The impact of genetic heterogeneity on biomarker development in kidney cancer assessed by multiregional sampling

MOICS, a novel classier deciphering immune heterogeneity and aid precise management of clear cell renal cell carcinoma at multiomics level

Recent studies have indicated that the tumor immune microenvironment plays a pivotal role in the initiation and progression of clear cell renal cell carcinoma (ccRCC). However, the characteristics and heterogeneity of tumor immunity in ccRCC, particularly at the multiomics level, remain poorly understood. We analyzed immune multiomics datasets to perform a consensus cluster analysis and validate the clustering results across multiple internal and external ccRCC datasets; and identified two distinctive immune phenotypes of ccRCC, which we named multiomics immune-based cancer subtype 1 (MOICS1) and subtype 2 (MOICS2). The former, MOICS1, is characterized by an immune-hot phenotype with poor clinical outcomes, marked by significant proliferation of CD4+ and CD8+ T cells, fibroblasts, and high levels of immune inhibitory signatures; the latter, MOICS2, exhibits an immune-cold phenotype with favorable clinical characteristics, characterized by robust immune activity and high infiltration of endothelial cells and immune stimulatory signatures. Besides, a significant negative correlation between immune infiltration and angiogenesis were identified. We further explored the mechanisms underlying these differences, revealing that negatively regulated endopeptidase activity, activated cornification, and neutrophil degranulation may promote an immune-deficient phenotype, whereas enhanced monocyte recruitment could ameliorate this deficiency. Additionally, significant differences were observed in the genomic landscapes between the subtypes: MOICS1 exhibited mutations in TTN, BAP1, SETD2, MTOR, MUC16, CSMD3, and AKAP9, while MOICS2 was characterized by notable alterations in the TGF-β pathway. Overall, our work demonstrates that multi-immune omics remodeling analysis enhances the understanding of the immune heterogeneity in ccRCC and supports precise patient management.

Impressive and Prolonged Response with Lenvatinib in a Highly Pretreated Patient with Metastatic Clear Cell Renal Cancer: A Case Report

Clear cell renal carcinoma (ccRCC) can occur in young people and could be associated with an aggressive behavior. While for the first-line treatment in metastatic disease, there is an agreement to rely on an immunotherapy (IO)-based combination regimen, no standard second-line regimens exist. Generally, tyrosine kinase inhibitors (TKIs) are employed, even in sequence, although no trials have demonstrated yet the best succession. Herein, we present the case of a 39-year-old male, with a very aggressive ccRCC with somatic VHL mutation and distant metastases at diagnosis. He was treated with four different lines of therapies, including TKIs, with progressive multiple tumor deposits. Lenvatinib alone as the fifth line was able to induce a remarkable and prolonged tumor shrinkage with manageable toxicities.

New Perspectives on the Role of Liquid Biopsy in Bladder Cancer: Applicability to Precision Medicine

Bladder cancer (BC) is one of the most common tumors in the world. Cystoscopy and tissue biopsy are the standard methods in screening and early diagnosis of suspicious bladder lesions. However, they are invasive procedures that may cause pain and infectious complications. Considering the limitations of both procedures, and the recurrence and resistance to BC treatment, it is necessary to develop a new non-invasive methodology for early diagnosis and multiple evaluations in patients under follow-up for bladder cancer. In recent years, liquid biopsy has proven to be a very useful diagnostic tool for the detection of tumor biomarkers. This non-invasive technique makes it possible to analyze single tumor components released into the peripheral circulation and to monitor tumor progression. Numerous biomarkers are being studied and interesting clinical applications for these in BC are being presented, with promising results in early diagnosis, detection of microscopic disease, and prediction of recurrence and response to treatment.

von-Hippel Lindau and Hypoxia-Inducible Factor at the Center of Renal Cell Carcinoma Biology

The most common form of kidney cancer is clear cell renal cell carcinoma (ccRCC). Biallelic VHL tumor suppressor gene inactivation is the usual initiating event in both hereditary (VHL Disease) and sporadic ccRCCs. The VHL protein, pVHL, earmarks the alpha subunits of the HIF transcription factor for destruction in an oxygen-dependent manner. Deregulation of HIF2 drives ccRCC pathogenesis. Drugs inhibiting the HIF2-responsive growth factor VEGF are now mainstays of ccRCC treatment. A first-in-class allosteric HIF2 inhibitor was recently approved for treating VHL Disease-associated neoplasms and appears active against sporadic ccRCC in early clinical trials.

Improving CNNs classification with pathologist-based expertise: the renal cell carcinoma case study

The prognosis of renal cell carcinoma (RCC) malignant neoplasms deeply relies on an accurate determination of the histological subtype, which currently involves the light microscopy visual analysis of histological slides, considering notably tumor architecture and cytology. RCC subtyping is therefore a time-consuming and tedious process, sometimes requiring expert review, with great impact on diagnosis, prognosis and treatment of RCC neoplasms. In this study, we investigate the automatic RCC subtyping classification of 91 patients, diagnosed with clear cell RCC, papillary RCC, chromophobe RCC, or renal oncocytoma, through deep learning based methodologies. We show how the classification performance of several state-of-the-art Convolutional Neural Networks (CNNs) are perfectible among the different RCC subtypes. Thus, we introduce a new classification model leveraging a combination of supervised deep learning models (specifically CNNs) and pathologist's expertise, giving birth to a hybrid approach that we termed ExpertDeepTree (ExpertDT). Our findings prove ExpertDT's superior capability in the RCC subtyping task, with respect to traditional CNNs, and suggest that introducing some expert-based knowledge into deep learning models may be a valuable solution for complex classification cases.

Identification and validation of a DNA methylation-driven gene-based prognostic model for clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is a malignant tumor with heterogeneous morphology and poor prognosis. This study aimed to establish a DNA methylation (DNAm)-driven gene-based prognostic model for ccRCC.

METHODS

Reduced representation bisulfite sequencing (RRBS) was performed on the DNA extracts from ccRCC patients. We analyzed the RRBS data from 10 pairs of patient samples to screen the candidate CpG sites, then trained and validated an 18-CpG site model, and integrated the clinical characters to establish a Nomogram model for the prognosis or risk evaluation of ccRCC.

RESULTS

We identified 2261 DMRs in the promoter region. After DMR selection, 578 candidates were screened, and was correspondence with 408 CpG dinucleotides in the 450 K array. We collected the DNAm profiles of 478 ccRCC samples from TCGA dataset. Using the training set with 319 samples, a prognostic panel of 18 CpGs was determined by univariate Cox regression, LASSO regression, and multivariate Cox proportional hazards regression analyses. We constructed a prognostic model by combining the clinical signatures. In the test set (159 samples) and whole set (478 samples), the Kaplan-Meier plot showed significant differences; and the ROC curve and survival analyses showed AUC greater than 0.7. The Nomogram integrated with clinicopathological characters and methylation risk score had better performance, and the decision curve analyses also showed a beneficial effect.

CONCLUSIONS

This work provides insight into the role of hypermethylation in ccRCC. The targets identified might serve as biomarkers for early ccRCC diagnosis and prognosis biomarkers for ccRCC. We believe our findings have implications for better risk stratification and personalized management of this disease.

The expression pattern of pyruvate dehydrogenase kinases predicts prognosis and correlates with immune exhaustion in clear cell renal cell carcinoma

Renal cancer cells constitute a paradigm of tumor cells with a glycolytic reprogramming which drives metabolic alterations favouring cell survival and transformation. We studied the expression and activity of pyruvate dehydrogenase kinases (PDK1-4), key enzymes of the energy metabolism, in renal cancer cells. We analysed the expression, subcellular distribution and clinicopathological correlations of PDK1-4 by immunohistochemistry of tumor tissue microarray samples from a cohort of 96 clear cell renal cell carcinoma (ccRCC) patients. Gene expression analysis was performed on whole tumor tissue sections of a subset of ccRCC samples. PDK2 and PDK3 protein expression in tumor cells correlated with lower patient overall survival, whereas PDK1 protein expression correlated with higher patient survival. Gene expression analysis revealed molecular association of PDK2 and PDK3 expression with PI3K signalling pathway, as well as with T cell infiltration and exhausted CD8 T cells. Inhibition of PDK by dichloroacetate in human renal cancer cell lines resulted in lower cell viability, which was accompanied by an increase in pAKT. Together, our findings suggest a differential role for PDK enzymes in ccRCC progression, and highlight PDK as actionable metabolic proteins in relation with PI3K signalling and exhausted CD8 T cells in ccRCC.

