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Received Date : 30-Jan-2014 Revised Date : 20-Feb-2014 Accepted Date : 20-Feb-2014 Article type
: Key Symposium
The challenge of intratumour heterogeneity in precision medicine
Running head: Tumour heterogeneity and precision medicine
Joan Seoane1,2,3 & Leticia De Mattos-Arruda1,2
From the
1
Vall d'Hebron Institute of Oncology, Vall d'Hebron University Hospital,
Barcelona, Spain; 2Universitat Autònoma de Barcelona, Barcelona, Spain; and 3Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
Abstract Cells within tumours have diverse genomes and epigenomes, and interact differentially with their surrounding microenvironment generating intratumour heterogeneity, which has critical implications for treating cancer patients. Understanding the cellular and microenvironment composition and characteristics in individual tumours is critical to stratify the patient This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/joim.12240 This article is protected by copyright. All rights reserved.
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population that is likely to benefit from specific treatment regimens. Here, we will review the current understanding of intratumour heterogeneity at the genomic, epigenomic and microenvironmental levels. We will also discuss the clinical implications and the challenges posed by intratumour heterogeneity, and evaluate non-invasive methods such as circulating biomarkers to characterise the cellular diversity of tumours. Comprehensive assessment of the molecular features of patients based on tumour specimen characterisation (including intratumour spatial and temporal variations), ancillary non-invasive methods (such as circulating biomarkers and molecular imaging approaches), and the correct design of clinical trials are required to guide administration of targeted therapy and to control therapeutic resistance. Finding the means to accurately determine and effectively control tumour heterogeneity and translate these achievements into patient benefit are major goals in modern oncology.
Keywords: cancer, genomics, intratumour heterogeneity, precision medicine, targeted therapy.
Introduction Progress in cancer management has frequently resulted from the interaction between clinical and laboratory research [1, 2]. The molecular characterisation of tumours provides a basis for personalising cancer treatment resulting in increased clinical benefit, reduced toxicity and overcoming therapeutic resistance [3].
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The genomic characterisation of cancers has contributed to our understanding of the molecular landscape of tumours and has provided a rationale for the use of matched targeted therapies based on specific molecular targets. Improved treatment for specific tumour subtypes has highlighted the potential of precision medicine [3]; for example, the use of trastuzumab in HER2-positive breast cancer [4], vemurafenib for advanced BRAFV600-mutant melanoma [5], the PARP inhibitor olaparib in patients whose tumours bear BRCA1/2 mutations [6] and crizotinib in non-small-cell lung cancer with anaplastic lymphoma kinase translocations [7].
However, at present, precision medicine is not entirely successful. There is growing recognition that the heterogenous nature of cancer, and in particular the intrinsic heterogeneity of individual tumours as well as between primary and metastatic tumours, presents an important challenge in cancer therapeutics. In addition to the fact that all tumours show a unique combination of genomic alterations (intertumour heterogeneity), they are composed of cells with different molecular characteristics (intratumour heterogeneity). This results in diversity between and within tumours [8, 9].
Here, we review the current knowledge of the diverse patterns of intratumour heterogeneity. Cells within tumours can comprise distinct genomic alterations. In addition, cells with the same genomic landscape can show different states of differentiation and, hence, epigenetic status. Moreover, tumour cells can be influenced by a complex and diverse environment generated by stromal cell heterogeneity. These three different levels of heterogeneity are crucial for explaining the biology of cancer and have critical therapeutic implications.
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Understanding intratumoural heterogeneity is essential for designing effective therapeutic strategies in the context of precision medicine.
Tumour heterogeneity Although tumour heterogeneity is a well- and long-established concept, recognition of its various challenges to cancer therapy has increased in recent years [10–20] (Table 1). The study of cancer genomics has been boosted by the development and use of massively parallel (or next-generation) sequencing technologies [21]. Global research initiatives such as the 1000 Genomes Project [22], the Cancer Genome Atlas [23–25] and the International Cancer Genome Consortium [26], and publicly available catalogues such as the Catalogue of Somatic Mutations in Cancer) [27] and Genomics of Drug Sensitivity in Cancer [28], have added to improved delineation of cancer genomics. Together, these demonstrate variability among individual tumours and how diverse genomic aberrations of tumours sharing a similar histology is crucial for predicting how patients will respond to targeted therapies [8, 9] (Fig. 1).
In addition to intertumour heterogeneity, another layer of complexity is created by the intermingling of diverse subpopulations of cancer cells with normal cells, which leads to intratumour heterogeneity that can progress and become modified over time. Intratumour heterogeneity influences the dynamic tumour landscape and plays a key role in shaping responses to specific therapies [29, 30].
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Intratumour genomic heterogeneity The findings of massively parallel sequencing studies have demonstrated that the compendium of genomic aberrations in cancer is complex and diverse and only a limited number of genes are frequently and recurrently mutated in a substantial proportion of cancer cases [31–33]. Genomic analyses of human cancers have provided evidence of spatial and temporal intratumour genomic heterogeneity [11, 14, 34–36], and have shown that tumours are composed of mosaics of cells [12, 36] with subclones harbouring both private (or unique) and ubiquitous (i.e. common to all tumour cells) genomic alterations. For example, Gerlinger et al. investigated intratumour genomic heterogeneity in renal cancer through an analysis of genomic alterations in multiple tumour regions [11]. Using exome sequencing, chromosome aberration and DNA ploidy analyses confirmed the heterogeneity of different regions within the same tumour mass and of the primary tumour versus matched metastases. Genomic aberrations were found in all cell subclones throughout individual tumour specimens, whereas other aberrations were heterogeneously distributed [11]. In this context, it was shown that performing single biopsies of primary tumours or metastatic deposits, as is usual clinical practice, is unlikely to reveal the complete profile of genomic alterations in any tumour [37].
Additionally, recent evidence shows that intratumoural heterogeneity is dynamic and versatile [14, 18, 38, 39]. For example, Nik-Zainal et al. studied the evolution of cancer genomic clones and quantified the extent and dynamics of subclonal variation in breast cancer [18]. Moreover, while reconstructing tumour phylogeny through a multisampling scheme devised for individual glioblastoma patients, Sottoriva et al. constructed a model that inferred tumour progression in time and space and showed that copy number aberrations in
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EGFR and CDKN2A/B/p14ARF were early events, and aberrations in PDGFRA and PTEN occurred later during cancer progression [14].
