Heterogeneity In Lung Cancer Research Articles

Mapping Immune-Tumor Bidirectional Dialogue Using Ultrasensitive Nanosensors for Accurate Diagnosis of Lung Cancer.

Lung cancer is one of the most common cancers with high mortality worldwide despite the development of molecularly targeted therapies and immunotherapies. A significant challenge in managing lung cancer is the accurate diagnosis of cancerous lesions owing to the lack of sensitive and specific biomarkers. The current procedure necessitates an invasive tissue biopsy for diagnosis and molecular subtyping, which presents patients with risk, morbidity, anxiety, and high false-positive rates. The high-risk diagnostic approach has highlighted the need to search for a reliable, low-risk noninvasive diagnostic approach to capture lung cancer heterogeneity precisely. The immune interaction profile of lung cancer is driven by immune cells' distinctive, precise interactions with the tumor microenvironment. Here, we hypothesize that immune cells, particularly T cells, can be used for accurate lung cancer diagnosis by exploiting the distinctive immune-tumor interaction by detecting the immune-diagnostic signature. We have developed an ultrasensitive T-sense nanosensor to probe these specific diagnostic signatures using the physical synthesis process of multiphoton ionization. Our research employed predictive in vitro models of lung cancers, cancer-associated T cells (PCAT, MCAT) and CSC-associated T cells (PCSCAT, MCSCAT), from primary and metastatic lung cancer patients to reveal the immune-diagnostic signature and uncover the molecular, functional, and phenotypic separation between patient-derived T cells (PDT) and healthy samples. We demonstrated this by adopting a machine learning model trained with SERS data obtained using cocultured T cells with preclinical models (CAT, CSCAT) of primary (H69AR) and metastatic lung cancer (H1915). Interrogating these distinct signatures with PDT captured the complexity and diversity of the tumor-associated T cell signature across the patient population, exposing the clinical feasibility of immune diagnosis in an independent cohort of patient samples. Thus, our predictive approach using T cells from the patient peripheral blood showed a highly accurate diagnosis with a specificity and sensitivity of 94.1% and 100%, respectively, for primary lung cancer and 97.9% and 94.4% for metastatic lung cancer. Our results prove that the immune-diagnostic signature developed in this study could be used as a clinical technology for cancer diagnosis and determine the course of clinical management with T cells.

Abstract 2805: Pharmacogenomic platform using patient-derived cells to delineate tumor heterogeneity and therapeutic response of refractory lung cancer

Abstract A pharmacogenomic platform using patient-derived cells (PDCs) was established to identify the underlying resistance mechanisms and tailored treatment for advanced or refractory lung cancer patients. Drug sensitivity screening was acquired from short-term PDC culture and multi-omics datasets were acquired from lung cancer PDCs (n = 199). Integrative analysis was performed to explore susceptible drug candidates from genetic variants, gene expression, and clinical profiles. To demonstrate PDC heterogeneity and its therapeutic response, cellular subpopulation was identified from single cell transcriptome from the patients (n = 8) harboring EGFR mutations with poor response to EGFR-TKIs. Mutation and gene expression profile of our PDC samples conserved with lung cancer tissue. Poor survival mutations were observed in KRAS, PDGFA, KEAP1, and MET. EGFR rare variants facilitated fast progression as well as poor susceptibility in EGFR-TKIs. PDC therapeutic subtypes classified patients into five groups: (1) epithelial-to-mesenchymal transition (EMT)-like (30.6 %), (2) EGFR-dominant (21.4 %), (3) stemness (19.4 %), (4) angiogenesis (17.3 %), and (5) inflammatory (11.2 %). The subtypes implicated in distinct pathway regulation activation, variant enrichment, pathologic type, and survival as well as therapeutic candidates. Meanwhile, single cell transcriptome uncovered IDO1 and MET activation of EGFR-TKI resistance cancer cells. Stromal cell clusters exhibited the variety according to combinatorial immunotherapy response. While VEGFA+, and TOP2A+ clusters exhibited well response to angiogenesis and DNA damage target drug combinations of patients, CALB2+ cluster was clearly abundant to non-responder patients. We could detect angiogenesis (dasatinib) and DNA damage (irinotecan) drug candidates according to stromal clusters’ scoring from bulk PDC samples. Our PDC platform recapitulated the molecular characteristics of solid lung tumor tissues including tumor microenvironment. Uncovered molecular mechanism implied drug candidates. Citation Format: Charny Park, Sehwa Hong, Mihwa Hwang, Youngjoo Lee, Bo Ram Song, Eun Hye Kang, Hanna Sim, Beung-Chul Ahn, Kum Hui Hwang, Namhee Yu, Sunshin Kim, Ji-Youn Han. Pharmacogenomic platform using patient-derived cells to delineate tumor heterogeneity and therapeutic response of refractory lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2805.

