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

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.

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Changes in tumor mutational burden in serially biopsied non-small cell lung cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e14286 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e14286-e14286

Author(s):

James W. Smithy ◽

David H. Hwang ◽

Yvonne Y. Li ◽

Liam Spurr ◽

Andrew D. Cherniack ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Predictive Biomarker ◽

Small Cell ◽

Small Cell Lung ◽

Tissue Samples ◽

Primary Tumors ◽

Mutational Burden ◽

Tumor Mutational Burden

e14286 Background: High tumor mutational burden (TMB) has been associated with response to checkpoint blockade in non-small cell lung cancer (NSCLC) and other malignancies. However, the degree to which TMB changes over time, across anatomical sites, and with intervening treatment remains unknown. To evaluate TMB changes across time points, we compared TMB in tissue specimens from patients with serially-biopsied NSCLC. Methods: Clinicopathologic characteristicsand changes in TMB were analyzed from patients with NSCLC and more than one biopsy that had undergone targeted next generation sequencing (NGS, OncoPanel) at the Dana-Farber Cancer Institute. Those representing distinct primary tumors by histology and/or discordant mutational profiles were excluded. Results: 134 NSCLC patients with more than one interpretable NGS result were identified; 23 were excluded due to separate primary tumors. Of the 111 remaining patients included in the analysis, the median time between samples was 14 months (range: 0 to 114 months). TMB correlated closely across all matched tumor pairs (Pearson’s r = 0.89), and greater variability in TMB was seen in biopsies taken from different anatomic sites (p = 0.04) compared to biopsies obtained from the same lesion. There was no significant change in median TMB with any intervening therapy, as TMB increased in some patients and decreased in others. Conclusions: In this observational study, TMB correlated closely across tumor pairs. However, these data suggest that sampling from different tumor sites may be associated with greater discrepancies in TMB. It is possible these differences could account for challenges in using TMB as a predictive biomarker for immunotherapy response. Further prospective investigation is needed to inform decisions regarding the need for repeat biopsy in patients starting immunotherapy with remote tissue samples.

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Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-000550 ◽

2020 ◽

Vol 8 (2) ◽

pp. e000550

Author(s):

Bingxi He ◽

Di Dong ◽

Yunlang She ◽

Caicun Zhou ◽

Mengjie Fang ◽

...

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Predictive Value ◽

Small Cell ◽

Small Cell Lung ◽

Training Cohort ◽

Mutational Burden ◽

Tumor Mutational Burden

BackgroundTumor mutational burden (TMB) is a significant predictor of immune checkpoint inhibitors (ICIs) efficacy. This study investigated the correlation between deep learning radiomic biomarker and TMB, including its predictive value for ICIs treatment response in patients with advanced non-small-cell lung cancer (NSCLC).MethodsCT images from 327 patients with TMB data (TMB median=6.067 mutations per megabase (range: 0 to 42.151)) were retrospectively collected and randomly divided into a training (n=236), validation (n=26), and test cohort (n=65). We used 3D-densenet to estimate the target tumor area, which used 1020 deep learning features to distinguish High-TMB from Low-TMB patients and establish the TMB radiomic biomarker (TMBRB). The TMBRB was developed in the training cohort combined with validation cohort and evaluated in the test cohort. The predictive value of TMBRB was assessed in a cohort of 123 NSCLC patients who had received ICIs (survival median=462 days (range: 16 to 1128)).ResultsTMBRB discriminated between High-TMB and Low-TMB patients in the training cohort (area under the curve (AUC): 0.85, 95% CI: 0.84 to 0.87))and test cohort (AUC: 0.81, 95% CI: 0.77 to 0.85). In this study, the predictive value of TMBRB was better than that of a histological subtype (AUC of training cohort: 0.75, 95% CI: 0.72 to 0.77; AUC of test cohort: 0.71, 95% CI: 0.66 to 0.76) or Radiomic model (AUC of training cohort: 0.75, 95% CI: 0.72 to 0.77; AUC of test cohort: 0.74, 95% CI: 0.69 to 0.79). When predicting immunotherapy efficacy, TMBRB divided patients into a high- and low-risk group with distinctly different overall survival (OS; HR: 0.54, 95% CI: 0.31 to 0.95; p=0.030) and progression-free survival (PFS; HR: 1.78, 95% CI: 1.07 to 2.95; p=0.023). Moreover, TMBRB had a better predictive ability when combined with the Eastern Cooperative Oncology Group performance status (OS: p=0.007; PFS: p=0.003). Visual analysis revealed that tumor microenvironment was important for predicting TMB.ConclusionBy combining deep learning technology and CT images, we developed an individual non-invasive biomarker that could distinguish High-TMB from Low-TMB, which might inform decisions on the use of ICIs in patients with advanced NSCLC.

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P1.04-72 Tumor Mutational Burden as a Potential Predictive Biomarker of Response to PD-1/PD-L1 Blockade in Non-Small Cell Lung Cancer

Journal of Thoracic Oncology ◽

10.1016/j.jtho.2019.08.975 ◽

2019 ◽

Vol 14 (10) ◽

pp. S470

Author(s):

H. Kim ◽

H.J.K. Kwon ◽

E.S.K. Kim ◽

Y.B. Han ◽

J. Lee ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Predictive Biomarker ◽

Small Cell ◽

Small Cell Lung ◽

Mutational Burden ◽

Tumor Mutational Burden

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Prospective clinical evaluation of blood-based tumor mutational burden (bTMB) as a predictive biomarker for atezolizumab (atezo) in 1L non-small cell lung cancer (NSCLC): Interim B-F1RST results.

Journal of Clinical Oncology ◽

10.1200/jco.2018.36.15_suppl.12001 ◽

2018 ◽

Vol 36 (15_suppl) ◽

pp. 12001-12001 ◽

Cited By ~ 24

Author(s):

Vamsidhar Velcheti ◽

Edward S. Kim ◽

Tarek Mekhail ◽

Christopher Dakhil ◽

Philip J. Stella ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Clinical Evaluation ◽

Cell Lung Cancer ◽

Predictive Biomarker ◽

Small Cell ◽

Small Cell Lung ◽

Mutational Burden ◽

Tumor Mutational Burden

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

Cancers ◽

10.3390/cancers11091271 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1271 ◽

Cited By ~ 9

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.

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