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Posts Tagged ‘Targeted therapy’


New Targeted Cancer Therapy may be ‘Possible Hope’ for Some Pancreatic Cancer Patients

Reporter: Irina Robu, PhD

 

UPDATED on 7/18/2019

BREAKTHROUGH PANCREATIC CANCER TREATMENT PHASE III TRIAL OPENS IN ISRAEL

Hope is that successful trials will allow Rafael Pharmaceuticals will receive expedited FDA approval by late 2020.

BY MAAYAN JAFFE-HOFFMAN  JULY 18, 2019 18:30

“What it does is feeds misinformation to these regulatory elements, making them feel that there is too much carbon flow through both of these complexes, causing them to be inhibited,” Pardee said. “It simultaneously inhibits both complexes so tumor cells that are primarily driven by glucose cannot utilize glucose in the TCA cycle. Tumor cells that are primarily driven by glutamine usage cannot use glutamine-derived carbons in the TCA cycle. And, importantly, tumors cannot switch from one source to the other in the presence of CPI-613,” he explained.

He said that hitting two complexes simultaneously has many advantages. One is that the carbon source the tumor is primarily dependent on does not matter; another is that evolved resistance for both complexes simultaneously is very unlikely to happen.

Pardee said CPI-613’s key differentiators are that it is highly selective on the uptake and target level in cancer cells, which leads to less toxicity to healthy cells. This allows for patients to receive extended treatment courses and for the drug to be used in combination with other drugs.

CPI-613 is being administered in this clinical trial with a chemotherapy combination of fluorouracil, leucovorin, irinotecan, and oxaliplatin, called FOLFIRINOX.

SOURCE

https://www.jpost.com/HEALTH-SCIENCE/Breakthrough-pancreatic-cancer-treatment-phase-III-trial-opens-in-Israel-596059

 

New Targeted Cancer Therapy may be ‘Possible Hope’ for Some Pancreatic Cancer Patients

Pancreatic cancer is the 12th maximum common cancer and the fourth leading cause of cancer death. The cancer is often difficult to diagnose as there is no cost-effective ways to screen for the illness. For over 52% of people who are diagnosed after the cancer has spread and with a 5-year survival rate.

Scientists at Sheba Medical Center in Israel developed a targeted cancer therapy drug together with AstraZeneca and Merck which can offer a possible new solution for patients with a specific kind of pancreatic cancer by delaying the progression of the disease. To evaluate the safety and test the efficacy of a new drug treatment regimen based on Lynparza tablets. The tablets are a pharmacological inhibitor of the enzyme poly (ADP-ribose) polymerase which inhibit the enzyme. They were developed for a number of indications, but most prominently for the treatment of cancer, as numerous forms of cancer are more dependent for their development on the enzyme than regular cells are. This makes poly (ADP-ribose) polymerase an attractive target for cancer therapy.

Their study included 154 patients who were randomly assigned to get the tablets at a dose of 300 mg twice a day with metastatic pancreatic cancer who carried the genetic mutation called BRCA 1 and BRCA 2. BRCA1 and BRCA2 are human genes that produce proteins accountable for repairing damaged DNA and play a substantial role in preserving the genetic stability of cells. Once either of these genes is mutated, DNA damage can’t be repaired properly and cells become unstable. As a result, cells are more likely to develop additional genetic alterations that can lead to cancer.

Patients with these mutations make up six to seven percent of the metastatic pancreatic cancer patients. The trial using the using the medicine Lynparza offers possible hope for those who suffer from metastatic pancreatic cancer and have a BRCA mutation and slows down the disease progression. According to the researchers this is the first Phase 3 biomarker that is positive in pancreatic cancer and the drug gives incredible hope for patients with the advanced stage of the cancer.

SOURCE
https://www.timesofisrael.com/israeli-researchers-find-potential-hope-for-some-pancreatic-cancer-patients/

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Targeted Therapy for Triple Negative Breast Cancer

Curator: Larry H. Bernstein, MD, FCAP

LPBI

 

Triple-Negative Breast Cancer Target Is Found

May 17, 2016   Researchers at UC Berkeley discover a target that drives cancer metabolism in triple-negative breast cancer.
http://www.technologynetworks.com/Genotyping/news.aspx?ID=191502

UC Berkeley researchers have found a long-elusive Achilles’ heel within “triple-negative” breast tumors, a common type of breast cancer that is difficult to treat. The scientists then used a drug-like molecule to successfully target this vulnerability, killing cancer cells in the lab and shrinking tumors in mice.

“We were looking for targets that drive cancer metabolism in triple-negative breast cancer, and we found one that was very specific to this type of cancer,” said Daniel K. Nomura, an associate professor of chemistry and of nutritional sciences and toxicology at UC Berkeley and senior author for the study, which is published online ahead of print in Cell Chemical Biology.

Triple-negative breast cancers account for about one in five breast cancers, and they are deadlier than other forms of breast cancer, in part because no drugs have been developed to specifically target these tumors.

Triple-negative breast cancers do not rely on the hormones estrogen and progesterone for growth, nor on human epidermal growth factor receptor 2 (HER2). Because they do not depend on these three targets, they are not vulnerable to modern hormonal therapies or to the HER2-targeted drug Herceptin (trastuzumab).

Instead, oncologists treat triple-negative breast cancer with older chemotherapies that target all dividing cells. If triple-negative breast cancer spreads beyond the breast to distant sites within the body, an event called metastasis, there are few treatment options.

Tumor cells develop abnormal metabolism, which they rely on to get the energy boost they need to fuel their rapid growth. In their new study, the research team used an innovative approach to search for active enzymes that triple-negative breast cancers use differently for metabolism in comparison to other cells and even other tumors.

Inhibiting cancer metabolism

They discovered that cells from triple-negative breast cancer cells rely on vigorous activity by an enzyme called glutathione-S-transferase Pi1 (GSTP1). They showed that in cancer cells, GSTP1 regulates a type of metabolism called glycolysis, and that inhibition of GSTP1 impairs glycolytic metabolism in triple-negative cancer cells, starving them of energy, nutrients and signaling capability. Normal cells do not rely as much on this particular metabolic pathway to obtain usable chemical energy, but cells within many tumors heavily favor glycolysis.

Co-author Eranthie Weerapana, an associate professor of chemistry at Boston College, developed a molecule named LAS17 that tightly and irreversibly attaches to the target site on the GSTP1 molecule. By binding tightly to GSTP1, LAS17 inhibits activity of the enzyme. The researchers found that LAS17 was highly specific for GSTP1, and did not attach to other proteins in cells.

According to Nomura, LAS17 did not appear to have toxic side effects in mice, where it shrank tumors grown to an invasive stage from surgically transplanted, human, triple-negative breast cancer cells that had long been maintained in lab cultures.

The research team intends to continue studying LAS17, Nomura said, with the next step being to study tumor tissue resected from human triple-negative breast cancers and transplanted directly into mice.

“Inhibiting GSTP1 impairs glycolytic metabolism,” Nomura said. “More broadly, this inhibition starves triple-negative breast cancer cells, preventing them from making the macromolecules they need, including the lipids they need to make membranes and the nucleic acids they need to make DNA. It also prevents these cells from making enough ATP, the molecule that is the basic energy fuel for cells.”

Beyond the metabolic role they first sought to track down, GSTP1 also appears to aid signaling within triple-negative breast cancer cells, helping to spur tumor growth, the researchers found.

Technique identifies Achilles’ heels

Nomura said it was surprising that a single, unique target emerged from the research team’s search.

The method used by the researchers, called “reactivity-based chemoproteomics,” can quickly lead to specific targetable sites — the Achilles’ heels — on proteins of interest, and eventually to drug development strategies, Nomura said.

The approach is to search for protein targets that are actively functioning within cells, instead of first using the well-trod path of surveying all genes to identify the specific genes that have taken the first step toward protein production. With that more conventional strategy, the switching on, or “expression,” of genes is evidenced by the easily quantified molecule called messenger RNA, made by the cell from a gene’s DNA template.

Nomura’s team instead first used chemical probes that can react with certain configurations of two of the amino acid building blocks of protein — cysteine and lysine — known to be involved in several kinds of important structural and functional transitions that active proteins can undergo.

“A lot can happen after the first step in protein production, and we believe our method for identifying fully formed, active proteins is more useful for tracking down relevant differences in cellular physiology,” Nomura said.

The researchers analyzed and compared cells from five distinct triple-negative breast cancers that had been grown in cell cultures for generations, along with cells from four distinct breast cancers that were not triple negative.

The scientists used a chemical identification technique known as mass spectrometry to narrow down the set of proteins that had active lysines and cysteines to just those that were metabolic enzymes. Only then did they use the more conventional approach of measuring gene expression in the different cancer cell types.