Genomic landscapes of canine splenic angiosarcoma (hemangiosarcoma) contain extensive heterogeneity within and between patients

Cancer genomic heterogeneity presents significant challenges for understanding oncogenic processes and for cancer’s clinical management. Variation in driver mutation frequency between patients with the same tumor type as well as within an individual patients’ cancer can shape the use of mutations as diagnostic, prognostic, and predictive biomarkers. We have characterized genomic heterogeneity between and within canine splenic hemangiosarcoma (HSA), a common naturally occurring cancer in pet dogs that is similar to human angiosarcoma (AS). HSA is a clinically, physiologically, and genomically complex canine cancer that may serve as a valuable model for understanding the origin and clinical impact of cancer heterogeneity. We conducted a prospective collection of 52 splenic masses from 43 dogs (27 HSA, 15 benign masses, and 1 stromal sarcoma) presenting for emergency care with hemoperitoneum secondary to a ruptured splenic mass. Multi-platform genomic analysis included matched tumor/normal targeted sequencing panel and exome sequencing. We found candidate somatic cancer driver mutations in 14/27 (52%) HSAs. Among recurrent candidate driver mutations, TP53 was most commonly mutated (30%) followed by PIK3CA (15%), AKT1 (11%), and CDKN2AIP (11%). We also identified significant intratumoral genomic heterogeneity, consistent with a branched evolution model, through multi-region exome sequencing of three distinct tumor regions from selected primary splenic tumors. These data provide new perspectives on the genomic landscape of this veterinary cancer and suggest a cross-species value for using HSA in pet dogs as a naturally occurring model of intratumoral heterogeneity.

Towards Personalized Sampling in Clear Cell Renal Cell Carcinomas

Simple Summary

Intratumor heterogeneity (ITH) is a constant event in malignant tumors and the cause of most therapeutic failures in modern oncology. Since clear cell renal cell carcinoma (CCRCC) is a paradigm of ITH, an appropriate tumor sampling is mandatory to unveil its histological and genomic complexity. Several strategies have been developed for such a purpose, trading-off cost and benefit. Here, we propose an evolution of the previous multisite tumor sampling (MSTS) strategy based on the last findings in the spatial distribution of metastasizing clones. This new personalized MSTS pays special attention to sample by sectors peripheral zones of the tumor, where ITH is high.

Abstract

Intratumor heterogeneity (ITH) is a constant evolutionary event in all malignant tumors, and clear cell renal cell carcinoma (CCRCC) is a paradigmatic example. ITH is responsible for most therapeutic failures in the era of precision oncology, so its precise detection remains a must in modern medicine. Unfortunately, classic sampling protocols do not resolve the problem as expected and several strategies have been being implemented in recent years to improve such detection. Basically, multisite tumor sampling (MSTS) and the homogenization of the residual tumor tissue are on display. A next step of the MSTS strategy considering the recently discovered patterns of ITH regionalization is presented here, the so-called personalized MSTS (pMSTS). This modification consists of paying more attention to sample the tumor periphery since it is this area with maximum levels of ITH.

Expression of GOT2 Is Epigenetically Regulated by DNA Methylation and Correlates with Immune Infiltrates in Clear-Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (KIRC) is the most common and highly malignant pathological type of kidney cancer, characterized by a profound metabolism dysregulation. As part of aspartate biosynthesis, mitochondrial GOT2 (glutamic-oxaloacetic transaminase 2) is essential for regulating cellular energy production and biosynthesis, linking multiple pathways. Nevertheless, the expression profile and prognostic significance of GOT2 in KIRC remain unclear. This study comprehensively analyzed the transcriptional levels, epigenetic regulation, correlation with immune infiltration, and prognosis of GOT2 in KIRC using rigorous bioinformatics analysis. We discovered that the expression levels of both mRNA and protein of GOT2 were remarkably decreased in KIRC tissues in comparison with normal tissues and were also significantly related to the clinical features and prognosis of KIRC. Remarkably, low GOT2 expression was positively associated with poorer overall survival (OS) and disease-free survival (DFS). Further analysis revealed that GOT2 downregulation is driven by DNA methylation in the promoter-related CpG islands. Finally, we also shed light on the influence of GOT2 expression in immune cell infiltration, suggesting that GOT2 may be a potential prognostic marker and therapeutic target for KIRC patients.

Molecular Characterization of m6A Modifications in Non-Clear Cell Renal Cell Carcinoma and Potential Relationship with Pathological Types

Background

N6-Methyladenosine (m6A) modification is a eukaryotic mRNA modification that modulates the fate of modified RNA and, therefore, the expression of proteins. m6A modifications are associated with important roles in several cancers. Most studies related to m6A modification are based on clear cell renal cell carcinoma (ccRCC) and little is known about its role in non-ccRCC.

Methods

We summarized the molecular features of different m6A modification patterns in non-ccRCC based on The Cancer Genome Atlas database and correlated them with phenotypes such as immune patterns and prognosis. We also computed the m6Ascore and assessed its prognostic value using multivariate Cox regression analysis.

Results

We found the immune-excluded phenotype to be predominant in non-ccRCC patients. We also found that in non-clear cell carcinoma, different m6A modification profiles determine different immune patterns and are associated with different prognosis. m6AgeneCluser typing strongly associated with pathological status. Based on our findings, we suggest that the m6Ascore can be used as an independent prognostic value for prognostic assessment in non-ccRCC.

Conclusion

This study confirms the important role of m6A modifications in non-ccRCC, reveals the heterogeneity of tumor immunity, and highlights the promise of non-ccRCC therapy.

A retrospective single-centered, comprehensive targeted genetic sequencing analysis of prognostic survival using tissues from Korean patients with metastatic renal cell carcinoma after targeted therapy

Purpose

To identify candidate gene mutations to significantly predict the risk of survival prognosis after treatment with systemic first-line targeted therapy (TT) in metastatic renal cell carcinoma (mRCC) patients.

Materials and Methods

Between 2005 and 2017, 168 triplet-tissue block samples from 56 mRCC patients were selected for targeted gene sequencing (TGS). Fifty-six patients’ medical records including overall survival (OS) and progression-free survival (PFS) at the time of mRCC diagnosis were evaluated. The patients were grouped into favorable (>12 months/>3 years), intermediate (3–12/12–36 months), and poor groups according to their PFS/OS (<3 months/<12 months). We identified any significant therapeutic targeted genes relating to the survival with a significance at p<0.050.

Results

The first line therapeutic response showed 1.8% complete remission, 14.2% partial response, 42.9% stable disease, and 41.1% progressive disease. Among the overall TGS results, the cumulative effect of CDH1, and/or PTK2 genes significantly reflected the therapeutic responses in terms of PFS/OS; CDH1 and PTK2 mutations were associated with poor prognostic outcomes (p<0.050). Among only triplet-quality check passed tissues, the SGO2, BRAF, URB1, and NEDD1 mutated genes significantly correlated with OS. Regarding metastasis, patients with liver metastasis had the worst OS (p=0.050). The combinational mutation number from these two candidate genes in the liver metastatic samples with mutated EGFR2 and FABP7 also showed a significantly worse OS than those with other metastatic lesions (p<0.050).

Conclusions

This study reports several significant mutated genes related to the survival prognosis in mRCC patients treated with first-line TT.

MCM2-7 in Clear Cell Renal Cell Carcinoma: MCM7 Promotes Tumor Cell Proliferation

Background

Clear cell renal cell carcinoma (ccRCC) accounts for 60-70% of renal cell carcinoma (RCC) cases. Finding more therapeutic targets for advanced ccRCC is an urgent mission. The minichromosome maintenance proteins 2-7 (MCM2-7) protein forms a stable heterohexamer and plays an important role in DNA replication in eukaryotic cells. In the study, we provide a comprehensive study of MCM2-7 genes expression and their potential roles in ccRCC.

Methods

The expression and prognosis of the MCM2-7 genes in ccRCC were analyzed using data from TCGA, GEO and ArrayExpress. MCM2-7 related genes were identified by weighted co-expression network analysis (WGCNA) and Metascape. CancerSEA and GSEA were used to analyze the function of MCM2–7 genes in ccRCC. The gene effect scores (CERES) of MCM2-7, which reflects carcinogenic or tumor suppressor, were obtained from DepMap. We used clinical and expression data of MCM2-7 from the TCGA dataset and the LASSO Cox regression analysis to develop a risk score to predict survival of patients with ccRCC. The correlations between risk score and other clinical indicators such as gender, age and stage were also analyzed. Further validation of this risk score was engaged in another cohort, E-MTAB-1980 from the ArrayExpress dataset.

