Intratumor heterogeneity, microenvironment, and mechanisms of drug resistance in glioma recurrence and evolution

Abstract

Glioma is the most common lethal tumor of the human brain. The median survival of patients with primary World Health Organization grade IV glioma is only 14.6 months. The World Health Organization classification of tumors of the central nervous system categorized gliomas into lower-grade gliomas and glioblastomas. Unlike primary glioblastoma that usually develop de novo in the elderly, secondary glioblastoma enriched with an isocitrate dehydrogenase mutant typically progresses from lower-grade glioma within 5–10 years from the time of diagnosis. Based on various evolutional trajectories brought on by clonal and subclonal alterations, the evolution patterns of glioma vary according to different theories. Some important features distinguish the normal brain from other tissues, e.g., the composition of the microenvironment around the tumor cells, the presence of the blood-brain barrier, and others. The underlying mechanism of glioma recurrence and evolution patterns of glioma are different from those of other types of cancer. Several studies correlated tumor recurrence with tumor heterogeneity and the immune microenvironment. However, the detailed reasons for the progression and recurrence of glioma remain controversial. In this review, we introduce the different mechanisms involved in glioma progression, including tumor heterogeneity, the tumor microenvironment and drug resistance, and their pre-clinical implements in clinical trials. This review aimed to provide new insights into further clinical strategies for the treatment of patients with recurrent and secondary glioma.

Keywords

glioma / evolution mechanism / strategies / tumor heterogeneity / secondary glioma

1 Introduction

Glioma is the most common and aggressive brain cancer in adults and can be classified as grades I–IV based on histological features [1–3]. According to the clinical course, glioblastoma multiforme (GBM), which is referred to as grade IV glioma, can generally be classified into two subtypes [4]. Primary GBM refers to the vast majority of GBMs considered to form de novo in the elderly. Secondary GBMs (sGBMs) typically progress from lower-grade tumors and affect younger patients [5]. Recurrence of lower-grade glioma (LGG) and other tumors appears to be unavoidable despite considerable research performed in this field using various technologies in the last decades. Many extracellular tumor microenvironment (ETM) cell types are prevalent in brain tumors, but some important features that distinguish the normal brain from other tissues exist, including the composition of the ETM (e.g., microglia, astrocytes, and neurons), blood-brain barrier, and a previously “immune privileged” organs consideration in the human brain. Moreover, the skull provides a physical barrier to the swelling that often occurs following inflammatory reactions. Thus, interactions with the ETM require regulation within the brain. Hence, the mechanism underlying glioma recurrence and the evolution patterns of glioma remain controversial.

Studies have revealed individual and spatiotemporal evolution patterns of the primary GBM genome [6,7] and have identified key genomic events and subgroup alterations during the progression of glioma [8–11]. Few researchers have systematically reviewed the mechanisms and patterns that occur in the progression and recurrence of glioma [12]. Moreover, few reviews have discussed the differences in intratumor heterogeneity (ITH), microenvironment, and differences in the mechanisms underlying drug resistance between glioma and other types of tumors.

We reviewed the currently available literature on the mechanisms of glioma progression, including tumor heterogeneity, ETM, and drug resistance. We described some evolutional patterns during the development and progression of glioma, to provide new insights into further strategies for the treatment of patients with recurrent or secondary glioma.

2 Gradualism and punctuated evolution of cancer

Approximately half of the mutations, e.g., driver mutations in tumor protein p53 (TP53), BRAF, and ATRX, identified in the initial tumor of LGG are no longer detectable at the time of recurrence. This phenomenon indicates that recurrent tumors are often seeded from heterogenetic cells derived from ancestors at early stages [9,11]. The long-standing debate on the evolution of cancer has focused on whether tumors evolve gradually through the sequential accumulation of alterations during clonal expansions or are characterized by punctuated progression [13]. The gradual evolution hypothesis is supported by the presence of clock-like alteration signatures observed in patients [14]. Different patterns of cancer evolution have been described in various cancer types. Evidence for the selection of driver events in cancer development and therapeutic pressure is currently limited. Therefore, ITH in the development of colorectal carcinoma can follow the laws of neutral growth after a “big bang” of diversity early events in its evolution [15]. Another evolution pattern known as punctuated or parallel evolution is described as an independent pattern of similar evolution characterization initiating from a single ancestral clone [16]. Clone cells harboring mutations did not expand, despite the proliferative phenotype typically conferred by this mutation in the recurrence of other cancer types [17,18]. This finding indicated the ineffectiveness of sequential monotherapy.

Similarly, glioma contains various heterogenetic tumor clones and evolutionary patterns. Multiregional biopsy and spatiotemporal genomic architectural study may provide information regarding the clonal evolution from initial glioma to recurrence [11,13,19]. Samples from the same tumor mass were determined to have shared genomic and expression patterns using bulk and single-cell data of 127 samples obtained from 53 patients. In contrast, multiregional, separately localized tumors, or long-term recurrent GBMs are seeded from different clones at an early stage [19]. Strikingly, more complex clonal evolution was found in recurrent GBM. Alternative models for the branching pattern are also proposed, in which the recurrent samples are seeded from a lineage nested within the tumor, perhaps selected by therapy [11,19]. Model I could be described as a clonal mutation in the initial sample and absent in recurrence, whereas model II could be described as a subclonal mutation in the initial sample and clonal in recurrence (Fig. 1A). Bao et al. showed that the PTPRZ1-MET (ZM) fusion was enriched in patients with sGBM [20] and subsequently found that MET exon 14 skipping (METex14) accompanied by ZM fusion promoted malignant phenotypes of glioma. The subclonal level of METex14 in initial tumors was associated with poorer overall survival than that reported in METex14-negative patients [21], indicating the model II evolution pattern in sGBM patients with MET alterations. A chemical screening of patient-derived glioma cells showed that therapeutic response was associated with genetic similarity [19]. Owing to the presence of the linear evolution mode in these tumor cells, local recurrent tumors respond well to targeted treatment. However, multifocal GBMs develop through parallel evolution [22]. The branching evolution pattern and estimates of evolutionary rate suggest that the relapse-related clones typically existed years before the diagnosis [11]. The recent glioma evolutional study “GLASS” demonstrated that the clonal architecture of each tumor remained similar, whereas the presence of subclonal selection was associated with the clinical outcome [12]. The evolution pattern in the landscape of driver clones was correlated with the distant appearance of a recurrent tumor from the initial tumor [7]. This phenomenon indicated the misleading therapy involving the targeting of the genomic profile of the initial tumor for the distally recurring tumors.

