scholarly journals Mutational burden and chromosomal aneuploidy synergistically predict survival from radiotherapy in non-small cell lung cancer

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Qingzhu Jia ◽  
Qian Chu ◽  
Anmei Zhang ◽  
Jing Yu ◽  
Fangfang Liu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Cox Proportional Hazards Model ◽  
Risk Scores ◽  
Small Cell Lung ◽  
Mutational Burden ◽  
Chromosomal Aneuploidy ◽  
Therapeutic Radiation

AbstractTherapeutic radiation can result in substantially different survival outcomes for patients with non-small cell lung cancer (NSCLC). Measures for identification of patients who can benefit most throughout radiotherapy remain limited. In this retrospective study, survival analysis was performed based on a discovery cohort from TCGA and a validation cohort from three independent hospitals. Tumor mutational burden (TMB) and chromosomal aneuploidy (ANE) were derived from the whole exome sequencing (WES) data from treatment-naïve tumors. Integrated risk scores were derived from TMB and ANE by a multivariate Cox proportional hazards model. TCGA reveal that TMB and ANE are associated positively and negatively, respectively, with survival throughout radiotherapy. Additionally, the synergistically predictive significance of these two genomic alterations, in differing responders and non-responders to radiotherapy is identified. These biomarkers may have clinical potential to improve personalized treatment management by rationally identifying highly likely responders to therapeutic radiation in patients with NSCLC.

scholarly journals Tumor mutational burden assessment in non-small-cell lung cancer samples: results from the TMB2 harmonization project comparing three NGS panels

2021 ◽  
Vol 9 (5) ◽  
pp. e001904
Author(s):  
Javier Ramos-Paradas ◽  
Susana Hernández-Prieto ◽  
David Lora ◽  
Elena Sanchez ◽  
Aranzazu Rosado ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Predictive Value ◽  
Predictive Biomarker ◽  
Small Cell ◽  
Small Cell Lung ◽  
Mutational Burden ◽  
Reproducibility Study ◽  
Tumor Mutational Burden

BackgroundTumor mutational burden (TMB) is a recently proposed predictive biomarker for immunotherapy in solid tumors, including non-small cell lung cancer (NSCLC). Available assays for TMB determination differ in horizontal coverage, gene content and algorithms, leading to discrepancies in results, impacting patient selection. A harmonization study of TMB assessment with available assays in a cohort of patients with NSCLC is urgently needed.MethodsWe evaluated the TMB assessment obtained with two marketed next generation sequencing panels: TruSight Oncology 500 (TSO500) and Oncomine Tumor Mutation Load (OTML) versus a reference assay (Foundation One, FO) in 96 NSCLC samples. Additionally, we studied the level of agreement among the three methods with respect to PD-L1 expression in tumors, checked the level of different immune infiltrates versus TMB, and performed an inter-laboratory reproducibility study. Finally, adjusted cut-off values were determined.ResultsBoth panels showed strong agreement with FO, with concordance correlation coefficients (CCC) of 0.933 (95% CI 0.908 to 0.959) for TSO500 and 0.881 (95% CI 0.840 to 0.922) for OTML. The corresponding CCCs were 0.951 (TSO500-FO) and 0.919 (OTML-FO) in tumors with <1% of cells expressing PD-L1 (PD-L1<1%; N=55), and 0.861 (TSO500-FO) and 0.722 (OTML-FO) in tumors with PD-L1≥1% (N=41). Inter-laboratory reproducibility analyses showed higher reproducibility with TSO500. No significant differences were found in terms of immune infiltration versus TMB. Adjusted cut-off values corresponding to 10 muts/Mb with FO needed to be lowered to 7.847 muts/Mb (TSO500) and 8.380 muts/Mb (OTML) to ensure a sensitivity >88%. With these cut-offs, the positive predictive value was 78.57% (95% CI 67.82 to 89.32) and the negative predictive value was 87.50% (95% CI 77.25 to 97.75) for TSO500, while for OTML they were 73.33% (95% CI 62.14 to 84.52) and 86.11% (95% CI 74.81 to 97.41), respectively.ConclusionsBoth panels exhibited robust analytical performances for TMB assessment, with stronger concordances in patients with negative PD-L1 expression. TSO500 showed a higher inter-laboratory reproducibility. The cut-offs for each assay were lowered to optimal overlap with FO.

scholarly journals In-house Implementation of Tumor Mutational Burden Testing to Predict Durable Clinical Benefit in Non-small Cell Lung Cancer and Melanoma Patients

