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Shin D, Cho KH. Critical transition and reversion of tumorigenesis. Exp Mol Med 2023; 55:692-705. [PMID: 37009794 PMCID: PMC10167317 DOI: 10.1038/s12276-023-00969-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 01/10/2023] [Accepted: 01/11/2023] [Indexed: 04/04/2023] Open
Abstract
Cancer is caused by the accumulation of genetic alterations and therefore has been historically considered to be irreversible. Intriguingly, several studies have reported that cancer cells can be reversed to be normal cells under certain circumstances. Despite these experimental observations, conceptual and theoretical frameworks that explain these phenomena and enable their exploration in a systematic way are lacking. In this review, we provide an overview of cancer reversion studies and describe recent advancements in systems biological approaches based on attractor landscape analysis. We suggest that the critical transition in tumorigenesis is an important clue for achieving cancer reversion. During tumorigenesis, a critical transition may occur at a tipping point, where cells undergo abrupt changes and reach a new equilibrium state that is determined by complex intracellular regulatory events. We introduce a conceptual framework based on attractor landscapes through which we can investigate the critical transition in tumorigenesis and induce its reversion by combining intracellular molecular perturbation and extracellular signaling controls. Finally, we present a cancer reversion therapy approach that may be a paradigm-changing alternative to current cancer cell-killing therapies.
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Affiliation(s)
- Dongkwan Shin
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
- Reasearch Institute, National Cancer Center, Goyang, 10408, Republic of Korea
| | - Kwang-Hyun Cho
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
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2
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Alkhatib H, Rubinstein AM, Vasudevan S, Flashner-Abramson E, Stefansky S, Chowdhury SR, Oguche S, Peretz-Yablonsky T, Granit A, Granot Z, Ben-Porath I, Sheva K, Feldman J, Cohen NE, Meirovitz A, Kravchenko-Balasha N. Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance. Genome Med 2022; 14:120. [PMID: 36266692 PMCID: PMC9583500 DOI: 10.1186/s13073-022-01121-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 09/28/2022] [Indexed: 11/22/2022] Open
Abstract
Background Drug resistance continues to be a major limiting factor across diverse anti-cancer therapies. Contributing to the complexity of this challenge is cancer plasticity, in which one cancer subtype switches to another in response to treatment, for example, triple-negative breast cancer (TNBC) to Her2-positive breast cancer. For optimal treatment outcomes, accurate tumor diagnosis and subsequent therapeutic decisions are vital. This study assessed a novel approach to characterize treatment-induced evolutionary changes of distinct tumor cell subpopulations to identify and therapeutically exploit anticancer drug resistance. Methods In this research, an information-theoretic single-cell quantification strategy was developed to provide a high-resolution and individualized assessment of tumor composition for a customized treatment approach. Briefly, this single-cell quantification strategy computes cell barcodes based on at least 100,000 tumor cells from each experiment and reveals a cell-specific signaling signature (CSSS) composed of a set of ongoing processes in each cell. Results Using these CSSS-based barcodes, distinct subpopulations evolving within the tumor in response to an outside influence, like anticancer treatments, were revealed and mapped. Barcodes were further applied to assign targeted drug combinations to each individual tumor to optimize tumor response to therapy. The strategy was validated using TNBC models and patient-derived tumors known to switch phenotypes in response to radiotherapy (RT). Conclusions We show that a barcode-guided targeted drug cocktail significantly enhances tumor response to RT and prevents regrowth of once-resistant tumors. The strategy presented herein shows promise in preventing cancer treatment resistance, with significant applicability in clinical use. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-022-01121-y.
