Barcoded Bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast

Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits.

PATH-29. GLIOBLASTOMA TUMOR CLASSIFICATION BASED ON SPATIAL ANALYSIS OF ONCOGENIC AMPLIFICATIONS AND PROTEIN EXPRESSION

Heterogeneity of glioblastoma (GBM) has been extensively studied in recent years with identification of oncogenic drivers of GBM cellular subtypes. However, little is known about how these cells interact with each other or with the surrounding tumor microenvironment (TME). We employed spatial protein profiling targeting immune and neuronal markers (79 proteins) coupled with single-cell spatial maps of fluorescence in situ hybridization (FISH) for EGFR, CDK4, and PDGFRA on human GBM tissue sections. Several cores from 20 GBM samples were collected to create a tissue microarray, and 96 regions of interests were profiled with 37,844 nuclei for oncogenic amplification screen. Spatial protein profiling identified strong correlation of certain immune markers, TAU-associated proteins, and oligodendrocyte-enriched protein groups and overall high intratumor heterogeneity of TME. Our single-cell quantification of FISH signals showed differences among tumors based on the prevalence of dual amplification of EGFR and CDK4 within a cell relative to single oncogene amplified cells. High relative frequency of dual amplification was associated with increased expression of immune-related markers and decreased expression of EGFR protein. Moreover, this protein expression signature was associated with survival in another GBM dataset. Here, we present spatial genetic analysis at the single cell level coupled with protein expression profiles associated with tumor microenvironment. Our results suggest that assessment of genetic heterogeneity in GBM could potentially drive improved patient stratification and treatment.

Response to comment on "Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2"

Further analysis of SARS-CoV-2 genome sequencing data identifies several highly recurrent genetic variants with low allele frequencies, which, if filtered out, provide estimates consistent with tighter transmission bottlenecks.

Impact of HER2 Heterogeneity on Treatment Response of Early-Stage HER2-Positive Breast Cancer: Phase II Neoadjuvant Clinical Trial of T-DM1 Combined with Pertuzumab

Intratumor heterogeneity is postulated to cause therapeutic resistance. To prospectively assess the impact of HER2 (ERBB2) heterogeneity on response to HER2-targeted therapy, we treated 164 patients with centrally confirmed HER2-positive early-stage breast cancer with neoadjuvant trastuzumab emtansine plus pertuzumab. HER2 heterogeneity was assessed on pretreatment biopsies from two locations of each tumor. HER2 heterogeneity, defined as an area with ERBB2 amplification in >5% but <50% of tumor cells, or a HER2-negative area by FISH, was detected in 10% (16/157) of evaluable cases. The pathologic complete response rate was 55% in the nonheterogeneous subgroup and 0% in the heterogeneous group (P < 0.0001, adjusted for hormone receptor status). Single-cell ERBB2 FISH analysis of cellular heterogeneity identified the fraction of ERBB2 nonamplified cells as a driver of therapeutic resistance. These data suggest HER2 heterogeneity is associated with resistance to HER2-targeted therapy and should be considered in efforts to optimize treatment strategies. SIGNIFICANCE: HER2-targeted therapies improve cure rates in HER2-positive breast cancer, suggesting chemotherapy can be avoided in a subset of patients. We show that HER2 heterogeneity, particularly the fraction of ERBB2 nonamplified cancer cells, is a strong predictor of resistance to HER2 therapies and could potentially be used to optimize treatment selection.See related commentary by Okines and Turner, p. 2369.This article is highlighted in the In This Issue feature, p. 2355.

Systematic tissue collection during clinical breast biopsy is feasible, safe and enables high-content translational analyses

Systematic collection of fresh tissues for research at the time of diagnostic image-guided breast biopsy has the potential to fuel a wide variety of innovative studies. Here we report the initial experience, including safety, feasibility, and laboratory proof-of-principle, with the collection and analysis of research specimens obtained via breast core needle biopsy immediately following routine clinical biopsy at a single institution over a 14-month period. Patients underwent one or two additional core biopsies following collection of all necessary clinical specimens. In total, 395 patients were approached and 270 consented to the research study, yielding a 68.4% consent rate. Among consenting patients, 238 lesions were biopsied for research, resulting in 446 research specimens collected. No immediate complications were observed. Representative research core specimens showed high diagnostic concordance with clinical core biopsies. Flow cytometry demonstrated consistent recovery of hundreds to thousands of viable cells per research core. Among a group of HER2 + tumor research specimens, HER2 assessment by flow cytometry correlated highly with immunohistochemistry (IHC) staining, and in addition revealed extensive inter- and intra-tumoral variation in HER2 levels of potential clinical relevance. Suitability for single-cell transcriptomic analysis was demonstrated for a triple-negative tumor core biopsy, revealing substantial cellular diversity in the tumor immune microenvironment, including a prognostically relevant T cell subpopulation. Thus, collection of fresh tissues for research purposes at the time of diagnostic breast biopsy is safe, feasible and efficient, and may provide a high-yield mechanism to generate a rich tissue repository for a wide variety of cross-disciplinary research.