Various pathways are involved in the evolution of GBM. Genetic alterations of the p53 pathway were identified as primary molecular markers with a high number of subclonal alterations in GBM [6]. Among recurrent GBMs, 11% of tumors harbor mutations in latent transforming growth factor b binding protein 4 (LTBP4), which encodes a protein that binds to the transforming growth factor b (TGF-b). Inhibition of LTBP4 in GBM cells restrains the TGF-b pathway and decreases cell proliferation, highlighting the function of this pathway as a potential predictive marker in GBM [11]. Other genetic alterations, including ETS variant transcription factor 1 (ETV1), cyclin dependent kinase 6 (CDK6), NF-κB (complex), interleukin 1B (IL1B), IL6, AKT, and vascular endothelial growth factor (VEGF) were identified as candidate genes and potential signaling regulators of chromosome gain or loss in recurrent GBM [23]. These findings are consistent with those generally observed in GBM samples in comparison with normal brain tissue.

Mazor et al. investigated the function of isocitrate dehydrogenase (IDH) mutation from LGG in recurrence and discovered that deletion of IDH1 was followed by clonal evolution and recurrence at a higher grade. Deletion of IDH1 leads to decreased concentration of 2-hydroxyglutarate, maintenance of the glioma CpG island methylator phenotype, and DNA methylation reprogramming outside CGI. This finding indicates that in some patients, mutant IDH1 and 2-hydroxyglutarate are not required for recurrence despite the initiation of gliomagenesis through the mutation of IDH1 [24]. Interestingly, genes transcriptionally dysregulated through promoter methylation and enriched in cell cycle pathways were associated with malignant progression to high-grade gliomas [24].

3 Mechanisms of glioma recurrence and malignant progression

Hanahan and Weinberg reviewed the different ways of activating cancer invasion and metastasis [25], including the epithelial-mesenchymal transition program, heterotypic contributions of stromal cells, plasticity, and the daunting complexity of metastatic colonization. In glioma, PTEN and p53 alterations [26], IDH [27] and ATRX [28] mutations, and telomerase reverse transcriptase promoter mutation [29] lead to the initiation of glioma and impact the overall survival time. However, the underlying mechanism of these alterations and other factors involved in glioma progression and recurrence remain unknown. ITH with genomic instability and hyper-mutation, the ETM (consisting of immune inflammatory cells, cancer stem cells (CSCs), endothelial cells, pericytes, cancer-associated fibroblasts (CAFs), and progenitor cells of the tumor stroma), and metabolic deregulation have recently been found as enabling and emerging characteristics of cancer pathogenesis and neoplastic proliferation.

4 ITH

ITH has been identified since the 1800s [30]. It is recognized as a key reason for therapeutic failure, drug resistance, and tumor progression [31,32]. However, ITH had not been distinctly elucidated until the development of advanced technology, such as next-generation sequencing. Through this approach (occasionally single-cell sequencing), studies demonstrated that distinct subpopulations cooperate to promote tumor maintenance, growth, and progression [33–36].

A recent study regarding LGG [9,24], GBM [7,37], and other tumor types [23,38] demonstrated the presence of different subclonal alterations between initial and recurrent tumors. The biological process and treatment implications of ITH have been recently reviewed. Divergent extents of ITH have been found in brain tumors, including diffuse intrinsic neuroblastoma [39], pontine glioma [40], LGG [9], and GBM [7,11]. The characteristics of heterogeneity in GBM have recently been identified. As implied by the term “multiforme,” ITH at a high level exists in this type of brain tumor [41]. Primary GBM samples showed strong association with classical and mesenchymal subtypes, as shown by RNA sequencing, thereby confirming the heterogeneity in GBM [22]. Analysis of copy number profiles revealed a high degree of genetic instability among different tumor cells with a high level of heterogeneity. This phenomenon indicated that genomic instability and ITH increase as GBM cells increase in tumorigenicity [23]. ITH is relatively well recognized in GBM [42]. However, fluorescence in situ hybridization [43], multi-region sequencing [9,44], and single-cell sequencing [45,46] revealed a more detailed characterization of LGG heterogeneity. IDH1 mutation occurs early in the course of LGG followed by deletion or amplification after tumor progression and clonal expansion at a higher grade [10]. As genome doubling and ongoing dynamic chromosomal instability are associated with ITH as an early event [47], further investigation should be performed regarding the correlation between ITH and IDH mutation. DNA methylation loss is identified during progression from LGG to high-grade glioma because of heterogeneity of the initial tumor [24]. However, ITH could not explain the majority of genetic alterations between the initial and progression tumors [9]. Geographic heterogeneity of the tumor is not responsible for the genomic divergence in distant recurrence samples [24]. Furthermore, intraGBM heterogeneity does not explain the large number of alterations uniquely detected in initial and recurrent tumors [6].

Future molecular targeted therapies should focus on at least four types of cancer cells based on their different properties and response to treatment, namely, primary glioma stem cells (GSCs), recurrence-initiating stem-like cancer cells, proliferating cells, and non-GSCs, as suggested by some researchers [48]. Inhibitors of the Wnt, sonic hedgehog signaling molecule (SHH), and Notch pathway are good candidate therapies for GSCs [8,49]. Targeting of adaptive resistance mechanisms and blocking of immune suppression can be accomplished by eliminating the populations of recurrence-initiating stem-like cancer cells [6,50], as shown in Fig. 1B.

5 ETM influences tumor progression

Multiple extracellular stromal cells converge to support the tumorigenic process by sustaining cell growth, invasion, and metastasis, leading to the following: inhibition of B and T cell responses, the recruitment of tumor-associated macrophages (TAMs), and the influence of CAFs. Unlike tumor cells, stromal cells within the ETM are genetically stable and thus become potential therapeutic targets with reduced risk of resistance and tumor relapse [51]. Interestingly, initial tumors with diverse recurrent potentials differ in their composition of both tumor- and stroma-derived ETM components [52]. Furthermore, the composition of the ETM is associated with clinical prognosis.

Another predominant factor in ETM associated with tumor progression is angiogenesis, which initiated a paradigm shift in cancer evolution [53,54]. Tumor vascularization requires the mutual interaction among multiple ETM cell types, such as TAMs, mesenchymal stem cells, and CAFs, whose phenotype is often regulated by hypoxia [55–57], as shown in Fig. 1C. Studies reported the effect of CAFs on the migration of glioma cells through their angiogenesis activity [58] or similar functional properties between CAFs and GBM stromal cells [59]. The role of CAFs in the malignant development of glioma needs further investigation.

In glioma, ETM is correlated with transcriptomic subtype multiplicity and ITH, which are involved in glioma cell clone expansion. Macrophages/microglia, CD4⁺ T lymphocytes, and neutrophils have been identified in the glioma microenvironment through in silico cell sorting. Deficiency of the neurofibromin 1 (NF1) gene leads to increased TAM infiltration and reveals a decrease in invading monocytes with a subtype-dependent increase in macrophages cells at recurrence. CD8⁺ T cell enrichment was further associated with hypermutation at diagnosis or recurrence [60]. These findings demonstrate that the microenvironment can normalize intrinsic tumor cells and re-regulate stromal cells. Therefore, targeting of the microenvironment rather than targeted ablation of tumor cells may be a more effective option for cancer treatment.

