Overcome tumor heterogeneity-imposed therapeutic barriers through convergent genomic biomarker discovery: A braided cancer river model of kidney cancer

Survival and treatment of stage IV renal cell carcinoma in academic vs non-academic medical centers

BACKGROUND

Renal cell carcinoma (RCC) is treated with surgical resection as the gold standard, as it is notoriously resistant to systemic therapy. Advancements with targeted therapies contribute to declining mortality, but metastatic RCC (mRCC) survival remains poor. One possible factor is treatment at academic centers, which employ advanced providers and novel therapies. This study compared outcomes of mRCC in patients treated at academic/research facilities compared to those treated at non-academic centers.

AIM

To compare survival outcomes of mRCC and their various etiologies between academic and non-academic centers.

METHODS

The National Cancer Database was used to identify mRCC patients including all histology subtypes and stage IV disease. Descriptive statistics and Kaplan-Meier curves measured survival outcomes for user file facility types sorted into a binary academic/research and non-academic research variable. Multivariate logistic regression and Cox proportional hazard testing generated odds ratio and hazard ratio. Data was analyzed using Statistical Package for the Social Sciences version 29.0 using a significance level of P < 0.05.

RESULTS

Overall, academic facility patients experienced greater 5-year and 10-year overall survival than non-academic facility patients. Treatment at non-academic facilities was associated with increased odds of death that persisted even after controlling for age, tumor size, sex, and distance traveled to treatment center. In comparison, non-academic facility patients also experienced greater risk of hazard.

CONCLUSION

Patients with mRCC treated at academic/research facilities experienced increased survival compared to patients treated at non-academic facilities, were more likely to be younger, carry private insurance, and come from a large metropolitan area. They also were significantly more likely to receive surgery and adjuvant immunotherapy.

Integrated machine learning reveals the role of tryptophan metabolism in clear cell renal cell carcinoma and its association with patient prognosis

BACKGROUND

Precision oncology's implementation in clinical practice faces significant constraints due to the inadequacies in tools for detailed patient stratification and personalized treatment methodologies. Dysregulated tryptophan metabolism has emerged as a crucial factor in tumor progression, encompassing immune suppression, proliferation, metastasis, and metabolic reprogramming. However, its precise role in clear cell renal cell carcinoma (ccRCC) remains unclear, and predictive models or signatures based on tryptophan metabolism are conspicuously lacking.

METHODS

The influence of tryptophan metabolism on tumor cells was explored using single-cell RNA sequencing data. Genes involved in tryptophan metabolism were identified across both single-cell and bulk-cell dimensions through weighted gene co-expression network analysis (WGCNA) and its single-cell data variant (hdWGCNA). Subsequently, a tryptophan metabolism-related signature was developed using an integrated machine-learning approach. This signature was then examined in multi-omics data to assess its associations with patient clinical features, prognosis, cancer malignancy-related pathways, immune microenvironment, genomic characteristics, and responses to immunotherapy and targeted therapy. Finally, the genes within the signature were validated through experiments including qRT-PCR, Western blot, CCK8 assay, and transwell assay.

RESULTS

Dysregulated tryptophan metabolism was identified as a potential driver of the malignant transformation of normal epithelial cells. The tryptophan metabolism-related signature (TMRS) demonstrated robust predictive capability for overall survival (OS) and progression-free survival (PFS) across multiple datasets. Moreover, a high TMRS risk score correlated with increased tumor malignancy, significant metabolic reprogramming, an inflamed yet dysfunctional immune microenvironment, heightened genomic instability, resistance to immunotherapy, and increased sensitivity to certain targeted therapeutics. Experimental validation revealed differential expression of genes within the signature between RCC and adjacent normal tissues, with reduced expression of DDAH1 linked to enhanced proliferation and metastasis of tumor cells.

CONCLUSION

This study investigated the potential impact of dysregulated tryptophan metabolism on clear cell renal cell carcinoma, leading to the development of a tryptophan metabolism-related signature that may provide insights into patient prognosis, tumor biological status, and personalized treatment strategies. This signature serves as a valuable reference for further exploring the role of tryptophan metabolism in renal cell carcinoma and for the development of clinical applications based on this metabolic pathway.

Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review

This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients' gene expression and clinical data through a variety of techniques to predict patients' outcomes, mechanistic models focus on investigating cells' and molecules' interactions within RCC tumors. These interactions are notably centered around immune cells, cytokines, tumor cells, and the development of lung metastases. The insights gained from both machine learning and mechanistic models encompass critical aspects such as signature gene identification, sensitive interactions in the tumors' microenvironments, metastasis development in other organs, and the assessment of survival probabilities. By reviewing the models of RCC, this study aims to shed light on opportunities for the integration of machine learning and mechanistic modeling approaches for treatment optimization and the identification of specific targets, all of which are essential for enhancing patient outcomes.

Single-Cell and Transcriptome-Based Immune Cell-Related Prognostic Model in Clear Cell Renal Cell Carcinoma

Traditional studies mostly focus on the role of single gene in regulating clear cell renal cell carcinoma (ccRCC), while it ignores the impact of tumour heterogeneity on disease progression. The purpose of this study is to construct a prognostic risk model for ccRCC by analysing the differential marker genes related to immune cells in the single-cell database to provide help in clinical diagnosis and targeted therapy. Single-cell data and ligand-receptor relationship pair data were downloaded from related publications, and ccRCC phenotype and expression profile data were downloaded from TCGA and CPTAC. Based on the DEGs of each cluster acquired from single-cell data, immune cell marker genes, and ligand-receptor gene data, we constructed a multilayer network. Then, the genes in the network and the genes in TCGA were used to construct the WGCNA network, which screened out prognosis-associated genes for subsequent analysis. Finally, a prognostic risk scoring model was obtained, and CPTAC data showed that the effectiveness of this model was good. A nomogram based on the predictive model for predicting the overall survival was established, and internal validation was performed well. Our findings suggest that the predictive model built and based on the immune cell scRNA-seq will enable us to judge the prognosis of patients with ccRCC and provide more accurate directions for basic relevant research and clinical practice.

