Response In Lung Cancer Patients Research Articles

FTIR monitoring of the 13-valent pneumococcal conjugate vaccine for lung cancer patients: Changes in amides vibrations correlated with biochemical assays

Lung cancer is one of the most lethal cancers. Unfortunately, respiratory tract infections are very common in lung cancer patients, delaying appropriate anticancer therapy. To increase therapy efficiency, in this study we examined the effect of 13-Valent Pneumococcal Conjugate Vaccine on the immune response in lung cancer patients, which indirectly affects the success of anticancer therapy. The study was done using biochemical tests and Fourier Transform InfraRed (FTIR) spectroscopy. For this purpose, serum from lung cancer patients aged 52 ± 9 years (III and IV clinical stage; 79 %; n = 103) before and seven as well as 30 days after vaccination was collected. Obtained results showed increasing concentrations of immunoglobulin IgG and IgG2 groups in patients after vaccination in comparison with group before vaccination. This result was confirmed by FTIR spectroscopy, where higher absorbances of amides vibrations were observed after vaccination. Interestingly, lack of differences in the amides absorbances between patients 7 and 30 days after vaccination were noticed. FTIR spectra also showed changes in the ratio between amide I and amide III as well as between amide II and amide III in the groups of patients after vaccination. From deconvolution of made I range (1600 cm−1–1700 cm−1) decrease of the ratio between α-helix and β-sheet around 0.05 was noticed in serum collected from patients after vaccination in comparison with patients before vaccination. Using Principal Component Analysis (PCA) analysis of FTIR data it was observed that serum collected from all three analyzed groups of samples was possible to differentiate. The highest accuracy in differentiation group of samples before and 7 days after vaccination was visible in amide I, while before and 30 days after vaccination using amide II. Correlation between immunoglobulin IgG and IgG2 concentrations obtained by biochemical assays and FTIR were noticed only in the group of serum collected 30 days after vaccination, which suggested that FTIR spectroscopy reflects biochemical data.

Identification of Immune-Related Gene Signature Model for Predicting Lung Cancer Survival and Response to Immunotherapy

Introduction: Studies have shown that immune-related genes play a crucial role in tumor development and treatment. However, the specific roles and potential value of these genes in lung cancer patients are still not fully understood. Therefore, this study aims to establish a novel risk model based on immune-related genes for evaluating the prognostic risk and response to immune therapy in lung cancer patients. Methods: Gene expression and clinical data of lung cancer patients were retrieved from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, while immune-related genes were obtained from the ImmPort database. A risk signature model was developed using univariate Cox analysis and LASSO regression analysis. The prognostic value of the model and its response to immunotherapy were analyzed by survival analysis, immune infiltration analysis, and immunotherapy response analysis. Results: We have developed a risk signature model based on eight key immune-related genes, which can classify patients into high-risk and low-risk groups. The prognosis of the high-risk group was significantly lower than that of the low-risk group and was validated in multiple GEO datasets. The mutation frequency was lower in the low-risk group compared to the high-risk group (TP53: 55% vs. 65%; TTN: 52% vs. 60%; CSMD3: 34% vs. 45%). Futhermore, CD274 expression was lower in the low-risk patients, and the high-risk patients in the IMvigor210 cohort had lower survival. Immune infiltration analyses showed that the high-risk group was negatively correlated with the infiltration level of B cells, CD4+ T cells, and NK cells. Importantly, patients in the low-risk group exhibit significantly lower TIDE scores, suggesting that they are more responsive to immunotherapy. Conclusion: Our study has established a novel and robust immune-related gene risk model that can assist in evaluating the prognostic risk and immune therapy response of lung cancer patients.

Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning

BackgroundLung cancer (LC) is a leading cause of cancer-related mortality, and immunotherapy (IO) has shown promise in treating advanced-stage LC. However, identifying patients likely to benefit from IO and monitoring treatment response remains challenging. This study aims to develop a predictive model for progression-free survival (PFS) in LC patients with IO based on clinical features and advanced imaging biomarkers.Materials and methodsA retrospective analysis was conducted on a cohort of 206 LC patients receiving IO treatment. Pre-treatment computed tomography images were used to extract advanced imaging biomarkers, including intratumoral and peritumoral-vasculature radiomics. Clinical features, including age, gene status, hematology, and staging, were also collected. Key radiomic and clinical features for predicting IO outcomes were identified using a two-step feature selection process, including univariate Cox regression and chi-squared test, followed by sequential forward selection. The DeepSurv model was constructed to predict PFS based on clinical and radiomic features. Model performance was evaluated using the area under the time-dependent receiver operating characteristic curve (AUC) and concordance index (C-index).ResultsCombining radiomics of intratumoral heterogeneity and peritumoral-vasculature with clinical features demonstrated a significant enhancement (p < 0.001) in predicting IO response. The proposed DeepSurv model exhibited a prediction performance with AUCs ranging from 0.76 to 0.80 and a C-index of 0.83. Furthermore, the predicted personalized PFS curves revealed a significant difference (p < 0.05) between patients with favorable and unfavorable prognoses.ConclusionsIntegrating intratumoral and peritumoral-vasculature radiomics with clinical features enabled the development of a predictive model for PFS in LC patients with IO. The proposed model’s capability to estimate individualized PFS probability and differentiate the prognosis status held promise to facilitate personalized medicine and improve patient outcomes in LC.

