INX-315, a Selective CDK2 Inhibitor, Induces Cell Cycle Arrest and Senescence in Solid Tumors.

Cyclin-dependent kinase 2 (CDK2) is thought to play an important role in driving proliferation of certain cancers, including those harboring CCNE1 amplification and breast cancers that have acquired resistance to CDK4/6 inhibitors (CDK4/6i). The precise impact of pharmacologic inhibition of CDK2 is not known due to the lack of selective CDK2 inhibitors. Here we describe INX-315, a novel and potent CDK2 inhibitor with high selectivity over other CDK family members. Using cell-based assays, patient-derived xenografts (PDX), and transgenic mouse models, we show that INX-315 (i) promotes retinoblastoma protein hypophosphorylation and therapy-induced senescence (TIS) in CCNE1-amplified tumors, leading to durable control of tumor growth; (ii) overcomes breast cancer resistance to CDK4/6i, restoring cell cycle control while reinstating the chromatin architecture of CDK4/6i-induced TIS; and (iii) delays the onset of CDK4/6i resistance in breast cancer by driving deeper suppression of E2F targets. Our results support the clinical development of selective CDK2 inhibitors. SIGNIFICANCE: INX-315 is a novel, selective inhibitor of CDK2. Our preclinical studies demonstrate activity for INX-315 in both CCNE1-amplified cancers and CDK4/6i-resistant breast cancer. In each case, CDK2 inhibition induces cell cycle arrest and a phenotype resembling cellular senescence. Our data support the development of selective CDK2 inhibitors in clinical trials. See related commentary by Watts and Spencer, p. 386. This article is featured in Selected Articles from This Issue, p. 384.

Light-based methods for predicting circadian phase in delayed sleep-wake phase disorder.

Methods for predicting circadian phase have been developed for healthy individuals. It is unknown whether these methods generalize to clinical populations, such as delayed sleep-wake phase disorder (DSWPD), where circadian timing is associated with functional outcomes. This study evaluated two methods for predicting dim light melatonin onset (DLMO) in 154 DSWPD patients using ~ 7 days of sleep-wake and light data: a dynamic model and a statistical model. The dynamic model has been validated in healthy individuals under both laboratory and field conditions. The statistical model was developed for this dataset and used a multiple linear regression of light exposure during phase delay/advance portions of the phase response curve, as well as sleep timing and demographic variables. Both models performed comparably well in predicting DLMO. The dynamic model predicted DLMO with root mean square error of 68 min, with predictions accurate to within ± 1 h in 58% of participants and ± 2 h in 95%. The statistical model predicted DLMO with root mean square error of 57 min, with predictions accurate to within ± 1 h in 75% of participants and ± 2 h in 96%. We conclude that circadian phase prediction from light data is a viable technique for improving screening, diagnosis, and treatment of DSWPD.