KIT and PDGFRA mutations and the risk of GI stromal tumor recurrence.

PURPOSE: Mutated KIT and platelet-derived growth factor alpha gene (PDGFRA) drive GI stromal tumor (GIST) oncogenesis, but the clinical significance of their single mutations is known incompletely. PATIENTS AND METHODS: We identified 11 population-based series of patients with GIST through a literature search and pooled individual data from 3,067 patients treated with macroscopically complete tumor excision. Mutation analysis was done from 1,505 tumors. We analyzed associations between KIT and PDGFRA mutations and recurrence-free survival (RFS) in the subsets in which patients were treated with surgery alone. RESULTS: We identified 301 different single mutations in KIT and 33 in PDGFRA. Patients with PDGFRA mutations had more favorable RFS than those with KIT mutations (hazard ratio, 0.34; P = .004). Only one of the 35 GISTs with KIT exon 11 duplication mutations recurred. Patients with deletions of only one codon of KIT exon 11 had better RFS than those with another deletion type, and some KIT exon 11 substitution mutations (Trp557Arg, Val559Ala, and Leu576Pro) were also associated with favorable RFS. Patients with an identical mutation had greatly variable outcomes depending on the standard prognostic factors, notably, mitotic count. Commonly used risk stratification schemes tended to overestimate the risk for recurrence in subgroups with prognostically favorable mutations. CONCLUSION: GISTs with an identical KIT or PDGFRA mutation may have widely varying risks for recurrence. Most of the patients with PDGFRA mutations and those with KIT exon 11 duplication mutation or deletion of one codon have favorable RFS with surgery alone and are usually not candidates for adjuvant therapy.

Risk of recurrence of gastrointestinal stromal tumour after surgery: an analysis of pooled population-based cohorts.

BACKGROUND: The risk of recurrence of gastrointestinal stromal tumour (GIST) after surgery needs to be estimated when considering adjuvant systemic therapy. We assessed prognostic factors of patients with operable GIST, to compare widely used risk-stratification schemes and to develop a new method for risk estimation. METHODS: Population-based cohorts of patients diagnosed with operable GIST, who were not given adjuvant therapy, were identified from the literature. Data from ten series and 2560 patients were pooled. Risk of tumour recurrence was stratified using the National Institute of Health (NIH) consensus criteria, the modified consensus criteria, and the Armed Forces Institute of Pathology (AFIP) criteria. Prognostic factors were examined using proportional hazards and non-linear models. The results were validated in an independent centre-based cohort consisting of 920 patients with GIST. FINDINGS: Estimated 15-year recurrence-free survival (RFS) after surgery was 59·9% (95% CI 56·2-63·6); few recurrences occurred after the first 10 years of follow-up. Large tumour size, high mitosis count, non-gastric location, presence of rupture, and male sex were independent adverse prognostic factors. In receiver operating characteristics curve analysis of 10-year RFS, the NIH consensus criteria, modified consensus criteria, and AFIP criteria resulted in an area under the curve (AUC) of 0·79 (95% CI 0·76-0·81), 0·78 (0·75-0·80), and 0·82 (0·80-0·85), respectively. The modified consensus criteria identified a single high-risk group. Since tumour size and mitosis count had a non-linear association with the risk of GIST recurrence, novel prognostic contour maps were generated using non-linear modelling of tumour size and mitosis count, and taking into account tumour site and rupture. The non-linear model accurately predicted the risk of recurrence (AUC 0·88, 0·86-0·90). INTERPRETATION: The risk-stratification schemes assessed identify patients who are likely to be cured by surgery alone. Although the modified NIH classification is the best criteria to identify a single high-risk group for consideration of adjuvant therapy, the prognostic contour maps resulting from non-linear modelling are appropriate for estimation of individualised outcomes. FUNDING: Academy of Finland, Cancer Society of Finland, Sigrid Juselius Foundation and Helsinki University Research Funds.