Cancers ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1271 ◽  
Author(s):  
Heeke ◽  
Benzaquen ◽  
Long-Mira ◽  
Audelan ◽  
Lespinet ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Clinical Benefit ◽  
Small Cell ◽  
Small Cell Lung ◽  
Mutational Burden ◽  
Potential Biomarker ◽  
Tumor Mutational Burden ◽  
Melanoma Patients

Tumor mutational burden (TMB) has emerged as an important potential biomarker for prediction of response to immune-checkpoint inhibitors (ICIs), notably in non-small cell lung cancer (NSCLC). However, its in-house assessment in routine clinical practice is currently challenging and validation is urgently needed. We have analyzed sixty NSCLC and thirty-six melanoma patients with ICI treatment, using the FoundationOne test (FO) in addition to in-house testing using the Oncomine TML (OTML) panel and evaluated the durable clinical benefit (DCB), defined by >6 months without progressive disease. Comparison of TMB values obtained by both tests demonstrated a high correlation in NSCLC (R2 = 0.73) and melanoma (R2 = 0.94). The association of TMB with DCB was comparable between OTML (area-under the curve (AUC) = 0.67) and FO (AUC = 0.71) in NSCLC. Median TMB was higher in the DCB cohort and progression-free survival (PFS) was prolonged in patients with high TMB (OTML HR = 0.35; FO HR = 0.45). In contrast, we detected no differences in PFS and median TMB in our melanoma cohort. Combining TMB with PD-L1 and CD8-expression by immunohistochemistry improved the predictive value. We conclude that in our cohort both approaches are equally able to assess TMB and to predict DCB in NSCLC.

scholarly journals Tumor Mutational Burden and PD-L1 Expression in Non-Small-Cell Lung Cancer (NSCLC) in Southwestern China

2020 ◽  
Vol Volume 13 ◽  
pp. 5191-5198
Author(s):  
Yuhui Ma ◽  
Quan Li ◽  
Yaxi Du ◽  
Wanlin Chen ◽  
Xing Liu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Southwestern China ◽  
Small Cell Lung ◽  
Mutational Burden ◽  
Tumor Mutational Burden

scholarly journals Tumor Mutational Burden and Efficacy of Nivolumab Monotherapy and in Combination with Ipilimumab in Small-Cell Lung Cancer

Cancer Cell ◽  
2018 ◽  
Vol 33 (5) ◽  
pp. 853-861.e4 ◽  
Author(s):  
Matthew D. Hellmann ◽  
Margaret K. Callahan ◽  
Mark M. Awad ◽  
Emiliano Calvo ◽  
Paolo A. Ascierto ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Mutational Burden ◽  
Tumor Mutational Burden

A twenty eight-gene signature and survival in completely-resected non-small cell lung cancer (NSCLC)

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 10583-10583
Author(s):  
N. Van Zandwijk
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Overall Survival ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Gene Signature ◽  
Small Cell ◽  
Risk Scores ◽  
Small Cell Lung ◽  
Independent Validation

10583 Background: Current staging methods are imprecise for predicting the outcome of treatment of non-small-cell lung cancer (NSCLC). We have developed a 28-gene signature that is closely associated with recurrence-free and overall survival. Methods: We used whole-genome gene expression microarrays to analyze frozen-tumor samples from 174 patients (pT1&2, N0&1, MO), who had undergone complete surgical resection in 5 European institutions. Randomly generated numbers were used to assign 2/3 of the samples to an algorithm training group with the remaining 1/3 set aside for independent validation. Cox proportional hazards models were used to evaluate the association between the level of expression and patient survival. We used risk scores and nearest centroid analysis to develop a gene-expression model for the prediction of treatment outcome. Leave-one-out cross validation was used to prevent model over-training. Results: 28 genes that correlated with survival were identified by analyzing microarray data and risk scores. Based on the expression of these genes, patients in training and validation groups were classified as either high (48%) or low (52%) risk. Analysis of predicted risk groups revealed significantly different survival distributions for patients in both the training set (p<0.001) and independent validation set (p=0.006). Genes in our prognostic signature encode for several membrane-bound proteins with previously demonstrated involvement in cell cycle regulation and cell proliferation processes. Conclusions: Our 28-gene signature is closely associated with time to recurrence and overall survival of completely-resected NSCLC patients. [Table: see text]

scholarly journals Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small-Cell Lung Cancer

2019 ◽  
Vol 23 (4) ◽  
pp. 507-520 ◽  
Author(s):  
Han Chang ◽  
Ariella Sasson ◽  
Sujaya Srinivasan ◽  
Ryan Golhar ◽  
Danielle M. Greenawalt ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Mutational Burden ◽  
Tumor Mutational Burden
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