Dissecting dual roles of MyoD during lineage conversion to mature myocytes and myogenic stem cells

The generation of myotubes from fibroblasts upon forced MyoD expression is a classic example of transcription factor-induced reprogramming. We recently discovered that additional modulation of signaling pathways with small molecules facilitates reprogramming to more primitive induced myogenic progenitor cells (iMPCs). Here, we dissected the transcriptional and epigenetic dynamics of mouse fibroblasts undergoing reprogramming to either myotubes or iMPCs using a MyoD-inducible transgenic model. Induction of MyoD in fibroblasts combined with small molecules generated Pax7⁺ iMPCs with high similarity to primary muscle stem cells. Analysis of intermediate stages of iMPC induction revealed that extinction of the fibroblast program preceded induction of the stem cell program. Moreover, key stem cell genes gained chromatin accessibility prior to their transcriptional activation, and these regions exhibited a marked loss of DNA methylation dependent on the Tet enzymes. In contrast, myotube generation was associated with few methylation changes, incomplete and unstable reprogramming, and an insensitivity to Tet depletion. Finally, we showed that MyoD's ability to bind to unique bHLH targets was crucial for generating iMPCs but dispensable for generating myotubes. Collectively, our analyses elucidate the role of MyoD in myogenic reprogramming and derive general principles by which transcription factors and signaling pathways cooperate to rewire cell identity.

Combined epigenetic and metabolic treatments overcome differentiation blockade in acute myeloid leukemia

A hallmark of acute myeloid leukemia (AML) is the inability of self-renewing malignant cells to mature into a non-dividing terminally differentiated state. This differentiation block has been linked to dysregulation of multiple cellular processes, including transcriptional, chromatin, and metabolic regulation. The transcription factor HOXA9 and the histone demethylase LSD1 are examples of such regulators that promote differentiation blockade in AML. To identify metabolic targets that interact with LSD1 inhibition to promote myeloid maturation, we screened a small molecule library to identify druggable substrates. We found that differentiation caused by LSD1 inhibition is enhanced by combined perturbation of purine nucleotide salvage and de novo lipogenesis pathways, and identified multiple lines of evidence to support the specificity of these pathways and suggest a potential basis of how perturbation of these pathways may interact synergistically to promote myeloid differentiation. In sum, these findings suggest potential drug combination strategies in the treatment of AML.

The impact of tumor epithelial and microenvironmental heterogeneity on treatment responses in HER2+ breast cancer

Despite the availability of multiple human epidermal growth factor receptor 2-targeted (HER2-targeted) treatments, therapeutic resistance in HER2+ breast cancer remains a clinical challenge. Intratumor heterogeneity for HER2 and resistance-conferring mutations in the PIK3CA gene (encoding PI3K catalytic subunit α) have been investigated in response and resistance to HER2-targeting agents, while the role of divergent cellular phenotypes and tumor epithelial-stromal cell interactions is less well understood. Here, we assessed the effect of intratumor cellular genetic heterogeneity for ERBB2 (encoding HER2) copy number and PIK3CA mutation on different types of neoadjuvant HER2-targeting therapies and clinical outcome in HER2+ breast cancer. We found that the frequency of cells lacking HER2 was a better predictor of response to HER2-targeted treatment than intratumor heterogeneity. We also compared the efficacy of different therapies in the same tumor using patient-derived xenograft models of heterogeneous HER2+ breast cancer and single-cell approaches. Stromal determinants were better predictors of response than tumor epithelial cells, and we identified alveolar epithelial and fibroblastic reticular cells as well as lymphatic vessel endothelial hyaluronan receptor 1-positive (Lyve1+) macrophages as putative drivers of therapeutic resistance. Our results demonstrate that both preexisting and acquired resistance to HER2-targeting agents involve multiple mechanisms including the tumor microenvironment. Furthermore, our data suggest that intratumor heterogeneity for HER2 should be incorporated into treatment design.

