Emerging next-generation sequencing-based discoveries for targeted osteosarcoma therapy

Cancer Letters 474 (2020) 158–167

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Cancer Letters journal homepage: www.elsevier.com/locate/canlet

Mini-review

Emerging next-generation sequencing-based discoveries for targeted osteosarcoma therapy

T

Jie Zhaoa,b,c, Dylan C. Deanb, Francis J. Hornicekb, Xiuchun Yuc,∗∗, Zhenfeng Duanb,∗ a

First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA c Department of Orthopaedic Surgery, The 960th Hospital of the PLA Joint Logistics Support Force, Jinan, Shandong, 250031, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Osteosarcoma Whole genome sequencing Targeted therapy Lung metastasis Biomarker

Osteosarcoma (OS) is the most common primary bone malignancy and is frequently lethal via metastasis to the lung. While surgical techniques and adjuvant chemotherapies have emerged to combat this deadly cancer, the 5year survival rate has plateaued over the past four decades. Therapeutic progress has been notably poor because past technologies have not been able to reveal obscured OS biomarkers and targets. With the advent and implementation of large-scale next-generation sequencing (NGS) studies, various somatic mutations and copy number changes involved in OS progression and metastasis have surfaced. These findings have significantly expanded the amount of genome-informed pathways and candidate genes suitable for targeting in pre-clinical models. Furthermore, NGS analyses comparing primary and matched pulmonary metastatic tumor tissues have catalogued previously unknown prognostic biomarkers in OS. In this review, we delineate the most recent findings in NGS for OS therapy and how this technology has advanced personalized therapy.

1. Introduction Osteosarcoma (OS) is a deadly bone cancer which occurs most frequently in children and adolescents [1]. In response to its known lethality, current OS treatment protocols are quite aggressive and combine neoadjuvant chemotherapy, wide surgical resection of the primary tumor and additional postoperative adjuvant chemotherapy. Despite these forceful measures, survival rate of OS patients has plateaued for the past four decades. In cases of localized OS, patients can expect a five-year survival rate of 60–70%, whereas those with metastatic involvement have a dismal 20–30% five-year survival rate [2–4]. While pulmonary metastasis has been well-documented within the literature as a prime cause of OS-related deaths, the exact molecular pathogenesis of this phenomenon is poorly understood. This has occurred, in part, due to the limited sensitivities of previous technologies seeking to reveal genetic alterations in a heterogeneous cancer such as OS. Relative to other cancers, OS has a complex and unstable genomic landscape with significant somatic copy-number alterations (SCNAs) and structural variants (SVs) [5]. While previous studies have identified several inherited tumor suppressor genes predisposing to OS such as TP53, RB1, RECQL4, BLM, and WRN, next-generation sequencing

(NGS) technology has revealed an entire landscape of genetic alterations in OS amenable to more personalized medicine [6–9]. Although several methodologies can detect genomic mutations in human cancer, NGS is the preferred platform for the detailed and unbiased profiling of cancer genomes [10,11]. NGS technology has enabled worldwide cancer genome-sequencing joint projects including The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) to sequence and analyze more than 50,000 cancer genomes [12,13]. Similarly, the Catalogue of Somatic Mutations in Cancer (COSMIC) has summarized coding mutations in over a million cancer samples, and identified many recurrent genetic events that promote cancer formation [14]. It is clear that NGS technology has and will continue to highlight targetable driver genes and novel metastatic and prognostic biomarkers, and is therefore the preferred technology for advancing precision OS medicine. In this review, we focus on the three NGS approaches (whole-genome, whole-exome, and RNA sequencing) and delineate their contribution to personalized and targeted OS therapy.

∗

Corresponding author. Corresponding author. E-mail addresses: [email protected] (J. Zhao), [email protected] (D.C. Dean), [email protected] (F.J. Hornicek), [email protected] (X. Yu), [email protected] (Z. Duan). ∗∗

https://doi.org/10.1016/j.canlet.2020.01.020 Received 19 November 2019; Received in revised form 18 January 2020; Accepted 20 January 2020 0304-3835/ © 2020 Elsevier B.V. All rights reserved.

Cancer Letters 474 (2020) 158–167

J. Zhao, et al.

Fig. 1. The workflow of NGS technology. First, DNA/ RNA is extracted from osteosarcoma tumor tissues, such as fresh frozen tumor tissues, formalin-fixed paraffin-embedded (FFPE) tissues, tumor cytology samples, and cell-free DNA/RNA. Second, DNA or RNA library construction is performed, and includes gDNA/cDNA fragments, adaptor ligation, and PCR amplification. Third, extracted gDNA or cDNA fragments are sequenced in a high throughput manner to obtain millions of raw short reads. Finally, bioinformatic analysis includes read filtering, alignment, variant calling, and variant annotation. Quality control is essential for accurate data analysis. Note: # For whole exome sequencing, an initial capture of the exome is required. Abbreviation: SNVs: single nucleotide variants; indels: insertions and deletions; SCNAs: somatic copy number alterations; SVs: structural variants; miRNAs: microRNA; lncRNAs: long non-coding RNAs; circRNAs: circular RNAs. Color should be used for this figure in print. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

2. NGS approaches and workflow

For RNA-Seq, it is crucial that ribosomal RNA is removed at this phase [24]. Second, the genomic DNA (gDNA) or complementary DNA (cDNA) sample is randomly fragmented into sizes ranging from 200bp to 300bp before adapters are added onto both ends of these library fragments [25]. Third, each extracted gDNA or cDNA fragment is independently sequenced in a high-throughput manner to obtain millions of short reads. Several leading short-read sequencing platforms are currently available in NGS, and include Illumina, Ion Torrent, 454 Life Science, and SOLiD [20,22]. This ultimately produces an abundance of raw data that must be channeled through a bioinformatics pipeline to produce meaningful results. The four main operations in this process include: read filtering, alignment, variant detection, and variant annotation [22,26,27]. Quality control is vital, as there is a tremendous amount of sequencing data that must be stored, managed, and analyzed to convert the raw data into robust and reliable tumor markers that are clinically useful [26,27]. Due to the variations in sequencing and data analysis among software platforms, it is essential to choose the most appropriate NGS technique at the onset to not miss clinically impactful mutations.

NGS is a massively parallel high-throughput sequencing technology relative to Sanger sequencing [11]. NGS facilitates the ability to analyze cancer genome profiles in a single test run with high sequencing accuracy [10]. The various NGS approaches are notable for their unique advantages to the clinician-researcher, and include whole exome sequencing (WES), whole genome sequencing (WGS), and RNA sequencing (RNA-Seq) [15]. WES is the preferred method for uncovering genetic variants in known protein-coding regions across an entire genome [16]. WGS is more comprehensive, as it can reveal an unbiased landscape of somatic mutations in non-coding and unannotated regions [17]. Finally, RNA-Seq can characterize an entire transcriptome, including coding messenger RNAs (mRNAs) and non-coding RNAs [18,19]. The workflow of NGS is divided into four steps: DNA or RNA extraction, library construction, sequencing, and data analysis [20–22]. (Fig. 1). First, high-quality DNA or RNA is isolated from the tumor specimens such as fresh-frozen OS tumor tissue, formalin-fixed paraffinembedded tissue, a tumor cytology sample, or cell-free DNA/RNA [23]. 159

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Table 1 The differences between WES, WGS, and RNA-Seq. NGS technology

WES

WGS

RNA-Seq

Sequencing range Sequencing region Sequencing coverage Recommended depth or reads PCR amplification bias Cost Time Downstream analysis Bioinformatics analysis

Less 2% of the genome Exons Non-uniform > 100x Frequent Relatively low Time-saving Relatively easy SNVs, indels, SCNAs

Nearly the entire genome Exons, introns, intergenic regions Uniform > 35x Possible High Time-consuming Difficult SNVs, Indels, SCNAs, SVs

The entire transcriptome mRNAs, ncRNAs Non-uniform > 200 millions Possible Relatively low Time-saving Relatively easy Gene expression, expressed miRNAs, lncRNAs, circRNAs

NGS, next-generation sequencing; WES, whole exome sequencing; WGS, whole genome sequencing; RNA-Seq, RNA sequencing; mRNAs, messenger RNAs; ncRNAs, non-coding RNAs; SNVs, single nucleotide variants; Indels, Insertions and deletions; SCNAs, somatic copy number alterations; SVs, structural variants; miRNAs, microRNA; lncRNAs, long non-coding RNAs; circRNAs, circular RNAs.

