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Posts Tagged ‘renal cell carcinoma’

Renal tumor macrophages linked to recurrence are identified using single-cell protein activity analysis

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

When malignancy returns after a period of remission, it is called a cancer recurrence. After the initial or primary cancer has been treated, this can happen weeks, months, or even years later. The possibility of recurrence is determined by the type of primary cancer. Because small patches of cancer cells might stay in the body after treatment, cancer might reoccur. These cells may multiply and develop large enough to cause symptoms or cause cancer over time. The type of cancer determines when and where cancer recurs. Some malignancies have a predictable recurrence pattern.

Even if primary cancer recurs in a different place of the body, recurrent cancer is designated for the area where it first appeared. If breast cancer recurs distantly in the liver, for example, it is still referred to as breast cancer rather than liver cancer. It’s referred to as metastatic breast cancer by doctors. Despite treatment, many people with kidney cancer eventually develop cancer recurrence and incurable metastatic illness.

The most frequent type of kidney cancer is Renal Cell Carcinoma (RCC). RCC is responsible for over 90% of all kidney malignancies. The appearance of cancer cells when viewed under a microscope helps to recognize the various forms of RCC. Knowing the RCC subtype can help the doctor assess if the cancer is caused by an inherited genetic condition and help to choose the best treatment option. The three most prevalent RCC subtypes are as follows:

  • Clear cell RCC
  • Papillary RCC
  • Chromophobe RCC

Clear Cell RCC (ccRCC) is the most prevalent subtype of RCC. The cells are clear or pale in appearance and are referred to as the clear cell or conventional RCC. Around 70% of people with renal cell cancer have ccRCC. The rate of growth of these cells might be sluggish or rapid. According to the American Society of Clinical Oncology (ASCO), clear cell RCC responds favorably to treatments like immunotherapy and treatments that target specific proteins or genes.

Researchers at Columbia University’s Vagelos College of Physicians and Surgeons have developed a novel method for identifying which patients are most likely to have cancer relapse following surgery.

The study

Their findings are detailed in a study published in the journal Cell entitled, “Single-Cell Protein Activity Analysis Identifies Recurrence-Associated Renal Tumor Macrophages.” The researchers show that the presence of a previously unknown type of immune cell in kidney tumors can predict who will have cancer recurrence.

According to co-senior author Charles Drake, MD, PhD, adjunct professor of medicine at Columbia University Vagelos College of Physicians and Surgeons and the Herbert Irving Comprehensive Cancer Center,

the findings imply that the existence of these cells could be used to identify individuals at high risk of disease recurrence following surgery who may be candidates for more aggressive therapy.

As Aleksandar Obradovic, an MD/PhD student at Columbia University Vagelos College of Physicians and Surgeons and the study’s co-first author, put it,

it’s like looking down over Manhattan and seeing that enormous numbers of people from all over travel into the city every morning. We need deeper details to understand how these different commuters engage with Manhattan residents: who are they, what do they enjoy, where do they go, and what are they doing?

To learn more about the immune cells that invade kidney cancers, the researchers employed single-cell RNA sequencing. Obradovic remarked,

In many investigations, single-cell RNA sequencing misses up to 90% of gene activity, a phenomenon known as gene dropout.

The researchers next tackled gene dropout by designing a prediction algorithm that can identify which genes are active based on the expression of other genes in the same family. “Even when a lot of data is absent owing to dropout, we have enough evidence to estimate the activity of the upstream regulator gene,” Obradovic explained. “It’s like when playing ‘Wheel of Fortune,’ because I can generally figure out what’s on the board even if most of the letters are missing.”

The meta-VIPER algorithm is based on the VIPER algorithm, which was developed in Andrea Califano’s group. Califano is the head of Herbert Irving Comprehensive Cancer Center’s JP Sulzberger Columbia Genome Center and the Clyde and Helen Wu professor of chemistry and systems biology. The researchers believe that by including meta-VIPER, they will be able to reliably detect the activity of 70% to 80% of all regulatory genes in each cell, eliminating cell-to-cell dropout.

Using these two methods, the researchers were able to examine 200,000 tumor cells and normal cells in surrounding tissues from eleven patients with ccRCC who underwent surgery at Columbia’s urology department.

The researchers discovered a unique subpopulation of immune cells that can only be found in tumors and is linked to disease relapse after initial treatment. The top genes that control the activity of these immune cells were discovered through the VIPER analysis. This “signature” was validated in the second set of patient data obtained through a collaboration with Vanderbilt University researchers; in this second set of over 150 patients, the signature strongly predicted recurrence.