GSTP1 was the only metabolically active enzyme that was specifically expressed only in triple-negative breast cancer cells compared to other breast cancer cell types, the researchers found. Separate analysis of databases of human breast cancer by UC San Francisco co-authors confirmed that GSTP1 is overexpressed in patients with triple-negative breast cancers in comparison to patients with other breast cancers.

In addition to Nomura and Weerapana, study authors included Sharon Louie, Elizabeth Grossman, Lucky Ding, Tucker Huffman and David Miyamoto, from UC Berkeley; Roman Camarda and Andrei Goga, from UC San Francisco, and Lisa Crawford, from Boston College. Study funders included the National Institutes of Health, the American Cancer Society, the U.S. Department of Defense, and the Searle Scholar Foundation.

 

Triple-negative breast cancer target is found

UC Berkeley researchers have found a long-elusive Achilles’ heel within “triple-negative” breast tumors, a common type of breast cancer that is difficult to treat. The scientists then used a drug-like molecule to successfully target this vulnerability, killing cancer cells in the lab and shrinking tumors in mice.

“We were looking for targets that drive cancer metabolism in triple-negative breast cancer, and we found one that was very specific to this type of cancer,” said Daniel K. Nomura, an associate professor of chemistry and of nutritional sciences and toxicology at UC Berkeley and senior author for the study, which is published online ahead of print on May 12 in Cell Chemical Biology.

Triple-negative breast cancers account for about one in five breast cancers, and they are deadlier than other forms of breast cancer, in part because no drugs have been developed to specifically target these tumors.

Triple-negative breast cancers do not rely on the hormones estrogen and progesterone for growth, nor on human epidermal growth factor receptor 2 (HER2). Because they do not depend on these three targets, they are not vulnerable to modern hormonal therapies or to the HER2-targeted drug Herceptin (trastuzumab).

Instead, oncologists treat triple-negative breast cancer with older chemotherapies that target all dividing cells. If triple-negative breast cancer spreads beyond the breast to distant sites within the body, an event called metastasis, there are few treatment options.

Tumor cells develop abnormal metabolism, which they rely on to get the energy boost they need to fuel their rapid growth. In their new study, the research team used an innovative approach to search for active enzymes that triple-negative breast cancers use differently for metabolism in comparison to other cells and even other tumors.

Inhibiting cancer metabolism

They discovered that cells from triple-negative breast cancer cells rely on vigorous activity by an enzyme called glutathione-S-transferase Pi1 (GSTP1). They showed that in cancer cells, GSTP1 regulates a type of metabolism called glycolysis, and that inhibition of GSTP1 impairs glycolytic metabolism in triple-negative cancer cells, starving them of energy, nutrients and signaling capability. Normal cells do not rely as much on this particular metabolic pathway to obtain usable chemical energy, but cells within many tumors heavily favor glycolysis.

for mor see.. http://news.berkeley.edu/2016/05/12/triple-negative-breast-cancer-target-is-found/

 

GSTP1 Is a Driver of Triple-Negative Breast Cancer Cell Metabolism and Pathogenicity

Sharon M. Louie, Elizabeth A. Grossman, Lisa A. Crawford….., Eranthie Weerapana, Daniel K. Nomura
Figure thumbnail fx1
  • We used chemoproteomics to profile metabolic drivers of breast cancer
  • GSTP1 is a novel triple-negative breast cancer-specific target
  • GSTP1 inhibition impairs triple-negative breast cancer pathogenicity
  • GSTP1 inhibition impairs GAPDH activity to affect metabolism and signaling

Breast cancers possess fundamentally altered metabolism that fuels their pathogenicity. While many metabolic drivers of breast cancers have been identified, the metabolic pathways that mediate breast cancer malignancy and poor prognosis are less well understood. Here, we used a reactivity-based chemoproteomic platform to profile metabolic enzymes that are enriched in breast cancer cell types linked to poor prognosis, including triple-negative breast cancer (TNBC) cells and breast cancer cells that have undergone an epithelial-mesenchymal transition-like state of heightened malignancy. We identified glutathione S-transferase Pi 1 (GSTP1) as a novel TNBC target that controls cancer pathogenicity by regulating glycolytic and lipid metabolism, energetics, and oncogenic signaling pathways through a protein interaction that activates glyceraldehyde-3-phosphate dehydrogenase activity. We show that genetic or pharmacological inactivation of GSTP1 impairs cell survival and tumorigenesis in TNBC cells. We put forth GSTP1 inhibitors as a novel therapeutic strategy for combatting TNBCs through impairing key cancer metabolism and signaling pathways.

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

Curator: Demet Sag, PhD, CRA, GCP

Through Histone Modulation

Renal cell carcinoma accounts for only 3% of total human malignancies but it is still the most common type of urological cancer with a high prevalence in elderly men (>60 years of age).

ICD10 C64
ICD9-CM 189.0
ICD-O M8312/3
OMIM 144700 605074
DiseasesDB 11245
MedlinePlus 000516
eMedicine med/2002

Most kidney cancers are renal cell carcinomas (RCC). RCC lacks early warning signs and 70 % of patients with RCC develop metastases. Among them, 50 % of patients having skeletal metastases developed a dismal survival of less than 10 % at 5 years.

There are three main histopathological entities:

  1. Clear cell RCC (ccRCC), dominant in histology (65%)
  2. Papillary (15-20%) and
  3. Chromophobe RCC (5%).

There are very rare forms of RCC shown in collecting duct, mucinous tubular, spindle cell, renal medullary, and MiTF-TFE translocation carcinomas.

Subtypes of clear cell and papillary RCC, and a new subtype, clear cell papillary http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f6.jpg

Different subtypes of clear cell RCC can be defined by HIF patterns as well as by transcriptomic expression as defined by ccA and ccB subtypes. Papillary RCC also demonstrates distinct histological subtypes. A recently described variant denoted as clear cell papillary RCC is VHL wildtype (VHL WT), while other clear cell tumors are characterized by VHL mutation, loss, or inactivation (VHL MT).

KEY POINTS

  • Renal cell cancer is a disease in which malignant (cancer) cells form in tubules of the kidney.
  • Smoking and misuse of certain pain medicines can affect the risk of renal cell cancer.
  • Signs of renal cell cancer include
  • Blood in your urine, which may appear pink, red or cola colored
  • A lump in the abdomen.
  • Back pain just below the ribs that doesn’t go away
  • Weight loss
  • Fatigue
  • Intermittent fever

 

Factors that can increase the risk of kidney cancer include:

  • Older age.
  • High blood pressure (hypertension).
  • Treatment for kidney failure.(long-term dialysis to treat chronic kidney failure)
  • Certain inherited syndromes.
  • von Hippel-Lindau disease

Tests that examine the abdomen and kidneys are used to detect (find) and diagnose renal cell cancer.

The following tests and procedures may be used:

There are 3 treatment approaches for Renal Cancer:

Stages of Renal Cancer:

Stage I Tumour of a diameter of 7 cm (approx. 23⁄4 inches) or smaller, and limited to the kidney. No lymph node involvement or metastases to distant organs.
Stage II Tumour larger than 7.0 cm but still limited to the kidney. No lymph node involvement or metastases to distant organs.
Stage III
any of the following
Tumor of any size with involvement of a nearby lymph node but no metastases to distant organs. Tumour of this stage may be with or without spread to fatty tissue around the kidney, with or without spread into the large veins leading from the kidney to the heart.
Tumour with spread to fatty tissue around the kidney and/or spread into the large veins leading from the kidney to the heart, but without spread to any lymph nodes or other organs.
Stage IV
any of the following
Tumour that has spread directly through the fatty tissue and the fascia ligament-like tissue that surrounds the kidney.
Involvement of more than one lymph node near the kidney
Involvement of any lymph node not near the kidney
Distant metastases, such as in the lungs, bone, or brain.
Grade Level Nuclear Characteristics
Grade I Nuclei appear round and uniform, 10 μm; nucleoli are inconspicuous or absent.
Grade II Nuclei have an irregular appearance with signs of lobe formation, 15 μm; nucleoli are evident.
Grade III Nuclei appear very irregular, 20 μm; nucleoli are large and prominent.
Grade IV Nuclei appear bizarre and multilobated, 20 μm or more; nucleoli are prominent

 

GENETICS:

90% or more of kidney cancers are believed to be of epithelial cell origin, and are referred to as renal cell carcinoma (RCC), which are further subdivided based on histology into clear-cell RCC (75%), papillary RCC (15%),

chromophobe tumor (5%), and oncocytoma (5%).

