Validation of serum amyloid α as an independent biomarker for progression-free and overall survival in metastatic renal cell cancer patients

Comparative Serum Proteome Profiling of Canine Benign Prostatic Hyperplasia before and after Castration

BPH is the most prevalent prostatic condition in aging dogs. Nevertheless, clinical diagnosis and management remain inconsistent. This study employed in-solution digestion coupled with nano-liquid chromatography tandem mass spectrometry to assess serum proteome profiling of dogs with BPH and those dogs after castration. Male dogs were divided into two groups; control and BPH groups. In the BPH group, each dog was evaluated at two time points: Day 0 (BF subgroup) and Day 30 after castration (AT subgroup). In the BF subgroup, three proteins were significantly upregulated and associated with dihydrotestosterone: solute carrier family 5 member 5, tyrosine-protein kinase, and FRAT regulator of WNT signaling pathway 1. Additionally, the overexpression of polymeric immunoglobulin receptors in the BF subgroup hints at its potential as a novel protein linked to the BPH development process. Conversely, alpha-1-B glycoprotein (A1BG) displayed significant downregulation in the BF subgroup, suggesting A1BG's potential as a predictive protein for canine BPH. Finasteride was associated with increased proteins in the AT subgroup, including apolipoprotein C-I, apolipoprotein E, apolipoprotein A-II, TAO kinase 1, DnaJ homolog subfamily C member 16, PH domain and leucine-rich repeat protein phosphatase 1, neuregulin 1, and pseudopodium enriched atypical kinase 1. In conclusion, this pilot study highlighted alterations in various serum proteins in canine BPH, reflecting different pathological changes occurring in this condition. These proteins could be a source of potential non-invasive biomarkers for diagnosing this disease.

Biomarkers for the Detection and Surveillance of Renal Cancer

Renal cell carcinoma (RCC) is a heterogeneous disease characterized by a broad spectrum of disorders in terms of genetics, molecular and clinical characteristics. There is an urgent need for noninvasive tools to stratify and select patients for treatment accurately. In this review, we analyze serum, urinary, and imaging biomarkers that have the potential to detect malignant tumors in patients with RCC. We discuss the characteristics of these numerous biomarkers and their ability to be used routinely in clinical practice. The development of biomarkers continues to evolve with promising prospects.

Sunitinib resistance in renal cell carcinoma: From molecular mechanisms to predictive biomarkers

Currently, renal cell carcinoma (RCC) is the most prevalent type of kidney cancer. Targeted therapy has replaced radiation therapy and chemotherapy as the main treatment option for RCC due to the lack of significant efficacy with these conventional therapeutic regimens. Sunitinib, a drug used to treat gastrointestinal tumors and renal cell carcinoma, inhibits the tyrosine kinase activity of a number of receptor tyrosine kinases, including vascular endothelial growth factor receptor (VEGFR), platelet-derived growth factor receptor (PDGFR), c-Kit, rearranged during transfection (RET) and fms-related receptor tyrosine kinase 3 (Flt3). Although sunitinib has been shown to be efficacious in the treatment of patients with advanced RCC, a significant number of patients have primary resistance to sunitinib or acquired drug resistance within the 6-15 months of therapy. Thus, in order to develop more efficacious and long-lasting treatment strategies for patients with advanced RCC, it will be crucial to ascertain how to overcome sunitinib resistance that is produced by various drug resistance mechanisms. In this review, we discuss: 1) molecular mechanisms of sunitinib resistance; 2) strategies to overcome sunitinib resistance and 3) potential predictive biomarkers of sunitinib resistance.