Further emerging examples of ETM-directed therapies neutralize tumor-associated chronic inflammation rather than targeting microenvironment cells. A colony stimulating factor 1 receptor (CSF-1R) inhibitor was recently used to target macrophages and microglia in the ETM of gliomas [61]. A patient with recurrent multifocal GBM received chimeric antigen receptor–engineered T cells targeting the tumor-associated antigen interleukin-13 receptor a 2 (IL13Ra2). Following chimeric antigen receptor T cell therapy, a decrease in all intracranial and spinal tumors was observed along with symptom relief and prolonged survival time [62].

6 Drug resistance and tumor relapse

Heterogeneity is associated with endogenous drug resistance in tumors. Heterogeneity-induced resistance can arise through two main mechanisms, namely, (1) pre-existing therapy-resistant clones prior to treatment and (2) de novo alterations acquired after treatment. Even with the most effective treatment, most patients exhibit incomplete drug response. Moreover, the residual tumor bulk commonly contains a small population of quiescent drug-resistant clones that survive the therapy due to alternative metabolic and epigenetic pathways [63–67]. Acquired drug resistance is often attributed to the selective expansion of pre-existing therapy-resistant subclones. However, some preclinical studies revealed de novo alterations of resistance alterations, which lead to the evolution of drug-tolerant cells [68,69]. Although these resistance alterations are often referred to as “acquired,” several studies identified de novo resistance alterations that are present at low frequencies in pretreated tumor tissues [70,71]. Bulk and single-cell sequencing demonstrated acquired malignant phenotypes after targeted therapy, including enhanced mesenchymal and growth factor signaling and decreased antigen presentation pathway, which may enable immune check-point avoidance. Some of the pre-existing subclones in pretreatment specimens with these phenotypes become dominant after chemotherapy, indicating the selection pressure for these resistance phenotypes [72].

Stem cells are responsible for the endogenous inducement of tumor progression. These cells are undifferentiated biological cells, which is a definition that is based on their capacity for long-term self-renewal to differentiate into multiple cell lineages. In numerous adult tumor tissues, stem cells (referred to as CSCs) are responsible for tumor homeostasis and regeneration [73]. CSC populations contribute to the occurrence of drug resistance through a variety of mechanisms, including self-renewal, quiescence maintenance, survival ability, and drug efflux [74]. Each category reflects specific CSC characteristics and provides anti-CSC strategies in the treatment of advanced cancers. In tumors constructed with CSCs and non-CSCs, the sensitivity of CSCs to chemotherapy inhibits tumor growth regardless of the remaining non-CSCs, inducing tumor regression. In case of non-CSC elimination, CSCs sustain tumor growth, inducing tumor relapse. The capacities to maintain tumor propagation, induce inherent resistance to clinical therapy, and contribute to tumor progression are fundamental properties of CSCs. Stem cells are located within the subventricular zone and express stem cell marker nestin in normal brain [75]. GSCs are identified in glioma tissue. Treatment with the standard drug temozolomide (TMZ) preferentially targets rapidly cycling tumor cells, whereas nestin-positive CSCs re-enter and retard the cell cycle after TMZ administration, thereby contributing to tumor relapse [76]. Resistant CSCs can be either intrinsically resistant to therapy (e.g., resistance to DNA damage and expression of multidrug resistance) or extrinsically instructed by the ETM (e.g., immune evasion, autophagy, and TAMs). Researchers demonstrated enriched CSCs following chemotherapy or radiotherapy, indicating the therapy-induced selection of cancer cells with CSC properties. Radiation induces high expression of CD133⁺ CSCs in GBM xenografts [77]. Epithelial-mesenchymal transition and CSC properties have been identified in GBM cell lines that acquired resistance to the anti-VEGFA bevacizumab [78]. In GBM, lineage-sorting experiments suggested that TMZ resistance is accompanied by the expansion of the CSC subclone [79]. Accordingly, targeting CSCs/GSCs necessitates a comprehensive elucidation of the mechanisms that lead to resistance to radiotherapy or chemotherapy.

Exogenous tumor resistance is associated with hypoxia and the ETM. Hypoxic cancer cells prevent the destabilization of the strands of DNA by ionizing radiation, indicating that molecular adaptations induced by oxygen-sensitive mechanisms impact responses to radiotherapy and tumor recurrence [80]. Homozygous mutation of Kelch-like ECH associated protein 1/TP53 (KEAP1/TP53) promotes airway basal stem cell self-renewal, leading to expansion of mutant stem cell clones. Meanwhile, deletion of KEAP1 increases tumor resistance to oxidative stress and radiotherapy [81]. This finding suggests that genomic mutations promote tumor progression, metastasis, and resistance to therapy through mutual effects between tumor and hypoxia in the microenvironment (Fig. 1D).

Models for the progression of glioma following radiotherapy or chemotherapy identified two patterns according to the presence of residue cell clones [6]. In the ancestral cell origin model, all dominant glioma clones from the primary tumor (unlike refractory cells) are removed after therapeutic intervention. The ancestral glioma cell accumulates new alterations that are identified in the recurrent tumor. In the clonal evolution model, radiotherapy or chemotherapy removed most of the initial tumor clones, but cells from the initial glioma clones survived and proliferated to a recurrent tumor. Endogenous pathways are altered after glioma progression. After treatment with TMZ, the refractory glioma is characterized by hyper-mutation and dysregulation of the retinoblastoma and AKT-mammalian target of rapamycin (AKT-mTOR) pathways [9]. In contrast to IDH1-mutated gliomas, IDH1-wild-type primary GBMs rarely present hypermutation after TMZ chemotherapy, demonstrating a low risk for TMZ-induced hypermutation in patients with GBM who received standard treatment [7]. Only 15% of GBM cases harbor hypermutation in highly expressed genes at recurrence [11]. The metabolic status of highly proliferative stem cells and cancer cells is similar [82,83]. Menendez et al. found that metabolic reprogramming in gliomas, which is influenced by the ETM, contributes to drug resistance and tumor relapse. Solute carrier family 2 member 3 (SLC2A3; previously termed GLUT3) is highly expressed in GSCs and indicates resistance to radiotherapy or chemotherapy [84]. Pyruvate dehydrogenase kinase 1 (PDK1) inhibitor dichloroacetate enhanced the activity of pyruvate dehydrogenase in rat GSCs and increased the sensitivity of cells to chemotherapy and radiotherapy in vitro [85].