3. The differences between WES, WGS, and RNA-Seq

predictive of pulmonary metastasis and poor prognosis. Stemming from these observations, somatic mutations and variants were analyzed for their potential in OS development and metastasis in vivo. In short, the OS mouse models Col1-Cre, Prx1-Cre, Rbfl/fl, ROSA26-NICD, Ctsk-Cre, Lkb1fl/fl, Ptch1fl/fl and p53 fl/fl confirmed TP53, RB1, Notch, mTOR and Hedgehog are driver genes in OS [34–42]. Perhaps swing to the heterogeneity of OS and its complex driver mutations, several of these studies concluded a synergism amongst driver genes was necessary to recapitulate human OS within the mouse models [37,40,41]. And while these works are encouraging, combination therapy are not without their challenges for clinical application as there can be an accumulation of side effects and possible drug interactions. There is, therefore, a need for more precise identification of the most tumorigenic gene mutations and tumor-specific copy number alterations in OS before pursuing optimal monotherapies in clinical trials.

We highlight the differences between WES, WGS, and RNA-Seq in Table 1. Because WES focuses on exome nucleotides, representing less than 2% of the genome, it neglects regulatory regions such as promoters and enhancers [28]. WGS determines the precise nucleotide order of the whole genome which include exons, introns, and intergenic regions. WES can detect single nucleotide variants (SNVs), insertions and deletions (indels), and SCNAs of the genome. WES has poor sensitivity in detecting SVs and non-coding variants due to its capture bias. In fact, compared to WES, even low-depth WGS can detect exponentially more variant types in the genome of OS samples, including SNVs, indels, CNVs, and SVs [28,29]. The strength of RNA-Seq is in its ability to detect expressed microRNA, long non-coding RNA, and circular RNAs [23]. Compared to WES and RNA-Seq, WGS produces more uniform and reliable sequences because it does not require an enrichment step [30,31]. For example, a sequencing depth in WES should exceed 100x to reduce GC bias [30]. For WGS, an average read depth of 30x is sufficient to achieve a comparable breadth of coverage seen in WES [32]. It is estimated that more than 200 million paired-end reads are required to identify the full range of transcripts in tumor samples using RNA-Seq [33]. However, WGS is not without its own unique disadvantages. The costs of genomic sequencing, bioinformatic analysis, and data storage for WGS are considerably greater than that of both WES and RNA-Seq, making large population-based genomic sequencing cost-prohibitive. In addition, even with the most powerful computer systems, the large amount of data generated from WGS is much more time-consuming and can delay clinical application or research [30].

4.1. Frequently mutated genes in OS Over the past few years, improvements in NGS and computational power have led to an explosion of data. Currently, over 595 driver genes have been reported in various cancers to promote oncogenesis and development [43]. Historically, driver mutations in the tumor suppressor genes TP53 and RB1 have been viewed as the primary events in OS tumorigenesis [5,44]. We outline a more thorough list which includes the 10 most frequently mutated genes in OS as identified by NGS in Table 2. 4.1.1. TP53 The TP53 tumor suppressor gene, localized to chromosome 17p13.1, is a transcription factor that inhibits cell division and survival in response to cellular stresses [45]. It spans approximately 25.8 kilobases (kb) of DNA, 2.6 kb of transcribed mRNA, and finally a 53-kDa nuclear protein product. Somatic mutations in the TP53 gene are one of the most common alterations in human cancers [46]. However, TP53 mutations vary widely across different cancers types. For example,

4. NGS-based discoveries of the OS genome Recent NGS studies have identified several frequently mutated genes and tumor-specific copy number alterations in OS tissues. In addition, comparisons between matched primary and metastatic lung OS tissues via NGS have exposed several novel candidate biomarkers Table 2 The top 10 most frequently mutated genes in human osteosarcoma. Gene

Location

Class

DNA sizea (kb)

mRNA size (kb)

Protein weight (kDa)

Signaling pathway

Frequency of mutation

Reference

TP53 RB1 PTEN DLG2 MYC ATRX NF1 CCNE1 CDKN2A PIK3CA

17p13.1 13q14.2 10q23.31 11q14.1 8q24.21 Xq21.1 17q11.2 19q12 9p21.3 3q26.32

Tumor suppressor Tumor suppressor Tumor suppressor Tumor suppressor Oncogene Tumor suppressor Tumor suppressor Oncogene Tumor suppressor Oncogene

25.8 178.2 108.8 2172.9 6.0 281.4 287.2 12.4 27.6 92.0

2.6 4.8 8.5 8.1 4.5 11.2 12.4 2.0 1.3 9.3

53 106.2 47.2 97.6 48.8 282.6 319.4 47.1 16.5 124.3

TP53 signaling Cell cycle/apoptosis PI3K/AKT/mTOR Wnt signaling Cell cycle DNA damage response Ras/MEK signaling Cell cycle Cell cycle PI3K/AKT/mTOR

16%–90% 10%–64% 4%–56% 24%–53% 8%–39% 9%–35% 6%–34% 8%–33% 11%–30% 3%–25%

[5,8,48–52] [5,8,51,56,57] [5,8,49,51,56,57] [5,8,51] [5,49,56,57] [5,8,51,56,57] [5,8,56] [5,49,56,57] [5,51,56,57] [5,8,49]

a

DNA size refers to hg19. PI3K/AKT/mTOR, phosphoinositide 3-kinase/AKT/mammalian target of rapamycin. 160

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Fig. 2. Single nucleotide variants and small insertions and deletions in TP53, RB1, and ATRX. Structure of the TP53 gene showing the transactivation domain, DNA binding domain, and tetramerization domain with missense mutations, splice sites, and frameshift mutations. Structure of the RB1 gene including the DUF3452 retinoblastoma-association, RB A domain, RB B domain, cyclin-like, RB C domain with stop gain, frameshift and missense mutation. Structure of the ATRX gene showing the helicase ATP-bd, SNF2 N domain, and helicase C domain with missense mutations, frameshift mutations, and stop gain. Abbreviation: DUF3452 retinoblastoma-association: domain of unknown function 3452 retinoblastoma-association; Helicase ATP-bd: helicase ATP-binding domain; SNF2 N domain: SNF2related N-terminal domain. Color should be used for this figure in print. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

not [50]. Similarly, a sequencing study which combined genomic and transcriptomic analysis identified rearrangements of TP53 in most OS cell lines (7/11) and many OS tumor samples (10/25) [52]. Taken together, these results suggest a specific TP53 alteration to induce OS pathogenesis.

TP53 mutations frequent high-grade ovarian cancers (95%) and pulmonary squamous cell carcinomas (80%) yet in renal clear cell carcinomas occur only 2.2% of the time [46,47]. With regards to OS, the frequency of TP53 mutations identified by NGS ranges between 16% and 90% [5,8,48–52]. Mechanistically, most TP53 mutations in human cancers are missense point mutations in the DNA-binding domain [46,47]. For example, in ovarian cancer, the most common single amino acid substitutions of TP53 are R273H, Y220C, R248Q, and R175H [47]. In OS, the four most frequent missense point mutations of TP53 are R248Q, R273H, D281E, and D281V. (Fig. 2). In addition, structural variations in the first intron of the TP53 gene also occur at high frequency in OS [53]. In a WGS study of 34 OS tumor tissues and their matched normal tissue counterparts from 32 patients, TP53 was mutated in 90% of the OS samples, with most of the TP53 somatic mutations being structural variations in the first intron (55%) [5]. An integrative analysis of the sequencing data from 13 cases via WGS, 59 cases via WES, and 35 via cases RNA-Seq was also supportive of this finding, as 75% of the OS samples showed inactivated TP53 [8]. In this cohort, TP53 point mutations, rearrangements, and deletions were observed in 22%, 14% and 39% of the cases, respectively. Interestingly, 34% of the cases had overlapping TP53 mutations, a finding that may have been overlooked had a diverse screening via multiple NGS modalities not been implemented [8]. In another work, screening of 288 OS alongside 1090 other type of cancers also revealed rearrangements in intron 1 of TP53 to be relatively specific to OS, as evidenced by 46 OS samples (16%) showcasing this mutation whereas other tumor types did