Evolutionary studies have focused on analyses of the relationship between primary tumours and metastatic deposits, and within and between metastatic deposits [10, 12, 14, 36], as well as subclone evolution within a given tumour mass [15, 18, 40]. Increasing evidence demonstrates that genome evolution reflects the Darwinian model and branched evolutionary tumour growth contributes to intratumour genomic heterogeneity [10, 37, 38]. In this regard, the fittest clones [11, 41] are selected during the metastatic process via external selective pressures (e.g. administration of systemic therapeutics) [10] or via pressures from the tumour microenvironment (e.g. hypoxia, growth factors) [30] (Fig. 2).
The functional impact of intratumour genomic heterogeneity on cancer remains to be fully understood. Further studies are required to assess how intratumour genomic diversity influences the response and resistance to therapies during tumour relapse and the generation of metastasis.
Heterogeneity in the cellular state of differentiation Diverse states of differentiation and, hence, diverse epigenomic states can be found in cancer cells that share the same genomic alterations [42]. This implies that tumour deposits with homogeneous genomic characteristics contain functionally heterogeneous cancer cells with
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diverse phenotypes, including different capacities to proliferate, migrate and invade and, most importantly, with different sensitivities to therapeutic agents.
With regard to the concept of heterogeneity in the cellular state of differentiation, recent evidence from transplantation and lineage tracing experiments shows that only a fraction of cells within a tumour have tumour-initiating capabilities [43, 44]. These cells, termed cancerinitiating cells (CICs) or cancer stem cells, are more tumorigenic than their surrounding cells. CICs are usually poorly differentiated (with stem cell features), have a different epigenetic status compared to the rest of the cells and are considered to be tumour drivers [44, 45]. CICs are responsible for the re-initiation of tumours, mediating relapse and metastatic dissemination; moreover, these cells seem to be more resistant to DNA-damaging therapeutic agents [46, 47] (Fig. 2).
The intratumoural heterogeneity generated through the different degrees of cellular differentiation, including CICs and non-CICs, is due to epigenetic regulation of cells guided by signals from the microenvironment [42, 46–48]. Of note, CICs are not randomly spread through the tumour mass; these cells tend to be found in specific locations or niches, depending on the local microenvironment [49, 50]. Such niches are composed of non-tumour cells (i.e. inflammatory cells, endothelial cells and fibroblasts) and the extracellular matrix, which enable direct cell-to-cell interactions and secrete factors that maintain the stem cells in a quiescent state, regulating their self-renewal capacity and pluripotency. CICs can be located near vessels in a perivascular niche or in a hypoxic niche, suggesting close communication between CICs and the tumour microenvironment. Several signalling pathways, including the Wnt, Notch, Hedgehog and transforming growth factor-beta pathways, have been implicated This article is protected by copyright. All rights reserved.
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in the regulation of CIC self-renewal, and also on differentiation or dedifferentiation of nonCICs into CICs [45, 51, 52].
Of note, CICs and non-CICs differ in their sensitivity to treatment [47] and, therefore, unravelling the mechanism(s) underlying their epigenetic heterogeneity will be crucial for cancer treatment.
Intratumour microenvironment heterogeneity Tumour cells are in intimate contact with a heterogeneous microenvironment which regulates their biology and sensitivity to treatment [53]. The tumour niche is created by a dynamic microenvironment that supports and interacts with tumour cells through a complex signal cross-talk. The non-tumoural component of cancer, the tumour stroma, is composed primarily of the extracellular matrix, fibroblasts, blood vessels and immune cells [54]. These stromal elements can vary substantially within the topography of primary tumours and/or metastatic deposits, both in terms of the number of cell types and the proportion of lineage relationships [55], generating intratumour heterogeneity. Of importance, the normal cells of the stroma coevolve with tumour cells in a dynamic way; thus, tumour deposits should be evaluated in the context of an evolving tumour microenvironment which has definite implications for the response and resistance to therapeutic agents [54].
The tumour stroma comprises a modified extracellular matrix [56] with a large number of fibroblasts with specific characteristics [57]. The so-called cancer-associated fibroblasts
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(CAFs) are not evenly distributed within the extracellular matrix and play a key role in tumorigenesis [58]. In addition, CAFs and tumour cells can generate an intricate and dense extracellular matrix, which contributes to increased interstitial fluid pressure that hampers the penetration of drugs through tissue [59, 60]. This limits the diffusion of therapeutic compounds to tumour regions, and thus might represent an obstacle to clinical treatment.
Tumours are nourished by a dynamic vascular network, which controls tumour growth [61]. The tumour vasculature is variable with distinct characteristics even within the same tumour mass [30]. The vascular heterogeneity within tumours may result from the formation of new vessels (angiogenesis) or modification of existing vessels within the tissue [57]. In some parts of a tumour, abnormalities in blood vessels and differences in hydrostatic pressure impair blood flow, delivery of nutrients and clearance of metabolic products leading to acidic and hypoxic tumour regions [62]. This generates a variable milieu for tumour cells ranging from hypoxic to well-irrigated and non-hypoxic regions.
On the other hand, the inflammatory and immune components of the stroma can also vary in different locations of the tumour mass [30]. Tumour-infiltrating immune cells include varied cell types with specific roles both as pro- and anti-tumour cells; in addition, their geographic location can vary within individual tumours. This intratumoural spatial diversity of immune response and inflammatory cells controls the tumour biology, and its understanding can be critical for designing therapies based on the cancer immune checkpoint [29, 63].
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Tackling tumour heterogeneity in the clinic Cancer heterogeneity challenges the potential benefit of precision medicine. Consequently, genomic and non-genomic biomarker analyses of single biopsies may differ according to the area of the sampled tumour [11]. This multiregional separation of molecular aberrations can lead to sampling bias [37, 64], potentially impairing the interpretation of the molecular characterisation of tumours and having an impact on the selection of treatment.
Accordingly, approaches that provide global assessment of the catalogue of somatic genomic and epigenomic aberrations and tumour microenvironment-based biomarkers are clearly important for accurately selecting targeted therapies for individual patients. Recent studies in the metastatic setting have provided evidence of the emergence of a small number of subclones within primary tumours [34, 35, 65, 66]. These subclones could clarify how treatment-resistant subpopulations of tumour cells can be monitored and targeted over time. Pilot initiatives (i.e. molecularly driven clinical trials) [9, 67] and also circulating biomarkers [i.e. liquid biopsies, circulating tumour cells (CTCs) or cell-free tumour DNA (ctDNA)] [67, 68] may play a key role in this context. Pilot initiatives The purpose of using biomarkers to provide information for clinical decision-making is to enable individualised cancer therapy. The fact that there are different levels of heterogeneity (i.e. inter- and intratumour heterogeneity) is one the major problems for the administration of targeted therapies and has influenced the design of translational pilot initiatives and other innovative clinical trials.