Nephrectomy continues to be the cornerstone of treatment for localized renal cell carcinoma (RCC). Research is still underway to developed targeted agents against the vascular endothelial growth factor (VEGF) molecule and related pathways as well as inhibitors of the mammalian target of rapamycin (mTOR),

clear cell RCC (ccRCC) doesn’t respond well to radiation chemotherapy due to high radiation resistancy.  The hallmark genetic features of solid tumors such as KRAS or TP53 mutations are also absent. However, there is a well-designed association presented between ccRCC and mutations in the VHL gene

Hereditary RCC, accounts for around 4% of cases, has been a relatively dominant area of RCC genetics.

Causative genes have been identified in several familial cancer syndromes that predispose to RCC including

  • VHLmutations in von Hippel-Lindau disease that predispose to ccRCC and VHL is somatically mutated in up to 80% of ccRCC
  • METmutations in familial papillary renal cancer,
  • dominantly activating kinase domainMET mutation reported in 4–10% of sporadic papillary RCC[2].
  • FH (fumarate hydratase) mutations in hereditary leiomyomatosis and renal cell cancer that predispose to papillary RCC
  • FLCN(folliculin) mutations in Birt-Hogg-Dubé syndrome that predispose to primarily chromophobe RCC.

In addition, there are germline mutations:

  • in theTSC1/2 genes predispose to tuberous sclerosis complex where approximately 3% of cases develop ccRCC,
  • in the SDHB(succinate dehydrogenase type B) in patients with paraganglioma syndrome shows elevated risk to develop multiple types of RCC.

GWAS in almost 6000 RCC cases demonstrated that loci on 2p21 and 11q13.3 play a role in RCC. Although EPAS1 gene encoding a transcription factor operative in hypoxia-regulated responses in  2p21 , 11q13.3 has no known coding genes.

There has been, however, comparatively less progress in the elaboration of the somatic genetics of sporadic RCC.

Absent mutations in sporadic RCC:

  • somaticFH mutations
  • somatic mutations ofTSC12 and SDHB

Present mutations in sporadic ccRCC (chromophobe RCC) are

  • TSC1mutations occur in 5% of ccRCCs and
  • somatic mutations inFLCN  rare
  • may predict for extraordinary sensitivity to mTORC1 inhibitors clinically.

The COSMIC database reports somatic point mutations in TP53 in 10% of cases, KRAS/HRAS/NRAS combined ≤1%, CDKN2A 10%, PTEN 3%, RB1 3%, STK11/LKB1 ≤1%, PIK3Ca ≤1%, EGFR1% and BRAF ≤1% in all histological samples. Further information can be found at (http://www.sanger.ac.uk/ genetics/CGP/cosmic/) for the  RCC somatic genetics.

HIF- and hypoxia-mediated epigenetic regulation work together due to histone modification because HIF activate several chromatin demethylases, including JMJD1A (KDM3A), JMJD2B (KDM4B), JMJD2C (KDM4C) and JARID1B (KDM5B), all of which are directly targeted by HIF.

Overview of Histone 3 modifications implicated in RCC genetics http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f1.jpg

A number of histone modifying genes are mutated in renal cell carcinoma. These include the H3K36 trimethylase SETD2, the H3K27 demethylase UTX/KDM6A, the H3K4 demethylase JARID1C/KDM5C and the SWI/SNF complex compenent PBRM1, shown in this cartoon to represent their relative activities on Histone H3.

Hyper-methylation is observed on RASSF1 highly (50% f RCC) yet less on VHL and CDKN2A, yet there is a methylation and silencing observed on TIMP3 and secreted frizzled-related protein 2.

RCC is ONE OF THE “CILIOPATHIES” among Polycystic Kidney Disease (PKD), Tuberous Sclerosis Complex (TSC) and VHL Syndrome. The main display of cysts is dysfunctional primary cilia.

Mol Cancer Res. Author manuscript; available in PMC 2013 Jan 1.

Mol Cancer Res. 2012 Jul; 10(7): 859–880. Published online 2012 May 25. doi:  10.1158/1541-7786.MCR-12-0117

pVHL mutants are categorized as Class A, B and C depending on the affected step in pVHL protein quality control http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f2.jpg

VHL proteostasis involves the chaperone mediated translocation of nascent VHL peptide from the ribosome to the TRiC/CCT chaperonin, where folding occurs in an ATP dependent process. The VBC complex is formed while VHL is bound to TRiC, and the mature complex is then released. Three different classes of mutation exist: Class A mutations prevent binding of VHL to TRiC, and abrogate folding into a mature complex. Class B mutations prevent association of Elongins C and B to VHL. Class C mutations inhibit interaction between VHL and HIF1 a.

# 193300. VON HIPPEL-LINDAU SYNDROME; VHL ICD+, Links
VON HIPPEL-LINDAU SYNDROME, MODIFIERS OF, INCLUDED
Cytogenetic locations: 3p25.3 , 11q13.3
Matching terms: lindau, disease, von, hippellindau, hippel
  • Birt-Hogg-Dube syndrome,
# 135150. BIRT-HOGG-DUBE SYNDROME; BHD ICD+, Links
Cytogenetic location: 17p11.2 
Matching terms: birthoggdube, syndrome, birt, hogg, dube
  • tuberous sclerosis
# 191100. TUBEROUS SCLEROSIS 1; TSC1 ICD+, Links
Cytogenetic location: 9q34.13 
Matching terms: tuber, sclerosi, tuberous
  • familial papillary renal cell carcinoma.
# 144700. RENAL CELL CARCINOMA, NONPAPILLARY; RCC ICD+, Links
NONPAPILLARY RENAL CARCINOMA 1 LOCUS, INCLUDED
Cytogenetic locations: 3p25.3 3p25.3 3q21.1 8q24.13 12q24.31 17p11.2 17q12 
Matching terms: renal, familial, papillary, carcinoma, cell

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4358399/bin/467fig3.jpg

Model for the control of the fate of nephron progenitor cells. Eya1 lies genetically upstream of Six2. Six2 labels the nephron progenitor cells, which can either maintain a progenitor state and self-renew or differentiate via the Wnt4-mediated MET. Wnt4 expression is under the direct control of Wt1. β-Catenin is involved in both progenitor cell fates through activation of different transcriptional programs. Active nuclear phosphorylated Yap/Taz shifts the progenitor balance toward the self-renewal fate. Eya1 and Six2 interact directly with Mycn, leading to dephosphorylation of Mycn pT58, stabilization of the protein, increased proliferation, and potentially a shift of the nephron progenitor toward self-renewal. Genes activated in Wilms’ tumors are depicted in green, and inactivated genes are in blue. Deregulation of Yap/Taz in Wilms’ tumors results in phosphorylated Yap not being retained in the cytoplasm as it should, but it translocates to the nucleus and thus shifts the progenitor cell balance toward self-renewal. This model is likely a simplification, as it presumes that all Wilms’ tumors, regardless of causative mutation, are caused by the same mechanism.

Epigenetic aberrations associated with Wilms’ tumor

Chinese Case Study: PMCID: PMC4471788

They u8ndertook this study based on association of low circulating adiponectin concentrations with a higher risk of several cancers, including renal cell carcinoma. Thus they demonstrated that by case–control study that ADIPOQ rs182052 is significantly associated with ccRCC risk.

They investigated the frequency of three single nucleotide polymorphisms (SNPs), rs182052G>A, rs266729C>G, rs3774262G>A, in the adiponectin gene (ADIPOQ).  1004 registered patients with clear cell renal cell carcinoma (ccRCC) compared with 1108 healthy subjects (= 1108).

The first table presents the characteristics of 1004 patients with clear cell renal cell carcinoma and 1108 cancer-free controls from a Chinese Han population. The Second and third table shows the SNP results.

Table 1: The characteristics of the examined population.

Variable Cases, n (%) Controls, n (%) P-value
1004 (100) 1108 (100)
Age, years
 ≤44 195 (19.4) 230 (20.8) 0.559
 45–64 580 (57.8) 644 (58.1)
 ≥65 229 (22.8) 234 (21.1)
Sex
 Male 711 (70.8) 815 (73.6) 0.160
 Female 293 (29.2) 293 (26.4)
BMI, kg/m2
 <25 480 (47.8) 589 (53.2) 0.014
 ≥25 524 (52.2) 519 (46.8)
Smoking status
 Never 455 (45.3) 529 (47.7) 0.265
 Ever/current 549 (54.7) 579 (52.3)
Hypertension
 No 639 (63.6) 780 (70.4) 0.001
 Yes 365 (36.4) 328 (29.6)
Fuhrman grade
 I 40 (4.0)
 II 380 (37.8)
 III 347 (34.6)
 IV 175 (17.4)
 Missing 62 (6.2)
Stage at diagnosis
 I 738 (73.5)
 II 71 (7.1)
 III 19 (1.9)
 IV 176 (17.5)

Pearson’s χ2-test.