Functions, Roles, and Biological Processes of Ferroptosis-Related Genes in Renal Cancer: A Pan-Renal Cancer Analysis

Ferroptosis is a cell death process discovered in recent years, highly related to cancer, acute kidney injury, and other diseases. In this study, a pan-renal cancer analysis of ferroptosis-associated genes in renal cancer was performed to construct a multigene joint signature for predicting prognosis in renal cancer patients. First, gene expression profiles were downloaded from the TCGA and GTEx databases to search for genes significantly associated with renal cancer prognosis through differential gene expression analysis, weighted gene co-expression network analysis (WGCNA), and survival analysis. Thereafter, the gene-set enrichment analysis (GSEA) was used to identify the biological processes in which ferroptosis-associated genes might be involved. Weighted gene co-expression network analysis resulted in 4,434 differentially expressed genes (DEGs) and 42 co-expression modules, among which ferroptosis-related genes were distributed in 11 gene modules. The survival analysis screening resulted in three DEGs associated with renal cancer prognosis, namely SLC7A11, HMOX1, and MT1G. Specifically, SLC7A11 and HMOX1 were upregulated in renal cancer tissues, while MT1G was downregulated. Receiver operating characteristic (ROC) curves, combined with Kaplan–Meier and Cox regression analysis, revealed that high expression of SLC7A11 was a prognostic risk factor for four different renal cancers, that low expression of HMOX1 was a poor prognostic marker for patients, and that increased expression of MT1G increased the prognostic risk for three additional classes of renal cancer patients, except for renal papillary cell carcinoma. The GSEA results showed that the ferroptosis-related genes from these screens were mainly associated with signaling pathways related to tumor progression and tumor immunity. This study provides potential biological markers for prognosis prediction in renal cancer patients with different subtypes, and these results imply that ferroptosis is highly associated with renal carcinogenesis progression.

MUC20 as a novel prognostic biomarker in ccRCC correlating with tumor immune microenvironment modulation

Tumor microenvironment (TME) broadly participates in genesis development of clear cell renal cell carcinoma (ccRCC). To recognize the immune and stromal modulation in TME, we screened the differentially expressed TME-related genes generated by the ESTIMATE algorithm in ccRCC specimens. Following the construction of protein-protein interaction (PPI) network and univariate COX regression, mucin 20 (MUC20) was judged to be a predictive factor. Further analysis, including immunohistochemistry (IHC) showed that MUC20 was positively correlated with survival and negatively correlated with the clinicopathologic characteristics (grade, clinical and TNM stages) in ccRCC patients. Gene Set Enrichment Analysis suggested that the low-expression MUC20 group was primarily enriched in immune-related activities, inflammation and epithelial-mesenchymal transition. Based on the CIBERSORT analysis for tumor-infiltrating immune cells (TICs), MUC20 was positively correlated with CD8⁺ T cells and resting mast cells and negatively correlated with activated CD4⁺ memory T cells, Treg cells, and plasma cells, implying that MUC20 may contribute to immune component in TME. Additionally, the patients with low MUC20 expression had better response to immune checkpoint blockades (ICBs) and 17 potential anticancer drugs were screened regarding calculating IC₅₀ value. Thus, MUC20 may contain a value of prognosis assessment for ccRCC patients and indicate the immune modulation status of TME, which provided a novel insight for comprehensive immunotherapy.

Clinical significance of novel DNA methylation biomarkers for renal clear cell carcinoma

OBJECTIVE

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney tumor characterized by the highest mortality rate of the genitourinary cancers, and, therefore, new diagnostic and/or prognostic biomarkers are urgently needed.

METHODS

Based on genome-wide DNA methylation profiling in 11 pairs of ccRCC and non-cancerous renal tissues (NRT), the methylation at regulatory regions of ZNF677, FBN2, PCDH8, TFAP2B, TAC1, and FLRT2 was analyzed in 168 renal tissues and 307 urine samples using qualitative and quantitative methylation-specific PCR (MSP).

RESULTS

Significantly higher methylation frequencies for all genes were found in ccRCC tissues compared to NRT (33-60% vs. 0-11%). The best diagnostic performance demonstrated a panel of ZNF677, FBN2, PCDH8, TFAP2B & TAC1 with 82% sensitivity and 96% specificity. Hypermethylation of ZNF677 and PCDH8 in the tissue samples was significantly related to numerous adverse clinicopathologic parameters. For the urine-based ccRCC detection, the highest diagnostic power (AUC = 0.78) was observed for a panel of ZNF677 & PCDH8 (with or without FBN2 or FLRT2) with 69-78% sensitivity and 69-80% specificity, albeit with lower values in the validation cohort. Besides, methylation of PCDH8 was significantly related to higher tumor stage and fat invasion in the study and validation cohorts. Moreover, PCDH8 was strongly predictive for OS (HR, 5.7; 95% CI 1.16-28.12), and its prognostic power considerably increased in combination with ZNF677 (HR, 12.5; 95% CI 1.47-105.58).

CONCLUSION

In summary, our study revealed novel, potentially promising DNA methylation biomarkers of ccRCC with the possibility to be applied for non-invasive urine-based ccRCC detection and follow-up.

A MUCINs expression signature impacts overall survival in patients with clear cell renal cell carcinoma

Background

Kidney cancer, especially clear cell renal cell carcinoma (ccRCC), is one of the most common cancers in the urinary system. Previous studies suggested that certain members of MUCINs could serve as independent predictors for the survival of ccRCC patients. None of them, however, is robust enough to predict prognosis accurately.

Objective

To analyze the correlation of MUCINs alterations and their expression levels with the prognosis of ccRCC patients and develop a prognosis‐related predictor.

Methods

We applied whole‐exome sequencing in samples from 22 Chinese ccRCC patients to identify genetic alterations in MUCIN genes and analyzed their genetic alterations, expression, and correlation with survival using the TCGA, GSE73731, and GSE29069 datasets.

Result

Genetic alternations in MUCINs were identified in 91% and 51% of ccRCC patients in our cohort and the TCGA database, respectively. No correlation with survival was found for the genetic alterations. Using unsupervised clustering analysis of gene expression, we identified two major clusters of MUCIN expression patterns. Cluster 1 was characterized by a global overexpression of MUC1, MUC12, MUC13, MUC16, and OVGP1; and cluster 2 was characterized by a global overexpression of MUC4, MUC5B, MUC6, MUC20, EMCN, and MCAM. Patients with cluster 1 expression pattern had significantly shorter overall survival time and worse clinical features, including higher tumor grades and metastasis. Meanwhile, they had a higher level of mutation counts and more infiltrated immune cells, but lower enrichment in angiogenesis signature genes. A five‐MUCINs expression signature was constructed from cluster 1, and notably, it was demonstrated to be associated with shorter overall survival. A similar worse clinical feature, lower angiogenesis but the more immune signature, was identified in samples presented with signature 1. In the validation data set GSE29069, patients with signature 1 were also associated with a trend of poor survival outcomes.

