MSK-IMPACT：肿瘤基因测序分析

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"MSK-IMPACT是一种由纪念斯隆凯特琳癌症中心（Memorial Sloan Kettering, MSK）开发的肿瘤基因检测技术，主要用于分析肿瘤组织中的体细胞单核苷酸变异、插入、缺失以及微卫星不稳定性。该测试通过下一代测序（Next Generation Sequencing, NGS）技术进行肿瘤谱系分析，旨在识别可操作的癌症靶点。" MSK-IMPACT（Integrated Mutation Profiling of Actionable Cancer Targets）是MSK设计的一种用于评估肿瘤中可治疗的基因变异的诊断工具。这个测试的主要目的是在固定并石蜡包埋的肿瘤组织中检测特定的热点突变和一系列基因的变异，包括单核苷酸变异、插入、缺失等。具体来说，它涵盖了多个与癌症相关的基因，具体列表可以在附录1a和1b中找到。该检测属于FDA的第二类医疗器械（Class II），根据21 CFR 866.6080法规进行监管，并分配了产品代码PZM。其被归类在病理学检测领域，适用于多种类型的肿瘤。MSK-IMPACT的主要用途是作为一个定性的体外诊断测试，帮助医生在临床上识别出可能影响治疗选择的遗传变化。 MSK-IMPACT的应用场景广泛，可用于指导患者的治疗决策。它能够检测到的体细胞变异可能涉及肿瘤生长、扩散或对特定疗法的反应，因此，这些信息对于选择个性化的癌症治疗方案至关重要。通过这个检测，医生可以确定哪些患者可能受益于靶向疗法，或者可能需要进一步的遗传咨询，以了解他们的遗传风险和潜在的预防策略。此外，由于MSK-IMPACT也涵盖了微卫星不稳定性（Microsatellite Instability, MSI）的检测，这使得它在识别具有错配修复缺陷的肿瘤方面也有价值，这些肿瘤可能对免疫检查点抑制剂等特定类型治疗更为敏感。因此，MSK-IMPACT不仅有助于传统化疗药物的选择，还可能影响免疫疗法的临床应用。 MSK-IMPACT是肿瘤精准医疗的一个重要组成部分，通过全面的基因组分析，为临床医生提供了更深入的癌症生物学理解，从而可能改善患者的治疗结果。其自动化的设计和广泛的基因覆盖范围使其在癌症研究和临床实践中具有广泛的影响。

A boxplot showing the observed mutation frequencies for the 862 common SNPs

genotyped in the pooled normal sample binned by their true underlying mutation

frequency is shown below. The results demonstrated that an observed VF range from

5.0% to 13.9% for a SNP with true underlying mutation frequency of 10% when the

mean coverage of the sample was 480X. This range in values is roughly in line with

what the theoretical statistical assessment for a coverage depth of 500X (7.5% to

13.0%). This data provided support for using a 5% as the lower limit for reporting

mutations detected with true underlying frequency of 10%.

The boxplot in Figure 2 shows the correlation is 0.975, with a slope of 0.971 and

intercept of -0.004. Consistent correlation is established as >0.9 as a QC metric for

the whole pool analyzed.

Figure 2. Observed vs. Expected Variant Frequency

b) Requirements on sample coverage: Ten normal (diploid) FFPE samples were

profiled in duplicate using the IMPACT assay (total = 20 replicates) to generate

summary statistics across all targeted exons. The mean coverage across all targeted

exons for the normal samples was 571X (SD = 373X). Summary statistics were also

computed on coverage values per exon normalized by per-sample coverage. There

were exons that presented with consistently low coverage values. None of the exons

of the genes in the clinical validation are among those with consistently low coverage.

It was determined the low coverage was due to sequence similarity with other loci,

and high GC content. The exons were removed from the MSK-IMPACT assay. Of

the remaining exons across all genes, 99.5% were sequenced to a depth of 100X or

greater while 98.6% were sequenced to a depth of 250X or greater. This analysis of

normal samples indicates that with a mean sample coverage of 571X, 98% of exons

are sequenced with coverage greater than 306X, or with normalized coverage greater

calculations, which showed 100X coverage was necessary to call mutations with true

underlying mutation frequency 10% or greater, with 95% power at an alpha level of

0.05).

To be conservative, a threshold of 200X on mean sample coverage is used to

determine if a sample is sequenced to sufficient depth for subsequent analysis. A

sample is flagged as being at increased risk of false negatives if its mean coverage is

below 200X.

To provide empirical data for these requirements, MSK utilizes the pool normal

sample with known expected single nucleotide mutations (n = 2436) and the

underlying mutation allele fractions (MAF). In silico downsampling analysis was

conducted with a pool normal mix down to 45% where the sample coverage

decreased from 452X to 203X. At this coverage level, 94% of the mutations with

expected underlying VAF of 10% were called.

c) Requirements on mutation coverage, allele depth and frequency for positive

calls: Permissive standard filters were used to intentionally generate false positives to

identify suitable thresholds for parameters such as mutation coverage (DP), alternate

allele depth (AD) and mutation frequency (VF) to optimize specificity. The following

criteria allows optimal rejection of false positive SNVs (stratified by whether they are

hotspots or not) and indel calls, while maintaining ability to detect true positive

events with underlying frequency of 10% (5-17.6% observable). Potential strand-bias

is also evaluated in the standard somatic mutation calling pipeline. An example of

the number of false positive events detected pre and post filtering for coverage

depth(DP), number of mutant reads (AD) and variant frequency (VF) is shown in

Table 4.

Table 4. Sample error correction by DP/AD/VF filter

2. Pre-Analytical performance:

Minimum DNA requirements were established by measuring assay performance based on

different inputs from normal blood and FFPE tumor samples. DNA samples are

normalized to yield 50 – 250 ng input and maximized to 55 ul prior to shearing. The

normalization and DNA quantification are performed.

DNA extraction method was validated based on the invalid rates across multiple tumor

types obtained from historical data. The data demonstrated that the DNA extraction has

been optimized across tumor types to reasonably conclude that the analytical

Mutations –Cosmic database

Mutations

Filter criteria

DP ≥ 20X, AD ≥ 8,VF ≥ 2%

DP ≥ 20X, AD ≥ 10,VF ≥ 5%

SNVs Indels

Pre-filter

Post-filter

Rejection Rate

1 24

0 0

1.00 1.00

342 40,793

0 8

1.00 0.999

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MSK-IMPACT：肿瘤基因测序分析

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