Table 2:

Association between ADIPOQ single nucleotide polymorphisms (SNP) and clear cell renal cell carcinoma risk

SNP HWE Cases, n(%) Controls, n(%) Crude OR (95% CI) P-value Adjusted OR (95% CI) P-value
rs182052
 GG 0.636 249 (24.8) 315 (28.4) 1.00 1.00
 AG 485 (48.3) 544 (49.1) 1.13 (0.92–1.39) 0.253 1.11 (0.90–1.37) 0.331
 AA 270 (26.9) 249 (22.5) 1.37 (1.08–1.75) 0.010 1.36 (1.07–1.74) 0.013
 AG/AA versusGG 1.20 (0.99–1.46) 0.060 1.19 (0.98–1.45) 0.086
 AA versusGG/AG 1.28 (1.04–1.57) 0.019 1.27 (1.04–1.56) 0.019
rs266729
 CC 0.143 502 (50.0) 572 (51.6) 1.00 1.00
 CG 398 (39.6) 434 (39.2) 1.05 (0.88–1.25) 0.635 1.05 (0.87–1.26) 0.633
 GG 104 (10.4) 102 (9.2) 1.16 (0.86–1.57) 0.324 1.17 (0.86–1.58) 0.307
 CG/GG versusCC 1.07 (0.91–1.29) 0.456 1.07 (0.90–1.27) 0.445
 GG versus CC/CG 1.19 (0.83–1.59) 0.377 1.15 (0.86–1.54) 0.353
rs3774262
 GG 0.106 482 (48.0) 523 (47.2) 1.00 1.00
 AG 420 (41.8) 459 (41.4) 0.99 (0.83–1.20) 0.938 0.99 (0.82–1.19) 0.905
 AA 102 (10.2) 126 (11.4) 0.88 (0.66–1.17) 0.381 0.90 (0.67–1.20) 0.463
 AG/AA versusGG 0.98 (0.80–1.16) 0.711 0.97 (0.82–1.15) 0.722
 AA versusGG/AG 0.88 (0.67–1.18) 0.372 0.90 (0.68–1.19) 0.465

Bold values indicate significance.

Adjusted for age, sex, BMI, smoking status, and hypertension. CI, confidence interval; OR, odds ratio; HWE, Hardy–Weinberg equilibrium.

Table 3:

Association between ADIPOQ single nucleotide polymorphisms (SNP) and clear cell renal cell carcinoma risk

SNP HWE Cases, n(%) Controls, n(%) Crude OR (95% CI) P-value Adjusted OR (95% CI) P-value
rs182052
 GG 0.636 249 (24.8) 315 (28.4) 1.00 1.00
 AG 485 (48.3) 544 (49.1) 1.13 (0.92–1.39) 0.253 1.11 (0.90–1.37) 0.331
 AA 270 (26.9) 249 (22.5) 1.37 (1.08–1.75) 0.010 1.36 (1.07–1.74) 0.013
 AG/AA versusGG 1.20 (0.99–1.46) 0.060 1.19 (0.98–1.45) 0.086
 AA versusGG/AG 1.28 (1.04–1.57) 0.019 1.27 (1.04–1.56) 0.019
rs266729
 CC 0.143 502 (50.0) 572 (51.6) 1.00 1.00
 CG 398 (39.6) 434 (39.2) 1.05 (0.88–1.25) 0.635 1.05 (0.87–1.26) 0.633
 GG 104 (10.4) 102 (9.2) 1.16 (0.86–1.57) 0.324 1.17 (0.86–1.58) 0.307
 CG/GG versusCC 1.07 (0.91–1.29) 0.456 1.07 (0.90–1.27) 0.445
 GG versus CC/CG 1.19 (0.83–1.59) 0.377 1.15 (0.86–1.54) 0.353
rs3774262
 GG 0.106 482 (48.0) 523 (47.2) 1.00 1.00
 AG 420 (41.8) 459 (41.4) 0.99 (0.83–1.20) 0.938 0.99 (0.82–1.19) 0.905
 AA 102 (10.2) 126 (11.4) 0.88 (0.66–1.17) 0.381 0.90 (0.67–1.20) 0.463
 AG/AA versusGG 0.98 (0.80–1.16) 0.711 0.97 (0.82–1.15) 0.722
 AA versusGG/AG 0.88 (0.67–1.18) 0.372 0.90 (0.68–1.19) 0.465

Bold values indicate significance.

Adjusted for age, sex, BMI, smoking status, and hypertension. CI, confidence interval; OR, odds ratio; HWE, Hardy–Weinberg equilibrium.

Molecular Genetics Level for Physiology (Function):

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4503866/bin/10585_2015_9731_Fig6_HTML.jpg

a The protein–protein interaction for the identified 8 proteins in STRING (10 necessary proteins/genes were added into the network so as to find the potential strong connection among them. The red dotted lines circled three main pathways. b The ingenuity pathway analysis (IPA) for all these 18 genes showing that oxidative phosphorylation, mitochondria dysfunction and granzyme A are the significantly activated pathways (fold change over 1.5, P < 0.05). c The possible mechanism related mitochondria functions: unspecific condition like inflammation, carcinogens, radiation (ionizing or ultraviolet), intermittent hypoxia, viral infections which is carcinogenesis in our study that damages a cell’s oxidative phosphorylation. Any of these conditions can damage the structure and function of mitochondria thus activating a respiratory chain changes (Complex I, II, III, IV) and also cytochrome c release. When the mitochondrial dysfunction persists, it produces genome instability (mtDNA mutation), and further lead to malignant transformation (metastasis) via increased ROS and apoptotic resistance. (Color figure online)

RENAL CELL CARCINOMA AND METABOLISM goes hand to hand in genes encoding enzymes of the Krebs cycle suppress tumor formation in kidney cells. This includes Succinate dehydrogenase (SDH), Fumarate hydratase (FH).  As a result of accumulation of succinate or fumarate causes the inhibition of a family of 2-oxoglutarate-dependent dioxygeneases.

The FH and SDH genes function as two-hit tumor suppressor genes.

SDH has a complex of 4 different polypeptides (SDHA-D) function in electron transfer, catalyzes the conversion of succinate to fumarate. Furthermore, heterozygous germline mutations in SDHsubunits predispose to pheochromocytoma/paraganglioma. FH function to convert fumarate to malate.  When its mutations presented as heterozygous germline, it predisposes hereditary leiomyomatosis and renal cell cancer (HLRCC). Among them about 20–50% of HLRCC families are typically papillary-type 2 (pRCC-2) and overwhelmingly aggressive.RCC is increasingly being recognized as a metabolic disease, and key lesions in nutrient sensing and processing have been detected.

Regulation of Prolyl Hydroxylases and Keap1 by Krebs cycle http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f4.jpg

Regulation of Prolyl Hydroxylases by Tricarboxylic Acid (TCA) Cycle Intermediates. Prolyl hydroxylases use TCA cycle intermediates to help catalyze the oxygen, iron and ascorbate dependent- addition of a hydroxyl side chain to a Pro402 and Pro564 of HIF alpha subunits, leading to VHL binding and degradation. Defects in either fumarate hydratase or succinate dehydrogenase will drive up levels of fumarate and succinate, which competitively bind prolyl hydroxylases, and prevent HIF prolyl hydroxylation. This results in higher intracellular HIF levels.

Regulation of mTORC1 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f5.jpg

HIF regulation and mTOR pathway connections. Hypoxia blocks HIF expression in a TSC1/2 and REDD dependent pathway [155]. HIF1α appears to be both TORC1 and TORC2 dependent, whereas HIF2α is only TORC2 dependent [275]. Signaling via TORC2 appears to upregulate HIF2α in an AKT dependent manner [69].

TREATMENT:

Based on the types of renal cancers the treatment method may vary but the general scheme is:

 

Drugs Approved for Kidney (Renal Cell) Cancer

Food and Drug Administration (FDA) approved drugs for kidney (renal cell) cancer. Some of the drug names link to NCI’s Cancer Drug Information summaries.

T cell regulation in RCC http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f7.jpg

Immune regulation of renal tumor cells. A: When an antigen presenting cell (APC) engages a T-cell via a cognate T-cell receptor (TCR) and CD28, T-cell cell activation occurs. B: Early and late T-cell inhibitory signals are mediated via CTLA-4 and PD-1 receptors, and this occurs via engagement of the APC via B7 and PD-L1, respectively. C: Inhibitory antibodies against CTLA-4 and PD-1 can overcome T-cell downregulation and once again allow cytokine production.