These findings raise the intriguing possibility that these macrophages are not only markers of more risky disease, but may also be responsible for the disease’s recurrence and progression,” Obradovic said, adding that targeting these cells could improve clinical outcomes

Drake said,

Our research shows that when the two techniques are combined, they are extremely effective at characterizing cells within a tumor and in surrounding tissues, and they should have a wide range of applications, even beyond cancer research.

Main Source

Single-cell protein activity analysis identifies recurrence-associated renal tumor macrophages

https://www.cell.com/cell/fulltext/S0092-8674(21)00573-0

Other Related Articles published in this Open Access Online Scientific Journal include the following:

Machine Learning (ML) in cancer prognosis prediction helps the researcher to identify multiple known as well as candidate cancer diver genes

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

https://pharmaceuticalintelligence.com/2021/05/04/machine-learning-ml-in-cancer-prognosis-prediction-helps-the-researcher-to-identify-multiple-known-as-well-as-candidate-cancer-diver-genes/

Renal (Kidney) Cancer: Connections in Metabolism at Krebs cycle  and Histone Modulation

Curator: Demet Sag, PhD, CRA, GCP

https://pharmaceuticalintelligence.com/2015/10/14/renal-kidney-cancer-connections-in-metabolism-at-krebs-cycle-through-histone-modulation/

Artificial Intelligence: Genomics & Cancer

https://pharmaceuticalintelligence.com/ai-in-genomics-cancer/

Bioinformatic Tools for Cancer Mutational Analysis: COSMIC and Beyond

Curator: Stephen J. Williams, Ph.D.

https://pharmaceuticalintelligence.com/2015/12/02/bioinformatic-tools-for-cancer-mutational-analysis-cosmic-and-beyond-2/

Deep-learning AI algorithm shines new light on mutations in once obscure areas of the genome

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2014/12/24/deep-learning-ai-algorithm-shines-new-light-on-mutations-in-once-obscure-areas-of-the-genome/

Premalata Pati, PhD, PostDoc in Biological Sciences, Medical Text Analysis with Machine Learning

https://pharmaceuticalintelligence.com/2021-medical-text-analysis-nlp/premalata-pati-phd-postdoc-in-pharmaceutical-sciences-medical-text-analysis-with-machine-learning/

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Tracking metabolism of renal cell carcinoma

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Charting Kidney Cancer Metabolism

http://www.technologynetworks.com/Metabolomics/news.aspx?ID=188237

 

Changes in cell metabolism are increasingly recognized as an important way tumors develop and progress, yet these changes are hard to measure and interpret. A new tool designed by MSK scientists allows users to identify metabolic changes in kidney cancer tumors that may one day be targets for therapy.

 

Much of what we know about cancer comes from studying genes. By sequencing genes in tumors, for example, scientists have learned what mutations are typically found in different cancer types. Genetic methods can also be used to survey which proteins are made in tumors.

Yet this information provides only an indirect measure of how cancer cells operate. To really capture that, you need to know about the dynamic chemical changes occurring in these cells; you need to know about cancer metabolism.

Tracing the products of cell metabolism, known as metabolites, is not easy to do. “Looking at metabolites in cancer has been very difficult because the technology was not available,” says James Hsieh, a physician-scientist at Memorial Sloan Kettering and an expert in kidney cancer. “Until recently, we didn’t have the capacity to look at hundreds, even thousands, of different metabolites inside of cells.”

But with advanced biochemical methods, these myriad metabolites are finally coming into focus. Dr. Hsieh’s team has used such methods to profile metabolic changes in hundreds of kidney cancer tumor samples. What’s more, they’ve developed a new online tool that will help researchers make sense of this vast data pool, highlighting previously unknown connections between metabolism and clear cell renal cell carcinoma — the most common, lethal form of the disease.

Metabolism Explained

Think of a cell as a factory. If genes provide the floor plan for the factory, and proteins make up the built environment, then metabolism is the movement of materials through the factory to make products.

For many years, investigators wanting to understand cancer metabolism looked at enzyme levels — proteins that catalyze chemical reactions. Publically accessible databases, such as those maintained by The Cancer Genome Atlas (TCGA), provide this information. The problem is that enzyme levels don’t necessarily tell you whether, and at what rate, metabolites are actually being made.

