NTRK fusions in a sarcomas series : pathology, molecular and clinical aspects

Targeting molecular alterations has been proven to be an inflecting point in tumor treatment. Especially in recent years, inhibitors that target the tyrosine receptor kinase show excellent response rates and durable effects in all kind of tumors that harbor fusions of one of the three neurotrophic tyrosine receptor kinase genes (NTRK1, NTRK2 and NTRK3). Today, the therapeutic options in most metastatic sarcomas are rather limited. Therefore, identifying which sarcoma types are more likely to harbor these targetable NTRK fusions is of paramount importance. At the moment, identification of these fusions is solely based on immunohistochemistry and confirmed by molecular techniques. However, a first attempt has been made to describe the histomorphology of NTRK-fusion positive sarcomas, in order to pinpoint which of these tumors are the best candidates for testing. In this study, we investigate the immunohistochemical expression of pan-TRK in 70 soft tissue and bone sarcomas. The pan-TRK positive cases were further investigated with molecular techniques for the presence of a NTRK fusion. Seven out of the 70 cases showed positivity for pan-TRK, whereas two of these seven cases presented an NTRK3 fusion. Further analysis of the fused sarcomas revealed some unique histological, molecular and clinical findings. The goal of this study is to expand the histomorphological spectrum of the NTRK-fused sarcomas, to identify their fusion partners and to correlate these parameters with the clinical outcome of the disease. In addition, we evaluated the immunohistochemical expression pattern of the pan-TRK and its correlation with the involved NTRK gene

Interobserver variability in upfront dichotomous histopathological assessment of ductal carcinoma in situ of the breast: the DCISion study

Histopathological assessment of ductal carcinoma in situ, a nonobligate precursor of invasive breast cancer, is characterized by considerable interobserver variability. Previously, post hoc dichotomization of multicategorical variables was used to determine the "ideal" cutoffs for dichotomous assessment. The present international multicenter study evaluated interobserver variability among 39 pathologists who performed upfront dichotomous evaluation of 149 consecutive ductal carcinomas in situ. All pathologists independently assessed nuclear atypia, necrosis, solid ductal carcinoma in situ architecture, calcifications, stromal architecture, and lobular cancerization in one digital slide per lesion. Stromal inflammation was assessed semiquantitatively. Tumor-infiltrating lymphocytes were quantified as percentages and dichotomously assessed with a cutoff at 50%. Krippendorff's alpha (KA), Cohen's kappa and intraclass correlation coefficient were calculated for the appropriate variables. Lobular cancerization (KA = 0.396), nuclear atypia (KA = 0.422), and stromal architecture (KA = 0.450) showed the highest interobserver variability. Stromal inflammation (KA = 0.564), dichotomously assessed tumor-infiltrating lymphocytes (KA = 0.520), and comedonecrosis (KA = 0.539) showed slightly lower interobserver disagreement. Solid ductal carcinoma in situ architecture (KA = 0.602) and calcifications (KA = 0.676) presented with the lowest interobserver variability. Semiquantitative assessment of stromal inflammation resulted in a slightly higher interobserver concordance than upfront dichotomous tumor-infiltrating lymphocytes assessment (KA = 0.564 versus KA = 0.520). High stromal inflammation corresponded best with dichotomously assessed tumor-infiltrating lymphocytes when the cutoff was set at 10% (kappa = 0.881). Nevertheless, a post hoc tumor-infiltrating lymphocytes cutoff set at 20% resulted in the highest interobserver agreement (KA = 0.669). Despite upfront dichotomous evaluation, the interobserver variability remains considerable and is at most acceptable, although it varies among the different histopathological features. Future studies should investigate its impact on ductal carcinoma in situ prognostication. Forthcoming machine learning algorithms may be useful to tackle this substantial diagnostic challenge.status: publishe