Precision Medicine In Lung Cancer Research Articles

Integration of clinical phenoms and metabolomics facilitates precision medicine for lung cancer

Lung cancer is a common malignancy that is frequently associated with systemic metabolic disorders. Early detection is pivotal to survival improvement. Although blood biomarkers have been used in its early diagnosis, missed diagnosis and misdiagnosis still exist due to the heterogeneity of lung cancer. Integration of multiple biomarkers or trans-omics results can improve the accuracy and reliability for lung cancer diagnosis. As metabolic reprogramming is a hallmark of lung cancer, metabolites, specifically lipids might be useful for lung cancer detection, yet systematic characterizations of metabolites in lung cancer are still incipient. The present study profiled the polar metabolome and lipidome in the plasma of lung cancer patients to construct an inclusive metabolomic atlas of lung cancer. A comprehensive analysis of lung cancer was also conducted combining metabolomics with clinical phenotypes. Furthermore, the differences in plasma lipid metabolites were compared and analyzed among different lung cancer subtypes. Alcohols, amides, and peptide metabolites were significantly increased in lung cancer, while carboxylic acids, hydrocarbons, and fatty acids were remarkably decreased. Lipid profiling revealed a significant increase in plasma levels of CER, PE, SM, and TAG in individuals with lung cancer as compared to those in healthy controls. Correlation analysis confirmed the association between a panel of metabolites and TAGs. Clinical trans-omics studies elucidated the complex correlations between lipidomic data and clinical phenotypes. The present study emphasized the clinical importance of lipidomics in lung cancer, which involves the correlation between metabolites and the expressions of other omics, ultimately influencing clinical phenotypes. This novel trans-omics network approach would facilitate the development of precision therapy for lung cancer.Graphical 1. Integrating multiple biomarkers or trans-omics results improves diagnostic accuracy and reliability in heterogeneous lung cancer.2. Metabolomics and lipidomics, along with clinical phenotypes, construct a comprehensive metabolic profile of lung cancer patients.3. TAG expression shows strong positive correlation with polar metabolites, potentially impacting clinical phenotypic changes in lung cancer patients.

Risk Prediction of Emergency Department Visits in Patients With Lung Cancer Using Machine Learning: Retrospective Observational Study.

Patients with lung cancer are among the most frequent visitors to emergency departments due to cancer-related problems, and the prognosis for those who seek emergency care is dismal. Given that patients with lung cancer frequently visit health care facilities for treatment or follow-up, the ability to predict emergency department visits based on clinical information gleaned from their routine visits would enhance hospital resource utilization and patient outcomes. This study proposed a machine learning-based prediction model to identify risk factors for emergency department visits by patients with lung cancer. This was a retrospective observational study of patients with lung cancer diagnosed at Seoul National University Bundang Hospital, a tertiary general hospital in South Korea, between January 2010 and December 2017. The primary outcome was an emergency department visit within 30 days of an outpatient visit. This study developed a machine learning-based prediction model using a common data model. In addition, the importance of features that influenced the decision-making of the model output was analyzed to identify significant clinical factors. The model with the best performance demonstrated an area under the receiver operating characteristic curve of 0.73 in its ability to predict the attendance of patients with lung cancer in emergency departments. The frequency of recent visits to the emergency department and several laboratory test results that are typically collected during cancer treatment follow-up visits were revealed as influencing factors for the model output. This study developed a machine learning-based risk prediction model using a common data model and identified influencing factors for emergency department visits by patients with lung cancer. The predictive model contributes to the efficiency of resource utilization and health care service quality by facilitating the identification and early intervention of high-risk patients. This study demonstrated the possibility of collaborative research among different institutions using the common data model for precision medicine in lung cancer.