Dietary Counseling Outcomes in Patients with Lung Cancer in an Upper-Middle-Income Country: An Open-Label Randomized Controlled Trial.

Background: Malnutrition harms treatment outcomes, QoL, and survival in lung cancer patients. Effective dietary counseling can improve nutrition, but few randomized controlled trials have focused on lung cancer patients. The objective of this study was to determine if dietary counseling improves nutritional and treatment outcomes when compared to routine care. Methods: This open-label parallel RCT was conducted at Maharaj Nakorn Chiang Mai Hospital in Thailand. The investigators used computer-generated blocked randomization to assign patients to dietary counseling by a nutritionist or routine care. The nutritionist sessions occurred before treatment, with follow-ups at 3-4 weeks and 12 weeks. The primary outcome was the mean percentage change in the body weight of patients at 12 weeks. Secondary outcomes included changes in the BMI, nutrition score, QoL, serum albumin level, lymphocyte count, energy and protein intake, treatment response, PFS, and OS. Results: Between April 2020 and May 2022, after completing recruitment, 80 lung cancer patients were randomized: 43 to dietary counseling and 37 to routine care. The dietary counseling group showed significant benefits, with smaller decreases in body weight at 3-4 weeks (-0.8% vs. -2.6%, p = 0.05) and 12 weeks (-1.1% vs. -4.3%, p = 0.05). They also had higher energy and protein intake levels and better treatment response rates. The secondary outcomes and significant adverse events did not differ significantly between the groups. Conclusions: Dietary counseling helps to maintain body weight, maintain dietary intake, and enhance treatment responses in lung cancer patients. Although not all nutritional markers or survival outcomes were affected, these findings highlight the importance of early nutritional interventions.

The Role of Serum IL-23 and Volatile Organic Compound Levels to RECIST 1.1 in The Evaluation of Therapeutic Response in Lung Cancer

The Response Evaluation Criteria in Solid Tumors (RECIST 1.1) is the gold standard for the assessment of lung cancer progression. However, the assessment and diagnosis of early treatment failure is challenging due to the limitations of current tools, as well as the long intervals and unavoidable side effects.This study aims to correlate volatile organic compound (VOC) patterns, serum level of interleukin-23 (IL-23), and RECIST 1.1 to assess chemotherapy response in lung cancer patients at Saiful Anwar Hospital. A prospective observational study was performed to 47 lung cancer patients who received three cycles of platinum-based chemotherapy. Using the Breath Analyzer to measure certain volatile organic compounds (VOCs), the study observed that three of the seven VOCs examined, formaldehyde (CH2O), toluene (C7H8), and hexane (C6H14), showed lower levels after three cycles of chemotherapy. Furthermore, there was a negative correlation between RECIST1.1 and acetone (C3H6O) (p=0.023), while RECIST1.1 and methane (CH4) had a positive correlation (p=0.011). Moreover, a significant positive correlation was observed between IL-23 after-chemotherapy and RECIST 1.1 (p=0.000). According to this study, a correlation exists between methane, IL-23, and RECIST 1.1 after three cycles of chemotherapy. The increase in methane and IL-23 aligns with the disease progression determined by RECIST 1.1. Furthermore, The decrease in acetone after chemotherapy showed a negative correlation with RECIST1.1, consistent with disease progression.Keywords: Volatile Organic Compound, Interleukin-23, RECIST 1.1.

