Pre-treatment with Bifidobacterium infantis and its specific antibodies enhance targeted radiosensitization in a murine model for lung cancer

2020 ◽  
Author(s):  
Juan Yang ◽  
ZhouXue Wu ◽  
Yao Chen ◽  
ChuanFei Hu ◽  
Dong Li ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Murine Model ◽  
Bifidobacterium Infantis ◽  
Specific Antibodies ◽  
Pre Treatment

scholarly journals Prognostic significance of pre-treatment ALBI grade in advanced non-small cell lung cancer receiving immune checkpoint therapy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryosuke Matsukane ◽  
Hiroyuki Watanabe ◽  
Kojiro Hata ◽  
Kimitaka Suetsugu ◽  
Toshikazu Tsuji ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Propensity Score ◽  
Liver Function ◽  
Small Cell Lung Cancer ◽  
Immune Checkpoint ◽  
Hepatic Function ◽  
Small Cell ◽  
Small Cell Lung ◽  
Hepatic Reserve ◽  
Pre Treatment

AbstractThe liver is an essential organ for regulating innate and acquired immunity. We hypothesized that the pre-treatment hepatic function affects the clinical outcome of immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC). We analyzed 140 patients with NSCLC who received ICIs. We investigated the association between pre-treatment liver function, assessed using the albumin–bilirubin (ALBI) grade, and clinical outcomes in univariate, multivariate, and propensity score matching analyses. Patients were divided into four grades according to pre-treatment liver function. Eighty-eight patients had good hepatic reserve (ALBI grade 1 or 2a), whereas 52 patients had poor hepatic reserve (ALBI grade 2b or 3). In the univariate Kaplan–Meier analysis, the ALBI grade 1, 2a group had a significantly prolonged progression-free survival (PFS, 5.3 versus 2.5 months, p = 0.0019) and overall survival (OS, 19.6 vs. 6.2 months, p = 0.0002). These results were consistent, regardless of whether the analysis was performed in patients with a performance status of 0 or 1 at pre-treatment (N = 124) or in those selected using propensity score matching (N = 76). In the multivariate analysis, pre-treatment ALBI grade was an independent prognostic factor for both PFS (hazard ratio [HR] 0.57, 95% confidence interval [95% CI] 0.38–0.86, p = 0.007) and OS (HR 0.45, 95% CI 0.29–0.72, p = 0.001). Our results suggest that pre-treatment hepatic function assessed by ALBI grade could be an essential biomarker for predicting the efficacy of treatment with ICIs in NSCLC.

A disease-based evaluation of lung cancer care quality in a community healthcare system.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 251-251
Author(s):  
Meghan Brooke Taylor ◽  
Meredith Ray ◽  
Nicholas Faris ◽  
Matthew Smeltzer ◽  
Carrie Fehnel ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Quality Improvement ◽  
Surgical Treatment ◽  
Healthcare System ◽  
Cancer Care ◽  
Care Quality ◽  
Nodal Points ◽  
Diagnostic Biopsy ◽  
Pre Treatment ◽  
Non Surgical Treatment