Conclusion

We established a five‐MUCINs expression signature as a new prognostic marker for ccRCC. The distinct tumor microenvironment feature between the two signatures may further affect ccRCC patients’ clinical management.

Mechanistic Target of Rapamycin Inhibitors in Renal Cell Carcinoma: Potential, Limitations, and Perspectives

Several elements highlight the importance of the mechanistic target of rapamycin (mTOR) in the biology of renal cell carcinoma (RCC). mTOR signaling pathway is indeed frequently activated in RCC, inducing cancer cell proliferation and survival. In addition, mTOR promotes tumor angiogenesis and regulates the expression of hypoxia-inducible factors that play an important role in a subset of RCC. Despite mTOR protumorigenic effects, mTOR inhibitors have failed to provide long-lasting anticancer benefits in RCC patients, highlighting the need to readdress their role in the treatment of RCC. This review aims to present the rationale and limitations of targeting mTOR in RCC. Future roles of mTOR inhibitors in the treatment of RCC are also discussed, in particular in the context of immunotherapies.

Exploiting the circuit breaker cancer evolution model in human clear cell renal cell carcinoma

The incessant interactions between susceptible humans and their respective macro/microenvironments registered throughout their lifetime result in the ultimate manifestation of individual cancers. With the average lifespan exceeding 50 years of age in humans since the beginning of 20th century, aging - the "time" factor - has played an ever-increasing role alongside host and environmental factors in cancer incidences. Cancer is a genetic/epigenetic disease due to gain-of-function mutations in cancer-causing genes (oncogene; OG) and/or loss-of-function mutations in tumor-suppressing genes (tumor suppressor genes; TSG). In addition to their integral relationship with cancer, a timely deployment of specific OG and/or TSG is in fact needed for higher organisms like human to cope with respective physiological and pathological conditions. Over the past decade, extensive human kidney cancer genomics have been performed and novel mouse models recapitulating human kidney cancer pathobiology have been generated. With new genomic, genetic, mechanistic, clinical and therapeutic insights accumulated from studying clear cell renal cell carcinoma (ccRCC)-the most common type of kidney cancer, we conceived a cancer evolution model built upon the OG-TSG signaling pair analogous to the electrical circuit breaker (CB) that permits necessary signaling output and at the same time prevent detrimental signaling overdrive. Hence, this viewpoint aims at providing a step-by-step mechanistic explanation/illustration concerning how inherent OG-TSG CBs intricately operate in concert for the organism's wellbeing; and how somatic mutations, the essential component for genetic adaptability, inadvertently triggers a sequential outage of specific sets of CBs that normally function to maintain and protect and individual tissue homeostasis.

Prognostic significance of proteomics and multi-omics studies in renal carcinoma

INTRODUCTION

Renal carcinoma, and in particular its most common variant, the clear cell subtype, is often diagnosed incidentally through abdominal imaging and frequently, the tumor is discovered at an early stage. However, 20% to 40% of patients undergoing nephrectomy for clinically localized renal cancer, even after accurate histological and clinical classification, will develop metastasis or recurrence, justifying the associated mortality rate. Therefore, even if renal carcinoma is not among the most frequent nor deadly cancers, a better prognostication is needed.

AREAS COVERED

Recently proteomics or other omics combinations have been applied to both cancer tissues, on the neoplasia itself and surrounding microenvironment, cultured cells, and biological fluids (so-called liquid biopsy) generating a list of prognostic molecular tools that will be reviewed in the present paper.

EXPERT OPINION

Although promising, none of the approaches listed above has been yet translated in clinics. This is likely due to the peculiar genetic and phenotypic heterogeneity of this cancer, which makes nearly each tumor different from all the others. Attempts to overcome this issue will be also revised. In particular, we will discuss how the application of omics-integrated approaches could provide the determinants of response to the different targeted drugs.

Glutathione transferase genotypes may serve as determinants of risk and prognosis in renal cell carcinoma

Renal cell carcinoma (RCC) represents a group of histologically similar neoplasms with significant intratumor and intertumor genetic heterogeneity. Recognized risk factors for RCC development include smoking, hypertension, obesity, as well as von Hippel-Lindau (VHL) disease. Inactivation of VHL, deregulated nuclear factor (erythroid-derived 2)-like 2 (Nrf2) pathway, and altered redox homeostasis, together with changes in glutathione transferase (GST) profile, are considered as important contributing factors in RCC development and progression. Although the available results of both gene-gene and gene-environment analysis are quite heterogeneous, they clearly indicate that certain GST genotypes may play a role as risk modifiers, either individually or in combination with other Phase I or Phase II gene polymorphisms, as well as in subjects exposed to relevant substrates. Seemingly, GST genotyping could identify individuals with impaired detoxification in renal parenchyma that are at higher risk of developing RCC. In addition to well established roles of GSTs in conjugation and biotransformation of xenobiotics, GSTs have emerged as significant regulators of pathways determining cell proliferation and survival. Indeed, there are evidence in favor of GST significance, not only in terms of risk for RCC development, but also with respect to progression and prognosis. So far, GSTM1-active genotype was confirmed to be an independent predictor of higher risk of overall mortality. Therefore, it is reasonable to assume that certain GST variants may assist in individual RCC risk assessment, as well as postoperative prognosis. Even more, GST profiling might contribute to development of personalized targeted therapy in RCC patients.

