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Weaver, C.;  Satter, K.B.;  Richardson, K.P.;  Tran, L.K.H.;  Tran, P.M.H.;  Purohit, S. Biomarkers in Renal Clear Cell Carcinoma. Encyclopedia. Available online: https://encyclopedia.pub/entry/36906 (accessed on 18 June 2024).
Weaver C,  Satter KB,  Richardson KP,  Tran LKH,  Tran PMH,  Purohit S. Biomarkers in Renal Clear Cell Carcinoma. Encyclopedia. Available at: https://encyclopedia.pub/entry/36906. Accessed June 18, 2024.
Weaver, Chaston, Khaled Bin Satter, Katherine P. Richardson, Lynn K. H. Tran, Paul M. H. Tran, Sharad Purohit. "Biomarkers in Renal Clear Cell Carcinoma" Encyclopedia, https://encyclopedia.pub/entry/36906 (accessed June 18, 2024).
Weaver, C.,  Satter, K.B.,  Richardson, K.P.,  Tran, L.K.H.,  Tran, P.M.H., & Purohit, S. (2022, November 28). Biomarkers in Renal Clear Cell Carcinoma. In Encyclopedia. https://encyclopedia.pub/entry/36906
Weaver, Chaston, et al. "Biomarkers in Renal Clear Cell Carcinoma." Encyclopedia. Web. 28 November, 2022.
Biomarkers in Renal Clear Cell Carcinoma
Edit

Renal clear cell carcinoma (ccRCC) comprises over 75% of all renal tumors and arises in the epithelial cells of the proximal convoluted tubule. Molecularly ccRCC is characterized by copy number alterations (CNAs) such as the loss of chromosome 3p and VHL inactivation.

clear cell carcinoma molecular pathology biomarkers gene and protein signatures

1. Introduction

Renal cell carcinoma (RCC) originates in the renal cortex and comprises 80–85% of all primary renal neoplasms [1]. RCC accounts for 2% of global cancer diagnoses and is one of the ten most common types of cancer diagnosed in the United States [2]. In recent years, RCC has become one of the fastest-growing cancers in North America, with the incidence doubling from 1975 to 2016 [2]. According to recent Surveillance, Epidemiology, and End Results Program (SEER) statistics, mortality rates remained relatively stable from 1975 to 2016, which may be associated with improved diagnostic and prognostic measures [2][3]. Despite the tremendous advancements, particularly in targeted therapeutics, RCC remains the most lethal urogenital cancer with a 5-year survival rate of roughly 76% [2][3]. However, the survival statistics depend highly on the initial stage at diagnosis, with localized patients having 93% 5-year survival, while distant cases have only 15.3% [3]. The major subtypes of RCC include clear cell carcinoma (ccRCC, ~75% cases), papillary cell (pRCC, ~10–15% cases), and chromophobe (chRCC, ~5% cases), and other rare types [4]. Each of these types arises from histologically distinct cells [4]. Each subtype arises from a series of complex genetic driver events and molecular aberrations [4]. Over the years, the knowledge has broadened on genetic heterogeneity, including mutational burden and targetable markers by high throughput assays and sequencing technologies [5][6]. Until the recent development of proteomic signature data, all of the research in RCC biomarker identification has focused on genomic alterations and gene expression signatures, which have various limitations preventing their integration into the clinical practice [7].
Current genomic profiling approaches have limitations, such as small numbers of individual mutations, which are both difficult to target therapeutically and fail to capture phenotypic consequences of aberrant gene expression [7][8]. Transcriptomic analyses suffer from a high degree of variability among expression signatures within individual tumors with the absence of validation of the gene signatures in independent population [9]. With the recent integration of protein signature data, a more robust molecular “landscape” for ccRCC may be revealed as the number of protein signature profiles begins to approach the level of genomic and gene expression data currently available [7].