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Affiliation(s)
- Heba Alkhatib
- The institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, 9103401, Jerusalem, Israel
| | - Ariel M Rubinstein
- The institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, 9103401, Jerusalem, Israel
| | - Swetha Vasudevan
- The institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, 9103401, Jerusalem, Israel
| | - Efrat Flashner-Abramson
- The institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, 9103401, Jerusalem, Israel
| | - Shira Stefansky
- The institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, 9103401, Jerusalem, Israel
| | - Sangita Roy Chowdhury
- The institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, 9103401, Jerusalem, Israel
| | - Solomon Oguche
- The institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, 9103401, Jerusalem, Israel
| | - Tamar Peretz-Yablonsky
- Sharett Institute of Oncology, Hebrew University-Hadassah Medical Center, 9103401, Jerusalem, Israel
| | - Avital Granit
- Sharett Institute of Oncology, Hebrew University-Hadassah Medical Center, 9103401, Jerusalem, Israel
| | - Zvi Granot
- Department of Developmental Biology and Cancer Research, Institute for Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, 91120, Jerusalem, Israel
| | - Ittai Ben-Porath
- Department of Developmental Biology and Cancer Research, Institute for Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, 91120, Jerusalem, Israel
| | - Kim Sheva
- The Legacy Heritage Oncology Center & Dr. Larry Norton Institute, Soroka University Medical Center, Ben Gurion University of the Negev, Faculty of Medicine, 8410101, Beer Sheva, Israel
| | - Jon Feldman
- Sharett Institute of Oncology, Hebrew University-Hadassah Medical Center, 9103401, Jerusalem, Israel
| | - Noa E Cohen
- School of Software Engineering and Computer Science, Azrieli College of Engineering, 9103501, Jerusalem, Israel
| | - Amichay Meirovitz
- The Legacy Heritage Oncology Center & Dr. Larry Norton Institute, Soroka University Medical Center, Ben Gurion University of the Negev, Faculty of Medicine, 8410101, Beer Sheva, Israel.
| | - Nataly Kravchenko-Balasha
- The institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, 9103401, Jerusalem, Israel.
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3
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Drug-Induced Resistance and Phenotypic Switch in Triple-Negative Breast Cancer Can Be Controlled via Resolution and Targeting of Individualized Signaling Signatures. Cancers (Basel) 2021; 13:cancers13195009. [PMID: 34638492 PMCID: PMC8507629 DOI: 10.3390/cancers13195009] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 09/29/2021] [Indexed: 12/28/2022] Open
Abstract
Simple Summary Patients with Triple Negative Breast Cancer (TNBC) have a poor prognosis due to high inter-tumor heterogeneity and absence of effective targeted treatments. Through quantification of ongoing processes in each individual with TNBC, we propose an explanation on why certain previously suggested monotherapies, such as anti-EGFR, are not effective. We experimentally demonstrate that monotherapies or drug combinations that are not adjusted accurately to the patient-specific ongoing processes may create an evolutionary pressure on a tumor leading to the emergence of previously undetected or untargeted cellular subpopulations. We show for example that certain TNBC tumors may benefit from therapies targeting estrogen receptors (ER), similarly to ER positive cancers. When untargeted, those tumors may develop large ER positive subpopulations. We propose that anti-TNBC therapy should be accurately tailored to the personalized molecular processes and that incomplete or “wrong” treatments may generate diverse evolutionary routes of TNBC tumors leading to drug resistance. Abstract Triple-negative breast cancer (TNBC) is an aggressive subgroup of breast cancers which is treated mainly with chemotherapy and radiotherapy. Epidermal growth factor receptor (EGFR) was considered to be frequently expressed in TNBC, and therefore was suggested as a therapeutic target. However, clinical trials of EGFR inhibitors have failed. In this study, we examine the relationship between the patient-specific TNBC network structures and possible mechanisms of resistance to anti-EGFR therapy. Using an information-theoretical analysis of 747 breast tumors from the TCGA dataset, we resolved individualized protein network structures, namely patient-specific signaling signatures (PaSSS) for each tumor. Each PaSSS was characterized by a set of 1–4 altered protein–protein subnetworks. Thirty-one percent of TNBC PaSSSs were found to harbor EGFR as a part of the network and were predicted to benefit from anti-EGFR therapy as long as it is combined with anti-estrogen receptor (ER) therapy. Using a series of single-cell experiments, followed by in vivo support, we show that drug combinations which are not tailored accurately to each PaSSS may generate evolutionary pressure in malignancies leading to an expansion of the previously undetected or untargeted subpopulations, such as ER+ populations. This corresponds to the PaSSS-based predictions suggesting to incorporate anti-ER drugs in certain anti-TNBC treatments. These findings highlight the need to tailor anti-TNBC targeted therapy to each PaSSS to prevent diverse evolutions of TNBC tumors and drug resistance development.
