Unravelling the myotonic dystrophy type 1 clinical spectrum: A systematic registry-based study with implications for disease classification.

The broad clinical spectrum of myotonic dystrophy type 1 (DM1) creates particular challenges for both medical care and design of clinical trials. Clinical onset spans a continuum from birth to late adulthood, with symptoms that are highly variable in both severity and nature of the affected organ systems. In the literature, this complex phenotype is divided into three grades (mild, classic, and severe) and four or five main clinical categories (congenital, infantile/juvenile, adult-onset and late-onset forms), according to symptom severity and age of onset, respectively. However, these classifications are still under discussion with no consensus thus far. While some specific clinical features have been primarily reported in some forms of the disease, there are no clear distinctions. As a consequence, no modifications in the management of healthcare or the design of clinical studies have been proposed based on the clinical form of DM1. The present study has used the DM-Scope registry to assess, in a large cohort of DM1 patients, the robustness of a classification divided into five clinical forms. Our main aim was to describe the disease spectrum and investigate features of each clinical form. The five subtypes were compared by distribution of CTG expansion size, and the occurrence and onset of the main symptoms of DM1. Analyses validated the relevance of a five-grade model for DM1 classification. Patients were classified as: congenital (n=93, 4.5%); infantile (n=303, 14.8%); juvenile (n=628, 30.7%); adult (n=694, 34.0%); and late-onset (n=326, 15.9%). Our data show that the assumption of a continuum from congenital to the late-onset form is valid, and also highlights disease features specific to individual clinical forms of DM1 in terms of symptom occurrence and chronology throughout the disease course. These results support the use of the five-grade model for disease classification, and the distinct clinical profiles suggest that age of onset and clinical form may be key criteria in the design of clinical trials when considering DM1 health management and research.

Can Routine Imaging After Neoadjuvant Chemotherapy in Breast Cancer Predict Pathologic Complete Response?

BACKGROUND: This study evaluated breast imaging procedures for predicting pathologic complete response (pCR = ypT0) after neoadjuvant chemotherapy (NACT) for breast cancer to challenge surgery as a diagnostic procedure after NACT. METHODS: This retrospective, exploratory, monocenter study included 150 invasive breast cancers treated by NACT. The patients received magnetic resonance imaging (MRI), mammography (MGR), and ultrasound (US). The results were classified in three response subgroups according to response evaluation criteria in solid tumors. To incorporate specific features of MRI and MGR, an additional category [clinical near complete response (near-cCR)] was defined. Residual cancer in imaging and pathology was defined as a positive result. Negative predictive values (NPVs), false-negative rates (FNRs), and false-positive rates (FPRs) of all imaging procedures were analyzed for the whole cohort and for triple-negative (TN), HER2-positive (HER2+), and HER2-negative/hormone-receptor-positive (HER2-/HR+) cancers, respectively. RESULTS: In 46 cases (31%), pCR (ypT0) was achieved. Clinical complete response (cCR) and near-cCR showed nearly the same NPVs and FNRs. The NPV was highest with 61% for near-cCR in MRI and lowest with 44% for near-cCR in MGR for the whole cohort. The FNRs ranged from 4 to 25% according to different imaging methods. The MRI performance seemed to be superior, especially in TN cancers (NPV 94%; FNR 5%). The lowest FPR was 10 % in MRI, and the highest FPR was 44% in US. CONCLUSION: Neither MRI nor MGR or US can diagnose a pCR (ypT0) with sufficient accuracy to replace pathologic diagnosis of the surgical excision specimen.