CRISPR screens reveal convergent targeting strategies against evolutionarily distinct chemoresistance in cancer.

Resistance to chemotherapy has been a major hurdle that limits therapeutic benefits for many types of cancer. Here we systematically identify genetic drivers underlying chemoresistance by performing 30 genome-scale CRISPR knockout screens for seven chemotherapeutic agents in multiple cancer cells. Chemoresistance genes vary between conditions primarily due to distinct genetic background and mechanism of action of drugs, manifesting heterogeneous and multiplexed routes towards chemoresistance. By focusing on oxaliplatin and irinotecan resistance in colorectal cancer, we unravel that evolutionarily distinct chemoresistance can share consensus vulnerabilities identified by 26 second-round CRISPR screens with druggable gene library. We further pinpoint PLK4 as a therapeutic target to overcome oxaliplatin resistance in various models via genetic ablation or pharmacological inhibition, highlighting a single-agent strategy to antagonize evolutionarily distinct chemoresistance. Our study not only provides resources and insights into the molecular basis of chemoresistance, but also proposes potential biomarkers and therapeutic strategies against such resistance.

Intratumoral microbiome is associated with gastric cancer prognosis and therapy efficacy.

The role of the intratumoral microbiome in gastric cancer (GC) has not been comprehensively assessed. Here, we explored the relationship between the microbial community and GC prognosis and therapy efficacy. Several cancer-associated microbial characteristics were identified, including increased α-diversity, differential ß-diversity, and decreased Helicobacter pylori abundance. After adjusting for clinical features, prognostic analysis revealed 2 phyla, 14 genera, and 5 species associated with the overall survival of patients with GC. Additionally, 2 phyla, 14 genera, and 6 species were associated with adjuvant chemotherapy (ACT) efficacy in patients with stage II - III GC. Furthermore, we classified GC microbiome structures into three microbial subtypes (MS1, MS2 and MS3) with distinguishing features. The MS1 subtype exhibited high immune activity and enrichment of microbiota related to immunotherapy and butyric acid-producing, as well as potential benefits in immunotherapy. MS2 featured the highest α-diversity and activation of the TFF pathway, MS3 was characterized by epithelial-mesenchymal transition and was associated with poor prognosis and reduced ACT efficacy. Collectively, the results of this study provide valuable insights into the microbial characteristics associated with GC prognosis and therapy efficacy.

Assessing base-resolution DNA mechanics on the genome scale.

Intrinsic DNA properties including bending play a crucial role in diverse biological systems. A recent advance in a high-throughput technology called loop-seq makes it possible to determine the bendability of hundred thousand 50-bp DNA duplexes in one experiment. However, it's still challenging to assess base-resolution sequence bendability in large genomes such as human, which requires thousands of such experiments. Here, we introduce 'BendNet'-a deep neural network to predict the intrinsic DNA bending at base-resolution by using loop-seq results in yeast as training data. BendNet can predict the DNA bendability of any given sequence from different species with high accuracy. To explore the utility of BendNet, we applied it to the human genome and observed DNA bendability is associated with chromatin features and disease risk regions involving transcription/enhancer regulation, DNA replication, transcription factor binding and extrachromosomal circular DNA generation. These findings expand our understanding on DNA mechanics and its association with transcription regulation in mammals. Lastly, we built a comprehensive resource of genomic DNA bendability profiles for 307 species by applying BendNet, and provided an online tool to assess the bendability of user-specified DNA sequences (http://www.dnabendnet.com/).

A deep learning-based drug repurposing screening and validation for anti-SARS-CoV-2 compounds by targeting the cell entry mechanism.

The recent outbreak of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a severe threat to the global public health and economy, however, effective drugs to treat COVID-19 are still lacking. Here, we employ a deep learning-based drug repositioning strategy to systematically screen potential anti-SARS-CoV-2 drug candidates that target the cell entry mechanism of SARS-CoV-2 virus from 2635 FDA-approved drugs and 1062 active ingredients from Traditional Chinese Medicine herbs. In silico molecular docking analysis validates the interactions between the top compounds and host receptors or viral spike proteins. Using a SARS-CoV-2 pseudovirus system, we further identify several drug candidates including Fostamatinib, Linagliptin, Lysergol and Sophoridine that can effectively block the cell entry of SARS-CoV-2 variants into human lung cells even at a nanomolar scale. These efforts not only illuminate the feasibility of applying deep learning-based drug repositioning for antiviral agents by targeting a specified mechanism, but also provide a valuable resource of promising drug candidates or lead compounds to treat COVID-19.

