Single-cell analyses of transcriptional heterogeneity in squamous cell carcinoma of urinary bladder

Clinical implications of single cell sequencing for bladder cancer

Bladder cancer (BC) is the 10th most common cancer worldwide, with about 0.5 million reported new cases and about 0.2 million deaths per year. In this scoping review, we summarize the current evidence regarding the clinical implications of single-cell sequencing for bladder cancer based on PRISMA guidelines. We searched PubMed, CENTRAL, Embase, and supplemented with manual searches through the Scopus, and Web of Science for published studies until February 2023. We included original studies that used at least one single-cell technology to study bladder cancer. Forty-one publications were included in the review. Twenty-nine studies showed that this technology can identify cell subtypes in the tumor microenvironment that may predict prognosis or response to immune checkpoint inhibition therapy. Two studies were able to diagnose BC by identifying neoplastic cells through single-cell sequencing urine samples. The remaining studies were mainly a preclinical exploration of tumor microenvironment at single cell level. Single-cell sequencing technology can discriminate heterogeneity in bladder tumor cells and determine the key molecular properties that can lead to the discovery of novel perspectives on cancer management. This nascent tool can advance the early diagnosis, prognosis judgment, and targeted therapy of bladder cancer.

Single-cell sequencing technologies in bladder cancer research: Applications and challenges

Bladder cancer is among the most common malignant tumors with highly heterogeneous molecular characteristics. Despite advancements of the available therapeutic options, several bladder cancer patients exhibit unsatisfactory clinical outcomes. The lack of specific biomarkers for effective targeted therapy or immunotherapy remains a major obstacle in treating bladder cancer. The rapid development of single-cell techniques is transforming our understanding of the intra-tumoral heterogeneity, thereby providing us with a powerful high-throughput sequencing tool that can reveal tumorigenesis, progression, and invasion in bladder tumors. In this review, we summarise and discuss how single-cell sequencing technologies have been applied in bladder cancer research, to advance our collective knowledge on the heterogeneity of bladder tumor cells, as well as to provide new insights into the complex ecosystem of the tumor microenvironment. The application of single-cell approaches also uncovers the therapeutic resistance mechanism in bladder cancer and facilitates the detection of urinary-exfoliated tumor cells. Moreover, benefiting from the powerful technical advantages of single-cell techniques, several key therapeutic targets and prognostic models of bladder cancer have been identified. It is hoped that this paper can provide novel insights into the precision medicine of bladder cancer.

Characterization of gene cluster heterogeneity in single-cell transcriptomic data within and across cancer types

Despite the remarkable progress in probing tumor transcriptomic heterogeneity by single-cell RNA sequencing (sc-RNAseq) data, several gaps exist in prior studies. Tumor heterogeneity is frequently mentioned but not quantified. Clustering analyses typically target cells rather than genes, and differential levels of transcriptomic heterogeneity of gene clusters are not characterized. Relations between gene clusters inferred from multiple datasets remain less explored. We provided a series of quantitative methods to analyze cancer sc-RNAseq data. First, we proposed two quantitative measures to assess intra-tumoral heterogeneity/homogeneity. Second, we established a hierarchy of gene clusters from sc-RNAseq data, devised an algorithm to reduce the gene cluster hierarchy to a compact structure, and characterized the gene clusters with functional enrichment and heterogeneity. Third, we developed an algorithm to align the gene cluster hierarchies from multiple datasets to a small number of meta gene clusters. By applying these methods to nine cancer sc-RNAseq datasets, we discovered that cancer cell transcriptomes were more homogeneous within tumors than the accompanying normal cells. Furthermore, many gene clusters from the nine datasets were aligned to two large meta gene clusters, which had high and low heterogeneity and were enriched with distinct functions. Finally, we found the homogeneous meta gene cluster retained stronger expression coherence and associations with survival times in bulk level RNAseq data than the heterogeneous meta gene cluster, yet the combinatorial expression patterns of breast cancer subtypes in bulk level data were not preserved in single-cell data. The inference outcomes derived from nine cancer sc-RNAseq datasets provide insights about the contributing factors for transcriptomic heterogeneity of cancer cells and complex relations between bulk level and single-cell RNAseq data. They demonstrate the utility of our methods to enable a comprehensive characterization of co-expressed gene clusters in a wide range of sc-RNAseq data in cancers and beyond.