EJS-4 Development of molecularly-stratified precision medicine in lung cancer

EJS-4 Development of molecularly-stratified precision medicine in lung cancer

Lung cancer organoids: models for preclinical research and precision medicine

Lung cancer is a malignancy with high incidence and mortality rates globally, and it has a 5-year survival rate of only 10%–20%. The significant heterogeneity in clinical presentation, histological features, multi-omics findings, and drug sensitivity among different lung cancer patients necessitate the development of personalized treatment strategies. The current precision medicine for lung cancer, primarily based on pathological and genomic multi-omics testing, fails to meet the needs of patients with clinically refractory lung cancer. Lung cancer organoids (LCOs) are derived from tumor cells within tumor tissues and are generated through three-dimensional tissue culture, enabling them to faithfully recapitulate in vivo tumor characteristics and heterogeneity. The establishment of a series of LCOs biobanks offers promising platforms for efficient screening and identification of novel targets for anti-tumor drug discovery. Moreover, LCOs provide supplementary decision-making factors to enhance the current precision medicine for lung cancer, thereby addressing the limitations associated with pathology-guided approaches in managing refractory lung cancer. This article presents a comprehensive review on the construction methods and potential applications of LCOs in both preclinical and clinical research. It highlights the significance of LCOs in biomarker exploration, drug resistance investigation, target identification, clinical precision drug screening, as well as microfluidic technology-based high-throughput drug screening strategies. Additionally, it discusses the current limitations and future prospects of this field.

Concordance between cryobiopsy and forceps biopsy specimens in assessment of immunohistochemistry staining for non-small cell lung carcinoma.

Cryobiopsy is recently being promoted for biopsy of tumors in the lung periphery in precision medicine for lung cancer; the obtained tissue samples have been reported to be more useful compared to those obtained using forceps, because of the larger volume and higher quality. However, the influence of freezing and thawing of tissues when performing cryobiopsy on the results of immunohistochemistry (IHC) has not been completely understood. In this study, consecutive patients who underwent diagnostic bronchoscopy with cryobiopsy for peripheral pulmonary lesions (PPLs) at our institution between June 2017 and November 2021 were reviewed retrospectively. Specimens of diagnosed cases of unresectable or recurrent non-small cell lung carcinoma (NSCLC) were selected. We compared the results of IHC assessment for programmed death-ligand 1 (PD-L1), human epidermal growth factor receptor 2 (HER2), and human epidermal growth factor receptor 3 (HER3) in cryobiopsy specimens versus conventional forceps biopsy specimens from the same site in the same procedure. Twenty-four of 40 patients were male (60%). The most frequent histologic type of cancer was adenocarcinoma (n=31, 77.5%), followed by NSCLC (n=4, 10%), squamous cell carcinoma (n=3, 7.5%), and others (n=2, 5%). The concordance rates of the tumor proportion scores (TPSs) for PD-L1, IHC score for HER2 and, IHC scores for HER3 were 85%, 72.5%, and 75%, respectively; the weighted kappa were 0.835, 0.637, and 0.697, respectively. Freezing and thawing associated with cryobiopsy had virtually no effect on the results of IHC. We suggest that cryobiopsy specimens would therefore be ideal for precision medicine and translational research.

[Research Progress in Immune Checkpoint Inhibitors Combination Therapy Applied to 
Non-small Cell Lung Cancer after EGFR Mutation-targeted Therapy Resistance].

With the development of precision medicine for lung cancer, targeted therapy has greatly improved the survival and prognosis of patients with advanced non-small cell lung cancer (NSCLC), but the occurrence of acquired drug resistance ultimately leads to patients with no targeted drugs available and no standard treatment options for this group of patients afterwards. The emergence of immune checkpoint inhibitors (ICIs) has revolutionized the treatment of advanced NSCLC. However, due to the unique features of NSCLC with epidermal growth factor receptor (EGFR) mutation, such as immunosuppressive tumor microenvironment (TME), single ICIs treatment has limited clinical benefits in NSCLC patients with EGFR mutation, and the combination of ICIs with chemotherapy and/or targeted therapies is the trend. This review further discusses potential subpopulations with EGFR mutations that may benefit from ICIs treatment, and analyzes how decisions can be made in the era of combined immunotherapy to maximize the efficacy of ICIs treatment in EGFR mutation targeted therapy for NSCLC patients with drug resistance, with the aim of achieving individualized treatment.

