Persistent Homology of Tumor CT Scans Predicts Survival In Lung Cancer

ABSTRACTRadiomics, the objective study of non-visual features in clinical imaging, has been useful in informing decisions in clinical oncology. However, radiomics currently lacks the ability to characterize the overall structure of the data. This field may benefit by incorporating persistent homology, a popular new algorithm that analyzes whole data structure. We hypothesized that persistent homology could be applied to lung tumor scans and predict clinical variables. We obtained computed tomography lung scans (n = 565) from the NSCLC-Radiomics and NSCLC-Radiogenomics datasets in The Cancer Imaging Archive. Segmentation data was used to create a cubical region centered on the primary tumor in each scan. For each scan, a cubical complex filtration based on Hounsfield units was generated. We created a feature curve that plotted the number of 0 dimensional topological features against each Hounsfield unit. The curve’s first moment of the distribution was utilized as a summary statistic to predict survival in a Cox proportional hazards model. The first moment of the distribution is equivalent to the area under the curve of our topological feature curves (AUC). The Kruskal-Wallis H Test and a post-hoc Dunn’s test with Bonferroni correction were used to test AUC differences among survival quartiles. After controlling for tumor image size, age, and stage, AUC, was associated with poorer survival (HR = 1.118; 95% CI = 1.026-1.218; p = 0.01). AUC was significantly higher for patients in the lowest survival quartile compared to the highest survival quartile (p < 0.001). We have shown that persistent homology can generate useful clinical correlates from tumor CT scans. Our 0-dimensional topological feature curve statistic predicts survival in lung cancer patients. This novel statistic may be used in tandem with standard radiomics variables to better inform clinical oncology decisions.

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Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans

Cancers ◽

10.3390/cancers13184585 ◽

2021 ◽

Vol 13 (18) ◽

pp. 4585

Author(s):

Wouter R. P. H. van de Worp ◽

Brent van der Heyden ◽

Georgios Lappas ◽

Ardy van Helvoort ◽

Jan Theys ◽

...

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Tumor Growth ◽

Tumor Volume ◽

Learning Algorithm ◽

Lung Tumor ◽

Whole Body ◽

Ct Scans ◽

Therapeutic Effects ◽

Deep Learning Algorithm

Lung cancer is the leading cause of cancer related deaths worldwide. The development of orthotopic mouse models of lung cancer, which recapitulates the disease more realistically compared to the widely used subcutaneous tumor models, is expected to critically aid the development of novel therapies to battle lung cancer or related comorbidities such as cachexia. However, follow-up of tumor take, tumor growth and detection of therapeutic effects is difficult, time consuming and requires a vast number of animals in orthotopic models. Here, we describe a solution for the fully automatic segmentation and quantification of orthotopic lung tumor volume and mass in whole-body mouse computed tomography (CT) scans. The goal is to drastically enhance the efficiency of the research process by replacing time-consuming manual procedures with fast, automated ones. A deep learning algorithm was trained on 60 unique manually delineated lung tumors and evaluated by four-fold cross validation. Quantitative performance metrics demonstrated high accuracy and robustness of the deep learning algorithm for automated tumor volume analyses (mean dice similarity coefficient of 0.80), and superior processing time (69 times faster) compared to manual segmentation. Moreover, manual delineations of the tumor volume by three independent annotators was sensitive to bias in human interpretation while the algorithm was less vulnerable to bias. In addition, we showed that besides longitudinal quantification of tumor development, the deep learning algorithm can also be used in parallel with the previously published method for muscle mass quantification and to optimize the experimental design reducing the number of animals needed in preclinical studies. In conclusion, we implemented a method for fast and highly accurate tumor quantification with minimal operator involvement in data analysis. This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models.

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Persistent homology of tumor CT scans is associated with survival in lung cancer

Medical Physics ◽

10.1002/mp.15255 ◽

2021 ◽

Author(s):

Eashwar Somasundaram ◽

Adam Litzler ◽

Raoul Wadhwa ◽

Steph Owen ◽

Jacob Scott

Keyword(s):

Lung Cancer ◽

Persistent Homology ◽

Ct Scans

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Improved Therapeutic Efficacy of Topotecan Against A549 Lung Cancer Cells with Folate-targeted Topotecan Liposomes

Current Drug Metabolism ◽

10.2174/1389200221999200820163337 ◽

2020 ◽

Vol 21 (11) ◽

pp. 902-909

Author(s):

Jingxin Zhang ◽

Weiyue Shi ◽

Gangqiang Xue ◽

Qiang Ma ◽

Haixin Cui ◽

...

