Changes in computer extracted features of vessel tortuosity on CT scans post-treatment in responders compared to non-responders for non-small cell lung cancer on immunotherapy.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 11518-11518 ◽  
Author(s):  
Vamsidhar Velcheti ◽  
Mehdi Alilou ◽  
Monica Khunger ◽  
Rajat Thawani ◽  
Anant Madabhushi
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Post Treatment ◽  
Ct Scans ◽  
Svm Classifier ◽  
Small Cell Lung ◽  
Vessel Tortuosity ◽  
Nsclc Patients

11518 Background: Immune-checkpoint blockade treatments demonstrate promising clinical efficacy in patients with non-small cell lung cancer (NSCLC). Nivolumab is a PD-1 inhibitor that is FDA approved for treatment of patients with chemotherapy refractory advanced NSCLC. The current standard clinical approach to evaluating tumor response is sub-optimal in defining clinical benefit from immunotherapy drugs. We sought to evaluate whether computer extracted measurements of vessel tortuosity significantly and differentially change post treatment between NSCLC patients who do and do not respond to immunotherapy. Methods: A total of 50 NSCLC patients including pre- and post- treatment CT scans were included in this study. The patients were either responders or non-responders to Nivolumab. Patients who did not receive Nivolumab after 2 cycles due to lack of response or progression as per RECIST were classified as ‘non-responders’. A total of 35 tortuosity features of the vessels around the lung nodules were investigated. In the training cohort (N = 25), the features were ranked based on the degree of change between pre- and post- treatment CT. The top 4 features were used for training a Support Vector Machine (SVM) classifier to identify which patients did and did not respond to immunotherapy on a validation cohort of N = 25 patients. Results: The top features identified were the ones associated with the curvature of the vessel branches. The AUC for the SVM classifier was 0.75 for the training and 0.79 for the test set. Conclusions: Changes in specific vessel tortuosity features between baseline and post-treatment CT scans following nivolumab were different between NSCLC patients who did and did not respond. Multi-site validation of the vessel tortuosity features is needed to establish it as a predictive biomarker for NSCLC patients treated with immunotherapy.

Computer extracted measurements of vessel tortuosity on baseline CT scans to predict response to nivolumab immunotherapy for non-small cell lung cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 11566-11566
Author(s):  
Monica Khunger ◽  
Mehdi Alilou ◽  
Rajat Thawani ◽  
Anant Madabhushi ◽  
Vamsidhar Velcheti
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Lung Nodule ◽  
Small Cell ◽  
Ct Scans ◽  
Support Vector ◽  
Svm Classifier ◽  
Small Cell Lung ◽  
Vessel Tortuosity

11566 Background: Immune-checkpoint blockade treatments, particularly drugs targeting the programmed death-1 (PD-1) receptor, demonstrate promising clinical efficacy in patients with non-small cell lung cancer (NSCLC). We sought to evaluate whether computer extracted measurements of tortuosity of vessels in lung nodules on baseline CT scans in NSCLC patients(pts) treated with a PD-1 inhibitor, nivolumab could distinguish responders and non-responders. Methods: A total of 61 NSCLC pts who underwent treatment with nivolumab were included in this study. Pts who did not receive nivolumab after 2 cycles due to lack of response or progression per RECIST were classified as ‘non-responders’, patients who had radiological response per RECIST or had clinical benefit (defined as stable disease >10 cycles) were classified as ‘responders’. A total of 35 quantitative tortuosity features of the vessels associated with lung nodule were investigated. In the training cohort (N=33), the features were ranked in their ability to identify responders to nivolumab using a support vector machine (SVM) classifier. The three most informative features were then used for training the SVM, which was then validated on a cohort of N=28 pts. Results: The maximum curvature ( f1), standard deviation of the torsion ( f2) and mean curvature ( f3) were identified as the most discriminating features. The area under Receiver operating characteristic (ROC) curve (AUC) of the SVM was 0.84 for the training and 0.72 for the validation cohort. Conclusions: Vessel tortuosity features were able to distinguish responders from non-responders for patients with NSCLC treated with nivolumab. Large scale multi-site validation will need to be done to establish vessel tortuosity as a predictive biomarker for immunotherapy. [Table: see text]

scholarly journals Biomarkers and Gene Signatures to Predict Durable Response to Pembrolizumab in Non-Small Cell Lung Cancer

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3828
Author(s):  
Anello Marcello Poma ◽  
Rossella Bruno ◽  
Iacopo Pietrini ◽  
Greta Alì ◽  
Giulia Pasquini ◽  
...  
Keyword(s):  
Lung Cancer ◽  
T Cells ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Immune Cell ◽  
Small Cell ◽  
Small Cell Lung ◽  
First Line ◽  
Durable Response ◽  
Nsclc Patients

