Combination of aneuploidy and high S-phase fraction indicates increased risk of relapse in stage I endometrioid endometrial carcinoma.

INTRODUCTION: Endometrioid endometrial carcinoma is a cancer type with generally excellent prognosis when diagnosed at an early stage, but there is a subset of patients with relapsing disease in spite of early diagnosis and surgical treatment. There is a need to find prognostic markers to identify these patients with increased risk of relapse. Depth of myometrial invasion, histological grade, and presence of lymphovascular invasion are known risk factors. DNA content (ploidy) and proliferation measured as S-phase fraction (SPF) have been discussed as prognostic markers but need additional evaluation. MATERIAL AND METHODS: We evaluated relapse-free survival (RFS) with respect to ploidy and SPF, which was analyzed by flow cytometry on fresh tumor tissue, in a cohort of 1001 women treated for stage I endometrioid endometrial carcinoma in northern Sweden during the period of 1993-2010, with a median follow up time of 12.0 years. Data were obtained from historical records. RESULTS: In simple analysis, both aneuploidy and high SPF were associated to increased risk of relapse with hazard ratios (HR) 2.37 (95% CI 1.52-3.70) and 1.94 (95% CI 1.24-3.02), respectively. Our data also confirmed stage, tumor grade, and ploidy as independent prognostic markers in an age adjusted cox regression multivariable analysis but we did not find SPF to contribute to prognosis. However, the combination of aneuploidy and high SPF identified a group of patients with increased risk of relapse, HR 2.02 (95% CI 1.19-3.44). CONCLUSION: In this study, which is the largest study of ploidy and SPF in stage I endometrioid endometrial carcinoma using fresh frozen tissue, aneuploidy was shown to be an independent prognostic marker. Furthermore, the combination of aneuploidy and high SPF could be used to identify patients with increased risk of relapse.

A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis.

Disease recurrence in surgically treated lung adenocarcinoma (AC) remains high. New approaches for risk stratification beyond tumor stage are needed. Gene expression-based AC subtypes such as the Cancer Genome Atlas Network (TCGA) terminal-respiratory unit (TRU), proximal-inflammatory (PI) and proximal-proliferative (PP) subtypes have been associated with prognosis, but show methodological limitations for robust clinical use. We aimed to derive a platform independent single sample predictor (SSP) for molecular subtype assignment and risk stratification that could function in a clinical setting. Two-class (TRU/nonTRU=SSP2) and three-class (TRU/PP/PI=SSP3) SSPs using the AIMS algorithm were trained in 1655 ACs (n = 9659 genes) from public repositories vs TCGA centroid subtypes. Validation and survival analysis were performed in 977 patients using overall survival (OS) and distant metastasis-free survival (DMFS) as endpoints. In the validation cohort, SSP2 and SSP3 showed accuracies of 0.85 and 0.81, respectively. SSPs captured relevant biology previously associated with the TCGA subtypes and were associated with prognosis. In survival analysis, OS and DMFS for cases discordantly classified between TCGA and SSP2 favored the SSP2 classification. In resected Stage I patients, SSP2 identified TRU-cases with better OS (hazard ratio [HR] = 0.30; 95% confidence interval [CI] = 0.18-0.49) and DMFS (TRU HR = 0.52; 95% CI = 0.33-0.83) independent of age, Stage IA/IB and gender. SSP2 was transformed into a NanoString nCounter assay and tested in 44 Stage I patients using RNA from formalin-fixed tissue, providing prognostic stratification (relapse-free interval, HR = 3.2; 95% CI = 1.2-8.8). In conclusion, gene expression-based SSPs can provide molecular subtype and independent prognostic information in early-stage lung ACs. SSPs may overcome critical limitations in the applicability of gene signatures in lung cancer.

A combined gene expression tool for parallel histological prediction and gene fusion detection in non-small cell lung cancer.