Abstract 3844: Clinical implications of mixed response on prognosis of non-small cell lung cancer (NSCLC) patients treated with Immunotherapy

Abstract Background: Current methods for assessing the response of lung cancer patients to therapy (e.g. RECIST criteria) lack consideration for mixed response (MR) where some lesions increase while others decrease simultaneously. MR poses a challenge in deciding the continuation of therapy. Understanding the clinical implications of MR on patient prognosis within radiographic response categories is crucial for establishing effective treatment strategies for NSCLC. Methods: This study analyzed 177 NSCLC patients with ≥2 lesions receiving first-line systemic treatment with immunotherapy. Clinical response was evaluated by RECIST v1.1 and immune-related RECIST (irRECIST). MR was defined as the simultaneous presence of ≥1 lesion increase and ≥1 lesion decrease during therapy. Two different definitions of MR were utilized. Definition-A (MR-A): the simultaneous presence of one or more lesions increasing ≥5mm and one or more lesions decreasing ≥5mm during therapy (Δ [baseline - first follow-up] ≥ 5 mm); and Definition-B (MR-B): the simultaneous presence of one or more lesions increasing &amp;gt;1mm and one or more lesions decreasing &amp;gt;1mm during therapy (any Δ [baseline - first follow-up]). Subgroup analysis was conducted using 94 patients with only intrathoracic lesions. Results: Among the 177 patients, tumor responses based on RECIST were 3 CR (1.7%),42 PR (23.7%), 87 SD (49.2%), and 45 PD (25.4%). Based on irRECIST, responses were 3 CR (1.7%), 46 PR (26.0%), 89 SD (50.3%), and 39 PD (22.0%). MR-A and MR-B occurred in 19.8% (n=35) and 45.2% (n=80) of cases, respectively. In the irRECIST PD group, MR-A demonstrated significantly different median progression-free survival (mPFS) and median overall survival (mOS) compared to non-MR-A (p&amp;lt;0.020, p&amp;lt;0.005, respectively). The mPFS for MR-A group was 1.4 months (range 0.5-52.9) and non-MR-A group was 5.5 months (range 0.2-97.2) with the hazard ratio being 2.91 (95% CI 1.13-7.51). The mOS for MR-A and non-MR-A groups were 4.8 months (range 0.7-61.5) and 11.8 months (range 0.2-97.2), respectively, hazard ratio 4.87 (95% CI 1.43-16.56). No other significant results were found for other RECIST and irRECIST categories. Subgroup analysis of only intrathoracic lesion patients revealed no statistically significant results. Conclusion: Contrary to our hypothesis, MR did not modify prognosis within the RECIST or irRECIST category. MR-A or MR-B in the RECIST or irRECIST PD group did not show a better prognosis, indicating the complexity of mixed responses in NSCLC patients treated with immunotherapy. Further research with larger cohorts are warranted. Citation Format: Seyoung Lee, Taegyu Um, Jeeyeon Lee, Peter Haseok Kim, Monica Yadav, Maria J. Chuchuca, Liam Il-Young Chung, Young Kwang Chae. Clinical implications of mixed response on prognosis of non-small cell lung cancer (NSCLC) patients treated with Immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3844.

Gene features of tumor-specific T cells relevant to immunotherapy, targeted therapy and chemotherapy in lung cancer

(1) BackgroundIn lung cancer, the use of small-molecule inhibitors, chemotherapy and immunotherapy has led to unprecedented survival benefits in selected patients. Considering most patients will experience a relapse within a short period of time due to single drug resistance, combination therapy is also particularly important to improve patient prognosis. Therefore, more robust biomarkers to predict responses to immunotherapy, targeted therapy, chemotherapy and rationally drug combination therapies may be helpful in clinical treatment choices. (2) MethodsWe defined tumor-specific T cells (TSTs) and their features (TSTGs) by single-cell RNA sequencing. We applied LASSO regression to filter out the most survival-relevant TSTGs to form the Tumor-specific T cell score (TSTS). Immunological characteristics, enriched pathways, and mutation were evaluated in high- and low TSTS groups. (3) ResultsWe identified six clusters of T cells as TSTs in lung cancer, and four most robust genes from 9 feature genes expressed only on tumor-specific T cells were screened to construct a tumor-specific T cells score (TSTS). TSTS was positively correlated with immune infiltration and angiogenesis and negatively correlated with malignant cell proliferation. Moreover, potential vascular-immune crosstalk in lung cancer provides the theoretical basis for combined anti-angiogenic and immunotherapy. Noticeable, patients in high TSTS had better response to ICB and targeted therapy and patients in the low TSTS group often benefit from chemotherapy. (4) ConclusionThe proposed TSTS is a promising indicator to predict immunotherapy, targeted therapy and chemotherapy responses in lung cancer patients for helping clinical treatment choices.

