Results of screening in early and advanced thoracic malignancies in the EORTC pan-European SPECTAlung platform

Abstract

Access to a comprehensive molecular alteration screening is patchy in Europe and quality of the molecular analysis varies. SPECTAlung was created in 2015 as a pan-European screening platform for patients with thoracic malignancies. Here we report the results of almost 4 years of prospective molecular screening of patients with thoracic malignancies, in terms of quality of the program and molecular alterations identified. Patients with thoracic malignancies at any stage of disease were recruited in SPECTAlung, from June 2015 to May 2019, in 7 different countries. Molecular tumour boards were organised monthly to discuss patients' molecular and clinical profile and possible biomarker-driven treatments, including clinical trial options. FFPE material was collected and analysed for 576 patients with diagnosis of pleural, lung, or thymic malignancies. Ultimately, 539 patients were eligible (93.6%) and 528 patients were assessable (91.7%). The turn-around time for report generation and molecular tumour board was 214 days (median). Targetable molecular alterations were observed in almost 20% of cases, but treatment adaptation was low (3% of patients). SPECTAlung showed the feasibility of a pan-European screening platform. One fifth of the patients had a targetable molecular alteration. Some operational issues were discovered and adapted to improve efficiency.

Figure 1

Quality metrics for the SPECTAlung platform. (a) Map of recruiting countries and number of patients enrolled per country (b) Number of registered, eligible, and evaluable patients per disease (c) FFPE QC (left) and sequencing (right) failure per stage in NSCLC, at the sample level. Colour code on the graph and in the legend are in the same order. (d) Number of genes covered by each platform, including overlapping genes. (e) Turnaround time between patient registration and pathology FFPE QC (left) or molecular tumor board (right). The bar below the graph represents the different platform used.

Figure 2

OS Kaplan Meier curve by AJCC v7 Stage. together with a summary of associated statistics (median OS, 3-year OS rate estimates including the corresponding two-sided 95% confidence interval intervals (calculated by Greenwood formula’s estimation of the standard deviation for rates and by Brookmeyer and Crowley technique for the median). OS censoring markers were displayed. Cox’s proportional hazards model was fit by AJCC v7 stage. Hazard ratios (HR) with 95% confidence intervals were computed with AJCC v.7 stage I as reference stratum (HR = 1.00). Log-rank test was computed at 5% significance. NE: Not Estimated. (a) NSCLC. There was a significant difference between stage (p < 0.0001). (b) Mesothelioma. There was no significant difference between stage (p = 0.70). (c) Thymic malignancies. There was a significant difference between stage (p = 0.0498).

Figure 3

Molecular landscape of patients. Only clinically relevant molecular alterations (SNVs, indels, CNVs) are represented. (a) Top 20 altered genes (y axis) in NSCLC patients (sorted by histology on the x axis). The platform used to sequence each sample is also represented on the x axis. The bar plots above the graph represent the mutation rate for each sample. Alterations are color coded by type (SNV in green, deletion in dark blue, insertion in purple, amplification in red, loss in clear blue and multi-hit in brown. Note: (*) TP53 gene is not covered by Oncomine platform. (b) Alteration frequency for clinically actionable alterations for NSCLC patients from SPECTAlung (grey bar), MSK (yellow bar) or TCGA (blue bar) cohorts. Frequency calculations for SPECTAlung cohort are adjusted for TP53, ATM and KEAP1 genes to include only samples covered by platforms that screened those genes (Illumina &14MG for TP53 and ATM, 14MG only for KEAP1) (c) Comparison of the mutational landscape of mesothelioma samples in SPECTAlung with top altered genes in TCGA cohort (87 patients). Note: (*) TP53 gene is not covered by Oncomine platform, NF2 and BAP1 covered only by 14MG. Frequency calculations are adjusted for those genes. (d) Top altered genes in the SPECTAlung thymoma population and TCGA thymoma cohort (123 patients). Note: GTF2I oncogene is not covered by any panel used in this study.

Figure 4

Actionability and limits of the platform. (a) Attrition rate between patient enrolled to treatment adaptation. Thymoma in red, NSCLC in clear blue and mesothelioma in dark blue. (b) Treatment recommendations for NSCLC patients. The different targetable alterations are color-coded and enlarged in the pie chart below. (c) Main reason for treatment adaptation (right) or absence of adaptation (left) per stage, for NSCLC patients and globally for other diseases.

Figure 5

Smoking status and molecular alterations. (a) EGFR mutations in exon 6, 18, 19 and 21 found in the current smokers (red), former smokers (yellow) and never smokers (blue). (b) KRAS mutations in exon 12, 13 and 61 found in the current smokers (red), former smokers (yellow) and never smokers (blue). (c) BRAF, TP53, MET mutations, JAK2 mutations and loss and fusions in ALK, RET and ROS1 found in the current smokers (red), former smokers (yellow) and never smokers (blue). NOTE: For alterations with *, BH adjusted p value from Fisher’s exact test was < 0.05.