Abstract 952: Prioritizing mutations associated with smoking as a variable in lung cancer precision medicine with immunotherapies

Abstract Background: In 2022, 230,000 new lung cancer cases will be diagnosed in the United States. The treatment regimen for non-small cell lung cancer (NSCLC) has drastically changed owing to the superior anti-cancer effects of immunotherapies. Immune checkpoint inhibitors (ICI) and chemo-immunotherapy (chemo-ICI) are the first-line treatments for NSCLC. Tumor Mutation Burden (TMB) and PD-L1 expression in tumor cells are potential biomarkers in predicting a patient’s survival and response to ICI. However, emerging data have shown that TMB and PD-L1 may no longer be an adequate biomarkers in predicting a patient’s response to ICI or chemo-ICI. We hypothesize that by using tumor-sequencing data and taking into effect a patient’s smoking status, we can identify biomarkers that predict survival to either ICI or chemo-ICI. Methods: To identify biomarkers, we collected genomic sequencing data and comprehensive clinical characteristics on 424 NSCLC patients who received ICI or chemo-ICI treatment at Atrium Health Wake Forest Baptist. Cox-proportional hazard regression models were fit to identify mutations that were “beneficial” (HR < 1) or “detrimental” (HR > 1) for patients on different treatment regimens, followed by the generation of mutation composite scores (MCS) for each treatment. Co-occurrence analysis was performed to identify novel co-occurring and mutually exclusive mutations in each treatment and smoking group by mutation interaction analysis. Results: We identify beneficial and harmful mutations in patients that received ICI or chemo-ICI treatment. We also identified unique biomarkers based on smoking statues. We then created an MCS for each smoking statues group and treatment type to assist personalize treatment. Future directions: We will validate these results in other institute cohorts and add other clinical characteristics to personalize treatment based on MCS for an individual patient. Citation Format: Margaret Rose Smith, Yuezhu Wang, Ralph D’Agostino, Yin Lin, Jimmy Ruiz, Thomas Lycan, Umit Topaloglu, Mohammed Abdulhaleem, Michael Chan, Fei Xing. Prioritizing mutations associated with smoking as a variable in lung cancer precision medicine with immunotherapies [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 952.

Lung cancer management in low and middle-income countries - current challenges and potential solutions

Lung cancer is the second-most prevalent cancer and, the most common cause of cancer-related mortality throughout the world. Evidence-based strategies to decrease its incidence and mortality are being implemented in different parts of the world. Smoking cessation policies are being actively publicized to decrease the incidence of tobacco related cancers including lung cancer. Screening program for early detection of localized lung cancer which can then be targeted by a multimodality approach utilizing surgery, radiation, chemoimmunotherapy to achieve cure is now becoming the standard in several high-income countries. Precision medicine in lung cancer is booming with metastatic non-curative cancers targeted with small molecule tyrosine kinase inhibitors. However, these advancements are not available all around the world. Inequalities exist in the management of lung cancer, mostly driven by the economic differences between the countries. Raising awareness to these international barriers is pivotal for improving lung cancer related incidence and mortality.

Omics sciences and precision medicine in lung cancer.

Lung cancer is a complex disease, with a wide range of genetic alterations and clinical presentations. Understanding the natural and clinical history of the disease is crucial for developing effective diagnostic and treatment strategies. Omics approaches, such as genomics, transcriptomics, proteomics, and metabolomics, have emerged as powerful tools for understanding the molecular mechanisms underlying lung cancer and for identifying novel biomarkers and therapeutic targets. These approaches enable researchers to examine the entire genome, transcriptome, proteome, or metabolome of a cell or tissue, providing a comprehensive view of the biological processes involved in lung cancer development and progression. Targeted therapies that address specific genetic mutations and pathways hold promise for improving the diagnosis and treatment of this disease.