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4
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Xue VW, Wong SCC, Cho WC. From proteomic landscape to single-cell oncoproteomics. Expert Rev Proteomics 2021; 18:1-6. [PMID: 33571016 DOI: 10.1080/14789450.2021.1890036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Introduction: Proteomic profiling plays an important role in the exploration of cancer from molecular mechanisms to clinical diagnosis and treatment. In recent years, the advent of new technologies has promoted oncoproteomics from the initial global style to a refined single-cell level.Areas Covered: Among them, the development of microfluidic devices, the improvement of liquid mass spectrometry in accuracy and trace sample handling processes, and the emergence of protein sequencing have contributed to the oncoproteomic analysis at the single-cell level.Expert Opinion: The proteomic analysis at the global level and the single-cell level gives different perspectives while combining them can reveal more comprehensive oncoproteomics and help cancer research and treatment strategies.
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Affiliation(s)
- Vivian Weiwen Xue
- School of Basic Medical Sciences, Shenzhen University Health Science Centre, Shenzhen University, Shenzhen, China
| | - Sze Chuen Cesar Wong
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - William Chi Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
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5
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Gao S, Zhou A, Cao B, Wang J, Li F, Tang G, Jiang Z, Yang A, Xiong R, Lei J, Huang C. A tunable temperature-responsive and tough platform for controlled drug delivery. NEW J CHEM 2021. [DOI: 10.1039/d1nj01356d] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
A tunable temperature-responsive site-specific drug-delivery platform for tumor therapy.
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6
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Yang Q, Cristea A, Roberts C, Liu K, Song Y, Xiao H, Shi H, Ma Y. Unveil early-stage nanocytotoxicity by a label-free single cell pH nanoprobe. Analyst 2020; 145:7210-7224. [PMID: 32960188 PMCID: PMC7655686 DOI: 10.1039/d0an01437k] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Single-cell analysis is an emerging research area that aims to reveal delicate cellular status and underlying mechanisms by conquering the intercellular heterogeneity. Current single-cell research methods, however, are highly dependent on cell-destructive protocols and cannot sequentially display the progress of cellular events. A recently developed pH nanoprobe in our lab conceptually showed its ability to detect intracellular pH (pHi) without cell labeling or disruption. In the present study, we took the cytotoxicity of nanoparticles (NPs) as a typical example of cell heterogeneity, to testify the practicality of the pH nanoprobe in interpreting cell status. Three types of NPs (CeO2, TiO2, and SiO2) were employed to generate varied toxic effects. Results showed that the traditional assays - including cell viability, intracellular ROS generation, and mitochondrial inner membrane depolarization - not only failed to report the nanotoxicity accurately and timely, but also drew confusing or misleading conclusions. The pH nanoprobe revealed explicit pHi changes induced by the NPs, which corresponded well with the cell damages found by the transmission electron microscopic (TEM) imaging. Besides, our results unveiled an unexpectedly devastating effect of SiO2 NPs on cells during the early stage NP-cell interaction. The developed novel pH nanoprobe demonstrated a rapid sensing capability at single-cell resolution with minimum invasiveness. Therefore, it may become a promising alternative for a wide range of applications in areas such as single-cell research and precision medicine.
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Affiliation(s)
- Qingbo Yang
- Department of Chemistry, and Center for Biomedical Research, Missouri University of Science and Technology, Rolla, MO 65409, USA.