Identification of biomarkers for hepatocellular carcinoma based on single cell sequencing and machine learning algorithms

Hepatocellular carcinoma (HCC) remains one of the most lethal cancers around the world. Precision oncology will be crucial for further improving the prognosis of HCC patients. Compared with traditional bulk RNA-seq, single-cell RNA sequencing (scRNA-seq) enables the transcriptomes of a great deal of individual cells assayed in an unbiased manner, showing the potential to deeply reveal tumor heterogeneity. In this study, based on the scRNA-seq results of primary neoplastic cells and paired normal liver cells from eight HCC patients, a new strategy of machine learning algorithms was applied to screen core biomarkers that distinguished HCC tumor tissues from the adjacent normal liver. Expression profiles of HCC cells and normal liver cells were first analyzed by maximum relevance minimum redundancy (mRMR) to get a top 50 signature gene feature. For further analysis, the incremental feature selection (IFS) method and leave-one-out cross validation (LOOCV) were conducted to build an optimal classification model and to extract 21 potentially essential biomarkers for HCC cells. Our results provided new insights into HCC pathogenesis that might be valuable for HCC diagnosis and therapy.

Serum amyloid A protein in cancer prognosis: a meta-analysis and systematic review

Background

Published studies showed divergent results of the prognostic value of serum amyloid A protein (SAA) in patients with different cancers. Therefore, we conducted this meta-analysis so as to assess the association between SAA and cancer prognosis.

Methods

A comprehensive search was conducted to identify the literatures working over SAA and survival in patients with cancers published until January 2020. Sufficient data for assessing overall survival in cancers were extracted descriptively and quantitatively from the studies and a pooled odds ratio was calculated using the Mantel-Haenszel fixed-effect or random-effect model.

Results

Ten eligible papers were identified by two reviewers independently, including 4 studies that evaluated renal cell carcinoma (RCC), 2 studies evaluated lung cancer and the other 3 studies evaluated melanoma, gastric cancer and different cancers. Elevated SAA expression and shorter overall survival (OS) had a statistically significant relation [pooled 1-year OR was 5.07, 95% confidence interval (CI), 3.71–6.94, Q=9.15, I²=0%; pooled 3-year OR was 4.21, 95% CI, 3.18–5.56, Q=14.94, I²=46%; pooled 5-year OR was 5.69, 95% CI, 2.66–12.18, Q=24.83, I²=80%]. Subgroup analysis of RCC patients showed remarkable association between SAA and shorter OS (pooled 1-year OR =4.76, 95% CI, 3.00–7.56, Q=4.18, I²=4%; pooled 3-year OR =4.89, 95% CI, 3.06–7.81, Q=2.88, I²=0%).

Conclusions

High SAA status is correlated with an unfavorable OS in different cancers, especially in RCC, and digestive cancer.

Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression

BACKGROUND

Renal Cell Carcinoma (RCC) is difficult to treat with 5-year survival rate of 10% in metastatic patients. Main reasons of therapy failure are lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression. Thus, the investigation of mechanisms regulating RCC progression is fundamental to improve RCC therapy.

METHODS

In order to identify molecular markers and gene processes involved in the steps of RCC progression, we generated several cell lines of higher aggressiveness by serially passaging mouse renal cancer RENCA cells in mice and, concomitantly, performed functional genomics analysis of the cells. Multiple cell lines depicting the major steps of tumor progression (including primary tumor growth, survival in the blood circulation and metastatic spread) were generated and analyzed by large-scale transcriptome, genome and methylome analyses. Furthermore, we performed clinical correlations of our datasets. Finally we conducted a computational analysis for predicting the time to relapse based on our molecular data.

RESULTS

Through in vivo passaging, RENCA cells showed increased aggressiveness by reducing mice survival, enhancing primary tumor growth and lung metastases formation. In addition, transcriptome and methylome analyses showed distinct clustering of the cell lines without genomic variation. Distinct signatures of tumor aggressiveness were revealed and validated in different patient cohorts. In particular, we identified SAA2 and CFB as soluble prognostic and predictive biomarkers of the therapeutic response. Machine learning and mathematical modeling confirmed the importance of CFB and SAA2 together, which had the highest impact on distant metastasis-free survival. From these data sets, a computational model predicting tumor progression and relapse was developed and validated. These results are of great translational significance.