Keyword(s):

Lung Cancer ◽

Drug Delivery ◽

Cancer Cells ◽

Targeted Delivery ◽

Therapeutic Efficacy ◽

Drug Delivery Systems ◽

Lung Tumor ◽

A549 Cells ◽

Delivery Systems ◽

Lung Cancer Cells

Background: Among all cancers, lung cancer has high mortality among patients in most of the countries in the world. Targeted delivery of anticancer drugs can significantly reduce the side effects and dramatically improve the effects of the treatment. Folate, a suitable ligand, can be modified to the surface of tumor-selective drug delivery systems because it can selectively bind to the folate receptor, which is highly expressed on the surface of lung tumor cells. Objective: This study aimed to construct a kind of folate-targeted topotecan liposomes for investigating their efficacy and mechanism of action in the treatment of lung cancer in preclinical models. Methods: We conjugated topotecan liposomes with folate, and the liposomes were characterized by particle size, entrapment efficiency, cytotoxicity to A549 cells and in vitro release profile. Technical evaluations were performed on lung cancer A549 cells and xenografted A549 cancer cells in female nude mice, and the pharmacokinetics of the drug were evaluated in female SD rats. Results: The folate-targeted topotecan liposomes were proven to show effectiveness in targeting lung tumors. The anti-tumor effects of these liposomes were demonstrated by the decreased tumor volume and improved therapeutic efficacy. The folate-targeted topotecan liposomes also lengthened the topotecan blood circulation time. Conclusion: The folate-targeted topotecan liposomes are effective drug delivery systems and can be easily modified with folate, enabling the targeted liposomes to deliver topotecan to lung cancer cells and kill them, which could be used as potential carriers for lung chemotherapy.

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Recombinant adeno-associated virus 9-mediated expression of Kallistatin suppresses lung tumor growth in mice

Current Gene Therapy ◽

10.2174/1566523220999201111194257 ◽

2020 ◽

Vol 20 ◽

Author(s):

Weihong Qu ◽

Jianguo Zhao ◽

Yaqing Wu ◽

Ruian Xu ◽

Shaowu Liu

Keyword(s):

Lung Cancer ◽

Gene Therapy ◽

Tumor Growth ◽

Lung Tumor ◽

Control Group ◽

High Dose ◽

Blank Control Group ◽

Adeno Associated Virus ◽

Tumor Tissues ◽

Subcutaneous Xenograft

Background:: Lung cancer remains the most common cause of cancer-related deaths in China and worldwide. Traditional surgery and chemotherapy do not offer an effective cure although gene therapy may be a promising future alter-native. Kallistatin (Kal) is an endogenous inhibitor of angiogenesis and tumorigenesis. Recombinant adeno-associated virus (rAAV) is considered the most promising vector for gene therapy of many diseases due to persistent and long-term transgen-ic expression. Objective:: The aim of this study was to investigate whether rAAV9-Kal inhibited NCI-H446 subcutaneous xenograft tumor growth in mice. Method:: The subcutaneous xenograft mode were induced by subcutaneous injection of 2×106 H446 cells into the dorsal skin of BALB/c nude mice. The mice were administered with ssrAAV9-Kal (single-stranded rAAV9) or dsrAAV9-Kal (double-stranded rAAV9）by intraperitoneal injection (I.P.). Tumor microvessel density (MVD) was examined by anti-CD34 stain-ing to evaluate tumor angiogenesis. Results:: Compared with the PBS (blank control) group, tumor growth in the high-dose ssrAAV9-Kal group was inhibited by 40% by day 49, and the MVD of tumor tissues was significantly decreased. Conclusion:: The results indicate that this therapeutic strategy is a promising approach for clinical cancer therapy and impli-cate rAAV9-Kal as a candidate for gene therapy of lung cancer.

