CRISPR activation screens identify the SWI/SNF ATPases as suppressors of ferroptosis.

Ferroptosis is an iron-dependent cell death mechanism characterized by the accumulation of toxic lipid peroxides and cell membrane rupture. GPX4 (glutathione peroxidase 4) prevents ferroptosis by reducing these lipid peroxides into lipid alcohols. Ferroptosis induction by GPX4 inhibition has emerged as a vulnerability of cancer cells, highlighting the need to identify ferroptosis regulators that may be exploited therapeutically. Through genome-wide CRISPR activation screens, we identify the SWI/SNF (switch/sucrose non-fermentable) ATPases BRM (SMARCA2) and BRG1 (SMARCA4) as ferroptosis suppressors. Mechanistically, they bind to and increase chromatin accessibility at NRF2 target loci, thus boosting NRF2 transcriptional output to counter lipid peroxidation and confer resistance to GPX4 inhibition. We further demonstrate that the BRM/BRG1 ferroptosis connection can be leveraged to enhance the paralog dependency of BRG1 mutant cancer cells on BRM. Our data reveal ferroptosis induction as a potential avenue for broadening the efficacy of BRM degraders/inhibitors and define a specific genetic context for exploiting GPX4 dependency.

heredERA Breast Cancer: a phase III, randomized, open-label study evaluating the efficacy and safety of giredestrant plus the fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection in patients with previously untreated HER2-positive, estrogen receptor-positive locally advanced or metastatic breast cancer.

BACKGROUND: HER2-positive, estrogen receptor-positive breast cancer (HER2+, ER+ BC) is a distinct disease subtype associated with inferior response to chemotherapy plus HER2-targeted therapy compared with HER2+, ER-negative BC. Bi-directional crosstalk leads to cooperation of the HER2 and ER pathways that may drive treatment resistance; thus, simultaneous co-targeting may optimize treatment impact and survival outcomes in patients with HER2+, ER+ BC. First-line (1L) treatment for patients with HER2+ metastatic BC (mBC) is pertuzumab, trastuzumab, and taxane chemotherapy. In clinical practice, dual HER2 blockade plus a fixed number of chemotherapy cycles are given as induction therapy to maximize tumor response, with subsequent HER2-targeted maintenance treatment given as a more tolerable regimen for long-term disease control. For patients whose tumors co-express ER, maintenance endocrine therapy (ET) can be added, but uptake varies due to lack of data from randomized clinical trials investigating the superiority of maintenance ET plus dual HER2 blockade versus dual HER2 blockade alone. Giredestrant, a novel oral selective ER antagonist and degrader, shows promising clinical activity and manageable safety across phase I-II trials of patients with ER+, HER2-negative BC, with therapeutic potential in those with HER2 co-expression. METHODS: This phase III, randomized, open-label, two-arm study aims to recruit 812 patients with HER2+, ER+ locally advanced (LA)/mBC into the induction phase (fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection [PH FDC SC] plus a taxane) to enable 730 patients to be randomized 1:1 to the maintenance phase (giredestrant plus PH FDC SC or PH FDC SC [plus optional ET]), stratified by disease site (visceral versus non-visceral), type of LA/metastatic presentation (de novo versus recurrent), best overall response to induction therapy (partial/complete response versus stable disease), and intent to give ET (yes versus no). The primary endpoint is investigator-assessed progression-free survival. Secondary endpoints include overall survival, objective response rate, clinical benefit rate, duration of response, safety, and patient-reported outcomes. DISCUSSION: heredERA BC will address whether giredestrant plus dual HER2 blockade is superior to dual HER2 blockade alone, to inform the use of this combination in clinical practice for maintenance 1L treatment of patients with HER2+, ER+ LA/mBC. TRIAL REGISTRATION: ClinicalTrials.gov, NCT05296798; registered on March 25, 2022. Protocol version 3.0 (November 18, 2022). SPONSOR: F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124 4070, Basel, Switzerland.

UGT1A4 Polymorphism is not Associated with a Clinically Relevant Change in Giredestrant Exposure.