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To date, most pilot initiatives have focused on individual patients through identifying groups of patients who share similar targetable biomarkers, and personalising appropriate targeted therapies to these individuals (Table 2) [45, 67, 69–77]. An example is the WinTHER study, which is a Phase II clinical trial launched by the Worldwide Innovative Networking Consortium in personalized cancer medicine (WIN Consortium) to assess the potential of selecting targeted therapies for ‘actionable’ targets found in the analysis of patients’ tumours [74, 78]. By contrast, few other initiatives (e.g. MOSCATO 01 study, NCT01566019) that included subgroups of patients enriched for specific genomic alterations have used the stratification approach, which focuses on molecular biomarkers rather than individual patients.
In addition, single subject clinical trials (i.e. n-of-1 trial design) investigate the efficacy of a given therapy or the side effect profiles of different therapeutic interventions in an individualised manner, primarily for patients harbouring rare molecular aberrations [9, 67]. Clinical trials using other designs are also being conducted, such as the ‘basket trials’ in which one study (the basket) includes one or more targets (e.g. NCT01219699 study enrolling patients whose tumours have a PIK3CA oncogene mutation to receive an alpha phosphatidylinositol 3-kinase (PI3K) inhibitor) and allows patients with multiple diseases to be
enrolled [11].
These studies to empirically test the concept of precision medicine are transforming the way tumour tissue is assessed for molecular aberrations, the design of clinical trials and the measurement of treatment efficacy. To date, these studies have usually been based on ‘biased‘ tumour tissue sampling, which relies on spatial variability in the expression of key This article is protected by copyright. All rights reserved.
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molecular biomarkers and, in some instances, on archival tumour specimens. This approach, which is rapidly becoming outdated, does not appropriately reflect tumour heterogeneity or dynamics.
Recent clinical and translational programmes have attempted to investigate intra- or intertumour heterogeneity (Table 3). These initiatives together with other non-invasive methods based on tumour molecular imaging [79, 80], which might identify and quantify actionable molecular biomarkers over time, are also leading the way to patient selection and longitudinal monitoring of response to therapy.
Circulating biomarkers The use of circulating biomarkers, in particular CTCs and ctDNA, is a promising approach that may illuminate the challenges posed by intratumour heterogeneity and sampling bias of single biopsies [64, 81–83]. CTCs and plasma-derived ctDNA have been widely investigated as potential non-invasive surrogates for tumour tissue biopsies [64]. The genomic characterisation of these blood-borne biomarkers has introduced a new method for investigating the metastatic process and the mechanisms of therapeutic resistance as well as for disease monitoring [65, 66, 82–85]. The ability to comprehensively identify tumour clones and subclones from cancer patients is likely to be instrumental in delineating the molecular and clinical heterogeneity of cancer and may facilitate the precise delivery of targeted therapy.
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CTCs and ctDNA are shed into the circulation from primary tumours and/or their metastases; therefore they constitute a potential source of tumour material from all disease sites, and offer a real-time, easily obtainable, cost-effective and minimally invasive tool for the development of molecular biomarkers [64, 68]. It should be noted, however, that CTCs may represent heterogeneous subpopulations of cancer cells [81, 86, 87] so that plasma-derived ctDNA might provide a better readout for bridging the gap between sample bias and the administration of targeted therapies. To this end, genomic characterisation of plasma-derived ctDNA may offer a way to confront the emergence of treatment-resistant clones, to track the evolution of the intratumour heterogeneity of cancer and to predict responses and resistance to treatment.
Conclusions and future perspectives Tumour diversity poses a challenge for managing the treatment of cancer patients. Decoding heterogeneity has important implications that will probably refine our understanding of cancer biology, its genomic, epigenomic and functional diversity and the mechanisms that lead to therapeutic resistance [10, 37, 88–90]. Nevertheless, the extent of tumour heterogeneity, and its multifaceted nature, remains poorly understood.
In addition to tumour heterogeneity, the design of studies appropriate for the era of precision medicine may be impeded by other barriers. These include the complexity of identifying and validating predictive molecular biomarkers, technical limitations of molecular tests, slow progress in unravelling the biology of some types of cancer, mechanisms of resistance per se,
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the high failure rate and cost of molecularly targeted agents and reimbursement and regulatory issues.
Current biomarker assessment programmes do not thoroughly address the issues of heterogeneity at the single-cell level (within and/or between patient tumours) or the dynamic environment of a cancer (i.e. changes in genomic, epigenomic and functional cancer characteristics). In addition, biomarker analysis does not consider minor genetic subclones or predict future tumour development. There is evidence to suggest that ’non-standard personalised strategies‘ using mathematic model-based genetic evolutionary dynamics and single-cell heterogeneity that personalise cancer therapeutics might lead to superior patient outcomes compared with the current model [91]. A comprehensive assessment of the molecular characteristics of patients and their therapeutic needs (Fig. 3), based on (i) tumour specimen characterisation, which includes intratumour spatial and temporal variations, (ii) ancillary non-invasive methods, such as circulating biomarkers and molecular imaging approaches, (iii) availability of clinical trials or compassionate use of specific therapeutic agents and (iv) functional characterisation of tumour-derived biomarkers based on in vivo cancer experimental models, would ideally guide targeted therapy administration and manage therapeutic resistance. Finding the means to accurately determine and effectively control tumour heterogeneity and translate these achievements into patient benefit are major goals in modern oncology. Precision medicine will not become a reality until heterogeneity-related issues are resolved.
Conflict of interest statement The authors have no conflicts of interest to declare. This article is protected by copyright. All rights reserved.