“There’s no good way to infer how changes in metabolite levels are connected to enzyme levels,” says Ed Reznik, a postdoctoral fellow in computational biology at the Sloan Kettering Institute who is a co-first author on the study. “You really have to go after the metabolites directly.” (To continue the factory analogy, just because a forklift is present on the shop floor doesn’t mean it’s being used.)

If genes provide the floor plan for the factory, and proteins make up the built environment, then metabolism is the movement of materials through the factory to make products.

To track metabolites, the team obtained samples of tumor tissue and normal tissue from 138 clear cell kidney cancer patients treated at MSK. A surgeon on the team and the paper’s other co-first author, Ari Hakimi, performed these operations.

The researchers then used mass spectrometry and liquid and gas chromatography to analyze the levels of more than 800 different metabolites in these samples. By comparing the levels of metabolites in tumors with those in normal tissues, they were able to chart the rise and fall of these chemicals.

There’s an App for That

Making sense of the metabolic data was challenging at first, since there was so much of it. “If you look at human metabolism, there are upward of 5,000 distinct biochemical reactions,” says Dr. Reznik. “It’s really hard to make sense of that in a way that humans can parse.”

So the team decided to build a tool that would help them visualize what was going on. Working with a team of programmers, Dr. Reznik developed what he calls a “metabologram,” which allows users to review the metabolite data for any number of different metabolic pathways, one pathway at a time. Users can compare metabolites between tumor samples and normal samples, or between lower-stage tumors and higher-stage tumors. They can also see how the metabolic data line up against the gene expression data obtained from TCGA.

With the help of their new tool, the team made some startling discoveries. They found that the genetic data from TCGA were not always reflective of what was happening to metabolites in kidney cancer cells, and that the metabolic data help to make better sense of the clinical behavior of kidney cancer tumors.

“Our data are actually much more consistent with the human data obtained from pathology,” Dr. Hsieh says.

Charting Aggressiveness

Taking a bird’s-eye view of the metabolic data, the team found four distinct groupings, or clusters, of tumor samples that they could distinguish based on levels of metabolites. The clusters differed in their level of tumor aggressiveness and highlighted who the high-risk patients were.

“You can use the metabologram to get a sense of what’s driving the aggressive tumors from a metabolic standpoint,” Dr. Hakimi says. Once you have that, you can then think about ways to target that altered metabolism.

The team hopes that the new tool, which is being made freely available online, will help researchers generate novel hypotheses about metabolism and kidney cancer, and even encourage other teams to create metabolograms for other cancer types.

“The goal is ultimately to use this information to improve clinical prediction for kidney cancer and to understand how best to treat it,” Dr. Hsieh says.

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Renal Cell Carcinoma Subclasses

Curator: Larry H. Bernstein, MD, FCAP

TCGA Analysis Points to Kidney Cancer Subtypes With Prognosis, Treatment Clues

NEW YORK (GenomeWeb) – Members of the Cancer Genome Atlas Research Network have described two molecularly distinct types within the kidney cancer papillary renal cell carcinoma, including one group containing three prognostically informative subtypes.

As they reported in the New England Journal of Medicine last night, the researchers used whole-exome sequencing, transcriptome sequencing, microRNA sequencing, proteomic analyses, and array-based methylation and copy number profiling to characterize 161 primary papillary renal cell carcinomas.

Bringing these data together, the researchers defined two main papillary renal cell carcinoma groups: type 1 tumors, which were frequently marked by glitches in the MET gene pathway, and type 2 tumors, which fell into three further subtypes with variable molecular features and patient outcomes.

The findings “really help us understand the phenotypes of sporadic papillary kidney cancer,” corresponding author Marston Linehan, a urologic oncology researcher with the National Cancer Institute, told GenomeWeb. “It also confirms that type 1 and type 2 [papillary renal cell carcinoma] really are two very separate diseases.”

It’s expected that molecular patterns within these groups could help predict disease aggressiveness in papillary renal cell carcinoma patients and, in some cases, highlight possible treatment targets.

Past work indicated the presence of two papillary renal cell carcinoma types as well, the team explained. But the original designations were based mainly on histological tumor features.

The disease — which represents around 15 percent of overall kidney cancers — is known for its heterogeneity, though what was known about it genetically largely stemmed from studies on inherited forms of papillary renal cell carcinoma.

