Assessment of Blood Tumor Mutational Burden as a Potential Biomarker for Immunotherapy in Patients With Non–Small Cell Lung Cancer With Use of a Next-Generation Sequencing Cancer Gene Panel

This study explores whether blood tumor mutational burden estimated by a next-generation sequencing gene panel is associated with clinical outcomes of patients with non–small cell lung cancer treated with anti–programmed cell death 1 and anti–programmed cell death ligand 1 agents.


eMethods 1. Gene Panel Design and Virtual Validation
WES data of 9205 total cases across 33 cancer types from TCGA (http://www.cbioportal.org/, version 1.11.3) was applied to explore the minimal number of genes needed and whether synonymous mutations should be considered for TMB calculation. We randomly extracted genes with a genomic scope to constitute randomized gene panels ranging from 10 to 700 genes (10, 20, 40, 60, 80, 100, 150, 200, 300, 400, 500, 600 and 700). The genes included in each size panel were extracted randomly 50 times. The correlations of TMB estimated by randomized gene panels with WES were evaluated. The estimated TMB was calculated by the sum of missense, stop loss, in-frame and frameshift mutations in protein coding regions, with or without silent mutations.
Based on the virtual deduction of panel size and improved TMB calculation formula, an NGS target CGP was designed and named as NCC-GP150 that covered whole exon regions of 150 genes. Gene selections were made considering several aspects for a combination of bTMB assessment and driver gene mutation Incorporation of additional positive and negative predictors for immunotherapy; and more importantly, D. One special consideration in our panel design: Incorporation of cancer genes that were reported to be associated to tumor mutation burden, and those with high mutation recurrence across TCGA database as identified from specific cancer types with high incidences or morality rates that will contribute to the accuracy of bTMB estimation. For example, some DNA damage repair (DDR) genes were included considering these DDR mutations are prone to increased somatic mutations and associated with improved survival of immunotherapy. 1 Taken together, we have integrated genes with above features into a complete panel and fine-tuned its cost-effectiveness.
To better explore and confirm the feasibility of NCC-GP150 for TMB estimation, we analyzed the correlation between the panel and TCGA-based WES data and compared it with other virtual randomsampling models and established NGS gene panels. We also applied NCC-GP150-based TMB to the

eMethods 3. Assessment of Clinical Outcomes
Radiographic imaging was acquired by indicated approaches such as CT or MRI for tumor response assessment, which was evaluated by both the investigator and an independent radiologist. Baseline tumor assessments were performed within 1-28 days prior to the initiation of the PD-1/PD-L1 treatment, with subsequent assessments performed every 6 to 8 weeks until objective disease progression. The objective response rate (ORR) was defined as the percentage of patients with confirmed complete response (CR) or partial response (PR) by RECIST version 1.1. 2 PFS was defined as the time from the start of anti-PD-1/PD-L1 treatment until disease progression (assessed by an investigator using RECIST version 1.1) or death from any cause.

eMethods 4. DNA Extraction
The blood was centrifuged in Streck tubes at 1600 g for 20 minutes at room temperature to separate the plasma. Then, the plasma layer was carefully transferred to a new 1.5 ml Eppendorf tube, followed by room-temperature centrifugation at 16000 g for 10 minutes to remove residual cells and debris. (http://gmt.genome.wustl.edu/packages/pindel). All raw variants were then filtered by an automated false positive filtering pipeline to guarantee sensitivity and specificity above an allele frequency of 5%. SNPs were filtered by dbSNP, 1000g and ESP6500 (population frequency >0.015). TMB was defined as the sum of missense, stop loss, in-frame and frameshift mutations in protein coding regions.

eMethods 7. bTMB Detection Pipeline
Our bTMB detection is based on the ctDNA variant-calling method, with integrated digital barcodes to tag the individual DNA molecules. Such barcodes enable the precise molecular tracking, making it possible to distinguish authentic somatic mutations arising in vivo from artifacts introduced ex vivo. 3 The variant-calling pipeline was developed according to mapping information from BWA Aligner. To improve specificity, especially for variants with low allele frequency in the ctDNA, we developed a filtering model based on the binomial test and determined optimal thresholds for the different parts of error generation using our own training data. 4 The filtering model contains error filters at different phases of noise generation, including sample conservation, wet experiment and data analysis. Therefore, in the filtering model, background error correction, strand bias, base quality, mapping quality, short tandem repeat regions and low-quality mapping ratio are considered.
bTMB was defined as the number of somatic SNVS and indels in examined coding region. All SNVs and indels in the coding region of targeted genes, including missense, silent, stop gain, stop loss, in-frame and frameshift mutations, are initially considered. Known germline SNVs, defined as population frequency more than 0.015, in dbSNP, 1000 genome, and ESP6500 were filtered. Variants with allele frequencies more than 30%, which are more likely germline mutations, were not counted.

Favors bTMB-H Favors bTMB-L eFigure 8. Association Between bTMB and Clinical Outcomes in Patients With Anti-PD1/PD-L1 Therapy as First-or Second-Line Treatment. (A)
Progression-free survival by bTMB status for ICB administered as a first-or second-line treatment. (B) Comparison of objective response rate between the bTMB-H and bTMB-L groups for ICB administered as a first-or second-line treatment. (C) Comparison of bTMB between nonresponse and response groups for ICB administered as a first-or second-line treatment.