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• W4200324152 abstract "Understanding tumor complexity and applying that knowledge to advance patient care is a cornerstone of cancer research. However, tumor heterogeneity obfuscates this vision.Contemporary research contains a diverse set of technologies and computational tools for detecting, characterizing, and quantifying tumor heterogeneity at the molecular, architectural, organ, patient, or population level.Evaluation of the clinical relevance of heterogeneity metrics and creating actionable intelligence for the clinics, requires a common lexicon across disciplines, and a unified look toward multimodal data acquisition and computational analysis, the constraints they present, and how they partake in whole workflows. Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care. Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care. Tumor heterogeneity: scales, dimensions, and implicationsTissue architecture is organized functionally, with several cell subpopulations (see Glossary) contributing with distinct roles in maintaining homeostasis. At the onset of oncogenesis, tissue organization is disrupted by an uncontrolled splurge of genetically unstable cell subpopulations in a limited spatial niche. Increased genomic variability gradually translates to elevated phenotypic and functional variability, contributing to what is commonly referred to as tumor heterogeneity. As these heterogeneous subpopulations expand and interact with one another and their microenvironment, they generate cancer ecosystems, following evolutionary principles of traditional ecology [1.Tabassum D.P. Polyak K. Tumorigenesis: it takes a village.Nat. Rev. Cancer. 2015; 15: 473-483Crossref PubMed Scopus (314) Google Scholar]. From the inner workings of a single cell to the patterns observed at a population level, heterogeneity is omnipresent across all scales of tumor organization. Stemming from intrinsic genetic instability and further amplified by other sources of variability at the epigenetic, transcriptional, or protein levels, tumor heterogeneity manifests in both a spatial and temporal dimension [2.Hiley C.T. Swanton C. Spatial and temporal cancer evolution: causes and consequences of tumour diversity.Clin. Med. 2014; 14: s33-s37Crossref PubMed Scopus (0) Google Scholar] (Figure 1). Temporal heterogeneity is present in the dynamic processes underlying cancer initiation, evolution, and metastasis [3.Hiley C. et al.Deciphering intratumor heterogeneity and temporal acquisition of driver events to refine precision medicine.Genome Biol. 2014; 15: 453Crossref PubMed Scopus (138) Google Scholar]. Spatial heterogeneity describes the complex tumor architecture, notably the interplay between cancer, immune, or stromal cells, and access to nutrients and growth factors through the vasculature or regional hypoxia [4.Yuan Y. Spatial heterogeneity in the tumor microenvironment.Cold Spring Harb. Perspect. Med. 2016; 6a026583Crossref PubMed Scopus (105) Google Scholar]. Moving up the scale of complexity, heterogeneity leads to spatiotemporal variability in tumor size, shape, texture, and organ localization, both within and across patients.Tumor heterogeneity has a substantial impact on treatment response and disease outcome [5.Bedard P.L. et al.Tumour heterogeneity in the clinic.Nature. 2013; 501: 355-364Crossref PubMed Scopus (754) Google Scholar,6.McGranahan N. Swanton C. Biological and therapeutic impact of intratumor heterogeneity in cancer evolution.Cancer Cell. 2015; 27: 15-26Abstract Full Text Full Text PDF PubMed Google Scholar]. Given that cancer cells constantly evolve disease progression mechanisms or treatment escape routes [7.Rybinski B. Yun K. Addressing intra-tumoral heterogeneity and therapy resistance.Oncotarget. 2016; 7: 72322-72342Crossref PubMed Scopus (47) Google Scholar], monitoring and quantifying tumor heterogeneity at diagnosis, during treatment, and possibly during disease resistance, is vital. To address this need, a plethora of data acquisition technologies can capture different aspects of tumor heterogeneity, with molecular techniques widely used for in-depth tumor characterization at smaller scales, and radiology imaging quantifying tumor size, spread, or morphology at larger scales. To summarize these measurements, several computational metrics that quantify the extent of heterogeneity have been devised. At smaller scales, statistical, entropic, or spatial metrics that quantify genomic, transcriptomic, proteomic, or microenvironmental heterogeneity from different ‘omics or tissue-imaging data are available. At larger scales, radiology-based metrics examine the spatial distribution of gray values and capture morphological or textural heterogeneity. Several studies have applied these metrics to a variety of cancer types, providing valuable insights and showing prognostic capabilities. Still, important limitations exist, because current metrics are often empirical, difficult to interpret, and challenging to integrate within a single framework, limiting their reproducibility and adoption in the clinics.In this review, we focus on the quantification of tumor heterogeneity from a unified experimental and computational perspective. We first illustrate different classes of technological innovation for data acquisition, highlighting methods able to extract information about spatiotemporal heterogeneity from patient samples at different ‘omic levels and across scales. We then elaborate on existing attempts to quantify different facets of tumor heterogeneity. We cover existing computational metrics, explaining their mathematical foundations, advantages, and limitations, and discussing the insights offered