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Acknowledgments The authors acknowledge finantial support from Fundación Rafael del Pino and la Asociación Española Contra el Cáncer. References
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Correspondence: Prof. Joan Seoane, Translational Research Program, Vall d’Hebron Institut d’Oncologia, Pg Vall d’Hebron 119-129 08035, Barcelona, Spain. Tel: +34 93 4894167; Fax: +34 93 489 40 15; E-mail: [email protected]
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Figure legends Fig. 1 The different patterns of tumour heterogeneity. (A) Intertumour heterogeneity: heterogeneity between tumours in different patients or within the same patient with different tumour deposits. (B) Intratumour heterogeneity: diversity within a tumour. (C) Intratumour genomic heterogeneity: cells with distinct genomic alterations within a tumour. (D) Intratumour epigenomic heterogeneity: cells with diverse states of differentiation based on different epigenomic states. (E) Intratumour microenvironment heterogeneity: differences in tumour stroma (extracellular matrix, vasculature and immune cells) within the same tumour. Fig. 2 Functional heterogeneity and the impact of treatment in the selection of the fittest cells/clones. (A) Genomic selection: the treatment affects a cellular clone with a specific genomic alteration. (B) Epigenomic selection: the treatment affects a cellular compartment with a specific state of differentiation/epigenetic status [e.g. non-cancer-initiating cells (CICs) vs. CICs]. (C) Microenvironment selection: the treatment affects cells present in a specific stromal niche. Fig. 3 Projected strategy of data integration to connect tumour characterisation and matched targeted therapy in the context of precision medicine. Clinical trial allocation would be based not only on the characterisation of tumours, which involves genomic/non-genomic aspects of tumour tissue, the use of surrogate tools (e.g. circulating blood-based biomarkers and molecular imaging) and functional studies, but would consider inter- and intratumour heterogeneity as core parameters for the specific choice of the most appropriate drug for each patient.
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Table 1 Selected examples of studies to assess tumour heterogeneity Tumour Description Type of assessment type Melanoma Intra- and intertumour heterogeneity of Intra- and intertumour heterogeneity the BRAFV600E mutation using BRAF mutant-specific PCR and conventional sequencing Renal Branched evolutionary growth through Intra- and intertumour multiregion exome sequencing heterogeneity Intratumour heterogeneity Glioblasto Integrated genomic analysis (copy ma number and gene expression) after multiple intratumour sample collection reveals multiple glioblastoma subtypes within the same tumour and inferred genomic evolution Medullobl Metastases from an individual are quite Intertumour heterogeneity astoma similar to each other but differ from the matched primary tumour Intratumour heterogeneity NSCLC Multiregion sampling from each tumour showed heterogeneous populations of both EGFR-mutated and non-mutated cancer cells, which was associated with reduced response to gefitinib Ovarian Intratumour variation in mutation, copy Intratumour heterogeneity number and gene expression profiles through the analysis of spatially and temporally separated high-grade serous ovarian tumour specimens. Key driver gene alterations (e.g. PIK3CA, CTNNB1 and NF1) present in a subset of samples; only TP53 somatic mutation present in all samples Intratumour heterogeneity Breast Triple-negative breast tumours vary widely in their clonal frequencies (e.g. TP53, PIK3CA and PTEN somatic mutations were not clonally dominant in all tumours) Breast The subclonal architecture of 21 breast Intratumour heterogeneity cancers was mapped by massively parallel sequencing analyses Pancreatic Whole-exome sequencing of pancreatic Intra- and intertumour cancer specimens showed the clonal heterogeneity relationships among primary and metastatic tumours. Timing of the genetic evolution (i.e. distant metastasis occurs late for these cancers) was inferred. This article is protected by copyright. All rights reserved.
Reference [13]
[11] [14]
[15]
[16]
[17]
[12]
[18]
[19]
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[20] Synchrono Massively parallel sequencing can help Intra- and intertumour define the origin of metastatic deposits heterogeneity us in patients with multiple primary tumours tumours and reveal genetic heterogeneity within and between lesions from the same patient EGFR, epidermal growth factor receptor, NSCLC, non-small-cell lung cancer; PCR, polymerase chain reaction.
Table 2 Selected pilot studies focusing on individual patients
Pilot study design
Matched actionable molecular alteration with targeted therapy
Treated patients
Reference
D
D
[71] [72]
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
Personalised Bisgrove trial MI-ONCOSEQ COMPACT WinTher
[73] [74]
Stratified MOSCATO 01 BATTLE I-SPY 2
The M. D. Anderson Cancer Center Initiative ‘Basket trials’
[75] [76] [77] -
Both personalised and stratified
Patient-derived xenografts
[45, 69, 70]
BATTLE, Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination; COMPACT, Canadian multicenter clinical trial; MI-ONCOSEQ, Michigan Oncology Sequencing Project; MOSCATO 01, The MOlecular Screening for CAncer Treatment and Optimisation; I-SPY 2; Investigation of Serial studies to Predict Your therapeutic response with imaging and molecular analysis; WinTHER, Worldwide Innovative Networking.
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Table 3 Selected studies that access tumour heterogeneity
Study NCT ID Tumour type Intratumour and inter-patient heterogeneity BEAUT Non-metastatic breast Y N/A cancer
Whole genome sequencing
TRACER NCT018 x 88601
Whole-genome and -exome sequencing
Xenograft models Circulating biomarker and functional imaging
MiSeq/Sequenom
Circulating biomarkers
Non-metastatic NSCLC Metastatic breast, colorectal or gynaecological cancer
NCT017 MATCH 03585 EPREDIC ISRCTN T/SPREDIC 2297960 Metastatic renal cell 4 cancer T Inter-patient heterogeneity
Main platform used
Ancillary approaches used
Whole exome sequencing, transcriptome
BATTLE -FL
NCT012 63782
Front-line treatment of NSCLC (stage IIIB or IV)
BATTLE -2
NCT012 48247
Metastatic NSCLC
NSCLC-based molecular biomarkers (PCR/FISH) NSCLC-based molecular biomarkers (PCR/FISH)
I-SPY 2
NCT010 42379
CanSeq
N/A
Breast cancer (stage III, neoadjuvant) NSCLC, colorectal, breast and prostate cancers
TargetPrint HER2, Mammaprint OncoMap/whole exome sequencing
-
-
Blood and breast magnetic resonance imaging -
BATTLE, Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination; BATTLE-FL: Front-Line Biomarker-Integrated Treatment Study in Non Small Cell Lung Cancer; BEAUTY, Breast Cancer Genome-Guided Therapy; FISH, fluorescence in situ hybridisation. HER2, Human Epidermal growth factor Receptor 2, I-SPY 2 TRIAL: Neoadjuvant and Personalized Adaptive Novel Agents to Treat Breast Cancer; ISRCTN, International Standard Randomised Controlled Trial Number; NCT ID, National
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Clinical Trials Identifier (ClinicalTrials.gov); MATCH, Feasibility Study of Genomic Profiling Methods and Timing in Tumor Samples; N/A, non available, NSCLC, non-smallcell lung cancer; PCR, polymerase chain reaction; PREDICT, Study of Preoperative Everolimus in Metastatic Renal Cancer; TRACERx, TRAcking Non-small Cell Lung Cancer Evolution Through Therapy.