Abstract 116: A novel murine model for interrogating tumor heterogeneity in non-small cell lung cancer

Abstract Treatment for non-small cell lung cancer (NSCLC) has been transformed by the use of immune checkpoint inhibitors (ICIs), which promote the ability of the immune system to attack cancer cells. Resistance to these therapies thus represents a major barrier to effectively treating patients with NSCLC. Tumor heterogeneity has been identified as an important factor that may mediate ICI resistance, and prior analyses of NSCLC tumors have accordingly shown that greater tumor heterogeneity is correlated with diminished response to ICIs. However, the exact mechanisms of heterogeneity-mediated resistance remain unclear, and there exist a lack of models for studying the impact of tumor heterogeneity on therapeutic efficacy in NSCLC. To address this issue, we have developed a novel murine model of NSCLC that incorporates a system of clonal cell lines to precisely modulate tumor heterogeneity. Specifically, we isolated single tumor cells from a murine NSCLC cell line harboring 3 common driver mutations (KrasG12DTp53+/−Lkb1−/−, or “KPL”). We additionally isolated single cells after mutagenizing the KPL line with the alkylating agent N-methyl-N-nitrosourea, in order to obtain clones that better reflect the higher tumor mutation burden often seen in NSCLC. Clonal cell lines were derived from these single cells and then analyzed using whole exome sequencing to verify clonality and characterize their mutational profile. Phylogenetic analysis of mutations yielded categorization of these clones into 5 clusters. Clones within the same cluster demonstrated comparable in vivo growth kinetics and similar response to anti-PD-1 immunotherapy. Combinations of these clones can be used to recapitulate various levels of heterogeneity and to form tumors with differing responses to PD-1 inhibition. Our results validate this system of clonal cell lines as a model of NSCLC that can modify multiple aspects of tumor heterogeneity and thus provide insights into the effects of tumor heterogeneity on ICIs and other immunotherapies. Citation Format: Michael S. Oh, Jensen W. Abascal, Linh M. Tran, Ramin Salehi-Rad, Raymond J. Lim, Bitta P. Kahangi, William P. Crosson, Edgar Perez Reyes, Jacey Yang, Cara Yeah, Kostyantyn Krysan, Bin Liu, Steven M. Dubinett. A novel murine model for interrogating tumor heterogeneity in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 116.

Radionuclide-based theranostics - a promising strategy for lung cancer.

This review aims to provide a comprehensive overview of the latest literature on personalized lung cancer management using different ligands and radionuclide-based tumor-targeting agents. Lung cancer is the leading cause of cancer-related deaths worldwide. Due to the heterogeneity oflung cancer, advances in precision medicine may enhance the disease management landscape.More recently, theranostics using the same molecule labeled with two different radionuclides forimaging and treatment has emerged as a promising strategy for systemic cancer management. Inradionuclide-based theranostics, the target, ligand, and radionuclide should all be carefullyconsidered to achieve an accurate diagnosis and optimal therapeutic effects for lung cancer. We summarize the latest radiotracers and radioligand therapeutic agents used in diagnosing andtreating lung cancer. In addition, we discuss the potential clinical applications and limitationsassociated with target-dependent radiotracers as well as therapeutic radionuclides. Finally, weprovide our views on the perspectives for future development in this field. Radionuclide-based theranostics show great potential in tailored medical care. We expect thatthis review can provide an understanding of the latest advances in radionuclide therapy for lungcancer and promote the application of radioligand theranostics in personalized medicine.