T-REX17 is a transiently expressed non-coding RNA essential for human endoderm formation.

Long non-coding RNAs (lncRNAs) have emerged as fundamental regulators in various biological processes, including embryonic development and cellular differentiation. Despite much progress over the past decade, the genome-wide annotation of lncRNAs remains incomplete and many known non-coding loci are still poorly characterized. Here, we report the discovery of a previously unannotated lncRNA that is transcribed 230 kb upstream of the SOX17 gene and located within the same topologically associating domain. We termed it T-REX17 (Transcript Regulating Endoderm and activated by soX17) and show that it is induced following SOX17 activation but its expression is more tightly restricted to early definitive endoderm. Loss of T-REX17 affects crucial functions independent of SOX17 and leads to an aberrant endodermal transcriptome, signaling pathway deregulation and epithelial to mesenchymal transition defects. Consequently, cells lacking the lncRNA cannot further differentiate into more mature endodermal cell types. Taken together, our study identified and characterized T-REX17 as a transiently expressed and essential non-coding regulator in early human endoderm differentiation.

Secuer: Ultrafast, scalable and accurate clustering of single-cell RNA-seq data.

Identifying cell clusters is a critical step for single-cell transcriptomics study. Despite the numerous clustering tools developed recently, the rapid growth of scRNA-seq volumes prompts for a more (computationally) efficient clustering method. Here, we introduce Secuer, a Scalable and Efficient speCtral clUstERing algorithm for scRNA-seq data. By employing an anchor-based bipartite graph representation algorithm, Secuer enjoys reduced runtime and memory usage over one order of magnitude for datasets with more than 1 million cells. Meanwhile, Secuer also achieves better or comparable accuracy than competing methods in small and moderate benchmark datasets. Furthermore, we showcase that Secuer can also serve as a building block for a new consensus clustering method, Secuer-consensus, which again improves the runtime and scalability of state-of-the-art consensus clustering methods while also maintaining the accuracy. Overall, Secuer is a versatile, accurate, and scalable clustering framework suitable for small to ultra-large single-cell clustering tasks.

Spatial intra-tumor heterogeneity is associated with survival of lung adenocarcinoma patients.

Intra-tumor heterogeneity (ITH) of human tumors is important for tumor progression, treatment response, and drug resistance. However, the spatial distribution of ITH remains incompletely understood. Here, we present spatial analysis of ITH in lung adenocarcinomas from 147 patients using multi-region mass spectrometry of >5,000 regions, single-cell copy number sequencing of ~2,000 single cells, and cyclic immunofluorescence of >10 million cells. We identified two distinct spatial patterns among tumors, termed clustered and random geographic diversification (GD). These patterns were observed in the same samples using both proteomic and genomic data. The random proteomic GD pattern, which is characterized by decreased cell adhesion and lower levels of tumor-interacting endothelial cells, was significantly associated with increased risk of recurrence or death in two independent patient cohorts. Our study presents comprehensive spatial mapping of ITH in lung adenocarcinoma and provides insights into the mechanisms and clinical consequences of GD.

Total Barley Maiya Alkaloids Prevent Increased Prolactin Levels Caused by Antipsychotic Drugs and Reduce Dopamine Receptor D2 via Epigenetic Mechanisms.