Abstract 6446: ResponderID™ KRAS: Biology-driven machine learning to personalize KRAS inhibitor therapeutics

Abstract Breakthroughs in targeted KRAS therapeutics (KRASi) have the potential to transform the treatment landscape for several of the most common cancers including lung, colorectal, and pancreatic. Despite the recent approvals of KRASi and the anticipation of more to come, both the rate of patient response and the durability of these responses remain significant areas requiring improvement. Biomarkers that can predict response to KRASi and guide effective patient selection and drug combination strategies will be key to realizing the full potential of this emerging therapeutic field. While most biomarkers predominantly rely on a single analyte (e.g. KRAS mutation status), Genialis’ biomarkers are constructed using high-dimensional and/or multimodal data that capture the underlying biological complexity unique to each individual patient. Genialis' ResponderID™ is a machine learning-based biomarker discovery framework that models fundamental aspects of cancer biology to predict the clinical benefit based on the patient’s own biology. Here we report progress towards the development of a first-in-class, RNA-based biomarker, ResponderID™ KRAS, capable of stratifying KRAS G12C inhibitor response in lung cancer patients using RNA sequencing data. Trained on thousands of lung cancer samples, our biomarker models therapeutic response by unifying two core KRAS biologic axes, dependency and activation, to identify those patients most likely to respond. The performance characteristics of ResponderID™ KRAS thus far has been evaluated on a real world dataset of lung cancer patients treated with Sotorasib. ResponderID™ KRAS serves as an independent biomarker designed to inform clinical trial design, select for therapeutic efficacy, identify rational combination strategies, and expedite approvals across various therapeutic contexts. Citation Format: Josh Wheeler, Anže Lovše, Klemen Žiberna, Miha Štajdohar, Luka Ausec, Janez Kokošar, Daniel Pointing, Aditya Pai, Rafael Rosengarten, Mark Uhlik. ResponderID™ KRAS: Biology-driven machine learning to personalize KRAS inhibitor therapeutics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6446.

Evaluating distinct KRAS subtypes as potential biomarkers for immune checkpoint inhibitor efficacy in lung adenocarcinoma.

Despite the acknowledged predictive value of KRAS in immune checkpoint inhibitor (ICI) responses, the heterogeneous behavior of its mutations in this sphere remains largely unexplored. As of now, no studies have definitively categorized KRAS subtype variations as independent prognostic indicators for ICI responses in lung cancer patients. We analyzed a cohort of 103 patients, all harboring different KRAS mutation subtypes, and complemented this data with information from TCGA and GEO databases. Our research focused on delineating the relationships between KRAS mutation subtypes and factors like immunotherapy markers and immune cell composition, in addition to examining survival rates, drug sensitivity, and PD-L1 responses corresponding to distinct KRAS subtypes. We found that the G12V and G12D subtypes demonstrated elevated expressions of immunotherapy markers, implying a potentially enhanced benefit from immunotherapy. Significant variations were identified in the distribution of naive B cells, activated CD4+ memory T cells, and regulatory T cells (Tregs) across different KRAS mutant subtypes. A notable difference was observed in the Tumor Mutation Burden (TMB) levels across the four KRAS subtypes, with the G12D subtype displaying the lowest TMB level. Furthermore, G12C subtype showcased the worst prognosis in terms of progression-free intervals (PFI), in stark contrast to the more favorable outcomes associated with the G12A subtype. Our study reveals that KRAS mutations exhibit considerable variability in predicting outcomes for LUAD patients undergoing ICI treatment. Thus, the evaluation of KRAS as a biomarker for ICIs necessitates recognizing the potential diversity inherent in KRAS mutations.

Tumor mutation burden in the prognosis and response of lung cancer patients to immune-checkpoint inhibition therapies

Immune checkpoint inhibition (ICI) therapies have reshaped the therapeutic landscape in lung cancer management, providing first-time improvements in patient response, prognosis, and overall survival. Despite their clinical effectiveness, variability in treatment responsiveness, as well as drug resistance, have led to a compelling need for predictive biomarkers facilitating the individualized selection of the most efficient therapeutic approach. Significant progress has been made in the identification of such biomarkers, with tumor mutation burden (ΤΜΒ) appearing as the leading and most promising predictive biomarker for the efficacy of ICIs in non-small cell lung cancer (NSCLC) among other tumors. Anti-PD-1/PD-L1 and anti-CTLA-4 antibodies have been extensively studied and clinically utilized. However, the overall efficiency of these drugs remains unsatisfactory, urging for the investigation of novel inhibitors, such as those targeting LAG-3, TIM-3, TIGIT and VISTA, which could be used either as a monotherapy or synergistically with the PD-1/PD-L1 or CTLA-4 blockers. Here, we investigate the role of TMB and cancer neoantigens as predictive biomarkers in the response of lung cancer patients to different ICI therapies, specifically focusing on the most recent immune checkpoint inhibitors, against LAG-3, TIM-3, TIGIT and VISTA. We further discuss the new trends in immunotherapies, including CAR T-cell therapy and personalized tumor vaccines. We also review further potential biomarkers that could be used in lung cancer response to immunotherapy, such as PD-L1+ IHC, MSI/dMMR, tumor infiltrating lymphocytes (TILs), as well as the role of the microbiome and circulating tumor DNA (ctDNA). Finally, we discuss the limitations and challenges of each.