Accurate histological classification and identification of fusion genes represent two cornerstones of clinical diagnostics in non-small cell lung cancer (NSCLC). Here, we present a NanoString gene expression platform and a novel platform-independent, single sample predictor (SSP) of NSCLC histology for combined, simultaneous, histological classification and fusion gene detection in minimal formalin fixed paraffin embedded (FFPE) tissue. The SSP was developed in 68 NSCLC tumors of adenocarcinoma (AC), squamous cell carcinoma (SqCC) and large-cell neuroendocrine carcinoma (LCNEC) histology, based on NanoString expression of 11 (CHGA, SYP, CD56, SFTPG, NAPSA, TTF-1, TP73L, KRT6A, KRT5, KRT40, KRT16) relevant genes for IHC-based NSCLC histology classification. The SSP was combined with a gene fusion detection module (analyzing ALK, RET, ROS1, MET, NRG1, and NTRK1) into a multicomponent NanoString assay. The histological SSP was validated in six cohorts varying in size (n = 11-199), tissue origin (early or advanced disease), histological composition (including undifferentiated cancer), and gene expression platform. Fusion gene detection revealed five EML4-ALK fusions, four KIF5B-RET fusions, two CD74-NRG1 fusion and three MET exon 14 skipping events among 131 tested cases. The histological SSP was successfully trained and tested in the development cohort (mean AUC = 0.96 in iterated test sets). The SSP proved successful in predicting histology of NSCLC tumors of well-defined subgroups and difficult undifferentiated morphology irrespective of gene expression data platform. Discrepancies between gene expression prediction and histologic diagnosis included cases with mixed histologies, true large cell carcinomas, or poorly differentiated adenocarcinomas with mucin expression. In summary, we present a proof-of-concept multicomponent assay for parallel histological classification and multiplexed fusion gene detection in archival tissue, including a novel platform-independent histological SSP classifier. The assay and SSP could serve as a promising complement in the routine evaluation of diagnostic lung cancer biopsies.

PD-L1 immunohistochemistry in clinical diagnostics of lung cancer: inter-pathologist variability is higher than assay variability.

Assessment of programmed cell death ligand 1 (PD-L1) immunohistochemical staining is used for decision on treatment with programmed cell death 1 and PD-L1 checkpoint inhibitors in lung adenocarcinomas and squamous cell carcinomas. This study aimed to compare the staining properties of tumor cells between the antibody clones 28-8, 22C3, SP142, and SP263 and investigate interrater variation between pathologists to see if these stainings can be safely evaluated in the clinical setting. Using consecutive sections from a tissue microarray with tumor tissue from 55 resected lung cancer cases, staining with five PD-L1 assays (28-8 from two different vendors, 22C3, SP142, and SP263) was performed. Seven pathologists individually evaluated the percentage of positive tumor cells, scoring each sample applying cutoff levels used in clinical studies: <1% positive tumor cells (score 0), 1-4% (score 1), 5-9% (score 2), 10-24% (score 3), 25-49% (score 4), and >50% positive tumor cells (score 5). Pairwise analysis of antibody clones showed weighted kappa values in the range of 0.45-0.91 with the highest values for comparisons with 22C3 and 28-8 and the lowest involving SP142. Excluding SP142 resulted in kappa 0.75-0.91. Weighted kappa for interobserver variation between pathologists was 0.71-0.96. Up to 20% of the cases were differently classified as positive or negative by any pathologist compared with consensus score using ≥1% positive tumor cells as cutoff. A significantly better agreement between pathologists was seen using ≥50% as cutoff (0-5% of cases). In conclusion, the concordance between the PD-L1 antibodies 22C3, 28-8 and SP263 is relatively good when evaluating lung cancers and suggests that any one of these assays may be sufficient as basis for decision on treatment with nivolumab, pembrolizumab, and durvalumab. The scoring of the pathologist presents an intrinsic source of error that should be considered especially at low PD-L1 scores.