CONCLUSION

A combination of experimental and mathematical modeling was able to generate meaningful data for the prediction of the clinical evolution of RCC.

Baseline and early changes in circulating Serum Amyloid A (SAA) predict survival outcomes in advanced non-small cell lung cancer patients treated with Anti-PD-1/PD-L1 monotherapy

BACKGROUND

Systemic inflammation plays an important role in carcinogenesis and is associated with overall survival in patients with different cancer types, including those treated with immune checkpoint blockade (ICB). Serum Amyloid A (SAA) is an acute-phase protein and a marker of persistent inflammation. We hypothesized that circulating SAA may predict outcomes in advanced non-small cell lung (aNSCLC) patients treated with PD-1/PD-L1 ICB.

MATERIALS AND METHODS

This retrospective study included 91 aNSCLC patients who received anti-PD-(L)1 monotherapy in Sun Yat-sen University Cancer Center (Guangzhou, China) between August 2016 and June 2018. We examined the impact of circulating SAA at baseline and 8 (±2) weeks later on overall survival (OS). X-tile program was used to determine the cut-off values which optimized the significance of the split between Kaplan-Meier survival curves. Kaplan-Meier methodology and Cox regression analyses were conducted for survival analyses.

RESULTS

The optimal cut-off value of baseline SAA for OS stratification was 137.6 mg/L. In univariate analysis, both high level of baseline SAA (hazard ratio [HR], 2.76; 95% confidence interval [CI], 1.47-5.18; P = 0.002) and lack of early SAA descent (HR, 1.51; 95% CI, 1.11-2.06; P = 0.009) were significantly associated with inferior OS. In multivariate analysis, gender, smoking status, performance status, liver metastasis, neutrophil-to-lymphocyte ratio, baseline SAA and early changes in SAA independently predicted OS (all with P < 0.05). A combined baseline SAA ≥ 137.6 mg/L and without early SAA descent identified a small cohort with remarkably worse OS (median, 3.2 months).

CONCLUSIONS

Both high baseline and lack of early decline in circulating SAA are significantly associated with inferior outcomes in aNSCLC patients treated with PD-1/PD-L1 ICB. Combined these two SAA indexes provided improved risk stratification. The prognostic value of this simple, readily-available, and cost-effective biomarker warrants larger, prospective validation before definitive recommendation can be made.

Identification of a Novel Glycolysis-Related Gene Signature Correlates With the Prognosis and Therapeutic Responses in Patients With Clear Cell Renal Cell Carcinoma

Background

Accumulating evidences indicate significant alterations in the aerobic glycolysis in clear cell renal cell carcinoma (ccRCC). We aim to develop and validate a glycolysis-related genes signature for predicting the clinical outcomes of patients with ccRCC.

Methods

mRNA expression profiling of ccRCC was obtained from The Cancer Genome Atlas database. Univariate Cox regression analysis and lasso Cox regression model were performed to identify and construct the prognostic gene signature. The protein expression levels of the core genes were obtained from the Human Protein Atlas database. We used four external independent data sets to verify the predictive power of the model for prognosis, tyrosine kinase inhibitor (TKI) therapy, and immunotherapy responses, respectively. Finally, we explored the potential mechanism of this signature through gene set enrichment analysis (GSEA).

Results

Through the GSEA, glycolysis-related gene sets were significantly different between ccRCC tissues and normal tissues. Next, we identified and constructed a seven-mRNA signature (GALM, TGFA, RBCK1, CD44, HK3, KIF20A, and IDUA), which was significantly correlated with worse survival outcome and was an independent prognostic indicator for ccRCC patients. Furthermore, the expression levels of hub genes were validated based on the Human Protein Atlas databases. More importantly, the model can predict patients’ response to TKI therapy and immunotherapy. These findings were successfully validated in the external independent ccRCC cohorts. The mechanism exploration showed that the model may influence the prognosis by influencing tumor proliferation, base mismatch repair system and immune status of patients.

