NTRK gene fusions are detected in both secretory and non-secretory breast cancers.

NTRK fusions represent a new biomarker-defined population that can be treated with TRK inhibitors. Although rare, NTRK fusions are detected across a wide range of solid tumors. Previous reports suggest that NTRK fusions are limited to the secretory subtype of breast cancer. Here we examined NTRK fusions in a large real world next-generation sequencing (NGS) dataset and confirmed secretory versus non-secretory status using H&E images. Of 23 NTRK fusion-positive cases, 11 were classified as secretory, 11 as non-secretory, and one as mixed status. The secretory subtype trended younger, was predominantly estrogen receptor (ER)-, had lower tumor mutational burden, and exhibited lower levels of genomic loss of heterozygosity. The non-secretory subtype was enriched for TP53 mutations. The secretory subtype was enriched for ETV6-NTRK3 fusions in 7 of 11 cases, and the non-secretory subtype had NTRK1 fusions in 7 of 11 cases, each with a different fusion partner. Our data suggests NTRK fusions are present in both secretory and non-secretory subtypes, and that comprehensive genomic profiling should be considered across all clinically advanced breast cancers to identify patients that could receive benefit from TRK inhibitors.

Interacting evolutionary pressures drive mutation dynamics and health outcomes in aging blood.

Age-related clonal hematopoiesis (ARCH) is characterized by age-associated accumulation of somatic mutations in hematopoietic stem cells (HSCs) or their pluripotent descendants. HSCs harboring driver mutations will be positively selected and cells carrying these mutations will rise in frequency. While ARCH is a known risk factor for blood malignancies, such as Acute Myeloid Leukemia (AML), why some people who harbor ARCH driver mutations do not progress to AML remains unclear. Here, we model the interaction of positive and negative selection in deeply sequenced blood samples from individuals who subsequently progressed to AML, compared to healthy controls, using deep learning and population genetics. Our modeling allows us to discriminate amongst evolutionary classes with high accuracy and captures signatures of purifying selection in most individuals. Purifying selection, acting on benign or mildly damaging passenger mutations, appears to play a critical role in preventing disease-predisposing clones from rising to dominance and is associated with longer disease-free survival. Through exploring a range of evolutionary models, we show how different classes of selection shape clonal dynamics and health outcomes thus enabling us to better identify individuals at a high risk of malignancy.