Lysophosphatidylcholine inhibits lung cancer cell proliferation by regulating fatty acid metabolism enzyme long-chain acyl-coenzyme A synthase 5.

Lung cancer is a widespread malignancy with a high death rate and disorder of lipid metabolism. Lysophosphatidylcholine (lysoPC) has anti-tumour effects, although the underlying mechanism is not entirely known. The purpose of this study aims at defining changes in lysoPC in lung cancer patients, the effects of lysoPC on lung cancer cells and molecular mechanisms. Lung cancer cell sensitivity to lysoPC was evaluated and decisive roles of long-chain acyl-coenzyme A synthase 5 (ACSL5) in lysoPC regulation were defined by comprehensively evaluating transcriptomic changes of ACSL5-downregulated epithelia. ACSL5 over-expressed in ciliated, club and Goblet cells in lung cancer patients, different from other lung diseases. LysoPC inhibited lung cancer cell proliferation, by inducing mitochondrial dysfunction, altering lipid metabolisms, increasing fatty acid oxidation and reprograming ACSL5/phosphoinositide 3-kinase/extracellular signal-regulated kinase-regulated triacylglycerol-lysoPC balance. Thus, this study provides a general new basis for the discovery of reprogramming metabolisms and metabolites as a new strategy of lung cancer precision medicine.

Lung fibrogenic microenvironment in mouse reconstitutes human alveolar structure and lung tumor

SummaryA mesenchymal cell activation is a hallmark event of pulmonary fibrosis. Alveolar type 2 (AT2) cells are progenitor cells that maintain alveolar homeostasis, and their damage is assumed to be an initiating event for pulmonary fibrosis. However, the interaction between the lung fibrogenic microenvironment and AT2 cell dynamics remains to be elucidated. Here, we report a unique role of the lung fibrogenic microenvironment, where cell type-specific tissue reconstruction is achieved by exogenous cell transplantation. We found that in the lung fibrogenic microenvironment the AT2 cell pool was depleted, whereas mesenchymal cells could promote intact AT2 cell proliferation in vitro. Furthermore, exogenously transplanted AT2 cells formed alveolar colonies and ameliorated pulmonary fibrosis. Exogenous tumor cells formed tumor nests with relevant histological and transcriptional properties. Human primary cells were adaptable to this microenvironment, facilitating epithelial cell-targeted therapy in pulmonary fibrosis and the establishment of patient-derived xenografts for precision medicine in lung cancer.

From icing to slicing the cake: the new hope of precision medicine for lung cancer

From icing to slicing the cake: the new hope of precision medicine for lung cancer

Patient-derived organoids to predict the drug response in locally advanced or metastatic lung cancer: A real-world study.