GSTO1*CC Genotype (rs4925) Predicts Shorter Survival in Clear Cell Renal Cell Carcinoma Male Patients

Omega class glutathione transferases, GSTO1-1 and GSTO2-2, exhibit different activities involved in regulation of inflammation, apoptosis and redox homeostasis. We investigated the the prognostic significance of GSTO1 (rs4925) and GSTO2 (rs156697 and rs2297235) polymorphisms in clear cell renal cell carcinoma (ccRCC) patients. GSTO1-1 and GSTO2-2 expression and phosphorylation status of phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt)/ /mammalian target of rapamycin (mTOR) and Raf/MEK/extracellular signal-regulated kinase (ERK) signaling pathways in non-tumor and tumor ccRCC tissue, as well as possible association of GSTO1-1 with signaling molecules were also assessed. GSTO genotyping was performed by quantitative PCR in 228 ccRCC patients, while expression and immunoprecipitation were analyzed by Western blot in 30 tissue specimens. Shorter survival in male carriers of GSTO1*C/C wild-type genotype compared to the carriers of at least one variant allele was demonstrated (p = 0.049). GSTO1*C/C genotype independently predicted higher risk of overall mortality among male ccRCC patients (p = 0.037). Increased expression of GSTO1-1 and GSTO2-2 was demonstrated in tumor compared to corresponding non-tumor tissue (p = 0.002, p = 0.007, respectively), while GSTO1 expression was correlated with interleukin-1β (IL-1β)/pro-interleukin-1β (pro-IL-1β) ratio (r = 0.260, p = 0.350). Interaction of GSTO1 with downstream effectors of investigated pathways was shown in ccRCC tumor tissue. This study demonstrated significant prognostic role of GSTO1 polymorphism in ccRCC. Up-regulated GSTO1-1 and GSTO2-2 in tumor tissue might contribute to aberrant ccRCC redox homeostasis.

Immunotherapy in renal cell carcinoma from poverty to the spoiled of choice

Management of metastatic renal cell carcinoma has been enriched by the advent of new therapeutic compounds. The approval of new combination strategies between targeted agents and immune-checkpoint inhibitors as well as the administration of combinations between immune-checkpoint inhibitors has clearly demonstrated significant improvement toward patients' prognosis and other clinical outcomes. Due to the availability of different treatments, the choice between them may be a difficult issue in our clinical practice. We have summarized current knowledge about available treatments focusing on criteria, which may help clinicians to make decisions.

Cell death-related molecules and biomarkers for renal cell carcinoma targeted therapy

Renal cell carcinoma (RCC) is not sensitive to conventional radio- and chemotherapies and is at least partially resistant to impairments in cell death-related signaling pathways. The hallmarks of RCC formation include diverse signaling pathways, such as maintenance of proliferation, cell death resistance, angiogenesis induction, immune destruction avoidance, and DNA repair. RCC diagnosed during the early stage has the possibility of cure with surgery. For metastatic RCC (mRCC), molecular targeted therapy, especially antiangiogenic therapy (e.g., tyrosine kinase inhibitors, TKIs, such as sunitinib), is one of the main partially effective therapeutics. Various forms of cell death that may be associated with the resistance to targeted therapy because of the crosstalk between targeted therapy and cell death resistance pathways were originally defined and differentiated into apoptosis, necroptosis, pyroptosis, ferroptosis and autophagic cell death based on cellular morphology. Particularly, as a new form of cell death, T cell-induced cell death by immune checkpoint inhibitors expands the treatment options beyond the current targeted therapy. Here, we provide an overview of cell death-related molecules and biomarkers for the progression, prognosis and treatment of mRCC by targeted therapy, with a focus on apoptosis and T cell-induced cell death, as well as other forms of cell death.

Toward a genome-based treatment landscape for renal cell carcinoma

Knowledge about molecular mechanisms driving development and progression of renal cell carcinoma has been elucidated by different studies. In few years we discovered a large difference between genomic landscapes of clear cell and non-clear cell carcinoma. Moreover, tumor heterogeneity and different acquisition of gene mutations during tumor progression are issues of particular interest. In this review we focalized our attention on principal genomic alterations identified among RCC subtypes. Acquired gene mutations may be an adaptive response to several external pressure including metabolic, treatment, genomic and immune-related external pressure. Thus we correlated and discussed principal genomic alterations adopted by tumor to escape from each external pressures. The aim of the present work is to summarize current knowledge about genomic alterations in RCC with special interest of treatment strategies tailored on the basis of disease mutations assessment.

Resistance to Systemic Therapies in Clear Cell Renal Cell Carcinoma: Mechanisms and Management Strategies

Renal cell carcinoma (RCC) is the most common form of kidney cancer. It is categorized into various subtypes, with clear cell RCC (ccRCC) representing about 85% of all RCC tumors. The lack of sensitivity to chemotherapy and radiation therapy prompted research efforts into novel treatment options. The development of targeted therapeutics, including multi-targeted tyrosine kinase inhibitors (TKI) and mTOR inhibitors, has been a major breakthrough in ccRCC therapy. More recently, other therapeutic strategies, including immune checkpoint inhibitors, have emerged as effective treatment options against advanced ccRCC. Furthermore, recent advances in disease biology, tumor microenvironment, and mechanisms of resistance formed the basis for attempts to combine targeted therapies with newer generation immunotherapies to take advantage of possible synergy. This review focuses on the current status of basic, translational, and clinical studies on mechanisms of resistance to systemic therapies in ccRCC. Mol Cancer Ther; 17(7); 1355-64. ©2018 AACR.

Resistance to Systemic Agents in Renal Cell Carcinoma Predict and Overcome Genomic Strategies Adopted by Tumor

The development of new systemic agents has led us into a "golden era" of management of metastatic renal cell carcinoma (RCC). Certainly, the approval of immune-checkpoint inhibitors and the combination of these with targeted compounds has irreversibly changed clinical scenarios. A deeper knowledge of the molecular mechanisms that correlate with tumor development and progression has made this revolution possible. In this amazing era, novel challenges are awaiting us in the clinical management of metastatic RCC. Of these, the development of reliable criteria which are able to predict tumor response to treatment or primary and acquired resistance to systemic treatments still remain an unmet clinical need. Thanks to the availability of data provided by studies evaluating genomic assessments of the disease, this goal may no longer be out of reach. In this review, we summarize current knowledge about genomic alterations related to primary and secondary resistance to target therapy and immune-checkpoint inhibitors in RCC.

Cell-free DNA diagnostics: current and emerging applications in oncology

Liquid biopsy is a noninvasive dynamic approach for monitoring disease over time. It offers advantages including limited risks of blood sampling, opportunity for more frequent sampling, lower costs and theoretically non-biased sampling compared with tissue biopsy. There is a high degree of concordance between circulating tumor DNA mutations versus primary tumor mutations. Remote sampling of circulating tumor DNA can serve as viable option in clinical diagnostics. Here, we discuss the progress toward broad adoption of liquid biopsy as a diagnostic tool and discuss knowledge gaps that remain to be addressed.