Background: The dopamine D2 receptor (DRD2) plays an important role in the increased prolactin (PRL) levels associated with the pathogenesis of antipsychotic drugs (ADs). Elevated prolactin levels can affect people's quality of life. Maiya alkaloids has been used to treat diseases associated with high PRL levels. Maiya, is a processed product of the mature fruits of Hordeum vulgare L. (a gramineous plant) after sprouting and drying and also a common Chinese herbal drug used in the clinic, is traditionally used to treat abnormal lactation, and is currently used clinically for the treatment of abnormal PRL levels. Aims: Epigenetic mechanisms can be related to DRD2 expression. We investigated the role of DRD2 methylation in the induction of PRL expression by ADs and the mechanism underlying the effects of total barley maiya alkaloids (TBMA) on this induction. Methods: The methylation rate of DRD2 in 46 people with schizophrenia who took risperidone was detected by MassARRAY sequencing. Humans were long term users of Ris. Seventy Sprague Dawley female rats were divided into seven groups. A rat model of risperidone-induced PRL was established, and the potential protective effects of TBMA and its components [e.g., hordenine (Hor)] on these increased PRL levels were investigated. The PRL concentration was detected by Enzyme-linked immunosorbent assay. PRL, DRD2, and DNA methyltransferase (DNMT1, DNMT3α, and DNMT3ß) protein and mRNA expression were detected by western blotting and real-time polymerase chain reaction (RT-PCR), respectively. The positive rate of methylation in the DRD2 promoter region of rats was detected by MassARRAY sequencing. Results: Clinical studies showed that the positive rate of DRD2 methylation associated with increased PRL levels induced by ADs was significantly higher than in the normal prolactinemia (NPRL) group. In vivo and vitro, TBMA and Hor inhibited this induction of PRL expression and increased DRD2 expression by inhibiting the expression of the DNMTs. Conclusions: TBMA and hordenine increased DRD2 expression by inhibiting DNMT-dependent DRD2 methylation.

Validating RRP12 Expression and Its Prognostic Significance in HCC Based on Data Mining and Bioinformatics Methods.

RRP12 (ribosomal RNA processing 12 homolog) is a nucleolar protein involved in the maturation and transport of eukaryotic ribosomal subunits and is a type of RNA binding protein. In recent years, considerable research has indicated that RRP12 is associated with the occurrence and development of multiple cancers. However, there is no research on RRP12 in hepatocellular carcinoma. Herein, we aimed to explore the role and significance of RRP12 in hepatocellular carcinoma.We used the TIMER and GEPIA databases to perform pan-cancer analyses of RRP12. The impact of RRP12 on the prognosis was analyzed through the GEPIA database. The relationship between RRP12 and immune cell infiltration was investigated by TIMER and GEPIA databases. Moreover, the expression of RRP12 in various liver cancer cells was evaluated by Western Blot to determine the cell line for the next experiment. Scratch test, Transwell test, and Edu tests were applied to validate the effects of RRP12 on the function of liver cancer cells. And the data were statistically analyzed.Pan-cancer analysis found that RPP12 was significantly upregulated in many cancers. Moreover, the prognostic analysis revealed that the difference in the expression of RRP12 has statistical significance for the overall survival rate and disease-free survival rate of liver cancer patients. In order to analyze the correlation between the expression level of RRP12 and clinical parameters, it was found that there was a significant negative correlation with tumor stage, tumor grade and tumor size. Univariate and multivariate analysis showed that RRP12 could be used as an independent prognostic factor for patients with hepatocellular carcinoma. Cellular experiments have proved that knocking down RRP12 can inhibit the proliferation, invasion, and metastasis of liver cancer cells.Therefore, RRP12 significantly affects the occurrence and development of HCC. Hence, RRP12 can become a potential target and prognostic biomarker for the treatment of hepatocellular carcinoma.

Topological isolation of developmental regulators in mammalian genomes.

Precise control of mammalian gene expression is facilitated through epigenetic mechanisms and nuclear organization. In particular, insulated chromosome structures are important for regulatory control, but the phenotypic consequences of their boundary disruption on developmental processes are complex and remain insufficiently understood. Here, we generated deeply sequenced Hi-C data for human pluripotent stem cells (hPSCs) that allowed us to identify CTCF loop domains that have highly conserved boundary CTCF sites and show a notable enrichment of individual developmental regulators. Importantly, perturbation of such a boundary in hPSCs interfered with proper differentiation through deregulated distal enhancer-promoter activity. Finally, we found that germline variations affecting such boundaries are subject to purifying selection and are underrepresented in the human population. Taken together, our findings highlight the importance of developmental gene isolation through chromosomal folding structures as a mechanism to ensure their proper expression.