7 Summary and perspectives

The mechanism underlying glioma evolution and the dynamic interaction between glioma cells and the ETM have been studied by many scientists [12,86,87]. However, the field of therapeutics targeting the evolution of glioma and the prevention of glioma progression is currently in its infancy (Table 1). The resulting genetic instability of glioma results in subsequent heterogeneity, which is maintained by treatment with TMZ or other targeted therapeutic selective pressures. Identification of clonal dynamics in glioma samples obtained from sites of resistance may lead to the development of further treatment options that address tumor heterogeneity. However, this approach should ideally be combined with the use of noninvasive liquid biopsy sampling, including CSF of patients with glioma, which enables easier surveillance. The clinical implications of relapse subclones need to be examined in CSF samples for the early diagnosis of recurrent glioma [72]. Technological and computational advances for exploring the glioma genome and its ETM will offer a deeper understanding of the evolutionary trajectories of this disease. On the basis of our current understanding of the evolution patterns of glioma, the use of combination therapies (e.g., combinations of targeted and immune therapies) may be the most promising approach. Clinical practice including clinical trials should also be dynamic with the timely adjustment of antitumor strategies. Although numerous clinical trials have investigated recurrent glioma, a deeper understanding of the mechanisms and identification of additional targets of glioma recurrence are warranted in this setting.

References

[1] Wang Y, Jiang T. Understanding high grade glioma: molecular mechanism, therapy and comprehensive management. Cancer Lett 2013; 331(2): 139–146

[2] Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 2007; 114(2): 97–109

[3] Schiff D, van den Bent M, Vogelbaum MA, Wick W, Miller CR, Taphoorn M, Pope W, Brown PD, Platten M, Jalali R, Armstrong T, Wen PY. Recent developments and future directions in adult lower-grade gliomas: Society for Neuro-Oncology (SNO) and European Association of Neuro-Oncology (EANO) Consensus. Neuro Oncol 2019; 21(7):837–853

[4] Ohgaki H, Kleihues P. Genetic alterations and signaling pathways in the evolution of gliomas. Cancer Sci 2009; 100(12): 2235–2241

[5] Cavenee WK, Furnari FB, Nagane M. Diffusely infiltrating astrocytomas. In: Kleihues P, Cavenee WK. Pathology and Genetics of Tumors of the Nervous System. WHO Classification of Tumors. Lyon: IARC press, 2000: 10–21

[6] Kim H, Zheng S, Amini SS, Virk SM, Mikkelsen T, Brat DJ, Grimsby J, Sougnez C, Muller F, Hu J, Sloan AE, Cohen ML, Van Meir EG, Scarpace L, Laird PW, Weinstein JN, Lander ES, Gabriel S, Getz G, Meyerson M, Chin L, Barnholtz-Sloan JS, Verhaak RG. Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution. Genome Res 2015; 25(3): 316–327

[7] Kim J, Lee IH, Cho HJ, Park CK, Jung YS, Kim Y, Nam SH, Kim BS, Johnson MD, Kong DS, Seol HJ, Lee JI, Joo KM, Yoon Y, Park WY, Lee J, Park PJ, Nam DH. Spatiotemporal evolution of the primary glioblastoma genome. Cancer Cell 2015; 28(3): 318–328

[8] Ceccarelli M, Barthel FP, Malta TM, Sabedot TS, Salama SR, Murray BA, Morozova O, Newton Y, Radenbaugh A, Pagnotta SM, Anjum S, Wang J, Manyam G, Zoppoli P, Ling S, Rao AA, Grifford M, Cherniack AD, Zhang H, Poisson L, Carlotti CG Jr, Tirapelli DP, Rao A, Mikkelsen T, Lau CC, Yung WK, Rabadan R, Huse J, Brat DJ, Lehman NL, Barnholtz-Sloan JS, Zheng S, Hess K, Rao G, Meyerson M, Beroukhim R, Cooper L, Akbani R, Wrensch M, Haussler D, Aldape KD, Laird PW, Gutmann DH; TCGA Research Network, Noushmehr H, Iavarone A, Verhaak RG. Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell 2016; 164(3): 550–563

[9] Johnson BE, Mazor T, Hong C, Barnes M, Aihara K, McLean CY, Fouse SD, Yamamoto S, Ueda H, Tatsuno K, Asthana S, Jalbert LE, Nelson SJ, Bollen AW, Gustafson WC, Charron E, Weiss WA, Smirnov IV, Song JS, Olshen AB, Cha S, Zhao Y, Moore RA, Mungall AJ, Jones SJM, Hirst M, Marra MA, Saito N, Aburatani H, Mukasa A, Berger MS, Chang SM, Taylor BS, Costello JF. Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma. Science 2014; 343(6167): 189–193

[10] Mazor T, Chesnelong C, Pankov A, Jalbert LE, Hong C, Hayes J, Smirnov IV, Marshall R, Souza CF, Shen Y, Viswanath P, Noushmehr H, Ronen SM, Jones SJM, Marra MA, Cairncross JG, Perry A, Nelson SJ, Chang SM, Bollen AW, Molinaro AM, Bengtsson H, Olshen AB, Weiss S, Phillips JJ, Luchman HA, Costello JF. Clonal expansion and epigenetic reprogramming following deletion or amplification of mutant IDH1. Proc Natl Acad Sci USA 2017; 114(40): 10743–10748

[11] Wang J, Cazzato E, Ladewig E, Frattini V, Rosenbloom DI, Zairis S, Abate F, Liu Z, Elliott O, Shin YJ, Lee JK, Lee IH, Park WY, Eoli M, Blumberg AJ, Lasorella A, Nam DH, Finocchiaro G, Iavarone A, Rabadan R. Clonal evolution of glioblastoma under therapy. Nat Genet 2016; 48(7): 768–776

[12] Barthel FP, Johnson KC, Varn FS, Moskalik AD, Tanner G, Kocakavuk E, Anderson KJ, Abiola O, Aldape K, Alfaro KD, Alpar D, Amin SB, Ashley DM, Bandopadhayay P, Barnholtz-Sloan JS, Beroukhim R, Bock C, Brastianos PK, Brat DJ, Brodbelt AR, Bruns AF, Bulsara KR, Chakrabarty A, Chakravarti A, Chuang JH, Claus EB, Cochran EJ, Connelly J, Costello JF, Finocchiaro G, Fletcher MN, French PJ, Gan HK, Gilbert MR, Gould PV, Grimmer MR, Iavarone A, Ismail A, Jenkinson MD, Khasraw M, Kim H, Kouwenhoven MCM, LaViolette PS, Li M, Lichter P, Ligon KL, Lowman AK, Malta TM, Mazor T, McDonald KL, Molinaro AM, Nam DH, Nayyar N, Ng HK, Ngan CY, Niclou SP, Niers JM, Noushmehr H, Noorbakhsh J, Ormond DR, Park CK, Poisson LM, Rabadan R, Radlwimmer B, Rao G, Reifenberger G, Sa JK, Schuster M, Shaw BL, Short SC, Smitt PAS, Sloan AE, Smits M, Suzuki H, Tabatabai G, Van Meir EG, Watts C, Weller M, Wesseling P, Westerman BA, Widhalm G, Woehrer A, Yung WKA, Zadeh G, Huse JT, De Groot JF, Stead LF, Verhaak RGW; GLASS Consortium. Longitudinal molecular trajectories of diffuse glioma in adults. Nature 2019; 576(7785): 112–120