Extrachromosomal DNA Amplification Contributes to Small Cell Lung Cancer Heterogeneity and Is Associated with Worse Outcomes.

MYC drives SCLC progression, but the genetic basis of MYC-driven SCLC evolution is unknown. Using SCLC as a paradigm, we report how ecDNA amplifications function as MYC-amplifying units, fostering tumor plasticity and a high degree of tumor heterogeneity. This article is highlighted in the In This Issue feature, p. 799.

Dissecting heterogeneity and immune cell populations in non-small cell lung cancer by single-cell RNA sequencing

Dissecting heterogeneity and immune cell populations in non-small cell lung cancer by single-cell RNA sequencing

Causes and Consequences of Lung Cancer Heterogeneity

Causes and Consequences of Lung Cancer Heterogeneity

Molecular basis of heterogeneity in small cell lung cancer

Molecular basis of heterogeneity in small cell lung cancer

Features of lymph node metastasis depending on intratumoral heterogeneity of non-small cell lung cancer in patients with different morphological changes in the bronchial epithelium

The aim of the study: to examine the relationship between the morphological diversity of non-small cell lung cancer and the frequency of lymph node metastasis in groups of patients with different epithelial conditions in the bronchi adjacent to the tumor. Material and methods. Surgical specimens from 90 patients with non-small cell lung cancer, who were treated in the Thoracoabdominal Department Of The Research Institute Of Oncology Of The Tomsk National Research Medical Center in the period from 2009 to 2017 were studied. The histological type of cancer was determined according to the who classification (2020). Lepidic, acinar, papillary, micropapillary, solid and solitary tumor cells were isolated in the parenchymal component of adenocarcinoma. In the parenchymal component of squamous cell carcinoma, 5 types of structures were distinguished: with keratinization, consisting of atypical cells of the prickly type without keratinization, consisting of atypical cells of the basaloid type, built of atypical cells with pronounced polymorphism, single tumor cells. Results. In patients with isolated basal cell hyperplasia, acinar (37 %), papillary (29 %) and solid (27 %) patterns were found less frequently in cases with metastatic regional lymph nodes compared to those without metastatic lymph nodes (63 %; p=0.05; 71 %; p=0.05; 73 %; p=0.01, respectively). In patients with isolated basal cell hyperplasia of the bronchial epithelium, in cases with the presence of lymph node metastasis in the parenchymal component of squamous cell carcinoma, structures 1 (with keratinization) (17 %), 2 (spiky pattern) (33 %) and 4 (polymorphic pattern) (29 %) were less frequently detected compared to those without metastases in regional lymph nodes (83 %; p=0.01; 67 %; p=0.02 and 71 %; p=0.01, respectively). In patients with a combination of basal cell hyperplasia and squamous metaplasia, a spiny pattern (65 %), a basaloid pattern (100 %), a polymorphic pattern (82 %) and single tumor cells (89 %) were more frequently detected in cases with metastatic lymph nodes than in cases without metastatic lymph nodes (35 %; р=0.04; 0 %; р=0.01; 18 %; р=0.01; 11 %; р=0.01, respectively). Conclusion. The data obtained clarify the available information on the significance of the morphological heterogeneity of the tumor for predicting the course of adenocarcinoma and squamous cell carcinoma of the lung.

Assessment of Mitochondrial Health in Cancer-Associated Fibroblasts Isolated from 3D Multicellular Lung Tumor Spheroids.