[The Clinical Characteristics and Influencing Factors of Patients with Severe COVID-19].

OBJECTIVE: To investigate the clinical characteristic of coagulation, possible causes and countermeasures of patients with severe corona virus disease 2019 (COVID-19). METHODS: The clinical data of the 142 patients diagnosed as COVID-19 at Wuhan Third Hospital in Wuhan, China, from February 10 to February 16, 2020 were collected and analyzed retrospective. Among the patients, 17 cases of dead patients were divided into observe group, and 125 cases of cured patients were divided into control group. The clinical characteristics, laboratory tests, influencing factors, anticoagulant therapy, embolization and bleeding events of the two groups were observed. RESULTS: The average hospital stay time in 142 patients was 22 d. For the 17 dead patients in the observe group, the average hospital stay time was 9.9 d, and the D-dimer, prothrombin time, WBC count and Padua score of the patients in the observe group were significantly higher as compared with the patients in the control group. PT(OR=1.064, 95%CI 1.012-1.119) and D-D(OR=1.045, 95%CI 1.027-1.064) were the independent risk factors that causing the death of COVID-19 patients. Among the patients, 36(25.4%) patients received low-molecular-weight heparin for anticoagulant therapy, with the average course of 9.6 d. The cumulative incidence of the embolism of the patients in the observe group was 7（41.2%）, while 2(11.8%) patients developed to deep vein thrombosis (DVT) and pulmonary embolism (PE), 3 (17.6%) patients occurred acute cerebral infarction and 2 (11.8%) patients occurred acute myocardial infarction. 3 (17.6%) dead patients revealed dominant disseminated intravascular coagulation (DIC). CONCLUSION: Most patients with severe COVID-19 shows a variety of risk factors for thrombus, and those with coagulation dysfunction shows a high dead rate and rapid disease progression. Therefore, coagulation indicators should be dynamically monitored, and mechanical and drug prevention should be actively carried out.

The expression profiles and prognostic values of HSP70s in hepatocellular carcinoma.

BACKGROUND: The HSP70 family of heat shock protein plays a critical role in protein synthesis and transport to maintain protein homeostasis. Several studies have indicated that HSP70s are related to the development and occurrence of various cancers. METHODS: The relationship between the overall survival rate of hepatocellular carcinoma patients and the expression of 14 HSP70s from multiple databases, such as TCGA, ONCOMINE, cBioPortal was investigated. Western Blot and PCR were used to evaluate HSPA4 and HSPA14 expressions in various HCC cells to identify suitable cell lines for further experiments .Wound-healing assays, Transwell assays and EdU assays were used to verify the effects of HSPA4 and HSPA14 on the function of hepatocellular carcinoma cells, and statistical analysis was performed. RESULTS: Hepatocellular carcinoma tissues significantly expressed the 14 HSP70s compared to the normal samples. Besides, the high HSPA1A, HSPA1B, HSPA4, HSPA5, HSPA8, HSPA13, and HSPA14 expressions were inversely associated with the overall survival rate of patients, tumor grade, and cancer stage. A PPI regulatory network was constructed using the 14 HSP70s proteins with HSPA5 and HSPA8 at the network center. Univariate and multivariate analyses showed that HSPA4 and HSPA14 could be independent risk factors for the prognosis of hepatocellular carcinoma patients. Cell experiments have also confirmed that reducing HSPA4 and HSPA14 expressions can inhibit the invasion, metastasis, and proliferation of hepatocellular carcinoma cells. CONCLUSIONS: Therefore, the HSP70s significantly influence the occurrence and development of hepatocellular carcinoma. For instance, HSPA4 and HSPA14 can be novel therapeutic targets and prognostic biomarkers for hepatocellular carcinoma.

Deconvolution of heterogeneous tumor samples using partial reference signals.

