Emergence of antiestrogen-resistant cells in MCF-7 cells during suppression of estrogen signaling is a widely accepted model of acquired breast cancer resistance to endocrine therapy. To obtain insight into the genomic basis of endocrine therapy resistance, we characterized MCF-7 monoclonal sublines that survived 21-day exposure to tamoxifen (T-series sublines) or fulvestrant (F-series sublines) and sublines unselected by drugs (U-series). All T/Fsublines were resistant to the cytocidal effects of both tamoxifen and fulvestrant. However, their responses to the cytostatic effects of fulvestrant varied greatly, and their remarkably diversified morphology showed no correlation with drug resistance. mRNA expression profiles of the U-sublines differed significantly from those of the T/F-sublines, whose transcriptomal responsiveness to fulvestrant was largely lost. A set of genes strongly expressed in the U-sublines successfully predicted metastasis-free survival of breast cancer patients. Most T/F-sublines shared highly homogeneous genomic DNA aberration patterns that were distinct from those of the U-sublines. Genomic DNA of the U-sublines harbored many aberrations that were not found in the T/F-sublines. These results suggest that the T/F-sublines are derived from a common monoclonal progenitor that lost transcriptomal responsiveness to antiestrogens as a consequence of genetic abnormalities many population doublings ago, not from the antiestrogen-sensitive cells in the same culture during the exposure to antiestrogens. Thus, the apparent acquisition of antiestrogen resistance by MCF-7 cells reflects selection of preexisting drug-resistant subpopulations without involving changes in individual cells. Our results suggest the importance of clonal selection in endocrine therapy resistance of breast cancer. acquired resistance ͉ clonal selection ͉ DNA copy number ͉ fulvestrant ͉ tumor heterogeneity A pproximately 50% to 70% of breast cancers are estrogen receptor (ER␣) positive without amplification of the ERBB2/HER2 gene (1). Such ER ϩ /HER2Ϫ breast cancers typically respond well to endocrine therapy (ET), which suppresses estrogen signaling in cancer cells and halts their proliferation (cytostatic effect) and/or induces apoptosis (cytocidal effect). Although ET is proven effective, its clinical benefit is seriously limited by drug resistance. Approximately 50% of ER ϩ metastatic breast cancers do not respond to ET at all (de novo resistance); even when they initially respond to ET, most eventually become resistant during the course of therapy (acquired resistance). Nearly 40% of early-stage breast cancers treated with ET relapse with ET-resistant disease. Thus, ET resistance is an important clinical challenge in breast cancer treatment.The MCF-7 cell line is an extensively studied cell culture model of ER ϩ /HER2 Ϫ breast cancer (2). Because proliferation and survival of MCF-7 cells in culture or as xenograft are strictly dependent on estrogen (3-5), this cell line has been widely used to study mechanisms of ET resis...

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.