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Su Y, Ko ME, Cheng H, Zhu R, Xue M, Wang J, Lee JW, Frankiw L, Xu A, Wong S, Robert L, Takata K, Yuan D, Lu Y, Huang S, Ribas A, Levine R, Nolan GP, Wei W, Plevritis SK, Li G, Baltimore D, Heath JR. Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line. Nat Commun 2020; 11:2345. [PMID: 32393797 PMCID: PMC7214418 DOI: 10.1038/s41467-020-15956-9] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 04/02/2020] [Indexed: 12/12/2022] Open
Abstract
The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge for understanding biological changes ranging from cellular differentiation to epigenetic responses of diseased cells upon drugging. We integrate experiments and theory to determine the trajectories that single BRAFV600E mutant melanoma cancer cells take between drug-naive and drug-tolerant states. Although single-cell omics tools can yield snapshots of the cell-state landscape, the determination of individual cell trajectories through that space can be confounded by stochastic cell-state switching. We assayed for a panel of signaling, phenotypic, and metabolic regulators at points across 5 days of drug treatment to uncover a cell-state landscape with two paths connecting drug-naive and drug-tolerant states. The trajectory a given cell takes depends upon the drug-naive level of a lineage-restricted transcription factor. Each trajectory exhibits unique druggable susceptibilities, thus updating the paradigm of adaptive resistance development in an isogenic cell population. Detailed understanding of how cancer cells transition from a drug sensitive to a tolerant state is lacking. Here, using single cell proteomic and metabolic data the authors uncover that isogenic BRAF mutant melanoma cells can take two distinct paths to become tolerant to BRAF inhibition.
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Affiliation(s)
- Yapeng Su
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, USA.,Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA.,Institute for Systems Biology, Seattle, Washington, USA
| | - Melissa E Ko
- Cancer Biology Program, Stanford University School of Medicine, Stanford, California, USA
| | - Hanjun Cheng
- Institute for Systems Biology, Seattle, Washington, USA
| | - Ronghui Zhu
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Min Xue
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, USA.,Department of Chemistry, University of California, Riverside, Riverside, California, USA
| | - Jessica Wang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Jihoon W Lee
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, USA
| | - Luke Frankiw
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Alexander Xu
- Institute for Systems Biology, Seattle, Washington, USA
| | - Stephanie Wong
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Lidia Robert
- Department of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Kaitlyn Takata
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Dan Yuan
- Institute for Systems Biology, Seattle, Washington, USA
| | - Yue Lu
- Institute for Systems Biology, Seattle, Washington, USA
| | - Sui Huang
- Institute for Systems Biology, Seattle, Washington, USA
| | - Antoni Ribas
- Department of Medicine, University of California, Los Angeles, Los Angeles, California, USA.,Department of Molecular and Medical Pharmacology, UCLA, Los Angeles, California, USA.,Department of Surgery, UCLA, Los Angeles, California, USA.,Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, California, USA
| | - Raphael Levine
- Department of Molecular and Medical Pharmacology, UCLA, Los Angeles, California, USA.,Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, California, USA.,The Fritz Haber Research Center, The Hebrew University, Jerusalem, Israel
| | - Garry P Nolan
- Department of Microbiology and Immunology, Stanford University, Stanford, California, USA
| | - Wei Wei
- Institute for Systems Biology, Seattle, Washington, USA.,Department of Molecular and Medical Pharmacology, UCLA, Los Angeles, California, USA.,Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, California, USA
| | | | - Guideng Li
- Center of Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. .,Suzhou Institute of Systems Medicine, Suzhou, China.
| | - David Baltimore
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA.
| | - James R Heath
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, USA. .,Institute for Systems Biology, Seattle, Washington, USA. .,Department of Molecular and Medical Pharmacology, UCLA, Los Angeles, California, USA. .,Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, California, USA.