Results

The mRNA and protein expression of MCM2-7 were increased in ccRCC compared with normal tissues. High MCM2, MCM4, MCM6 and MCM7 expression were associated with a poor prognosis of ccRCC patients. Functional enrichment analysis revealed that MCM2-7 might influence the progress of ccRCC by regulating the cell cycle. Knockdown of MCM7 can inhibit the proliferation of ccRCC cells. A two-gene risk score including MCM4 and MCM6 can predict overall survival (OS) of ccRCC patients. The risk score was successfully verified by further using Arrayexpress cohort.

Conclusion

We analyze MCM2-7 mRNA and protein levels in ccRCC. MCM7 is determined to promote tumor proliferation. Meanwhile, our study has determined a risk score model composed of MCM2-7 can predict the prognosis of ccRCC patients, which may help future treatment strategies.

Analysis and verification of N6-methyladenosine-modified genes as novel biomarkers for clear cell renal cell carcinoma

N⁶-methyladenosine (m⁶A) has been involved in diverse biological processes in cancer, but its function and clinical value in clear cell renal cell carcinoma (ccRCC) remain largely unknown. In this study, we found that 1453 m⁶A-modified differentially expressed genes (DEGs) of ccRCC were mainly enriched in cell cycle, PI3K-AKT, and p53 signaling pathways. Then we constructed a co-expression network of the 1453 m⁶A-modified DEGs and identified a most clinically relevant module, where NUF2, CDCA3, CKAP2L, KIF14, and ASPM were hub genes. NUF2, CDCA3, and KIF14 could combine with a major RNA m⁶A methyltransferase METTL14, serving as biomarkers for ccRCC. Real-time quantitative PCR assay confirmed that NUF2, CDCA3, and KIF14 were highly expressed in ccRCC cell lines and ccRCC tissues. Furthermore, these three genes were modified by m⁶A and negatively regulated by METTL14. This study revealed that NUF2, CDCA3, and KIF14 were m⁶A-modified biomarkers, representing a potential diagnostic, prognostic, and therapeutic target for ccRCC.

Abbreviations: m⁶A: N⁶-methyladenosine; ccRCC: clear cell renal cell carcinoma; DEGs: differentially expressed genes; NUF2: NUF2 component of NDC80 kinetochore complex; CDCA3: cell division cycle associated 3; CKAP2L: cytoskeleton associated protein 2 like; KIF14: kinesin family member 14; ASPM: assembly factor for spindle microtubules; METTL14: methyltransferase 14; OS: overall survival; FPKM: fragments per kilobase million; GEO: gene expression omnibus; TCGA: the Cancer Genome Atlas; RMA: robust multi-array average expression measure; WGCNA: weighted gene co-expression network analysis; GO: gene ontology; KEGG: kyoto encyclopedia of genes and genomes; ROC: receiver operating characteristic curve; AUC: area under the curve; RIP: RNA immunoprecipitation; qPCR: real-time quantitative PCR.

Somatic mutations as preoperative predictors of metastases in patients with localized clear cell renal cell carcinoma - An exploratory analysis

OBJECTIVE

Recurrent genomic alterations in clear cell renal cell carcinoma (ccRCC) have been associated with treatment outcomes; however, current preoperative predictive models do not include known genetic predictors. We aimed to explore the value of common somatic mutations in the preoperative prediction of metastatic disease among patients treated for localized ccRCC.

MATERIALS AND METHODS

After obtaining institutional review board approval, data of 254 patients with localized ccRCC treated between 2005 and 2015 who underwent genetic sequencing was collected. The mutation status of VHL, PBRM1, SETD2, BAP1 and KDM5C were evaluated in the nephrectomy tumor specimen, which served as a proxy for biopsy mutation status. The Raj et al. preoperative nomogram was used to predict the 12-year metastatic free probability (MFP). The study outcome was MFP; the relationship between MFP and mutation status was evaluated with Cox-regression models adjusting for the preoperative nomogram variables (age, gender, incidental presentation, lymphadenopathy, necrosis, and size).

RESULTS

The study cohort included 188 males (74%) and 66 females (26%) with a median age of 58 years. VHL mutations were present in 152/254 patients (60%), PBRM1 in 91/254 (36%), SETD2 in 32/254 (13%), BAP1 in 19/254 (8%), and KDM5C in 19/254 (8%). Median follow-up for survivors was 8.1 years. Estimated 12-year MFP was 70% (95% CI: 63%-75%). On univariable analysis SETD2 (HR: 3.30), BAP1 (HR: 2.44) and PBRM1 (HR: 1.78) were significantly associated with a higher risk of metastases. After adjusting for known preoperative predictors in the existing nomogram, SETD2 mutations remained associated with a higher rate of metastases after nephrectomy (HR: 2.09, 95% CI: 1.19-3.67, P = 0.011).

CONCLUSION

In the current exploratory analysis, SETD2 mutations were significant predictors of MFP among patients treated for localized ccRCC. Our findings support future studies evaluating genetic alterations in preoperative renal biopsy samples as potential predictors of treatment outcome.

A narrative review of tumor heterogeneity and challenges to tumor drug therapy

Objective

To accurately evaluate tumor heterogeneity, make multidimensional diagnosis according to the causes and phenotypes of tumor heterogeneity, and assist in the individualized treatment of tumors.

Background

Tumor heterogeneity is one of the most essential characteristics of malignant tumors. In tumor recurrence, development, and evolution, tumor heterogeneity can lead to the formation of different cell groups with other molecular characteristics. Tumor heterogeneity can be characterized by the uneven distribution of tumor cell subsets of other genes between and within the disease site (spatial heterogeneity) or the time change of cancer cell molecular composition (temporal heterogeneity). The discovery of tumor targeting drugs has dramatically promoted tumor therapy. However, the existence of heterogeneity seriously affects the effect of tumor treatment and the prognosis of patients.

Methods

The literature discussing tumor heterogeneity and its resistance to tumor therapy was broadly searched to analyze tumor heterogeneity as well as the challenges and solutions for gene detection and tumor drug therapy.

Conclusions

Tumor heterogeneity is affected by many factors consist of internal cell factors and cell microenvironment. Tumor heterogeneity greatly hinders effective and individualized tumor treatment. Understanding the fickle of tumors in multiple dimensions and flexibly using a variety of detection methods to capture the changes of tumors can help to improve the design of diagnosis and treatment plans for cancer and benefit millions of patients.

The Hypoxic Microenvironment Induces Stearoyl-CoA Desaturase-1 Overexpression and Lipidomic Profile Changes in Clear Cell Renal Cell Carcinoma

Simple Summary

Clear cell renal cell carcinoma (ccRCC) is characterized by a high rate of cell proliferation and an extensive accumulation of lipids. Uncontrolled cell growth usually generates areas of intratumoral hypoxia that define the tumor phenotype. In this work, we show that, under these microenvironmental conditions, stearoyl-CoA desaturase-1 is overexpressed. This enzyme induces changes in the cellular lipidomic profile, increasing the oleic acid levels, a metabolite that is essential for cell proliferation. This work supports the idea of considering stearoyl-CoA desaturase-1 as an exploitable therapeutic target in ccRCC.

Abstract

Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cell carcinoma (RCC). It is characterized by a high cell proliferation and the ability to store lipids. Previous studies have demonstrated the overexpression of enzymes associated with lipid metabolism, including stearoyl-CoA desaturase-1 (SCD-1), which increases the concentration of unsaturated fatty acids in tumor cells. In this work, we studied the expression of SCD-1 in primary ccRCC tumors, as well as in cell lines, to determine its influence on the tumor lipid composition and its role in cell proliferation. The lipidomic analyses of patient tumors showed that oleic acid (18:1n-9) is one of the major fatty acids, and it is particularly abundant in the neutral lipid fraction of the tumor core. Using a ccRCC cell line model and in vitro-generated chemical hypoxia, we show that SCD-1 is highly upregulated (up to 200-fold), and this causes an increase in the cellular level of 18:1n-9, which, in turn, accumulates in the neutral lipid fraction. The pharmacological inhibition of SCD-1 blocks 18:1n-9 synthesis and compromises the proliferation. The addition of exogenous 18:1n-9 to the cells reverses the effects of SCD-1 inhibition on cell proliferation. These data reinforce the role of SCD-1 as a possible therapeutic target.