4.1.2. RB1 The retinoblastoma 1 (RB1) gene, mapped on chromosome 13q14.2, is a negative regulator of the cell cycle and tumor suppressor gene important in osteoblast and bone formation. The RB1 gene is approximately 178.2 kb, its resultant mRNA is 4.8 kb, and its protein product Rb is 106.2-kDa [54]. Similar to TP53, mutated RB1 has been implicated in various solid tumors such as OS [55]. In a previous study of 3281 tumors across 12 cancer types, RB1 mutations were shown to most frequently occur in bladder urothelial carcinoma (14.3%), glioblastoma (8.3%), and lung squamous cell carcinoma (6.9%) [46]. With respect to OS, RB1 mutations are present in 10%–64% of the cases as detected by NGS [5,8,51,56,57]. We outline the common SNVs of RB1 in OS in Fig. 2. Aside from these classic tumor suppressor genes, other commonly mutated genes in OS include ATRX, PTEN, DLG2, MYC, NF1, CCNE1, CDKN2A, and PIK3CA (Table 2). And although these genes are wellknown regulators of on copathways, their exact molecular functions in OS are relatively poorly understood. As an example, the ATRX gene in OS has a mutation frequency of 9%–35% and is proposed to regulate chromatin remodeling and telomere maintenance [5,8,51,56,57]. When 161

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Fig. 3. Genes with recurrent copy-number alterations in osteosarcoma as identified by next-generation sequencing. The 10 most frequently amplified genes in OS identified by NGS include MYC, CCNE1, VEGFA, AURKB, CDK4, MDM2, CCND3, KDR, PDGFRA, and KIT. The 10 most frequently deleted genes in OS identified by NGS are BRCA1/2, TP53, RB1, PTEN, NF1, ATRX, DLG2, CDKN2A/B. Note: red line, recurrently amplified genes; blue line, recurrently deleted genes. Color should be used for this figure in print. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

expression to be associated with chemoresistance, tumor progression, and poor prognosis in breast cancer, ovarian cancer, and OS [62–64]. NGS has identified CCNE1 amplification in 8%–33% of OS tumor samples [49,56,57]. In a copy number analysis obtained via WES of 59 OS samples, CCNE1 was one of the most significantly amplified genes [8]. In another WGS study, CCNE1 amplification was present in 33% of enrolled patients with OS [56]. Furthermore, an in vivo work of five patient-derived tumor xenografts (PDTX) models showed they harbored CCNE1 amplification [56].

this tumor suppressor is mutated, it promotes OS development [58]. Common ATRX mutations in OS include missense mutations, frameshift mutations, and stop gain mutations. (Fig. 2). 4.2. Frequently amplified or deleted genes in OS NGS studies have demonstrated copy number changes which often reflect chromosomal instability may be critical drivers of OS initiation and progression. (Fig. 3). We delineate the most frequently amplified and deleted genes implicated in the pathogenesis and development of OS in Table 3 and Table 4, respectively.

4.2.3. CDK4 The Cyclin-dependent kinase 4 (CDK4) gene located on chromosome 12q14.1 encodes a 33.7-kDa Cdk4 protein. Functionally, CDK4 is an intracellular kinase which controls cell cycle progression by regulating the G1–S transition; in addition, the complex of CDK4/6 has been implicated in many cancers, including OS [65]. NGS has detected CDK4 amplification in OS to occur at a frequency of 11%–13% [56,57]. In one WGS study, 11% of OS patients exhibited CDK4 amplification, with five cases notable for having more than 12 copies of amplified CDK4 [56]. Elevated CDK4 protein was also seen in two PDTX models with CDK4 amplification [56]. In another NGS study, CDK4 amplification was found in 9 of 71 OS samples (13%) [57]. These researchers used an OncoKB classification to annotate the potentially targetable somatic alterations [66]. Among the 66 OS patients with IMPACT (Integrated Mutation Profiling of Actionable Cancer Targets) data, nine patients (14%) with CDK4 amplification were classified as OncoKB Level 2B. Interestingly, 6 patients (9%) also had co-amplification of CDK4 and MDM2. A series of NGS studies have shown MDM2 is amplified in approximately 5%–15% of OS cases [5,8,56,57].

4.2.1. MYC Amplifications at 8q24 involving MYC frequent numerous human cancers, including OS [59,60]. A series of genome sequencing studies have shown MYC amplification is both common and essential to OS pathogenesis [5,8,49,56]. Specifically, NGS works have revealed MYC amplification in 8%–39% of OS samples [49,56,57,61]. A comprehensive WES study comparing genetic alterations between 13 paired primary OS and metastatic pulmonary samples found amplification at chromosome 8q involving MYC in both primary and metastatic OS tumors within the same patient [61]. Another WGS study performed on 63 OS samples from 54 patients reported 39% of cases had at least four copies of MYC, with 5% of OS patients having 12 or more copies of MYC [56]. Most recently, a large clinical genomic panel sequencing 71 OS samples from 66 pediatric and adult patients revealed that among several targetable alterations, amplified MYC was a robust option [57]. 4.2.2. CCNE1 CCNE1, mapped on chromosome 19q12, encodes the cyclin E1 protein. Previous studies have found CCNE1 amplification or overTable 3 The top 10 most frequently amplified genes in human osteosarcoma. Gene

Location

DNA sizea (kb)

mRNA size (kb)

Protein weight (kDa)

Signaling pathway

Frequency of amplification

Reference

KDR MYC CCNE1 VEGFA CCND3 PDGFRA KIT MDM2 CDK4 AURKB

4q12 8q24.21 19q12 6p21.1 6p21.1 4q12 4q12 12q15 12q14.1 17p13.1

47.3 6.0 12.4 16.3 115.4 69.2 82.8 37.3 8.3 5.9

5.8 4.5 2.0 3.7 2.1 2.9 5.2 12.2 1.9 1.2

151.5 48.8 47.1 27.0 32.5 122.7 109.9 55.2 33.7 39.3

Receptor tyrosine kinases Cell cycle Cell cycle Receptor tyrosine kinases Cell cycle/apoptosis Receptor tyrosine kinases PI3K/AKT/mTOR Cell cycle/apoptosis Cell cycle/apoptosis Mitosis

11%–50% 8%–39% 8%–33% 23%–24% 18%–23% 5%–18% 11%–15% 5%–15% 11%–13% 6%–13%

[56,57,103] [49,56,57,61] [49,56,57] [56,57] [56,57] [8,56,57] [56,57] [5,8,56,57] [56,57] [56,57]

a

DNA size refers to hg19. PI3K/AKT/mTOR, phosphoinositide 3-kinase/AKT/mammalian target of rapamycin. 162

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Table 4 The top 10 most frequently deleted genes in human osteosarcoma. Gene

Location

DNA sizea (kb)

mRNA size (kb)

Protein weight (kDa)

Signaling pathway

Frequency of deletion

Reference

BRCA1 BRCA2 TP53 RB1 PTEN NF1 ATRX DLG2 CDKN2A CDKN2B

17q21.31 13q13.1 17p13.1 13q14.2 10q23.31 17q11.2 Xq21.1 11q14.1 9p21.3 9p21.3

81.2 84.2 25.8 178.2 108.8 287.2 281.4 2172.9 27.6 6.5

7.1 11.4 2.6 4.8 8.5 12.4 11.2 8.1 1.3 3.9

207.7 384.2 53 106.2 47.2 319.4 282.6 97.6 16.5 14.7

DNA damage control DNA damage control TP53 signaling Cell cycle/apoptosis PI3K/AKT/mTOR Ras/MEK signaling DNA damage response Wnt signaling Cell cycle Cell cycle

91% 3%–78% 10%–74% 8%–64% 1%–56% 3%–34% 2%–26% 10%–24% 15%–23% 15%–22%

[51] [51,56] [8,56,57] [8,49,56,57] [8,49,51,56,57] [8,56] [8,56,57] [8,51] [8,51,56,57] [8,51,56,57]

a

DNA size refers to hg19. PI3K/AKT/mTOR, phosphoinositide 3-kinase/AKT/mammalian target of rapamycin.