Genotyping, in silico screening and molecular dynamics simulation of SNPs of MGMT and ERCC1 gene in lung cancer patients treated with platinum-based doublet chemotherapy

Lung cancer, the leading cause of death worldwide, arises from an intricate combination of genetic and environmental factors. Genetic variations can influence the chemotherapeutic response of lung cancer patients in DNA repair genes. This study examines the response to platinum-based drugs among lung cancer patients of North Indian descent who possess genetic variations in the MGMT and ERCC1 genes. P CR-RFLP method was used for genotypic analysis. MedCalc statistical software was used to calculate odds ratios and Median Survival Time (MST). GROMACS software was used to perform Molecular dynamic simulation. ADCC Patients revealed a significant association with MGMT in the heterozygous genotype (HR= 1.56, p=0.02) and also with ERCC1 in both mutant and combined variants (HR= 1.25, p=0.01; HR=0.78, p=0.03). SQCC subjects harbouring ERCC1 polymorphism also reported a 2-fold increase in hazard ratio and a corresponding decrease in survival time for heterozygous and combined variants (HR= 2.55, p=0.02; HR 2.33, p=0.01, respectively). MD simulation results demonstrate a lower RMSD, stable radius of gyration, and lower RMSF, indicating the mutated MGMT protein is more stable than the wild. Further, the docking score for DNA-Wild and DNA-L84F mutants are -201.6 and -131.8, respectively. MD Simulation of the complexes further validated the results. Our study concludes that MGMT and ERCC1 polymorphisms are associated with decreased overall survival. Further, computational analysis of MGMT (rs12917) polymorphism revealed that mutated MGMT cannot bind properly to the DNA and hence cannot properly repair DNA, resulting in lower overall survival. Communicated by Ramaswamy H. Sarma

Serological Response to SARS-CoV-2 after COVID-19 Vaccination in Lung Cancer Patients: Short Review.

In comparison to the general population, lung cancer patients are more likely to suffer from severe Coronavirus disease (COVID-19) and associated mortality. Considering this increased risk, and in order to prevent symptoms and severe disease, patients with lung cancer have been prioritized for COVID-19 vaccination primary and booster doses. Despite this, the pivotal clinical trials did not include these patients, which leaves open questions regarding vaccine efficacy and humoral immune response. This review outlines the findings of recent investigations into the humoral responses of lung cancer patients to COVID-19 vaccination, particularly the primary doses and first boost.

Abstract B015: Wildtype RAS activity and PI3K signaling as new vulnerabilities in cells with acquired resistance to sotorasib