Phase III Trials of Targeted Therapy in Metastatic Renal Cell Carcinoma

Trial Number
of
patients
Clinical setting RR (%) PFS (months) OS (months)
VEGF-Targeted Therapy
*AVOREN

Bevacizumab +
IFNa
vs.IFNa[270]

649 First-line 31 vs. 12 10.2 vs. 5.5
(p<0.001)
23.3 vs. 21.3
(p=0.129)
*CALBG 90206

Bevacizumab +
IFNa
vs.IFNa[271]

732 First-line 25.5 vs. 13 8.4 vs. 4.9
(p<0.001)
18.3 vs. 17.4
(p=0.069)
Sunitinib vs.
IFNa[248]
750 First-line 47 vs. 12 11 vs. 5
(p=0.0001)
26.4 vs. 21.8
(p=0.051)
*TARGET

Sorafenib vs.
Placebo[272]

903 Second-line

(post-cytokine)

10 vs. 2 5.5 vs. 2.8
(p<0.01)
17.8vs.15.2
(p=0.88)
Pazopanib vs.
placebo[273]
435 First line/second line

(post-cytokine)

30 vs. 3 9.2 vs. 4.2
(p<0.0001)
22.9 vs. 20.5
(p=0.224)
*AXIS

Axitinib vs.
sorafenib [269]

723 Second line

(post-sunitinib, cytokine,
bevacizumab or
temsirolimus)

19 vs. 9
(p=0.0001)
6.7 vs. 4.7
(p<0.0001)
Not reported
mTOR-Targeted Therapy
*ARCC
Temsirolimus
vs. Tem + IFNa
vs. IFNa[249]
624 First line, ≥ 3 poor risk
featuresa
9 vs. 5 3.8 vs. 1.9 for
IFNa
monotherapy
(p=0.0001)
10.9 vs. 7.3 for
IFNa(p=0.008)
*RECORD-1
Everolimus vs.
placebo [274]
410 Second line
(post sunitinib and/or
sorafenib)
2 vs. 0 4.9 vs. 1.9

(p<0.0001)

14.8 vs. 14.5

RCC renal cell carcinoma, RR response rate, OS overall survival, PFS progression free survival, VEGFvascular endothelial growth factor, IFNa interferon alphamTOR mammalian target of rapamycin. AVORENAVastin fOr RENal cell cancer, CALBG Cancer and Leukemia Group B. TARGET Treatment Approaches in Renal Cancer Global Evaluation Trial. AXIS Axitinib in Second Line. ARCC Advanced Renal-Cell Carcinoma. RECORD-1 REnal Cell cancer treatment withOral RAD001 given Daily.

aIncluding serum lactate dehydrogenase level of more than 1.5 times the upper limit of the normal range, a hemoglobin level below the lower limit of the normal range; a corrected serum calcium level of more than 10 mg per deciliter (2.5 mmol per liter), a time from initial diagnosis of renal-cell carcinoma to randomization of less than 1 year, a Karnofsky performance score of 60 or 70, or metastases in multiple organs.

PMC full text: Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.

Open Access J Urol. 2010 Aug; 2010(2): 125–141. doi:  10.2147/RRU.S7242

Table: RCC-Associated Antigens (RCCAA) Recognized by T Cells.

Antigen Antigen
Category
Frequency of
Expression
Among RCC
Tumors (%)
CD8+ T cell
recognition:
Patients with
HLA Class I
Allele(s)
CD4+ T cell
recognition:
Patients with
HLA Class II
Allele(s)
References found in Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.
Survivina ML 100 Multiple Multiple 114
OFA-iLR OF 100 A2 NR 115116
IGFBP3ab ML 97 NR Multiple 117118
EphA2a ML > 90 A2 DR4 1744119
RU2AS Antisense
transcript
> 90 B7 NR 120
G250
(CA-IX) ab
RCC 90 A2, A24 Multiple 4751
EGFRab ML 85 A2 NR 121122
HIFPH3a ML 85 A24 NR 123
c-Meta ML > 80 A2 NR 124
WT-1a ML 80 A2, A24 NR 125128
MUC1ab ML 76 A2 DR3 46129130
5T4 ML 75 A2, Cw7 DR4 54131133
iCE aORF 75 B7 NR 134
MMP7a ML 75 A3 Multiple 117135136
Cyclin D1a ML 75 A2 Multiple 117137138
HAGE b CT 75 A2 DR4 139
hTERT ab ML > 70 Mutliple Multiple 140142
FGF-5 Protein splice variant > 60 A3 NR 143
mutVHLab ML > 60 NR NR 144
MAGE-A3 b CT 60 Multiple Multiple 145
SART-3 ML 57 Mulitple NR 146149
SART-2 ML 56 A24 NR 150
PRAME b CT 40 Multiple NR 151154
p53ab Mutant/WT
ML
32 Mutliple Multiple 155156
MAGE-A9b CT >30 A2 NR 157
MAGE-A6b CT 30 Mutliple DR4 18158
MAGE-D4b CT 30 A25 NR 159
Her2/neua ML 1030 Multiple Multiple 45160164
SART-1a ML 25 Multiple NR 165167
RAGE-1 CT (ORF2/5) 21 Mutliple Multiple 151157168169
TRP-1/ gp75 ML 11 A31 DR4 151170172

A summary is provided for RCCAA that have been defined at the molecular level. RCCAA are characterized with regard to their antigen category, their prevalence of (over)expression among total RCC specimens evaluated, whether RCCAA expression is modulated by hypoxia or tumor DNA methylation status, and which HLA class I and class II alleles have been reported to serve as presenting molecules for T cell recognition of peptides derived from a given RCCAA.

Abbreviations: CT = Cancer-Testis Antigens; ML = Multi-lineage Antigens; NR = Not Reported; OF = Oncofetal Antigen; aORF = altered open reading frame; ORF = open reading frame; RCC = Renal cell carcinoma; WT = Wild-Type;

aHypoxia-Induced;

bHypomethylation-Induced.

PMC full text: Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.

Open Access J Urol. 2010 Aug; 2010(2): 125–141. doi:  10.2147/RRU.S7242

Expected Impact on Teff versus Suppressor Cells
Co-Therapeutic Agent Teff
priming
Teff
function
Teff
survival
Teff
(TME)
Treg/
MDSC
References found in Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.
Cytokines
IL-2 +/− ↑ (Treg) 173175
IL-7 ↑ (Treg) 176178
IL-12 – (Treg), ↓ (MDSC) 179181
IL-15 ↑ (Treg)* 182183
IL-18 ↓ (Treg) 184186
IL-21 ? +/− (Treg) 187190
IFN-α +/− (Treg) 175191194
IFN-γ -? ? ↑ ↑ (Treg); ↑ ?(MDSC) 195197
GM-CSF ? ↑ (Treg); ↑(MDSC) 198202
Coinhibitory Antagonist
CTLA-4 ? ↓ (Treg) 203204
PD1/PD1L ↓ (Treg) 205207
Costimulatory Agonist
CD40/CD40L ↑ (Treg); ↑(MDSC) 208211
GITR/GITRL ↓ (Treg); ↓ (MDSC) 212213
OX40/OX86 ↑↓ (Treg); ↓ (MDSC) 214219
4-1BB/4-1BBL ↑ (Treg) 220224
TLR Agonists
Imiquimod (TLR7) ? 225227
Resiquimod (TLR8) ? ? 228229
CpG (TLR9) ↓ (Treg) 230232
Anti-Angiogenic
VEGF-Trap ? ? 233
Sunitinib ? ↓ (Treg/MDSC) 98100234
Sorafenib ? ↓ (MDSC) 235
Bevacizumab ? ? ↓ (MDSC) 236237
Gefitinib (IRESSA) ? ? ? ? ? 238239
Cetuximab ? ? ? ? 240
mTOR Inhibitors
Temsirolimus/Everolimus ? ↓ (Treg) 241
Treg/MDSC Inhibitors
Iplimumab (CTLA-4) ? ↓ (Treg) 242243
ONTAK (CD25) +/− +/− ? ? ↓ (Treg) 244
Anti-TGFβ/TGFβR ↓ (Treg) 245247
Anti-IL10/IL10R +/− ↓ (Treg) 248249
Anti-IL35/IL35R ↑? ↑? ↑? ↑? ↓ (Treg) 250
1-methyl trytophan ? ? ↓ (MDSC) 251
ATRA ? ? ↑ (Treg), ↓ (MDSC) 9093

Agents that are currently or soon-to-be in clinical trials are summarized with regard to their anticipated impact(s) on Type-1 anti-tumor T cell (Te) activation, function, survival and recruitment into the TME. Additional anticipated effects of drugs on suppressor cells (Treg and MDSC) are also summarized. Key: ↑, agent is expected to increase parameter; ↓, agent is expected to inhibit parameter; +/−, minimal increase or decrease is expected in parameter as a consequence of treatment with agent; ?, unknown effect of agent on parameter.