251 Background: Lung cancer care is complex, but, for quality improvement, can be simplified into five ‘nodal points’: lesion detection, diagnostic biopsy, radiologic staging, invasive staging, and treatment. We previously demonstrated great heterogeneity in passage through these nodal points in patients who received surgical resection for lung cancer in our healthcare system. However, examining only surgical patients may underestimate the enormity of the opportunity for quality improvement. With the aim of identifying quality gaps in pre-treatment evaluation for lung cancer, we evaluated the flow of care through these nodal points within a community-based healthcare system. Methods: We classified lung cancer care procedures received by all suspected lung cancer patients treated within the Multidisciplinary Thoracic Oncology Program at Baptist Cancer Center, Memphis TN between 2014 and 2019, into five nodal points. We compared the frequency of, and time intervals between, nodal points among patients receiving surgical, nonsurgical (chemotherapy/radiation), or no definitive treatment, using Chi-square or Kruskal Wallis tests, where appropriate. Results: Of 1304 eligible patients: 11% had no pre-treatment diagnostic procedure, 20% no PET/CT, and 39% no invasive staging. 39% of patients underwent surgical resection, 51% received non-surgical treatment, and 10% received no treatment. Patients who had surgery were less likely than those who had non-surgical treatment to get a diagnostic test, radiologic staging, and invasive staging (Table). Patients who had non-surgical treatment were more likely to pass through all five nodal points (50% v 68%, p<0.0001). The median (IQR) duration from initial lesion identification to treatment (n=1126) was 77 days (45-190); 27 days (10-90) from lesion identification to diagnostic biopsy (n=1115); and 38 days (26-63) from diagnostic biopsy to treatment (n=1041). Patients who had surgery received less timely care than those who had non-surgical or no treatment: median 122 v 66 v 68 days from lesion identification to treatment; 40 v 21 v 29 days from lesion identification to diagnostic biopsy; 46 v 38 v 31 days from diagnostic biopsy to treatment (p<0.0001 all comparisons). Conclusions: Quality improvement initiatives within our healthcare system, such as the establishment of a coordinated multidisciplinary program, enhanced care quality over previous benchmarks. Despite improvements, lung cancer patients who had surgery received less frequent and less timely pre-treatment evaluation than those without surgery. Implementing a standardized cancer care pathway from diagnosis to surgery could help to reduce variations in optimal care delivery.[Table: see text]

scholarly journals Evaluation of cyclooxygenase-2 inhibition in an orthotopic murine model of lung cancer for dose-dependent effect

2005 ◽  
Vol 129 (6) ◽  
pp. 1242-1249 ◽  
Author(s):  
Eric M. Sievers ◽  
Robert D. Bart ◽  
Leah M. Backhus ◽  
Yuanguang Lin ◽  
Margaret Starnes ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Murine Model ◽  
Cyclooxygenase 2 ◽  
Dependent Effect ◽  
Dose Dependent ◽  
Dose Dependent Effect

scholarly journals 240 Discovery of biomarkers of resistance to immune checkpoint blockade in non-small-cell lung cancer (NSCLC) using high-plex digital spatial profiling

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A258-A258
Author(s):  
Myrto Moutafi ◽  
Sandra Martinez-Morilla ◽  
Prajan Divakar ◽  
Ioannis Vathiotis ◽  
Niki Gavrielatou ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Immune Checkpoint ◽  
Biomarker Discovery ◽  
Univariate Analysis ◽  
Predictive Biomarkers ◽  
Protein Detection ◽  
Statistical Testing ◽  
Multiple Testing Correction ◽  
Stromal Compartment ◽  
Pre Treatment

BackgroundDespite the clinical effectiveness of Immune Checkpoint Inhibitors (ICI) in lung cancer, only around 20% remain disease free at 5 years. Predictive biomarkers for ICIs are neither sensitive nor specific. Here, we used the GeoMx Digital Spatial Profiler (DSP) (NanoString, Inc.) to analyze high-plex protein in a quantitative and spatially resolved manner from single formalin-fixed paraffin embedded tissue sections toward the goal of identification of new biomarkers with better predictive value.MethodsPre-treatment samples from 56 patients with NSCLC treated with ICI were collected, represented in Yale tissue microarray 471 (YTMA471), and analyzed. A panel of 71 photocleavable oligonucleotide-labeled primary antibodies (NanoString Human IO panel) was used for protein detection. Protein expression was measured in 4 molecularly defined tissue compartments, defined by fluorescence co-localization (tumor [panCK+], leukocytes [CD45+/CD68-], macrophages [CD68+] and an aggregate stromal immune cell compartment, defined as the sum of leukocyte and macrophage expression [panCK-/CD45+/CD68+]) generating 284 variables representing potential predictive biomarkers. Promising candidates were orthogonally validated with Quantitative Immunofluorescence (QIF). Pre-treatment samples from 40 patients with NSCLC (YTMA404) that received ICI, and 174 non-ICI treated operable NSCLC patients (YTMA423) were analyzed to provide independent cohort validation. All statistical testing was performed using a two-sided significance level of α=0.05 and multiple testing correction (Benjamini-Hochberg method, FDR < 0.1).ResultsInitial biomarker discovery on 284 protein variables were generated by univariate analysis using continuous log-scaled data. High PD-L1 expression in tumor cells predicted longer survival (PFS; HR 0.67, p=0.017) and validated the training cohort. We found 4 markers associated with PFS, and 3 with OS in the stromal compartment. Of these, expression of CD66b in stromal immune cells predicted significantly shorter OS (HR 1.31, p=0.016) and shorter PFS (HR 1.24, p = 0.04). Tertile analysis using QIF on all three tissue cohorts for CD66b expression, assessed by QIF, showed that CD66b was indicative but not prognostic for survival [discovery cohort, YTMA471 (OS; HR 3.02, p=0.013, PFS; HR 2.38, p=0.023), validation cohort; YTMA404 (OS; HR 2.97, p=0.018, PFS; HR 1.85, p=0.1), non-ICI treated cohort YTMA423 (OS; HR 1.02, p>0.9, PFS; HR 0.72, p=0.4)].ConclusionsUsing the DSP technique, we have discovered that CD66b expressed in the stromal immune [panCK-/CD45+/CD68+] molecular compartment is associated with resistance to ICI therapy in NSCLC. This observation was validated by an orthogonal approach in an independent ICI treated NSCLC cohort. Since CD66b identifies neutrophils, further studies are warranted to characterize the role of neutrophils in ICI resistance.AcknowledgementsDr Moutafi is supported by a scholarship from the Hellenic Society of Medical Oncologists (HESMO)Ethics ApprovalAll tissue samples were collected and used under the approval from the Yale Human Investigation Committee protocol #9505008219 with an assurance filed with and approved by the U.S. Department of Health and Human Services