Abstract Purpose: Sotorasib (AMG510) has demonstrated remarkable response in lung cancer patients with tumors driven by oncogenic KRASG12C mutation. However, recently published clinical data identified acquired mutations in RAS and other genomic alterations as potential mechanisms of resistance. The cause of resistance in more than half of the patient cohort was undetermined with genomic sequencing. We hypothesize that rewiring of signaling networks could be the newly acquired vulnerabilities in cells progressing after sotorasib treatment. Experimental Design: We performed whole exome sequencing, transcriptomic profiling, and mass spectrometry-based proteomics/phosphoproteomics of the parental and sotorasib resistant isogenic line of LU65 (LU65-AMGR). The omics analyses were integrated with functional screens to prioritize a set of druggable targets/pathways. Results: We did not observe obvious acquired mutations that drive resistance to sotorasib in LU65-AMGR cells. To investigate compensatory signaling critically important for the survival/growth of LU65-AMGR cells, we studied signaling perturbations using mass spectrometry-based phosphoproteomics approach. We identified 430 and 1,574 phosphosites differentially expressed in resistant cells (± 1.5-fold &amp; p &amp;lt; 0.05) with phosphotyrosine (pY) and global phosphoproteome (pS/T/Y) enrichment, respectively. Analysis of phosphoproteomics data identified increased phosphorylation of multiple RTKs including EGFR, HER2, HER3, IGF1R, AXL and PDGFRA, including others. RAS-GTP pull-down show increased levels of RAS-GTP and NRAS-GTP in the resistant cells. RAS isoform quantification using parallel reaction monitoring (MRM) mass spectrometry further confirmed increased activity of NRAS in pull-down samples. Transcriptomic analysis revealed elevated genes responsible for anti-apoptosis pathways mediated by PI3K/AKT signaling. Enrichment of transcription factors such as AP1 and AP-2A could drive enhanced EGF expression and EGFR phosphorylation in sotorasib resistant cells. Consistent with enhanced RTK and RAS activity, Western blots confirmed higher phosphorylation of ERK and AKT in LU65-AMGR cells. We show higher EGFR phosphorylation in resistant cells when compared to the parental counterpart. While LU65 cells viability is reduced markedly by sotorasib combination with afatinib, an irreversible HER family inhibitor, resistant cells showed lesser effect on cell viability with afatinib combination. Consistent with our in vitro observations, the sotorasib and afatinib combination treatment significantly regressed tumor growth only in LU65 xenografts, but not in LU65-AMGR xenografts. Conclusions: Our data suggest that activation of multiple RTKs maintains RAS activity and PI3K signaling in LU65-AMGR cells. Thus, dual pan-RAS and PI3K inhibition could serve as promising strategy of treatment in relapsed tumors identified with WT RAS and PI3K signaling activation. Citation Format: Denis Imbody, Hitendra S. Solanki, Bina Desai, Paul A. Stewart, Yaakov Stern, Ryoji Kato, Anurima Majumder, Liznair Bridenstine, Aobuli Xieraili, Bin Fang, Lancia Darville, Fumi Kinose, John M. Koomen, Uwe Rix, Andriy Marusyk, Eric B. Haura. Wildtype RAS activity and PI3K signaling as new vulnerabilities in cells with acquired resistance to sotorasib [abstract]. In: Proceedings of the AACR Special Conference: Targeting RAS; 2023 Mar 5-8; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Res 2023;21(5_Suppl):Abstract nr B015.

Predicting lung cancer treatment response from CT images using deep learning

AbstractLung cancer is the deadliest type of cancer and is one of the most frequently occurring cancers. It is primarily diagnosed in later stages when treatment becomes difficult. For better treatment and higher chances of survival, the treatment response of lung cancer patients needs to be analyzed to check whether the patients are responding to the treatment or not. This analysis can be done with the help of follow‐up computed tomography (CT) imaging before and after the treatment. However, manually analyzing the baseline and post‐treatment CT scan images of so many lung cancer patients is a tedious task. This study proposes an intuitive approach based on deep learning to analyze lung cancer through CT scan images before and after the treatment. In this approach, we utilized a segmentation network to segment the lung tumor in the follow‐up CT images. The segmented tumor is then analyzed to check the treatment effect, as suggested by the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines. The segmentation network combines a vision transformer and a convolutional neural network. The segmentation network is first trained on a public dataset and then fine‐tuned on the local dataset to improve the segmentation performance. For this study, we have collected a lung cancer dataset from an Indian hospital. The dataset is divided into two parts dataset I and dataset II. Dataset I consists of 100 CT scans, which we use to fine‐tune the proposed segmentation network. Dataset II comprises 220 CT scans of 110 patients, consisting of baseline and post‐treatment scans. We use dataset II for testing. We achieved significant performance.

Abstract 5784: Spatial clustering reveals immune hub interaction with reservoir of stem-like CD8 T cells and predicts immunotherapy response in lung cancer patients