2. RCC Subtypes

Major subtypes of RCC include clear-cell (ccRCC), papillary (pRCC), and chromophobe (chRCC), as mentioned earlier [10] (Table 1). The vast majority of RCC cases are of clear-cell morphology (75%), while pRCC (10%), chRCC (5%), and other unclassified and rare subtypes make up the remainder of renal cancer [11]. Clear cell RCC tumors arise from epithelial cells of the proximal convoluted tubule in the nephron and are histologically confirmed by their abundant lipid and glycogen-rich cytoplasmic droplets [12]. Roughly 2–3% of ccRCC are hereditary, originating from VHL disease-induced renal neoplastic cysts [13][14]. Hereditary and sporadic tumors alike may degenerate into malignant tumors as the result of a combination of early driver and somatic mutations, DNA methylation, and copy number alterations (CNAs) [14]. These molecular changes promote oncogenesis through the proliferation of a multitude of growth factors and dysregulated pathways, i.e., VEGF, PDGF, and HIF pathways [15]. PRCC tumors are histologically classified as type 1 or type 2, which have distinct molecular and survival differences [16] (Table 1). Most pRCC cases are sporadic; however, type 1 tumors have a hereditary component arising from germline mutations of MET [17]. In comparison, type 2 tumors are linked to a greater number of chromosomal aberrations and are associated with higher grade, stage, and an overall worse prognosis [18]. ChRCC tumors are histologically subdivided into a classical type, consisting of pale and eosinophilic cells, and an eosinophilic variant, which contains predominantly eosinophilic cells [19] (Table 1). These tumors are generally viewed as less aggressive compared to the more frequent RCC subtypes [20]. Molecular features unique to chRCC include copy number variations involving complete loss of chromosomes 1, 2, 6, 10, 13, and 17 [19]. Despite these distinctions, much of the current multi-omics analyses have been directed towards ccRCC, as it is the most frequently diagnosed and most lethal subtype [21] (Table 1).
Over 50% of cases of RCC in the clinic are discovered incidentally, showing no common clinical symptoms of flank pain, hematuria, and/or palpable abdominal mass(es), usually associated with RCC [14]. Surgical removal of tumors is the preferred treatment for RCC when patients are in stages I-III; however, up to 1/3 of these patients will experience disease recurrence [22]. For advanced-stage disease, intratumor heterogeneity and tumor clonality are important factors for predicting prognostic outcome [5].
Table 1. Renal Cell Carcinoma Types.
Risk stratification and targeted therapeutic development for these patients have generally relied on certain physiological and biochemical markers expressed within the genome, transcriptome, and proteome [24]. Recent progress in whole genome sequencing techniques has led to the identification of a number of genes with clinical and prognostic relevance for the ccRCC [21]. Additional analysis techniques, such as functional impact mutation ranking, phylogenetic analysis, and ploidy profiling, have revealed distinct driver mutations in the early development of ccRCC tumors [6][25]. The identification of common mutation patterns that initiate tumor progression can improve early detection and prognostication methods, which are two important factors for RCC survival outcomes [11][26].

3. Molecular Changes in RCC

The identification of genomic and transcriptomic biomarkers has added tremendous biological value for ccRCC characterization [27]. The development of ccRCC has been described by a series of molecular changes associated with tumor initiation, driver gene mutations, lethal events, and, ultimately, tumor metastasis [28]. Various DNA alterations are involved in tumor development and progression, including copy number alterations (CNAs), methylation, and mutations that drive genomic instability [28]. The resulting biological state of these alterations is often reflected in the gene expression profiles of tumor cells, from which expression signatures may be identified and associated with clinical metrics, such as diagnosis and prognosis [29]. When analyzed together, the correlation between mRNA transcripts and protein expression for RCC tumors has been shown to be quite variable [27]. As such, protein expression signatures may adequately summarize the consequences of genomic and transcriptomic alterations, while also providing new targetable agents for precision medicine [27].

