Dataset Information

High frequency of exon 20 S768I EGFR mutation detected in malignant pleural effusions: A poor prognosticator of NSCLC.

ABSTRACT:

Background

Lung cancer is the cause of a fourth of all cancer-related deaths. About a third of all lung adenocarcinoma tumours harbour mutations on exons 18 to 21 of the epidermal growth factor receptor (EGFR) gene. Detection of these mutations allows for targeted therapies in the form of EGFR Tyrosine kinase inhibitors. Recently, "liquid biopsies" have emerged as an alternative to conventional tissue mutation detection.

Aim

In this pilot study, we attempted to optimize EGFR mutation detection from malignant pleural effusions (MPEs) as "liquid biopsies" when tissue biopsies were unavailable. Resulting mutations were then to be mapped on the EGFR gene and explored using cBioPortal, a public cancer genomic database.

Methods and results

We first attempted a direct sequencing approach and showed that single nucleotide variants (SNVs) were likely to be missed in MPEs. We then switched to and optimized an EGFR mutant-specific quantitative polymerase chain reaction-based assay. This assay was piloted on n = 10 pleural effusion samples (one non-malignant pleural effusion as a negative control). 5/9 (55.55%) samples harboured EGFR mutations with 2/9 (22.22%) being exon 19 deletions and 3/9 (33.33%) the S768I mutation. The frequency of the S768I SNV in our study was significantly higher than that observed in other studies (~0.2%). Utilizing cBioPortal data, we report that patients with S768I have a shorter median survival time (6 months vs 38 months), progression-free survival time (8 months vs 44 months) and lower tumor mutation count compared to patients with other EGFR mutations.

Conclusions

The shorter survival of patients with the S768I SNV predicts aggressive disease and poor prognosis as a result of this mutation. Studies in larger cohorts and/or animal models are necessary to confirm these findings.

SUBMITTER: D'Souza G

PROVIDER: S-EPMC7941414 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:IntroductionPleural effusions frequently signal disseminated cancer. Diagnostic markers of pleural malignancy at presentation that would assess cancer risk and would streamline diagnostic decisions remain unidentified.MethodsA consecutive cohort of 323 patients with pleural effusion (PE) from different etiologies were recruited between 2013 and 2017 and was retrospectively analyzed. Data included history, chest X-ray, and blood/pleural fluid cell counts and biochemistry. Group comparison, receiver-operator characteristics, unsupervised hierarchical clustering, binary logistic regression, and random forests were used to develop the malignant pleural effusion detection (MAPED) score. MAPED was validated in an independent retrospective UK cohort (n = 238).ResultsFive variables showed significant diagnostic power and were incorporated into the 5-point MAPED score. Age > 55 years, effusion size > 50% of the most affected lung field, pleural neutrophil count 〈 2,500/mm3, effusion protein 〉 3.5 g/dL, and effusion lactate dehydrogenase > 250 U/L, each scoring one point, predicted underlying cancer with the area under curve(AUC) = 0.819 (P < 10-15) in the derivation cohort. The integrated discrimination improvement of MAPED scores showed an increase compared to cytology (p <0.001). Decision curve analysis indicated that the MAPED score generated net clinical benefit. In the validation dataset, the AUC of MAPED scores was 0.723 ( P = 3 × 10-9) for the MAPED score. Interestingly, MAPED correctly identified 33/42(79%) of cytology-negative patients that indeed had cancer.ConclusionsThe MAPED score identifies malignant pleural effusions with satisfactory accuracy and can be used complementary to cytology to streamline diagnostic procedures.Condensed abstractDiagnostic markers for malignant pleural effusions remain uncertain. The MAPED score identifies malignant pleural effusions and complements cytology and confers no additional risk to the patient or cost to the healthcare system.