Therapies targeting HER2 amplification are a success of modern oncology, yet resistance to these agents remains a major challenge. For example, the mertansine- (DM1-) armed antibody-drug conjugate trastuzumab emtansine (T-DM1; Kadcyla) extends the survival of HER2-positive metastatic breast cancer patients after failure of prior trastuzumab-based therapy. However, T-DM1 provides objective responses in less than half of patients and its effectiveness is limited by both acquired and intrinsic resistance.
We employed whole-genome CRISPR knockout screens using the GeCKOv2 sgRNA library in two trastuzumab-refractory breast cancer cell lines (MDA-MB-361 and MDA-MB-453) to identify and characterise genomic modifiers of sensitivity to T-DM1 and free DM1 in vitro. Genome-scale knockout libraries developed resistance to T-DM1 and DM1 after eight and thirteen weeks of escalating drug treatment, respectively. Positive and negative selection screen hits were identified by sequencing and bioinformatics analysis using MaGECK and PinAPL-Py. In parallel, we undertook RNA sequencing analysis of the DM1 and T-DM1 resistant cells to identify transcriptional adaptations to therapy.
Fourteen genes, highly enriched for positive or negative selection after T-DM1 or DM1 treatment, were selected to assess the impact of individual knockout on T-DM1 sensitivity. This confirmed the role of the putative lysosomal DM1 transporter SLC46A3, in T-DM1 resistance, and identified a set of genes potentially involved in T-DM1 activity. This study revealed consistent changes for both drugs with the genes involved in mitosis and cell cycle regulation highly overrepresented among differentially expressed transcripts. Adaptation to T-DM1 alone involved upregulation of genes related to proteolysis, acidification of vacuoles, intracellular trafficking of vesicles and maturation of lysosomes.
A panel of 550 candidate genes from both the genome-wide knockout screen and transcriptomic profiling informed the generation of a custom sgRNA library for secondary in vitro and in vivo validation screens. Finally, the predictive utility of these genes is being explored in clinical datasets.
The University of Auckland.
Roche.
All authors have declared no conflicts of interest.