Clinical management of molecular alterations identified by high throughput sequencing in patients with advanced solid tumors in treatment failure: Real-world data from a French hospital

Abstract

Background: In the context of personalized medicine, screening patients to identify targetable molecular alterations is essential for therapeutic decisions such as inclusion in clinical trials, early access to therapies, or compassionate treatment. The objective of this study was to determine the real-world impact of routine incorporation of FoundationOne analysis in cancers with a poor prognosis and limited treatment options, or in those progressing after at least one course of standard therapy.

Methods: A FoundationOneCDx panel for solid tumor or liquid biopsy samples was offered to 204 eligible patients.

Results: Samples from 150 patients were processed for genomic testing, with a data acquisition success rate of 93%. The analysis identified 2419 gene alterations, with a median of 11 alterations per tumor (range, 0-86). The most common or likely pathogenic variants were on TP53, TERT, PI3KCA, CDKN2A/B, KRAS, CCDN1, FGF19, FGF3, and SMAD4. The median tumor mutation burden was three mutations/Mb (range, 0-117) in 143 patients with available data. Of 150 patients with known or likely pathogenic actionable alterations, 13 (8.6%) received matched targeted therapy. Sixty-nine patients underwent Molecular Tumor Board, which resulted in recommendations in 60 cases. Treatment with genotype-directed therapy had no impact on overall survival (13 months vs. 14 months; p = 0.95; hazard ratio = 1.04 (95% confidence interval, 0.48-2.26)].

Conclusions: This study highlights that an organized center with a Multidisciplinary Molecular Tumor Board and an NGS screening system can obtain satisfactory results comparable with those of large centers for including patients in clinical trials.

Keywords: FoundationOne CDx; cancer; liquid biopsy; next-generation sequencing; precision oncology; targeted therapy.

Figure 1

(A) Overview of our organization. (B) Study flow chart. F1LCDx, FoundationOne Liquid CDx; FCDX, FoundationOne CDx; FFPE, formalin-fixed paraffin embedded.

Figure 2

Distribution of the number of genetic alterations identified by sequencing (according to cancer type). For each tumor type, genetic alterations were classified into class 4/5 (corresponding to known and/or likely pathogenic) or VUS (corresponding to variant of unknown significance). Only tumor types with at least five cases were included and are presented according to decreasing number of patients: glioma (n=36), lung cancer (n=29), breast cancer (n=25), colorectal and prostate cancers (n=10), pancreatic cancer (n=9), ovarian cancer (n=7), head and neck cancer (n=6), and gastric cancer (n=5). Boxes indicate the median and quartile values, and points correspond to the number of alterations retrieved for each patient. Yellow or blue correspond to class 4/5 or VUS, respectively.

Figure 3

Distribution of the most frequently altered genes among our cohort of 162 patients. The numbers of patients with class 4/5 (known and/or likely pathogenic) and VUS (variant of unknown significance) genetic alterations are shown as black and grey bars, respectively: mutation (A), amplification (B), or loss (C), of each gene.

Figure 4

Distribution of TMB (number of mutations per megabase) according to the type of cancer. Only tumor types with at least five cases with sequencing results were included in the representation, and are listed by decreasing number of patients: glioma (n=36), lung cancer (n=29), breast cancer (n=25), colorectal and prostate cancers (n=10), pancreatic cancer (n=9), ovarian cancer (n=7), head and neck cancer (n=6) and gastric cancer (n=5). Boxes indicate the median and quartile values, and points correspond to individual TMB values. A dashed grey line indicates the cutoff (10) for high and low TMB values.

Figure 5

Association between the frequency of class 4-5 gene alterations and survival rates. Kaplan-Meier curves are depicted according to low frequency of genetic alterations (none, one, or two altered genes per patient) versus high frequency (more than three altered genes per patient). P-values were determined by the log-rank test and used to compare two survival curves.

Figure 6

Kaplan-Meier plots of overall survival comparing patients treated with and without genotype-directed therapy.