Linking Binary Gene Relationships to Drivers of Renal Cell Carcinoma Reveals Convergent Function in Alternate Tumor Progression Paths

Renal cell carcinoma (RCC) subtypes are characterized by distinct molecular profiles. Using RNA expression profiles from 1,009 RCC samples, we constructed a condition-annotated gene coexpression network (GCN). The RCC GCN contains binary gene coexpression relationships (edges) specific to conditions including RCC subtype and tumor stage. As an application of this resource, we discovered RCC GCN edges and modules that were associated with genetic lesions in known RCC driver genes, including VHL, a common initiating clear cell RCC (ccRCC) genetic lesion, and PBRM1 and BAP1 which are early genetic lesions in the Braided Cancer River Model (BCRM). Since ccRCC tumors with PBRM1 mutations respond to targeted therapy differently than tumors with BAP1 mutations, we focused on ccRCC-specific edges associated with tumors that exhibit alternate mutation profiles: VHL-PBRM1 or VHL-BAP1. We found specific blends molecular functions associated with these two mutation paths. Despite these mutation-associated edges having unique genes, they were enriched for the same immunological functions suggesting a convergent functional role for alternate gene sets consistent with the BCRM. The condition annotated RCC GCN described herein is a novel data mining resource for the assignment of polygenic biomarkers and their relationships to RCC tumors with specific molecular and mutational profiles.

VEGI174 protein and its functional domain peptides exert antitumour effects on renal cell carcinoma

Vascular endothelial growth inhibitor (VEGI) has been identified as an anti‑angiogenic cytokine. However, the effects of VEGI174 protein, and its functional domain peptides V7 and V8, on renal cell carcinoma (RCC) remain unknown. In the present study, the protein and peptides were biosynthesised as experimental agents. The A498 and 786‑O RCC cell lines, and an established mouse xenograft model, were separately treated with VEGI174, V7 or V8. Cellular functions, including proliferation, migration and invasion, were subsequently detected. Cell migration and invasion were monitored using the xCELLigence system. Furthermore, tumour growth and mouse behaviours, including mobility, appetite and body weight, were assessed. The results demonstrated that VEGI174, V7 and V8 inhibited the proliferation, migration and invasion of A498 and 786‑O cell lines when administered at concentrations of 1 and 100 pM, 10 nM and 1 µM. The inhibitory effects exhibited dose‑ and time‑dependent antitumour activity. Furthermore, VEGI174, V7 and V8 inhibited tumour growth in A498 and 786‑O xenograft mice. In the A498 xenografts, the tumour growth inhibition (TGI) rates in the VEGI174‑, V7‑ and V8‑treated groups were 71, 20 and 31%, respectively. In the 786‑O xenografts, the TGI rates in the VEGI174‑, V7‑ and V8‑treated groups were 34, 26 and 31%, respectively. There was no significant loss in body weight and no cases of mortality were observed for all treated mice. In conclusion, VEGI174, V7 and V8 exhibited potential antitumour effects and were well tolerated in vivo. V7 and V8, as functional domain peptides of the VEGI174 protein, may be studied for the future treatment of RCC.

Tumor hypoxia directed multimodal nanotherapy for overcoming drug resistance in renal cell carcinoma and reprogramming macrophages

Drug resistance is one of the significant clinical burden in renal cell carcinoma (RCC). The development of drug resistance is attributed to many factors, including impairment of apoptosis, elevation of carbonic anhydrase IX (CA IX, a marker of tumor hypoxia), and infiltration of tumorigenic immune cells. To alleviate the drug resistance, we have used Sorafenib (Sor) in combination with tumor hypoxia directed nanoparticle (NP) loaded with a new class of apoptosis inducer, CFM 4.16 (C4.16), namely CA IX-C4.16. The NP is designed to selectively deliver the payload to the hypoxic tumor (core), provoke superior cell death in parental (WT) and Everolimus-resistant (Evr-res) RCC and selectively downmodulate tumorigenic M2-macrophage. Copper-free 'click' chemistry was utilized for conjugating SMA-TPGS with Acetazolamide (ATZ, a CA IX-specific targeting ligand). The NP was further tagged with a clinically approved NIR dye (S0456) for evaluating hypoxic tumor core penetration and organ distribution. Imaging of tumor spheroid treated with NIR dye-labeled CA IX-SMA-TPGS revealed remarkable tumor core penetration that was modulated by CA IX-mediated targeting in hypoxic-A498 RCC cells. The significant cell killing effect with synergistic combination index (CI) of CA IX-C4.16 and Sor treatment suggests efficient reversal of Evr-resistance in A498 cells. The CA IX directed nanoplatform in combination with Sor has shown multiple benefits in overcoming drug resistance through (i) inhibition of p-AKT, (ii) upregulation of tumoricidal M1 macrophages resulting in induction of caspase 3/7 mediated apoptosis of Evr-res A498 cells in macrophage-RCC co-culturing condition, (iii) significant in vitro and in vivo Evr-res A498 tumor growth inhibition as compared to individual therapy, and (iv) untraceable liver and kidney toxicity in mice. Near-infrared (NIR) imaging of CA IX-SMA-TPGS-S0456 in Evr-res A498 RCC model exhibited significant accumulation of CA IX-oligomer in tumor core with >3-fold higher tumor uptake as compared to control. In conclusion, this proof-of-concept study demonstrates versatile tumor hypoxia directed nanoplatform that can work in synergy with existing drugs for reversing drug-resistance in RCC accompanied with re-education of tumor-associated macrophages, that could be applied universally for several hypoxic tumors.

In situ characterization of stem cells-like biomarkers in meningiomas

Background

Meningioma cancer stem cells (MCSCs) contribute to tumor aggressiveness and drug resistance. Successful therapies developed for inoperable, recurrent, or metastatic tumors must target these cells and restrict their contribution to tumor progression. Unfortunately, the identity of MCSCs remains elusive, and MSCSs’ in situ spatial distribution, heterogeneity, and relationship with tumor grade, remain unclear.

Methods

Seven tumors classified as grade II or grade III, including one case of metastatic grade III, and eight grade I meningioma tumors, were analyzed for combinations of ten stem cell (SC)-related markers using immunofluorescence of consecutive sections. The correlation of expression for all markers were investigated. Three dimensional spatial distribution of markers were qualitatively analyzed using a grid, designed as a repository of information for positive staining. All statistical analyses were completed using Statistical Analysis Software Package.