Progression signature underlies clonal evolution and dissemination of multiple myeloma

Clonal evolution drives tumor progression, dissemination, and relapse in multiple myeloma (MM), with most patients dying of relapsed disease. This multistage process requires tumor cells to enter the circulation, extravasate, and colonize distant bone marrow (BM) sites. Here, we developed a fluorescent or DNA-barcode clone-tracking system on MM PrEDiCT (progression through evolution and dissemination of clonal tumor cells) xenograft mouse model to study clonal behavior within the BM microenvironment. We showed that only the few clones that successfully adapt to the BM microenvironment can enter the circulation and colonize distant BM sites. RNA sequencing of primary and distant-site MM tumor cells revealed a progression signature sequentially activated along human MM progression and significantly associated with overall survival when evaluated against patient data sets. A total of 28 genes were then computationally predicted to be master regulators (MRs) of MM progression. HMGA1 and PA2G4 were validated in vivo using CRISPR-Cas9 in the PrEDiCT model and were shown to be significantly depleted in distant BM sites, indicating their role in MM progression and dissemination. Loss of HMGA1 and PA2G4 also compromised the proliferation, migration, and adhesion abilities of MM cells in vitro. Overall, our model successfully recapitulates key characteristics of human MM disease progression and identified potential new therapeutic targets for MM.

Computational modelling of perivascular-niche dynamics for the optimization of treatment schedules for glioblastoma

Glioblastoma stem-like cells dynamically transition between a chemoradiation-resistant state and a chemoradiation-sensitive state. However, physical barriers in the tumour microenvironment restrict the delivery of chemotherapy to tumour compartments that are distant from blood vessels. Here, we show that a massively parallel computational model of the spatiotemporal dynamics of the perivascular niche that incorporates glioblastoma stem-like cells and differentiated tumour cells as well as relevant tissue-level phenomena can be used to optimize the administration schedules of concurrent radiation and temozolomide-the standard-of-care treatment for glioblastoma. In mice with platelet-derived growth factor (PDGF)-driven glioblastoma, the model-optimized treatment schedule increased the survival of the animals. For standard radiation fractionation in patients, the model predicts that chemotherapy may be optimally administered about one hour before radiation treatment. Computational models of the spatiotemporal dynamics of the tumour microenvironment could be used to predict tumour responses to a broader range of treatments and to optimize treatment regimens.

Breast tumours maintain a reservoir of subclonal diversity during expansion

Our knowledge of copy number evolution during the expansion of primary breast tumours is limited^1,2. Here, to investigate this process, we developed a single-cell, single-molecule DNA-sequencing method and performed copy number analysis of 16,178 single cells from 8 human triple-negative breast cancers and 4 cell lines. The results show that breast tumours and cell lines comprise a large milieu of subclones (7-22) that are organized into a few (3-5) major superclones. Evolutionary analysis suggests that after clonal TP53 mutations, multiple loss-of-heterozygosity events and genome doubling, there was a period of transient genomic instability followed by ongoing copy number evolution during the primary tumour expansion. By subcloning single daughter cells in culture, we show that tumour cells rediversify their genomes and do not retain isogenic properties. These data show that triple-negative breast cancers continue to evolve chromosome aberrations and maintain a reservoir of subclonal diversity during primary tumour growth.

Reconstructing the Lineage Histories and Differentiation Trajectories of Individual Cancer Cells in Myeloproliferative Neoplasms

Some cancers originate from a single mutation event in a single cell. Blood cancers known as myeloproliferative neoplasms (MPNs) are thought to originate when a driver mutation is acquired by a hematopoietic stem cell (HSC). However, when the mutation first occurs in individuals and how it affects the behavior of HSCs in their native context is not known. Here we quantified the effect of the JAK2-V617F mutation on the self-renewal and differentiation dynamics of HSCs in treatment-naive individuals with MPNs and reconstructed lineage histories of individual HSCs using somatic mutation patterns. We found that JAK2-V617F mutations occurred in a single HSC several decades before MPN diagnosis-at age 9 ± 2 years in a 34-year-old individual and at age 19 ± 3 years in a 63-year-old individual-and found that mutant HSCs have a selective advantage in both individuals. These results highlight the potential of harnessing somatic mutations to reconstruct cancer lineages.

