scholarly journals Paired analysis of tumor mutation burden for lung adenocarcinoma and associated idiopathic pulmonary fibrosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yasuto Yoneshima ◽  
Eiji Iwama ◽  
Shingo Matsumoto ◽  
Taichi Matsubara ◽  
Testuzo Tagawa ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Lung Adenocarcinoma ◽  
Genetic Alterations ◽  
Driver Mutations ◽  
Somatic Variant ◽  
Tumor Mutation Burden ◽  
Mutation Burden ◽  
And Oxidative Stress

AbstractGenetic alterations underlying the development of lung cancer in individuals with idiopathic pulmonary fibrosis (IPF) have remained unclear. To explore whether genetic alterations in IPF tissue contribute to the development of IPF-associated lung cancer, we here evaluated tumor mutation burden (TMB) and somatic variants in 14 paired IPF and tumor samples from patients with IPF-associated lung adenocarcinoma. We also determined TMB for 22 samples of lung adenocarcinoma from patients without IPF. TMB for IPF-associated lung adenocarcinoma was significantly higher than that for matched IPF tissue (median of 2.94 vs. 1.26 mutations/Mb, P = 0.002). Three and 102 somatic variants were detected in IPF and matched lung adenocarcinoma samples, respectively, with only one pair of specimens sharing one somatic variant. TMB for IPF-associated lung adenocarcinoma was similar to that for lung adenocarcinoma samples with driver mutations (median of 2.94 vs. 2.51 mutations/Mb) and lower than that for lung adenocarcinoma samples without known driver mutations (median of 2.94 vs. 5.03 mutations/Mb, P = 0.130) from patients without IPF. Our findings suggest that not only the accumulation of somatic mutations but other factors such as inflammation and oxidative stress might contribute to the development and progression of lung cancer in patients with IPF.

Common driver mutations and smoking history affect tumor mutation burden in lung adenocarcinoma

2018 ◽  
Vol 230 ◽  
pp. 181-185 ◽  
Author(s):  
Masayuki Nagahashi ◽  
Seijiro Sato ◽  
Kizuki Yuza ◽  
Yoshifumi Shimada ◽  
Hiroshi Ichikawa ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Smoking History ◽  
Driver Mutations ◽  
Tumor Mutation Burden ◽  
Mutation Burden

Genetic alterations in squamous cell lung cancer associated with idiopathic pulmonary fibrosis

2021 ◽  
Author(s):  
Atsushi Hata ◽  
Takahiro Nakajima ◽  
Keisuke Matsusaka ◽  
Masaki Fukuyo ◽  
Manabu Nakayama ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Squamous Cell ◽  
Cell Lung Cancer ◽  
Genetic Alterations ◽  
Squamous Cell Lung Cancer

scholarly journals Decoding tumor mutation burden and driver mutations in early stage lung adenocarcinoma using CT‐based radiomics signature

2019 ◽  
Vol 10 (10) ◽  
pp. 1904-1912 ◽  
Author(s):  
Xiaoxiao Wang ◽  
Cheng Kong ◽  
Weizhang Xu ◽  
Sheng Yang ◽  
Dan Shi ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Early Stage ◽  
Driver Mutations ◽  
Tumor Mutation Burden ◽  
Mutation Burden ◽  
Radiomics Signature

scholarly journals Integration of Tumor Mutation Burden and PD-L1 Testing in Routine Laboratory Diagnostics in Non-Small Cell Lung Cancer

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1685
Author(s):  
Stefanie Schatz ◽  
Markus Falk ◽  
Balázs Jóri ◽  
Hayat O. Ramdani ◽  
Stefanie Schmidt ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Genetic Alterations ◽  
Small Cell ◽  
Precision Oncology ◽  
Checkpoint Inhibition ◽  
Small Cell Lung ◽  
Tumor Mutation Burden ◽  
Mutation Burden