Deconvolution of heterogeneous bulk tumor samples into distinct cellular populations is an important yet challenging problem, particularly when only partial references are available. A common approach to dealing with this problem is to deconvolve the mixed signals using available references and leverage the remaining signal as a new cell component. However, as indicated in our simulation, such an approach tends to over-estimate the proportions of known cell types and fails to detect novel cell types. Here, we propose PREDE, a partial reference-based deconvolution method using an iterative non-negative matrix factorization algorithm. Our method is verified to be effective in estimating cell proportions and expression profiles of unknown cell types based on simulated datasets at a variety of parameter settings. Applying our method to TCGA tumor samples, we found that proportions of pure cancer cells better indicate different subtypes of tumor samples. We also detected several cell types for each cancer type whose proportions successfully predicted patient survival. Our method makes a significant contribution to deconvolution of heterogeneous tumor samples and could be widely applied to varieties of high throughput bulk data. PREDE is implemented in R and is freely available from GitHub (https://xiaoqizheng.github.io/PREDE).

A simple and highly efficient method for multi-allelic CRISPR-Cas9 editing in primary cell cultures.

BACKGROUND: CRISPR-Cas9-based technologies have revolutionized experimental manipulation of mammalian genomes. None-the-less, limitations of the delivery and efficacy of these technologies restrict their application in primary cells. AIMS: To create an optimized protocol for penetrant, reproducible, and fast targeted genome editing in cell cultures derived from primary cells, using patient-derived glioblastoma stem-like cells (GSCs) and human neural stem/progenitor cells (NSCs) for proof-of-concept experiments. METHODS AND RESULTS: We employed transient nucleofection of Cas9:sgRNA ribonucleoprotein complexes composed of chemically synthesized 2'-O-methyl 3'phosphorothioate-modified sgRNAs and purified Cas9 protein. Insertion-deletion mutation (indel) frequency and size distribution were measured via computational deconvolution of Sanger sequencing trace data. We found that this optimized technique routinely allows for >90% indel formation in only 3 days, without the need to create clonal lines for simple loss-of-function experiments. Using Western blotting, we observed near-total protein loss of target genes in cell pools. Additionally, we found that this approach allows for the creation of targeted genomic deletions. Furthermore, by using RNA-seq in edited NSCs to assess gene expression changes resulting from knockout of tumor suppressors commonly altered in glioblastoma, we also demonstrated the utility of this method for quickly creating a series of gene knockouts that allow for the study of oncogenic activities. CONCLUSION: Our data suggest that this relatively simple method can be used for highly efficient and fast gene knockout, as well as for targeted genomic deletions, even in hyperdiploid cells (such as GSCs). This represents an extremely useful tool for the cancer research community when wishing to inactivate not only coding genes, but also non-coding RNAs, UTRs, enhancers, and promoters. This method can be readily applied to diverse cell types by varying the nucleofection conditions.

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.

Detection of differentially methylated CpG sites between tumor samples with uneven tumor purities.

MOTIVATION: Inference of differentially methylated (DM) CpG sites between two groups of tumor samples with different geno- or pheno-types is a critical step to uncover the epigenetic mechanism of tumorigenesis, and identify biomarkers for cancer subtyping. However, as a major source of confounding factor, uneven distributions of tumor purity between two groups of tumor samples will lead to biased discovery of DM sites if not properly accounted for. RESULTS: We here propose InfiniumDM, a generalized least square model to adjust tumor purity effect for differential methylation analysis. Our method is applicable to a variety of experimental designs including with or without normal controls, different sources of normal tissue contaminations. We compared our method with conventional methods including minfi, limma and limma corrected by tumor purity using simulated datasets. Our method shows significantly better performance at different levels of differential methylation thresholds, sample sizes, mean purity deviations and so on. We also applied the proposed method to breast cancer samples from TCGA database to further evaluate its performance. Overall, both simulation and real data analyses demonstrate favorable performance over existing methods serving similar purpose. AVAILABILITY AND IMPLEMENTATION: InfiniumDM is a part of R package InfiniumPurify, which is freely available from GitHub (https://github.com/Xiaoqizheng/InfiniumPurify). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Long non-coding RNA NEAT1 modulates hypoxia/reoxygenation-induced cardiomyocyte injury via targeting microRNA-520a.

