Biology subjects

Zehir, A.

Publications and source records attributed to Zehir, A..

3 recordsLinked to original sources

bioRxiv2024

Targetable Treatment Resistance in Thyroid Cancer with Clonal Hematopoiesis

Anaplastic thyroid cancer (ATC) is a clinically aggressive malignancy with a dismal prognosis. Combined BRAF/MEK inhibition offers significant therapeutic benefit in patients with BRAFV600E-mutant ATCs. However, relapses are common and overall survival remains poor. Compared with differentiated thyroid cancer, a hallmark of ATCs is significant infiltration with myeloid cells, particularly macrophages. ATCs are most common in the aging population, which also has an increased incidence of TET2-mutant clonal hematopoiesis (CH). CH-mutant macrophages have been shown to accelerate CH-associated pathophysiology including atherosclerosis. However, the clinical and mechanistic contribution of CH-mutant clones to solid tumour biology, prognosis and therapeutic response has not been elucidated. Here we show that TET2-mutant CH is enriched in the tumour microenvironment of patients with solid tumours and associated with adverse prognosis in ATC patients. We find that Tet2-mutant macrophages selectively infiltrate mouse BrafV600E-mutant ATC and that their overexpression of Tgf{beta}-family ligands mediates resistance to BRAF/MEK inhibition. Importantly, inhibition of Tgf{beta} signaling restores sensitivity to MAPK pathway inhibition, opening a path for synergistic strategies to improve outcomes of patients with ATCs and concurrent CH.

cancer biology↗

bioRxiv2020

MiMSI - a deep multiple instance learning framework improves microsatellite instability detection from tumor next-generation sequencing

Microsatellite instability (MSI) is a critical phenotype of cancer genomes and an FDA-recognized biomarker that can guide treatment with immune checkpoint inhibitors. Recent work has demonstrated that next-generation sequencing data can be used to identify samples with MSI-high phenotype. However, low tumor purity, as frequently observed in routine clinical samples, poses a challenge to the sensitivity of existing algorithms. To overcome this critical issue, we developed MiMSI, an MSI classifier based on deep neural networks and trained using a dataset that included low tumor purity MSI cases in a multiple instance learning framework. On a challenging yet representative set of cases, MiMSI showed higher sensitivity (0.940) and auROC (0.988) than MSISensor(sensitivity: 0.57; auROC: 0.911), an open-source software previously validated for clinical use at our institution using MSK-IMPACT large panel targeted NGS data.

bioinformatics↗

bioRxiv2018

Isoform switching as a mechanism of acquired resistance to isocitrate dehydrogenase inhibition

Somatic mutations in cytosolic or mitochondrial isoforms of isocitrate dehydrogenase (IDH1 or IDH2, respectively) contribute to oncogenesis via production of the metabolite 2-hydroxyglutarate (2HG). Isoform-selective IDH inhibitors suppress 2HG production and induce clinical responses in patients with IDH1- and IDH2-mutant malignancies. Despite the promising activity of IDH inhibitors, the mechanisms that mediate resistance to IDH inhibition are poorly understood. Here, we describe four clinical cases that identify mutant IDH isoform switching, either from mutant IDH1 to mutant IDH2 or vice versa, as a mechanism of acquired clinical resistance to IDH inhibition in solid and liquid tumors.\n\nSignificanceIDH-mutant cancers can develop resistance to isoform-selective IDH inhibition by \"isoform switching\" from mutant IDH1 to mutant IDH2 or vice versa, thereby restoring 2-hydroxyglutarate (2HG) production by the tumor. These findings underscore a role for continued 2HG production in tumor progression and suggest therapeutic strategies to prevent or overcome resistance.

cancer biology↗