ESTIpop: a computational tool to simulate and estimate parameters for continuous-time Markov branching processes

SUMMARY

ESTIpop is an R package designed to simulate and estimate parameters for continuous-time Markov branching processes with constant or time-dependent rates, a common model for asexually reproducing cell populations. Analytical approaches to parameter estimation quickly become intractable in complex branching processes. In ESTIpop, parameter estimation is based on a likelihood function with respect to a time series of cell counts, approximated by the Central Limit Theorem for multitype branching processes. Additionally, simulation in ESTIpop via approximation can be performed many times faster than exact simulation methods with similar results.

AVAILABILITY AND IMPLEMENTATION

ESTIpop is available as an R package on Github (https://github.com/michorlab/estipop).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2

Superspreading events shaped the coronavirus disease 2019 (COVID-19) pandemic, and their rapid identification and containment are essential for disease control. Here, we provide a national-scale analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) superspreading during the first wave of infections in Austria, a country that played a major role in initial virus transmissions in Europe. Capitalizing on Austria's well-developed epidemiological surveillance system, we identified major SARS-CoV-2 clusters during the first wave of infections and performed deep whole-genome sequencing of more than 500 virus samples. Phylogenetic-epidemiological analysis enabled the reconstruction of superspreading events and charts a map of tourism-related viral spread originating from Austria in spring 2020. Moreover, we exploited epidemiologically well-defined clusters to quantify SARS-CoV-2 mutational dynamics, including the observation of low-frequency mutations that progressed to fixation within the infection chain. Time-resolved virus sequencing unveiled viral mutation dynamics within individuals with COVID-19, and epidemiologically validated infector-infectee pairs enabled us to determine an average transmission bottleneck size of 103 SARS-CoV-2 particles. In conclusion, this study illustrates the power of combining epidemiological analysis with deep viral genome sequencing to unravel the spread of SARS-CoV-2 and to gain fundamental insights into mutational dynamics and transmission properties.

Abstract PO-133: Breast tumors maintain a reservoir of subclonal diversity during primary expansion

Our knowledge of copy number evolution during the expansion of primary breast tumors is limited. To investigate this process, we developed a single-cell, single-molecule DNA sequencing method and performed copy number analysis of 13,808 single cells from six triple-negative breast cancers (TNBCs) and two breast cancer cell lines. Our data shows that breast tumors and cell lines are comprised of a large milieu (17-26) of subclones that are organized into a few (4-8) major superclones. Phylogenetic analysis and mathematical modeling show that copy number evolution was ongoing during the expansion of the primary tumor, after the initial punctuated burst of genomic instability. By expanding single daughter cells in culture, we show that cancer cells re-diversify their genomes and do not retain isogenic properties. These data elucidate modes of evolution and show that TNBCs maintain a vast reservoir of subclonal copy number diversity during the expansion of the primary tumor mass.

Citation Format: Darlan Conterno Minussi, Michael Nicholson, Hanghui Ye, Alexander Davis, Kaile Wang, Emi Sei, Haowei Du, Mashiat Rabbani, Cheng Peng, Min Hu, Shanshan Bai, Thomas McDonald, Aislyn Schalck, Anna Casasent, Angelica Barrera, Hui Chen, Bora Lim, Banu Arun, Funda Meric-Bernstam, Franziska Michor, Nicholas Navin. Breast tumors maintain a reservoir of subclonal diversity during primary expansion [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-133.

Pharmacokinetic Profiles Determine Optimal Combination Treatment Schedules in Computational Models of Drug Resistance

Identification of optimal schedules for combination drug administration relies on accurately estimating the correct pharmacokinetics, pharmacodynamics, and drug interaction effects. Misspecification of pharmacokinetics can lead to wrongly predicted timing or order of treatments, leading to schedules recommended on the basis of incorrect assumptions about absorption and elimination of a drug and its effect on tumor growth. Here, we developed a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data. The software can be used to compare prespecified schedules on the basis of the number of resistant cells where drug interactions and pharmacokinetic curves can be estimated from user-provided data or models. We applied our approach to publicly available in vitro data of treatment with different tyrosine kinase inhibitors of BT-20 triple-negative breast cancer cells and of treatment with erlotinib of PC-9 non-small cell lung cancer cells. Our approach is publicly available in the form of an R package called ACESO (https://github.com/Michorlab/aceso) and can be used to investigate optimum dosing for any combination treatment. SIGNIFICANCE: These findings introduce a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data.