Abbreviations: ATRA, all-trans retinoic acid; CTLA-4, cytotoxic T Lymphocyte antigen 4; GITR(L), glucocorticoid-induced TNF receptor (ligand); GM-CSF, granulocyte-macrophage colony stimulating factor; IFN, interferon; IL, interleukin; MDSC, myeloid-derived suppressor cell; PD1/PD1L, programmed cell death 1 (ligand); TGF-β(R), tumor necrosis factor-β(receptor); TLR, Toll-like receptor; TME, tumor microenvironment; Treg, regulatory T cell; VEGF, vascular endothelial growth factor.

Alternative and Complementary Therapies for Cancer:

  • Art therapy
  • Dance or movement therapy
  • Exercise
  • Meditation
  • Music therapy
  • Relaxation exercises

Mol Cancer Res. 2012 Jul; 10(7): 859–880. Published online 2012 May 25. doi:  10.1158/1541-7786.MCR-12-0117 PMCID: PMC3399969 NIHMSID: NIHMS380694

State-of-the-science: An update on renal cell carcinoma

Eric Jonasch,1 Andrew Futreal,1 Ian Davis,2 Sean Bailey,2 William Y. Kim,2 James Brugarolas,3 Amato Giaccia,4 Ghada Kurban,5 Armin Pause,6 Judith Frydman,4 Amado Zurita,1 Brian I. Rini,7 Pam Sharma,8Michael Atkins,9 Cheryl Walker,8,* and W. Kimryn Rathmell2,*

Go to:

REFERENCES

Germline and somatic mutations in the tyrosine kinase domain of the MET proto-oncogene in papillary renal carcinomas.[Nat Genet. 1997]

Germline mutations in FH predispose to dominantly inherited uterine fibroids, skin leiomyomata and papillary renal cell cancer.[Nat Genet. 2002]

Mutations in a novel gene lead to kidney tumors, lung wall defects, and benign tumors of the hair follicle in patients with the Birt-Hogg-Dubé syndrome.[Cancer Cell. 2002]

Tuberous sclerosis-associated renal cell carcinoma. Clinical, pathological, and genetic features.[Am J Pathol. 1996]

Tumor risks and genotype-phenotype-proteotype analysis in 358 patients with germline mutations in SDHB and SDHD.[Hum Mutat. 2010]

Genome-wide association study of renal cell carcinoma identifies two susceptibility loci on 2p21 and 11q13.3.[Nat Genet. 2011]

Mutations of the VHL tumour suppressor gene in renal carcinoma.[Nat Genet. 1994]

Germline and somatic mutations in the tyrosine kinase domain of the MET proto-oncogene in papillary renal carcinomas.[Nat Genet. 1997]

Few FH mutations in sporadic counterparts of tumor types observed in hereditary leiomyomatosis and renal cell cancer families.[Cancer Res. 2002]

Interplay between pVHL and mTORC1 pathways in clear-cell renal cell carcinoma.[Mol Cancer Res. 2011]

Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.[N Engl J Med. 2012]

Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma.[Nature. 2011]

Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes.[Nature. 2010]

Histone methyltransferase gene SETD2 is a novel tumor suppressor gene in clear cell renal cell carcinoma.[Cancer Res. 2010]

The von Hippel-Lindau tumor suppressor protein regulates gene expression and tumor growth through histone demethylase JARID1C.[Oncogene. 2012]

HIFalpha targeted for VHL-mediated destruction by proline hydroxylation: implications for O2 sensing.[Science. 2001]

Targeting of HIF-alpha to the von Hippel-Lindau ubiquitylation complex by O2-regulated prolyl hydroxylation.[Science. 2001]

Hypoxia induces trimethylated H3 lysine 4 by inhibition of JARID1A demethylase.[Cancer Res. 2010]

Silencing of the VHL tumor-suppressor gene by DNA methylation in renal carcinoma.[Proc Natl Acad Sci U S A. 1994]

Inactivation of the von Hippel-Lindau (VHL) tumour suppressor gene and allelic losses at chromosome arm 3p in primary renal cell carcinoma: evidence for a VHL-independent pathway in clear cell renal tumourigenesis.[Genes Chromosomes Cancer. 1998]

DNA methylation and histone modifications cause silencing of Wnt antagonist gene in human renal cell carcinoma cell lines.[Int J Cancer. 2008]

Global levels of histone modifications predict prognosis in different cancers.[Am J Pathol. 2009]

A phase II trial of panobinostat, a histone deacetylase inhibitor, in the treatment of patients with refractory metastatic renal cell carcinoma.[Cancer Invest. 2011]

Review Oxygen sensing by metazoans: the central role of the HIF hydroxylase pathway.[Mol Cell. 2008]

Review Targeting HIF-1 for cancer therapy.[Nat Rev Cancer. 2003]

Review Hypoxia-inducible factors: central regulators of the tumor phenotype.[Curr Opin Genet Dev. 2007]

Review Role of VHL gene mutation in human cancer.[J Clin Oncol. 2004]

Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes.[Nature. 2010]

Genetic and functional studies implicate HIF1α as a 14q kidney cancer suppressor gene.[Cancer Discov. 2011]

HIF-alpha effects on c-Myc distinguish two subtypes of sporadic VHL-deficient clear cell renal carcinoma.[Cancer Cell. 2008]

Software and database for the analysis of mutations in the VHL gene.[Nucleic Acids Res. 1998]

Genetic analysis of von Hippel-Lindau disease.[Hum Mutat. 2010]

The von Hippel-Lindau tumor suppressor protein is required for proper assembly of an extracellular fibronectin matrix.[Mol Cell. 1998]

pVHL modification by NEDD8 is required for fibronectin matrix assembly and suppression of tumor development.[Mol Cell Biol. 2004]

Contrasting effects on HIF-1alpha regulation by disease-causing pVHL mutations correlate with patterns of tumourigenesis in von Hippel-Lindau disease.[Hum Mol Genet. 2001]

Characterization of a von Hippel Lindau pathway involved in extracellular matrix remodeling, cell invasion, and angiogenesis.[Cancer Res. 2006]

The von Hippel-Lindau tumor suppressor gene inhibits hepatocyte growth factor/scatter factor-induced invasion and branching morphogenesis in renal carcinoma cells.[Mol Cell Biol. 1999]

A role for mitochondrial enzymes in inherited neoplasia and beyond.[Nat Rev Cancer. 2003]

Mitochondrial tumour suppressors: a genetic and biochemical update.[Nat Rev Cancer. 2005]

Mitochondrial tumour suppressors: a genetic and biochemical update.[Nat Rev Cancer. 2005]

Identification of the von Hippel-Lindau disease tumor suppressor gene.[Science. 1993]

Vascular tumors in livers with targeted inactivation of the von Hippel-Lindau tumor suppressor.[Proc Natl Acad Sci U S A. 2001]

mTOR: from growth signal integration to cancer, diabetes and ageing.[Nat Rev Mol Cell Biol. 2011]

Genomic expression and single-nucleotide polymorphism profiling discriminates chromophobe renal cell carcinoma and oncocytoma.[BMC Cancer. 2010]

Classification of renal neoplasms based on molecular signatures.[J Urol. 2006]

Genetic subtyping of renal cell carcinoma by comparative genomic hybridization.[Recent Results Cancer Res. 2003]

Papillary renal cell carcinoma. Prognostic value of morphological subtypes in a clinicopathologic study of 43 cases.[Virchows Arch. 2003]

Prognostic impact of carbonic anhydrase IX expression in human renal cell carcinoma.[BJU Int. 2007]

Survivin expression in renal cell carcinoma.[Cancer Invest. 2008]

High expression levels of survivin protein independently predict a poor outcome for patients who undergo surgery for clear cell renal cell carcinoma.[Cancer. 2006]

Mcm2, Geminin, and KI67 define proliferative state and are prognostic markers in renal cell carcinoma.[Clin Cancer Res. 2005]

Prognostic impacts of cytogenetic findings in clear cell renal cell carcinoma: gain of 5q31-qter predicts a distinct clinical phenotype with favorable prognosis.[Cancer Res. 2001]

Chromosome 14q loss defines a molecular subtype of clear-cell renal cell carcinoma associated with poor prognosis.[Mod Pathol. 2011]

Loss of chromosome 9p is an independent prognostic factor in patients with clear cell renal cell carcinoma.[Mod Pathol. 2008]

Chromosome 9p deletions identify an aggressive phenotype of clear cell renal cell carcinoma.[Cancer. 2010]

Gene expression profiling predicts survival in conventional renal cell carcinoma.[PLoS Med. 2006]