Conclusions

Our study has built up a robust glycolysis-based molecular signature that predicts the prognosis and TKI therapy and immunotherapy responses of patients with ccRCC with high accuracy, which might provide important guidance for clinical assessment. Also, clinical investigations in large ccRCC cohorts are greatly needed to validate our findings.

LINC00160 mediates sunitinib resistance in renal cell carcinoma via SAA1 that is implicated in STAT3 activation and compound transportation

Patients with advanced renal cell carcinoma who are resistant to sunitinib currently have limited clinical options for treatment. Therefore, it is necessary to explore the biological basis of sunitinib resistance and to uncover new targets for the intervention of sunitinib resistance. In this study, we identified that LINC00160 was associated with sunitinib resistance in renal cell carcinoma. Resistant tumor cells highly expressed LINC00160 to recruit transcriptional factor TFAP2A, which bound to SAA1 promoter regions and activated its expression. On one hand, SAA1 linked to ABCB1 protein, which facilitated sunitinib cellular efflux and diminished drug accumulation. On the other hand, SAA1 stimulated JAK-STAT signaling pathways, which countered cellular survival inhibition from drug. All these regulatory networks were well organized and collaborated, thus promoting sunitinib resistance in renal cell carcinoma. LINC00160 mediates sunitinib resistance in renal cell carcinoma via SAA1 that is implicated in STAT3 activation and compound transportation, which offers an opportunity for targeted intervention and molecular therapies in the future.

The prognostic value of integration of pretreatment serum amyloid A (SAA)-EBV DNA (S-D) grade in patients with nasopharyngeal carcinoma

Background

Serum amyloid A (SAA) has been associated with the development and prognosis of cancer. The purpose of this study was to evaluate the predictive value of integration of pretreatment SAA–EBV DNA (S-D) grade and comparison with the TNM staging system in patients with nasopharyngeal carcinoma (NPC). The S-D grade was calculated based on the cut-off values of serum SAA and EBV DNA copy numbers which were determined by receiver operating characteristic (ROC) curves. We evaluated the prognostic value of pretreatment SAA, EBV DNA and S-D grade on overall survival (OS) of NPC patients. We also evaluated the predictive power of S-D grade with TNM staging system using 4 indices: concordance statistics (C-index), time-dependent ROC (ROCt) curve, net reclassification index (NRI) and integrated discrimination improvement (IDI).

Results

A total of 304 NPC patients were enrolled in this study. Multivariate analysis showed that TNM stage (P = 0.007), SAA (P = 0.013), and EBV DNA (P = 0.033) were independent prognostic factors in NPC. The S-D grade was divided into S-D grade 1, S-D grade 2, and S-D grade 3, which had more predictive accuracy for OS than TNM staging according to all 4 indices.

Conclusions

We found that the S-D grade could be used as a new tool to predict the OS in NPC patients.

Pretreatment Serum Amyloid A and C-reactive Protein Comparing with Epstein-Barr Virus DNA as Prognostic Indicators in Patients with Nasopharyngeal Carcinoma: A Prospective Study

Purpose

The measuring Epstein-Barr virus (EBV) DNA is an important predictor of nasopharyngeal carcinoma (NPC). This study evaluated the predictive value of pretreatment serum amyloid A (SAA) and C-reactive protein (CRP) comparing with EBV DNA in patients with NPC.

Materials and Methods

In an observational study of 419 non-metastatic NPC patients, we prospectively evaluated the prognostic effects of pretreatment SAA, CRP, and EBV DNA on survival. The primary end-point was progress-free survival (PFS).