4.2.7. CDKN2A/B Cyclin dependent kinase inhibitor 2A/2B (CDKN2A/B), located at chromosome 9p21.3, controls cell cycle progression in G1 and G2 phases. Both CDKN2A and CDKN2B function as tumor suppressors and are frequently mutated and deleted in a several tumors, including OS [70]. CDKN2A/B deletions are present in 15%–23% of OS cases as detected by NGS [8,51,56,57]. In a sequencing of 71 OS samples from 66 pediatric and adult patients, they found deletions at 9p21 involving CDKN2A/B (22%) to be the second-most frequent copy number alteration [57]. We suggest future clinical works to focus on CDKN2A/B targeted therapy in patients with these deletions, as no corresponding clinical trials have been conducted which address this notable genetic aberration. For completeness, we briefly mention here the other OS genes revealed by NGS to be frequently amplified including CCND3, KDR, PDGFRA, and KIT and those commonly deleted such as BRCA1/2, TP53, RB1, NF1, ATRX, and DLG2 (Tables 3 and 4).

4.2.4. VEGFA Vascular endothelial growth factor A (VEGFA), located on chromosome 6p21.1, is a member of the platelet-derived growth factor (PDGF)/VEGF growth factor family. Copy number amplifications in VEGF pathway genes (including VEGFA gene) have been previously validated in OS [67]. NGS has shown VEGFA amplification in OS to range from 23% to 24% [56,57]. In one WGS study, the authors reported 23% of OS patients to have VEGFA amplification, with a significant concomitant increase in VEGFR2 protein (the main VEGFA receptor) in one PDTX model with VEGFA amplification [56]. Of note, another recent NGS study showed VEGFA amplification in 17 of 71 OS patients (24%) [57].

4.2.5. AURKB AURKB, located on chromosome 17q13.1, is a member of the chromosomal passenger complex and a key regulator of mitosis [68]. NGS has shown AURKB amplification in OS to occur at a rate of 6%–13% [56,57]. In one WGS study, copy number gain of AURKB was found in 13% of assessed OS patients [56]. Higher levels of AURKB protein was observed in one PDTX model via western blot and immunohistochemistry. In another NGS study, amplification at 17q13.1 involving AURKB was found in 4 of 71 OS samples (6%) [57]. They did not, however, analyze how this AURKB amplification correlated with OS tumorigenesis as the cohort proportion was too low. Future studies should therefore be conducted with a greater sample size to ensure sufficient power and reveal the possible role of AURKB amplification in the pathogenesis of OS.

4.3. Transcriptomic variants in OS RNA-Seq is a highly sensitive screening tool, and has revealed a number of previously unidentified transcriptome alterations in OS. In an analysis of a single OS patient, their transcriptome showed 65 genes differentially expressed between OS tissue and normal tissue. Specifically, there were seven upregulated genes in the normal sample compared to 58 upregulated genes in the tumor tissue [71]. In another RNA-Seq study including 14 OS and six non-tumoral paired samples, the genes COL1A2, COL5A2, and GJA1 were differentially expressed [72]. In a whole transcriptome analysis of 18 OS samples and their paired normal samples, researchers found 3399 upregulated genes and 1966 downregulated genes in the OS tissues compared to the normal samples. Among these genes, BTNL9, MMP14, ABCA10, ACACB, COL11A1, and PKM2 were the most differentially expressed [73]. In an extracellular vesicle RNA sequencing analysis of 10 matched primary and metastatic OS samples, the mutations, gene expressions, fusion transcripts, and alternative splicing events were evaluated during OS metastasis. Of note, the pyruvate kinase M (PKM) gene showed aberrant alternative splicing, with the M2 isoform (exon 10) being elevated in tumor cells whereas the M1 isoform (exon 9) remained relatively unchanged [74].

4.2.6. PTEN PTEN, located on chromosome 10q23.31, is the major negative regulator of the phosphatidylinositol 3-kinase/mammalian target of rapamycin (PI3K/mTOR) pathway; its loss of function by mutation or deletion therefore promotes bone proliferation, angiogenesis, and lung metastasis in OS [69]. The PTEN gene is deleted in approximately 6%–13% of OS samples [8,49,51,56,57]. A comprehensive analysis of WGS, WES, and RNA-Seq data from 59 human OS tumors with paired normal samples revealed a high frequency of mutations in the PI3K/ AKT/mTOR signaling pathway [8]. In this study, PTEN deletions were observed in 5 of 59 OS samples. In another WES study including 13 advanced OS patients with available molecular profiling results, the researchers also confirmed the most common actionable targets to occur within the PI3K/AKT/mTOR pathway [49]. In this study, three patients (25%) had PTEN losses and two patients (17%) had PIK3CA mutations. In a recent WGS study comprising 30 OS tumor samples and 15 PDTX models, copy number alterations in the PI3K/AKT/mTOR signaling pathway were confirmed [56]. Loss of PTEN was observed in 56% of these patients assessed by WGS. Alterations in this pathway were also identified in two PDTX models, one with biallelic PTEN loss and one with AKT amplification.

4.4. Targetable genes and pathways in OS In recent years, NGS studies have identified several promising targetable genes and related driver pathways in OS. Subsequent pre-clinical studies and clinical trials have analyzed the efficacy of their inhibition and will be discussed herein. 4.4.1. TP53 The TP53 gene synthesizes proteins central to the cell cycle, apoptosis, senescence, DNA repair, and metabolism [47]. Despite its fame as 163

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pazopanib, which has activity against VEGF, has displayed antitumor action in both preclinical mouse models and small patient cohorts [93,94]. A multicenter phase II trial demonstrated another multi-kinase inhibitor, sorafenib, also has significant activity in a small subset of patients with relapsed and unresectable OS [95].

a tumor suppressor, however, the correlation between TP53 mutations and OS patients outcomes remains controversial. Several studies have suggested OS patients carrying TP53 missense mutations or deletions have a worse prognosis and have prompted preclinical studies targeting mutant TP53 [75,76]. Recently, an in vitro study found both the Clustered Regularly Interspaced Short Palindromic Repeats-associated protein-9 nuclease (CRISPR-Cas9) system and the TP53 small molecular inhibitor NSC59984 could knock-out and inhibit mutated TP53 in the human OS cell lines KHOS and KHOSR2. This resulted in diminished OS cell proliferation and migration [77]. In addition, they showed that the knock-out or inhibition of mutant TP53 decreases expression of the IGF1R oncogene as well as the anti-apoptotic proteins Bcl-2 and Survivin in OS cells. Collectively, these results suggest TP53 targeting is a potential therapeutic strategy for OS treatment and warrants further preclinical studies and trials for verification. The PDTX model, which directly implants fresh tumor tissue from cancer patients into immunocompromised mice, is a powerful preclinical platform to bridge basic translational cancer research to the clinical trial stage [78,79]. A primary strength in this technique lies in its ability to retain inter- and intra-tumoral heterogeneity, a prominent OS feature. In a recent WGS study, researchers demonstrated that targeting patient-specific copy number alterations significantly decreases in tumor growth, a promising finding for precision therapy in OS [56]. In this study, six candidate genes and pathways were evaluated using PDTX model systems, including MYC, CCNE1, CDK4, VEGFA, PI3K/ AKT/mTOR pathway, and AURKB. After the mice were treated with their matched inhibitors, 60% of the PDTX models showed significant tumor growth inhibition in vivo, an encouraging finding for future OS patient studies.