Summary: We propose quantitative methods to analyze cancer sc-RNAseq data: measures of intra-tumoral heterogeneity, characterization of a hierarchy of gene clusters, and alignment of gene cluster hierarchies from multiple datasets.

Role of Epstein-Barr Virus and Human Papillomavirus Coinfection in Cervical Intraepithelial Neoplasia in Chinese Women Living With HIV

Given that only a small percentage of human papillomavirus (HPV)-positive women develop cancer, HPV is necessary but insufficient for carcinogenesis. Mucosally transmitted viral cofactors appear to contribute to HPV-related cervical cancer, such as Epstein-Barr virus (EBV), but previous studies have shown inconsistent outcomes. The exact role of EBV in cervical cancer remains unclear, and more studies are needed to determine its involvement. In this study, we describe the prevalence of EBV and HPV coinfection in HIV-positive women and explore how abnormal host immune status induced by viral coinfections modulates epithelial gene expression. We found a significant correlation between EBV-HPV coinfection and the incidence of high-grade cervical intraepithelial neoplasia (CIN2+). RNA sequencing indicated that CIN tissues coinfected with EBV and HPV led to significant changes in the gene expression of epithelial differentiation and development compared to normal tissues with HPV infection alone. In particular, several differentially expressed genes (DEGs) are closely associated with cancer, such as CACNG4, which was confirmed to be upregulated at both the mRNA and protein levels. Therefore, these findings provide some evidence that EBV may act as a cofactor or mediator in HPV-related cervical cancer. Specific genes or proteins, such as CACNG4, may serve as biomarkers that can risk stratify patients based on pathological changes in the cervix.

Identification and Validation of the Prognostic Stemness Biomarkers in Bladder Cancer Bone Metastasis

Background

Bladder urothelial carcinoma (BLCA) is one of the most common urinary system malignancies with a high metastasis rate. Cancer stem cells (CSCs) play an important role in the occurrence and progression of BLCA, however, its roles in bone metastasis and the prognostic stemness biomarkers have not been identified in BLCA.

Method

In order to identify the roles of CSC in the tumorigenesis, bone metastasis and prognosis of BLCA, the RNA sequencing data of patients with BLCA were retrieved from The Cancer Genome Atlas (TCGA) databases. The mRNA expression-based stemness index (mRNAsi) and the differential expressed genes (DEGs) were evaluated and identified. The associations between mRNAsi and the tumorigenesis, bone metastasis, clinical stage and overall survival (OS) were also established. The key prognostic stemness-related genes (PSRGs) were screened by Lasso regression, and based on them, the predict model was constructed. Its accuracy was tested by the area under the curve (AUC) of the receiver operator characteristic (ROC) curve and the risk score. Additionally, in order to explore the key regulatory network, the relationship among differentially expressing TFs, PSRGs, and absolute quantification of 50 hallmarks of cancer were also identified by Pearson correlation analysis. To verify the identified key TFs and PSRGs, their expression levels were identified by our clinical samples via immunohistochemistry (IHC).

Results

A total of 8,647 DEGs were identified between 411 primary BLCAs and 19 normal solid tissue samples. According to the clinical stage, mRNAsi and bone metastasis of BLCA, 2,383 stage-related DEGs, 3,680 stemness-related DEGs and 716 bone metastasis-associated DEGs were uncovered, respectively. Additionally, compared with normal tissue, mRNAsi was significantly upregulated in the primary BLCA and also associated with the prognosis (P = 0.016), bone metastasis (P < 0.001) and AJCC clinical stage (P < 0.001) of BLCA patients. A total of 20 PSRGs were further screened by Lasso regression, and based on them, we constructed the predict model with a relatively high accuracy (AUC: 0.699). Moreover, we found two key TFs (EPO, ARID3A), four key PRSGs (CACNA1E, LINC01356, CGA and SSX3) and five key hallmarks of cancer gene sets (DNA repair, myc targets, E2F targets, mTORC1 signaling and unfolded protein response) in the regulatory network. The tissue microarray of BLCA and BLCA bone metastasis also revealed high expression of the key TFs (EPO, ARID3A) and PRSGs (SSX3) in BLCA.