Results

The median level of SAA and CRP was 4.28 mg/L and 1.88 mg/L, respectively. For the high-SAA group (> 4.28 mg/L) versus the low-SAA (≤ 4.28 mg/L) group and the high-CRP group (> 1.88 mg/L) versus the low-CRP (≤ 1.88 mg/L) group, the 5-year PFS was 64.5% versus 73.1% (p=0.013) and 65.2% versus 73.3% (p=0.064), respectively. EBV DNA detection showed a superior predictive result, the 5-year PFS in the EBV DNA ≥ 1,500 copies/mL group was obviously different than the EBV DNA < 1,500 copies/mL group (62.2% versus 77.8%, p < 0.001). Multifactorial Cox regression analysis confirmed that in the PFS, the independent prognostic factors were including EBV DNA (hazard ratio [HR], 1.788; p=0.009), tumour stage (HR, 1.903; p=0.021), and node stage (HR, 1.498; p=0.049), but the SAA and CRP were not included in the independent prognostic factors.

Conclusion

The results of SAA and CRP had a certain relationship with the prognosis of NPC, and the prognosis of patients with high level of SAA and CRP were poor. However, the predictive ability of SAA and CRP was lower than that of EBV DNA.

ABCG2 is a potential prognostic marker of overall survival in patients with clear cell renal cell carcinoma

Background

ATP-binding cassette sub-family G member 2 (ABCG2) is a semi-transport protein that plays a major role in multidrug resistance. We aimed to evaluate the prognostic significance of ABCG2 expression in patients with clear cell renal cell carcinoma.

Methods

From 2008 to 2013, 120 patients with clear cell kidney cancer underwent surgery with paraffin-embedded specimens and necessary clinical information available. Immunohistochemistry staining was performed to grade the expression of ABCG2 as ABCG2(−): less than 10% of tumor cells stained; ABCG2(+): weak membrane staining; and ABCG2(++): moderate or strong membrane staining. The overall survival was analyzed using Kaplan-Meier method. Multivariable Cox regression evaluated the independent predictors for overall survival.

Results

ABCG2(−) was diagnosed in 57 (48%) patients, ABCG2(+) in 52 (43%) patients, and ABCG2 (++) in 11(9.2%) patients. ABCG2 expression significantly correlated with the five-year survival (p < 0.001) and distant metastasis (p = 0.001). In the multivariable analysis, besides Fuhrman grade, the ABCG2 expression was an independent prognostic marker for overall survival (p < 0.001) when incorporating other relevant tumor and clinical parameters (HR = 3.84, 95% CI: 1.92–7.70).

Conclusion

The current data suggests that ABCG2 may serve as a prognostic marker for overall survival in patients with clear cell renal cell carcinoma. Further studies with large cohorts of patients will be essential for validating these findings and defining the clinical utility of ABCG2 in the patient population.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3224-6) contains supplementary material, which is available to authorized users.

Clinical and molecular prognostic and predictive biomarkers in clear cell renal cell cancer

ABSTRACT 

The natural history of clear cell renal cell cancer is highly unpredictable with various progressors and with populations where small renal masses may be accompanied by metastatic disease. Currently, there is a critical need to determine patient risk and optimize treatment regimes. For these patients, molecular markers may offer significant information in terms of prognostic and predictive values, as well as determination of valid therapeutic targets. Until now, only a few of the many identified clear cell renal cell cancer biomarkers have been clinically validated in large cohorts. And only several biomarkers are integrated in predictive or prognostic models. Therefore, a large cohesive effort is required to advance the field of clear cell renal cell cancer prognostic biomarkers through systematic discovery, verification, validation and clinical implementation.

Predictive biomarkers in renal cell cancer: insights in drug resistance mechanisms

INTRODUCTION

VEGF-targeted therapy is currently the first line treatment for patients with metastatic clear cell renal cell carcinoma (ccRCC), but most patients either display primary (intrinsic) resistance or acquire drug resistance. In recent years multiple mechanisms of resistance to VEGF-targeted therapy emerged from preclinical research, but it is currently unknown to what extent these drug resistance modalities play a role in the clinic. Here we reviewed the current literature on biomarkers that predict treatment outcome in patients with ccRCC to gain insight in clinical drug resistance mechanisms.