Cancer-associated fibroblasts (CAFs) are among the most abundant stromal cells present in the tumor microenvironment, facilitating tumor growth and progression. Complexity within the tumor microenvironment, including tumor secretome, low-grade inflammation, hypoxia, and redox imbalance, fosters heterotypic interaction and allows the transformation of inactive resident fibroblasts to become active CAFs. CAFs are metabolically distinguished from normal fibroblasts (NFs) as they are more glycolytically active, produce higher levels of reactive oxygen species (ROS), and overexpress lactate exporter MCT-4, leading to the opening of the mitochondrial permeability transition pore (MPTP). Here a method has been described to analyze the mitochondrial health of activated CAFs isolated from the multicellular 3D tumor spheroids comprising of human lung adenocarcinoma cells (A549), human monocytes (THP-1), and human lung fibroblast cells (MRC5). Tumor spheroids were disintegrated at different time intervals and through magnetic-activated cell sorting, CAFs were isolated. The mitochondrial membrane potential of CAFs was assessed using JC-1 dye, ROS production by 2',7'-dichlorodihydrofluorescein diacetate (DCFDA) staining, and enzyme activity in the isolated CAFs. Analyzing the mitochondrial health of isolated CAFs provides a better understanding of the reverse Warburg effect and can also be applied to study the consequences of CAF mitochondrial changes, such as metabolic fluxes and the corresponding regulatory mechanisms on lung cancer heterogeneity. Thus, the present study advocates an understanding of tumor-stroma interactions on mitochondrial health. It would provide a platform to check mitochondrial-specific drug candidates for their efficacies against CAFs as potential therapeutics in the tumor microenvironment, thereby preventing CAF involvement in lung cancer progression.

Single-cell transcriptomic profiling reveals the tumor heterogeneity of small-cell lung cancer

Small-cell lung cancer (SCLC) is the most aggressive and lethal subtype of lung cancer, for which, better understandings of its biology are urgently needed. Single-cell sequencing technologies provide an opportunity to profile individual cells within the tumor microenvironment (TME) and investigate their roles in tumorigenic processes. Here, we performed high-precision single-cell transcriptomic analysis of ~5000 individual cells from primary tumors (PTs) and matched normal adjacent tissues (NATs) from 11 SCLC patients, including one patient with both PT and relapsed tumor (RT). The comparison revealed an immunosuppressive landscape of human SCLC. Malignant cells in SCLC tumors exhibited diverse states mainly related to the cell cycle, immune, and hypoxic properties. Our data also revealed the intratumor heterogeneity (ITH) of key transcription factors (TFs) in SCLC and related gene expression patterns and functions. The non-neuroendocrine (non-NE) tumors were correlated with increased inflammatory gene signatures and immune cell infiltrates in SCLC, which contributed to better responses to immune checkpoint inhibitors. These findings indicate a significant heterogeneity of human SCLC, and intensive crosstalk between cancer cells and the TME at single-cell resolution, and thus, set the stage for a better understanding of the biology of SCLC as well as for developing new therapeutics for SCLC.

Positron emission tomography imaging of lung cancer: An overview of alternative positron emission tomography tracers beyond F18 fluorodeoxyglucose.

Lung cancer has been the leading cause of cancer-related mortality in China in recent decades. Positron emission tomography-computer tomography (PET/CT) has been established in the diagnosis of lung cancer. 18F-FDG is the most widely used PET tracer in foci diagnosis, tumor staging, treatment planning, and prognosis assessment by monitoring abnormally exuberant glucose metabolism in tumors. However, with the increasing knowledge on tumor heterogeneity and biological characteristics in lung cancer, a variety of novel radiotracers beyond 18F-FDG for PET imaging have been developed. For example, PET tracers that target cellular proliferation, amino acid metabolism and transportation, tumor hypoxia, angiogenesis, pulmonary NETs and other targets, such as tyrosine kinases and cancer-associated fibroblasts, have been reported, evaluated in animal models or under clinical investigations in recent years and play increasing roles in lung cancer diagnosis. Thus, we perform a comprehensive literature review of the radiopharmaceuticals and recent progress in PET tracers for the study of lung cancer biological characteristics beyond glucose metabolism.