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Received Date : 30-Jan-2014 Revised Date : 20-Feb-2014 Accepted Date : 20-Feb-2014 Article type
: Key Symposium
The challenge of intratumour heterogeneity in precision medicine
Running head: Tumour heterogeneity and precision medicine
Joan Seoane1,2,3 & Leticia De Mattos-Arruda1,2
From the
1
Vall d'Hebron Institute of Oncology, Vall d'Hebron University Hospital,
Barcelona, Spain; 2Universitat Autònoma de Barcelona, Barcelona, Spain; and 3Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
Abstract Cells within tumours have diverse genomes and epigenomes, and interact differentially with their surrounding microenvironment generating intratumour heterogeneity, which has critical implications for treating cancer patients. Understanding the cellular and microenvironment composition and characteristics in individual tumours is critical to stratify the patient This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/joim.12240 This article is protected by copyright. All rights reserved.
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population that is likely to benefit from specific treatment regimens. Here, we will review the current understanding of intratumour heterogeneity at the genomic, epigenomic and microenvironmental levels. We will also discuss the clinical implications and the challenges posed by intratumour heterogeneity, and evaluate non-invasive methods such as circulating biomarkers to characterise the cellular diversity of tumours. Comprehensive assessment of the molecular features of patients based on tumour specimen characterisation (including intratumour spatial and temporal variations), ancillary non-invasive methods (such as circulating biomarkers and molecular imaging approaches), and the correct design of clinical trials are required to guide administration of targeted therapy and to control therapeutic resistance. Finding the means to accurately determine and effectively control tumour heterogeneity and translate these achievements into patient benefit are major goals in modern oncology.
Keywords: cancer, genomics, intratumour heterogeneity, precision medicine, targeted therapy.
Introduction Progress in cancer management has frequently resulted from the interaction between clinical and laboratory research [1, 2]. The molecular characterisation of tumours provides a basis for personalising cancer treatment resulting in increased clinical benefit, reduced toxicity and overcoming therapeutic resistance [3].
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The genomic characterisation of cancers has contributed to our understanding of the molecular landscape of tumours and has provided a rationale for the use of matched targeted therapies based on specific molecular targets. Improved treatment for specific tumour subtypes has highlighted the potential of precision medicine [3]; for example, the use of trastuzumab in HER2-positive breast cancer [4], vemurafenib for advanced BRAFV600-mutant melanoma [5], the PARP inhibitor olaparib in patients whose tumours bear BRCA1/2 mutations [6] and crizotinib in non-small-cell lung cancer with anaplastic lymphoma kinase translocations [7].
However, at present, precision medicine is not entirely successful. There is growing recognition that the heterogenous nature of cancer, and in particular the intrinsic heterogeneity of individual tumours as well as between primary and metastatic tumours, presents an important challenge in cancer therapeutics. In addition to the fact that all tumours show a unique combination of genomic alterations (intertumour heterogeneity), they are composed of cells with different molecular characteristics (intratumour heterogeneity). This results in diversity between and within tumours [8, 9].
Here, we review the current knowledge of the diverse patterns of intratumour heterogeneity. Cells within tumours can comprise distinct genomic alterations. In addition, cells with the same genomic landscape can show different states of differentiation and, hence, epigenetic status. Moreover, tumour cells can be influenced by a complex and diverse environment generated by stromal cell heterogeneity. These three different levels of heterogeneity are crucial for explaining the biology of cancer and have critical therapeutic implications.
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Understanding intratumoural heterogeneity is essential for designing effective therapeutic strategies in the context of precision medicine.
Tumour heterogeneity Although tumour heterogeneity is a well- and long-established concept, recognition of its various challenges to cancer therapy has increased in recent years [10–20] (Table 1). The study of cancer genomics has been boosted by the development and use of massively parallel (or next-generation) sequencing technologies [21]. Global research initiatives such as the 1000 Genomes Project [22], the Cancer Genome Atlas [23–25] and the International Cancer Genome Consortium [26], and publicly available catalogues such as the Catalogue of Somatic Mutations in Cancer) [27] and Genomics of Drug Sensitivity in Cancer [28], have added to improved delineation of cancer genomics. Together, these demonstrate variability among individual tumours and how diverse genomic aberrations of tumours sharing a similar histology is crucial for predicting how patients will respond to targeted therapies [8, 9] (Fig. 1).
In addition to intertumour heterogeneity, another layer of complexity is created by the intermingling of diverse subpopulations of cancer cells with normal cells, which leads to intratumour heterogeneity that can progress and become modified over time. Intratumour heterogeneity influences the dynamic tumour landscape and plays a key role in shaping responses to specific therapies [29, 30].
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Intratumour genomic heterogeneity The findings of massively parallel sequencing studies have demonstrated that the compendium of genomic aberrations in cancer is complex and diverse and only a limited number of genes are frequently and recurrently mutated in a substantial proportion of cancer cases [31–33]. Genomic analyses of human cancers have provided evidence of spatial and temporal intratumour genomic heterogeneity [11, 14, 34–36], and have shown that tumours are composed of mosaics of cells [12, 36] with subclones harbouring both private (or unique) and ubiquitous (i.e. common to all tumour cells) genomic alterations. For example, Gerlinger et al. investigated intratumour genomic heterogeneity in renal cancer through an analysis of genomic alterations in multiple tumour regions [11]. Using exome sequencing, chromosome aberration and DNA ploidy analyses confirmed the heterogeneity of different regions within the same tumour mass and of the primary tumour versus matched metastases. Genomic aberrations were found in all cell subclones throughout individual tumour specimens, whereas other aberrations were heterogeneously distributed [11]. In this context, it was shown that performing single biopsies of primary tumours or metastatic deposits, as is usual clinical practice, is unlikely to reveal the complete profile of genomic alterations in any tumour [37].
Additionally, recent evidence shows that intratumoural heterogeneity is dynamic and versatile [14, 18, 38, 39]. For example, Nik-Zainal et al. studied the evolution of cancer genomic clones and quantified the extent and dynamics of subclonal variation in breast cancer [18]. Moreover, while reconstructing tumour phylogeny through a multisampling scheme devised for individual glioblastoma patients, Sottoriva et al. constructed a model that inferred tumour progression in time and space and showed that copy number aberrations in
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EGFR and CDKN2A/B/p14ARF were early events, and aberrations in PDGFRA and PTEN occurred later during cancer progression [14].