4.4.5. PI3K/AKT/mTOR pathway The PI3K/AKT/mTOR pathway is one of the most critical kinaseenriched pathways and participates in cell survival, growth, proliferation, metabolism, angiogenesis, and metastasis [96,97]. Several recent high-throughput sequencing and genotyping studies of human OS have highlighted the PI3K/mTOR pathway is not only common, but highly vulnerable to targeted therapy in OS [8,49,56,98]. Another genomeinformed targeting study found the pan-AKT inhibitor MK2206 or the mTOR inhibitor rapamycin to arrest tumor growth in OS PDTX models with genetic alterations in this pathway [56]. Furthermore, some preclinical models and clinical trials have showcased the efficacy of targeting the PI3K/mTOR pathway for OS treatment [99,100]. In OS preclinical models, a combination therapy of the multikinase inhibitor sorafenib with everolimus yielded enhanced antitumor activity, impaired tumor growth, and reduced migratory and metastatic potential by absolute mTOR pathway inhibition [99]. In a phase II trial, two OS patients achieved confirmed partial responses and one OS patient achieved an unconfirmed partial response after treatment with the mTOR inhibitor ridaforolimus [100]. 4.4.6. Receptor tyrosine kinase (RTK) signaling pathway RTKs are high-affinity cell surface receptors with roles in cell proliferation, differentiation, survival, motility and invasion [101]. Aberrant expression and activation of RTKs are associated with tumor progression in various human cancers, including OS [101,102]. Amplifications at 4q12 involving the RTK encoding genes KIT, KDR and PDGFRA have been identified in 11%–15%, 11%–50%, and 5%–18% of OS tumor samples by NGS, respectively [8,56,57,103]. Therefore, these genes represent promising targetable pathways in OS [104]. Recently, clinical trials have demonstrated efficacy in RTK signaling pathway targeting for OS therapy [105–108]. An open label phase II clinical trial reported the anti-angiogenesis tyrosine kinase inhibitor apatinib is effective for patients with advanced OS, showing a high objective response rate (43.24%) [105]. In a randomized double-blind phase II study of the multi-kinase inhibitor regorafenib in patients with metastatic OS, median progression-free survival was significantly improved with regorafenib compared to placebo (3.6 months versus 1.7 months) [108].

4.4.2. MYC MYC is a major proto-oncogene which promotes cell growth, cell cycle progression, transformation, cell apoptosis, and metabolism [80]. Some previous studies suggest overexpression of MYC in human OS encourages tumor cell invasion and worsens patient prognosis [81,82]. In one notable study, researchers found OS super enhancer genes were bound by MYC; subsequent treatment with super enhancer inhibitors such as JQ1 and THZ1 sufficiently suppressed MYC driven transcriptional amplification and tumor progression [83]. As MYC is the most commonly amplified gene in OS and therefore an attractive target, a recent study mimicked this phenomenon via utilization of targeted MYC therapy in MYC-amplified OS PDTX [56]. Significant tumor shrinkage was observed after treatment with inhibitors [56]. Other studies have employed nanocarriers encapsulated with MYC small interference RNA (siRNA) as anticancer therapy in mouse models, resulting in pronounced tumor inhibition [84,85].

4.4.7. Insulin-like growth factor (IGF) signaling pathway Previous studies have shown the IGF signaling axis is involved in OS pathogenesis [109,110]. In a large sample sequencing study of 112 childhood and adult OS tumors, recurrent mutations in IGF signaling genes were seen in 8/112 (7%) of cases [9]. Additionally, several clinical trials of IGF-1R inhibitors have demonstrated targeting the IGF signaling pathway to be promising in OS treatment [111–113]. In a multi-institutional phase 2 trial of the IGF-1R inhibitor R1507 in patients with recurrent or refractory sarcomas, partial responses were observed in four patients, including in two patients with OS, one patient with rhabdomyosarcoma, and one patient with alveolar soft part sarcoma [111]. In another phase II study of the IGF-1R inhibitor robatumumab in patients with relapsed OS and Ewing sarcoma, complete or partial responses were observed in three of 31 OS patients [113].

4.4.3. CDK4 CDK4 is an important regulator of cell cycle progression. Several studies have demonstrated CDK4 is highly expressed in OS and associated with chemotherapeutic responses [86,87]. One work found elevated CDK4 expression is related to metastatic potential and poor prognosis for OS patients, and that CDK4 inhibition via palbociclib or specific siRNA decreases OS cell proliferation, growth and migration [88]. In short, targeted CDK4 therapy is as a potential therapeutic option in OS. 4.4.4. VEGFA VEGFA is an oncogenic driver and targetable through kinase inhibition. Its expression is correlated with OS development, pulmonary metastasis, and shorter disease-free and overall survival [89]. When inhibited, tumor-induced angiogenesis and growth are suppressed and some preclinical and clinical studies show antiangiogenic therapies are promising for OS treatment [90,91]. Furthermore, several anti-angiogenic drugs have shown activity against OS both in vitro and in vivo [67,92]. For example, the small molecule tyrosine kinase inhibitor

4.5. Novel metastatic and prognostic biomarkers OS patients with pulmonary metastasis can expect an especially grim prognosis. However, the cellular and genetic mechanisms enhancing the dissemination of OS tumor cells to the lungs is poorly understood. In response, an emergence of NGS studies have analyzed paired 164

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primary and matched lung metastatic OS specimens. Several genes have subsequently been shown to enrich metastatic potential and gained traction as prognostic biomarkers. A WES study comparing genetic alterations between primary and metastatic OS tumors found metastatic lung tumors of three patients to have notably higher KEAP1 amplification compared to the paired primary OS samples [61]. Consistent with this finding, their study based on public Gene Expression Omnibus (GEO) datasets also showed high KEAP1 is associated with lung metastasis at the time of initial diagnosis [114]. These results, confirmed by NGS, show KEAP1 amplification or overexpression is a novel predictive biomarker for increased metastatic risk in OS patients. OS patients with elevated KDR have poorer prognosis, and in addition, the KDR inhibitor Apatinib represses OS growth in vivo [115]. More recently, an integrative analysis of WGS and WES data from primary and matched metastatic pediatric OS samples identified amplification of KDR in 7 of 13 primary tumors (54%) and 7 matched pulmonary metastatic OS samples [103]. By analyzing two independent gene expression microarray datasets (GSE21257 and GSE42352), this study also showed high expression of KDR mRNA is directly associated with shorter metastasis-free survival [103]. Moreover, immunohistochemistry revealed a strong association between elevated KDR protein and poor outcomes. Amplifications on ch4q12, including KDR, KIT, and PDGFRA, were reported in another sequencing study of 30 OS tumor samples [56]. Taken collectively, KDR is a potential prognostic biomarker and actionable target for OS. A multiregional WES and WGS study performed on 86 tumor regions from resected primary and metastatic OS tumors from 10 patients identified strong variance between primary tumors and their corresponding pulmonary metastatic lesions [116]. Metastatic OS tumors exhibited significantly higher mutational burden and genomic instability compared with primary OS tumors. A deficiency of DNA damage response (DDR) genes including MSH2, MSH6, MLH1, and PMS2 were proposed to be drivers of OS metastasis [116]. Furthermore, aberrant driver gene-defining subclones, such as NRF1, MDM2, PRAMEF4, HLF, FNDC8, ERC1, CACNG8, and IKZF1 may contribute to metastatic progression [116].