[13] McGranahan N, Swanton C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell 2017; 168(4): 613–628

[14] Alexandrov LB, Jones PH, Wedge DC, Sale JE, Campbell PJ, Nik-Zainal S, Stratton MR. Clock-like mutational processes in human somatic cells. Nat Genet 2015; 47(12): 1402–1407

[15] Sottoriva A, Kang H, Ma Z, Graham TA, Salomon MP, Zhao J, Marjoram P, Siegmund K, Press MF, Shibata D, Curtis C. A Big Bang model of human colorectal tumor growth. Nat Genet 2015; 47(3): 209–216

[16] Campbell PJ, Yachida S, Mudie LJ, Stephens PJ, Pleasance ED, Stebbings LA, Morsberger LA, Latimer C, McLaren S, Lin ML, McBride DJ, Varela I, Nik-Zainal SA, Leroy C, Jia M, Menzies A, Butler AP, Teague JW, Griffin CA, Burton J, Swerdlow H, Quail MA, Stratton MR, Iacobuzio-Donahue C, Futreal PA. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 2010; 467(7319): 1109–1113

[17] Landau DA, Carter SL, Stojanov P, McKenna A, Stevenson K, Lawrence MS, Sougnez C, Stewart C, Sivachenko A, Wang L, Wan Y, Zhang W, Shukla SA, Vartanov A, Fernandes SM, Saksena G, Cibulskis K, Tesar B, Gabriel S, Hacohen N, Meyerson M, Lander ES, Neuberg D, Brown JR, Getz G, Wu CJ. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell 2013; 152(4): 714–726

[18] Xue Y, Martelotto L, Baslan T, Vides A, Solomon M, Mai TT, Chaudhary N, Riely GJ, Li BT, Scott K, Cechhi F, Stierner U, Chadalavada K, de Stanchina E, Schwartz S, Hembrough T, Nanjangud G, Berger MF, Nilsson J, Lowe SW, Reis-Filho JS, Rosen N, Lito P. An approach to suppress the evolution of resistance in BRAF^V600E-mutant cancer. Nat Med 2017; 23(8): 929–937

[19] Lee JK, Wang J, Sa JK, Ladewig E, Lee HO, Lee IH, Kang HJ, Rosenbloom DS, Camara PG, Liu Z, van Nieuwenhuizen P, Jung SW, Choi SW, Kim J, Chen A, Kim KT, Shin S, Seo YJ, Oh JM, Shin YJ, Park CK, Kong DS, Seol HJ, Blumberg A, Lee JI, Iavarone A, Park WY, Rabadan R, Nam DH. Spatiotemporal genomic architecture informs precision oncology in glioblastoma. Nat Genet 2017; 49(4): 594–599

[20] Bao ZS, Chen HM, Yang MY, Zhang CB, Yu K, Ye WL, Hu BQ, Yan W, Zhang W, Akers J, Ramakrishnan V, Li J, Carter B, Liu YW, Hu HM, Wang Z, Li MY, Yao K, Qiu XG, Kang CS, You YP, Fan XL, Song WS, Li RQ, Su XD, Chen CC, Jiang T. RNA-seq of 272 gliomas revealed a novel, recurrent PTPRZ1-MET fusion transcript in secondary glioblastomas. Genome Res 2014; 24(11): 1765–1773

[21] Hu H, Mu Q, Bao Z, Chen Y, Liu Y, Chen J, Wang K, Wang Z, Nam Y, Jiang B, Sa JK, Cho HJ, Her NG, Zhang C, Zhao Z, Zhang Y, Zeng F, Wu F, Kang X, Liu Y, Qian Z, Wang Z, Huang R, Wang Q, Zhang W, Qiu X, Li W, Nam DH, Fan X, Wang J, Jiang T. Mutational landscape of secondary glioblastoma guides MET-targeted trial in brain tumor. Cell 2018; 175(6): 1665–1678.e18

[22] Abou-El-Ardat K, Seifert M, Becker K, Eisenreich S, Lehmann M, Hackmann K, Rump A, Meijer G, Carvalho B, Temme A, Schackert G, Schröck E, Krex D, Klink B. Comprehensive molecular characterization of multifocal glioblastoma proves its monoclonal origin and reveals novel insights into clonal evolution and heterogeneity of glioblastomas. Neuro-oncol 2017; 19(4): 546–557

[23] Baysan M, Woolard K, Cam MC, Zhang W, Song H, Kotliarova S, Balamatsias D, Linkous A, Ahn S, Walling J, Belova GI, Fine HA. Detailed longitudinal sampling of glioma stem cells in situ reveals Chr7 gain and Chr10 loss as repeated events in primary tumor formation and recurrence. Int J Cancer 2017; 141(10): 2002–2013

[24] Mazor T, Pankov A, Johnson BE, Hong C, Hamilton EG, Bell RJA, Smirnov IV, Reis GF, Phillips JJ, Barnes MJ, Idbaih A, Alentorn A, Kloezeman JJ, Lamfers MLM, Bollen AW, Taylor BS, Molinaro AM, Olshen AB, Chang SM, Song JS, Costello JF. DNA methylation and somatic mutations converge on the cell cycle and define similar evolutionary histories in brain tumors. Cancer Cell 2015; 28(3): 307–317

[25] Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011; 144(5): 646–674

[26] Alcantara Llaguno S, Chen J, Kwon CH, Jackson EL, Li Y, Burns DK, Alvarez-Buylla A, Parada LF. Malignant astrocytomas originate from neural stem/progenitor cells in a somatic tumor suppressor mouse model. Cancer Cell 2009; 15(1): 45–56

[27] Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W, Kos I, Batinic-Haberle I, Jones S, Riggins GJ, Friedman H, Friedman A, Reardon D, Herndon J, Kinzler KW, Velculescu VE, Vogelstein B, Bigner DD. IDH1 and IDH2 mutations in gliomas. N Engl J Med 2009; 360(8): 765–773

[28] Jiao Y, Killela PJ, Reitman ZJ, Rasheed AB, Heaphy CM, de Wilde RF, Rodriguez FJ, Rosemberg S, Oba-Shinjo SM, Nagahashi Marie SK, Bettegowda C, Agrawal N, Lipp E, Pirozzi C, Lopez G, He Y, Friedman H, Friedman AH, Riggins GJ, Holdhoff M, Burger P, McLendon R, Bigner DD, Vogelstein B, Meeker AK, Kinzler KW, Papadopoulos N, Diaz LA, Yan H. Frequent ATRX, CIC, FUBP1 and IDH1 mutations refine the classification of malignant gliomas. Oncotarget 2012; 3(7): 709–722