Evolutionary studies have focused on analyses of the relationship between primary tumours and metastatic deposits, and within and between metastatic deposits [10, 12, 14, 36], as well as subclone evolution within a given tumour mass [15, 18, 40]. Increasing evidence demonstrates that genome evolution reflects the Darwinian model and branched evolutionary tumour growth contributes to intratumour genomic heterogeneity [10, 37, 38]. In this regard, the fittest clones [11, 41] are selected during the metastatic process via external selective pressures (e.g. administration of systemic therapeutics) [10] or via pressures from the tumour microenvironment (e.g. hypoxia, growth factors) [30] (Fig. 2).
The functional impact of intratumour genomic heterogeneity on cancer remains to be fully understood. Further studies are required to assess how intratumour genomic diversity influences the response and resistance to therapies during tumour relapse and the generation of metastasis.
Heterogeneity in the cellular state of differentiation Diverse states of differentiation and, hence, diverse epigenomic states can be found in cancer cells that share the same genomic alterations [42]. This implies that tumour deposits with homogeneous genomic characteristics contain functionally heterogeneous cancer cells with
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diverse phenotypes, including different capacities to proliferate, migrate and invade and, most importantly, with different sensitivities to therapeutic agents.
With regard to the concept of heterogeneity in the cellular state of differentiation, recent evidence from transplantation and lineage tracing experiments shows that only a fraction of cells within a tumour have tumour-initiating capabilities [43, 44]. These cells, termed cancerinitiating cells (CICs) or cancer stem cells, are more tumorigenic than their surrounding cells. CICs are usually poorly differentiated (with stem cell features), have a different epigenetic status compared to the rest of the cells and are considered to be tumour drivers [44, 45]. CICs are responsible for the re-initiation of tumours, mediating relapse and metastatic dissemination; moreover, these cells seem to be more resistant to DNA-damaging therapeutic agents [46, 47] (Fig. 2).
The intratumoural heterogeneity generated through the different degrees of cellular differentiation, including CICs and non-CICs, is due to epigenetic regulation of cells guided by signals from the microenvironment [42, 46–48]. Of note, CICs are not randomly spread through the tumour mass; these cells tend to be found in specific locations or niches, depending on the local microenvironment [49, 50]. Such niches are composed of non-tumour cells (i.e. inflammatory cells, endothelial cells and fibroblasts) and the extracellular matrix, which enable direct cell-to-cell interactions and secrete factors that maintain the stem cells in a quiescent state, regulating their self-renewal capacity and pluripotency. CICs can be located near vessels in a perivascular niche or in a hypoxic niche, suggesting close communication between CICs and the tumour microenvironment. Several signalling pathways, including the Wnt, Notch, Hedgehog and transforming growth factor-beta pathways, have been implicated This article is protected by copyright. All rights reserved.
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in the regulation of CIC self-renewal, and also on differentiation or dedifferentiation of nonCICs into CICs [45, 51, 52].
Of note, CICs and non-CICs differ in their sensitivity to treatment [47] and, therefore, unravelling the mechanism(s) underlying their epigenetic heterogeneity will be crucial for cancer treatment.
Intratumour microenvironment heterogeneity Tumour cells are in intimate contact with a heterogeneous microenvironment which regulates their biology and sensitivity to treatment [53]. The tumour niche is created by a dynamic microenvironment that supports and interacts with tumour cells through a complex signal cross-talk. The non-tumoural component of cancer, the tumour stroma, is composed primarily of the extracellular matrix, fibroblasts, blood vessels and immune cells [54]. These stromal elements can vary substantially within the topography of primary tumours and/or metastatic deposits, both in terms of the number of cell types and the proportion of lineage relationships [55], generating intratumour heterogeneity. Of importance, the normal cells of the stroma coevolve with tumour cells in a dynamic way; thus, tumour deposits should be evaluated in the context of an evolving tumour microenvironment which has definite implications for the response and resistance to therapeutic agents [54].
The tumour stroma comprises a modified extracellular matrix [56] with a large number of fibroblasts with specific characteristics [57]. The so-called cancer-associated fibroblasts
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(CAFs) are not evenly distributed within the extracellular matrix and play a key role in tumorigenesis [58]. In addition, CAFs and tumour cells can generate an intricate and dense extracellular matrix, which contributes to increased interstitial fluid pressure that hampers the penetration of drugs through tissue [59, 60]. This limits the diffusion of therapeutic compounds to tumour regions, and thus might represent an obstacle to clinical treatment.
Tumours are nourished by a dynamic vascular network, which controls tumour growth [61]. The tumour vasculature is variable with distinct characteristics even within the same tumour mass [30]. The vascular heterogeneity within tumours may result from the formation of new vessels (angiogenesis) or modification of existing vessels within the tissue [57]. In some parts of a tumour, abnormalities in blood vessels and differences in hydrostatic pressure impair blood flow, delivery of nutrients and clearance of metabolic products leading to acidic and hypoxic tumour regions [62]. This generates a variable milieu for tumour cells ranging from hypoxic to well-irrigated and non-hypoxic regions.
On the other hand, the inflammatory and immune components of the stroma can also vary in different locations of the tumour mass [30]. Tumour-infiltrating immune cells include varied cell types with specific roles both as pro- and anti-tumour cells; in addition, their geographic location can vary within individual tumours. This intratumoural spatial diversity of immune response and inflammatory cells controls the tumour biology, and its understanding can be critical for designing therapies based on the cancer immune checkpoint [29, 63].
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Tackling tumour heterogeneity in the clinic Cancer heterogeneity challenges the potential benefit of precision medicine. Consequently, genomic and non-genomic biomarker analyses of single biopsies may differ according to the area of the sampled tumour [11]. This multiregional separation of molecular aberrations can lead to sampling bias [37, 64], potentially impairing the interpretation of the molecular characterisation of tumours and having an impact on the selection of treatment.
Accordingly, approaches that provide global assessment of the catalogue of somatic genomic and epigenomic aberrations and tumour microenvironment-based biomarkers are clearly important for accurately selecting targeted therapies for individual patients. Recent studies in the metastatic setting have provided evidence of the emergence of a small number of subclones within primary tumours [34, 35, 65, 66]. These subclones could clarify how treatment-resistant subpopulations of tumour cells can be monitored and targeted over time. Pilot initiatives (i.e. molecularly driven clinical trials) [9, 67] and also circulating biomarkers [i.e. liquid biopsies, circulating tumour cells (CTCs) or cell-free tumour DNA (ctDNA)] [67, 68] may play a key role in this context. Pilot initiatives The purpose of using biomarkers to provide information for clinical decision-making is to enable individualised cancer therapy. The fact that there are different levels of heterogeneity (i.e. inter- and intratumour heterogeneity) is one the major problems for the administration of targeted therapies and has influenced the design of translational pilot initiatives and other innovative clinical trials.