Conclusion

Our study identifies mRNAsi as a reliable index in predicting the tumorigenesis, bone metastasis and prognosis of patients with BLCA and provides a well-applied model for predicting the OS for patients with BLCA based on 20 PSRGs. Besides, we also identified the regulatory network between key PSRGs and cancer gene sets in mediating the BLCA bone metastasis.

Identification and Analysis of An Epigenetically Regulated Five-lncRNA Signature Associated With Outcome and Chemotherapy Response in Ovarian Cancer

The deregulation of long non-coding RNAs (lncRNAs) by epigenetic alterations has been implicated in cancer initiation and progression. However, the epigenetically regulated lncRNAs and their association with clinical outcome and therapeutic response in ovarian cancer (OV) remain poorly investigated. This study performed an integrative analysis of DNA methylation data and transcriptome data and identified 419 lncRNAs as potential epigenetically regulated lncRNAs. Using machine-learning and multivariate Cox regression analysis methods, we identified and developed an epigenetically regulated lncRNA expression signature (EpiLncRNASig) consisting of five lncRNAs from the list of 17 epigenetically regulated lncRNAs significantly associated with outcome. The EpiLncRNASig could stratify patients into high-risk groups and low-risk groups with significantly different survival and chemotherapy response in different patient cohorts. Multivariate Cox regression analyses, after adjusted by other clinical features and treatment response, demonstrated the independence of the DEpiLncSig in predicting survival. Functional analysis for relevant protein-coding genes of the DEpiLncSig indicated enrichment of known immune-related or cancer-related biological pathways. Taken together, our study not only provides a promising prognostic biomarker for predicting outcome and chemotherapy response but also will improve our understanding of lncRNA epigenetic regulation mechanisms in OV.

Comprehensive analysis of metastatic gastric cancer tumour cells using single-cell RNA-seq

Gastric cancer (GC) is a leading cause of cancer-induced mortality, with poor prognosis with metastasis. The mechanism of gastric carcinoma lymph node metastasis remains unknown due to traditional bulk-leveled approaches masking the roles of subpopulations. To answer questions concerning metastasis from the gastric carcinoma intratumoural perspective, we performed single-cell level analysis on three gastric cancer patients with primary cancer and paired metastatic lymph node cancer tissues using single-cell RNA-seq (scRNA-seq). The results showed distinct carcinoma profiles from each patient, and diverse microenvironmental subsets were shared across different patients. Clustering data showed significant intratumoural heterogeneity. The results also revealed a subgroup of cells bridging the metastatic group and primary group, implying the transition state of cancer during the metastatic process. In the present study, we obtained a more comprehensive picture of gastric cancer lymph node metastasis, and we discovered some GC lymph node metastasis marker genes (ERBB2, CLDN11 and CDK12), as well as potential gastric cancer evolution-driving genes (FOS and JUN), which provide a basis for the treatment of GC.

Tumor heterogeneity in muscle-invasive bladder cancer

Muscle-invasive bladder cancer (MIBC), a highly heterogeneous disease, shows genomic instability and a high mutation rate. Clinical outcomes are variable and responses to conventional chemotherapy differ among patients (due to inter-patient tumor heterogeneity and inter-tumor heterogeneity) and even within each individual tumor (intra-tumor heterogeneity). Emerging evidence indicates that tumor heterogeneity may play an important role in cancer progression, resistance to therapy, and metastasis. Comprehensive molecular subtyping classifies MIBC into distinct categories that have potential to guide prognosis, patient stratification, and treatment. Genomic characterization of time-series analyses at the single cell level, and of cell-free circulating tumor DNA or circulating tumor cells, are emerging technologies that enable dissection of the complex clonal architecture of MIBC. This review provides insight into the clinical significance of the molecular mechanisms underlying heterogeneity, focusing on inter- and intra-tumor heterogeneity, with special emphasis on molecular classification and methods used to analyze the complex patterns involved.