when applied to a variety of data sets and cancer types. Finally, we outline their limitations and highlight possible future directions, focusing on workflows able to integrate data modalities and fill the gaps across technologies. As the field is moving toward multisite, multi-timepoint and multimodal data generation and integration, standardizing such workflows requires effort toward an increased adoption of pan-scale technologies and computational metrics. We advocate here that clinical adoption of such workflows has the potential to reduce information complexity into actionable intelligence, aiding diagnosis, prognosis, or prediction of therapy response, with the grand vision to significantly advance patient care.From sample to measurement: heterogeneity data acquisition techniquesHeterogeneity manifests across all levels of tumor organization, its quantification thus requiring measurements at multiple scales. Clinical application of any experimental measurement requires that it is precise and unique to each patient (‘diagnostic’), generalizable over a population to indicate outcome (‘prognostic’), and/or indicative of response to therapy (‘predictive’). Therefore, the scale and intended application of a measurement defines the attributes of a technology: ‘where to measure’, ‘when to measure’, and ‘how many entities to measure’ (Figure 2).Figure 2Relation between scope and scale of data acquisition.Show full captionThe scope of clinical and fundamental investigations defines the challenge the technology is meant to solve. The challenge also defines the necessary technological attributes: spatial (where), temporal (when), or statistical (how many) analyses. The more technologies advance the knowledge base, the more refinement is possible on the specificity of the challenges, giving the highly interlinked relationship between these aspects.View Large Image Figure ViewerDownload Hi-res image Download (PPT)The attribute ‘where to measure’ defines the scale of data obtained. Technologies interface with clinical samples by dissociating tumor specimens and analyzing molecular constituents through lysing of whole specimens (homogenized samples) or individual single cells, preserving the specimen topology by in situ imaging or processing sections of a specimen, or noninvasively with medical imaging (Figure 3). With regard to ‘when’, workflows at the cellular or tumor level extract temporal information using multisite biopsies with space as a surrogate for time, and workflows at the patient level use radiological screening for continuous noninvasive tumor monitoring. For the attribute ‘how many’, the entities that a technology can process is defined by the degree of multiplexing (number) and throughput (rate). Some low-throughput technologies allow manipulation of single cells, while others can process millions of cells or tens of biopsy sections for several biological entities simultaneously. As the size of the clinical sample under investigation grows, the throughput often drops, culminating in single organs or patients being profiled in radiology. Here, we describe tumor heterogeneity data acquisition technologies with respect to scales ranging from molecular analysis to medical imaging, with their attributes and potential scope of application. We organize the following section with respect to increasing scales of data, but note that the corresponding technological implementations are often modular (Figure 4) and can be customized based on the nature of investigation. We conclude this section with a brief overview, listing some of the modules being integrated for multimodal data acquisition.Figure 3Technologies to measure tumor properties at different scales and their comparative attributes.Show full captionBased on the scale of interest for tumor examination, technologies can be classified under those that require dissociation of tissues, conserve the intratissue spatial component, or can be noninvasively imaged in patients (left panel). This allows for the extraction of different data forms, such as molecular information from a population or a single cell, spatial molecular or architectural information, or organ-level images (middle panel). The attributes that define each of these classes can be compared with respect to spatial and temporal data, the scale of tissue organization it addresses, the resolution achievable, and the throughput and multiplexing capabilities (right panel). Abbreviations: CT, computer tomography; CyTOF, cytometry time of flight; IF, immunofluorescence; IMC, imaging mass cytometry; ISH, in situ hybridization; LCM, laser capture microdissection; MFP, microfluidic probe; MRI, magnetic resonance imaging; PET, position emission tomography; scRNAseq, single cell RNA sequencing.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure 4Schematic showing workflows and modules of technological platforms that allow molecular analysis of tumors.Show full captionThe clinical sample is first preprocessed (left panel) to reach the data form of interest. The sample can then be processed to uncover its molecular content nonspatially at higher throughputs or spatially at lower throughputs (middle left panel), and these can be later examined in a downstream process or via integrated detection in the assay (middle right panel) using hybridization or immunodetection. Finally, the data are scaled and analyzed (right panel) to extract clinically relevant parameters. Abbreviations: CyTOF, cytometry time of flight; ISH, in situ hybridization; LCM, laser capture microdissection; MFP, microfluidic probe.