Acquired Cross-Resistance in Small Cell Lung Cancer due to Extrachromosomal DNA Amplification of MYC Paralogs.

Small cell lung cancer (SCLC) presents as a highly chemosensitive malignancy but acquires cross-resistance after relapse. This transformation is nearly inevitable in patients but has been difficult to capture in laboratory models. Here, we present a preclinical system that recapitulates acquired cross-resistance, developed from 51 patient-derived xenograft (PDX) models. Each model was tested in vivo against three clinical regimens: cisplatin plus etoposide, olaparib plus temozolomide, and topotecan. These drug-response profiles captured hallmark clinical features of SCLC, such as the emergence of treatment-refractory disease after early relapse. For one patient, serial PDX models revealed that cross-resistance was acquired through MYC amplification on extrachromosomal DNA (ecDNA). Genomic and transcriptional profiles of the full PDX panel revealed that MYC paralog amplifications on ecDNAs were recurrent in relapsed cross-resistant SCLC, and this was corroborated in tumor biopsies from relapsed patients. We conclude that ecDNAs with MYC paralogs are recurrent drivers of cross-resistance in SCLC. SIGNIFICANCE: SCLC is initially chemosensitive, but acquired cross-resistance renders this disease refractory to further treatment and ultimately fatal. The genomic drivers of this transformation are unknown. We use a population of PDX models to discover that amplifications of MYC paralogs on ecDNA are recurrent drivers of acquired cross-resistance in SCLC. This article is featured in Selected Articles from This Issue, p. 695.

Cellular harmonics for the morphology-invariant analysis of molecular organization at the cell surface.

The spatiotemporal organization of membrane-associated molecules is central to the regulation of cellular signals. Powerful new microscopy techniques enable the three-dimensional visualization of localization and activation of these molecules; however, the quantitative interpretation and comparison of molecular organization on the three-dimensional cell surface remains challenging because cells themselves vary greatly in morphology. Here we introduce u-signal3D, a framework to assess the spatial scales of molecular organization at the cell surface in a cell-morphology-invariant manner. We validated the framework by analyzing synthetic signaling patterns painted onto observed cell morphologies, as well as measured distributions of cytoskeletal and signaling molecules. To demonstrate the framework's versatility, we further compared the spatial organization of cell surface signals both within, and between, cell populations, and powered an upstream machine-learning-based analysis of signaling motifs.