Single-Cell Sequencing in Genitourinary Malignancies

Single-cell sequencing (SCS) is a powerful new tool that applies Next Generation Sequencing at the cellular level. SCS has revolutionized our understanding of tumor heterogeneity and the tumor microenvironment, immune infiltration, cancer stem cells (CSCs), circulating tumor cells (CTCs), and clonal evolution. The following chapter highlights the current literature on SCS in genitourinary (GU) malignancies and discusses future applications of SCS technology. The renal cell carcinoma (RCC) section highlights the use of SCS in characterizing the initial cells driving tumorigenesis, the intercellular mutational landscape of RCC, intratumoral heterogeneity (ITH) between primary and metastatic lesions, and genes driving RCC cancer stem cells (CSCs). The bladder cancer section will also illustrate molecular drivers of bladder cancer stem cells (BCSCs), SCS use in reconstructing tumor developmental history and underlying subclones, and understanding the effect of cisplatin on intratumoral heterogeneity in vitro and potential mechanisms behind platinum resistance. The final section featuring prostate cancer will discuss how SCS can be used to identify the cellular origins of benign prostatic hyperplasia and prostate cancer, the plasticity and heterogeneity of prostate cancer cells with regard to androgen dependence, and the use of SCS in CTCs to understand chemotherapy resistance and gene expression changes after androgen deprivation therapy (ADT). The studies listed in this chapter illustrate many translational applications of SCS in GU malignancies, including diagnostic, prognostic, and treatment-related approaches. The ability of SCS to resolve intratumor heterogeneity and better define the genomic landscape of tumors and CTCs will be fundamental in the new era of precision-based care.

Traditional Classification and Novel Subtyping Systems for Bladder Cancer

Bladder cancer is the most common tumor in the urinary system, with approximately 420,000 new cases and 160,000 deaths per year. The European Organization for Research and Treatment of Cancer (EOTRC) classifies non-muscular invasive bladder cancer (NMIBC) into low-risk, medium-risk and high-risk groups based on a comprehensive analysis of NMIBC pathological parameters and the risk of recurrence and progression to muscular invasive bladder cancer (MIBC). Traditional classification systems are based on pathologic grading, staging systems, and clinical prognosis. However, the pathological parameters of the tumor cannot fully reflect the “intrinsic characteristics” of bladder cancer, and tumors with a similar pathology exhibit different biological behaviors. Furthermore, although the traditional classification system cannot accurately predict the risk of recurrence or the progression of bladder cancer patients (BCs) individually, this method is widely used in clinical practice because of its convenient operation. With the development of sequencing and other technologies, the genetics-based molecular subtyping of bladder cancer has become increasingly studied. Compared with the traditional classification system, it provides more abundant tumor biological information and is expected to assist or even replace the traditional typing system in the future.

Identification of biomarkers associated with progression and prognosis in bladder cancer via co-expression analysis

BACKGROUND

Bladder cancer is one of the most common genitourinary malignancies, with a high rate of recurrence and progression. The prognosis for patients with bladder cancer, especially muscle-invasive bladder cancer, remains poor despite systemic therapy.

OBJECTIVE

To explore the underlying disease mechanisms and identify more effective biomarkers for bladder cancer.

METHODS

Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were applied to identify hub genes correlated with the bladder cancer progression. Survival analyses were then conducted to identify potential biomarkers correlated with the prognosis of bladder cancer. Finally, validation and analysis of these potential biomarkers were conducted by a series of bioinformatics analyses.

RESULTS

Based on the results of weighted gene co-expression network analysis and protein-protein interaction network analysis, ten hub genes closely correlated with bladder cancer progression were identified in the relevant module. Survival analyses of these genes indicated that elevated expressions of six potential biomarkers (COL3A1, FN1, COL5A1, FBN1, COL6A1 and THBS2) were significantly associated with a worse overall survival. Furthermore, these 6 potential biomarkers were validated in association with the progression of bladder cancer. Bladder cancer samples with higher expression of these genes were most significantly enriched in gene set associated with ECM-receptor interaction.

CONCLUSIONS

This study identified several biomarkers associated with bladder cancer progression and prognosis. As novel findings, these may have important clinical implications for diagnosis, treatment and prognosis prediction.

