Abstract 1201P
Background
Assessment of TMB for response stratification of cancer patients is emerging as a new biomarker for immunotherapy. TMB is defined as the total number of non-synonymous somatic mutations and it approximates the amount of neoantigens. Whole exome sequencing is recognized as the best approach to quantify TMB, nevertheless the cost and access represent relevant limitations. The aim of this investigation is to evaluate the capacity of out small panel to predict response to immunotherapy based on the calculation of a min-TMB score.
Methods
Patients with advanced tumors were screened for r targetable alterations. A customized hotspots NGS panel containing 87 genes, including 4 full CDS genes was used. Somatic mutations were selected as follow: 1) present in our somatic variant database; 2) mutation within cancer hotspot regions, in non-intronic regions with a population allele frequency < 0.1; 3) mutations with a known population frequency < 0.05%, with an unknown population frequency and not intronic; 4) Mutations in samples with a VAF over the selected thresholds (3%, 5% and 10%). A descriptive observational analysis was performed. Multivariable Cox regression analysis was carried out evaluating age, PS and TMB with R v3.6.2.
Results
Among 255 patients analyzed with our customized panel, only 46 received immunotherapy after the molecular screening as I or II line and were included. 25 received treatment with standard checkpoint inhibitors, while 21 received experimental immunotherapy. Median age was 61 (54-65). Tumor types represented were NSCLC, Melanoma HNSCC, GI and gynecological tumors. Median PFS was 5.4 (3.7-8.2) months. TMB was calculated and over patients treated with standard immunotherapy, the 3% non-synonymous mutations remained the only significant prognostic factor of PFS in the multivariable model (p=0.045).
Conclusions
The feasibility of the TMB score calculation depending on the panel size remains controversial. Our customized small panel shows that it could help in the identification of a predictive TMB score for those patients candidate to receive checkpoint inhibitors. Although these finding are promising, further investigation is needed.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
This study was supported by grants from the Instituto de Salud Carlos III (PI15/02180 and PI18/01909 to AC; PI18/01508 to TF). VG was supported by the ESMO 2014 fellowship program, and by Rio Hortega contract CM18/00241 from the Carlos III Health Institute; TF is supported by Joan Rodes contract 17/ 00026 from the Carlos III Health Institute. NT was supported by Rio Hortega contract CM15/00246 from the Instituto de Salud Carlos III and by the ESMO 2013 fellowship program; DR was supported by Joan Rodes contract 16/00040 from the Instituto de Salud Carlos III. SZ has a CA18/00042 contract for bio-informaticians from the Instituto de Salud Carlos III. R JMC is supported by a SEOM-Rio HOrtega contract 2019. MT has a predoctoral from the AECC (Spanish Cancer Association in Valencia). Part of the equipment used in this study has been funded by Generalitat Valenciana and co-financed with ERDF funds (OP ERDF of Comunitat Valenciana 2014-2020).
Disclosure
A. Cervantes: Honoraria (institution): Genentech; Honoraria (institution): F. Hoffmann-La Roche; Honoraria (institution): Astellas; Honoraria (institution): Merk-Serono; Honoraria (institution): BMS; Honoraria (institution): MSD; Honoraria (institution): Novartis; Honoraria (institution): Beigene; Honoraria (institution): Bayer; Honoraria (institution): Servier; Honoraria (institution): Lilly; Honoraria (institution): Takeda; Honoraria (institution): Fibrogen. All other authors have declared no conflicts of interest.