Adenovirus vector-mediated transfer of 9 kDa granulysin induces DNA fragmentation in HuD antigen-expressing small cell lung cancer murine model cells

Respirology ◽  
2002 ◽  
Vol 7 (1) ◽  
pp. 29-35 ◽  
Author(s):  
MITSUAKI SEKIYA ◽  
AKIHIKO OHWADA ◽  
MASAHARU KATAE ◽  
TAKASHI DAMBARA ◽  
ISAO NAGAOKA ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Dna Fragmentation ◽  
Cell Lung Cancer ◽  
Murine Model ◽  
Adenovirus Vector ◽  
Small Cell ◽  
Small Cell Lung

MRI-based radiomics signature for the prediction of response of lung cancer brain metastases after whole-brain radiotherapy.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e21096-e21096
Author(s):  
Ruizhe Xu ◽  
Ye Tian ◽  
Bo Zhang
Keyword(s):  
Lung Cancer ◽  
Brain Metastases ◽  
Whole Brain Radiotherapy ◽  
Gray Level ◽  
Local Response ◽  
Prediction Of Response ◽  
Whole Brain ◽  
Brain Radiotherapy ◽  
Pre Treatment ◽  
Radiomics Signature

e21096 MRI-based Radiomics signature for the Prediction of Response of Lung Cancer Brain Metastases After Whole-Brain Radiotherapy Background: Local response prediction for brain metastases (BMs) from lung cancer after Whole-Brain Radiotherapy (WBRT) is challenging, as existing criteria are based solely on unidimensional measurements. This study sought to determine whether radiomic features of lung cancer BMs derived from pre-treatment magnetic resonance imaging (MRI) could be used to predict local response following WBRT. Methods: A total of 88 Lung Cancer patients with BMs treated with WBRT were analyzed. After volumes of interest were drawn, 944 radiomic features including first-order, shape, Gray Level Co-occurrence Matrix (GLCM), Gray Level Dependence Matrix (GLDM), Gray Level Run Length Matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), Neighborhood Gray Tone Difference Matrix (NGTDM), and Laplacian of Gaussian (LoG) features were extracted, using the baseline pre-treatment post-contrast T1 (T1c) and T2 fluid-attenuated inversion recovery (FLAIR) MRI sequences, respectively. Local response status was determined by contrasting the baseline and follow-up MRI according to the RANO-BM criteria. The independent samples t test or Mann-Whitney U test, and then least absolute shrinkage and selection operator (LASSO) were used for dimensionality reduction and feature selection. An adaboost classifier was trained using the selected radiomic features and evaluated using the area under the receiver operating characteristic curve (AUC) in both the training and testing sets. Other discrimination metrics, including classification accuracy, positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity, were also calculated. Results: The optimal radiomics signature was developed based on a multivariable logistic regression with 4, 5, 6 radiomic features on T1c, T2 FLAIR and T1c+T2 FLAIR, respectively. The radiomics model based on T1c features presented the AUC of (0.920 vs. 0.805, respectively) for both the training and testing sets, followed by T2 FLAIR features (0.893 vs. 0.701, respectively), and T1c+T2 FLAIR features (0.971 vs. 0.857, respectively). The classification accuracy of the radiomics model also well predicted the local response of BMs for both the the training and testing sets (T1c: 82.9% vs. 77.8%, T2 FLAIR: 82.9% vs. 77.8%, T1c+T2 FLAIR: 90.0% vs. 77.8%, respectively). Conclusions: Radiomics holds promise for predicting local tumor response following WBRT in patients with lung cancer and brain metastases. A predictive model built on radiomic features from an institutional cohort performed well on cross-validation testing. These results warrant further validation in independent datasets. Such work could prove invaluable for guiding management of individual patients and assessing outcomes of novel interventions.