In a Nature Genetics paper published in 1997, for example, Linehan and his colleagues identified mutations in MET — a gene coding for a kidney cell receptor protein that binds a hepatoctype growth factor — in individuals with hereditary papillary renal cell carcinoma, which often shows type 1 histology. Mutations in MET also turned up in a subset of sporadic type 1 cases, hinting at the gene’s importance in papillary renal cell carcinoma.

Mutations in other sorts of genes were more common in studies of hereditary leiomyomatosis, a condition that puts individuals at risk of aggressive papillary renal cell carcinoma with type 2 histological features, Linehan explained, including mutations in the gene coding for the fumarate hydratase enzyme.

Nevertheless, researchers suspected additional genetic complexity in sporadic forms of the disease, particularly within type 2 tumors.

For the TCGA analysis, members of the team focused on 161 papillary renal cell carcinoma tumors, including 75 tumors with type 1 histology, 60 tumors that were histologically type 2, and 26 tumors that could not be classified neatly into either group based on histological features.

Using Nimblegen capture kits and the Illumina HiSeq 2000 instrument, they generated exome sequences for matched tumor and normal samples, successfully uncovering almost 10,400 somatic mutations across 157 of the tumors.

The team also used sequencing to assess transcript expression, gene fusions, and miRNA profiles in the tumors and in some adjacent or non-adjacent kidney samples, while Affymetrix and Illumina arrays offered a look at copy number profiles and methylation patterns, and proteomic profiles were produced by reverse phase protein arrays.

Almost one-quarter of the tumors contained mutations in the top five recurrently mutated genes: MET, SETD2, NF2, KDM6A, and SMARCB1. But molecular features differed dramatically between the type 1 and type 2 tumors, as did patient outcomes, Linehan explained.

For example, more than 80 percent of type 1 tumors carried some form of MET pathway alteration, including mutations to MET itself, glitches in genes coding for other components in the pathway, or chromosome 7 gains involving sequences coding for MET and other genes.

Such findings hint that at least some type 1 tumors might be vulnerable to treatments targeting the MET pathway, Linehan noted, such as tyrosine kinase inhibitors — a possibility being explored through ongoing and anticipated clinical trials at NCI and beyond.

Within the type 2 tumors, the researchers saw enhanced NRF2-antioxidant response element (ARE) pathway activity, mutations in the SETD2 gene or other chromatin modifying genes, and fusions involving TFE3 that were previously characterized primarily in kidney cancers affecting children and young adults.

Still, the team found significant heterogeneity and sub-stratification within the three molecular subtypes that made up the group.

The increased NRF2-ARE pathway activity was most pronounced in an aggressive type 2 subtype that had a so-called CpG island methylator phenotype (CIMP), which was marked by hypermethylation increased glycolysis and increased NQO1 gene expression.

CIMP tumors within type 2 were also prone to mutations affecting the fumarate hydratase gene. In the absence of optimal fumarate hydratase, past studies suggest problems may arise with the function of other enzymes, including those tasked with regulating DNA methylation.

Based on available clinical data, the researchers found that the nine individuals with CIMP type 2 tumors had relatively early onset, coupled with the worst papillary renal cell carcinoma outcomes and shortest survival times.

A second subtype of type 2 papillary renal cell carcinoma included 22 cases with slightly better outcomes, though survival times were still diminished compared to those for individuals with type 1 tumors.

This intermediate group included tumors that tended to contain the SETD2 mutations and/or CDKN2A silencing. The remaining 35 type 2 tumors tested fell into a third subtype with survival patterns that were more comparable to the 93 tumors in the type 1 group.

Along with trials focused on exploring targeted treatment options for papillary renal cell carcinoma, Linehan noted that TCGA members are involved in a related effort to do whole-genome sequencing on papillary renal cell carcinoma alongside chromophobe and clear cell renal carcinoma.

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Renal Cell Carcinoma Classified

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

TCGA Analysis Points to Kidney Cancer Subtypes With Prognosis, Treatment Clues

NEW YORK (GenomeWeb) – Members of the Cancer Genome Atlas Research Network have described two molecularly distinct types within the kidney cancer papillary renal cell carcinoma, including one group containing three prognostically informative subtypes.

As they reported in the New England Journal of Medicine last night, the researchers used whole-exome sequencing, transcriptome sequencing, microRNA sequencing, proteomic analyses, and array-based methylation and copy number profiling to characterize 161 primary papillary renal cell carcinomas.