Abstract Background: Immunologic tumor control involves coordination of multiple cell types, but the organization of these interactions in human tumors is poorly understood. We recently reported that immunogenic tumors contain spatially-localized multicellular immunity hubs characterized by expression of interferon-stimulated genes, including CXCL10/CXCL11 (T cell attracting chemokines), and the presence of IFNG+ T cells. This suggested a positive feedback loop where T cell-derived IFNγ stimulates production of CXCR3 ligands, thereby attracting more T cells. However, we did not know (1) whether these hubs predict response to immunotherapy and (2) how hubs intersect with the various CD8 T cell states which play central roles in anti-tumor immunity. Methods: To understand the composition of this immunity hub and its potential association with immunotherapy response, we performed multiplex RNA FISH to visualize hub components in NSCLC tissue from 68 patients prior to PD1-blockade. Cells were segmented and phenotyped automatically. We additionally imaged serial sections with a second panel for markers of CD8 T cell state. We then computationally registered sequential images, integrating our panels. To identify hubs in an unbiased manner, we employed kmeans clustering. Results: We found that the presence of the immunity hub is predictive of subsequent response to PD1-blockade. Image registration revealed that hubs are enriched for CD8 T cells in multiple states, confirming their role as key sites of anti-tumor T cell activity. Furthermore, immunity hubs in responders contained more activated CD8 T cells and IFNG+ cells than those in nonresponders. To determine how hub heterogeneity may influence hub functions, we subclustered hubs by phenotypic composition. This analysis uncovered a ‘hybrid hub’ subclass that spatially overlaps with structures containing stem-like TCF7+ CD8 T cells, resembling the interfollicular zone of lymph nodes. The presence of a single hybrid hub was strongly associated with RECIST response (p = 0.0005), found in 85% of responders and only 24% of non-responders. Hybrid hubs also showed a striking association with patient PFS (p = 0.0014). Using ultra-highplex RNA ISH technologies, we further explored the hybrid hubs and found they are enriched in macrophages that express CXCR3 ligands. Conclusions: Our study provides insight into the multicellular networks that underlie anti-tumor immunity. Immunity hubs are predictive of response to immunotherapy in human lung cancer and organize intratumoral CD8 T cell activity. Moreover, hybrid hubs may represent an active intratumoral niche for tumor-specific stem-like T cells that sustain anti-tumor immunity. These multicellular networks are excellent candidates for biomarker development and targets for immunotherapy. Citation Format: Jonathan Chen, Linda Nieman, Maxwell Spurrell, Justin Gainor, Nir Hacohen. Spatial clustering reveals immune hub interaction with reservoir of stem-like CD8 T cells and predicts immunotherapy response in lung cancer patients. [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 5784.

Nanoparticles overcome adaptive immune resistance and enhance immunotherapy via targeting tumor microenvironment in lung cancer.

Lung cancer is one of the common malignant cancers worldwide. Immune checkpoint inhibitor (ICI) therapy has improved survival of lung cancer patients. However, ICI therapy leads to adaptive immune resistance and displays resistance to PD-1/PD-L1 blockade in lung cancer, leading to less immune response of lung cancer patients. Tumor microenvironment (TME) is an integral tumor microenvironment, which is involved in immunotherapy resistance. Nanomedicine has been used to enhance the immunotherapy in lung cancer. In this review article, we described the association between TME and immunotherapy in lung cancer. We also highlighted the importance of TME in immunotherapy in lung cancer. Moreover, we discussed how nanoparticles are involved in regulation of TME to improve the efficacy of immunotherapy, including Nanomedicine SGT-53, AZD1080, Nanomodulator NRF2, Cisplatin nanoparticles, Au@PG, DPAICP@ME, SPIO NP@M-P, NBTXR3 nanoparticles, ARAC nanoparticles, Nano-DOX, MS NPs, Nab-paclitaxel, GNPs-hPD-L1 siRNA. Furthermore, we concluded that targeting TME by nanoparticles could be helpful to overcome resistance to PD-1/PD-L1 blockade in lung cancer.

Signature stemmed from two transcription factor families determines histological fate and regulates immune infiltration in patients with lung cancer.

Earlier research has reported that transcription factors play a crucial role in the anti-tumorigenic immune response of lung cancer patients. The aim of this study is to determine the relationship between post-translational modifications of transcription factors and histological fate and patient prognosis. Based on the information of 293 lung cancer patients in the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) related to the interferon regulatory factor (IRF) and signal transducer and activator of transcription (STAT) families between patients experiencing early death and those with long-term survival were identified and characterized. A survival prediction model was established by incorporating 7 STAT genes and 9 IRF genes into the least absolute shrinkage and selection operator (LASSO) algorithm. Gene Ontology (GO) enrichment analysis indicated that these two transcription factor families can govern lung cancer tissue differentiation and predict patient prognosis. Moreover, the Cox proportional hazards regression model was applied to select the genes with the highest predictive capability to construct a gene-based signature. Lastly, the data of 1,803 and 784 lung cancer patients from the Kaplan-Meier plotter (KMPLOT) and The Cancer Genome Atlas (TCGA) databases were used to evaluate the accuracy and sensitivity of the model. Based on the minimum criterion, TRIM28, IRF3, and STAT3 were employed to generate the prognostic model. The 1-, 3-, and 5-year area under the curve (AUC) values of the three-gene-based signature showed consistent results, signifying that the model had excellent accuracy and sensitivity in predicting overall survival (OS) for patients with lung cancer. Finally, the three-gene signature and tumor-node-metastasis (TNM) staging system were combined to construct a nomogram for evaluating the OS of lung cancer patients. TRIM28 may affect the stability of IRF3. Encouragingly, the predicted OS was highly consistent with the observed OS in multiple cohorts. Taken together, these findings implied that the predictive model based on the three-gene signature showed robust discriminatory performance.