Development of a mechanically matched silk scaffolded 3D clear cell renal cell carcinoma model

Development of a 3D, biomaterials-based model for clear cell renal cell carcinoma (ccRCC) would be advantageous for understanding disease progression in vitro. This study demonstrated the development of lyophilized silk scaffolds that mechanically match the experimentally determined Young's modulus for ex vivo ccRCC samples and normal kidney tissue. Scaffolds fabricated from silk solutions ranging from 3 to 12% (w/v) were evaluated through mechanical testing. Following mechanical characterization of ccRCC samples, it was demonstrated that 6% silk scaffolds mechanically matched ccRCC samples. No impact of pathological grade and stage on the calculated ccRCC modulus was observed and all tumors evaluated mechanically matched the 6% silk scaffold formulation. Stratifying tissue specimens based upon histological observations (e.g. evidence of high levels of collagen deposition) resulted in no significant differences between groups. To investigate the impact of a mechanically matched culturing environment on in vitro ccRCC disease characteristics a model ccRCC cell line, 786-O, was utilized. Scaffolded 786-O cells demonstrated increased lipid droplet accumulation, a hallmark of ccRCC, compared to standard two-dimensional (2D) culture conditions. Additionally, scaffolded 786-O cells demonstrated increased expression of genes associated with ccRCC aggressiveness (ex. VEGFA, TNF, and IL-6) or immune markers under investigation as therapeutic targets (ex. PDL1, CTLA4). Comparison with 786-O cells grown on non-mechanically matched scaffolds demonstrated that these improved ccRCC characteristics were driven by scaffold modulus. Overall, our findings support the use of silk scaffolds in replicating physiologic tumor behavior for clear cell renal cell carcinoma and provide a platform for investigating disease progression.

Radiomic profiling of clear cell renal cell carcinoma reveals subtypes with distinct prognoses and molecular pathways

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Radiomics profile of clear cell renal cell carcinoma is heterogeneity.

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Multi-scale Radiogenomics could link molecular features and images.

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Radiomic subtypes could be used for risk stratification.

Background

To identify radiomic subtypes of clear cell renal cell carcinoma (ccRCC) patients with distinct clinical significance and molecular characteristics reflective of the heterogeneity of ccRCC.

Methods

Quantitative radiomic features of ccRCC were extracted from preoperative CT images of 160 ccRCC patients. Unsupervised consensus cluster analysis was performed to identify robust radiomic subtypes based on these features. The Kaplan–Meier method and chi-square test were used to assess the different clinicopathological characteristics and gene mutations among the radiomic subtypes. Subtype-specific marker genes were identified, and gene set enrichment analyses were performed to reveal the specific molecular characteristics of each subtype. Moreover, a gene expression-based classifier of radiomic subtypes was developed using the random forest algorithm and tested in another independent cohort (n = 101).

Results

Radiomic profiling revealed three ccRCC subtypes with distinct clinicopathological features and prognoses. VHL, MUC16, FBN2, and FLG were found to have different mutation frequencies in these radiomic subtypes. In addition, transcriptome analysis revealed that the dysregulation of cell cycle-related pathways may be responsible for the distinct clinical significance of the obtained subtypes. The prognostic value of the radiomic subtypes was further validated in another independent cohort (log-rank P = 0.015).

Conclusion

In the present multi-scale radiogenomic analysis of ccRCC, radiomics played a central role. Radiomic subtypes could help discern genomic alterations and non-invasively stratify ccRCC patients.

Tumor heterogeneity and the potential role of liquid biopsy in bladder cancer

Bladder cancer (BC) is a heterogeneous disease that characterized by genomic instability and a high mutation rate. Heterogeneity in tumor may partially explain the diversity of responses to targeted therapies and the various clinical outcomes. A combination of cytology and cystoscopy is the standard methodology for BC diagnosis, prognosis, and disease surveillance. However, genomics analyses of single tumor‐biopsy specimens may underestimate the mutational burden of heterogeneous tumors. Liquid biopsy, as a promising technology, enables analysis of tumor components in the bodily fluids, such as blood and urine, at multiple time points and provides a minimally invasive approach that can track the evolutionary dynamics and monitor tumor heterogeneity. In this review, we describe the multiple faces of BC heterogeneity at the genomic and transcriptional levels and how they affect clinical care and outcomes. We also summarize the outcomes of liquid biopsy in BC, which plays a potential role in revealing tumor heterogeneity. Finally, we discuss the challenges that must be addressed before liquid biopsy can be widely used in clinical treatment.

Three-Dimensional Printed Molds for Image-Guided Surgical Biopsies: An Open Source Computational Platform

PURPOSE

Spatial heterogeneity of tumors is a major challenge in precision oncology. The relationship between molecular and imaging heterogeneity is still poorly understood because it relies on the accurate coregistration of medical images and tissue biopsies. Tumor molds can guide the localization of biopsies, but their creation is time consuming, technologically challenging, and difficult to interface with routine clinical practice. These hurdles have so far hindered the progress in the area of multiscale integration of tumor heterogeneity data.

METHODS

We have developed an open-source computational framework to automatically produce patient-specific 3-dimensional-printed molds that can be used in the clinical setting. Our approach achieves accurate coregistration of sampling location between tissue and imaging, and integrates seamlessly with clinical, imaging, and pathology workflows.

RESULTS

We applied our framework to patients with renal cancer undergoing radical nephrectomy. We created personalized molds for 6 patients, obtaining Dice similarity coefficients between imaging and tissue sections ranging from 0.86 to 0.96 for tumor regions and between 0.70 and 0.76 for healthy kidneys. The framework required minimal manual intervention, producing the final mold design in just minutes, while automatically taking into account clinical considerations such as a preference for specific cutting planes.

CONCLUSION

Our work provides a robust and automated interface between imaging and tissue samples, enabling the development of clinical studies to probe tumor heterogeneity on multiple spatial scales.

Progress in Neoantigen Targeted Cancer Immunotherapies

Immunotherapies that harness the immune system to kill cancer cells have showed significant therapeutic efficacy in many human malignancies. A growing number of studies have highlighted the relevance of neoantigens in recognizing cancer cells by intrinsic T cells. Cancer neoantigens are a direct consequence of somatic mutations presenting on the surface of individual cancer cells. Neoantigens are fully cancer-specific and exempt from central tolerance. In addition, neoantigens are important targets for checkpoint blockade therapy. Recently, technological innovations have made neoantigen discovery possible in a variety of malignancies, thus providing an impetus to develop novel immunotherapies that selectively enhance T cell reactivity for the destruction of cancer cells while leaving normal tissues unharmed. In this review, we aim to introduce the methods of the identification of neoantigens, the mutational patterns of human cancers, related clinical trials, neoantigen burden and sensitivity to immune checkpoint blockade. Moreover, we focus on relevant challenges of targeting neoantigens for cancer treatment.

Identification and Comprehensive Validation of a DNA Methylation-Driven Gene-Based Prognostic Model for Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most prevalent renal malignancy in adults with generally poor prognosis. This study aimed to establish a DNA methylation-driven gene-based prognostic model for ccRCC. We collected DNA methylation and gene expression profiles of over 1500 ccRCC samples from The Cancer Genome Atlas (TCGA) dataset, four Gene Expression Omnibus (GEO) datasets, the Genotype-Tissue Expression (GTEx) dataset, and cancer cell lines from Cancer Cell Line Encyclopedia database and performed comprehensive bioinformatics analysis. As a result, a total of 31 differentially expressed methylation-driven genes (DEMDGs) were identified. After univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, four (NFE2L3, HHLA2, IFI16, and ZNF582) were finally selected to construct a risk score prognostic model. The high-risk group demonstrated significantly poor prognosis than the low-risk group did in TCGA training (hazard ratio [HR] = 3.533, p < 0.001), TCGA internal, and GEO external validation datasets. Furthermore, the nomogram, including the prognostic model and clinical factors, showed promising prognostic value (HR = 5.756, p < 0.001, and area under the curve at 1 year = 0.856). In addition, the model was found to be significantly associated with drug sensitivity of eight targeted agents. These findings provided a novel and reliable four DEMDG-based prognostic model for ccRCC.

Intratumoral heterogeneity of hepatocellular carcinoma: From single-cell to population-based studies

Hepatocellular carcinoma (HCC) is characterized by high heterogeneity in both intratumoral and interpatient manners. While interpatient heterogeneity is related to personalized therapy, intratumoral heterogeneity (ITH) largely influences the efficacy of therapies in individuals. ITH contributes to tumor growth, metastasis, recurrence, and drug resistance and consequently limits the prognosis of patients with HCC. There is an urgent need to understand the causes, characteristics, and consequences of tumor heterogeneity in HCC for the purposes of guiding clinical practice and improving survival. Here, we summarize the studies and technologies that describe ITH in HCC to gain insight into the origin and evolutionary process of heterogeneity. In parallel, evidence is collected to delineate the dynamic relationship between ITH and the tumor ecosystem. We suggest that conducting comprehensive studies of ITH using single-cell approaches in temporal and spatial dimensions, combined with population-based clinical trials, will help to clarify the clinical implications of ITH, develop novel intervention strategies, and improve patient prognosis.