This work was supported, in part, by the Department of Orthopaedic Surgery at UCLA. Dr. Duan is supported, in part, through a Grant from Sarcoma Foundation of America (SFA) (222433), and a Grant from National Cancer Institute (NCI, National Institutes of Health (NIH), UO1, CA151452-01. References [1] Y.C. Yang, L. Han, Z.W. He, et al., Advances in limb salvage treatment of osteosarcoma, J Bone Oncol 10 (2018) 36–40. [2] P.A. Meyers, C.L. Schwartz, M. Krailo, et al., Osteosarcoma: a randomized, prospective trial of the addition of ifosfamide and/or muramyl tripeptide to cisplatin, doxorubicin, and high-dose methotrexate, J. Clin. Oncol. 23 (2005) 2004–2011. [3] P.A. Meyers, G. Heller, J.H. Healey, et al., Osteogenic sarcoma with clinically detectable metastasis at initial presentation, J. Clin. Oncol. 11 (1993) 449–453. [4] L. Kager, A. Zoubek, U. Potschger, et al., Primary metastatic osteosarcoma: presentation and outcome of patients treated on neoadjuvant cooperative osteosarcoma study group protocols, J. Clin. Oncol. 21 (2003) 2011–2018. [5] X. Chen, A. Bahrami, A. Pappo, et al., Recurrent somatic structural variations contribute to tumorigenesis in pediatric osteosarcoma, Cell Rep. 7 (2014) 104–112. [6] J.F. Mcintyre, B. Smith-Sorensen, S.H. Friend, et al., Germline mutations of the p53 tumor suppressor gene in children with osteosarcoma, J. Clin. Oncol. 12 (1994) 925–930. [7] M. Kansara, D.M. Thomas, Molecular pathogenesis of osteosarcoma, DNA Cell Biol. 26 (2007) 1–18. [8] J.A. Perry, A. Kiezun, P. Tonzi, et al., Complementary genomic approaches highlight the PI3K/mTOR pathway as a common vulnerability in osteosarcoma, Proc. Natl. Acad. Sci. U.S.A. 111 (2014) 5564–5573. [9] S. Behjati, P.S. Tarpey, K. Haase, et al., Recurrent mutation of IGF signalling genes and distinct patterns of genomic rearrangement in osteosarcoma, Nat. Commun. 8 (2017) 15936. [10] M. Meyerson, S. Gabriel, G. Getz, Advances in understanding cancer genomes through second-generation sequencing, Nat. Rev. Genet. 11 (2010) 685–696. [11] V.G. Leblanc, M.A. Marra, Next-generation sequencing approaches in cancer: where have they brought us and where will they take us? Cancer 7 (2015) 1925–1958. [12] C. Hutter, J.C. Zenklusen, The cancer genome atlas: creating lasting value beyond its data, Cell 173 (2018) 283–285. [13] International Cancer Genome Consortium, T.J. Hudson, W. Anderson, et al., International network of cancer genome projects, Nature 464 (2010) 993–998. [14] S.A. Forbes, D. Beare, P. Gunasekaran, et al., COSMIC: exploring the world's knowledge of somatic mutations in human cancer, Nucleic Acids Res. 43 (2015) 805–811. [15] M. Tuna, C.I. Amos, Genomic sequencing in cancer, Canc. Lett. 340 (2013) 161–170. [16] H. Dong, S. Wang, Exploring the cancer genome in the era of next-generation sequencing, Front. Med. 6 (2012) 48–55. [17] H. Nakagawa, M. Fujita, Whole genome sequencing analysis for cancer genomics and precision medicine, Canc. Sci. 109 (2018) 513–522. [18] R. Hrdlickova, M. Toloue, B. Tian, RNA-Seq methods for transcriptome analysis, Wiley Interdiscip Rev RNA 8 (2017) 1364. [19] Z. Wang, M. Gerstein, M. Snyder, RNA-Seq: a revolutionary tool for transcriptomics, Nat. Rev. Genet. 10 (2009) 57–63. [20] E.L. Van Dijk, H. Auger, Y. Jaszczyszyn, et al., Ten years of next-generation sequencing technology, Trends Genet. 30 (2014) 418–426. [21] J.M. Heather, B. Chain, The sequence of sequencers: the history of sequencing DNA, Genomics 107 (2016) 1–8. [22] S. Morganti, P. Tarantino, E. Ferraro, et al., Complexity of genome sequencing and reporting: next generation sequencing (NGS) technologies and implementation of precision medicine in real life, Crit. Rev. Oncol. Hematol. 133 (2019) 171–182. [23] X. Zhang, Z. Liang, S. Wang, et al., Application of next-generation sequencing technology to precision medicine in cancer: joint consensus of the Tumor Biomarker Committee of the Chinese Society of Clinical Oncology, Cancer Biol Med 16 (2019) 189–204. [24] K.R. Kukurba, S.B. Montgomery, RNA sequencing and analysis, Cold Spring Harb. Protoc. (2015) 951–969. [25] M. Bousquet, C. Noirot, F. Accadbled, et al., Whole-exome sequencing in osteosarcoma reveals important heterogeneity of genetic alterations, Ann. Oncol. 27 (2016) 738–744. [26] Y.O. Alekseyev, R. Fazeli, S. Yang, et al., A next-generation sequencing primer—how does it work and what can it do? Acad Pathol 5 (2018) 1–11. [27] T.S. Alioto, I. Buchhalter, S. Derdak, et al., A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing, Nat. Commun. 6 (2015) 10001. [28] L.G. Biesecker, K.V. Shianna, J.C. Mullikin, Exome sequencing: the expert view, Genome Biol. 12 (2011) 128–131. [29] S. Lacey, J.Y. Chung, H. Lin, A comparison of whole genome sequencing with exome sequencing for family-based association studies, BMC Proc. 8 (2014) 538–544. [30] D. Sims, I. Sudbery, N.E. Ilott, et al., Sequencing depth and coverage: key

5. Conclusions and perspectives NGS has emerged as the preferred platform for detailing the heterogeneous genomic alterations that hallmark OS. More specifically, it has successfully identified recurrent somatic mutations, copy number alterations, and driver pathways prominent in OS tumorigenesis and lung metastasis. A major challenge to previous targeted therapies in OS has been in the accurate identification of targets within the individual patient, as OS is highly variable between and within patients. Highthroughput NGS technologies can answer this dilemma, as they are able to expose which targets are most prominent within a given patient, and therefore well-suited for optimizing target selection and overall precision medicine for OS patients. However, further preclinical studies and clinical trials are required to explore the therapeutic efficacy of these gnome-informed targeted pathways. Aside from therapeutic selection, NGS is able to detect novel predictors for pulmonary metastasis and poor prognosis in OS and can better inform patient outcomes. Furthermore, as an emerging tool of NGS for OS research and targeted therapy, single-cell sequencing may be an ideal platform to clarify OS heterogeneity [117]. Given the relatively low incidence rate and diversity of the pathological subtypes of this disease, future global multicenter genome-wide sequencing studies with larger sample sizes are required to fully realize the potential of NGS in OS therapy and prognostics. Declaration of competing interest The authors declare that there are no conflict of interest. 165