[29] Yang P, Cai J, Yan W, Zhang W, Wang Y, Chen B, Li G, Li S, Wu C, Yao K, Li W, Peng X, You Y, Chen L, Jiang C, Qiu X, Jiang T; CGGA project. Classification based on mutations of TERT promoter and IDH characterizes subtypes in grade II/III gliomas. Neuro-oncol 2016; 18(8): 1099–1108

[30] Brown TM, Fee E. Rudolf Carl Virchow: medical scientist, social reformer, role model. Am J Public Health 2006; 96(12): 2104–2105

[31] Heppner GH, Miller BE. Tumor heterogeneity: biological implications and therapeutic consequences. Cancer Metastasis Rev 1983; 2(1): 5–23

[32] Fidler IJ. Tumor heterogeneity and the biology of cancer invasion and metastasis. Cancer Res 1978; 38(9): 2651–2660

[33] Wu M, Pastor-Pareja JC, Xu T. Interaction between Ras(V12) and scribbled clones induces tumour growth and invasion. Nature 2010; 463(7280): 545–548

[34] Cleary AS, Leonard TL, Gestl SA, Gunther EJ. Tumour cell heterogeneity maintained by cooperating subclones in Wnt-driven mammary cancers. Nature 2014; 508(7494): 113–117

[35] Dominguez CX, Muller S, Keerthivasan S, Koeppen H, Hung J, Gierke S, Breart B, Foreman O, Bainbridge TW, Castiglioni A, Senbabaoglu Y, Madrusan Z, Liang Y, Junttila MR, Klijn C, Bourgon R, Turley SJ. Single-cell RNA sequencing reveals stromal evolution into LRRC15⁺ myofibroblasts as a determinant of patient response to cancer immunotherapy. Cancer Discov 2020; 10(2): 232–253

[36] Neftel C, Laffy J, Filbin MG, Hara T, Shore ME, Rahme GJ, Richman AR, Silverbush D, Shaw ML, Hebert CM, Dewitt J, Gritsch S, Perez EM, Gonzalez Castro LN, Lan X, Druck N, Rodman C, Dionne D, Kaplan A, Bertalan MS, Small J, Pelton K, Becker S, Bonal D, Nguyen QD, Servis RL, Fung JM, Mylvaganam R, Mayr L, Gojo J, Haberler C, Geyeregger R, Czech T, Slavc I, Nahed BV, Curry WT, Carter BS, Wakimoto H, Brastianos PK, Batchelor TT, Stemmer-Rachamimov A, Martinez-Lage M, Frosch MP, Stamenkovic I, Riggi N, Rheinbay E, Monje M, Rozenblatt-Rosen O, Cahill DP, Patel AP, Hunter T, Verma IM, Ligon KL, Louis DN, Regev A, Bernstein BE, Tirosh I, Suva ML. An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell 2019; 178(4): 835–849.e21

[37] Favero F, McGranahan N, Salm M, Birkbak NJ, Sanborn JZ, Benz SC, Becq J, Peden JF, Kingsbury Z, Grocok RJ, Humphray S, Bentley D, Spencer-Dene B, Gutteridge A, Brada M, Roger S, Dietrich PY, Forshew T, Gerlinger M, Rowan A, Stamp G, Eklund AC, Szallasi Z, Swanton C. Glioblastoma adaptation traced through decline of an IDH1 clonal driver and macro-evolution of a double-minute chromosome. Ann Oncol 2015; 26(5): 880–887

[38] Yachida S, Jones S, Bozic I, Antal T, Leary R, Fu B, Kamiyama M, Hruban RH, Eshleman JR, Nowak MA, Velculescu VE, Kinzler KW, Vogelstein B, Iacobuzio-Donahue CA. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 2010; 467(7319): 1114–1117

[39] Eleveld TF, Oldridge DA, Bernard V, Koster J, Colmet Daage L, Diskin SJ, Schild L, Bentahar NB, Bellini A, Chicard M, Lapouble E, Combaret V, Legoix-Né P, Michon J, Pugh TJ, Hart LS, Rader J, Attiyeh EF, Wei JS, Zhang S, Naranjo A, Gastier-Foster JM, Hogarty MD, Asgharzadeh S, Smith MA, Guidry Auvil JM, Watkins TB, Zwijnenburg DA, Ebus ME, van Sluis P, Hakkert A, van Wezel E, van der Schoot CE, Westerhout EM, Schulte JH, Tytgat GA, Dolman ME, Janoueix-Lerosey I, Gerhard DS, Caron HN, Delattre O, Khan J, Versteeg R, Schleiermacher G, Molenaar JJ, Maris JM. Relapsed neuroblastomas show frequent RAS-MAPK pathway mutations. Nat Genet 2015; 47(8): 864–871

[40] Nikbakht H, Panditharatna E, Mikael LG, Li R, Gayden T, Osmond M, Ho CY, Kambhampati M, Hwang EI, Faury D, Siu A, Papillon-Cavanagh S, Bechet D, Ligon KL, Ellezam B, Ingram WJ, Stinson C, Moore AS, Warren KE, Karamchandani J, Packer RJ, Jabado N, Majewski J, Nazarian J. Spatial and temporal homogeneity of driver mutations in diffuse intrinsic pontine glioma. Nat Commun 2016; 7(1): 11185

[41] Wen PY, Kesari S. Malignant gliomas in adults. N Engl J Med 2008; 359(5): 492–507

[42] Holland EC. Glioblastoma multiforme: the terminator. Proc Natl Acad Sci USA 2000; 97(12): 6242–6244

[43] Snuderl M, Fazlollahi L, Le LP, Nitta M, Zhelyazkova BH, Davidson CJ, Akhavanfard S, Cahill DP, Aldape KD, Betensky RA, Louis DN, Iafrate AJ. Mosaic amplification of multiple receptor tyrosine kinase genes in glioblastoma. Cancer Cell 2011; 20(6): 810–817

[44] Sottoriva A, Spiteri I, Piccirillo SG, Touloumis A, Collins VP, Marioni JC, Curtis C, Watts C, Tavaré S. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci USA 2013; 110(10): 4009–4014

[45] Francis JM, Zhang CZ, Maire CL, Jung J, Manzo VE, Adalsteinsson VA, Homer H, Haidar S, Blumenstiel B, Pedamallu CS, Ligon AH, Love JC, Meyerson M, Ligon KL. EGFR variant heterogeneity in glioblastoma resolved through single-nucleus sequencing. Cancer Discov 2014; 4(8): 956–971

[46] Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, Cahill DP, Nahed BV, Curry WT, Martuza RL, Louis DN, Rozenblatt-Rosen O, Suvà ML, Regev A, Bernstein BE. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 2014; 344(6190): 1396–1401