3.1. DNA Alterations

DNA mutations are the most common form of alteration found in all cancers including ccRCC. Genomic alterations in ccRCC are summarized by copy number variations involving whole chromosome alterations (7 and 9), arm-level deletions (3p and 14q) and gains (5q), and additional somatic mutations [30][31]. Many of the mutations associated with ccRCC development follow the two-hit hypothesis of tumorigenesis; the loss of heterozygosity (LOH) occurs via the loss of 3p and inactivation of the remaining allele by somatic mutation [31] (Table 2). The loss of 3p leads to the loss of one copy of VHL, PBRM1, SETD2, and BAP1, the most commonly mutated genes in ccRCC. In addition to chromosomal aberrations, promoter CpG hypermethylation, missense, and truncating mutations account for a large percentage of the observed DNA dysregulation [6][31]. The most commonly dysregulated pathways include the well-known VHL/HIF pathway, chromatin remodeling/histone methylation activity, and the PI3K/AKT/mTOR pathway [31] (Table 2). The models for ccRCC development and progression consistently depict the importance of early driver mutations, which leave surroundings cells vulnerable to additional subclonal mutations [26]. Somatic mutations of genes with chromatin remodeling and histone modification capabilities (PBRM1, BAP1, SETD2, KDM5C) contribute to increased chromosome instability and alterations in gene expression control, which has been associated with higher-grade tumors and poorer survival [32][33].
Table 2. Driver Mutations for ccRCC (Clark et al. [30]).
Driver mutations are defined as a specific group of mutations that arise in the early stages of cancer and are highly influential in the malignant transformation of tumor cells [34] (Table 2). In certain analyses of the evolution of ccRCC tumor mutations, driver mutations are differentiated by the time at which they occur along phylogenetic trees [5][6]. “Truncal” mutations represent the earliest mutational events in tumor progression, while “branched” mutations occur later and characterize distinct trajectories of the tumor development [5][6]. Despite largely ubiquitous VHL inactivation and 3p loss in ccRCC tumors, there is a wide variation in clinical outcomes, which brings into question the role of subclonal and passenger mutations in tumor progression and drug resistance [5][6]. Branched mutations and epigenetic changes often involve gene products associated with chromatin remodeling complexes and hypermethylation, which present unique challenges to the therapeutic targeting [32][35]. Somatic mutations of genes with chromatin remodeling and histone modification capabilities (PBRM1, BAP1, SETD2, KDM5C) contribute to increased chromosome instability and alterations in gene expression control, which has been associated with higher grade tumors [32][33] (Table 2). The role of DNA hypermethylation has also been investigated extensively in ccRCC, as silencing of tumor suppressor genes, such as FBN2, PCDH8, BNC1, and SFRP1, plays an integral role in the tumor progression [36].

