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Caroline Truntzer
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EP1.01 - Advanced NSCLC (ID 150)
- Event: WCLC 2019
- Type: E-Poster Viewing in the Exhibit Hall
- Track: Advanced NSCLC
- Presentations: 1
- Moderators:
- Coordinates: 9/08/2019, 08:00 - 18:00, Exhibit Hall
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EP1.01-25 - An Interactive Interface for Prediction of Anti-PD-1 Efficacy in Lung Cancer Patients (ID 2460)
08:00 - 18:00 | Author(s): Caroline Truntzer
- Abstract
Background
The past decade has seen a new field of promising treatments for non-small cell lung cancer patients (NSCLC) with immune checkpoint inhibitors (ICI). Medicine community embarks on a biomarker race to identify the one-quarter of responders and potential hyper-progressors. The assessment of PD-L1 tumor expression by IHC is used to select responder patients and is considered as the gold standard biomarker in many studies but it does not predict the absence of anti-PD-1 efficacy. Recent review from Keenan TE et al. summaries all potential studied markers in literature as intratumoral T cell infiltration, tumor neoantigens or else mismatch repair deficiency. Despite this abundance of potential markers, recommendations only rely on high PD-L1 expression and more recently on high tumor mutational burden (TMB). In this study, we propose estimations of response probability based on the different data that may be available for clinicians according their molecular biology and material means.
Method
Based on a cohort of 100 patients with advanced NSCLC treated with nivolumab in second line of treatment, we developed an algorithm enabling the calculation of the probability of survival without progression at 6 or 12 months when treated with ICI. Using Cox proportional hazards multiple regression, we adjusted these three stages of information to estimate the probability of response of a patient based on the type of available data: only clinical data and/or exome analysis and/or RNA sequencing. Stability and predictive ability of these models where evaluated internally and externally through bootstrap procedure.
Result
Among the 100 patients, 90 had both somatic and constitutional exome sequencings available and 50 had an RNA sequencing. We built a main model based on clinical and pathological data easily available for clinicians. As mandatory criteria, the age, sex, performance status, tumor histology, routine mutational status and PD-L1 immunohistochimical expression were first included in the predictive model. In addition, RECIST response to previous chemotherapy line and antibiotics usage were added into the initial model as they were described significantly associated with respectively good and poor survival to ICI in previous publications. Additional criterion were obtained thanks to extensive DNA tumor analysis such as TMB, mutations in DNA repair pathways, the number of large deletions and intratumoral TCR clones. A third stage of data was added with CD8A and CD274 RNA expressions obtained by RNA sequencing. Thus, several models are available for clinicians to estimate the probability of survival without progression at 6 or 12 months for their patients according to the type of available data. An interactive on-line interface based on R Shiny enables the questioning of all these models for further predictions.
Conclusion
Altogether, these data provide validated and more complex biomarkers according to the type of available data to predict probability of survival without progression to anti-PD-1 in patients with advanced NLCSC. Further predictions can be obtained thanks to a shiny R free and interactive interface available on-line.
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P1.14 - Targeted Therapy (ID 182)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Targeted Therapy
- Presentations: 1
- Now Available
- Moderators:
- Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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P1.14-19 - Exome Analysis of Patients Treated with Afatinib Reveals Genetic Variations Discriminating Extreme Responders (Now Available) (ID 2298)
09:45 - 18:00 | Author(s): Caroline Truntzer
- Abstract
Background
Non-small cell lung cancer is a dramatic disease. For several years, molecular analyses highlighted several genetic alterations (mutations, gene fusions) enabling use of targeted therapies. Among targetable mutations, the most frequent (11% of lung adenocarcinomas) are EGFR mutations spanning from exon 18 to exon 21, except insertion in exon 20 and T790M mutation. In clinical practice, progression free survival under EGFR Tyrosine Kinase Inhibitor is between 12 and 14 months, but some patients rapidly progress in less than 6 months, whereas other patients are treated with EGFR TKI during more than 16 months, and even more in some cases.
Method
ALCAPONE clinical trial (NCT02281214) included 165 patients with a non-small cell lung cancer divided in 2 different groups: EGFR mutated (n=63) and EGFR wild-type tumors (n=102). All tumors at baseline had an exome analysis performed with SureSelect Human all exon v5 or v6 kit. After adapter trimming and quality check, GATK tools were applied to process the data. After variant calling (Haplotype Caller) and annotation, genetic variations were separated in 3 categories: intron variants, synonymous variants and transcripted variants. Only variants with a general population frequency <1% were conserved for statistical analyses. For the first analysis of the trial, we focused on a training set of 33 EGFR mutated patients homogenously treated by afatinib. We selected 18 extreme responders (10 short responders with PFS<180 days and 8 long responders with PFS>500 days) to select genetic markers predictive of extreme responses and used them to evaluate survival including 15 patients with PFS between 180 and 500 days.
Result
Thanks to 2 different predictive models, it appeared that 5 genes were able to discriminate short responders from long responders: AKR1B1, WNK1, IHH*, PLA2G16*, and SMYD3*, whose those with an asterisk selected by the 2 different predictive models. For these genes, the presence of a non-synonymous variant in transcripted (UTR and coding) sequences of the genes was associated with a worse response to afatinib. By studying PFS probability with the 33 EGFR mutated patients of the training set, it appeared that 2 genes discriminated responders from non-responders. Indeed, patients with a variation in IHH or in WNK1 gene had a significant worse PFS than patients with no variation (p=0.0003, median PFS=9 vs 16 months for IHH and p=0.0052, median PFS=9 vs 17 months for WNK1). Interestingly, in the literature, IHH (Indian HedgeHog) decreased expression are correlated with increased sensitivity to treatment, and WNK1 (With No lysine Kinase 1) activation are linked to cellular migration and epithelial mesenchymal transition in non-small cell lung cancer.
Conclusion
This first analysis from the ALCAPONE clinical trial identified 2 genes that could discriminate responders from non-responders. As the analysis was performed from 33 EGFR mutated patients, it will be confirmed thanks to a validation set of 30 new EGFR mutated patients treated with afatinib. If these results are confirmed, the analysis of genetic variations on both genes could be new biomarkers bringing new information to clinicians for the choice of EGFR TKI treatment sequence.
ALCAPONE study was supported by Boehringer Ingelheim