METHODS

A search syntax was compiled by combining different synonyms of "biomarker" AND "renal" AND "cancer". MEDLINE was accessed through PubMed, where this syntax was entered and used to search titles and abstracts of publications. Articles were selected based on three criteria: (1) description of patients with clear cell RCC, (2) treatment with VEGF targeted therapy and (3) discussion of biomarkers that were studied for potential association with treatment response.

RESULTS

The literature search was performed on March 4th 2014 and yielded 1882 articles. After carefully reading the titles and abstracts based on the three previously mentioned criteria, 103 publications were evaluated. Backward citation screening was performed on all eligible studies and revealed another 24 articles. This search revealed that (1) High glucose uptake and low contrast enhancement on PET- and CT-imaging before start of treatment may correlate with poor response to therapy, (2) Low dose intensity due to treatment intolerance is related to shorter progression free survival. (3) Acquired resistance appears to be associated with rebound vascularization based on both longitudinal monitoring of contrast enhancement by CT and blood vessel counts in tumor tissue, and (4) Based on plasma cytokine and single nucleotide polymorphism (SNP) studies, interleukin-8, VEGFR-3, FGFR2 and HGF/MET emerged as potential clinical markers for chemoresistance.

CONCLUSION

Low dose intensity, specific tumor-imaging techniques and potential biological biomarkers may be predictive for response to VEGF-targeted therapy in ccRCC. Some of these plausible biomarkers may also provide more insight into the underlying mechanisms of resistance such as altered glucose metabolism and rapid rebound vascularization.

Molecular marker for predicting treatment response in advanced renal cell carcinoma: does the promise fulfill clinical need?

Renal cell carcinoma (RCC) is largely diagnosed incidentally on imaging taken for unrelated reasons. The management of localized lesions is primarily extirpative with excellent results. Treatment of advanced RCC has evolved over recent years with the use of targeted therapies such as tyrosine kinase inhibitors, mammalian target of rapamycin inhibitors, and antibody-mediated therapies. The treatment response to these targeted therapies is highly variable, with no clear clinical method of identifying patients who will benefit from or not tolerate therapy. The field of molecular markers has evolved significantly in the last decade, with a multitude of markers identified that predict treatment response and drug toxicity. The following review critically evaluates those molecular markers that have been assessed for their utility in predicting treatment response in patients with advanced/metastatic renal cell carcinoma (mRCC). Identifying the ideal treatment for these patients will improve responses to therapy, minimize morbidity, and save significant healthcare dollars.

Comparative proteomic profiles indicating genetic factors may involve in hepatocellular carcinoma familial aggregation

Familial aggregation of hepatocellular carcinoma (HCC), the third leading cause of cancer death worldwide, has shown to be a common phenomenon. We investigated the association between the genetic background and HCC familial aggregation. Serum samples were collected from HCC family members and normal control family members for screening the differentially expressed protein peaks with the approach of surface-enhanced laser desorption ionization time-of-flight mass spectrometry. Potential genetically associated protein peaks were selected and further identified by matrix assisted laser desorption ionization-time of flight mass spectrometry. A panel of six protein peaks (m/z 6432.94, 8478.35, 9381.91, 17284.67, 17418.34, and 18111.04) were speculated to reflect the genetic susceptibility of HCC familial aggregation. Three of them (m/z 6432.94, 8478.35, and 9381.91) were selected to identify as the candidate proteins. Nine identified proteins, including mostly apolipoprotein family (ApoA1, ApoA2, ApoC3, ApoE) and serum amyloid A protein (SAA), were found overexpressed in the multiple HCC cases family members. The comparative proteomic profiles have suggested that genetic factors ought to be taken into account for familial aggregation of HCC.