3.2. Gene Expression Signatures

The application of gene expression signatures for clinical use has remained a long-standing question since the advent of expression analysis over 20 years ago [37]. Gene expression signatures are defined as a single gene, or group of genes, with an expression pattern that associates with some clinically relevant metric such as diagnosis, prognosis, or predictive treatment response [37]. As a potential biomarker, RNA expression provides a readily and easily available resource for detecting cellular changes reflected in mRNA and other types of extracellular RNA (exRNA) [38]. Extracellular RNA transcripts are also stable in a number of bio-fluids, including urine, serum, and plasma, providing a potentially promising resource for non-invasive collection methods [38]. Changes in gene expression patterns are directly correlated with biologically diseased states and ultimately may represent a surrogate phenotype for the cancer [29]. With recent developments in next-generation sequencing (NGS) transcriptomic signatures can be easily identified. It can also predict splice variants, gene fusions, and epigenetic changes, which are missed in the DNA analysis [39]. Low sample numbers and lack of validation are major obstacles to the clinical transition [40]. The gene expression signatures provided in Table 3 incorporate diagnostic, prognostic, and predictive response outcomes, representing the current state of expression signature analyses.
Classification of RCC, based on gene expression and survival outcomes, was proposed in 2010 as a molecular stratification tool to investigate metastasis and tumor aggressiveness [41][42]. This approach suggested using clear cell type A (ccA) and clear cell type B (ccB) for the classification of RCC to include metastasis and aggressive nature of the RCC tumors [41]. Built off of the ccA/ccB classifiers, ClearCode34 is a prognostic signature that can reliably predict the ccRCC recurrence risk [43]. This gene signature has been shown to identify patients who would benefit from surveillance versus adjuvant therapy following surgery [43]. Gene expression signatures can also be used to differentiate tumor and normal tissue [40]. Therefore, targeting driver mutation-specific expression profiles is a logical strategy to detect early oncogenic changes in pre-neoplastic cells as well as to supplement diagnosis by tumor biopsy and guide treatment decisions [44][45]. Ujfaludi et al. (2022) analyzed the transcriptomic signature of key ccRCC driver genes, VHL, SETD2, PBRM1, and BAP1, to find that the median transcription of these genes distinguished ccRCC from normal tissue with a moderate level of sensitivity and specificity (87% and 77%, respectively) [44]. There have been a number of recent clinical trials [46] which have revolutionized the treatment landscape of ccRCC, from which a wealth of biomarker data can be extracted. Biomarker analysis from the recent phase III CheckMate 214 clinical trial compared survival outcomes, progression-free survival (PFS), and overall survival (OS), of combination immune checkpoint inhibitor (ICI) treatment versus sunitinib, with established gene expression signatures [47]. Their findings suggest that combined signatures, such as tumor inflammation with angiogenesis or myeloid changes, may predict better response to immunotherapy versus tyrosine kinase inhibitor (TKI) alone [47]. However, the accumulated wealth of signature data has not been successfully implemented in the clinical setting [48].
One of the greatest barriers to signature implementation in the clinic is reliable data reproducibility, from which further analyses can build upon [49]. In an effort to overcome this, The Cancer Genome Atlas (TCGA) compiled pan-cancer data sets, which have been used as both discovery and validation sets for novel expression signatures [49][50][51]. Other issues in expression signature development include opposing views in the method of analysis, such as “top-down” and “bottom-up” supervised approaches. Supervised, “top-down”, approaches attempt to associate some clinical outcome (survival or metastasis) with an expression profile. Conversely, supervised, “bottom-up”, approaches utilize a biological basis for gene expression, which can be connected to some factors associated with tumor progression [52]. Predictive treatment responses encompass a much smaller range of the available expression signatures, as the individual signatures are tied to specific therapeutic agents [52]. Additionally, expression profiles can represent downstream alterations in proteins which may eventually become therapeutic targets [52].
Table 3. List of gene expression studies for diagnostic (ccRCC vs. normal tissue), prognostic (overall survival, recurrence, disease-free survival, and cancer-specific survival), and therapeutic outcomes.
Serial No. of Genes Metric No. of Samples Measure Outcome Ref
1. 3 AUC = 0.912 413 5-year survival post-nephrectomy Prognostic [53]
2. 34 RFS: HR = 2.3 (1.6–3.3);
CSS: HR = 2.9 (1.6–5.6);
OS: HR = 2.4, (1.6–3.7)
530 RFS, CSS, and OS (ccA vs. ccB) Prognostic [43]
3. 5 AUC = 0.783 523 Overall Survival (OS) Prognostic [54]
4. 16 HR = 3.37 615 RFS, CSS, and OS Prognostic [55]
5. 10 HR = 2.85 468 Overall Survival (OS) Prognostic [56]
6. 8 AUC = 0.821 888 Fuhrman grade (high grade) Prognostic [57]
7. 9 OR = 3.08 443 Recurrence post-nephrectomy, immune signature Prognostic [51]
8. 1 AUC = 0.9451 605 Overall Survival (OS) and DFS Prognostic [58]
9. 3 AUC = 0.9235–0.9451 605 Normal vs. ccRCC tissue Diagnostic [58]
10. 17 HR = 51.37 46 Overall Survival Prognostic [50]
11. 4 SN = 87%/SP = 77% 60 Normal vs. ccRCC tissue Diagnostic [44]

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