Results

The patterns of expression for SC-related markers were determined in the context of two dimensional distribution and cellular features. All markers could be detected in all tumors, however, Frizzled 9 and GFAP had differential expression in grade II/III compared with grade I meningioma tissues. Correlation analysis showed significant relationships between the expression of GFAP and CD133 as well as SSEA4 and Vimentin. Data from three dimensional analysis showed a complex distribution of SC markers, with increased gene hetero-expression being associated with grade II/III tumors. Sub regions that showed multiple co-staining of markers including CD133, Frizzled 9, GFAP, Vimentin, and SSEA4, but not necessarily the proliferation marker Ki67, were highly associated with grade II/III meningiomas.

Conclusion

The distribution and level of expression of CSCs markers in meningiomas are variable and show hetero-expression patterns that have a complex spatial nature, particularly in grade II/III meningiomas. Thus, results strongly support the notion of heterogeneous populations of CSCs, even in grade I meningiomas, and call for the use of multiple markers for the accurate identification of individual CSC subgroups. Such identification will lead to practical clinical diagnostic protocols that can quantitate CSCs, predict tumor recurrence, assist in guiding treatment selection for inoperable tumors, and improve follow up of therapy.

Electronic supplementary material

The online version of this article (10.1186/s12935-018-0571-6) contains supplementary material, which is available to authorized users.

Association between vascular endothelial growth factor gene polymorphisms and the risk and prognosis of renal cell carcinoma: A systematic review and meta-analysis

The aim of the meta-analysis was to clarify the associations between vascular endothelial growth factor (VEGF) polymorphisms and the risk and prognosis of renal cell carcinoma (RCC). A meta-analysis was performed by searching the databases PubMed, EMBASE and Web of Science for the relevant available studies until August 1st, 2016, and fourteen studies met the inclusion criteria. The pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to evaluate the strength of such associations. Besides, the pooled hazard ratios (HRs) with 95% CIs were used to evaluate the overall survival (OS). Fixed- or random-effects models were conducted according to existence of heterogeneity. Publication bias was evaluated using Begg's funnel plots and Egger's regression test. Overall, this meta-analysis included a total of 8,275 patients, who had been accrued between November 2002 and September 2015. Meta-analysis indicated that -2578C/A, +936C/T and +405G/C polymorphisms in the VEGF gene correlated with elevated RCC risk, especially in Asian populations. Moreover, VEGF -1154G/A and -634C/G polymorphisms were found significantly associated with poor OS of RCC. Therefore, this meta-analysis revealed that VEGF -2578C/A, +936C/T, +405G/C polymorphisms were associated with an elevated susceptibility to RCC, indicating that these three polymorphisms might be risk factors for RCC, especially in Asian populations.

Genomic classifications of renal cell carcinoma: a critical step towards the future application of personalized kidney cancer care with pan-omics precision

Over the past 20 years, classifications of kidney cancer have undergone major revisions based on morphological refinements and molecular characterizations. The 2016 WHO classification of renal tumors recognizes more than ten different renal cell carcinoma (RCC) subtypes. Furthermore, the marked inter- and intra-tumor heterogeneity of RCC is now well appreciated. Nevertheless, contemporary multi-omics studies of RCC, encompassing genomics, transcriptomics, proteomics, and metabolomics, not only highlight apparent diversity but also showcase and underline commonality. Here, we wish to provide an integrated perspective concerning the future 'functional' classification of renal cancer by bridging gaps among morphology, biology, multi-omics, and therapeutics. This review focuses on recent progress and elaborates the potential value of contemporary pan-omics approaches with a special emphasis on cancer genomics unveiled through next-generation sequencing technology, and how an integrated multi-omics approach might impact precision-based personalized kidney cancer care in the near future. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Vascular endothelial growth factor gene polymorphisms and the risk of renal cell carcinoma: Evidence from eight case-control studies

Background

Vascular endothelial growth factor (VEGF) protein plays important role in renal cell carcinoma (RCC) development and progression. VEGF gene polymorphisms can alter the protein concentrations and might be associated with renal cell carcinoma risk. However, the results of studies investigating the association between VEGF polymorphisms and renal cell carcinoma risk are inconsistent. Thus, a meta-analysis was performed.

Methods

We selected eligible studies via electronic searches. Only high-quality studies were included based on specific inclusion criteria and the Newcastle-Ottawa Scale (NOS).

Results

Eight studies primarily focusing on seven polymorphisms were included in our meta-analysis. Our results showed dramatically high risks for renal cell carcinoma were found regarding most genetic models and alleles of the +936C/T polymorphism (except CT vs. CC). In addition, significant increased renal cell carcinoma risks were found regarding all genetic models and alleles of the -2578C/A polymorphism. However, no significant associations were found between renal cell carcinoma risk and the +1612G/A, -460T/C, -634G/C, -405G/C or -1154G/A polymorphisms.

Conclusions

Our meta-analysis indicates that the +936C/T and -2578C/A polymorphisms of VEGF are associated with an increased risk for renal cell carcinoma. Additional rigorous analytical studies are needed to confirm our results.

Nutrient-induced FNIP degradation by SCFβ-TRCP regulates FLCN complex localization and promotes renal cancer progression

Folliculin-interacting protein 1 and 2 (FNIP1 and FNIP2) play critical roles in preventing renal malignancy through their association with the tumor suppressor FLCN. Mutations in FLCN are associated with Birt-Hogg-Dubé (BHD) syndrome, a rare disorder with increased risk of renal cancer. Recent studies indicated that FNIP1/FNIP2 double knockout mice display enlarged polycystic kidneys and renal carcinoma, which phenocopies FLCN knockout mice, suggesting that these two proteins function together to suppress renal cancer. However, the molecular mechanism functionally linking FNIP1/FNIP2 and FLCN remains largely elusive. Here, we demonstrated that FNIP2 protein is unstable and subjected to proteasome-dependent degradation via β-TRCP and Casein Kinase 1 (CK1)-directed ubiquitination in a nutrition-dependent manner. Degradation of FNIP2 leads to lysosomal dissociation of FLCN and subsequent lysosomal association of mTOR, which in turn promotes the proliferation of renal cancer cells. These results indicate that SCF^β-TRCP negatively regulates the FLCN complex by promoting FNIP degradation and provide molecular insight into the pathogenesis of BHD-associated renal cancer.