Biomarkers predicting outcome in patients with advanced renal cell carcinoma: Results from sorafenib phase III Treatment Approaches in Renal Cancer Global Evaluation Trial.[Clin Cancer Res. 2010]

Interleukin-8 mediates resistance to antiangiogenic agent sunitinib in renal cell carcinoma.[Cancer Res. 2010]

Therapeutic vaccination against metastatic renal cell carcinoma by autologous dendritic cells: preclinical results and outcome of a first clinical phase I/II trial.[Cancer Immunol Immunother. 2002]

Immunotherapy for metastatic renal cell carcinoma.[BJU Int. 2007]

Results of treatment of 255 patients with metastatic renal cell carcinoma who received high-dose recombinant interleukin-2 therapy.[J Clin Oncol. 1995]

Phase II study of vinorelbine in patients with androgen-independent prostate cancer.[Ann Oncol. 2001]

CD28-mediated signalling co-stimulates murine T cells and prevents induction of anergy in T-cell clones.[Nature. 1992]

CD28 and CTLA-4 have opposing effects on the response of T cells to stimulation.[J Exp Med. 1995]

 Mechanisms of T-cell inhibition: implications for cancer immunotherapy.[Expert Rev Vaccines. 2010]

Enhancement of antitumor immunity by CTLA-4 blockade.[Science. 1996]

Combination immunotherapy of B16 melanoma using anti-cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) and granulocyte/macrophage colony-stimulating factor (GM-CSF)-producing vaccines induces rejection of subcutaneous and metastatic tumors accompanied by autoimmune depigmentation.[J Exp Med. 1999]

Biologic activity of cytotoxic T lymphocyte-associated antigen 4 antibody blockade in previously vaccinated metastatic melanoma and ovarian carcinoma patients.[Proc Natl Acad Sci U S A. 2003]

Preoperative CTLA-4 blockade: tolerability and immune monitoring in the setting of a presurgical clinical trial.[Clin Cancer Res. 2010]

Ipilimumab (anti-CTLA4 antibody) causes regression of metastatic renal cell cancer associated with enteritis and hypophysitis.[J Immunother. 2007]

PD-1 and its ligands in tolerance and immunity.[Annu Rev Immunol. 2008]

New strategies in kidney cancer: therapeutic advances through understanding the molecular basis of response and resistance.[Clin Cancer Res. 2010]

Efficacy of everolimus in advanced renal cell carcinoma: a double-blind, randomised, placebo-controlled phase III trial.[Lancet. 2008]

Comparative effectiveness of axitinib versus sorafenib in advanced renal cell carcinoma (AXIS): a randomised phase 3 trial.[Lancet. 2011]

Efficacy of everolimus in advanced renal cell carcinoma: a double-blind, randomised, placebo-controlled phase III trial.[Lancet. 2008]

American Cancer Society. Cancer Facts & Figures 2014. 2014. http://www.cancer.org/research/cancerfactsstatistics/cancerfactsfigures2014/. Accessed Oct. 1, 2014.

Amin A, Plimack ER, Infante JR, Ernstoff MS, Rini BI, Mcdermott DF, Knox JJ, Pal SK, Voss MH, Sharma P, Kollmannsberger CK, Heng DYC, Sprattin JL, Shen Y, Kurland JF, Gagnier P, Hammers HJ. Nivolumab (anti-PD-1; BMS-936558, ONO-4538) in combination with sunitinib or pazopanib in patients (pts) with metastatic renal cell carcinoma (mRCC). J Clin Oncol 32:5s (suppl; abstr 5010), 2014.

Atkins MB, Kudchadkar RR, Sznol M, Mcdermott DF, Lotem M, Schacther J, Wolchok JD, Urba WJ, Kuzel T, Schuchter LM, Slingluff CL, Ernstoff MS, Fay JW, Friedlander PA, Gajewski T, Zarour H, Rotem-Yehudar R, Sosman JA. Phase 2, multicenter, safety and efficacy study of pidilizumab in patients with metastatic melanoma. J Clin Oncol 32:5s (suppl; abstr 9001), 2014.

Beck KE, Blansfield JA, Tran KQ, Feldman AL, Hughes MS, Royal RE, Kammula US, Topalian SL, Sherry RM, Kleiner D, Quezado M, Lowy I, Yellin M, Rosenberg SA, Yang JC. Enterocolitis in patients with cancer after antibody blockade of cytotoxic T-lymphocyte-associated antigen 4. J Clin Oncol 24(15):2283-2289, 2006.

Berger R, Rotem-Yehudar R, Slama G, Landes S, Kneller A, Leiba M, Koren-Michowitz M, Shimoni A, Nagler A. Phase I safety and pharmacokinetic study of CT-011, a humanized antibody interacting with PD-1, in patients with advanced hematologic malignancies. Clin Cancer Res 14(10):3044-3051, 2008.

Brahmer JR, Drake CG, Wollner I, Powderly JD, Picus J, Sharfman WH, Stankevich E, Pons A, Salay TM, Mcmiller TL, Gilson MM, Wang C, Selby M, Taube JM, Anders R, Chen L, Korman AJ, Pardoll DM, Lowy I, Topalian SL. Phase I study of single-agent anti-programmed death-1 (MDX-1106) in refractory solid tumors: safety, clinical activity, pharmacodynamics, and immunologic correlates. J Clin Oncol 28(19):3167-3175, 2010.

Camacho LH, Antonia S, Sosman J, Kirkwood JM, Gajewski TF, Redman B, Pavlov D, Bulanhagui C, Bozon VA, Gomez-Navarro J, Ribas A. Phase I/II trial of tremelimumab in patients with metastatic melanoma. J Clin Oncol 27(7):1075-1081, 2009.

Cho DC, Sosman JA, Sznol M, Gordon MS, Hollebecque A, Mcdermott DF, Delord JP, Rhee IP, Mokatrin A, Kowantez M, Funke RP, Fine GD, Powles T. Clinical activity, safety, and biomarkers of MPDL3280A, an engineered PD-L1 antibody in patients with metastatic renal cell carcinoma (mRCC). J Clin Oncol 31 (suppl; abstr 4505), 2013.

Choueiri TK, Fishman MN, Escudier B, Kim JJ, Kluger H, Stadler WM, Perez-Gracia JL, Mcneel DG, Curti BD, Harrison MR, Plimack ER, Appleman LJ, Fong L, Drake CG, Cohen LJ, Srivastava S, Jure-Kunkel M, Hong Q, Kurland JF, Sznol M. Immunomodulatory activity of nivolumab in previously treated and untreated metastatic renal cell carcinoma (mRCC): Biomarker-based results from a randomized clinical trial. J Clin Oncol 32:5s (suppl; abstr 5012), 2014.

Coppin C, Porzsolt F, Awa A, Kumpf J, Coldman A, Wilt T. Immunotherapy for advanced renal cell cancer. Cochrane Database Syst Rev (1):CD001425, 2005.

Downey SG, Klapper JA, Smith FO, Yang JC, Sherry RM, Royal RE, Kammula US, Hughes MS, Allen TE, Levy CL. Prognostic factors related to clinical response in patients with metastatic melanoma treated by CTL-associated antigen-4 blockade. Clin Cancer Res 13(22):6681-6688, 2007.

Drake CG, Mcdermott DF, Sznol M. Survival, safety, and response duration results of nivolumab (Anti-PD-1; BMS-936558; ONO-4538) in a phase I trial in patients with previously treatedmetastatic renal cell carcinoma (mRCC): long-term patient followup. J Clin Oncol 31 (suppl; abstr 4514), 2013.

Dunn GP, Bruce AT, Ikeda H, Old LJ, Schreiber RD. Cancer immunoediting: from immunosurveillance to tumor escape. Nat Immunol 3(11):991-998, 2002.

Elfiky AA, Sonpavde G. Novel molecular targets for the therapy of renal cell carcinoma. Discov Med13(73):461-471, 2012.

FDA. Pembrolizumab. http://www.fda.gov/Drugs/InformationOnDrugs/ApprovedDrugs/ucm412861.htm. 2014. Accessed Oct. 16, 2014.

Fyfe G, Fisher RI, Rosenberg SA, Sznol M, Parkinson DR, Louie AC. Results of treatment of 255 patients with metastatic renal cell carcinoma who received high-dose recombinant interleukin-2 therapy. J Clin Oncol 13(3):688-696, 1995.

Gupta K, Miller JD, Li JZ, Russell MW, Charbonneau C. Epidemiologic and socioeconomic burden of metastatic renal cell carcinoma (mRCC): a literature review. Cancer Treat Rev 34(3):193-205, 2008.