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8
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Dagan H, Flashner-Abramson E, Vasudevan S, Jubran MR, Cohen E, Kravchenko-Balasha N. Exploring Alzheimer's Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures. Biomolecules 2020; 10:biom10040503. [PMID: 32225014 PMCID: PMC7226317 DOI: 10.3390/biom10040503] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 03/23/2020] [Accepted: 03/24/2020] [Indexed: 12/27/2022] Open
Abstract
Despite huge investments and major efforts to develop remedies for Alzheimer’s disease (AD) in the past decades, AD remains incurable. While evidence for molecular and phenotypic variability in AD have been accumulating, AD research still heavily relies on the search for AD-specific genetic/protein biomarkers that are expected to exhibit repetitive patterns throughout all patients. Thus, the classification of AD patients to different categories is expected to set the basis for the development of therapies that will be beneficial for subpopulations of patients. Here we explore the molecular heterogeneity among a large cohort of AD and non-demented brain samples, aiming to address the question whether AD-specific molecular biomarkers can progress our understanding of the disease and advance the development of anti-AD therapeutics. We studied 951 brain samples, obtained from up to 17 brain regions of 85 AD patients and 22 non-demented subjects. Utilizing an information-theoretic approach, we deciphered the brain sample-specific structures of altered transcriptional networks. Our in-depth analysis revealed that 7 subnetworks were repetitive in the 737 diseased and 214 non-demented brain samples. Each sample was characterized by a subset consisting of ~1–3 subnetworks out of 7, generating 52 distinct altered transcriptional signatures that characterized the 951 samples. We show that 30 different altered transcriptional signatures characterized solely AD samples and were not found in any of the non-demented samples. In contrast, the rest of the signatures characterized different subsets of sample types, demonstrating the high molecular variability and complexity of gene expression in AD. Importantly, different AD patients exhibiting similar expression levels of AD biomarkers harbored distinct altered transcriptional networks. Our results emphasize the need to expand the biomarker-based stratification to patient-specific transcriptional signature identification for improved AD diagnosis and for the development of subclass-specific future treatment.
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Affiliation(s)
- Hila Dagan
- The Rachel and Selim Benin School of Computer Science and Engineering, Hebrew University, Jerusalem 9190416, Israel;
| | - Efrat Flashner-Abramson
- Department for Bio-Medical Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel; (E.F.-A.); (S.V.); (M.R.J.)
| | - Swetha Vasudevan
- Department for Bio-Medical Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel; (E.F.-A.); (S.V.); (M.R.J.)
| | - Maria R. Jubran
- Department for Bio-Medical Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel; (E.F.-A.); (S.V.); (M.R.J.)
| | - Ehud Cohen
- Department of Biochemistry and Molecular Biology, The Institute for Medical Research Israel—Canada, The Hebrew University School of Medicine, Jerusalem 9112102, Israel;
| | - Nataly Kravchenko-Balasha
- Department for Bio-Medical Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel; (E.F.-A.); (S.V.); (M.R.J.)
- Correspondence:
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9
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Kravchenko-Balasha N. Translating Cancer Molecular Variability into Personalized Information Using Bulk and Single Cell Approaches. Proteomics 2020; 20:e1900227. [PMID: 32072740 DOI: 10.1002/pmic.201900227] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 01/13/2020] [Indexed: 12/17/2022]
Abstract
Cancer research is striving toward new frontiers of assigning the correct personalized drug(s) to a given patient. However, extensive tumor heterogeneity poses a major obstacle. Tumors of the same type often respond differently to therapy, due to patient-specific molecular aberrations and/or untargeted tumor subpopulations. It is frequently not possible to determine a priori which patients will respond to a certain therapy or how an efficient patient-specific combined therapy should be designed. Large-scale datasets have been growing at an accelerated pace and various technologies and analytical tools for single cell and bulk level analyses are being developed to extract significant individualized signals from such heterogeneous data. However, personalized therapies that dramatically alter the course of the disease remain scarce, and most tumors still respond poorly to medical care. In this review, the basic concepts of bulk and single cell approaches are discussed, as well as their emerging role in individualized designs of drug therapies, including the advantages and limitations of their applications in personalized medicine.