Circadian clock effects on cellular proliferation: Insights from theory and experiments

Oscillations of the cellular circadian clock have emerged as an important regulator of many physiological processes, both in health and in disease. One such process, cellular proliferation, is being increasingly recognized to be affected by the circadian clock. Here, we review how a combination of experimental and theoretical work has furthered our understanding of the way circadian clocks couple to the cell cycle and play a role in tissue homeostasis and cancer. Finally, we discuss recently introduced methods for modeling coupling of clocks based on techniques from survival analysis and machine learning and highlight their potential importance for future studies.

Synthetic Lethal and Resistance Interactions with BET Bromodomain Inhibitors in Triple-Negative Breast Cancer

BET bromodomain inhibitors (BBDIs) are candidate therapeutic agents for triple-negative breast cancer (TNBC) and other cancer types, but inherent and acquired resistance to BBDIs limits their potential clinical use. Using CRISPR and small-molecule inhibitor screens combined with comprehensive molecular profiling of BBDI response and resistance, we identified synthetic lethal interactions with BBDIs and genes that, when deleted, confer resistance. We observed synergy with regulators of cell cycle progression, YAP, AXL, and SRC signaling, and chemotherapeutic agents. We also uncovered functional similarities and differences among BRD2, BRD4, and BRD7. Although deletion of BRD2 enhances sensitivity to BBDIs, BRD7 loss leads to gain of TEAD-YAP chromatin binding and luminal features associated with BBDI resistance. Single-cell RNA-seq, ATAC-seq, and cellular barcoding analysis of BBDI responses in sensitive and resistant cell lines highlight significant heterogeneity among samples and demonstrate that BBDI resistance can be pre-existing or acquired.

Stochastic Evolution of Pancreatic Cancer Metastases During Logistic Clonal Expansion

Despite recent progress in diagnostic and multimodal treatment approaches, most cancer deaths are still caused by metastatic spread and the subsequent growth of tumor cells in sites distant from the primary organ. So far, few quantitative studies are available that allow for the estimation of metastatic parameters and the evaluation of alternative treatment strategies. Most computational studies have focused on situations in which the tumor cell population expands exponentially over time; however, tumors may eventually be subject to resource and space limitations so that their growth patterns deviate from exponential growth to adhere to density-dependent growth models. In this study, we developed a stochastic evolutionary model of cancer progression that considers alterations in metastasis-related genes and intercellular growth competition leading to density effects described by logistic growth. Using this stochastic model, we derived analytical approximations for the time between the initiation of tumorigenesis and diagnosis, the expected number of metastatic sites, the total number of metastatic cells, the size of the primary tumor, and survival. Furthermore, we investigated the effects of drug administration and surgical resection on these quantities and predicted outcomes for different treatment regimens. Parameter values used in the analysis were estimated from data obtained from a pancreatic cancer rapid autopsy program. Our theoretical approach allows for flexible modeling of metastatic progression dynamics.

Abstract B16: Mathematical modeling identifies optimum palbociclib dosing schedules for the treatment of estrogen receptor-positive (ER+) breast cancer patients

Fulvestrant and other endocrine therapies are widely used to treat estrogen receptor-positive (ER+) breast cancer, although most patients eventually develop treatment resistance. Endocrine therapy-resistant tumors have been shown to be sensitive to cyclin-dependent kinases 4/6 (CDK4/6) inhibitors, including palbociclib, abemaciclib, and ribociclib, which have recently been approved to be used in combination with endocrine therapies to treat ER+ breast cancers. Given the large number of possible pairwise drug combinations, systematically comparing different combinations and administration schedules experimentally is difficult, though it has been shown that altering therapy administration schedules can substantially improve treatment outcomes. Here, we develop a novel mathematical model to characterize the pharmacodynamic response to fulvestrant-palbociclib combination therapy in fulvestrant-sensitive and -resistant MCF7 cells. We use a cell cycle explicit model to describe changes in the G1/S transition rates in response to the combination therapy, and a Bayesian statistical inference approach to estimate model parameters from the in vitro data. We then combine the results from our pharmacodynamic model with published pharmacokinetic data to systematically compare dose administration schedules computationally. We use in silico clinical trials to identify treatment schedules within the clinical toxicity limits that are predicted to be more effective than the current standard of care. Our results suggest that continuous dosing of 75mg of palbociclib with no treatment holiday is more effective for slowing down tumor growth and lowering overall tumor burden than the current palbociclib treatment standard (125mg of palbociclib per day, three weeks on, followed by a one-week holiday). We predict that both palbociclib treatment strategies are most effective when combined with the current standard fulvestrant administration schedule, even when considering various levels of fulvestrant resistance. Thus, we plan to compare the two palbocilib schedules described above in combination with the current standard fulvestrant therapy in an investigator-led clinical trial at Dana-Farber Cancer Institute. Our mathematical modeling and statistical analysis platform provides a rational method for comparing treatment strategies in search of optimal combination dosing strategies for ER+ breast cancer.