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To date, most pilot initiatives have focused on individual patients through identifying groups of patients who share similar targetable biomarkers, and personalising appropriate targeted therapies to these individuals (Table 2) [45, 67, 69–77]. An example is the WinTHER study, which is a Phase II clinical trial launched by the Worldwide Innovative Networking Consortium in personalized cancer medicine (WIN Consortium) to assess the potential of selecting targeted therapies for ‘actionable’ targets found in the analysis of patients’ tumours [74, 78]. By contrast, few other initiatives (e.g. MOSCATO 01 study, NCT01566019) that included subgroups of patients enriched for specific genomic alterations have used the stratification approach, which focuses on molecular biomarkers rather than individual patients.
In addition, single subject clinical trials (i.e. n-of-1 trial design) investigate the efficacy of a given therapy or the side effect profiles of different therapeutic interventions in an individualised manner, primarily for patients harbouring rare molecular aberrations [9, 67]. Clinical trials using other designs are also being conducted, such as the ‘basket trials’ in which one study (the basket) includes one or more targets (e.g. NCT01219699 study enrolling patients whose tumours have a PIK3CA oncogene mutation to receive an alpha phosphatidylinositol 3-kinase (PI3K) inhibitor) and allows patients with multiple diseases to be
enrolled [11].
These studies to empirically test the concept of precision medicine are transforming the way tumour tissue is assessed for molecular aberrations, the design of clinical trials and the measurement of treatment efficacy. To date, these studies have usually been based on ‘biased‘ tumour tissue sampling, which relies on spatial variability in the expression of key This article is protected by copyright. All rights reserved.
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molecular biomarkers and, in some instances, on archival tumour specimens. This approach, which is rapidly becoming outdated, does not appropriately reflect tumour heterogeneity or dynamics.
Recent clinical and translational programmes have attempted to investigate intra- or intertumour heterogeneity (Table 3). These initiatives together with other non-invasive methods based on tumour molecular imaging [79, 80], which might identify and quantify actionable molecular biomarkers over time, are also leading the way to patient selection and longitudinal monitoring of response to therapy.
Circulating biomarkers The use of circulating biomarkers, in particular CTCs and ctDNA, is a promising approach that may illuminate the challenges posed by intratumour heterogeneity and sampling bias of single biopsies [64, 81–83]. CTCs and plasma-derived ctDNA have been widely investigated as potential non-invasive surrogates for tumour tissue biopsies [64]. The genomic characterisation of these blood-borne biomarkers has introduced a new method for investigating the metastatic process and the mechanisms of therapeutic resistance as well as for disease monitoring [65, 66, 82–85]. The ability to comprehensively identify tumour clones and subclones from cancer patients is likely to be instrumental in delineating the molecular and clinical heterogeneity of cancer and may facilitate the precise delivery of targeted therapy.
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CTCs and ctDNA are shed into the circulation from primary tumours and/or their metastases; therefore they constitute a potential source of tumour material from all disease sites, and offer a real-time, easily obtainable, cost-effective and minimally invasive tool for the development of molecular biomarkers [64, 68]. It should be noted, however, that CTCs may represent heterogeneous subpopulations of cancer cells [81, 86, 87] so that plasma-derived ctDNA might provide a better readout for bridging the gap between sample bias and the administration of targeted therapies. To this end, genomic characterisation of plasma-derived ctDNA may offer a way to confront the emergence of treatment-resistant clones, to track the evolution of the intratumour heterogeneity of cancer and to predict responses and resistance to treatment.
Conclusions and future perspectives Tumour diversity poses a challenge for managing the treatment of cancer patients. Decoding heterogeneity has important implications that will probably refine our understanding of cancer biology, its genomic, epigenomic and functional diversity and the mechanisms that lead to therapeutic resistance [10, 37, 88–90]. Nevertheless, the extent of tumour heterogeneity, and its multifaceted nature, remains poorly understood.
In addition to tumour heterogeneity, the design of studies appropriate for the era of precision medicine may be impeded by other barriers. These include the complexity of identifying and validating predictive molecular biomarkers, technical limitations of molecular tests, slow progress in unravelling the biology of some types of cancer, mechanisms of resistance per se,
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the high failure rate and cost of molecularly targeted agents and reimbursement and regulatory issues.
Current biomarker assessment programmes do not thoroughly address the issues of heterogeneity at the single-cell level (within and/or between patient tumours) or the dynamic environment of a cancer (i.e. changes in genomic, epigenomic and functional cancer characteristics). In addition, biomarker analysis does not consider minor genetic subclones or predict future tumour development. There is evidence to suggest that ’non-standard personalised strategies‘ using mathematic model-based genetic evolutionary dynamics and single-cell heterogeneity that personalise cancer therapeutics might lead to superior patient outcomes compared with the current model [91]. A comprehensive assessment of the molecular characteristics of patients and their therapeutic needs (Fig. 3), based on (i) tumour specimen characterisation, which includes intratumour spatial and temporal variations, (ii) ancillary non-invasive methods, such as circulating biomarkers and molecular imaging approaches, (iii) availability of clinical trials or compassionate use of specific therapeutic agents and (iv) functional characterisation of tumour-derived biomarkers based on in vivo cancer experimental models, would ideally guide targeted therapy administration and manage therapeutic resistance. Finding the means to accurately determine and effectively control tumour heterogeneity and translate these achievements into patient benefit are major goals in modern oncology. Precision medicine will not become a reality until heterogeneity-related issues are resolved.
Conflict of interest statement The authors have no conflicts of interest to declare. This article is protected by copyright. All rights reserved.