PURPOSE: Giredestrant is a potent, orally bioavailable, small-molecule selective estrogen receptor antagonist and degrader (SERD) that is being developed for the treatment of patients with estrogen receptor (ER)-positive breast cancer. In vitro, giredestrant was primarily metabolized by UGT1A4. The goal of this study was to investigate if UGT1A4 polymorphism had a clinically relevant impact on giredestrant exposure. METHODS: Genotyping and pharmacokinetic data were obtained from 118 and 61 patients in two clinical studies, GO39932 [NCT03332797] and acelERA Breast Cancer [NCT04576455], respectively. RESULTS: The overall allelic frequencies of UGT1A4*2 and UGT1A4*3 were 3.3% and 11%, respectively. Giredestrant exposure was consistent between patients with wild-type UGT1A4 and UGT1A4*2 and *3 polymorphisms, with no clinically relevant difference observed. In addition, haplotype analysis indicated that no other UGT1A4 variants were significantly associated with giredestrant exposure. CONCLUSION: Therefore, this study indicates that UGT1A4 polymorphism status is unlikely a clinically relevant factor to impact giredestrant exposure and giredestrant can be administered at the same dose level regardless of patients' UGT1A4 polymorphism status.

CDK4/6i-treated HR+/HER2- breast cancer tumors show higher ESR1 mutation prevalence and more altered genomic landscape.

As CDK4/6 inhibitor (CDK4/6i) approval changed treatment strategies for patients with hormone receptor-positive HER2-negative (HR+/HER2-) breast cancer (BC), understanding how exposure to CDK4/6i affects the tumor genomic landscape is critical for precision oncology. Using real-world data (RWD) with tumor genomic profiling from 5910 patients with metastatic HR+/HER2- BC, we investigated the evolution of alteration prevalence in commonly mutated genes across patient journeys. We found that ESR1 is more often altered in tumors exposed to at least 1 year of adjuvant endocrine therapy, contrasting with TP53 alterations. We observed a similar trend after first-line treatments in the advanced setting, but strikingly exposure to aromatase inhibitors (AI) combined with CDK4/6i led to significantly higher ESR1 alteration prevalence compared to AI alone, independent of treatment duration. Further, CDK4/6i exposure was associated with higher occurrence of concomitant alterations in multiple oncogenic pathways. Differences based on CDK4/6i exposure were confirmed in samples collected after 2L and validated in samples from the acelERA BC clinical trial. In conclusion, our work uncovers opportunities for further treatment personalization and stresses the need for effective combination treatments to address the altered tumor genomic landscape following AI+CDK4/6i exposure. Further, we demonstrated the potential of RWD for refining patient treatment strategy and guiding clinical trial design.

Proteomic profiling across breast cancer cell lines and models.

We performed quantitative proteomics on 60 human-derived breast cancer cell line models to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics.

An allosteric pan-TEAD inhibitor blocks oncogenic YAP/TAZ signaling and overcomes KRAS G12C inhibitor resistance.

The Hippo pathway is a key growth control pathway that is conserved across species. The downstream effectors of the Hippo pathway, YAP (Yes-associated protein) and TAZ (transcriptional coactivator with PDZ-binding motif), are frequently activated in cancers to drive proliferation and survival. Based on the premise that sustained interactions between YAP/TAZ and TEADs (transcriptional enhanced associate domain) are central to their transcriptional activities, we discovered a potent small-molecule inhibitor (SMI), GNE-7883, that allosterically blocks the interactions between YAP/TAZ and all human TEAD paralogs through binding to the TEAD lipid pocket. GNE-7883 effectively reduces chromatin accessibility specifically at TEAD motifs, suppresses cell proliferation in a variety of cell line models and achieves strong antitumor efficacy in vivo. Furthermore, we uncovered that GNE-7883 effectively overcomes both intrinsic and acquired resistance to KRAS (Kirsten rat sarcoma viral oncogene homolog) G12C inhibitors in diverse preclinical models through the inhibition of YAP/TAZ activation. Taken together, this work demonstrates the activities of TEAD SMIs in YAP/TAZ-dependent cancers and highlights their potential broad applications in precision oncology and therapy resistance.

Systematic pharmacological analysis of agonistic and antagonistic fibroblast growth factor receptor 1 MAbs reveals a similar unique mode of action.

Fibroblast growth factor receptor 1 (FGFR1) is a receptor tyrosine kinase that plays a major role in developmental processes and metabolism. The dysregulation of FGFR1 through genetic aberrations leads to skeletal and metabolic diseases as well as cancer. For this reason, FGFR1 is a promising therapeutic target, yet a very challenging one due to potential on-target toxicity. More puzzling is that both agonistic and antagonistic FGFR1 antibodies are reported to exhibit similar toxicity profiles in vivo, namely weight loss. In this study, we aimed to assess and compare the mechanism of action of these molecules to better understand this apparent contradiction. By systematically comparing the binding of these antibodies and the activation or the inhibition of the major FGFR1 signaling events, we demonstrated that the molecules displayed similar properties and can behave either as an agonist or antagonist depending on the presence or the absence of the endogenous ligand. We further demonstrated that these findings translated in xenografts mice models. In addition, using time-resolved FRET and mass spectrometry analysis, we showed a functionally distinct FGFR1 active conformation in the presence of an antibody that preferentially activates the FGFR substrate 2 (FRS2)-dependent signaling pathway, demonstrating that modulating the geometry of a FGFR1 dimer can effectively change the signaling outputs and ultimately the activity of the molecule in preclinical studies. Altogether, our results highlighted how bivalent antibodies can exhibit both agonistic and antagonistic activities and have implications for targeting other receptor tyrosine kinases with antibodies.