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Affiliation(s)
- Nataly Kravchenko-Balasha
- Department for Bio-Medical Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem, 91120, Israel
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10
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Li N, Zhang W, Li Y, Lin JM. Analysis of cellular biomolecules and behaviors using microfluidic chip and fluorescence method. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2019.05.029] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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11
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Su Y, Bintz M, Yang Y, Robert L, Ng AHC, Liu V, Ribas A, Heath JR, Wei W. Phenotypic heterogeneity and evolution of melanoma cells associated with targeted therapy resistance. PLoS Comput Biol 2019; 15:e1007034. [PMID: 31166947 PMCID: PMC6576794 DOI: 10.1371/journal.pcbi.1007034] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 06/17/2019] [Accepted: 04/15/2019] [Indexed: 01/26/2023] Open
Abstract
Phenotypic plasticity is associated with non-genetic drug tolerance in several cancers. Such plasticity can arise from chromatin remodeling, transcriptomic reprogramming, and/or protein signaling rewiring, and is characterized as a cell state transition in response to molecular or physical perturbations. This, in turn, can confound interpretations of drug responses and resistance development. Using BRAF-mutant melanoma cell lines as the prototype, we report on a joint theoretical and experimental investigation of the cell-state transition dynamics associated with BRAF inhibitor drug tolerance. Thermodynamically motivated surprisal analysis of transcriptome data was used to treat the cell population as an entropy maximizing system under the influence of time-dependent constraints. This permits the extraction of an epigenetic potential landscape for drug-induced phenotypic evolution. Single-cell flow cytometry data of the same system were modeled with a modified Fokker-Planck-type kinetic model. The two approaches yield a consistent picture that accounts for the phenotypic heterogeneity observed over the course of drug tolerance development. The results reveal that, in certain plastic cancers, the population heterogeneity and evolution of cell phenotypes may be understood by accounting for the competing interactions of the epigenetic potential landscape and state-dependent cell proliferation. Accounting for such competition permits accurate, experimentally verifiable predictions that can potentially guide the design of effective treatment strategies. Cancer cells exhibit varied degrees of phenotypic heterogeneity. These phenotypes, each of them with unique molecular and functional profiles, display dynamic interconversion in response to drug perturbations, and can evolve to form new drug-tolerant phenotypes. Such phenotypic plasticity, in turn, renders tumor cells extremely difficult to treat. To get a quantitative biophysical understanding of the origins of the phenotypic equilibrium and evolution associated with drug tolerance development in highly plastic patient-derived melanoma cells, we employed joint experimental and computational approaches, using either bulk or single cell measurements as input, to interrogate the epigenetic landscape of the phenotypic evolution. We found that the observed phenotypic equilibria were established via competition between state-dependent net proliferation rates and landscape potential. The results reveal how the tumor cells maintain a phenotypic heterogeneity that facilitates appropriate responses to external cues. They implicate that, in certain phenotypically plastic tumor cells, drug targeting the driver oncogenes may not have sustained efficacy unless the phenotypic plasticity of the tumor is co-targeted.
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Affiliation(s)
- Yapeng Su
- Institute for Systems Biology, Seattle, Washington, United State of America
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, United State of America
| | - Marcus Bintz
- Department of Molecular and Medical Pharmacology, University of California – Los Angeles, Los Angeles, California, United State of America
| | - Yezi Yang
- Department of Molecular and Medical Pharmacology, University of California – Los Angeles, Los Angeles, California, United State of America
| | - Lidia Robert
- Department of Medicine, University of California – Los Angeles, Los Angeles, California, United State of America
| | - Alphonsus H. C. Ng
- Institute for Systems Biology, Seattle, Washington, United State of America
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, United State of America
| | - Victoria Liu
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, United State of America
| | - Antoni Ribas
- Department of Molecular and Medical Pharmacology, University of California – Los Angeles, Los Angeles, California, United State of America
- Department of Medicine, University of California – Los Angeles, Los Angeles, California, United State of America
- Department of Surgery, Division of Surgical-Oncology, University of California – Los Angeles, Los Angeles, California, United State of America
- Jonsson Comprehensive Cancer Center, University of California – Los Angeles, Los Angeles, California, United State of America
| | - James R. Heath
- Institute for Systems Biology, Seattle, Washington, United State of America
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, United State of America
- Jonsson Comprehensive Cancer Center, University of California – Los Angeles, Los Angeles, California, United State of America
- * E-mail: (J.R.H.); (W.W.)
| | - Wei Wei
- Institute for Systems Biology, Seattle, Washington, United State of America
- Department of Molecular and Medical Pharmacology, University of California – Los Angeles, Los Angeles, California, United State of America
- Jonsson Comprehensive Cancer Center, University of California – Los Angeles, Los Angeles, California, United State of America
- * E-mail: (J.R.H.); (W.W.)