MA05.07 Intertumoral Molecular Heterogeneity of Non-small Cell Lung Cancer with MET Exon 14 Skipping

MET exon 14 skipping, as a rare driver for non-small cell lung cancer (NSCLC), can be successfully targeted by MET specific tyrosine kinase inhibitors (TKI) like Savolitinib, Tepotinib, and Capmatinib. Recent clinical trials of MET selective TKIs displayed encouraging clinical efficacy. While nearly half of NSCLC patients with MET exon 14 skipping did not benefit from MET-TKI treatment, which indicated biological heterogeneity in NSCLCs driven by METex14 skipping. This study aims to disclose the intertumoral heterogeneity of METex14 skipping positive NSCLCs at the functional level.

EP16.03-033 Transcriptomic Heterogeneity in Non-Small Cell Lung Cancer with Four Structure-Based EGFR-Mutation Subgroups

EGFR mutations were classified into four subtypes (Classical-like, P-loop and αC-helical compressing (PACC), T790M-like, and exon 20 loop insertions) based on unique structures as Jacqulyne previously reported (Nature, 2021).Thus, EGFR mutant non-small cell lung cancer (NSCLC) should be considered as a heterogeneous disease. And there is clearly an unmet need understanding the biology trait of NSCLC with distinct EGFR mutation subtypes.

EP05.01-014 Circulating Tumor DNA to Identify Genomic Biomarkers of Radiation Sensitivity in Locally Advanced Non-Small Cell Lung Cancer

Despite our increasing understanding of the genomic heterogeneity of non-small cell lung cancer (NSCLC), patients with locally advanced inoperable disease are treated with the same dose of radiation therapy (RT). Tissue-based analyses have identified likely biomarkers of RT response. We evaluated the feasibility of prospective molecular profiling with liquid biopsy ctDNA testing to identify genomic markers of RT sensitivity and resistance.

MA01.05 Immuno-microenvironment (TIME) Heterogeneity of Small Cell Lung Cancer (SCLC) Stratified by Molecular Subtypes

Small cell lung cancer (SCLC) is a highlyaggressive and refractory lung cancer. Most patients respond to first-linechemotherapy but relapse rapidly, resulting in a relatively poor prognosis. Inthe past few years, immunotherapy, especially the immune checkpoint inhibitorsrepresented by PD-L1 inhibitors, has made initial progress in SCLC. Butdifferent patients respond differently to treatment, and it is necessary todetermine which part of patients benefit the most.

ATAXIC: An Algorithm to Quantify Transcriptomic Perturbation Heterogeneity in Single Cancer Cells.

The single-cell RNA sequencing (scRNA-seq) has recently been widely utilized to quantify transcriptomic profiles in single cells of bulk tumors. The transcriptomic profiles in single cells facilitate the investigation of intratumor heterogeneity that is unlikely confounded by the nontumor components. We proposed an algorithm (ATAXIC) to quantify the heterogeneity of transcriptomic perturbations (TPs) in single cancer cells. ATAXIC calculated the TP heterogeneity level of a single cell based on the standard deviations of the absolute z-scored gene expression values for tens of thousands of genes, reflecting the asynchronous degree of transcriptomic alterations relative to the central (mean) tendency. By analyzing scRNA-seq datasets for eight cancer types, we revealed that ATAXIC scores were likely to correlate positively with the enrichment scores of various proliferation and oncogenic signatures, DNA damage repair, treatment resistance, and unfavorable phenotypes and outcomes in cancer. The ATAXIC scores varied among different cancer types, with lung cancer and melanoma having the lowest average scores and clear cell renal cell carcinoma having the highest average scores. The low TP heterogeneity in lung cancer and melanoma could bestow relatively higher response rates to immune checkpoint inhibitors on both cancer types. In conclusion, ATAXIC is a useful algorithm to quantify the TP heterogeneity in single cancer cells, as well as providing new insights into tumor biology.