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.

Analysis of the concordance of 20 immunohistochemical tissue markers in metastasectomy lesions in patients with metastatic renal cell carcinoma: A retrospective study using tissue microarray

Purpose

This study aimed to characterize the different expressions of 20 tissue markers in multiple metastatic lesions and organs in patients with metastatic renal cell carcinoma (mRCC).

Materials and Methods

Sixty-six patients with mRCC, harboring 162 metastasectomy tissue lesions (MTLs), were enrolled. Immunohistochemical analysis for the following tissue markers was performed: BAP1; CD31; CD 34; HIF1α and 2α; Ki67; pS6; PBRM1; PDGFRα and β; PDL1; PSMA; PTEN; α-SMA; TGase2; VEGFR1, 2, and 3; VHL loss; and CA9. Cases were identified pathologically using the semi-quantitative H-score (0–300), including the intensity score (0, 1, 2, 3). The concordance rate was calculated as the number of patients with concordant binary score out of the total number of patients in that comparison.

Results

The specimens from 66 patients were divided into those from the same organs and those from different organs. Forty-two patients (44 cases) with 96 MTLs and 39 with 83 MTLs were examined. Among the 20 tissue markers, only BAP1, PSMA, VEGFR3, PDGFRα, and pS6 tissue showed high concordance ratio (>0.7) regardless of different metastatic tissues and different metastatic lesions within the tumor.

Conclusions

The study demonstrated the intratumoral heterogeneity of mRCC with a low-concordance index of most tissue markers. However, some had high concordance with a similar expression regardless of the metastatic organs, metastatic sites, or presence of recurrence.

Identification of a three-m6A related gene risk score model as a potential prognostic biomarker in clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent malignancies worldwide, N6-methyladenosine (m6A) has been shown to play important roles in regulating gene expression and phenotypes in both health and disease. Here, our purpose is to construct a m6A-regulrator-based risk score (RS) for prediction of the prognosis of ccRCC.

Methods

We used clinical and expression data of m6A related genes from The Cancer Genome Atlas (TCGA) dataset and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis to develop an RS to predict survival of patients with ccRCC, and analyzed correlations between RS and other clinical indicators such as age, grade and stage. Validation of this RS was then engaged in another cohort, E-MTAB-1980 from the ArrayExpress dataset. Finally, we used quantitative real-time PCR to analyze the expression profile of genes consists of the RS.

Results

A three-gene RS including METTL3, METTL14 and HNRNPA2B1 which can predict overall survival (OS) of ccRCC patients from TCGA. After applying this RS into the validation cohort from Arrayexpress, we found that it successfully reproduced the result; furthermore, the results of PCR validation were in line with our analysis.

Conclusion

To sum up, our study has identified an RS composed of m6A related genes that may predict the prognosis of ccRCC patients, which might be helpful for future therapeutic strategies. Our results call for further experimental studies for validations.

Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) comprises the majority of kidney cancer death worldwide, whose incidence and mortality are not promising. Identifying ideal biomarkers to construct a more accurate prognostic model than conventional clinical parameters is crucial.

METHODS

Raw count of RNA-sequencing data and clinicopathological data were acquired from The Cancer Genome Atlas (TCGA). Tumor samples were divided into two sets. Differentially expressed genes (DEGs) were screened in the whole set and prognosis-related genes were identified from the training set. Their common genes were used in LASSO and best subset regression which were performed to identify the best prognostic 5 genes. The gene-based risk score was developed based on the Cox coefficient of the individual gene. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analysis were used to assess its prognostic power. GSE29609 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to screen independent prognostic parameters to construct a nomogram. The predictive power of the nomogram was revealed by time-dependent ROC curves and the calibration plot and verified in the validation set. Finally, Functional enrichment analysis of DEGs and 5 novel genes were performed to suggest the potential biological pathways.

RESULTS

PADI1, ATP6V0D2, DPP6, C9orf135 and PLG were screened to be significantly related to the prognosis of ccRCC patients. The risk score effectively stratified the patients into high-risk group with poor overall survival (OS) based on survival analysis. AJCC-stage, age, recurrence and risk score were regarded as independent prognostic parameters by Cox regression analysis and were used to construct a nomogram. Time-dependent ROC curves showed the nomogram performed best in 1-, 3- and 5-year survival predictions compared with AJCC-stage and risk score in validation sets. The calibration plot showed good agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis suggested several enriched biological pathways related to cancer.

CONCLUSIONS

In our study, we constructed a gene-based model integrating clinical prognostic parameters to predict prognosis of ccRCC well, which might provide a reliable prognosis assessment tool for clinician and aid treatment decision-making in the clinic.

Ontological analyses reveal clinically-significant clear cell renal cell carcinoma subtypes with convergent evolutionary trajectories into an aggressive type

Background

Clear cell renal cell carcinoma (ccRCC) is a particularly challenging tumor type because of its extensive phenotypic variability as well as intra-tumoral heterogeneity (ITH). Clinically, this complexity has been reduced to a handful of pathological variables such as stage, grade and necrosis, but these variables fail to capture the breadth of the disease. How different phenotypes affect patient prognosis and influence therapeutic response is poorly understood. Extensive ITH illustrates remarkable plasticity, providing a framework to study tumor evolution. While multiregional genomic analyses have shown evolution from an ancient clone that acquires metastatic competency over time, these studies have been conducted agnostic to morphological cues and phenotypic plasticity.

Methods

We established a systematic ontology of ccRCC phenotypic variability by developing a multi-scale framework along three fundamental axes: tumor architecture, cytology and the microenvironment. We defined 33 parameters, which we comprehensively evaluated in 549 consecutive ccRCCs retrospectively. We systematically evaluated the impact of each parameter on patient outcomes, and assessed their contribution through multivariate analyses. We measured therapeutic impact in the context of anti-angiogenic therapies. We applied dimensionality reduction by t-distributed stochastic neighbor embedding (t-SNE) algorithms to tumor architectures for the study of tumor evolution superimposing tumor size and grade vectors. Evolutionary models were refined through empirical analyses of directed evolution of tumor intravascular extensions, and metastatic competency (as determined by tumor reconstitution in a heterologous host).

Findings

We discovered several novel ccRCC phenotypes, developed an integrated taxonomy, and identified features that improve current prognostic models. We identified a subset of ccRCCs refractory to anti-angiogenic therapies. We developed a model of tumor evolution, which revealed converging evolutionary trajectories into an aggressive type.

Interpretation

This work serves as a paradigm for deconvoluting tumor complexity and illustrates how morphological analyses can improve our understanding of ccRCC pleiotropy. We identified several subtypes associated with aggressive biology, and differential response to targeted therapies. By analyzing patterns of spatial and temporal co-occurrence, intravascular tumor extensions and metastatic competency, we were able to identify distinct trajectories of convergent phenotypic evolution.

Mouse- and patient-derived CAM xenografts for studying metastatic renal cell carcinoma

Renal cell carcinoma is the seventh most common cancer in the United States, and its metastatic form has a very poor prognosis due to a lack of effective treatment and thorough understanding on metastatic mechanism. This chapter will demonstrate a novel concept that intratumoral heterogeneity is essential for metastasis in renal cell carcinoma. We will first introduce the in vitro system and the mouse model that led to the finding of the cooperative mechanism for metastasis. Then, the results from the CAM model illustrate the cooperative interactions that lead to metastasis also occur in this model. We believe that the CAM model, as a unique and sustainable system, can open up new opportunities to study the metastatic disease.

Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders

Clear cell renal cell carcinoma (ccRCC) is highly heterogeneous and is the most lethal cancer of all urologic cancers. We developed an unsupervised deep learning method, stacked denoising autoencoders (SdA), by integrating multi-platform genomic data for subtyping ccRCC with the goal of assisting diagnosis, personalized treatments and prognosis. We successfully found two subtypes of ccRCC using five genomics datasets for Kidney Renal Clear Cell Carcinoma (KIRC) from The Cancer Genome Atlas (TCGA). Correlation analysis between the last reconstructed input and the original input data showed that all the five types of genomic data positively contribute to the identification of the subtypes. The first subtype of patients had significantly lower survival probability, higher grade on neoplasm histology and higher stage on pathology than the other subtype of patients. Furthermore, we identified a set of genes, proteins and miRNAs that were differential expressed (DE) between the two subtypes. The function annotation of the DE genes from pathway analysis matches the clinical features. Importantly, we applied the model learned from KIRC as a pre-trained model to two independent datasets from TCGA, Lung Adenocarcinoma (LUAD) dataset and Low Grade Glioma (LGG), and the model stratified the LUAD and LGG patients into clinical associated subtypes. The successful application of our method to independent groups of patients supports that the SdA method and the model learned from KIRC are effective on subtyping cancer patients and most likely can be used on other similar tasks. We supplied the source code and the models to assist similar studies at https://github.com/tjgu/cancer_subtyping.

Major challenges in accurate mutation detection of multifocal lung adenocarcinoma by next-generation sequencing

Background: Many patients with advanced non-small cell lung cancer manifested with metastasis, and molecular heterogeneity may exhibit between primary and metastatic tumors. We sought to investigate the clinical detection strategy of primary and metastatic tumors in Chinese patients with NSCLC.Methods: Here, 77 paired tumors of Chinese patients with lung adenocarcinoma were analyzed, and 1836 mutation in hotspot regions of 22 genes were identified by next-generation sequencing. The expression of ALK in these paired tumors was also detected by immunohistochemistry.Results: The results showed that the concordance rate in multiple pulmonary nodules, primary-LN metastasis pairs and primary-distant metastasis pairs was 67.7%, 94.1% and 86.7%, respectively. In multiple pulmonary nodules, the concordance rate was 100% when the pathologic diagnosis was intrapulmonary metastasis, whereas the concordance rate was 23.1% when the pathologic diagnosis was multiple primary tumors. TP53 and CTNNB1 mutations were detected as the recurrent alterations in LN metastasis. Moreover, the concordance of ALK status was observed in these pairs.Conclusions: Our data suggested that hotspot mutations and ALK status in the primary-metastasis pairs had a high concordance in lung adenocarcinoma. Clinical detection of one lesion may be enough to identify the key alterations except that patients are diagnosed with multiple primary tumors or have disease progression after benefiting from target therapy.

Somatic Mitochondrial Mutations in Oral Cavity Cancers among Senegalese Patients

Background: Somatic mutations affecting the mitochondrial DNA (mtDNA) have been frequently observed in human cancers and proposed as important oncological biomarkers. However, the exact mtDNA mutations that is responsible for the pathogenesis of cancer remains unclear. The aim of this study was to investigate somatic mutations in the MT-CYB and D-Loop regions of mitochondrial DNA (mtDNA) in oral cavity cancers from Senegalese patients. Methods: MT-CYB and the D-Loop of mtDNA derived from 45 oral cavity cancer tissues and 21 control blood samples were assessed by PCR and sequencing. The sequences of MT-CYB and the D-Loop from cancerous tissues were compared with control sequences, and sequence differences were recognized as somatic mutations. Results: Overall, 389 somatic mtDNA mutations were identified, most of which (79.43%) were located in the D-Loop region. The majority of base substitution mutations were G-to-A (63.93%) and T-to-C (16.39%) transitions. In the protein-coding MT-CYB gene, 29 missense mutations were observed. The pathogenic mutation load of MT-CYB was 3.11%. Pathogenic mutations were carried by 25% of patients. pArg76Pro (pArg282Pro in rCRS) was novel and was the most common pathogenic mutation observed. Conclusion: These results strongly indicate that mtDNA mutations are a potential marker of oral cavity cancer.

Harnessing the Genomic Landscape of the Small Renal Mass to Guide Clinical Management

CONTEXT

Small renal masses (SRMs; tumors <4 cm) encompass a diagnostic and therapeutic challenge. Genomic profiling has the potential to improve risk stratification and personalize treatment selection.

OBJECTIVE

Herein, we review the evidence regarding the utility, challenges, and potential implications of genomic profiling in the management of SRMs.

EVIDENCE ACQUISITION

Pertinent publications available on PubMed database pertaining to kidney cancer, tumor size, genomics, and clinical management were reviewed.

EVIDENCE SYNTHESIS

Compared with larger tumors, SRMs range from benign to lethal, necessitating strategies for improved treatment selection. Recent advances in the molecular characterization of renal cell carcinoma have improved our understanding of the disease; however, utility of these tools for the management of SRMs is less clear. While intratumoral heterogeneity (ITH) reduces the accuracy and reliability of sequencing, relative genomic uniformity of SRMs somewhat lessens the impact of ITH. Therefore, renal mass biopsy of SRMs represents an appealing opportunity to evaluate how incorporation of molecular profiles may improve management strategies.

CONCLUSIONS

Ongoing research into the genomic landscape of SRMs has advanced our understanding of the spectrum of disease aggressiveness and may hold promise in matching disease biology to treatment intensity.

PATIENT SUMMARY

Small renal masses are a clinical challenge, as they range from benign to lethal. Genomic profiling may eventually improve treatment selection, but more research is needed.

Radiomics in Oncological PET/CT: a Methodological Overview

Radiomics is a medical imaging analysis approach based on computer-vision. Metabolic radiomics in particular analyses the spatial distribution patterns of molecular metabolism on PET images. Measuring intratumoral heterogeneity via image is one of the main targets of radiomics research, and it aims to build a image-based model for better patient management. The workflow of radiomics using texture analysis follows these steps: 1) imaging (image acquisition and reconstruction); 2) preprocessing (segmentation & quantization); 3) quantification (texture matrix design & texture feature extraction); and 4) analysis (statistics and/or machine learning). The parameters or conditions at each of these steps are effect on the results. In statistical testing or modeling, problems such as multiple comparisons, dependence on other variables, and high dimensionality of small sample size data should be considered. Standardization of methodology and harmonization of image quality are one of the most important challenges with radiomics methodology. Even though there are current issues in radiomics methodology, it is expected that radiomics will be clinically useful in personalized medicine for oncology.

Prospective Evaluation of Unprocessed Core Needle Biopsy DNA and RNA Yield from Lung, Liver, and Kidney Tumors: Implications for Cancer Genomics

Context

Targeted needle biopsies are increasingly performed for the genetic characterization of cancer. While the nucleic acid content of core needle biopsies after standard pathology processing (i.e., formalin fixation and paraffin embedding (FFPE)) has been previously reported, little is known about the potential yield for molecular analysis at the time of biopsy sample acquisition.

Objectives

Our objective was to improve the understanding of DNA and RNA yields from commonly used core needle biopsy techniques prior to sample processing.

Methods

We performed 552 ex vivo 18 and 20G core biopsies in the lungs, liver, and kidneys. DNA and RNA were extracted from fresh-frozen core samples and quantified for statistical comparisons based on needle gauge, biopsy site, and tissue type.

Results

Median tumor DNA yields from all 18G and 20G samples were 5880 ng and 2710 ng, respectively. Median tumor RNA yields from all 18G and 20G samples were 1100 ng and 230 ng, respectively. A wide range of DNA and RNA quantities (1060–13,390 ng and 370–6280 ng, respectively) were acquired. Median DNA and RNA yields from 18G needles were significantly greater than those from 20G needles across all organs (p < 0.001).

Conclusions

Core needle biopsy techniques for cancer diagnostics yield a broad range of DNA and RNA for molecular pathology, though quantities are greater than what has been reported for FFPE processed material. Since non-formalin-fixed DNA is advantageous for molecular studies, workflows that optimize core needle biopsy yield for molecular characterization should be explored.

Spatial and temporal responses of metastatic renal cell carcinoma lesions to sequential treatments over a 10-year period

Introduction

Survival among patients with metastatic renal cell carcinoma has been prolonged with advancements in treatment; however, its cure remains challenging. We describe three patients with metastatic renal cell carcinoma who showed long-term survival and discuss their response to sequential therapy.

Case presentation

Three patients underwent radical nephrectomy and subsequent treatment for metastatic renal cell carcinoma. They received cytokine therapy, target therapy, and an immuno-oncology drug. Metastatic growth patterns based on computed tomography scans were plotted using a line or bar graph to visualize the response to sequential therapy, and the numbers of International Metastatic renal cell carcinoma Database Consortium risk score were also calculated.

Conclusion

The metastatic lesions responded differently to the administered drugs, and the sum of the axis of measurable metastasis is valuable to get an overview of treatment. Furthermore, having the same or lower International Metastatic renal cell carcinoma Database Consortium risk score during sequential therapy, and long duration of each target therapy contributed to prolonged survival.