Pembrolizumab has been approved as first-line treatment for advanced Non-small cell lung cancer (NSCLC) patients with tumors expressing PD-L1 and in the absence of other targetable alterations. However, not all patients that meet these criteria have a durable benefit. In this monocentric study, we aimed at refining the selection of patients based on the expression of immune genes. Forty-six consecutive advanced NSCLC patients treated with pembrolizumab in first-line setting were enrolled. The expression levels of 770 genes involved in the regulation of the immune system was analysed by the nanoString system. PD-L1 expression was evaluated by immunohistochemistry. Patients with durable clinical benefit had a greater infiltration of cytotoxic cells, exhausted CD8, B-cells, CD45, T-cells, CD8 T-cells and NK cells. Immune cell scores such as CD8 T-cell and NK cell were good predictors of durable response with an AUC of 0.82. Among the immune cell markers, XCL1/2 showed the better performance in predicting durable benefit to pembrolizumab, with an AUC of 0.85. Additionally, CD8A, CD8B and EOMES showed a high specificity (>0.86) in identifying patients with a good response to treatment. In the same series, PD-L1 expression levels had an AUC of 0.61. The characterization of tumor microenvironment, even with the use of single markers, can improve patients’ selection for pembrolizumab treatment.

scholarly journals Circulating Biomarkers of Response and Toxicity of Immunotherapy in Advanced Non-Small Cell Lung Cancer (NSCLC): A Comprehensive Review

Cancers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1794
Author(s):  
Alice Indini ◽  
Erika Rijavec ◽  
Francesco Grossi
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Liquid Biopsy ◽  
Cytotoxic T Lymphocyte ◽  
Checkpoint Inhibitors ◽  
Small Cell ◽  
Circulating Biomarkers ◽  
Small Cell Lung ◽  
Nsclc Patients

Immune checkpoint inhibitors (ICIs) targeting the programmed cell death (PD)-1 protein and its ligand, PD-L1, and cytotoxic T-lymphocyte-associated antigen (CTLA)-4, have revolutionized the management of patients with advanced non-small cell lung cancer (NSCLC). Unfortunately, only a small portion of NSCLC patients respond to these agents. Furthermore, although immunotherapy is usually well tolerated, some patients experience severe immune-related adverse events (irAEs). Liquid biopsy is a non-invasive diagnostic procedure involving the isolation of circulating biomarkers, such as circulating tumor cells (CTC), cell-free DNA (cfDNA), and microRNAs (miRNAs). Thanks to recent advances in technologies, such as next-generation sequencing (NGS) and digital polymerase chain reaction (dPCR), liquid biopsy has become a useful tool to provide baseline information on the tumor, and to monitor response to treatments. This review highlights the potential role of liquid biomarkers in the selection of NSCLC patients who could respond to immunotherapy, and in the identification of patients who are most likely to experience irAEs, in order to guide improvements in care.

scholarly journals 1318P Association between soluble PD-L1 and prognosis of non-small cell lung cancer (NSCLC) patients treated with immunotherapy

2020 ◽  
Vol 31 ◽  
pp. S851
Author(s):  
C. Dellepiane ◽  
S. Coco ◽  
M.G. Dal Bello ◽  
G. Rossi ◽  
E. Rijavec ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Nsclc Patients

scholarly journals Molecular profile of KRAS G12C-mutant colorectal and non-small-cell lung cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Luiz Henrique Araujo ◽  
Bianca Mendes Souza ◽  
Laura Rabelo Leite ◽  
Sabrina A. F. Parma ◽  
Natália P. Lopes ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Driver Mutations ◽  
Molecular Profile ◽  
Common Mutation ◽  
Small Cell Lung ◽  
The South ◽  
Nsclc Patients

Abstract Background KRAS is the most frequently mutated oncogene in cancer, however efforts to develop targeted therapies have been largely unsuccessful. Recently, two small-molecule inhibitors, AMG 510 and MRTX849, have shown promising activity in KRAS G12C-mutant solid tumors. The current study aims to assess the molecular profile of KRAS G12C in colorectal (CRC) and non-small-cell lung cancer (NSCLC) tested in a clinical certified laboratory. Methods CRC and NSCLC samples submitted for KRAS testing between 2017 and 2019 were reviewed. CRC samples were tested for KRAS and NRAS by pyrosequencing, while NSCLC samples were submitted to next generation sequencing of KRAS, NRAS, EGFR, and BRAF. Results The dataset comprised 4897 CRC and 4686 NSCLC samples. Among CRC samples, KRAS was mutated in 2354 (48.1%). Most frequent codon 12 mutations were G12D in 731 samples (14.9%) and G12V in 522 (10.7%), followed by G12C in 167 (3.4%). KRAS mutations were more frequent in females than males (p = 0.003), however this difference was exclusive of non-G12C mutants (p < 0.001). KRAS mutation frequency was lower in the South and North regions (p = 0.003), but again KRAS G12C did not differ significantly (p = 0.80). In NSCLC, KRAS mutations were found in 1004 samples (21.4%). As opposed to CRC samples, G12C was the most common mutation in KRAS, in 346 cases (7.4%). The frequency of KRAS G12C was higher in the South and Southeast regions (p = 0.012), and lower in patients younger than 50 years (p < 0.001). KRAS G12C mutations were largely mutually exclusive with other driver mutations; only 11 NSCLC (3.2%) and 1 CRC (0.6%) cases had relevant co-mutations. Conclusions KRAS G12C presents in frequencies higher than several other driver mutations, and may represent a large volume of patients in absolute numbers. KRAS testing should be considered in all CRC and NSCLC patients, independently of clinical or demographic characteristics.

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