Emerging roles for multifunctional ion channel auxiliary subunits in cancer

Several superfamilies of plasma membrane channels which regulate transmembrane ion flux have also been shown to regulate a multitude of cellular processes, including proliferation and migration. Ion channels are typically multimeric complexes consisting of conducting subunits and auxiliary, non-conducting subunits. Auxiliary subunits modulate the function of conducting subunits and have putative non-conducting roles, further expanding the repertoire of cellular processes governed by ion channel complexes to processes such as transcellular adhesion and gene transcription. Given this expansive influence of ion channels on cellular behaviour it is perhaps no surprise that aberrant ion channel expression is a common occurrence in cancer. This review will focus on the conducting and non-conducting roles of the auxiliary subunits of various Ca2+, K+, Na+ and Cl- channels and the burgeoning evidence linking such auxiliary subunits to cancer. Several subunits are upregulated (e.g. Cavβ, Cavγ) and downregulated (e.g. Kvβ) in cancer, while other subunits have been functionally implicated as oncogenes (e.g. Navβ1, Cavα2δ1) and tumour suppressor genes (e.g. CLCA2, KCNE2, BKγ1) based on in vivo studies. The strengthening link between ion channel auxiliary subunits and cancer has exposed these subunits as potential biomarkers and therapeutic targets. However further mechanistic understanding is required into how these subunits contribute to tumour progression before their therapeutic potential can be fully realised.

Bladder cancer stage-associated hub genes revealed by WGCNA co-expression network analysis

Background

Bladder cancer was a malignant disease in patients, our research aimed at discovering the possible biomarkers for the diseases.

Results

The gene chip GSE31684, including 93samples, was downloaded from the GEO datasets and co-expression network was constructed by the data. Molecular complex detection(MCODE) was used to identify hub genes. The most significant cluster including 16 genes: CDH11, COL3A1, COL6A3, COL5A1, AEBP1, COL1A2, NTM, COL11A1, THBS2, COL8A1, COL1A1, BGN, MMP2, PXDN, THY1, and TGFB1I1 was identified. After annotated by BiNGO, they were suggested associated with collagen fibril organization and blood vessel development. In addition, the Kaplan Meier curves were obtained by UALCAN. The high expression of THY1, AEBP1, CDH11, COL1A1, COL1A2, COL11A1, MMP2, PXDN, BGN, COL5A1, COL8A1, and TGFB1I1 indicated poor prognosis of the patients(P < 0.05). Finally, we examined genes’ expression between low and high tumor stage by the Wilcoxon test(P < 0.05), TGFB1I1 was excluded.

Conclusion

THY1, AEBP1, CDH11, COL1A1, COL1A2, COL11A1, MMP2, PXDN, BGN, COL5A1, COL8A1 associated with the tumor stage as well as tumor patients’ prognosis. COL5A1, COL8A1(P < 0.01) may serve as therapeutic targets for the disease.

Single-cell analysis of tumors: Creating new value for molecular biomarker discovery of cancer stem cells and tumor-infiltrating immune cells

Biomarker-driven individualized treatment in oncology has made tremendous progress through technological developments, new therapeutic modalities and a deeper understanding of the molecular biology for tumors, cancer stem cells and tumor-infiltrating immune cells. Recent technical developments have led to the establishment of a variety of cancer-related diagnostic, prognostic and predictive biomarkers. In this regard, different modern OMICs approaches were assessed in order to categorize and classify prognostically different forms of neoplasia. Despite those technical advancements, the extent of molecular heterogeneity at the individual cell level in human tumors remains largely uncharacterized. Each tumor consists of a mixture of heterogeneous cell types. Therefore, it is important to quantify the dynamic cellular variations in order to predict clinical parameters, such as a response to treatment and or potential for disease recurrence. Recently, single-cell based methods have been developed to characterize the heterogeneity in seemingly homogenous cancer cell populations prior to and during treatment. In this review, we highlight the recent advances for single-cell analysis and discuss the challenges and prospects for molecular characterization of cancer cells, cancer stem cells and tumor-infiltrating immune cells.

Emerging Biomarkers in Bladder Cancer Identified by Network Analysis of Transcriptomic Data

Bladder cancer is a very common malignancy. Although new treatment strategies have been developed, the identification of new therapeutic targets and reliable diagnostic/prognostic biomarkers for bladder cancer remains a priority. Generally, they are found among differentially expressed genes between patients and healthy subjects or among patients with different tumor stages. However, the classical approach includes processing these data taking into consideration only the expression of each single gene regardless of the expression of other genes. These complex gene interaction networks can be revealed by a recently developed systems biology approach called Weighted Gene Co-expression Network Analysis (WGCNA). It takes into account the expression of all genes assessed in an experiment in order to reveal the clusters of co-expressed genes (modules) that, very probably, are also co-regulated. If some genes are co-expressed in controls but not in pathological samples, it can be hypothesized that a regulatory mechanism was altered and that it could be the cause or the effect of the disease. Therefore, genes within these modules could play a role in cancer and thus be considered as potential therapeutic targets or diagnostic/prognostic biomarkers. Here, we have reviewed all the studies where WGCNA has been applied to gene expression data from bladder cancer patients. We have shown the importance of this new approach in identifying candidate biomarkers and therapeutic targets. They include both genes and miRNAs and some of them have already been identified in the literature to have a role in bladder cancer initiation, progression, metastasis, and patient survival.