Intratumoral heterogeneity of esophageal squamous cell carcinoma and its clinical significance

Recent studies have shown that intratumoral heterogeneity is prevalent in esophageal squamous cell cancer (ESCC) based on DNA sequencing and chromosome analysis in multiple regions from the same tumor. This study aimed to investigate the expression of ZNF750, EP300, MTOR and KMT2D and their intratumoral heterogeneity (ITH) in patients with ESCC. A total of 106 cases, who underwent esophagectomy from 2008 to 2010, with two foci from each case, were tested by immunohistochemistry(IHC) as well as 12 cases were tested by RNAscope in this study.We found that 58/106 (54.72%), 66/106 (62.26%), 75/106 (70.75%%) of ESCC showed high expression of ZNF750, EP300, MTOR, respectively by IHC, and 8/12 (66.67%), 10/12 (83.33%), 4/12 (33.33%) and 6/12 (50%) showed high expression of ZNF750, EP300, MTOR and KMT2D, respectively by RNAscope. Multivariate analysis showed that MTOR expression was an independent infavorable prognostic factor of overall survival (OS) (HR = 1.921; P = 0.000). This study also found that 44/106(4151%), 37/106 (34.91%), 39/106(36.79%) of ESCC showed heterogeneous expression of ZNF750, EP300 and MTOR respectively by IHC, 8/12(66.67%), 8/12(66.67%), 4/12(33.33%), 4/12(33.33%) of ZNF750, EP300, MTOR and KMT2D respectively by RNAscope, IHC and RNAscope could successfully detect a high prevalence of ITH. In conclusion, findings of this study showed that ZNF750, EP300, MTOR and KMT2D heterogeneously expressed in ESCC. High expression of ZNF750 related to a better outcome, while EP300 and MTOR related to a poor prognosis.

Genomically annotated risk model for advanced renal-cell carcinoma: a retrospective cohort study

BACKGROUND

The Memorial Sloan Kettering Cancer Center (MSKCC) risk model is an established prognostic tool for metastatic renal-cell carcinoma that integrates clinical and laboratory data, but is agnostic to tumour genomics. Several mutations, including BAP1 and PBRM1, have prognostic value in renal-cell carcinoma. Using two independent clinical trial datasets of patients with metastatic renal-cell carcinoma, we aimed to study whether the addition of the mutation status for several candidate prognostic genes to the MSKCC model could improve the model's prognostic performance.

METHODS

In this retrospective cohort study, we used available formalin-fixed paraffin-embedded tumour tissue and clinical outcome data from patients with metastatic renal-cell carcinoma assigned to treatment with tyrosine kinase inhibitors in the COMPARZ trial (training cohort; n=357) and RECORD-3 trial (validation cohort; n=258). Eligible patients in both trials were treatment-naive; had histologically confirmed, advanced, or metastatic renal-cell carcinoma; and a Karnofsky performance status score of at least 70. For each cohort, data from patients in all treatment groups (sunitinib and pazopanib in the training cohort, and everolimus and sunitinib in the validation cohort) were pooled for this analysis. In the training cohort, tumour tissue was used to evaluate somatic mutations by next-generation sequencing, and the association between cancer-specific outcomes (overall survival, progression-free survival, and overall response) and the mutation status of six genes of interest (BAP1, PBRM1, TP53, TERT, KDM5C, and SETD2) was tested. Only those genes with prognostic value in this setting were added to the MSKCC risk model to create a genomically annotated version. The validation cohort was used to independently test the prognostic value of the annotated model compared with the original MSKCC risk model.

FINDINGS

357 (32%) of 1110 patients assigned to protocol treatment in the COMPARZ study between August, 2008, and September, 2011, were evaluable for mutation status and clinical outcomes in the training cohort. The independent validation cohort included 258 (55%) of 471 evaluable patients, enrolled between October, 2009, and June, 2011, on the RECORD-3 study. In the training cohort, the presence of any mutation in BAP1 or TP53, or both, and absence of any mutation in PBRM1 were prognostic in terms of overall survival (TP53^wt/BAP1^mut, TP53^mut/BAP1^wt o TP53^mut/BAP1^mut vs TP53^wt/BAP1^wt hazard ratio [HR] 1·57, 95% CI 1·21-2·04; p=0·0008; PBRM1^wt vs PBRM^mut, HR 1·58, 1·16-2·14; p=0·0035). The mutation status for these three prognostic genes were added to the original MSKCC risk model to create a genomically annotated version. Distribution of participants in the training cohort into the three risk groups of the original MSKCC model changed from 87 (24%) of 357 patients deemed at favourable risk, 217 (61%) at intermediate risk, and 53 (15%) at poor risk, to distribution across four risk groups in the genomically annotated risk model, with 36 (10%) of 357 deemed at favourable risk, 77 (22%) at good risk, 108 (30%) at intermediate risk, and 136 (38%) at poor risk. Addition of genomic information improved model performance for predicting overall survival (C-index: original model, 0·595 [95% CI 0·557-0·634] vs new model, 0·637 [0·595-0·679]) and progression-free survival (0·567 [95% CI 0·529-0·604] vs 0·602 [0·560-0·643]) with adequate discrimination of the proportion of patients who achieved an objective response (Cochran-Armitage one-sided p=0·0014). Analyses in the validation cohort confirmed the superiority of the genomically annotated risk model over the original version.

INTERPRETATION

The mutation status of BAP1, PBRM1, and TP53 has independent prognostic value in patients with advanced or metastatic renal-cell carcinoma treated with first-line tyrosine kinase inhibitors. Improved stratification of patients across risk groups by use of a genomically annotated model including the mutational status of these three genes warrants further investigation in prospective trials and could be of use as a model to stratify patients with metastatic renal-cell carcinoma in clinical trials.

FUNDING

Novartis Pharmaceuticals Corporation, MSKCC Support Grant/Core Grant, and the J Randall & Kathleen L MacDonald Research Fund.

Clonal heterogeneity influences drug responsiveness in renal cancer assessed by ex vivo drug testing of multiple patient-derived cancer cells

Renal cell cancer (RCC) has become a prototype example of the extensive intratumor heterogeneity and clonal evolution of human cancers. However, there is little direct evidence on how the genetic heterogeneity impacts on drug response profiles of the cancer cells. Our goal was to determine how genomic clonal evolution impacts drug responses. Finding from our study could help to define the challenge that clonal evolution poses on cancer therapy. We established multiple patient-derived cells (PDCs) from different tumor regions of four RCC patients, verified their clonal relationship to each other and to the uncultured tumor tissue by genome sequencing. Furthermore, comprehensive drug-sensitivity testing with 460 oncological drugs was performed on all PDC clones. The PDCs retained many cancer-specific copy number alterations and mutations in driver genes such as VHL, PBRM1, PIK3C2A, KMD5C and TSC2 genes. The drug testing highlighted vulnerability in the PDCs toward approved RCC drugs, such as the mTOR-inhibitor temsirolimus, but also novel sensitivities were uncovered. The individual PDC clones from different tumor regions in a patient showed distinct drug-response profiles, suggesting that genomic heterogeneity contributes to the variability in drug responses. Studies of multiple PDCs from a patient with cancer are informative for elucidating cancer heterogeneity and for the determination on how the genomic evolution is manifested in cancer drug responsiveness. This approach could facilitate tailoring of drugs and drug combinations to individual patients.

Multiple tumor suppressors regulate a HIF-dependent negative feedback loop via ISGF3 in human clear cell renal cancer

Whereas VHL inactivation is a primary event in clear cell renal cell carcinoma (ccRCC), the precise mechanism(s) of how this interacts with the secondary mutations in tumor suppressor genes, including PBRM1, KDM5C/JARID1C, SETD2, and/or BAP1, remains unclear. Gene expression analyses reveal that VHL, PBRM1, or KDM5C share a common regulation of interferon response expression signature. Loss of HIF2α, PBRM1, or KDM5C in VHL-/-cells reduces the expression of interferon stimulated gene factor 3 (ISGF3), a transcription factor that regulates the interferon signature. Moreover, loss of SETD2 or BAP1 also reduces the ISGF3 level. Finally, ISGF3 is strongly tumor-suppressive in a xenograft model as its loss significantly enhances tumor growth. Conversely, reactivation of ISGF3 retards tumor growth by PBRM1-deficient ccRCC cells. Thus after VHL inactivation, HIF induces ISGF3, which is reversed by the loss of secondary tumor suppressors, suggesting that this is a key negative feedback loop in ccRCC.