Multiplexed and reproducible high content screening of live and fixed cells using Dye Drop.

High-throughput measurement of cells perturbed using libraries of small molecules, gene knockouts, or different microenvironmental factors is a key step in functional genomics and pre-clinical drug discovery. However, it remains difficult to perform accurate single-cell assays in 384-well plates, limiting many studies to well-average measurements (e.g., CellTiter-Glo®). Here we describe a public domain Dye Drop method that uses sequential density displacement and microscopy to perform multi-step assays on living cells. We use Dye Drop cell viability and DNA replication assays followed by immunofluorescence imaging to collect single-cell dose-response data for 67 investigational and clinical-grade small molecules in 58 breast cancer cell lines. By separating the cytostatic and cytotoxic effects of drugs computationally, we uncover unexpected relationships between the two. Dye Drop is rapid, reproducible, customizable, and compatible with manual or automated laboratory equipment. Dye Drop improves the tradeoff between data content and cost, enabling the collection of information-rich perturbagen-response datasets.

Giredestrant reverses progesterone hypersensitivity driven by estrogen receptor mutations in breast cancer.

ESR1 (estrogen receptor 1) hotspot mutations are major contributors to therapeutic resistance in estrogen receptor-positive (ER+) breast cancer. Such mutations confer estrogen independence to ERα, providing a selective advantage in the presence of estrogen-depleting aromatase inhibitors. In addition, ESR1 mutations reduce the potency of tamoxifen and fulvestrant, therapies that bind ERα directly. These limitations, together with additional liabilities, inspired the development of the next generation of ERα-targeted therapeutics, of which giredestrant is a high-potential candidate. Here, we generated Esr1 mutant-expressing mammary gland models and leveraged patient-derived xenografts (PDXs) to investigate the biological properties of the ESR1 mutations and their sensitivity to giredestrant in vivo. In the mouse mammary gland, Esr1 mutations promote hypersensitivity to progesterone, triggering pregnancy-like tissue remodeling and profoundly elevated proliferation. These effects were driven by an altered progesterone transcriptional response and underpinned by gained sites of ERα-PR (progesterone receptor) cobinding at the promoter regions of pro-proliferation genes. PDX experiments showed that the mutant ERα-PR proliferative program is also relevant in human cancer cells. Giredestrant suppressed the mutant ERα-PR proliferation in the mammary gland more so than the standard-of-care agents, tamoxifen and fulvestrant. Giredestrant was also efficacious against the progesterone-stimulated growth of ESR1 mutant PDX models. In addition, giredestrant demonstrated activity against a molecularly characterized ESR1 mutant tumor from a patient enrolled in a phase 1 clinical trial. Together, these data suggest that mutant ERα can collaborate with PR to drive protumorigenic proliferation but remain sensitive to inhibition by giredestrant.

EHMT2 methyltransferase governs cell identity in the lung and is required for KRAS G12D tumor development and propagation.

Lung development, integrity and repair rely on precise Wnt signaling, which is corrupted in diverse diseases, including cancer. Here, we discover that EHMT2 methyltransferase regulates Wnt signaling in the lung by controlling the transcriptional activity of chromatin-bound ß-catenin, through a non-histone substrate in mouse lung. Inhibition of EHMT2 induces transcriptional, morphologic, and molecular changes consistent with alveolar type 2 (AT2) lineage commitment. Mechanistically, EHMT2 activity functions to support regenerative properties of KrasG12D tumors and normal AT2 cells-the predominant cell of origin of this cancer. Consequently, EHMT2 inhibition prevents KrasG12D lung adenocarcinoma (LUAD) tumor formation and propagation and disrupts normal AT2 cell differentiation. Consistent with these findings, low gene EHMT2 expression in human LUAD correlates with enhanced AT2 gene expression and improved prognosis. These data reveal EHMT2 as a critical regulator of Wnt signaling, implicating Ehmt2 as a potential target in lung cancer and other AT2-mediated lung pathologies.