In recent years, Non-small cell lung cancer (NSCLC) has evolved into a prime example for precision oncology with multiple FDA-approved “precision” drugs. For the majority of NSCLC lacking targetable genetic alterations, immune checkpoint inhibition (ICI) has become standard of care in first-line treatment or beyond. PD-L1 tumor expression represents the only approved predictive biomarker for PD-L1/PD-1 checkpoint inhibition by therapeutic antibodies. Since PD-L1-negative or low-expressing tumors may also respond to ICI, additional factors are likely to contribute in addition to PD-L1 expression. Tumor mutation burden (TMB) has emerged as a potential candidate; however, it is the most complex biomarker so far and might represent a challenge for routine diagnostics. We therefore established a hybrid capture (HC) next-generation sequencing (NGS) assay that covers all oncogenic driver alterations as well as TMB and validated TMB values by correlation with the assay (F1CDx) used for the CheckMate 227 study. Results of the first consecutive 417 patients analyzed in a routine clinical setting are presented. Data show that fast reliable comprehensive diagnostics including TMB and targetable alterations are obtained with a short turn-around time. Thus, even complex biomarkers can easily be implemented in routine practice to optimize treatment decisions for advanced NSCLC.

scholarly journals Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Apaar Sadhwani ◽  
Huang-Wei Chang ◽  
Ali Behrooz ◽  
Trissia Brown ◽  
Isabelle Auvigne-Flament ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lung Adenocarcinoma ◽  
Demographic Data ◽  
Characteristic Curve ◽  
Learning System ◽  
Histologic Subtype ◽  
Tumor Mutation Burden ◽  
Mutation Burden ◽  
Weakly Supervised ◽  
Whole Slide Images

AbstractBoth histologic subtypes and tumor mutation burden (TMB) represent important biomarkers in lung cancer, with implications for patient prognosis and treatment decisions. Typically, TMB is evaluated by comprehensive genomic profiling but this requires use of finite tissue specimens and costly, time-consuming laboratory processes. Histologic subtype classification represents an established component of lung adenocarcinoma histopathology, but can be challenging and is associated with substantial inter-pathologist variability. Here we developed a deep learning system to both classify histologic patterns in lung adenocarcinoma and predict TMB status using de-identified Hematoxylin and Eosin (H&E) stained whole slide images. We first trained a convolutional neural network to map histologic features across whole slide images of lung cancer resection specimens. On evaluation using an external data source, this model achieved patch-level area under the receiver operating characteristic curve (AUC) of 0.78–0.98 across nine histologic features. We then integrated the output of this model with clinico-demographic data to develop an interpretable model for TMB classification. The resulting end-to-end system was evaluated on 172 held out cases from TCGA, achieving an AUC of 0.71 (95% CI 0.63–0.80). The benefit of using histologic features in predicting TMB is highlighted by the significant improvement this approach offers over using the clinical features alone (AUC of 0.63 [95% CI 0.53–0.72], p = 0.002). Furthermore, we found that our histologic subtype-based approach achieved performance similar to that of a weakly supervised approach (AUC of 0.72 [95% CI 0.64–0.80]). Together these results underscore that incorporating histologic patterns in biomarker prediction for lung cancer provides informative signals, and that interpretable approaches utilizing these patterns perform comparably with less interpretable, weakly supervised approaches.

scholarly journals Chemotherapy in idiopathic pulmonary fibrosis and small-cell lung cancer with poor lung function

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiyue Zhang ◽  
Wei Li ◽  
Chunyan Li ◽  
Jie Zhang ◽  
Zhenzhong Su
Keyword(s):  
Lung Cancer ◽  
Lung Function ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
High Resolution Computed Tomography ◽  
Chemotherapeutic Regimen

Abstract Background Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease with unclear pathogenesis. IPF is considered as a risk factor for lung cancer. Compared to other lung cancers, small-cell lung cancer (SCLC) has a lower incidence, but has a more aggressive course. Patients with IPF and SCLC have a lower survival rate, more difficult treatment, and poorer prognosis. Case presentation Case 1 was of a 66-year-old man with IPF for 5 years, who was admitted to our hospital for dyspnea. Case 2 was of a 68-year-old woman, who presented with chest pains, cough, and dyspnea. Both patients had extremely poor lung function. High-resolution computed tomography and pathology revealed that both patients had IPF and SCLC. Chemotherapy comprising nedaplatin (80 mg/m2) and etoposide (100 mg for 5 days) was initiated for both patients. Antifibrotic agents were continued during the chemotherapeutic regimen. Both patients showed improvement in their condition after treatment. Conclusion The favorable outcomes in these 2 cases suggests that chemotherapy is worth considering in the management of patients having SCLC and IPF with poor lung function.

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