Citation Format: Shayna Stein, Agostina Nardone, Weihan Liu, Gabriella Cohen, Cristina Guarducci, Myles Brown, Rinath Jeselsohn, Franziska Michor. Mathematical modeling identifies optimum palbociclib dosing schedules for the treatment of estrogen receptor-positive (ER+) breast cancer patients [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr B16.

Acquired resistance to combined BET and CDK4/6 inhibition in triple-negative breast cancer

BET inhibitors are promising therapeutic agents for the treatment of triple-negative breast cancer (TNBC), but the rapid emergence of resistance necessitates investigation of combination therapies and their effects on tumor evolution. Here, we show that palbociclib, a CDK4/6 inhibitor, and paclitaxel, a microtubule inhibitor, synergize with the BET inhibitor JQ1 in TNBC lines. High-complexity DNA barcoding and mathematical modeling indicate a high rate of de novo acquired resistance to these drugs relative to pre-existing resistance. We demonstrate that the combination of JQ1 and palbociclib induces cell division errors, which can increase the chance of developing aneuploidy. Characterizing acquired resistance to combination treatment at a single cell level shows heterogeneous mechanisms including activation of G1-S and senescence pathways. Our results establish a rationale for further investigation of combined BET and CDK4/6 inhibition in TNBC and suggest novel mechanisms of action for these drugs and new vulnerabilities in cells after emergence of resistance.

Drug Sensitivity and Allele Specificity of First-Line Osimertinib Resistance EGFR Mutations

Osimertinib, a mutant-specific third-generation EGFR tyrosine kinase inhibitor, is emerging as the preferred first-line therapy for EGFR-mutant lung cancer, yet resistance inevitably develops in patients. We modeled acquired resistance to osimertinib in transgenic mouse models of EGFR^L858R -induced lung adenocarcinoma and found that it is mediated largely through secondary mutations in EGFR-either C797S or L718V/Q. Analysis of circulating free DNA data from patients revealed that L718Q/V mutations almost always occur in the context of an L858R driver mutation. Therapeutic testing in mice revealed that both erlotinib and afatinib caused regression of osimertinib-resistant C797S-containing tumors, whereas only afatinib was effective on L718Q mutant tumors. Combination first-line osimertinib plus erlotinib treatment prevented the emergence of secondary mutations in EGFR. These findings highlight how knowledge of the specific characteristics of resistance mutations is important for determining potential subsequent treatment approaches and suggest strategies to overcome or prevent osimertinib resistance in vivo. SIGNIFICANCE: This study provides insight into the biological and molecular properties of osimertinib resistance EGFR mutations and evaluates therapeutic strategies to overcome resistance. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/10/2017/F1.large.jpg.

Ascl2-Dependent Cell Dedifferentiation Drives Regeneration of Ablated Intestinal Stem Cells

Ablation of LGR5⁺ intestinal stem cells (ISCs) is associated with rapid restoration of the ISC compartment. Different intestinal crypt populations dedifferentiate to provide new ISCs, but the transcriptional and signaling trajectories that guide this process are unclear, and a large body of work suggests that quiescent "reserve" ISCs contribute to regeneration. By timing the interval between LGR5⁺ lineage tracing and lethal injury, we show that ISC regeneration is explained nearly completely by dedifferentiation, with contributions from absorptive and secretory progenitors. The ISC-restricted transcription factor ASCL2 confers measurable competitive advantage to resting ISCs and is essential to restore the ISC compartment. Regenerating cells re-express Ascl2 days before Lgr5, and single-cell RNA sequencing (scRNA-seq) analyses reveal transcriptional paths underlying dedifferentiation. ASCL2 target genes include the interleukin-11 (IL-11) receptor Il11ra1, and recombinant IL-11 enhances crypt cell regenerative potential. These findings reveal cell dedifferentiation as the principal means for ISC restoration and highlight an ASCL2-regulated signal that enables this adaptive response.