Genomic Heterogeneity and the Small Renal Mass

Purpose: Tumor heterogeneity may represent a barrier to preoperative genomic characterization by needle biopsy in clear cell renal cell carcinoma (ccRCC). The extent of heterogeneity in small renal tumors remains unknown. Therefore, we set out to evaluate heterogeneity in resected large and small renal tumors.Experimental Design: We conducted a study from 2013 to 2016 that evaluated 47 consecutive ccRCC tumors resected during radical or partial nephrectomy. Cases were designated as small (<4 cm) and large (>7 cm) tumors. Each tumor had three regions sampled. Copy-number variation (CNV) was assessed and gene expression analysis was performed to characterize the clear-cell A and B (ccA/ccB) profile and the cell-cycle progression (CCP) score. Genomic heterogeneity between three regions was evaluated using CNV subclonal events, regional expression profiles, and correlation between gene expression.Results: Twenty-three small and 24 large tumors were analyzed. Total CNVs and subclonal CNVs events were less frequent in small tumors (P < 0.001). Significant gene expression heterogeneity was observed for both CCP scores and ccA/ccB classifications. Larger tumors had more variance in CCP scores (P = 0.026). The distribution of ccA/ccB differed between small and large tumors with mixed ccA/ccB tumors occurring more frequently in the larger tumors (P = 0.024). Analysis of five mixed tumors (with both ccA/ccB regions) demonstrated the more aggressive ccB phenotype had greater CNV events (P = 0.014).Conclusions: Small renal tumors have much less genomic complexity and fewer subclonal events. Pretreatment genomic characterization with single-needle biopsy in small tumors may be useful to assess biologic potential and may influence therapy. Clin Cancer Res; 24(17); 4137-44. ©2018 AACR.

Genomics and clinical correlates of renal cell carcinoma

PURPOSE

Clear cell, papillary cell, and chromophobe renal cell carcinomas (RCCs) have now been well characterised thanks to large collaborative projects such as The Cancer Genome Atlas (TCGA). Not only has knowledge of the genomic landscape helped inform the development of new drugs, it also promises to fine tune prognostication.

METHODS

A literature review was performed summarising the current knowledge on the genetic basis of RCC.

RESULTS

The Von Hippel-Lindau (VHL) tumour suppressor gene undergoes bi-allelic knockout in the vast majority of clear cell RCCs. The next most prevalent aberrations include a cohort of chromatin-modifying genes with diverse roles including PBRM1, SETD2, BAP1, and KMD5C. The most common non-clear cell renal cancers have also undergone genomic profiling and are characterised by distinct genomic landscapes. Many recurrent mutations have prognostic value and show promise in aiding decisions regarding treatment stratification. Intra-tumour heterogeneity appears to hamper the clinical applicability of sparsely sampled tumours. Ways to abrogate heterogeneity will be required to optimise the genomic classification of tumours.

CONCLUSION

The somatic mutational landscape of the more common renal cancers is well known. Correlation with outcome needs to be more comprehensively furnished, particularly for small renal masses, rarer non-clear cell renal cancers, and for all tumours undergoing targeted therapy.

Fifteen-gene expression based model predicts the survival of clear cell renal cell carcinoma

Clear-cell renal cell carcinoma (ccRCC) is the major renal cell carcinoma subtype, but its postsurgical prognosis varies among individual patients.We used gene expression, machine learning (random forest variable hunting), and Cox regression analysis to develop a risk score model based on 15 genes to predict survival of patients with ccRCC in the The Cancer Genome Atlas dataset (N = 533). We validated this model in another cohort, and analyzed correlations between risk score and other clinical indicators.Patients in the high-risk group had significantly worse overall survival (OS) than did those in the low-risk group (P = 5.6e-16); recurrence-free survival showed a similar pattern. This result was reproducible in another dataset, E-MTAB-1980 (N = 101, P = .00029). We evaluated correlations between risk score and other clinical indicators. Risk was independent of age and sex, but was significantly associated with hemoglobin level, primary tumor size, and grade. Radiation therapy also had no effect on the prognostic value of the risk score. Cox multivariate regression showed risk score to be an important indicator for ccRCC prognosis. We plotted a nomogram for 3-year OS to facilitate use of risk score and other indicators.The risk score model based on expression of the 15 selected genes can predict survival of patients with ccRCC.

The genomics of renal cell carcinoma and its role in renal mass biopsy

PURPOSE OF REVIEW

Large-scale genomic profiling has shed new light on the molecular underpinnings of renal cell carcinoma (RCC), spurring a much needed refinement of RCC subclassification based on an integrative assessment of histopathologic features and molecular alterations. At the same time, renal mass biopsies have become increasingly commonplace, necessitating ancillary tools to help guide clinical management. Herein, we briefly review our current understanding of RCC genomics, highlighting areas of possible clinical utility, as well as potential limitations, for renal mass biopsies.

RECENT FINDINGS

Distinct RCC subtypes harbor characteristic molecular features, including somatic mutations, copy number alterations, and genomic rearrangements. Existing ancillary tools, including fluorescent in-situ hybridization and immunohistochemistry, may be useful for diagnostic subclassification. Recurrent secondary molecular alterations in clear cell RCC (BAP1, SETD2, PBRM1, and TP53) and papillary RCC (CDKN2A) may be associated with poor prognosis; however, intratumoral genomic heterogeneity may limit the clinical utility of these molecular biomarkers in renal mass biopsies.

SUMMARY

Recent technological advances have the potential to fundamentally alter the clinical management of RCC by leveraging our increasing understanding of RCC genomics to assess hundreds of molecular biomarkers simultaneously. Additional focused molecular analyses of renal mass biopsy cohorts are needed prior to widespread implementation of molecular biomarker assays.

Comprehensive Genomic Profiling of Metastatic Tumors in a Phase 2 Biomarker Study of Everolimus in Advanced Renal Cell Carcinoma

INTRODUCTION

Genomic events leading to activation of mechanistic target of rapamycin (mTOR) are common in renal cell carcinoma (RCC). Everolimus is an allosteric mTOR inhibitor with efficacy in metastatic RCC. We characterized the genomic profile of RCC tumors from metastatic sites and assessed whether particular alterations correlate with clinical response to everolimus.

PATIENTS AND METHODS

An open-label, single-arm phase 2 biomarker study of everolimus 10 mg daily was conducted in metastatic RCC patients. Needle biopsy or metastasectomy was performed on metastatic tumors before everolimus initiation. Next-generation sequencing was performed using a targeted hybrid capture panel detecting alterations within exons and key introns of ≥ 300 cancer-associated genes. Disease assessments were obtained every 8 weeks using standard radiographic modalities and evaluated by Response Evaluation Criteria in Solid Tumors criteria.

RESULTS

Objective response was seen in 1 (4.2%) of 24 patients. Two patients (8.3%) had stable disease lasting > 6 months. Median (90% confidence interval) overall and progression-free survival were 20.1 (8.6, NA) and 3.8 (2.4, 5.4) months, respectively. Next-generation sequencing was successful on 18 pretreatment specimens and 3 on-treatment specimens. Alterations in the phosphatidylinositol 3-kinase-protein kinase B-mammalian target of rapamycin (PI3K-AKT-mTOR) pathway were identified in 8 (44%) of 18 pretreatment samples. An mTOR E2419D mutation was identified in the patient who experienced partial response. Alterations in VHL, PBRM1, SETD2, KDM5C, and ATM were common in the RCC metastases before initiation of everolimus.

CONCLUSION

Nearly half of heavily pretreated RCC metastases may harbor mutations in components of the PI3K-AKT-mTOR pathway. Commonly mutated genes in primary RCC were also altered at a high frequency in RCC metastases.

Reconstruction of kidney renal clear cell carcinoma evolution across pathological stages

Although numerous studies on kidney renal clear cell carcinoma (KIRC) were carried out, the dynamic process of tumor formation was not clear yet. Inadequate attention was paid on the evolutionary paths among somatic mutations and their clinical implications. As the tumor initiation and evolution of KIRC were primarily associated with SNVs, we reconstructed an evolutionary process of KIRC using cross-sectional SNVs in different pathological stages. KIRC driver genes appeared early in the evolutionary tree, and the genes with moderate mutation frequency showed a pattern of stage-by-stage expansion. Although the individual gene mutations were not necessarily associated with survival outcome, the evolutionary paths such as VHL-PBRM1 and FMN2-PCLO could indicate stage-specific prognosis. Our results suggested that, besides mutation frequency, the evolutionary relationship among the mutated genes could facilitate to identify novel drivers and biomarkers for clinical utility.

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.