scholarly journals Mutational signature analysis in non-small cell lung cancer patients with a high tumor mutational burden

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Guus R. M. van den Heuvel ◽  
Leonie I. Kroeze ◽  
Marjolijn J. L. Ligtenberg ◽  
Katrien Grünberg ◽  
Erik A. M. Jansen ◽  
...  

Abstract Background Lung cancer is the leading cause of cancer death worldwide. With the growing number of targeted therapies and the introduction of immuno-oncology (IO), personalized medicine has become standard of care in patients with metastatic disease. The development of predictive and prognostic biomarkers is of great importance. Mutational signatures harbor potential clinical value as predictors of therapy response in cancer. Here we set out to investigate particular mutational processes by assessing mutational signatures and associations with clinical features, tumor mutational burden (TMB) and targetable mutations. Methods In this retrospective study, we studied tumor DNA from patients with non-small cell lung cancer (NSCLC) irrespective of stage. The samples were sequenced using a 2 megabase (Mb) gene panel. On each sample TMB was determined and defined as the total number of single nucleotide mutations per Mb (mut/Mb) including non-synonymous mutations. Mutational signature profiling was performed on tumor samples in which at least 30 somatic single base substitutions (SBS) were detected. Results In total 195 samples were sequenced. Median total TMB was 10.3 mut/Mb (range 0–109.3). Mutational signatures were evaluated in 76 tumor samples (39%; median TMB 15.2 mut/Mb). SBS signature 4 (SBS4), associated with tobacco smoking, was prominently present in 25 of 76 samples (33%). SBS2 and/or SBS13, both associated with activity of the AID/APOBEC family of cytidine deaminases, were observed in 11 of 76 samples (14%). SBS4 was significantly more present in early stages (I and II) versus advanced stages (III and IV; P = .005). Conclusion In a large proportion of NSCLC patients tissue panel sequencing with a 2 Mb panel can be used to determine the mutational signatures. In general, mutational signature SBS4 was more often found in early versus advanced stages of NSCLC. Further studies are needed to determine the clinical utility of mutational signature analyses.

2021 ◽  
Vol 9 (5) ◽  
pp. e001904
Author(s):  
Javier Ramos-Paradas ◽  
Susana Hernández-Prieto ◽  
David Lora ◽  
Elena Sanchez ◽  
Aranzazu Rosado ◽  
...  

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.


Cancers ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1271 ◽  
Author(s):  
Heeke ◽  
Benzaquen ◽  
Long-Mira ◽  
Audelan ◽  
Lespinet ◽  
...  

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.


2020 ◽  
Vol Volume 13 ◽  
pp. 5191-5198
Author(s):  
Yuhui Ma ◽  
Quan Li ◽  
Yaxi Du ◽  
Wanlin Chen ◽  
Xing Liu ◽  
...  

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 ◽  
...  

2019 ◽  
Vol 23 (4) ◽  
pp. 507-520 ◽  
Author(s):  
Han Chang ◽  
Ariella Sasson ◽  
Sujaya Srinivasan ◽  
Ryan Golhar ◽  
Danielle M. Greenawalt ◽  
...  

2020 ◽  
Vol 31 ◽  
pp. S1217-S1218
Author(s):  
G. Van den Heuvel ◽  
L. Kroeze ◽  
M. Ligtenberg ◽  
K. Grunberg ◽  
D. von Rhein ◽  
...  

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