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Acknowledgments The authors acknowledge finantial support from Fundación Rafael del Pino and la Asociación Española Contra el Cáncer. References
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Correspondence: Prof. Joan Seoane, Translational Research Program, Vall d’Hebron Institut d’Oncologia, Pg Vall d’Hebron 119-129 08035, Barcelona, Spain. Tel: +34 93 4894167; Fax: +34 93 489 40 15; E-mail: [email protected]
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Figure legends Fig. 1 The different patterns of tumour heterogeneity. (A) Intertumour heterogeneity: heterogeneity between tumours in different patients or within the same patient with different tumour deposits. (B) Intratumour heterogeneity: diversity within a tumour. (C) Intratumour genomic heterogeneity: cells with distinct genomic alterations within a tumour. (D) Intratumour epigenomic heterogeneity: cells with diverse states of differentiation based on different epigenomic states. (E) Intratumour microenvironment heterogeneity: differences in tumour stroma (extracellular matrix, vasculature and immune cells) within the same tumour. Fig. 2 Functional heterogeneity and the impact of treatment in the selection of the fittest cells/clones. (A) Genomic selection: the treatment affects a cellular clone with a specific genomic alteration. (B) Epigenomic selection: the treatment affects a cellular compartment with a specific state of differentiation/epigenetic status [e.g. non-cancer-initiating cells (CICs) vs. CICs]. (C) Microenvironment selection: the treatment affects cells present in a specific stromal niche. Fig. 3 Projected strategy of data integration to connect tumour characterisation and matched targeted therapy in the context of precision medicine. Clinical trial allocation would be based not only on the characterisation of tumours, which involves genomic/non-genomic aspects of tumour tissue, the use of surrogate tools (e.g. circulating blood-based biomarkers and molecular imaging) and functional studies, but would consider inter- and intratumour heterogeneity as core parameters for the specific choice of the most appropriate drug for each patient.
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Table 1 Selected examples of studies to assess tumour heterogeneity Tumour Description Type of assessment type Melanoma Intra- and intertumour heterogeneity of Intra- and intertumour heterogeneity the BRAFV600E mutation using BRAF mutant-specific PCR and conventional sequencing Renal Branched evolutionary growth through Intra- and intertumour multiregion exome sequencing heterogeneity Intratumour heterogeneity Glioblasto Integrated genomic analysis (copy ma number and gene expression) after multiple intratumour sample collection reveals multiple glioblastoma subtypes within the same tumour and inferred genomic evolution Medullobl Metastases from an individual are quite Intertumour heterogeneity astoma similar to each other but differ from the matched primary tumour Intratumour heterogeneity NSCLC Multiregion sampling from each tumour showed heterogeneous populations of both EGFR-mutated and non-mutated cancer cells, which was associated with reduced response to gefitinib Ovarian Intratumour variation in mutation, copy Intratumour heterogeneity number and gene expression profiles through the analysis of spatially and temporally separated high-grade serous ovarian tumour specimens. Key driver gene alterations (e.g. PIK3CA, CTNNB1 and NF1) present in a subset of samples; only TP53 somatic mutation present in all samples Intratumour heterogeneity Breast Triple-negative breast tumours vary widely in their clonal frequencies (e.g. TP53, PIK3CA and PTEN somatic mutations were not clonally dominant in all tumours) Breast The subclonal architecture of 21 breast Intratumour heterogeneity cancers was mapped by massively parallel sequencing analyses Pancreatic Whole-exome sequencing of pancreatic Intra- and intertumour cancer specimens showed the clonal heterogeneity relationships among primary and metastatic tumours. Timing of the genetic evolution (i.e. distant metastasis occurs late for these cancers) was inferred. This article is protected by copyright. All rights reserved.
Reference [13]
[11] [14]
[15]
[16]
[17]
[12]
[18]
[19]
Accepted Article
[20] Synchrono Massively parallel sequencing can help Intra- and intertumour define the origin of metastatic deposits heterogeneity us in patients with multiple primary tumours tumours and reveal genetic heterogeneity within and between lesions from the same patient EGFR, epidermal growth factor receptor, NSCLC, non-small-cell lung cancer; PCR, polymerase chain reaction.
Table 2 Selected pilot studies focusing on individual patients
Pilot study design
Matched actionable molecular alteration with targeted therapy
Treated patients
Reference
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D
[71] [72]
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D
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D
Personalised Bisgrove trial MI-ONCOSEQ COMPACT WinTher
[73] [74]
Stratified MOSCATO 01 BATTLE I-SPY 2
The M. D. Anderson Cancer Center Initiative ‘Basket trials’
[75] [76] [77] -
Both personalised and stratified
Patient-derived xenografts
[45, 69, 70]
BATTLE, Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination; COMPACT, Canadian multicenter clinical trial; MI-ONCOSEQ, Michigan Oncology Sequencing Project; MOSCATO 01, The MOlecular Screening for CAncer Treatment and Optimisation; I-SPY 2; Investigation of Serial studies to Predict Your therapeutic response with imaging and molecular analysis; WinTHER, Worldwide Innovative Networking.
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Table 3 Selected studies that access tumour heterogeneity
Study NCT ID Tumour type Intratumour and inter-patient heterogeneity BEAUT Non-metastatic breast Y N/A cancer
Whole genome sequencing
TRACER NCT018 x 88601
Whole-genome and -exome sequencing
Xenograft models Circulating biomarker and functional imaging
MiSeq/Sequenom
Circulating biomarkers
Non-metastatic NSCLC Metastatic breast, colorectal or gynaecological cancer
NCT017 MATCH 03585 EPREDIC ISRCTN T/SPREDIC 2297960 Metastatic renal cell 4 cancer T Inter-patient heterogeneity
Main platform used
Ancillary approaches used
Whole exome sequencing, transcriptome
BATTLE -FL
NCT012 63782
Front-line treatment of NSCLC (stage IIIB or IV)
BATTLE -2
NCT012 48247
Metastatic NSCLC
NSCLC-based molecular biomarkers (PCR/FISH) NSCLC-based molecular biomarkers (PCR/FISH)
I-SPY 2
NCT010 42379
CanSeq
N/A
Breast cancer (stage III, neoadjuvant) NSCLC, colorectal, breast and prostate cancers
TargetPrint HER2, Mammaprint OncoMap/whole exome sequencing
-
-
Blood and breast magnetic resonance imaging -
BATTLE, Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination; BATTLE-FL: Front-Line Biomarker-Integrated Treatment Study in Non Small Cell Lung Cancer; BEAUTY, Breast Cancer Genome-Guided Therapy; FISH, fluorescence in situ hybridisation. HER2, Human Epidermal growth factor Receptor 2, I-SPY 2 TRIAL: Neoadjuvant and Personalized Adaptive Novel Agents to Treat Breast Cancer; ISRCTN, International Standard Randomised Controlled Trial Number; NCT ID, National
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Clinical Trials Identifier (ClinicalTrials.gov); MATCH, Feasibility Study of Genomic Profiling Methods and Timing in Tumor Samples; N/A, non available, NSCLC, non-smallcell lung cancer; PCR, polymerase chain reaction; PREDICT, Study of Preoperative Everolimus in Metastatic Renal Cancer; TRACERx, TRAcking Non-small Cell Lung Cancer Evolution Through Therapy.
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