In the present study, a hypoxia/reoxygenation (H/R) model of cardiomyocytes was established to investigate the effects of long non-coding RNA (LncRNA) Nuclear Enriched Abundant Transcript 1 (NEAT1) and microRNA (miR)-520a on H/R-induced cardiomyocyte apoptosis. Flow cytometry and terminal deoxynucleotidyl transferase dUTP nick end labeling staining were used to evaluate cell apoptosis. Luciferase activity assay was used to investigate whether miR-520a targets NEAT1. Results revealed that NEAT1 was significantly upregulated and miR-520a was downregulated in the ischemia/reperfusion myocardium and the cardiomyocytes that received H/R treatment. Further study demonstrated that knockdown of NEAT1 and overexpression of miR-520a serves a protective role against H/R-induced cardiomyocyte apoptosis. miR-520a directly targets NEAT1 and its expression level is negatively correlated with that of NEAT1. The findings suggested that NEAT1 and miR-520a may protect cardiomyocytes from apoptosis through regulating apoptotic proteins B-cell lymphoma 2 (Bcl-2) and Bcl-2-associated X protein, and altering cleaved caspase3 expression levels.

MicroRNA-10b expression predicts long-term survival in patients with solid tumor.

BACKGROUND: Numerous studies have evaluated the significance of the microRNA-10b (miR-10b) in the development and progression of many cancers. Their findings revealed that increased expression of miR-10b is associated with unfavorable prognosis in patients with cancer. RESULTS: A total of 1,834 patients from 19 studies were included in this study. A significantly shorter overall survival was observed in patients with increased expression of miR-10b (hazard ratio [HR] = 1.99, 95% confidence interval [CI]: 1.51-2.61). Statistical significance was also observed in subgroup meta-analysis stratified by the cancer type, cutoff value, analysis type, and sample size. Also, patients with a high expression level of miR-10b had a poorer disease-free survival rate (HR = 1.18, 95% CI: 1.05-1.33). In addition, the pooled odds ratios (ORs) showed that increased miR-10b was also associated with positive lymph node metastasis (OR = 2.09, 95% CI: 1.45-3.03), distant metastasis (OR = 2.40, 95% CI: 1.57-3.67), tumor size (OR = 3.86, 95% CI: 2.25-6.64), and poor clinical stage (OR = 5.02, 95% CI: 3.37-7.47). MATERIALS AND METHODS: A systematic literature search was conducted on a number of electronic databases, including PubMed, Embase, Web of Science, China National Knowledge Infrastructure, Springer, Google Scholar, and Gene expression omnibus. We retrieved the relevant articles to examine the association between the miR-10b expression levels and patients' prognosis. The meta-analysis was conducted using the RevMan 5.2 software and Stata SE12.0 software. CONCLUSIONS: High miR-10b expression was correlated with poor clinical outcome, which indicated the potential clinical use of miR-10b as a molecular biomarker for cancer, particularly in assessing prognosis for patients with cancers. Further studies should be performed to verify the clinical utility of miR-10b in human solid tumors.

KDM5 Histone Demethylase Activity Links Cellular Transcriptomic Heterogeneity to Therapeutic Resistance.

Members of the KDM5 histone H3 lysine 4 demethylase family are associated with therapeutic resistance, including endocrine resistance in breast cancer, but the underlying mechanism is poorly defined. Here we show that genetic deletion of KDM5A/B or inhibition of KDM5 activity increases sensitivity to anti-estrogens by modulating estrogen receptor (ER) signaling and by decreasing cellular transcriptomic heterogeneity. Higher KDM5B expression levels are associated with higher transcriptomic heterogeneity and poor prognosis in ER+ breast tumors. Single-cell RNA sequencing, cellular barcoding, and mathematical modeling demonstrate that endocrine resistance is due to selection for pre-existing genetically distinct cells, while KDM5 inhibitor resistance is acquired. Our findings highlight the importance of cellular phenotypic heterogeneity in therapeutic resistance and identify KDM5A/B as key regulators of this process.