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FlowCell-enriched circulating tumor cells as a predictor of lung cancer metastasis

Human Cell ◽

10.1007/s13577-021-00500-8 ◽

2021 ◽

Author(s):

Yan Lu ◽

Yushuang Zheng ◽

Yuhong Wang ◽

Dongmei Gu ◽

Jun Zhang ◽

...

Keyword(s):

Lung Cancer ◽

Tumor Cells ◽

Circulating Tumor Cells ◽

Cancer Metastasis ◽

Lung Tumor ◽

Early Stage ◽

High Rate ◽

Early Stage Lung Cancer ◽

Lung Cancer Metastasis ◽

Number Of Patients

AbstractLung cancer is the most fetal malignancy due to the high rate of metastasis and recurrence after treatment. A considerable number of patients with early-stage lung cancer relapse due to overlooked distant metastasis. Circulating tumor cells (CTCs) are tumor cells in blood circulation that originated from primary or metastatic sites, and it has been shown that CTCs are critical for metastasis and prognosis in various type of cancers. Here, we employed novel method to capture, isolate and classify CTC with FlowCell system and analyzed the CTCs from a cohort of 302 individuals. Our results illustrated that FlowCell-enriched CTCs effectively differentiated benign and malignant lung tumor and the total CTC counts increased as the tumor developed. More importantly, we showed that CTCs displayed superior sensitivity and specificity to predict lung cancer metastasis in comparison to conventional circulating biomarkers. Taken together, our data suggested CTCs can be used to assist the diagnosis of lung cancer as well as predict lung cancer metastasis. These findings provide an alternative means to screen early-stage metastasis.

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Lung Cancer Detection and Classification from Chest CT Scans Using Machine Learning Techniques

2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA) ◽

10.1109/caida51941.2021.9425269 ◽

2021 ◽

Author(s):

Amjad Rehman ◽

Muhammad Kashif ◽

Ibrahim Abunadi ◽

Noor Ayesha

Keyword(s):

Machine Learning ◽

Lung Cancer ◽

Cancer Detection ◽

Chest Ct ◽

Machine Learning Techniques ◽

Ct Scans ◽

Learning Techniques ◽

Lung Cancer Detection

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Kras-driven intratumoral heterogeneity triggers infiltration of M2 polarized macrophages via the circHIPK3/PTK2 immunosuppressive circuit

Scientific Reports ◽

10.1038/s41598-021-94671-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Theodora Katopodi ◽

Savvas Petanidis ◽

Kalliopi Domvri ◽

Paul Zarogoulidis ◽

Doxakis Anestakis ◽

...

Keyword(s):

Lung Cancer ◽

Tumor Growth ◽

Lung Tumor ◽

Quantitative Polymerase Chain Reaction ◽

Macrophage Polarization ◽

Metastatic Potential ◽

Intratumoral Heterogeneity ◽

M2 Macrophage

AbstractIntratumoral heterogeneity in lung cancer is essential for evasion of immune surveillance by tumor cells and establishment of immunosuppression. Gathering data reveal that circular RNAs (circRNAs), play a role in the pathogenesis and progression of lung cancer. Particularly Kras-driven circRNA signaling triggers infiltration of myeloid-associated tumor macrophages in lung tumor microenvironment thus establishing immune deregulation, and immunosuppression but the exact pathogenic mechanism is still unknown. In this study, we investigate the role of oncogenic Kras signaling in circRNA-related immunosuppression and its involvement in tumoral chemoresistance. The expression pattern of circRNAs HIPK3 and PTK2 was determined using quantitative polymerase chain reaction (qPCR) in lung cancer patient samples and cell lines. Apoptosis was analyzed by Annexin V/PI staining and FACS detection. M2 macrophage polarization and MDSC subset analysis (Gr1−/CD11b−, Gr1−/CD11b+) were determined by flow cytometry. Tumor growth and metastatic potential were determined in vivo in C57BL/6 mice. Findings reveal intra-epithelial CD163+/CD206+ M2 macrophages to drive Kras immunosuppressive chemoresistance through myeloid differentiation. In particular, monocytic MDSC subsets Gr1−/CD11b−, Gr1−/CD11b+ triggered an M2-dependent immune response, creating an immunosuppressive tumor-promoting network via circHIPK3/PTK2 enrichment. Specifically, upregulation of exosomal cicHIPK3/PTK2 expression prompted Kras-driven intratumoral heterogeneity and guided lymph node metastasis in C57BL/6 mice. Consequent co-inhibition of circPTK2/M2 macrophage signaling suppressed lung tumor growth along with metastatic potential and prolonged survival in vivo. Taken together, these results demonstrate the key role of myeloid-associated macrophages in sustaining lung immunosuppressive neoplasia through circRNA regulation and represent a potential therapeutic target for clinical intervention in metastatic lung cancer.

