advancing precision diagnostics, liquid biopsy technologies could transform lung cancer management, improve survival rates, and reduce the disease burden.

Genomic alterations and transcriptional phenotypes in circulating free DNA and matched metastatic tumor

BACKGROUND

Profiling circulating cell-free DNA (cfDNA) has become a fundamental practice in cancer medicine, but the effectiveness of cfDNA at elucidating tumor-derived molecular features has not been systematically compared to standard single-lesion tumor biopsies in prospective cohorts of patients. The use of plasma instead of tissue to guide therapy is particularly attractive for patients with small cell lung cancer (SCLC), due to the aggressive clinical course of this cancer, which makes obtaining tumor biopsies exceedingly challenging.

METHODS

In this study, we analyzed a prospective cohort of 49 plasma samples obtained before, during, and after treatment from 20 patients with recurrent SCLC. We conducted cfDNA low-pass whole genome sequencing (0.1X coverage), comparing it with time-point matched tumor characterized using whole-exome (130X) and transcriptome sequencing.

RESULTS

A direct comparison of cfDNA and tumor biopsy revealed that cfDNA not only mirrors the mutation and copy number landscape of the corresponding tumor but also identifies clinically relevant resistance mechanisms and cancer driver alterations not detected in matched tumor biopsies. Longitudinal cfDNA analysis reliably tracks tumor response, progression, and clonal evolution. Sequencing coverage of plasma DNA fragments around transcription start sites showed distinct treatment-related changes and captured the expression of key transcription factors such as NEUROD1 and REST in the corresponding SCLC tumors. This allowed for the prediction of SCLC neuroendocrine phenotypes and treatment responses.

CONCLUSIONS

cfDNA captures a comprehensive view of tumor heterogeneity and evolution. These findings have significant implications for the non-invasive stratification of SCLC, a disease currently treated as a single entity.

Cell-free and extrachromosomal DNA profiling of small cell lung cancer

Small cell lung cancer (SCLC) is highly aggressive with poor prognosis. Despite a relative prevalence of circulating tumour DNA (ctDNA) in SCLC, liquid biopsies are not currently implemented, unlike non-SCLC where cell-free DNA (cfDNA) mutation profiling in the blood has utility for guiding targeted therapies and assessing minimal residual disease. cfDNA methylation profiling is highly sensitive for SCLC detection and holds promise for disease monitoring and molecular subtyping; cfDNA fragmentation profiling has also demonstrated clinical potential. Extrachromosomal DNA (ecDNA), that is often observed in SCLC, promotes tumour heterogeneity and chemotherapy resistance and can be detected in blood. We discuss how these cfDNA profiling modalities can be harnessed to expand the clinical applications of liquid biopsy in SCLC.

Next-Generation Cancer Phenomics: A Transformative Approach to Unraveling Lung Cancer Complexity and Advancing Precision Medicine

Lung cancer remains one of the leading causes of cancer-related deaths globally, with its complexity driven by intricate and intertwined genetic, epigenetic, and environmental factors. Despite advances in genomics, transcriptomics, and proteomics, understanding the phenotypic diversity of lung cancer has lagged behind. Next-generation phenomics, which integrates high-throughput phenotypic data with multiomics approaches and digital technologies such as artificial intelligence (AI), offers a transformative strategy for unraveling the complexity of lung cancer. This approach leverages advanced imaging, single-cell technologies, and AI to capture dynamic phenotypic variations at cellular, tissue, and whole organism levels and in ways resolved in temporal and spatial contexts. By mapping the high-throughput and spatially and temporally resolved phenotypic profiles onto molecular alterations, next-generation phenomics provides deeper insights into the tumor microenvironment, cancer heterogeneity, and drug efficacy, safety, and resistance mechanisms. Furthermore, integrating phenotypic data with genomic and proteomic networks allows for the identification of novel biomarkers and therapeutic targets in ways informed by biological structure and function, fostering precision medicine in lung cancer treatment. This expert review examines and places into context the current advances in next-generation phenomics and its potential to redefine lung cancer diagnosis, prognosis, and therapy. It highlights the emerging role of AI and machine learning in analyzing complex phenotypic datasets, enabling personalized therapeutic interventions. Ultimately, next-generation phenomics holds the promise of bridging the gap between molecular alterations and clinical and population health outcomes, providing a holistic understanding of lung cancer biology that could revolutionize its management and improve patient survival rates.

Recent advances in immunotherapy for small cell lung cancer

PURPOSE OF REVIEW

This review aims to provide an overview of recent advances in immunotherapy for small cell lung cancer (SCLC), with a focus on the current status of immune checkpoint inhibitors (ICIs), novel combination strategies, and key biomarkers.

RECENT FINDINGS

The integration of ICIs into standard chemotherapy has established them as the first-line treatment for extensive-stage SCLC (ES-SCLC). The ADRIATIC trial further demonstrated the efficacy of ICI maintenance therapy in limited-stage SCLC. Additionally, combining radiotherapy with ICIs has shown promising synergistic effects, including the abscopal and radscopal effects. Ongoing investigations into the combination of ICIs with targeted therapies, such as antiangiogenic agents and DNA damage response inhibitors, have yielded encouraging preliminary results. Notably, the novel therapeutic agent tarlatamab, the first bispecific DLL3-directed CD3 T-cell engager, has recently received FDA approval for second-line treatment of ES-SCLC. Advances in omics technologies have shed light on the intra-tumor and inter-tumor heterogeneity of SCLC, leading to the identification of new molecular subtypes and biomarkers, thereby paving the way for precision medicine.

