Up-front mutation detection in circulating tumor DNA by droplet digital PCR has added diagnostic value in lung cancer

Identification of actionable mutations in advanced stage non-squamous non-small-cell lung cancer (NSCLC) patients is recommended by guidelines as it enables treatment with targeted therapies. In current practice, mutations are identified by next-generation sequencing of tumor DNA (tDNA-NGS), which requires tissue biopsies of sufficient quality. Alternatively, circulating tumor DNA (ctDNA) could be used for mutation analysis. This prospective, multicenter study establishes the diagnostic value of ctDNA analysis by droplet digital PCR (ctDNA-ddPCR) in patients with primary lung cancer. CtDNA from 458 primary lung cancer patients was analyzed using a panel of multiplex ddPCRs for EGFR (Ex19Del, G719S, L858R, L861Q and S768I) , KRAS G12/G13 and BRAF V600 mutations. For 142 of 175 advanced stage non-squamous NSCLC patients tDNA-NGS results were available to compare to ctDNA-ddPCR. tDNA-NGS identified 98 mutations, of which ctDNA-ddPCR found 53 mutations (54%), including 32 of 45 (71%) targetable driver mutations. In 2 of these 142 patients,a mutation was found by ctDNA-ddPCR only. In 33 advanced stage patients lacking tDNA-NGS results, ctDNA-ddPCR detected 15 additional mutations, of which 7 targetable. Overall, ctDNA-ddPCR detected 70 mutations and tDNA-NGS 98 mutations in 175 advanced NSCLC patients. Using an up-front ctDNA-ddPCR strategy, followed by tDNA-NGS only if ctDNA-ddPCR analysis is negative, increases the number of mutations found from 98 to 115 (17%). At the same time, up-front ctDNA-ddPCR reduces tDNA-NGS analyses by 40%, decreasing the need to perform (additional) biopsies

Liquid biopsy-based decision support algorithms for diagnosis and subtyping of lung cancer

OBJECTIVES: Pathologic subtyping of tissue biopsies is the gold standard for the diagnosis of lung cancer (LC), which could be complicated in cases of e.g. inconclusive tissue biopsies or unreachable tumors. The diagnosis of LC could be supported in a minimally invasive manner using protein tumor markers (TMs) and circulating tumor DNA (ctDNA) measured in liquid biopsies (LBx). This study evaluates the performance of LBx-based decision-support algorithms for the diagnosis of LC and subtyping into small- and non-small-cell lung cancer (SCLC and NSCLC) aiming to directly impact clinical practice. MATERIALS AND METHODS: In this multicenter prospective study (NL9146), eight protein TMs (CA125, CA15.3, CEA, CYFRA 21-1, HE4, NSE, proGRP and SCCA) and ctDNA mutations in EGFR, KRAS and BRAF were analyzed in blood of 1096 patients suspected of LC. The performance of individual and combined TMs to identify LC, NSCLC or SCLC was established by evaluating logistic regression models at pre-specified positive predictive values (PPV) of ≥95% or ≥98%. The most informative protein TMs included in the multi-parametric models were selected by recursive feature elimination. RESULTS: Single TMs could identify LC, NSCLC and SCLC patients with 46%, 25% and 40% sensitivity, respectively, at pre-specified PPVs. Multi-parametric models combining TMs and ctDNA significantly improved sensitivities to 65%, 67% and 50%, respectively. CONCLUSION: In patients suspected of LC, the LBx-based decision-support algorithms allowed identification of about two-thirds of all LC and NSCLC patients and half of SCLC patients. These models therefore show clinical value and may support LC diagnostics, especially in patients for whom pathologic subtyping is impossible or incomplete