[47] Jamal-Hanjani M, Wilson GA, McGranahan N, Birkbak NJ, Watkins TBK, Veeriah S, Shafi S, Johnson DH, Mitter R, Rosenthal R, Salm M, Horswell S, Escudero M, Matthews N, Rowan A, Chambers T, Moore DA, Turajlic S, Xu H, Lee SM, Forster MD, Ahmad T, Hiley CT, Abbosh C, Falzon M, Borg E, Marafioti T, Lawrence D, Hayward M, Kolvekar S, Panagiotopoulos N, Janes SM, Thakrar R, Ahmed A, Blackhall F, Summers Y, Shah R, Joseph L, Quinn AM, Crosbie PA, Naidu B, Middleton G, Langman G, Trotter S, Nicolson M, Remmen H, Kerr K, Chetty M, Gomersall L, Fennell DA, Nakas A, Rathinam S, Anand G, Khan S, Russell P, Ezhil V, Ismail B, Irvin-Sellers M, Prakash V, Lester JF, Kornaszewska M, Attanoos R, Adams H, Davies H, Dentro S, Taniere P, O’Sullivan B, Lowe HL, Hartley JA, Iles N, Bell H, Ngai Y, Shaw JA, Herrero J, Szallasi Z, Schwarz RF, Stewart A, Quezada SA, Le Quesne J, Van Loo P, Dive C, Hackshaw A, Swanton C; TRACERx Consortium. Tracking the evolution of non-small-cell lung cancer. N Engl J Med 2017; 376(22): 2109–2121

[48] Osuka S, Van Meir EG. Overcoming therapeutic resistance in glioblastoma: the way forward. J Clin Invest 2017; 127(2): 415–426

[49] Suvà ML, Rheinbay E, Gillespie SM, Patel AP, Wakimoto H, Rabkin SD, Riggi N, Chi AS, Cahill DP, Nahed BV, Curry WT, Martuza RL, Rivera MN, Rossetti N, Kasif S, Beik S, Kadri S, Tirosh I, Wortman I, Shalek AK, Rozenblatt-Rosen O, Regev A, Louis DN, Bernstein BE. Reconstructing and reprogramming the tumor-propagating potential of glioblastoma stem-like cells. Cell 2014; 157(3): 580–594

[50] Bouffet E, Larouche V, Campbell BB, Merico D, de Borja R, Aronson M, Durno C, Krueger J, Cabric V, Ramaswamy V, Zhukova N, Mason G, Farah R, Afzal S, Yalon M, Rechavi G, Magimairajan V, Walsh MF, Constantini S, Dvir R, Elhasid R, Reddy A, Osborn M, Sullivan M, Hansford J, Dodgshun A, Klauber-Demore N, Peterson L, Patel S, Lindhorst S, Atkinson J, Cohen Z, Laframboise R, Dirks P, Taylor M, Malkin D, Albrecht S, Dudley RW, Jabado N, Hawkins CE, Shlien A, Tabori U. Immune checkpoint inhibition for hypermutant glioblastoma multiforme resulting from germline biallelic mismatch repair deficiency. J Clin Oncol 2016; 34(19): 2206–2211

[51] Quail DF, Joyce JA. Microenvironmental regulation of tumor progression and metastasis. Nat Med 2013; 19(11): 1423–1437

[52] Naba A, Clauser KR, Hoersch S, Liu H, Carr SA, Hynes RO. The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Mol Cell Proteomics 2012; 11(4): M111.014647

[53] Folkman J. Tumor angiogenesis: therapeutic implications. N Engl J Med 1971; 285(21): 1182–1186

[54] Fridman WH, Pagès F, Sautès-Fridman C, Galon J. The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer 2012; 12(4): 298–306

[55] Weis SM, Cheresh DA. Tumor angiogenesis: molecular pathways and therapeutic targets. Nat Med 2011; 17(11): 1359–1370

[56] Du R, Lu KV, Petritsch C, Liu P, Ganss R, Passegué E, Song H, Vandenberg S, Johnson RS, Werb Z, Bergers G. HIF1α induces the recruitment of bone marrow-derived vascular modulatory cells to regulate tumor angiogenesis and invasion. Cancer Cell 2008; 13(3): 206–220

[57] Semenza GL. Cancer-stromal cell interactions mediated by hypoxia-inducible factors promote angiogenesis, lymphangiogenesis, and metastasis. Oncogene 2013; 32(35): 4057–4063

[58] Trylcova J, Busek P, Smetana K Jr, Balaziova E, Dvorankova B, Mifkova A, Sedo A. Effect of cancer-associated fibroblasts on the migration of glioma cells in vitro. Tumour Biol 2015; 36(8): 5873–5879

[59] Lemée JM, Clavreul A, Menei P. Intratumoral heterogeneity in glioblastoma: don’t forget the peritumoral brain zone. Neuro-oncol 2015; 17(10): 1322–1332

[60] Wang Q, Hu B, Hu X, Kim H, Squatrito M, Scarpace L, deCarvalho AC, Lyu S, Li P, Li Y, Barthel F, Cho HJ, Lin YH, Satani N, Martinez-Ledesma E, Zheng S, Chang E, Sauve CG, Olar A, Lan ZD, Finocchiaro G, Phillips JJ, Berger MS, Gabrusiewicz KR, Wang G, Eskilsson E, Hu J, Mikkelsen T, DePinho RA, Muller F, Heimberger AB, Sulman EP, Nam DH, Verhaak RGW. Tumor evolution of glioma-intrinsic gene expression subtypes associates with immunological changes in the microenvironment. Cancer Cell 2017; 32(1): 42–56.e6

[61] Pyonteck SM, Akkari L, Schuhmacher AJ, Bowman RL, Sevenich L, Quail DF, Olson OC, Quick ML, Huse JT, Teijeiro V, Setty M, Leslie CS, Oei Y, Pedraza A, Zhang J, Brennan CW, Sutton JC, Holland EC, Daniel D, Joyce JA. CSF-1R inhibition alters macrophage polarization and blocks glioma progression. Nat Med 2013; 19(10): 1264–1272

[62] Brown CE, Alizadeh D, Starr R, Weng L, Wagner JR, Naranjo A, Ostberg JR, Blanchard MS, Kilpatrick J, Simpson J, Kurien A, Priceman SJ, Wang X, Harshbarger TL, D’Apuzzo M, Ressler JA, Jensen MC, Barish ME, Chen M, Portnow J, Forman SJ, Badie B. Regression of glioblastoma after chimeric antigen receptor T-cell therapy. N Engl J Med 2016; 375(26): 2561–2569

[63] Shafee N, Smith CR, Wei S, Kim Y, Mills GB, Hortobagyi GN, Stanbridge EJ, Lee EY. Cancer stem cells contribute to cisplatin resistance in Brca1/p53-mediated mouse mammary tumors. Cancer Res 2008; 68(9): 3243–3250

