t(4;12)(q12;p13) ETV6-rearranged AML without eosinophilia does not involve PDGFRA: relevance for imatinib insensitivity.

Acute myeloid leukemia (AML) with t(4;12)(q12;p13) translocation is rare and often associated with an aggressive clinical course and poor prognosis. Previous reports based on fluorescence in situ hybridization (FISH) analysis have suggested that ETV6::PDGFRA fusions are present in these patients, despite the absence of eosinophilia, which is typically found in other hematopoietic malignancies with PDGFRA-containing fusions. We first detected an ETV6-SCFD2 fusion by targeted RNA sequencing in a patient with t(4;12)(q12;p13) who had been diagnosed with an ETV6-PDGFRA fusion by FISH analysis but failed to respond to imatinib. We then retrospectively identified 4 additional patients with AML and t(4;12)(q12;p13) with apparent ETV6-PDGFRA fusions using chromosome and FISH analysis and applied targeted RNA sequencing to archival material. We again detected rearrangements between ETV6 and non-PDGFRA 4q12 genes, including SCFD2, CHIC2, and GSX2. None of the 3 patients who received imatinib based on the incorrect assumption of an ETV6-PDGFRA fusion responded. Our findings highlight the importance of using a sequencing-based assay to confirm the presence of targetable gene fusions, particularly in genomic regions, such as 4q12, with many clinically relevant genes that are too close to resolve by chromosome or FISH analysis. Finally, combining our data and review of the literature, we show that sequence-confirmed ETV6-PDGFRA fusions are typically found in eosinophilic disorders (3/3 cases), and patients with t(4;12)(q12;p13) without eosinophilia are found to have other 4q12 partners on sequencing (17/17 cases).

Development of a qualitative real-time RT-PCR assay for the detection of SARS-CoV-2: a guide and case study in setting up an emergency-use, laboratory-developed molecular microbiological assay.

Developing and deploying new diagnostic tests are difficult, but the need to do so in response to a rapidly emerging pandemic such as COVID-19 is crucially important. During a pandemic, laboratories play a key role in helping healthcare providers and public health authorities detect active infection, a task most commonly achieved using nucleic acid-based assays. While the landscape of diagnostics is rapidly evolving, PCR remains the gold-standard of nucleic acid-based diagnostic assays, in part due to its reliability, flexibility and wide deployment. To address a critical local shortage of testing capacity persisting during the COVID-19 outbreak, our hospital set up a molecular-based laboratory developed test (LDT) to accurately and safely diagnose SARS-CoV-2. We describe here the process of developing an emergency-use LDT, in the hope that our experience will be useful to other laboratories in future outbreaks and will help to lower barriers to establishing fast and accurate diagnostic testing in crisis conditions.

A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients.

Unlocking the full potential of pathology data by gaining computational access to histological pixel data and metadata (digital pathology) is one of the key promises of computational pathology. Despite scientific progress and several regulatory approvals for primary diagnosis using whole-slide imaging, true clinical adoption at scale is slower than anticipated. In the U.S., advances in digital pathology are often siloed pursuits by individual stakeholders, and to our knowledge, there has not been a systematic approach to advance the field through a regulatory science initiative. The Alliance for Digital Pathology (the Alliance) is a recently established, volunteer, collaborative, regulatory science initiative to standardize digital pathology processes to speed up innovation to patients. The purpose is: (1) to account for the patient perspective by including patient advocacy; (2) to investigate and develop methods and tools for the evaluation of effectiveness, safety, and quality to specify risks and benefits in the precompetitive phase; (3) to help strategize the sequence of clinically meaningful deliverables; (4) to encourage and streamline the development of ground-truth data sets for machine learning model development and validation; and (5) to clarify regulatory pathways by investigating relevant regulatory science questions. The Alliance accepts participation from all stakeholders, and we solicit clinically relevant proposals that will benefit the field at large. The initiative will dissolve once a clinical, interoperable, modularized, integrated solution (from tissue acquisition to diagnostic algorithm) has been implemented. In times of rapidly evolving discoveries, scientific input from subject-matter experts is one essential element to inform regulatory guidance and decision-making. The Alliance aims to establish and promote synergistic regulatory science efforts that will leverage diverse inputs to move digital pathology forward and ultimately improve patient care.