Bringing these data together, the researchers defined two main papillary renal cell carcinoma groups: type 1 tumors, which were frequently marked by glitches in the MET gene pathway, and type 2 tumors, which fell into three further subtypes with variable molecular features and patient outcomes.

The findings “really help us understand the phenotypes of sporadic papillary kidney cancer,” corresponding author Marston Linehan, a urologic oncology researcher with the National Cancer Institute, told GenomeWeb. “It also confirms that type 1 and type 2 [papillary renal cell carcinoma] really are two very separate diseases.”

It’s expected that molecular patterns within these groups could help predict disease aggressiveness in papillary renal cell carcinoma patients and, in some cases, highlight possible treatment targets.

 

Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma

The Cancer Genome Atlas Research Network Group

NEJM   Nov 4, 2015     DOI: http://dx.doi.org:/10.1056/NEJMoa1505917

Papillary renal-cell carcinoma, which accounts for 15 to 20% of renal-cell carcinomas, is a heterogeneous disease that consists of various types of renal cancer, including tumors with indolent, multifocal presentation and solitary tumors with an aggressive, highly lethal phenotype. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist.

Type 1 and type 2 papillary renal-cell carcinomas were shown to be different types of renal cancer characterized by specific genetic alterations, with type 2 further classified into three individual subgroups on the basis of molecular differences associated with patient survival. Type 1 tumors were associated with METalterations, whereas type 2 tumors were characterized by CDKN2Asilencing, SETD2 mutations, TFE3 fusions, and increased expression of the NRF2–antioxidant response element (ARE) pathway. A CpG island methylator phenotype (CIMP) was observed in a distinct subgroup of type 2 papillary renal-cell carcinomas that was characterized by poor survival and mutation of the gene encoding fumarate hydratase (FH).

Type 1 and type 2 papillary renal-cell carcinomas were shown to be clinically and biologically distinct. Alterations in the MET pathway were associated with type 1, and activation of the NRF2-ARE pathway was associated with type 2; CDKN2A loss and CIMP in type 2 conveyed a poor prognosis. Furthermore, type 2 papillary renal-cell carcinoma consisted of at least three subtypes based on molecular and phenotypic features. (Funded by the National Institutes of Health.)

Figure 1. Somatic Alterations in Papillary Renal-Cell Carcinoma and Molecular Differences between Type 1 and Type 2 Cancers.

http://www.nejm.org/na101/home/literatum/publisher/mms/journals/content/nejm/0/nejm.ahead-of-print/nejmoa1505917/20151104-02/images/small/nejmoa1505917_f1.gif

Unsupervised clustering of DNA copy profiles of 161 papillary renal-cell carcinomas (PRCCs) (Panel A) revealed three molecular subtypes, one of which was highly enriched for type 1 tumors and the other two for type 2 tumors. SCNA denotes somatic copy-number alterations. Significantly mutated genes (SMGs) in PRCC (Panel B) were determined by considering all genes (q<0.1 [range, 0.0 to 1.0]) or focusing on the set of 260 genes previously implicated in cancer by large-scale, pan-cancer exome analyses15 (q<0.1). P values were calculated with the MutSigCV algorithm, version 2.0. A pathway-centric view of gene mutations in PRCC (Panel C) shows key pathways and genes implicated in cancer, either in the current study or elsewhere.15 The tumors were classified according to histologic type (from left to right) and according to gene or pathway altered (from top to bottom). Pathways and genes represented include MET, the Hippo pathway (NF2, SAV1, and WWC1), the NRF2 pathway (NFE2L2, KEAP1, CUL3, SIRT1, and FH), chromatin modification (CREBBP, DOTL1, EHMT1/2, EP300, EZH1/2, KAT2A/B, KDM1A/B, KDM4A/B, KDM5A/B/C, KDM6A/B, MLL1/2/3/4/5, NSD1, SETD2, SMYD4, and SRCAP), the SWI/SNF complex (ACTB, ACTL6A/B, ARID1A/B, ARID2, BCL6A/B/C, BCL11A/B, BRD7/9, DPF1/2/3, PHF10, PBRM1, SMARCA2/4

Figure 2. Alterations in Papillary Renal-Cell Carcinoma Involving the MET Oncogene.

http://www.nejm.org/na101/home/literatum/publisher/mms/journals/content/nejm/0/nejm.ahead-of-print/nejmoa1505917/20151104-02/images/small/nejmoa1505917_f2.gif

Panel A is a schematic representation of somatic mutations in MET, along with germline variant H1112R, which was previously implicated in hereditary papillary renal-cell carcinoma,17 and the novel RNA transcript variant of MET lacking the canonical exons 1 and 2 but containing a novel exon 1 that splices to the canonical exon 3. IPT denotes immunoglobulin-like, plexins, and transcription factors, and PSI plexins, semaphorins, and integrins. Panel B shows the crystal structure for the MET tyrosine kinase catalytic domain (RCSB-PDB 3I5 N18), on which are mapped the residues that are altered in papillary renal-cell carcinoma. All numbering of amino acids is based on the MET protein sequences.