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Effect of APOE ε4 on multimodal brain connectomic traits: a persistent homology study

BMC Bioinformatics ◽

10.1186/s12859-020-03877-9 ◽

2020 ◽

Vol 21 (S21) ◽

Author(s):

Jin Li ◽

◽

Chenyuan Bian ◽

Dandan Chen ◽

Xianglian Meng ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Genetic Risk ◽

Brain Connectivity ◽

Persistent Homology ◽

Brain Network ◽

Resonance Imaging ◽

Modeling Framework ◽

Apoe Ε4 ◽

Topological Features

Abstract Background Although genetic risk factors and network-level neuroimaging abnormalities have shown effects on cognitive performance and brain atrophy in Alzheimer’s disease (AD), little is understood about how apolipoprotein E (APOE) ε4 allele, the best-known genetic risk for AD, affect brain connectivity before the onset of symptomatic AD. This study aims to investigate APOE ε4 effects on brain connectivity from the perspective of multimodal connectome. Results Here, we propose a novel multimodal brain network modeling framework and a network quantification method based on persistent homology for identifying APOE ε4-related network differences. Specifically, we employ sparse representation to integrate multimodal brain network information derived from both the resting state functional magnetic resonance imaging (rs-fMRI) data and the diffusion-weighted magnetic resonance imaging (dw-MRI) data. Moreover, persistent homology is proposed to avoid the ad hoc selection of a specific regularization parameter and to capture valuable brain connectivity patterns from the topological perspective. The experimental results demonstrate that our method outperforms the competing methods, and reasonably yields connectomic patterns specific to APOE ε4 carriers and non-carriers. Conclusions We have proposed a multimodal framework that integrates structural and functional connectivity information for constructing a fused brain network with greater discriminative power. Using persistent homology to extract topological features from the fused brain network, our method can effectively identify APOE ε4-related brain connectomic biomarkers.

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Targeted inhibition of GRP78 by HA15 promotes apoptosis of lung cancer cells accompanied by ER stress and autophagy

Biology Open ◽

10.1242/bio.053298 ◽

2020 ◽

Vol 9 (11) ◽

pp. bio053298

Author(s):

Jingjing Wu ◽

Youqile Wu ◽

Xuemei Lian

Keyword(s):

Lung Cancer ◽

Protein Expression ◽

Cell Viability ◽

Er Stress ◽

Cancer Cells ◽

Lung Tumor ◽

Lung Cancer Cells ◽

Dependent Manner ◽

Mice Model ◽

Targeted Inhibition

ABSTRACTThis study investigated the pathophysiological role of GRP78 in the survival of lung cancer cells. Lung cancer patient data from public databases were used to analyze the expression of GRP78 and its influence on prognoses. In vivo, GRP78 protein expression was analyzed in an established urethane-induced lung tumor mouse model. In vitro, the effects of targeted inhibition of GRP78 by HA15 in lung cancer cells were assessed, with cell viability analyzed using a CCK-8 assay, cell proliferation using an EdU assay, apoptosis and cell cycle using flow cytometry, subcellular structure using electron microscopy, and relative mRNA and protein expression using RT-PCR, western blotting or immunofluorescence assays. The results showed that GRP78 was highly expressed in the lung tissue of lung cancer mice model or patients, and was associated with a poor prognosis. After inhibition of GRP78 in lung cancer cells by HA15, cell viability was decreased in a dose- and time-dependent manner, proliferation was suppressed and apoptosis promoted. Unfolded protein response signaling pathway proteins were activated, and the autophagy-related proteins and mRNAs were upregulated. Therefore, targeted inhibition of GRP78 by HA15 promotes apoptosis of lung cancer cells accompanied by ER stress and autophagy.

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