LncRNA LINC00880 promotes cell proliferation, migration, and invasion while inhibiting apoptosis by targeting CACNG5 through the MAPK signaling pathway in spinal cord ependymoma

The present study was to investigate the effect of lncRNA LINC00880 targeting CACNG5 on cell proliferation, migration, invasion, and apoptosis in spinal cord ependymoma (SCE) through the MAPK signaling pathway. GEO database was used to download gene expression data related with SCE (GSE50161 and GSE66354) and annotation file. LncRNA with differential expression was predicted by Multi Experiment Matrix website (MEM). The target gene was analyzed by KEGG pathway enrichment analysis. SCE tissues and adjacent tissues were collected. The positive expression of CACNG5 protein was tested by immunohistochemistry. Expression of LINC00880, CACNG5, and MAPK signaling pathway-related proteins was measured with qRT-PCR and Western blotting. Cell proliferation, migration, invasion, cycle, and apoptosis were detected using MTT, Transwell assay, Scratch test, and Flow cytometry. SCE tissues showed increased LINC00880 expression. CACNG5 was a target gene of LINC00880 and correlated with MAPK signaling pathway. Compared with adjacent tissues, SCE tissues showed lower positive expression of CACNG5. Compared with the blank group, LINC00880 expression was higher in the LINC00880 vector and LINC00880 vector + CACNG5 vector groups, and lower in the si-LINC00880 and si-LINC00880 + si-CACNG5 groups; in the LINC00880 vector and si-CACNG5 groups, expression of survivin, p38MAPK, ERK1/2, JNK1/2/3 increased and CACNG5 and Bax expression reduced, the proliferation, invasion and migration of tumor cells increased, and apoptosis rate decreased. Opposite results were found in the si-LINC00880 and CACNG5 vector groups. The findings indicate that lncRNA LINC00880 targeting CACNG5 inhibits cell apoptosis and promotes proliferation, migration, and invasion in SCE through the MAPK signaling pathway.

Single-cell sequencing and tumorigenesis: improved understanding of tumor evolution and metastasis

Extensive genomic and transcriptomic heterogeneity in human cancer often negatively impacts treatment efficacy and survival, thus posing a significant ongoing challenge for modern treatment regimens. State-of-the-art DNA- and RNA-sequencing methods now provide high-resolution genomic and gene expression portraits of individual cells, facilitating the study of complex molecular heterogeneity in cancer. Important developments in single-cell sequencing (SCS) technologies over the past 5 years provide numerous advantages over traditional sequencing methods for understanding the complexity of carcinogenesis, but significant hurdles must be overcome before SCS can be clinically useful. In this review, we: (1) highlight current methodologies and recent technological advances for isolating single cells, single-cell whole-genome and whole-transcriptome amplification using minute amounts of nucleic acids, and SCS, (2) summarize research investigating molecular heterogeneity at the genomic and transcriptomic levels and how this heterogeneity affects clonal evolution and metastasis, and (3) discuss the promise for integrating SCS in the clinical care arena for improved patient care.

Mechanisms and clinical implications of tumor heterogeneity and convergence on recurrent phenotypes

Tumor heterogeneity has been identified at various -omic levels. The tumor genome, transcriptome, proteome, and phenome can vary widely across cells in patient tumors and are influenced by tumor cell interactions with heterogeneous physical conditions and cellular components of the tumor microenvironment. Here, we explore the concept that while variation exists at multiple -omic levels, changes at each of these levels converge on the same pathways and lead to convergent phenotypes in tumors that can provide common drug targets. These phenotypes include cellular growth and proliferation, sustained oncogenic signaling, and immune avoidance, among others. Tumor heterogeneity complicates treatment of patient cancers as it leads to varied response to therapies. Identification of convergent cellular phenotypes arising in patient cancers and targeted therapies that reverse them has the potential to transform the way clinicians treat these cancers and to improve patient outcome.