Metabolic inhibitor screening identifies dihydrofolate reductase as an inducer of the tumor immune escape mediator CD24.

Immune checkpoint inhibitors have improved the clinical management of some cancer cases, yet patients still fail to respond to immunotherapy. Dysregulated metabolism is a common feature of many cancers, and metabolites are known to modulate functions in cancer cells. To identify potential metabolic pathways involved in anti-tumor immune response, we employed a metabolic inhibitor-based drug screen in human lung cancer cell lines and examined expression changes in a panel of immune regulator genes. Notably, pharmacologic inhibition of dihydrofolate reductase (DHFR) downregulated cancer cell expression of cluster of differentiation 24 (CD24), an anti-phagocytic surface protein. Genetic modulation of DHFR resulted in decrease of CD24 expression, whereas tetrahydrofolate, the product of DHFR, enhanced CD24 expression. DHFR inhibition and the consequent CD24 decrease enhanced T cell-mediated tumor cell killing, whereas replenishment of DHFR or CD24 partially mitigated the immune-mediated tumor cell killing that resulted from methotrexate treatment in cancer cells. Moreover, publicly available clinical data analyses further revealed the link between DHFR, CD24, and the antitumor immune response in lung cancer patients. Our study highlights a novel connection between folate metabolism and the anti-tumor immune response and partially interprets how DHFR inhibitors lead to clinical benefits when combined with cancer immunotherapy agents.

Immunotherapy response and microenvironment provide biomarkers of immunotherapy options for patients with lung adenocarcinoma.

Background: Immunotherapy has been a promising approach option for lung cancer. Method: All the open-accessed data was obtained from the Cancer Genome Atlas (TCGA) database. All the analysis was conducted using the R software analysis. Results: Firstly, the genes differentially expressed in lung cancer immunotherapy responders and non-responders were identified. Then, the lung adenocarcinoma immunotherapy-related genes were determined by LASSO logistic regression and SVM-RFE, respectively. A total of 18 immunotherapy response-related genes were included in our investigation. Subsequently, we constructed the logistics score model. Patients with high logistics score had a better clinical effect on immunotherapy, with 63.2% of patients responding to immunotherapy, while only 12.1% of patients in the low logistics score group responded to immunotherapy. Moreover, we found that pathways related to immunotherapy were mainly enriched in metabolic pathways such as fatty acid metabolism, bile acid metabolism, oxidative phosphorylation, and carcinogenic pathways such as KRAS signaling. Logistics score was positively correlated with NK cells activated, Mast cells resting, Monocytes, Macrophages M2, dendritic cells resting, dendritic cells activated and eosinophils, while was negatively related to Tregs, macrophages M0, macrophages M1, and mast cells activated. In addition, ERVH48-1 was screened for single-cell exploration. The expression of ERVH48-1 increased in patients with distant metastasis, and ERVH48-1 was associated with pathways such as pancreas beta cells, spermatogenesis, G2M checkpoints and KRAS signaling. The result of quantitative real-time PCR showed that ERVH48-1 was upregulated in lung cancer cells. Conclusion: Our study developed an effective signature to predict the immunotherapy response of lung cancer patients.

From COPD to Lung Cancer: Mechanisms Linking, Diagnosis, Treatment, and Prognosis.

Many studies have proved that the pathogenesis of the chronic obstructive pulmonary disease (COPD) and lung cancer is related, and may cause and affect each other to a certain extent. In fact, the change of chronic airway obstruction will continue to have an impact on the screening, treatment, and prognosis of lung cancer.In this comprehensive review, we outlined the links and heterogeneity between COPD and lung cancer and finds that factors such as gene expression and genetic susceptibility, epigenetics, smoking, epithelial mesenchymal transformation (EMT), chronic inflammation, and oxidative stress injury may all play a role in the process. Although the relationship between these two diseases have been largely determined, the methods to prevent lung cancer in COPD patients are still limited. Early diagnosis is still the key to a better prognosis. Thus, it is necessary to establish more intuitive screening evaluation criteria and find suitable biomarkers for lung cancer screening in high-risk populations with COPD. Some studies have indicated that COPD may change the efficacy of anti-tumor therapy by affecting the response of lung cancer patients to immune checkpoint inhibitors (ICIs). And for lung cancer patients with COPD, the standardized management of COPD can improve the prognosis. The treatment of lung cancer patients with COPD is an individualized, comprehensive, and precise process. The development of new targets and new strategies of molecular targeted therapy may be the breakthrough for disease treatment in the future.