Pathological Bases and Clinical Impact of Intratumor Heterogeneity in Clear Cell Renal Cell Carcinoma

PURPOSE OF REVIEW

Intratumor heterogeneity is an inherent event in tumor development that is receiving much attention in the last years since it is responsible for most failures of current targeted therapies. The purpose of this review is to offer clinicians an updated insight of the multiple manifestations of a complex event that impacts significantly patient's life.

RECENT FINDINGS

Clear cell renal cell carcinoma is the most common renal tumor and a paradigmatic example of a heterogeneous neoplasm. Next-generation sequencing has demonstrated that intratumor heterogeneity encompasses genetic, epigenetic, and microenvironmental variability. Currently accepted protocols of tumor sampling seem insufficient in unveiling intratumor heterogeneity with reliability and need to be updated. This variability challenges the precise morphological diagnosis, its molecular characterization, and the selection of optimal personalized therapies in clear cell renal cell carcinoma, a neoplasm traditionally considered chemo- and radio-resistant. We review the state of the art of the different approaches to intratumor heterogeneity in clear cell renal cell carcinomas, from the simple morphology to the most sophisticated massive sequencing tools.

Comparative Genomic Profiling of Matched Primary and Metastatic Tumors in Renal Cell Carcinoma

BACKGROUND

Next-generation sequencing (NGS) studies of matched pairs of primary and metastatic tumors in renal cell carcinoma (RCC) have been limited to small cohorts.

OBJECTIVE

To evaluate the discordance in somatic mutations between matched primary and metastatic RCC tumors.

DESIGN, SETTING, AND PARTICIPANTS

Primary tumor (P), metastasis (M), and germline DNA from 60 patients with RCC was subjected to NGS with a targeted exon capture-based assay of 341 cancer-associated genes. Somatic mutations were called using a validated pipeline.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Mutations were classified as shared (S) or private (Pr) in relation to each other within individual P-M pairs. The concordance score was calculated as (S-Pr)/(S+Pr). To calculate enrichment of Pr/S mutations for a particular gene, we calculated a two-sided p value from a binomial model for each gene with at least ten somatic mutation events, and also implemented a separate permutation test procedure. We adjusted p values for multiple hypothesis testing using the Benjamini-Hochberg procedure. The mutation discordance was calculated using Mann-Whitney U tests according to gene mutations or metastatic sites.

RESULTS AND LIMITATIONS

Twenty-one pairs (35%) showed Pr mutations in both P and M samples. Of the remaining 39 pairs (65%), 14 (23%) had Pr mutations specific to P samples, 12 (20%) had Pr mutations to M samples, and 13 (22%) had identical somatic mutations. No individual gene mutation was preferentially enriched in either P or M samples. P-M pairs with SETD2 mutations demonstrated higher discordance than pairs with wild-type SETD2. We observed that patients who received therapy before sampling of the P or M tissue had higher concordance of mutations for P-M pairs than patients who did not (Mann-Whitney p=0.088).

CONCLUSIONS

Our data show mutation discordance within matched P-M RCC tumor pairs. As most contemporary precision medicine trials do not differentiate mutations detected in P and M tumors, the prognostic and predictive value of mutations in P versus M tumors warrants further investigation.

PATIENT SUMMARY

In this study we evaluated the concordance of mutations between matched primary and metastatic tumors for 60 kidney cancer patients using a panel of 341 cancer genes. Forty-seven patients carried nonidentical cancer gene mutations within their matched primary-metastatic pair. The mutation profile of the primary tumor alone could compromise precision in selecting effective targeted therapies and result in suboptimal clinical outcomes.

Molecular Classification of Renal Cell Carcinoma and Its Implication in Future Clinical Practice

Renal cell carcinoma (RCC) encompasses a wide spectrum of morphologically and molecularly distinct (>10) cancer subtypes originated from the kidney epithelium. Metastatic RCC (mRCC) is lethal and refractory to conventional chemotherapeutic agents. The incorporation of targeted therapies and immune checkpoint inhibitors into the current practice of mRCC has markedly improved the median overall survival of clear cell RCC (ccRCC) patients, the most common subtype, but not rare kidney cancer (RKC or non-ccRCC, nccRCC). Varied treatment response in mRCC patients is observed, which presents clinical challenges/opportunities at the modern mRCC therapeutic landscape consisting of 12 approved drugs representing 6 different effective mechanisms. Key contributing factors include inter- and intra-RCC heterogeneity. With the advances in pan-omics technologies, we now have a better understanding of the molecular pathobiology of individual RCC subtype. Here, we attempt to classify ccRCC based on contemporary molecular features with emphasis on their respective potential significance in clinical practice.

SWI/SNF tumor suppressor gene PBRM1/BAF180 in human clear cell kidney cancer

Mutations within chromatin modulating protein complexes have dominated the novel cancer gene landscape. However, little is known about how individual aberrations contribute to cancer formation. A novel Pbrm1 kidney cancer mouse model examining the role of Pbrm1 provides much needed clue concerning how SWI/SNF complexes might function as tumor suppressors.

Genomic landscape and evolution of metastatic chromophobe renal cell carcinoma

Chromophobe renal cell carcinoma (chRCC) typically shows ~7 chromosome losses (1, 2, 6, 10, 13, 17, and 21) and ~31 exonic somatic mutations, yet carries ~5%-10% metastatic incidence. Since extensive chromosomal losses can generate proteotoxic stress and compromise cellular proliferation, it is intriguing how chRCC, a tumor with extensive chromosome losses and a low number of somatic mutations, can develop lethal metastases. Genomic features distinguishing metastatic from nonmetastatic chRCC are unknown. An integrated approach, including whole-genome sequencing (WGS), targeted ultradeep cancer gene sequencing, and chromosome analyses (FACETS, OncoScan, and FISH), was performed on 79 chRCC patients including 38 metastatic (M-chRCC) cases. We demonstrate that TP53 mutations (58%), PTEN mutations (24%), and imbalanced chromosome duplication (ICD, duplication of ≥ 3 chromosomes) (25%) were enriched in M-chRCC. Reconstruction of the subclonal composition of paired primary-metastatic chRCC tumors supports the role of TP53, PTEN, and ICD in metastatic evolution. Finally, the presence of these 3 genomic features in primary tumors of both The Cancer Genome Atlas kidney chromophobe (KICH) (n = 64) and M-chRCC (n = 35) cohorts was associated with worse survival. In summary, our study provides genomic insights into the metastatic progression of chRCC and identifies TP53 mutations, PTEN mutations, and ICD as high-risk features.