From data to diagnosis: A review of the current state of the art in lung cancer prediction using machine learning

Lung cancer remains a leading cause of cancer-related mortality worldwide, with 1.8 million cases of lung cancer-related death recorded in 2022 alone. This is often due to late-stage diagnosis and the complexity of its molecular subtypes, necessitating the need for early detection and personalized treatment, to improve patient outcomes. Predictive biomarkers—biological indicators that help detect, monitor, and guide treatment—help in addressing this challenge. However, traditional methods of biomarker discovery often struggle to cope with the heterogeneity of lung cancer and the vast datasets generated from genomic, proteomic, and imaging technologies. In response, machine learning (ML) has emerged as a tool, capable of analyzing data and identifying novel biomarkers that may be overlooked through conventional techniques. This paper reviews the current state of predictive biomarker discovery in lung cancer, focusing on the application of machine learning approaches. It examines the types of biomarkers used in lung cancer diagnosis and treatment, recent advancements in ML-driven biomarker discovery, and the challenges that persist—such as data quality, model overfitting, and interpretability. This paper concludes with recommendations for future research directions, emphasizing the need for improved data integration, better model interpretability, and clinical validation of biomarkers to ensure that machine learning can fully realize its potential in revolutionizing lung cancer care.

ITHscore: comprehensive quantification of intra-tumor heterogeneity in NSCLC by multi-scale radiomic features.

To quantify intra-tumor heterogeneity (ITH) in non-small cell lung cancer (NSCLC) from computed tomography (CT) images. We developed a quantitative ITH measurement-ITHscore-by integrating local radiomic features and global pixel distribution patterns. The associations of ITHscore with tumor phenotypes, genotypes, and patient's prognosis were examined on six patient cohorts (n = 1399) to validate its effectiveness in characterizing ITH. For stage I NSCLC, ITHscore was consistent with tumor progression from stage IA1 to IA3 (p < 0.001) and captured key pathological change in terms of malignancy (p < 0.001). ITHscore distinguished the presence of lymphovascular invasion (p = 0.003) and pleural invasion (p = 0.001) in tumors. ITHscore also separated patient groups with different overall survival (p = 0.004) and disease-free survival conditions (p = 0.005). Radiogenomic analysis showed that the level of ITHscore in stage I and stage II NSCLC is correlated with heterogeneity-related pathways. In addition, ITHscore was proved to be a stable measurement and can be applied to ITH quantification in head-and-neck cancer (HNC). ITH in NSCLC can be quantified from CT images by ITHscore, which is an indicator for tumor phenotypes and patient's prognosis. • ITHscore provides a radiomic quantification of intra-tumor heterogeneity in NSCLC. • ITHscore is an indicator for tumor phenotypes and patient's prognosis. • ITHscore has the potential to be generalized to other cancer types such as HNC.

Genomic and Gene Expression Studies Helped to Define the Heterogeneity of Small-Cell Lung Cancer and Other Lung Neuroendocrine Tumors and to Identify New Therapeutic Targets

Small-cell lung cancer (SCLC) is a high-grade neuroendocrine carcinoma, corresponding to about 15% of lung cancers, occurring predominantly in smokers and associated with a very poor prognosis. Key genetic alterations very frequently observed in SCLC are represented by the loss of TP53 and RB1, due to mutational events or deletions; frequent amplification or overexpression of MYC family genes (MYC, MYCL and MYCN); frequent genetic alterations by mutation/deletion of KMT2D, RB family members p107 (RBL1) and p130 (RBL2), PTEN, NOTCH receptors and CREBBP. The profile of expression of specific transcription factors allowed to differentiate four subtypes of SCLC defined according to levels of ASCL1 (SCLC-A), NEUROD1 (SCLC-N), POUF23 (SCLC-P) or YAP1 (SCLC-Y). A recent study identified the subgroup SCLC-I, characterized by the expression of inflammatory/immune-related genes. Recent studies have characterized at molecular level other lung neuroendocrine tumors, including large cell neuroendocrine cancers (LCNECs) and lung carcinoids. These molecular studies have identified some therapeutic vulnerabilities that can be targeted using specific drugs and some promising biomarkers that can predict the response to this treatment. Furthermore, the introduction of immunotherapy (immune checkpoint blockade) into standard first-line treatment has led to a significant clinical benefit in a limited subset of patients.