9136 Background: Lung cancer organoids (LCOs) were expected to be the potential precision medicine approach for clinical response prediction. However, the clinical applications of both tissue and malignant serous effusions (MSE) derived LCOs were rarely reported. Our previous work demonstrated that MSE-derived LCOs maintain the genomic signature of the original tumor. In this study, we aimed to create LCOs using tissue or MSE, then validate the reliability of the model by comparing LCOs and their origin from the pathological and molecular levels. Furthermore, drug sensitivity tests of LCOs were also performed to evaluate the feasibility of LCO drug test as an approach for personalized medicine. Methods: Primary or metastatic tumor tissues were obtained from advanced lung cancer patients through core biopsy or surgically resected biopsy at the Guangdong Provincial People’s Hospital. MSEs were also collected. LCOs were generated from the obtained tissue and MSE, and the pathological features and genomic profiles were verified by analyzing the consistency with their origin. Then, the drug sensitivity scheme was formulated to follow the principles of clinical medication. In addition, proteomics analysis by 4D LC-MS/MS was also performed to analysis the molecular details of combinational therapy. Results: In our study, we generated 213 LCOs from 106 patients, mainly from MSE. The success rate to generate LCOs derived from MSE was 81.4% (131/161). The concordance rate of pathological phenotypes of LCOs samples verified by immunohistochemistry with clinical samples was 75% (63/84). In our cohort, LCO based drug sensitivity tests (LCO-DST) of targeted therapies were performed to predict the tumor response, and the AUC value of ROC analysis of osimertinib in EGFR-mutant adenocarcinoma reached 0.94(LCOs samples = 15, p= 0.0047). There were 2 patients with advanced lung adenocarcinoma, one with de novo EGFR mutation /MET amplification and the other with EGFR mutation combined with acquired RET fusion. The results of LCO based drug tests of 2 patients showed that combined targeted therapy (osimertinib plus savolitinib/cabozantinib) showed high tumor inhibition rate validated in clinical treatment and made differences. Then, 4D label-free high through-put proteomic analysis was performed in the patient with EGFR mutation and acquired RET fusion, demonstrating caspase 3 increased dramatically in combination of osimertinib and BLU-667 and the downstream proteins of EGFR and RET were down-regulated. Conclusions: LCOs derived from MSE faithfully reflected the pathological and genomic features of their original patients. The LCOs based drug test results are remarkably consistent with the tumor response. These results suggested the important prospects of LCO as an in vitro model for lung cancer precision medicine.

Development of Precision Medical Technology and its Current Clinical Applications

Background: Precision medicine has emerged with the development of science and technology and the rise of big data. This study first defines and presents the advantages of precision medicine and then introduces the development of three technologies: gene sequencing, cellular immunotherapy, and gene editing. The clinical applications of precision medicine in lung cancer, cervical cancer, breast cancer, and prostate cancer are thus analyzed. Lastly, the existing problems and future development directions of precision medicine are identified. The introduction of gene sequencing, bioanalytical techniques, and big data analysis tools has propelled medicine into the era of precision medicine. Key technologies in precision medicine form the foundation of its development. Therefore, this study elaborates on the development of key technologies in precision medicine, the current status of its clinical application, and the main problems that currently exist. This study also suggests solutions to the problems. Objective: To systematically explain the development and principle of three core technologies in precision medicine and to predict the main research trends of precision medicine. Results: Research in gene sequencing, cell immunotherapy, and gene editing technology has shown significant progress, and accurate medical treatment has achieved remarkable results, effectively prolonging the survival time and improving the quality of life of patients. Conclusion: Precision medicine has made significant achievements, but problems remain. Ensuring safety and efficiency in precision medicine should be the focus of future research.

Molecular Docking Studies with Garlic Phytochemical Constituents to Inhibit the Human EGFR Protein for Lung Cancer Therapy

In both developed and emerging economies, carcinoma is one of the most frequent malignancies. Non-small cell lung cancer accounts for 80% of all melanoma within India. The basic concept of molecular precision medicine for lung cancer is the EGFR (Epidermal growth factor receptor), which belongs to the tyrosine kinase receptor family (RTKs). Garlic (Allium sativum L), an Indian Traditional spice from the Allium family, appears to be capable of inhibiting cancer cell formation through an apoptotic route. In the present research, the competence and binding ability of various phytochemical compounds from garlic targeting lung melanoma marker protein were assessed. Garlic's medicinal benefits are assumed to be due to key components within the elemental composition, such as organosulfur compounds and flavonoids. There were 19 compounds along with 2 reference compounds that were used for molecular docking purposes. The study revealed that the chemicals investigated are bound effectively to EGFR. When compared to water-soluble and lipid-soluble organosulfur compounds, garlic flavonoids had the anticipated free energy of interaction. Especially in contrast to Alliin, S-Methyl-L-cysteine, Kaempferol, Quercetin, Myrecetin, Apigenin along with Erlotinib demonstrated excellent interaction. Alliin, the much more prevalent component throughout the whole garlic bulb, has a stronger affinity via intermolecular hydrogen bonding, with a binding energy of -7.4 kcal/ Mol. The water-soluble chemical S-Methyl-L-cysteine also had positive interactions with EGFR (-7.3 Kcal/mol). Among the flavonoid groups studied, Quercetin, Myrecetin and Apigenin were found to be the best suitable compound with a binding energy of -8.5 kcal/ Mol. The current findings pave the way for a deeper understanding of garlic's active substance interactions with EGFR. The substances discovered might be utilised as a prototype for anti-cancer treatmentHowever, more research into these compounds' biochemical functions is required before they can be used to treat lung cancer.