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knowledge base, JCO Precis Oncol (2017), https://doi.org/10.1200/PO.17.00011. [67] J. Yang, D. Yang, Y. Sun, et al., Genetic amplification of the vascular endothelial growth factor (VEGF) pathway genes, including VEGFA, in human osteosarcoma, Cancer 117 (2011) 4925–4938. [68] M. Carmena, M. Wheelock, H. Funabiki, et al., The chromosomal passenger complex (CPC): from easy rider to the godfather of mitosis, Nat. Rev. Mol. Cell Biol. 13 (2012) 789–803. [69] S.S. Freeman, S.W. Allen, R. Ganti, et al., Copy number gains in EGFR and copy number losses in PTEN are common events in osteosarcoma tumors, Cancer 113 (2008) 1453–1461. [70] E.K. Karlsson, S. Sigurdsson, E. Ivansson, et al., Genome-wide analyses implicate 33 loci in heritable dog osteosarcoma, including regulatory variants near CDKN2A/B, Genome Biol. 14 (2013) 132. [71] A. Martson, S. Koks, E. Reimann, et al., Transcriptome analysis of osteosarcoma identififies suppression of WNT pathway and up-regulation of adiponectin as potential biomarker, Genom. Discov. 1 (1) (2013) 3. [72] D.J. Wu, K. Chen, Y.S. Bai, et al., Screening of diagnostic markers for osteosarcoma, Mol. Med. Rep. 10 (5) (2014) 2415–2420. [73] X.D. Ho, P. Phung, Q. Le V, et al., Whole transcriptome analysis identifies differentially regulated networks between osteosarcoma and normal bone samples, Exp. Biol. Med. 242 (18) (2017) 1802–1811. [74] Q.Y. Bao, L.Z. Gong, J.Z. Wang, et al., Extracellular vesicle RNA sequencing reveals dramatic transcriptomic alterations between metastatic and primary osteosarcoma in a liquid biopsy approach, Ann. Surg Oncol. 25 (9) (2018) 2642–2651. [75] N. Gokgoz, J.S. Wunder, S. Mousses, et al., Comparison of p53 mutations in patients with localized osteosarcoma and metastatic osteosarcoma, Cancer 92 (2001) 2181–2189. [76] A.L. Seidinger, M.J. Mastellaro, F. Paschoal Fortes, et al., Association of the highly prevalent TP53 R337H mutation with pediatric choroid plexus carcinoma and osteosarcoma in southeast Brazil, Cancer 117 (2011) 2228–2235. [77] F. Tang, L. Min, N.A. Seebacher, et al., Targeting mutant TP53 as a potential therapeutic strategy for the treatment of osteosarcoma, J. Orthop. Res. 37 (2019) 789–798. [78] D. Siolas, G.J. Hannon, Patient-derived tumor xenografts: transforming clinical samples into mouse models, Canc. Res. 73 (2013) 5315–5319. [79] W. Meohas, R.A. Granato, J.A.M. Guimarães, et al., Patient-derived xenografts as a preclinical model for bone sarcomas, Acta Ortopédica Bras. 26 (2018) 98–102. [80] V. Posternak, M.D. Cole, Strategically Targeting MYC in Cancer. F1000Res, (2016), https://doi.org/10.12688/f1000research.7879.1. [81] G. Han, Y. Wang, W.Z. Bi, C-Myc overexpression promotes osteosarcoma cell invasion via activation of MEK-ERK pathway, Oncol. Res. 20 (2012) 149–156. [82] X. Wu, Z.D. Cai, L.M. Lou, et al., Expressions of p53, c-MYC, BCL-2 and apoptotic index in human osteosarcoma and their correlations with prognosis of patients, Cancer Epidemiol 36 (2012) 212–216. [83] D. Chen, Z. Zhao, Z. Huang, et al., Super enhancer inhibitors suppress MYC driven transcriptional amplification and tumor progression in osteosarcoma, Bone Res 6 (2018) 11–18. [84] Y. Chen, J.J. Wu, L. Huang, Nanoparticles targeted with NGR motif deliver c-myc siRNA and doxorubicin for anticancer therapy, Mol. Ther. 18 (2010) 828–834. [85] Y. Zhang, L. Peng, R.J. Mumper, et al., Combinational delivery of c-myc siRNA and nucleoside analogs in a single, synthetic nanocarrier for targeted cancer therapy, Biomaterials 34 (2013) 8459–8468. [86] A. Yoshida, T. Ushiku, T. Motoi, et al., Immunohistochemical analysis of MDM2 and CDK4 distinguishes low-grade osteosarcoma from benign mimics, Mod. Pathol. 23 (2010) 1279–1288. [87] F. Dujardin, M.B. Binh, C. Bouvier, et al., MDM2 and CDK4 immunohistochemistry is a valuable tool in the differential diagnosis of low-grade osteosarcomas and other primary fibro-osseous lesions of the bone, Mod. Pathol. 24 (2011) 624–637. [88] Y.B. Zhou, J.K. Shen, Z.J. Yu, et al., Expression and therapeutic implications of cyclin-dependent kinase 4 (CDK4) in osteosarcoma, Biochim. Biophys. Acta (BBA) - Mol. Basis Dis. 1864 (2018) 1573–1582. [89] X.W. Yu, T.Y. Wu, X. Yi, et al., Prognostic significance of VEGF expression in osteosarcoma: a meta-analysis, Tumour Biol 35 (2014) 155–160. [90] H.F. Dvorak, Vascular permeability factor/vascular endothelial growth factor: a critical cytokine in tumor angiogenesis and a potential target for diagnosis and therapy, J. Clin. Oncol. 20 (2002) 4368–4380. [91] G.M. Quan, P.F. Choong, Anti-angiogenic therapy for osteosarcoma, Canc. Metastasis Rev. 25 (2006) 707–713. [92] M. Kaya, T. Wada, S. Nagoya, et al., Prevention of postoperative progression of pulmonary metastases in osteosarcoma by antiangiogenic therapy using endostatin, J. Orthop. Sci. 12 (2007) 562–567. [93] T. Tanaka, Y. Yui, N. Naka, et al., Dynamic analysis of lung metastasis by mouse OS LM8: VEGF is a candidate for anti-metastasis therapy, Clin. Exp. Metastasis 30 (2013) 369–379. [94] A. Safwat, A. Boysen, A. Lucke, et al., Pazopanib in metastatic osteosarcoma: significant clinical response in three consecutive patients, Acta Oncol. 53 (2014) 1451–1454. [95] G. Grignani, E. Palmerini, P. Dileo, et al., A phase II trial of sorafenib in relapsed and unresectable high-grade OS after failure of standard multimodal therapy: an Italian Sarcoma Group study, Ann. Oncol. 23 (2012) 508–516. [96] B. Vanhaesebroeck, J. Guillermet-Guibert, M. Graupera, et al., The emerging mechanisms of isoform-specific PI3K signalling, Nat. Rev. Mol. Cell Biol. 11 (2010) 329–341. [97] M. Laplante, D.M. Sabatini, mTOR signaling in growth control and disease, Cell 149 (2012) 274–293. [98] E. Choy, F. Hornicek, L. MacConaill, et al., High-throughput genotyping in

considerations in genomic analyses, Nat. Rev. Genet. 15 (2014) 121–132. [31] D. Aird, M.G. Ross, W.S. Chen, et al., Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries, Genome Biol. 12 (2011) 18–32. [32] S.S. Ajay, S.C. Parker, H.O. Abaan, et al., Accurate and comprehensive sequencing of personal genomes, Genome Res. 21 (2011) 1498–1505. [33] S. Tarazona, F. García-Alcalde, J. Dopazo, et al., Differential expression in RNAseq: a matter of depth, Genome Res. 21 (2011) 2213–2223. [34] C.J. Lengner, H.A. Steinman, J. Gagnon, et al., Osteoblast differentiation and skeletal development are regulated by Mdm2-p53 signaling, J. Cell Biol. 172 (2006) 909–921. [35] P.P. Lin, M.K. Pandey, F. Jin, et al., Targeted mutation of p53 and Rb in mesenchymal cells of the limb bud produces sarcomas in mice, Carcinogenesis 30 (2009) 1789–1795. [36] A.J. Ng, A.J. Mutsaers, E.K. Baker, et al., Genetically engineered mouse models and human osteosarcoma, Clin. Sarcoma Res. 2 (1) (2012) 19–33. [37] C. Jacques, N. Renema, B. Ory, et al., Murine models of bone sarcomas, Methods Mol. Biol. (2019) 1914. [38] E. Calo, J.A. Quintero-Estades, P.S. Danielian, et al., Rb regulates fate choice and lineage commitment in vivo, Nature 466 (2010) 1110–1114. [39] T. Quist, H.F. Jin, J.F. Zhu, et al., The impact of osteoblastic differentiation on osteosarcomagenesis in the mouse, Oncogene 34 (32) (2015) 4278–4284. [40] J.N. Tao, M.M. Jiang, L.C. Jiang, et al., Notch activation as a driver of osteogenic sarcoma, Canc. Cell 26 (3) (2014) 390–401. [41] Y.J. Han, H. Feng, J. Sun, et al., Lkb1 deletion in periosteal mesenchymal progenitors induces osteogenic tumors through mTORC1 activation, J. Clin. Invest. 129 (5) (2019) 1895–1909. [42] Q. Deng, P. Li, M.J. Che, et al., Activation of hedgehog signaling in mesenchymal stem cells induces cartilage and bone tumor formation via Wnt/β-Catenin, Elife 8 (2019) e50208. [43] P.A. Futreal, L. Coin, M. Marshall, et al., A census of human cancer genes, Nat. Rev. Canc. 4 (2004) 177–183. [44] K. Rickel, F. Fang, J. Tao, Molecular genetics of osteosarcoma, Bone 102 (2017) 69–79. [45] E.R. Kastenhuber, S.W. Lowe, Putting p53 in context, Cell 170 (2017) 1062–1078. [46] C. Kandoth, M.D. McLellan, F. Vandin, et al., Mutational landscape and significance across 12 major cancer types, Nature 502 (2013) 333–339. [47] V.J.N. Bykov, S.E. Eriksson, J. Bianchi, et al., Targeting mutant p53 for efficient cancer therapy, Nat. Rev. Canc. 18 (2018) 89–102. [48] C.G. Joseph, H. Hwang, Y. Jiao, et al., Exomic analysis of myxoid liposarcomas, synovial sarcomas, and osteosarcomas, Genes Chromosomes Cancer 53 (2014) 15–24. [49] D. Egas-Bejar, P.M. Anderson, R. Agarwal, et al., Theranostic profiling for actionable aberrations in advanced high risk osteosarcoma with aggressive biology reveals high molecular diversity: the human fingerprint hypothesis, Oncoscience 1 (2014) 167–179. [50] S. Ribi, D. Baumhoer, K. Lee, et al., TP53 intron 1 hotspot rearrangements are specific to sporadic osteosarcoma and can cause Li-Fraumeni syndrome, Oncotarget 6 (2015) 7727–7740. [51] M. Kovac, C. Blattmann, S. Ribi, et al., Exome sequencing of osteosarcoma reveals mutation signatures reminiscent of BRCA deficiency, Nat. Commun. 6 (2015) 8940–8948. [52] S. Lorenz, T. Barøy, J. Sun, et al., Unscrambling the genomic chaos of osteosarcoma reveals extensive transcript fusion, recurrent rearrangements and frequent novel TP53 aberrations, Oncotarget 7 (2016) 5273–5288. [53] H. Masuda, C. Miller, H.P. Koeffler, et al., Rearrangement of the p53 gene in human osteogenic sarcomas, Proc. Natl. Acad. Sci. U.S.A. 84 (1987) 7716–7719. [54] A.L. Murphree, W.F. Benedict, Retinoblastoma: clues to human oncogenesis, Science 223 (1984) 1028–1033. [55] S.H. Friend, R. Bernards, S. Rogelj, et al., A human DNA segment with properties of the gene that predisposes to retinoblastoma and osteosarcoma, Nature 323 (1986) 643–646. [56] L.C. Sayles, M.R. Breese, A.L. Koehne, et al., Genome-informed targeted therapy for osteosarcoma, Canc. Discov. 9 (2019) 46–63. [57] Y. Suehara, D. Alex, A.S. Bowman, et al., Clinical genomic sequencing of pediatric and adult osteosarcoma reveals distinct molecular subsets with potentially targetable alterations, Clin. Canc. Res. 25 (21) (2019) 6346–6356. [58] N.K.V. Cheung, J. Zhang, C. Lu, et al., Association of age at diagnosis and genetic mutations in patients with neuroblastoma, J. Am. Med. Assoc. 307 (2012) 1062–1071. [59] R. Beroukhim, C.H. Mermel, D. Porter, et al., The landscape of somatic copynumber alteration across human cancers, Nature 463 (2010) 899–905. [60] A.L. Hsieh, Z.E. Walton, B.J. Altman, et al., MYC and metabolism on the path to cancer, Semin. Cell Dev. Biol. 43 (2015) 11–21. [61] H.Y. Xu, X.J. Zhu, H. Bao, et al., Genetic and clonal dissection of osteosarcoma progression and lung metastasis, Int. J. Canc. 143 (2018) 1134–1142. [62] N. Nakayama, K. Nakayama, Y. Shamima, et al., Gene amplification CCNE1 is related to poor survival and potential therapeutic target in ovarian cancer, Cancer 116 (2010) 2621–2634. [63] W.W. Lockwood, D. Stack, T. Morris, et al., Cyclin E1 is amplified and overexpressed in osteosarcoma, J. Mol. Diagn. 13 (2011) 289–296. [64] H.Y. Zhao, J.L. Wang, Y. Zhang, et al., Prognostic values of CCNE1 amplification and overexpression in cancer patients: a systematic review and meta-analysis, J. Canc. 9 (2018) 2397–2407. [65] E. Hamilton, J.R. Infante, Targeting CDK4/6 in patients with cancer, Canc. Treat Rev. 45 (2016) 129–138. [66] D. Chakravarty, J. Gao, S.M. Phillips, et al., OncoKB: a precision oncology