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12
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Personalized disease signatures through information-theoretic compaction of big cancer data. Proc Natl Acad Sci U S A 2018; 115:7694-7699. [PMID: 29976841 DOI: 10.1073/pnas.1804214115] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Every individual cancer develops and grows in its own specific way, giving rise to a recognized need for the development of personalized cancer diagnostics. This suggested that the identification of patient-specific oncogene markers would be an effective diagnostics approach. However, tumors that are classified as similar according to the expression levels of certain oncogenes can eventually demonstrate divergent responses to treatment. This implies that the information gained from the identification of tumor-specific biomarkers is still not sufficient. We present a method to quantitatively transform heterogeneous big cancer data to patient-specific transcription networks. These networks characterize the unbalanced molecular processes that deviate the tissue from the normal state. We study a number of datasets spanning five different cancer types, aiming to capture the extensive interpatient heterogeneity that exists within a specific cancer type as well as between cancers of different origins. We show that a relatively small number of altered molecular processes suffices to accurately characterize over 500 tumors, showing extreme compaction of the data. Every patient is characterized by a small specific subset of unbalanced processes. We validate the result by verifying that the processes identified characterize other cancer patients as well. We show that different patients may display similar oncogene expression levels, albeit carrying biologically distinct tumors that harbor different sets of unbalanced molecular processes. Thus, tumors may be inaccurately classified and addressed as similar. These findings highlight the need to expand the notion of tumor-specific oncogenic biomarkers to patient-specific, comprehensive transcriptional networks for improved patient-tailored diagnostics.
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Su Y, Shi Q, Wei W. Single cell proteomics in biomedicine: High-dimensional data acquisition, visualization, and analysis. Proteomics 2017; 17. [PMID: 28128880 DOI: 10.1002/pmic.201600267] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 01/20/2017] [Accepted: 01/23/2017] [Indexed: 11/11/2022]
Abstract
New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features, and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions.
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Affiliation(s)
- Yapeng Su
- NanoSystems Biology Cancer Center, Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Qihui Shi
- Key Laboratory of Systems Biomedicine (Ministry of Education), School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Wei Wei
- NanoSystems Biology Cancer Center, Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.,Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California - Los Angeles, Los Angeles, CA, USA
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Kravchenko-Balasha N, Johnson H, White FM, Heath JR, Levine RD. A Thermodynamic-Based Interpretation of Protein Expression Heterogeneity in Different Glioblastoma Multiforme Tumors Identifies Tumor-Specific Unbalanced Processes. J Phys Chem B 2016; 120:5990-7. [PMID: 27035264 DOI: 10.1021/acs.jpcb.6b01692] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We describe a thermodynamic-motivated, information theoretic analysis of proteomic data collected from a series of 8 glioblastoma multiforme (GBM) tumors. GBMs are considered here as prototypes of heterogeneous cancers. That heterogeneity is viewed here as manifesting in different unbalanced biological processes that are associated with thermodynamic-like constraints. The analysis yields a molecular description of a stable steady state that is common across all tumors. It also resolves molecular descriptions of unbalanced processes that are shared by several tumors, such as hyperactivated phosphoprotein signaling networks. Further, it resolves unbalanced processes that provide unique classifiers of tumor subgroups. The results of the theoretical interpretation are compared against those of statistical multivariate methods and are shown to provide a superior level of resolution for identifying unbalanced processes in GBM tumors. The identification of specific constraints for each GBM tumor suggests tumor-specific combination therapies that may reverse this imbalance.
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Affiliation(s)
- Nataly Kravchenko-Balasha
- NanoSystems Biology Cancer Center, Division of Chemistry, Caltech , Pasadena, California 91125, United States.,Bio-Medical Sciences Department, The Faculty of Dental Medicine, The Hebrew University of Jerusalem , Jerusalem 9112001, Israel
| | - Hannah Johnson
- Signaling Programme, The Babraham Institute , Cambridge CB22 3AT, United Kingdom
| | - Forest M White
- Department of Biological Engineering, MIT , Cambridge, Massachusetts 02139, United States
| | - James R Heath
- NanoSystems Biology Cancer Center, Division of Chemistry, Caltech , Pasadena, California 91125, United States
| | - R D Levine
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine and Department of Chemistry and Biochemistry, UCLA , Los Angeles, California 90095, United States.,The Institute of Chemistry, The Hebrew University of Jerusalem , Jerusalem 91904, Israel
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