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Measuring molecular heterogeneity in mixed cellular populationsExtraction of individual molecules by whole-tissue/biopsy lysis provides total DNA, mRNA, or protein levels, allowing for the evaluation of total genomic, transcriptomic, or proteomic content in the specimen, at the expense of intratumoral spatial context. Biopsies and surgical resections are excised using imaging-guided surgery from a single or multiple anatomic sites and either preserved through fixation/cryopreservation or directly used while viable. Comparative analysis between different tumor samples provides a measure of intertumor heterogeneity, while the content of a single sample provides general information on intratumor heterogeneity. Each individual specimen can then be dissociated [8.Leelatian N. et al.Preparing viable single cells from human tissue and tumors for cytomic analysis.Curr. Protoc. Mol. Biol. 2017; 118: 25C.1.1-25C.1.23Crossref Scopus (26) Google Scholar] using mechanical (e.g., mechanical rotor-stator or bead beating), chemical (e.g., bases or detergents), or enzymatic homogenization (e.g., lysozyme, proteinase-K or collagenase). By controlling the extent of mechanical stress, chemical or enzymatic strength, and incubation time, it is possible to dissociate the tumor into cell suspensions, which can then be pelleted and lysed by repeating the process. Dissociation and lysis parameters need to be selected based on tissue type and the extent of preservation. Upon release of biomolecules, they are processed through multistep downstream processes that include molecular isolation, enrichment, and characterization/detection.A low quality or quantity of biomolecules can adversely affect data analysis and needs to be an essential factor when choosing a technology for heterogeneity measurements [9.Padula M.P. et al.A comprehensive guide for performing sample preparation and top-down protein analysis.Proteomes. 2017; 5: 1-31Crossref Scopus (22) Google Scholar, 10.Capriotti A.L. et al.Recent applications of magnetic solid-phase extraction for sample preparation.Chromatographia. 2019; 82: 1251-1274Crossref Scopus (40) Google Scholar, 11.Hosic S. et al.Microfluidic sample preparation for single cell analysis.Anal. Chem. 2016; 88: 354-380Crossref PubMed Scopus (77) Google Scholar]. Examples of downstream techniques for purification are chromatography [9.Padula M.P. et al.A comprehensive guide for performing sample preparation and top-down protein analysis.Proteomes. 2017; 5: 1-31Crossref Scopus (22) Google Scholar] for protein isolation, and solid phase extraction [10.Capriotti A.L. et al.Recent applications of magnetic solid-phase extraction for sample preparation.Chromatographia. 2019; 82: 1251-1274Crossref Scopus (40) Google Scholar], and electrophoresis [11.Hosic S. et al.Microfluidic sample preparation for single cell analysis.Anal. Chem. 2016; 88: 354-380Crossref PubMed Scopus (77) Google Scholar], which have specific implementations for DNA or RNA (Table 1). Once isolated, nucleic acids can be enriched for target genes through PCR-based strategies. After isolation and enrichment, detection is enabled by hybridization- or PCR-based techniques for nucleic acids and immunodetection using antibody specificity for protein antigens. These provide presence/absence estimates of genomic traits, transcript quantities, or protein abundance. However, the analysis of variants and isoforms necessitates sequencing methodologies, which are downstream modules to several techniques and are described later in detail.Table 1Advantages and disadvantages of ex situ and in situ processing stepsaAbbreviations: FISH, fluorescence in situ hybridization; IMC, imaging mass cytometry; LCM, laser capture microdissection; MS, mass spectrometry; SBL, sequencing by ligation; SBS, sequencing by synthesis.Processing stepSystemSmallest measured unitAdvantagesDisadvantagesRefsEx situ sample processingbEx situ processing is divided into methods for recovery and methods for detection and quantification of molecules of interest.Ex situ recoveryFlow cytometry and derivativesSingle cellHigh throughputHigh multiplexing capabilitiesLoss of spatial information[21.Chang Q. Hedley D. 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Biotechnol. 2008; 26: 317-325Crossref PubMed Scopus (1463) Google Scholar]a Abbreviations: FISH, fluorescence in situ hybridization; IMC, imaging mass cytometry; LCM, laser capture microdissection; MS, mass spectrometry; SBL, sequencing by ligation; SBS, sequencing by synthesis.b Ex situ processing is divided into methods for recovery and methods for detection and quantification of molecules of interest.c Requires downstream processing. Open table in a new tab At the genomic level, gene loci of interest can be enriched from the total genomic isolate by hybridization capture on oligo-functionalized surfaces [for gene panel sequencing and whole-exome sequencing (WES)] or the totality of genomic isolate can be sequenced as is [for whole-genome sequencing (WGS)]. At the transcriptomic level, mRNA isolated from the lysates is introduced into sequencing workflows after reverse transcription to DNA, with addition of a known quantity of reference transcript to provide quantitative information. Therefore, the same sequencing platforms [12.Heather J.M. Chain B. The sequence of sequencers: the history of sequencing DNA.Genomics. 2016; 107: 1-8Crossref PubMed Scopus (464) Google Scholar] are often used to sequence both DNA and RNA. First- and second-generation sequencers use polymerase-based identification of nucleic acid sequences. S" @default.
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• W4200324152 title "Quantification of tumor heterogeneity: from data acquisition to metric generation" @default.
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