Growth-rate model predicts in vivo tumor response from in vitro data.

A major challenge in oncology drug development is to elucidate why drugs that show promising results in cancer cell lines in vitro fail in mouse studies or human trials. One of the fundamental steps toward solving this problem is to better predict how in vitro potency translates into in vivo efficacy. A common approach to infer whether a model will respond in vivo is based on in vitro half-maximal inhibitory concentration values (IC50 ), but yields limited quantitative comparison between cell lines and drugs, potentially because cell division and death rates differ between cell lines and in vivo models. Other methods based either on mechanistic modeling or machine learning require molecular insights or extensive training data, limiting their use for early drug development. To address these challenges, we propose a mathematical model integrating in vitro growth rate inhibition values with pharmacokinetic parameters to estimate in vivo drug response. Upon calibration with a drug-specific factor, our model yields precise estimates of tumor growth rate inhibition for in vivo studies based on in vitro data. We then demonstrate how our model can be used to study dosing schedules and perform sensitivity analyses. In addition, it provides meaningful metrics to assess association with genotypes and guide clinical trial design. By relying on commonly collected data, our approach shows great promise for optimizing drug development, better characterizing the efficacy of novel molecules targeting proliferation, and identifying more robust biomarkers of sensitivity while limiting the number of in vivo experiments.

Evaluation of statistical approaches for association testing in noisy drug screening data.

BACKGROUND: Identifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment. RESULTS: To address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets. CONCLUSIONS: We observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics.

PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis.

Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose-response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0, https://pharmacodb.ca/), we present (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug-response analysis such as tissue distribution of dose-response metrics and biomarker analysis; and (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug-response phenotypes of cancer models.

RTK-Dependent Inducible Degradation of Mutant PI3Kα Drives GDC-0077 (Inavolisib) Efficacy.

PIK3CA is one of the most frequently mutated oncogenes; the p110a protein it encodes plays a central role in tumor cell proliferation. Small-molecule inhibitors targeting the PI3K p110a catalytic subunit have entered clinical trials, with early-phase GDC-0077 studies showing antitumor activity and a manageable safety profile in patients with PIK3CA-mutant breast cancer. However, preclinical studies have shown that PI3K pathway inhibition releases negative feedback and activates receptor tyrosine kinase signaling, reengaging the pathway and attenuating drug activity. Here we discover that GDC-0077 and taselisib more potently inhibit mutant PI3K pathway signaling and cell viability through unique HER2-dependent mutant p110a degradation. Both are more effective than other PI3K inhibitors at maintaining prolonged pathway suppression. This study establishes a new strategy for identifying inhibitors that specifically target mutant tumors by selective degradation of the mutant oncoprotein and provide a strong rationale for pursuing PI3Kα degraders in patients with HER2-positive breast cancer. SIGNIFICANCE: The PI3K inhibitors GDC-0077 and taselisib have a unique mechanism of action; both inhibitors lead to degradation of mutant p110a protein. The inhibitors that have the ability to trigger specific degradation of mutant p110a without significant change in wild-type p110a protein may result in improved therapeutic index in PIK3CA-mutant tumors.See related commentary by Vanhaesebroeck et al., p. 20.This article is highlighted in the In This Issue feature, p. 1.

Orchestrating and sharing large multimodal data for transparent and reproducible research.

Reproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon. However, the intrinsic complexity and continuous growth of biomedical data makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. To overcome these issues, we created a cloud-based platform called ORCESTRA ( orcestra.ca ), which provides a flexible framework for the reproducible processing of multimodal biomedical data. It enables processing of clinical, genomic and perturbation profiles of cancer samples through automated processing pipelines that are user-customizable. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI) and manages multiple dataset versions, which can be shared for future studies.

Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients.

Cell-line screens create expansive datasets for learning predictive markers of drug response, but these models do not readily translate to the clinic with its diverse contexts and limited data. In the present study, we apply a recently developed technique, few-shot machine learning, to train a versatile neural network model in cell lines that can be tuned to new contexts using few additional samples. The model quickly adapts when switching among different tissue types and in moving from cell-line models to clinical contexts, including patient-derived tumor cells and patient-derived xenografts. It can also be interpreted to identify the molecular features most important to a drug response, highlighting critical roles for RB1 and SMAD4 in the response to CDK inhibition and RNF8 and CHD4 in the response to ATM inhibition. The few-shot learning framework provides a bridge from the many samples surveyed in high-throughput screens (n-of-many) to the distinctive contexts of individual patients (n-of-one).