SUMMARY

Despite the improved outcomes associated with immunotherapy in SCLC, the overall clinical benefit remains modest. Further preclinical and clinical studies are essential to identify optimal treatment regimens and enhance therapeutic efficacy.

Unveiling the Molecular Features of SCLC With a Clinical RNA Expression Panel

Introduction

The translation of gene expression profiles of SCLC to clinical testing remains relatively unexplored. In this study, gene expression variations in SCLC were evaluated to identify potential biomarkers.

Methods

RNA expression profiling was performed on 44 tumor samples from 35 patients diagnosed with SCLC using the clinically validated RNA Salah Targeted Expression Panel (RNA STEP). RNA sequencing (RNA-Seq) and immunohistochemistry were performed on two different SCLC cohorts, and correlation analyses were performed for the ASCL1, NEUROD1, POU2F3, and YAP1 genes and their corresponding proteins. RNA STEP and RNA-Seq results were evaluated for gene expression profiles and heterogeneity between SCLC primary and metastatic sites. RNA STEP gene expression profiles of independent SCLC samples (n = 35) were compared with lung adenocarcinoma (n = 160) and squamous cell carcinoma results (n = 25).

Results

The RNA STEP results were highly correlated with RNA-Seq and immunohistochemistry results. The dominant transcription regulator by RNA STEP was ASCL1 in 74.2% of the samples, NEUROD1 in 20%, and POU2F3 in 2.9%. The ASCL1, NEUROD1, and POU2F3 gene expression profiles were heterogeneous between primary and metastatic sites. SCLCs displayed markedly high expression for targetable genes DLL3, EZH2, TERT, and RET. SCLCs were found to have relatively colder immune profiles than lung adenocarcinomas and squamous cell carcinomas, characterized by lower expression of HLA genes, immune cell, and immune checkpoint genes, except the LAG3 gene.

Conclusions

Clinical-grade SCLC RNA expression profiling has value for SCLC subtyping, design of clinical trials, and identification of patients for trials and potential targeted therapy.

Liquid biopsy for early cancer detection: technological revolutions and clinical dilemma

INTRODUCTION

Liquid biopsy is an innovative advancement in oncology, offering a noninvasive method for early cancer detection and monitoring by analyzing circulating tumor cells, DNA, RNA, and other biomarkers in bodily fluids. This technique has the potential to revolutionize precision oncology by providing real-time analysis of tumor dynamics, enabling early detection, monitoring treatment responses, and tailoring personalized therapies based on the molecular profiles of individual patients.

AREAS COVERED

In this review, the authors discuss current methodologies, technological challenges, and clinical applications of liquid biopsy. This includes advancements in detecting minimal residual disease, tracking tumor evolution, and combining liquid biopsy with other diagnostic modalities for precision oncology. Key areas explored are the sensitivity, specificity, and integration of multi-omics, AI, ML, and LLM technologies.

EXPERT OPINION

Liquid biopsy holds great potential to revolutionize cancer care through early detection and personalized treatment strategies. However, its success depends on overcoming technological and clinical hurdles, such as ensuring high sensitivity and specificity, interpreting results amidst tumor heterogeneity, and making tests accessible and affordable. Continued innovation and collaboration are crucial to fully realize the potential of liquid biopsy in improving early cancer detection, treatment, and monitoring.

Small cell lung cancer profiling: an updated synthesis of subtypes, vulnerabilities, and plasticity

Small cell lung cancer (SCLC) is a devastating disease with high proliferative and metastatic capacity. SCLC has been classified into molecular subtypes based on differential expression of lineage-defining transcription factors. Recent studies have proposed new subtypes that are based on both tumor-intrinsic and -extrinsic factors. SCLC demonstrates substantial intratumoral subtype heterogeneity characterized by highly plastic transcriptional states, indicating that the initially dominant subtype can shift during disease progression and in association with resistance to therapy. Strategies to promote or constrain plasticity and cell fate transitions have nominated novel targets that could prompt the development of more durably effective therapies for patients with SCLC. In this review, we describe the latest advances in SCLC subtype classification and their biological and clinical implications.

Noninvasive Lung Cancer Subtype Classification Using Tumor-Derived Signatures and cfDNA Methylome

Accurate diagnosis of lung cancer is important for treatment decision-making. Tumor biopsy and histologic examination are the standard for determining histologic lung cancer subtypes. Liquid biopsy, particularly cell-free DNA (cfDNA), has recently shown promising results in cancer detection and classification. In this study, we investigate the potential of cfDNA methylome for the noninvasive classification of lung cancer histologic subtypes. We focused on the two most prevalent lung cancer subtypes, lung adenocarcinoma and lung squamous cell carcinoma. Using a fragment-based marker discovery approach, we identified robust subtype-specific methylation markers from tumor samples. These markers were successfully validated in independent cohorts and associated with subtype-specific transcriptional activity. Leveraging these markers, we constructed a subtype classification model using cfDNA methylation profiles, achieving an AUC of 0.808 in cross-validation and an AUC of 0.747 in the independent validation. Tumor copy-number alterations inferred from cfDNA methylome analysis revealed potential for treatment selection. In summary, our study demonstrates the potential of cfDNA methylome analysis for noninvasive lung cancer subtyping, offering insights for cancer monitoring and early detection.

SIGNIFICANCE

This study explores the use of cfDNA methylomes for the classification of lung cancer subtypes, vital for effective treatment. By identifying specific methylation markers in tumor tissues, we developed a robust classification model achieving high accuracy for noninvasive subtype detection. This cfDNA methylome approach offers promising avenues for early detection and monitoring.