Renal cell carcinoma

Renal cell carcinoma (RCC) denotes cancer originated from the renal epithelium and accounts for >90% of cancers in the kidney. The disease encompasses >10 histological and molecular subtypes, of which clear cell RCC (ccRCC) is most common and accounts for most cancer-related deaths. Although somatic VHL mutations have been described for some time, more-recent cancer genomic studies have identified mutations in epigenetic regulatory genes and demonstrated marked intra-tumour heterogeneity, which could have prognostic, predictive and therapeutic relevance. Localized RCC can be successfully managed with surgery, whereas metastatic RCC is refractory to conventional chemotherapy. However, over the past decade, marked advances in the treatment of metastatic RCC have been made, with targeted agents including sorafenib, sunitinib, bevacizumab, pazopanib and axitinib, which inhibit vascular endothelial growth factor (VEGF) and its receptor (VEGFR), and everolimus and temsirolimus, which inhibit mechanistic target of rapamycin complex 1 (mTORC1), being approved. Since 2015, agents with additional targets aside from VEGFR have been approved, such as cabozantinib and lenvatinib; immunotherapies, such as nivolumab, have also been added to the armamentarium for metastatic RCC. Here, we provide an overview of the biology of RCC, with a focus on ccRCC, as well as updates to complement the current clinical guidelines and an outline of potential future directions for RCC research and therapy.

Genomic Biomarkers of a Randomized Trial Comparing First-line Everolimus and Sunitinib in Patients with Metastatic Renal Cell Carcinoma

BACKGROUND

Metastatic renal cell carcinoma (RCC) patients are commonly treated with vascular endothelial growth factor (VEGF) inhibitors or mammalian target of rapamycin inhibitors. Correlations between somatic mutations and first-line targeted therapy outcomes have not been reported on a randomized trial.

OBJECTIVE

To evaluate the relationship between tumor mutations and treatment outcomes in RECORD-3, a randomized trial comparing first-line everolimus (mTOR inhibitor) followed by sunitinib (VEGF inhibitor) at progression with the opposite sequence in 471 metastatic RCC patients.

DESIGN, SETTING, AND PARTICIPANTS

Targeted sequencing of 341 cancer genes at ∼540× coverage was performed on available tumor samples from 258 patients; 220 with clear cell histology (ccRCC).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Associations between somatic mutations and median first-line progression free survival (PFS1L) and overall survival were determined in metastatic ccRCC using Cox proportional hazards models and log-rank tests.

RESULTS AND LIMITATIONS

Prevalent mutations (≥ 10%) were VHL (75%), PBRM1 (46%), SETD2 (30%), BAP1 (19%), KDM5C (15%), and PTEN (12%). With first-line everolimus, PBRM1 and BAP1 mutations were associated with longer (median [95% confidence interval {CI}] 12.8 [8.1, 18.4] vs 5.5 [3.1, 8.4] mo) and shorter (median [95% CI] 4.9 [2.9, 8.1] vs 10.5 [7.3, 12.9] mo) PFS1L, respectively. With first-line sunitinib, KDM5C mutations were associated with longer PFS1L (median [95% CI] of 20.6 [12.4, 27.3] vs 8.3 [7.8, 11.0] mo). Molecular subgroups of metastatic ccRCC based on PBRM1, BAP1, and KDM5C mutations could have predictive values for patients treated with VEGF or mTOR inhibitors. Most tumor DNA was obtained from primary nephrectomy samples (94%), which could impact correlation statistics.

CONCLUSIONS

PBRM1, BAP1, and KDM5C mutations impact outcomes of targeted therapies in metastatic ccRCC patients.

PATIENT SUMMARY

Large-scale genomic kidney cancer studies reported novel mutations and heterogeneous features among individual tumors, which could contribute to varied clinical outcomes. We demonstrated correlations between somatic mutations and treatment outcomes in clear cell renal cell carcinoma, supporting the value of genomic classification in prospective studies.

A braided cancer river connects tumor heterogeneity and precision medicine

With the ever-increasing complexity of tumor heterogeneity (TH) discovered through cancer genome sequencing, it is apparent that TH has become the biggest hurdle for precision cancer therapeutics. Through studying the genomics of exceptional responders to targeted therapeutic agents in kidney cancer, we demonstrated parallel convergent gene/pathway/capability/function evolution of kidney cancer in the context of TH, which prompted us to propose a new cancer evolution model “the braided cancer river model”. Based on this model, we might be able to outsmart a given cancer type within an individual patient through simultaneously inhibiting preferred parallel pathways or sequential nodes. Thus, the goals of this perspective are to define tumor heterogeneity, discuss tumor evolution, introduce braided cancer river model, and improve precision medicine.

Systems heterogeneity: An integrative way to understand cancer heterogeneity

The concept of systems heterogeneity was firstly coined and explained in the Special Issue, as a new alternative to understand the importance and complexity of heterogeneity in cancer. Systems heterogeneity can offer a full image of heterogeneity at multi-dimensional functions and multi-omics by integrating gene or protein expression, epigenetics, sequencing, phosphorylation, transcription, pathway, or interaction. The Special Issue starts with the roles of epigenetics in the initiation and development of cancer heterogeneity through the interaction between permanent genetic mutations and dynamic epigenetic alterations. Cell heterogeneity was defined as the difference in biological function and phenotypes between cells in the same organ/tissue or in different organs, as well as various challenges, as exampled in telocytes. The single cell heterogeneity has the value of identifying diagnostic biomarkers and therapeutic targets and clinical potential of single cell systems heterogeneity in clinical oncology. A number of signaling pathways and factors contribute to the development of systems heterogeneity. Proteomic heterogeneity can change the strategy and thinking of drug discovery and development by understanding the interactions between proteins or proteins with drugs in order to optimize drug efficacy and safety. The association of cancer heterogeneity with cancer cell evolution and metastasis was also overviewed as a new alternative for diagnostic biomarkers and therapeutic targets in clinical application.