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R. Soong
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P1.02 - Poster Session with Presenters Present (ID 454)
- Event: WCLC 2016
- Type: Poster Presenters Present
- Track: Biology/Pathology
- Presentations: 1
- Moderators:
- Coordinates: 12/05/2016, 14:30 - 15:45, Hall B (Poster Area)
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P1.02-059 - Evaluation of Plasma DNA Extraction, Droplet PCR and Droplet next Generation Sequencing Methods for Liquid Biopsy Analysis (ID 6407)
14:30 - 14:30 | Author(s): R. Soong
- Abstract
Background:
The ability to detect tumour mutations from blood and other bodily fluids promises many sample access, convenience, and monitoring benefits. However, the extremely rare levels at which mutations are present in these fluids obliges the use of optimal extraction and detection methods. Here, the performance of two extraction, two droplet PCR (dPCR) and a droplet next generation sequencing (dNGS) method for blood plasma analysis was systematically evaluated.
Methods:
Limits of detection were assessed using 15 blinded healthy donor blood samples spiked with equivolume mixtures of H1975 (containing EGFR L858R and T790M mutations) and H1650 (EGFR exon 19 deletion, e19del) cells at 10%, 1%, 0.1%, 0.01%, 0.001% H1650 cells in triplicate. A series of 32 blinded blood plasma samples from non-small cell lung cancer (NSCLC) patients with known tumour EGFR mutation status was also tested. Samples were processed for plasma, and 1ml plasma each underwent Qiagen Circulating Nucleic Acid and Promega Maxwell Circulation Cell Free DNA extraction processing. The plasma DNA samples were analysed using the Biorad dPCR and Raindance Raindrop dPCR method for L858R and exon 19 deletion mutations, and the 50-gene Raindance Thunderbolts dNGS protocol.
Results:
No significant difference in DNA yield and detection patterns was observed between the two extraction methods. L858R mutations were detected by both dPCR methods at 0.001% in 1/3 replicates and 0.01% in 3/3 replicates. Of 4 cases with L858R tumour mutations, mutations were detected in the same 3 plasma samples by both Biorad and Raindance dPCR (sensitivity 75%). “False positive” L858R mutations were identified in 2 (specificity 92%) and 7 (75%) cases respectively. For 8 tumour e19del mutations, the sensitivities were 38% and 25%, and specificities were 96% and 75% respectively. Of 16 clinical samples analysed by dNGS, an average of 20 mutations per sample were identified after filtering for quality, non-synomyous, and non-germline variant status. The sensitivity and specificity for detecting 2 L858R tumour mutation was 100% and 50%, and 1 e19del tumour mutation was 100% and 55% respectively. The allele frequencies for the majority of “false positives” for dPCR and dNGS were less than 5%, although some “true positives” were also detected at that level.
Conclusion:
dPCR and dNGS methods can enable detection of tumour mutations in blood, albeit imperfectly. Future work to determine optimal detection thresholds will help to maximize sensitivity and specificity.
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P2.01 - Poster Session with Presenters Present (ID 461)
- Event: WCLC 2016
- Type: Poster Presenters Present
- Track: Biology/Pathology
- Presentations: 2
- Moderators:
- Coordinates: 12/06/2016, 14:30 - 15:45, Hall B (Poster Area)
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P2.01-027 - A Comparison of Five Different Immunohistochemistry Assays for Programmed Death Ligand-1 Expression in Non-Small Cell Lung Cancer Samples (ID 4414)
14:30 - 14:30 | Author(s): R. Soong
- Abstract
Background:
Randomised trials have shown treatment with programmed death-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors can provide a survival benefit to patients with advanced stage non-small cell lung cancer (NSCLC). PD-L1 expression, determined by immunohistochemistry (IHC) using different protocols, antibodies and thresholds for positivity for different inhibitors, has been reported to be potentially predictive of clinical outcome. The objective of this study was to compare the staining patterns of prominent PD-L1 IHC assays in clinically relevant NSCLC samples.
Methods:
Consecutive full sections of 20 NSCLC samples, comprising five each of resection, core biopsy, cytology, and pleural fluid samples, underwent IHC with the following anti-PD-L1 antibodies/autostainers: 22C3/Link 48, 28-8/Bondmax, SP142/Bondmax, SP263/Benchmark XT, E1L3N/Benchmark XT according to publicly-available protocols. PD-L1 expression were scored manually by pathologists according to the percentage of tumour cells (%TC) stained on a continuous scale.