166

Cancer Letters 474 (2020) 158–167

J. Zhao, et al.

[99]

[100]

[101] [102] [103]

[104] [105]

[106] [107]

[108]

(2019) 1424–1431. [109] R. Yang, S. Piperdi, Y. Zhang, et al., Transcriptional profifiling identififies the signaling axes of IGF and transforming growth factor-b as involved in the pathogenesis of osteosarcoma, Clin. Orthop. Relat. Res. 474 (1) (2016) 178–189. [110] Y.S. Li, Q. Liu, H.B. He, et al., The possible role of insulin-like growth factor-1 in osteosarcoma, Curr. Probl. Canc. 43 (3) (2019) 228–235. [111] A.S. Pappo, G. Vassal, J.J. Crowley, et al., A phase 2 trial of R1507, a monoclonal antibody to the insulin-like growth factor-1 receptor (IGF-1R), in patients with recurrent or refractory rhabdomyosarcoma, osteosarcoma, synovial sarcoma, and other soft tissue sarcomas: results of a Sarcoma Alliance for Research through Collaboration study, Cancer 120 (16) (2014) 2448–2456. [112] L.M. Wagner, M. Fouladi, A. Ahmed, et al., Phase II study of cixutumumab in combination with temsirolimus in pediatric patients and young adults with recurrent or refractory sarcoma: a report from the Children's Oncology Group, Pediatr. Blood Canc. 62 (3) (2015) 440–444. [113] P.M. Anderson, S.S. Bielack, R.G. Gorlick, et al., A phase II study of clinical activity of SCH 717454 (robatumumab) in patients with relapsed osteosarcoma and Ewing sarcoma, Pediatr. Blood Canc. 63 (10) (2016) 1761–1770. [114] E. Clough, T. Barrett, The gene expression omnibus database, Methods Mol. Biol. 1418 (2016) 93–110. [115] K. Liu, T. Ren, Y. Huang, et al., Apatinib promotes autophagy and apoptosis through VEGFR2/STAT3/BCL-2 signaling in osteosarcoma, Cell Death Dis. 8 (2017) 3015. [116] D. Wang, X.H. Niu, Z.J. Wang, et al., Multiregion sequencing reveals the genetic heterogeneity and evolutionary history of osteosarcoma and matched pulmonary metastases, Canc. Res. 79 (1) (2019) 7–20. [117] M. Rantalainen, Application of single-cell sequencing in human cancer, Brief Funct Genomics 17 (4) (2018) 273–282.

osteosarcoma identifies multiple mutations in phosphoinositide-3-kinase and other oncogenes, Cancer 118 (2012) 2905–2914. Y. Pignochino, C. Dell'Aglio, M. Basiricò, et al., The combination of sorafenib and everolimus abrogates mTORC1 and mTORC2 upregulation in osteosarcoma preclinical models, Clin. Canc. Res. 19 (2013) 2117–2131. S.P. Chawla, A.P. Staddon, L.H. Baker, et al., Phase II study of the mammalian target of rapamycin inhibitor ridaforolimus in patients with advanced bone and soft tissue sarcomas, J. Clin. Oncol. 30 (2012) 78–84. Z.F. Du, C.M. Lovly, Mechanisms of receptor tyrosine kinase activation in cancer, Mol. Canc. 17 (1) (2018) 58. V. Sangwan, M. Park, Receptor tyrosine kinases: role in cancer progression, Curr. Oncol. 13 (5) (2006) 191–193. G.L. Negri, B.M. Grande, A. Delaidelli, et al., Integrative genomic analysis of matched primary and metastatic pediatric osteosarcoma, J. Pathol. 249 (2019) 319–331. N. Entz-Werle, M.P. Gaub, T. Lavaux, et al., KIT gene in pediatric OS: could it be a new therapeutic target? Int. J. Canc. 120 (2007) 2510–2516. L. Xie, J. Xu, X. Sun, et al., Apatinib for advanced osteosarcoma after failure of standard multimodal therapy: an open label phase II clinical trial, Oncol. 24 (7) (2019) e542–e550. A. Longhi, A. Paioli, E. Palmerini, et al., Pazopanib in relapsed osteosarcoma patients: report on 15 cases, Acta Oncol. 58 (1) (2019) 124–128. F. Duffaud, O. Mir, P. Boudou-Rouquette, et al., Efficacy and safety of regorafenib in adult patients with metastatic osteosarcoma: a non-comparative, randomised, double-blind, placebo-controlled, phase 2 study, Lancet Oncol. 20 (1) (2019) 120–133. L.E. Davis, V. Bolejack, C.W. Ryan, et al., Randomized double-blind phase II study of regorafenib in patients with metastatic osteosarcoma, J. Clin. Oncol. 37 (16)

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