Whole-genome Sequencing-based Lung Cancer Precision Medicine in Japan

Whole-genome Sequencing-based Lung Cancer Precision Medicine in Japan

Precision Medicine in Lung Cancer Theranostics: Paving the Way from Traditional Technology to Advance Era

Precision medicine for lung cancer theranostics is an advanced model combining prevention, diagnosis, and treatment for individual or specific population diseases to match individual patient differences. It involves collection and integration of genome, transcriptome, proteome, and metabolome features of lung cancer patients, combined with clinical characteristics. Subsequently, large data and artificial intelligence (AI) analysis have emerged to identify the most suitable therapeutic targets and personal treatment strategies for treatment of patients with lung cancer. We review the development and challenges associated with diagnosis and therapy of lung cancer from traditional technology, including immunotherapy prediction markers, liquid biopsy, surgery, and tumor immune microenvironment and patient-derived xenograft models, to AI in the era of precision medicine. AI has improved precision medicine and the predictive ability and accuracy of patient outcomes. Finally, we discuss some opportunities and challenges for lung cancer theranostics. Precision medicine in lung cancer can help us find the optimum treatment dose and time for a specific patient, which can advance the development of lung cancer therapeutics.

Benefits of precision medicine in lung cancer: experience from a single institution

Lung cancer remains the leading cause of cancer death worldwide and in China. Over the past two decades, targeted therapy and immunotherapy have improved the five-year survival rate of lung cancer, and precision medicine has played a great part in this progress. However, owing to the heterogeneity, complexity, and economic disparity, the usage of precision medicine is still low, which generally results in a poor prognosis. In view of the current bottlenecks, such as low early diagnosis rates and the poor prognosis of lung cancer patients, our team predicted and identified early diagnosis markers of lung cancer. We have described the genetic characteristics of lung cancer patients, identified potential treatment targets, and explored accurate treatment schemes for the disease. We have also promoted their application by conducting bioinformatic analysis, high-throughput sequencing, liquid biopsy, and improved FISH and other technologies. This has formed an integrated system for the precise diagnosis, treatment, and transformation of lung cancer research, obtaining good social benefits after their application.

RNF8-mediated regulation of Akt promotes lung cancer cell survival and resistance to DNA damage.

Despite the tremendous success of targeted and conventional therapies for lung cancer, therapeutic resistance is a common and major clinical challenge. RNF8 is a ubiquitin E3 ligase that plays essential roles in the DNA damage response; however, its role in the pathogenesis of lung cancer is unclear. Here, we report that RNF8 is overexpressed in lung cancer and positively correlates with the expression of p-Akt and poor survival of patients with non-small-cell lung cancer. In addition, we identify RNF8 as the E3 ligase for regulating the activation of Akt by K63-linked ubiquitination under physiological and genotoxic conditions, which leads to lung cancer cell proliferation and resistance to chemotherapy. Together, our study suggests that RNF8 could be a very promising target in precision medicine for lung cancer.

PSY1-2 Expanding the reach of precision medicine in lung cancer

PSY1-2 Expanding the reach of precision medicine in lung cancer