scholarly journals Ascertaining an Appropriate Diagnostic Algorithm Using EGFR Mutation-Specific Antibodies to Detect EGFR Status in Non-Small-Cell Lung Cancer

PLoS ONE ◽  
2013 ◽  
Vol 8 (3) ◽  
pp. e59183 ◽  
Author(s):  
Guiyang Jiang ◽  
Chuifeng Fan ◽  
Xiupeng Zhang ◽  
Qianze Dong ◽  
Liang Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Egfr Mutation ◽  
Diagnostic Algorithm ◽  
Small Cell ◽  
Small Cell Lung ◽  
Specific Antibodies

scholarly journals Imaging Features and Patterns of Metastasis in Non-Small Cell Lung Cancer with RET Rearrangements

Cancers ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 693 ◽  
Author(s):  
Subba R. Digumarthy ◽  
Dexter P. Mendoza ◽  
Jessica J. Lin ◽  
Marguerite Rooney ◽  
Andrew Do ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Imaging Features ◽  
Small Cell Lung ◽  
Primary Tumors ◽  
Imaging Characteristics ◽  
Metastatic Patterns ◽  
Pre Treatment

Rearranged during transfection proto-oncogene (RET) fusions represent a potentially targetable oncogenic driver in non-small cell lung cancer (NSCLC). Imaging features and metastatic patterns of advanced RET fusion-positive (RET+) NSCLC are not well established. Our goal was to compare the imaging features and patterns of metastases in RET+, ALK+ and ROS1+ NSCLC. Patients with RET+, ALK+, or ROS1+ NSCLC seen at our institution between January 2014 and December 2018 with available pre-treatment imaging were identified. The clinicopathologic features, imaging characteristics, and the distribution of metastases were reviewed and compared. We identified 215 patients with NSCLC harboring RET, ALK, or ROS1 gene fusion (RET = 32; ALK = 116; ROS1 = 67). Patients with RET+ NSCLC were older at presentation compared to ALK+ and ROS1+ patients (median age: RET = 64 years; ALK = 51 years, p < 0.001; ROS = 54 years, p = 0.042) and had a higher frequency of neuroendocrine histology (RET = 12%; ALK = 2%, p = 0.025; ROS1 = 0%, p = 0.010). Primary tumors in RET+ patients were more likely to be peripheral (RET = 69%; ALK = 47%, p = 0.029; ROS1 = 36%, p = 0.003), whereas lobar location, size, and density were comparable across the three groups. RET+ NSCLC was associated with a higher frequency of brain metastases at diagnosis compared to ROS1+ NSCLC (RET = 32%, ROS1 = 10%; p = 0.039. Metastatic patterns were otherwise similar across the three molecular subgroups, with high incidences of lymphangitic carcinomatosis, pleural metastases, and sclerotic bone metastases. RET+ NSCLC shares several distinct radiologic features and metastatic spread with ALK+ and ROS1+ NSCLC. These features may suggest the presence of RET fusions and help identify patients who may benefit from further molecular genotyping.

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