We used a comprehensive genomics approach to characterize the biologic foundation of papillary renal-cell carcinoma and found that type 1 and type 2 papillary renal-cell carcinoma are distinctly different diseases and that type 2 papillary renal-cell carcinoma is a heterogeneous disease with multiple distinct subgroups. Common driver mutations among the different subtypes were relatively rare, as had been observed in two recent studies.7,30 Molecular and phenotypic differences between type 1 and type 2 papillary renal-cell carcinoma were reflected in individual and combined analyses of various data platforms. The usefulness of CDKN2A alterations as an independent prognostic marker associated with type 2 tumors requires validation. This study suggests that gene fusions involving TFE3 or TFEB are underappreciated in type 2 tumors in adults and should be considered in any patient with type 2 disease. Although papillary renal-cell carcinomas with fusions involving TFE3 or TFEB are generally considered to be diseases of children and young adults,16the mean age in our study was 52 years, and we found tumors with TFEB fusions in patients 64 and 71 years of age.

The most distinct of the three type 2 subgroups was the subgroup defined by the CIMP, which was associated with the worst overall survival. CIMP hypermethylation patterns have been observed in a number of other cancer subtypes, including glioblastoma,31 lung adenocarcinoma,32 and gastric adenocarcinoma.33 The CIMP-associated tumors showed low levels of FH mRNA expression, and five had germline or somatic mutation of FH. Germline mutation of FH has been observed in the aggressive type 2 tumor associated with the hereditary leiomyomatosis and renal-cell cancer syndrome.9,34 In this syndrome, the high levels of fumarate accumulating from loss of fumarate hydratase enzyme activity result in impaired function of enzymes such as the TET family of enzymes, which play a role in maintaining appropriate DNA methylation within the genome.35 The subgrouping of type 2 tumors according to molecular features and the presence of specific subsets of type 2 tumors, such as those with TFE3 fusions or CIMP, suggest that substratification of type 2 papillary renal-cell carcinoma according to specific molecular markers may allow more accurate diagnosis that could lead to the development of mechanistic, disease-specific targeted therapies.

This classification of papillary renal-cell carcinoma could potentially have a substantial effect on clinical and therapeutic management and on the design of clinical trials. Alteration of MET or gain of chromosome 7 was observed in a large percentage (81%) of type 1 tumors. Antitumor activity of an agent targeting the MET and VEGFR2 pathways has been shown in a phase 2 trial involving patients with papillary renal-cell carcinoma, with a particularly high response rate among patients who had tumors with MET mutations.36 Mutation of the Hippo pathway tumor suppressor, NF2, was observed in a number of papillary renal-cell carcinomas. This pathway has been targeted in other cancers with agents such as dasatinib, an inhibitor of the YES1 kinase that interacts with the YAP transcription factor that is up-regulated with Hippo pathway dysregulation.37 The CIMP-associated tumors showed a Warburg-like metabolic shift, similar to that observed in fumarate hydratase–deficient tumors in patients with the hereditary leiomyomatosis and renal-cell cancer syndrome.11,25,26 A clinical trial targeting this metabolic shift in papillary renal-cell carcinoma is currently under way (ClinicalTrials.gov number, NCT01130519). Increased expression of the NRF2-ARE pathway has been observed in both hereditary and sporadic type 2 papillary renal-cell carcinomas.12 Immunohistochemical analysis for NQO1 could provide a valuable marker of activation of the NRF2-ARE pathway. Currently, there is intense interest in the NRF2-ARE pathway in cancer,38 and novel strategies have recently been developed to target this pathway.39

The identification of altered genes and pathways provides a comprehensive foundation for an understanding of the molecular basis of papillary renal-cell carcinoma. This refined classification more accurately reflects the genotypic and phenotypic differences among the various types of these tumors and may lead to more appropriate clinical management and development of more effective forms of therapy.

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