Design of a brain-penetrant CDK4/6 inhibitor for glioblastoma.

CDK4 and CDK6 are kinases with similar sequences that regulate cell cycle progression and are validated targets in the treatment of cancer. Glioblastoma is characterized by a high frequency of CDKN2A/CCND2/CDK4/CDK6 pathway dysregulation, making dual inhibition of CDK4 and CDK6 an attractive therapeutic approach for this disease. Abemaciclib, ribociclib, and palbociclib are approved CDK4/6 inhibitors for the treatment of HR+/HER2- breast cancer, but these drugs are not expected to show strong activity in brain tumors due to poor blood brain barrier penetration. Herein, we report the identification of a brain-penetrant CDK4/6 inhibitor derived from a literature molecule with low molecular weight and topological polar surface area (MW = 285 and TPSA = 66 Å2), but lacking the CDK2/1 selectivity profile due to the absence of a basic amine. Removal of a hydrogen bond donor via cyclization of the pyrazole allowed for the introduction of basic and semi-basic amines, while maintaining in many cases efflux ratios reasonable for a CNS program. Ultimately, a basic spiroazetidine (cpKa = 8.8) was identified that afforded acceptable selectivity over anti-target CDK1 while maintaining brain-penetration in vivo (mouse Kp,uu = 0.20-0.59). To probe the potency and selectivity, our lead compound was evaluated in a panel of glioblastoma cell lines. Potency comparable to abemaciclib was observed in Rb-wild type lines U87MG, DBTRG-05MG, A172, and T98G, while Rb-deficient cell lines SF539 and M059J exhibited a lack of sensitivity.

A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines.

Evidence that some high-impact biomedical results cannot be repeated has stimulated interest in practices that generate findable, accessible, interoperable, and reusable (FAIR) data. Multiple papers have identified specific examples of irreproducibility, but practical ways to make data more reproducible have not been widely studied. Here, five research centers in the NIH LINCS Program Consortium investigate the reproducibility of a prototypical perturbational assay: quantifying the responsiveness of cultured cells to anti-cancer drugs. Such assays are important for drug development, studying cellular networks, and patient stratification. While many experimental and computational factors impact intra- and inter-center reproducibility, the factors most difficult to identify and control are those with a strong dependency on biological context. These factors often vary in magnitude with the drug being analyzed and with growth conditions. We provide ways to identify such context-sensitive factors, thereby improving both the theory and practice of reproducible cell-based assays.

Therapeutic Ligands Antagonize Estrogen Receptor Function by Impairing Its Mobility.

Estrogen receptor-positive (ER+) breast cancers frequently remain dependent on ER signaling even after acquiring resistance to endocrine agents, prompting the development of optimized ER antagonists. Fulvestrant is unique among approved ER therapeutics due to its capacity for full ER antagonism, thought to be achieved through ER degradation. The clinical potential of fulvestrant is limited by poor physicochemical features, spurring attempts to generate ER degraders with improved drug-like properties. We show that optimization of ER degradation does not guarantee full ER antagonism in breast cancer cells; ER "degraders" exhibit a spectrum of transcriptional activities and anti-proliferative potential. Mechanistically, we find that fulvestrant-like antagonists suppress ER transcriptional activity not by ER elimination, but by markedly slowing the intra-nuclear mobility of ER. Increased ER turnover occurs as a consequence of ER immobilization. These findings provide proof-of-concept that small molecule perturbation of transcription factor mobility may enable therapeutic targeting of this challenging target class.

Multiomics Profiling Establishes the Polypharmacology of FDA-Approved CDK4/6 Inhibitors and the Potential for Differential Clinical Activity.

The target profiles of many drugs are established early in their development and are not systematically revisited at the time of FDA approval. Thus, it is often unclear whether therapeutics with the same nominal targets but different chemical structures are functionally equivalent. In this paper we use five different phenotypic and biochemical assays to compare approved inhibitors of cyclin-dependent kinases 4/6-collectively regarded as breakthroughs in the treatment of hormone receptor-positive breast cancer. We find that transcriptional, proteomic, and phenotypic changes induced by palbociclib, ribociclib, and abemaciclib differ significantly; abemaciclib in particular has advantageous activities partially overlapping those of alvocidib, an older polyselective CDK inhibitor. In cells and mice, abemaciclib inhibits kinases other than CDK4/6 including CDK2/cyclin A/E-implicated in resistance to CDK4/6 inhibition-and CDK1/cyclin B. The multifaceted experimental and computational approaches described here therefore uncover underappreciated differences in CDK4/6 inhibitor activities with potential importance in treating human patients.