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Competing Interests: The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO’s conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/cci/author-center. Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians (Open Payments). Carl R. PelzConsulting or Advisory Role, Immediate Family Member: NovartisCharles D. LopezConsulting or Advisory Role: Boston Scientific, Celgene, Boston Biomedical, Pfizer, Exelixis, Astellas Pharma Research Funding: Taiho Pharmaceutical Travel, Accommodations, Expenses: RenovoRxLisa M. CoussensEmployment: Oregon Health & Science University (OHSU) Honoraria: Prospect Creek Foundation, Lustgarten Foundation for Pancreatic Cancer Research, Syndax Pharmaceuticals, Inc: External Advisory Board, Carisma Therapeutics Inc: Scientific Advisory Board, Verseau Therapeutics, Inc, Scientific Advisory Board, Zymeworks, Inc, Scientific Advisory Board, CytomX Therapeutics, Inc, Kineta Inc, (P30) Koch Institute for Integrated Cancer Research, Massachusetts Inst. of Tech, (P30) Salk Institute Cancer Center, Bloomberg-Kimmel Institute for Cancer Immunotherapy, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Dana Farber Cancer Center Breast SPORE, (P30) Dana Farber/Harvard Cancer Center, (P30) University of California, San Diego Moores Cancer Center, Starr Cancer Consortium, Lustgarten Foundation for Pancreatic Cancer Research, Therapeutics Working Group, NIH/NCI-Frederick National Laboratory Advisory Committee (FNLAC), Susan G Komen Foundation, Komen Scholar Consulting or Advisory Role: Cell Signaling Technologies, Pharmacyclics, CytomX Therapeutics, Syndax, Carisma Therapeutics, Verseau Therapeutics, Zymeworks, Kineta, Inc, Abbvie, Shasqi Inc Research Funding: Syndax Pharmaceuticals Inc, Pharmacyclics, Cell Signaling Technologies, Innate Pharma, Deciphera Travel, Accommodations, Expenses: Cell Signaling Technologies, AstraZeneca, Pharmacyclics, Verseau, Carisma Therapeutics, CytomX Therapeutics, Zymeworks Other Relationship: Prospect Creek Foundation, Lustgarten Foundation for Pancreatic Cancer Research, (P30) Koch Institute for Integrated Cancer Research, Massachusetts Inst. of Tech. (2012-present; honorarium), (P30) Salk Institute Cancer Center: (2016-2020; honorarium), Bloomberg-Kimmel Institute for Cancer Immunotherapy, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins: (2016-present; honorarium), Dana Farber Cancer Center Breast SPORE: (2017-present; honorarium), (P30) Dana Farber/Harvard Cancer Center: (2019-present; honorarium), (P30) University of California, San Diego Moores Cancer Center (2019-present; honorarium), Cancer Research Institute (CRI): (2013-present; unpaid), The V Foundation for Cancer Research): (2013-present; unpaid), Starr Cancer Consortium: (2011-present, honorarium), Lustgarten Foundation for Pancreatic Cancer Research, Therapeutics Working Group: (2019-present; paid), NIH/NCI-Frederick National Laboratory Advisory Committee (FNLAC): (2016-present; daily honorarium), Susan G Komen Foundation, Komen Scholar (2020 – 2023; honorarium)Rosalie C. SearsConsulting or Advisory Role: Novartis No other potential conflicts of interest were reported

Intratumoral heterogeneity in cancers arises from genomic instability and epigenomic plasticity and is associated with resistance to cytotoxic and targeted therapies. We show here that cell-state heterogeneity, defined by differentiation-state marker expression, is high in triple-negative and basal-like breast cancer subtypes, and that drug tolerant persister (DTP) cell populations with altered marker expression emerge during treatment with a wide range of pathway-targeted therapeutic compounds. We show that MEK and PI3K/mTOR inhibitor-driven DTP states arise through distinct cell-state transitions rather than by Darwinian selection of preexisting subpopulations, and that these transitions involve dynamic remodeling of open chromatin architecture. Increased activity of many chromatin modifier enzymes, including BRD4, is observed in DTP cells. Co-treatment with the PI3K/mTOR inhibitor BEZ235 and the BET inhibitor JQ1 prevents changes to the open chromatin architecture, inhibits the acquisition of a DTP state, and results in robust cell death in vitro and xenograft regression in vivo.

Background: Microarray technology has become very popular for globally evaluating gene expression in biological samples. However, non-linear variation associated with the technology can make data interpretation unreliable. Therefore, methods to correct this kind of technical variation are critical. Here we consider a method to reduce this type of variation applied after three common procedures for processing microarray data: MAS 5.0, RMA, and dChip., Results: We commonly observe intensity-dependent technical variation between samples in a single microarray experiment. This is most common when MAS 5.0 is used to process probe level data, but we also see this type of technical variation with RMA and dChip processed data. Datasets with unbalanced numbers of up and down regulated genes seem to be particularly susceptible to this type of intensity-dependent technical variation. Unbalanced gene regulation is common when studying cancer samples or genetically manipulated animal models and preservation of this biologically relevant information, while removing technical variation has not been well addressed in the literature. We propose a method based on using rank-invariant, endogenous transcripts as reference points for normalization (GRSN). While the use of rank-invariant transcripts has been described previously, we have added to this concept by the creation of a global rank-invariant set of transcripts used to generate a robust average reference that is used to normalize all samples within a dataset. The global rank-invariant set is selected in an iterative manner so as to preserve unbalanced gene expression. Moreover, our method works well as an overlay that can be applied to data already processed with other probe set summary methods. We demonstrate that this additional normalization step at the "probe set level" effectively corrects a specific type of technical variation that often distorts samples in datasets., Conclusion: We have developed a simple post-processing tool to help detect and correct non-linear technical variation in microarray data and demonstrate how it can reduce technical variation and improve the results of downstream statistical gene selection and pathway identification methods.