Results:
Using published tumour cell percentage thresholds for 22C3, 28-8, SP142 and SP263 of ≥50%, ≥1%, ≥5%, and ≥25%, the frequency of PD-L1 positive cases were 10%, 15%, 70%, and 15% of cases respectively. When a ≥1% threshold was applied, the corresponding frequencies were 70%, 15%, 95%, 65% respectively, and 55% for E1L3N. Using published thresholds, cases were positive according to 1, 2, 3, 4 and 5 antibodies in 15%, 25%, 25%, 0% and 5% of cases respectively. Sorting of cases according to increasing %TC staining revealed a similar order of cases between antibodies, albeit with differences in %TC quanta and occasional exceptions to the order. Spearman rho analysis indicated %TC staining significantly (p<0.05) correlated between most antibody pairs, except 28-8 and 22C3, 28-8 and SP142, and 28-8 and E1L3N. Unsupervised hierarchical clustering revealed two subgroups, comprising of SP142/SP263 and 22C3/28-8/E1L3N.
Conclusion:
The classification of cases as PD-L1 positive can vary significantly according to the antibody and protocol used. Differences were more likely due to protocol dependent staining intensities and nominated thresholds for positivity, rather than differences in antibody affinity for different epitopes.
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P2.01-058 - Mutational Features Associated with Immunoreactivity in Non-Small Cell Lung Cancer (ID 5315)
14:30 - 14:30 | Author(s): R. Soong
- Abstract
Background:
Recent reports convey that the abundance and patterns of DNA mutational features in human cancers could modulate their antigenicity and sensitivity to immune checkpoint blockade. We thus sought to comprehensively characterise the immunogenomic landscape of NSCLC.
Methods:
We used publicly-available molecular (DNA mutation and RNA expression profiles) and clinical data of 658 NSCLC patients from The Cancer Genome Atlas project. HLA-type was inferred using POLYSOLVER, and we identified neoantigens with patient-specific HLA-binding affinity of IC50<500nm using NetMHC. The relative tumour infiltration by 22 immune cell types was enumerated using CIBERSORT. Finally, we developed and applied MUTPROFILER, a computational approach for mutational signature analysis capable of decrypting sequence alterations across 96 trinucleotide contexts and indels of varying lengths.
Results:
This analysis recapitulated a pervasive and prominent association between neoantigen levels and the molecular smoking signature (that is characterised by C:G>A:T transversions; Spearman ρ=0.78, P=1.37×10[-69]), and identified several novel and powerful immunogenetic associations in NSCLC. For instance, the proportion of activated natural killer (NK) cells was greater in tumours which showed a higher abundance of 1-3bp insertion/deletions (indels; ρ=0.23, P=1.34×10[-9]). Furthermore, small (1bp) indels were associated with increased expression of markers of immune cytolytic activity, including TNFA (TNFα; ρ=0.30, P=4.77×10[-15]), GZMA (ρ=0.21, P=4.92×10[-8]) and PRF1 (ρ=0.15, P=1.84×10[-4]). Fourteen trinucleotide alterations, including GCA>GTA, TCG>TAG and TCG>TGG, were more strongly correlated with PDCD1LG2 (PD-L2) expression compared to small indels (q<5×10[-8], Fisher’s Z-transformation). Interestingly, the number of small frameshifting indels was associated with downregulation of the antigen-presenting machinery (APM) such as TAP1 (ρ=–0.22, P=7.93×10[-9]) and TAP2 (ρ=–0.23, P=1.69×10[-9]), suggesting a potential immunoediting mechanism by which NSCLC tumours co-opt APM pathways to prevent neoantigen-recognition. Finally, by analysing the mutational signatures of the AID/APOBEC family, which has important roles in adaptive and innate immunity, we identified a potential novel mutagenic contribution of APOBEC3H, whose expression levels was associated with a pattern of C>A (ρ=0.18, P=4.76×10[-6]) and C>G (ρ=0.14, P=3.67×10[-4]) within TC motifs (with the mutated base underlined).
Conclusion:
Our study portrays an atlas of immunogenetic features in NSCLC. The results sheds light on the dynamics of tumour-immune cell interactions which are likely to form the driving force behind the clinical activity of novel immunologic strategies, and may lead to new biomarkers and targets for cancer immunotherapy.