[64] Li X, Lewis MT, Huang J, Gutierrez C, Osborne CK, Wu MF, Hilsenbeck SG, Pavlick A, Zhang X, Chamness GC, Wong H, Rosen J, Chang JC. Intrinsic resistance of tumorigenic breast cancer cells to chemotherapy. J Natl Cancer Inst 2008; 100(9): 672–679

[65] Raha D, Wilson TR, Peng J, Peterson D, Yue P, Evangelista M, Wilson C, Merchant M, Settleman J. The cancer stem cell marker aldehyde dehydrogenase is required to maintain a drug-tolerant tumor cell subpopulation. Cancer Res 2014; 74(13): 3579–3590

[66] Haq R, Shoag J, Andreu-Perez P, Yokoyama S, Edelman H, Rowe GC, Frederick DT, Hurley AD, Nellore A, Kung AL, Wargo JA, Song JS, Fisher DE, Arany Z, Widlund HR. Oncogenic BRAF regulates oxidative metabolism via PGC1α and MITF. Cancer Cell 2013; 23(3): 302–315

[67] Bivona TG, Doebele RC. A framework for understanding and targeting residual disease in oncogene-driven solid cancers. Nat Med 2016; 22(5): 472–478

[68] Ramirez M, Rajaram S, Steininger RJ, Osipchuk D, Roth MA, Morinishi LS, Evans L, Ji W, Hsu CH, Thurley K, Wei S, Zhou A, Koduru PR, Posner BA, Wu LF, Altschuler SJ. Diverse drug-resistance mechanisms can emerge from drug-tolerant cancer persister cells. Nat Commun 2016; 7(1): 10690

[69] Hata AN, Niederst MJ, Archibald HL, Gomez-Caraballo M, Siddiqui FM, Mulvey HE, Maruvka YE, Ji F, Bhang HE, Krishnamurthy Radhakrishna V, Siravegna G, Hu H, Raoof S, Lockerman E, Kalsy A, Lee D, Keating CL, Ruddy DA, Damon LJ, Crystal AS, Costa C, Piotrowska Z, Bardelli A, Iafrate AJ, Sadreyev RI, Stegmeier F, Getz G, Sequist LV, Faber AC, Engelman JA. Tumor cells can follow distinct evolutionary paths to become resistant to epidermal growth factor receptor inhibition. Nat Med 2016; 22(3): 262–269

[70] Shaw AT, Friboulet L, Leshchiner I, Gainor JF, Bergqvist S, Brooun A, Burke BJ, Deng YL, Liu W, Dardaei L, Frias RL, Schultz KR, Logan J, James LP, Smeal T, Timofeevski S, Katayama R, Iafrate AJ, Le L, McTigue M, Getz G, Johnson TW, Engelman JA. Resensitization to crizotinib by the lorlatinib ALK resistance mutation L1198F. N Engl J Med 2016; 374(1): 54–61

[71] Yu HA, Arcila ME, Hellmann MD, Kris MG, Ladanyi M, Riely GJ. Poor response to erlotinib in patients with tumors containing baseline EGFR T790M mutations found by routine clinical molecular testing. Ann Oncol 2014; 25(2): 423–428

[72] Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol 2018; 15(2): 81–94

[73] Blanpain C, Fuchs E. Stem cell plasticity. Plasticity of epithelial stem cells in tissue regeneration. Science 2014; 344(6189): 1242281

[74] Wang A, Qu L, Wang L. At the crossroads of cancer stem cells and targeted therapy resistance. Cancer Lett 2017; 385: 87–96

[75] Chen J, Li Y, Yu TS, McKay RM, Burns DK, Kernie SG, Parada LF. A restricted cell population propagates glioblastoma growth after chemotherapy. Nature 2012; 488(7412): 522–526

[76] Nassar D, Blanpain C. Cancer stem cells: basic concepts and therapeutic implications. Annu Rev Pathol 2016; 11(1): 47–76

[77] Bao S, Wu Q, McLendon RE, Hao Y, Shi Q, Hjelmeland AB, Dewhirst MW, Bigner DD, Rich JN. Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature 2006; 444(7120): 756–760

[78] Hamerlik P, Lathia JD, Rasmussen R, Wu Q, Bartkova J, Lee M, Moudry P, Bartek J Jr, Fischer W, Lukas J, Rich JN, Bartek J. Autocrine VEGF-VEGFR2-Neuropilin-1 signaling promotes glioma stem-like cell viability and tumor growth. J Exp Med 2012; 209(3): 507–520

[79] Auffinger B, Tobias AL, Han Y, Lee G, Guo D, Dey M, Lesniak MS, Ahmed AU. Conversion of differentiated cancer cells into cancer stem-like cells in a glioblastoma model after primary chemotherapy. Cell Death Differ 2014; 21(7): 1119–1131

[80] Barker HE, Paget JT, Khan AA, Harrington KJ. The tumour microenvironment after radiotherapy: mechanisms of resistance and recurrence. Nat Rev Cancer 2015; 15(7): 409–425

[81] Jeong Y, Hoang NT, Lovejoy A, Stehr H, Newman AM, Gentles AJ, Kong W, Truong D, Martin S, Chaudhuri A, Heiser D, Zhou L, Say C, Carter JN, Hiniker SM, Loo BW Jr, West RB, Beachy P, Alizadeh AA, Diehn M. Role of KEAP1/NRF2 and TP53 mutations in lung squamous cell carcinoma development and radiation resistance. Cancer Discov 2017; 7(1): 86–101

[82] Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 2009; 324(5930): 1029–1033

[83] Menendez JA. Metabolic control of cancer cell stemness: lessons from iPS cells. Cell Cycle 2015; 14(24): 3801–3811

[84] Flavahan WA, Wu Q, Hitomi M, Rahim N, Kim Y, Sloan AE, Weil RJ, Nakano I, Sarkaria JN, Stringer BW, Day BW, Li M, Lathia JD, Rich JN, Hjelmeland AB. Brain tumor initiating cells adapt to restricted nutrition through preferential glucose uptake. Nat Neurosci 2013; 16(10): 1373–1382

[85] Morfouace M, Lalier L, Bahut M, Bonnamain V, Naveilhan P, Guette C, Oliver L, Gueguen N, Reynier P, Vallette FM. Comparison of spheroids formed by rat glioma stem cells and neural stem cells reveals differences in glucose metabolism and promising therapeutic applications. J Biol Chem 2012; 287(40): 33664–33674

[86] Molinaro AM, Taylor JW, Wiencke JK, Wrensch MR. Genetic and molecular epidemiology of adult diffuse glioma. Nat Rev Neurol 2019; 15(7): 405–417

[87] Ding X, He M, Chan AWH, Song QX, Sze SC, Chen H, Man MKH, Man K, Chan SL, Lai PBS, Wang X, Wong N. Genomic and epigenomic features of primary and recurrent hepatocellular carcinomas. Gastroenterology 2019; 157(6): 1630–1645.e6

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