Assessment of programmed cell death ligand-1 expression by 4 diagnostic assays and its clinicopathological correlation in a large cohort of surgical resected non-small cell lung carcinoma.

Immune checkpoint blockade targeting the PD-1/PD-L1 axis has recently demonstrated efficacy and promise in cancer treatment. Appropriate biomarker selection is therefore essential for improving treatment efficacy. However, the establishment of PD-L1 assay in pathology laboratories is complicated by the presence of multiple testing platforms using different scoring systems. Here we assessed the PD-L1 expression in 713 consecutive non-small cell lung carcinomas by four commercially available PD-L1 immunohistochemical assays, namely, 22C3, 28-8, SP142 and SP263. The analytical performances of the four assays and diagnostic performances across clinically relevant cutoffs were evaluated. The prevalence of PD-L1 (22C3) expression was 21% with a ≥50% cutoff and 56% with a ≥1% cutoff. High PD-L1 expression (using a ≥50% cutoff) was significantly associated with male sex (P = 0.001), ever smoking history (P < 0.001), squamous cell carcinoma (P = 0.001), large cell carcinoma (P < 0.001), lymphoepithelioma-like carcinoma (P = 0.006), sarcomatoid carcinoma (P < 0.001), mutant KRAS (P = 0.005) and wild-type EGFR (P = 0.003). Elevated PD-L1 expression was also significantly associated with shorter survival in patients with adenocarcinoma (log-rank P = 0.026) and remained an independent prognostic factor by multivariable analysis. Among the four assays, 22C3, 28-8 and SP263 were highly concordant for tumor cell scoring. With a cutoff of ≥50% (i.e., the threshold for first-line patient selection), inter-rater agreement was high among the three assays with percentage agreement >97%. In conclusion, three PD-L1 assays showed good analytical performance and a high agreement with each other, but not all cases were correctly classified using the same clinical cutoff. Further studies comparing the predictive value of these assays are required to address the interchangeability of these assays for clinical use.

Novel sib pair selection strategy increases power in quantitative association analysis.

Quantitative-trait association studies have been widely used in search for genetic loci for complex traits in recent years. Yet, fiscal constraints still prohibit many on-going research projects from recruiting a large number of individuals for genotyping to reach a desired level of statistical power. Accordingly, in this article, we describe a novel sib pair sampling strategy for genotyping in QTL association studies. With the use of phenotypic scores (and IBD allele-sharing probabilities if available), the genetic effect of a biallelic additive trait locus can be properly modelled within the maximum-likelihood variance components framework proposed by Fulker et al. (Am J Hum Genet 64(1):259-267, 1999) and sib pairs can be rank-ordered by use of informativeness indices. The performance of our method was investigated using simulation. The power of our approach was shown to be higher when compared with other phenotypic selection schemes. An R-script implementing all the selection approaches (including the traditional phenotype-based ones) used in the simulation is available at http://statgen.hku.hk/jshkwan .