Hamid O, Robert C, Daud A, Hodi FS, Hwu WJ, Kefford R, Wolchok JD, Hersey P, Joseph RW, Weber JS, Dronca R, Gangadhar TC, Patnaik A, Zarour H, Joshua AM, Gergich K, Elassaiss-Schaap J, Algazi A, Mateus C, Boasberg P, et al. Safety and tumor responses with lambrolizumab (anti-PD-1) in melanoma. N Engl J Med 369(2):134-144, 2013.

Hammers H, Plimack ER, Infante JR, Ernstoff MS, Rini BI, Mcdermott B, Razak AR, Pal SK, Voss MH, Sharma P, Kollmannsberger C, Heng DY, Spratlin J, Shen Y, Kurland J, Gagnier P, Amin A. Phase I study of nivolumab in combination with ipilimumab in metastatic renal cell carcinoma. ASCO Annual Meeting. J Clin Oncol 32:5s (suppl; abstr 4504), 2014.

Hodi FS, O’day SJ, Mcdermott DF, Weber RW, Sosman JA, Haanen JB, Gonzalez R, Robert C, Schadendorf D, Hassel JC. Improved survival with ipilimumab in patients with metastatic melanoma.N Engl J Med 363(8):711-723, 2010.

Infante JR, Powderly JD, Burris HA, Kittaneh M, Grice JH, Smothers JF, Brett S, Fleming ME, May R, Marshall S, Devenport M, Pillemer S, Pardoll DM, Chen L, Langermann S, Lorusso P. Clinical and pharmacodynamic (PD) results of a phase I trial with AMP-224 (B7-DC Fc) that binds to the PD-1 receptor. J Clin Oncol 31 (suppl; abstr 3044), 2013.

Intlekofer AM, Thompson CB. At the bench: preclinical rationale for CTLA-4 and PD-1 blockade ascancer immunotherapyJ Leukoc Biol 94(1):25-39, 2013.

Keir ME, Butte MJ, Freeman GJ, Sharpe AH. PD-1 and its ligands in tolerance and immunity. Annu Rev Immunol 26:677-704, 2008.

Kirkwood JM, Lorigan P, Hersey P, Hauschild A, Robert C, Mcdermott D, Marshall MA, Gomez-Navarro J, Liang JQ, Bulanhagui CA. Phase II trial of tremelimumab (CP-675,206) in patients with advanced refractory or relapsed melanoma. Clin Cancer Res 16(3):1042-1048, 2010.

Lieu C, Bendell J, Powderly JD, Pishvaian MJ, Hochster H, Eckhardt SG, Funke RP, Rossi C, Waterkamp D, Hurwitz H. Safety and efficacy of MPDL3280A (anti-PDL1) in combination with bevacizumab (bev) and/or chemotherapy (chemo) in patients (pts) with locally advanced or metastatic solid tumors. Ann Oncol 25 (suppl 4; abstr 1049o), 2014.

McDermott DF, Regan MM, Clark JI, Flaherty LE, Weiss GR, Logan TF, Kirkwood JM, Gordon MS, Sosman JA, Ernstoff MS, Tretter CP, Urba WJ, Smith JW, Margolin KA, Mier JW, Gollob JA, Dutcher JP, Atkins MB. Randomized phase III trial of high-dose interleukin-2 versus subcutaneous interleukin-2 and interferon in patients with metastatic renal cell carcinoma. J Clin Oncol 23(1):133-141, 2005.

McDermott DF, Sznol M, Sosman JA, Soria JC, Gordon MS, Hamid O, Delord JP, Fasso M, Wang Y, Bruey J, Fine GD, Powles T. Immune correlates and long term follow up of a phase Ia study of MPDL3280A, an engineered PD-L1 antibody, in patients with metastatic renal cell carcinoma (mRCC). Ann Oncol 25 (Suppl 4; abstr 809o), 2014.

Melero I, Hervas-Stubbs S, Glennie M, Pardoll DM, Chen L. Immunostimulatory monoclonal antibodies for cancer therapy. Nat Rev Cancer 7(2):95-106, 2007.

Millward M, Underhill C, Lobb S, Mcburnie J, Meech SJ, Gomez-Navarro J, Marshall MA, Huang B, Mather CB. Phase I study of tremelimumab (CP-675 206) plus PF-3512676 (CPG 7909) in patients with melanoma or advanced solid tumours. Br J Cancer 108(10):1998-2004, 2013.

Motzer RJ, Hutson TE, Cella D, Reeves J, Hawkins R, Guo J, Nathan P, Staehler M, De Souza P, Merchan JR, Boleti E, Fife K, Jin J, Jones R, Uemura H, De Giorgi U, Harmenberg U, Wang J, Sternberg CN, Deen K, et al. Pazopanib versus sunitinib in metastatic renal-cell carcinoma. N Engl J Med 369(8):722-731, 2013.

Motzer RJ, Jonasch E, Agarwal N. NCCN Clinical Practice Guidelines in Oncology: Kidney Cancer. Version 1. 2015. National Comprehensive Cancer Network.http://www.nccn.org/professionals/physician_gls/f_guidelines.asp. Accessed Oct. 1, 2014.

Motzer RJ, Rini BI, McDermott DF, Redman BG, Kuzel TM, Harrison MR, Vaishampayan UN, Drabkin HA, George S, Logan TF, Margolin KA, Plimack ER, Lambert AM, Waxman IM, Hammers HJ. Nivolumab for Metastatic Renal Cell Carcinoma: Results of a Randomized Phase II Trial. J Clin Oncol, epub ahead of print, Dec. 1, 2014.

National Cancer Institute. SEER Stat Fact Sheets: Kidney and renal pelvis cancer. Surveillance,Epidemiology, and End Results Program. 2014. http://seer.cancer.gov/statfacts/html/kidrp.html. Accessed Oct. 1, 2014.

Negrier S, Escudier B, Lasset C, Douillard JY, Savary J, Chevreau C, Ravaud A, Mercatello A, Peny J, Mousseau M, Philip T, Tursz T. Recombinant human interleukin-2, recombinant human interferon alfa-2a, or both in metastatic renal-cell carcinoma. Groupe Francais d’Immunotherapie. N Engl J Med 338(18):1272-1278, 1998.

O’Day SJ, Hamid O, Urba WJ. Targeting cytotoxic T-lymphocyte antigen-4 (CTLA-4). Cancer110(12):2614-2627, 2007.

Page DB, Postow MA, Callahan MK, Allison JP, Wolchok JD. Immune modulation in cancer with antibodies. Annu Rev Med 65:185-202, 2014.

Pages C, Gornet JM, Monsel G, Allez M, Bertheau P, Bagot M, Lebbe C, Viguier M. Ipilimumab-induced acute severe colitis treated by infliximab. Melanoma Res 23(3):227-230, 2013.

Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer12(4):252-264, 2012.

Park J-J, Omiya R, Matsumura Y, Sakoda Y, Kuramasu A, Augustine MM, Yao S, Tsushima F, Narazaki H, Anand S. B7-H1/CD80 interaction is required for the induction and maintenance of peripheral T-cell tolerance. Blood 116(8):1291-1298, 2010.

Patnaik A, Kang SP, Tolcher AW, Rasco DW, Papadopoulos KP, Beeram M, Drengler R, Chen C, Smith L, Perez C, Gergich K, Lehnert M. Phase I study of MK-3475 (anti-PD-1 monoclonal antibody) in patients with advanced solid tumors. J Clin Oncol 30 (suppl; abstr 2512), 2012.

Ribas A, Hodi FS, Kefford R, Hamid O, Daud A, Wolchok JD, Hwu WJ, Gangadhar TC, Patnaik A, Joshua AM, Hersey P, Weber JS, Dronca R, Zarour H, Gergich K, Li XN, Iannone R, Kang SP, Ebbinghaus SW, Robert C. Efficacy and safety of the anti-PD-1 monoclonal antibody MK-3475 in 411 patients (pts) with melanoma (MEL). J Clin Oncol 32:5s (suppl; abstr LBA9000), 2014.

Ribas A, Kefford R, Marshall MA, Punt CJ, Haanen JB, Marmol M, Garbe C, Gogas H, Schachter J, Linette G, Lorigan P, Kendra KL, Maio M, Trefzer U, Smylie M, Mcarthur GA, Dreno B, Nathan PD, Mackiewicz J, Kirkwood JM, et al. Phase III randomized clinical trial comparing tremelimumab with standard-of-care chemotherapy in patients with advanced melanoma. J Clin Oncol 31(5):616-622, 2013.

Rini BI, Stein M, Shannon P, Eddy S, Tyler A, Stephenson JJ Jr, Catlett L, Huang B, Healey D, Gordon M. Phase 1 dose-escalation trial of tremelimumab plus sunitinib in patients with metastatic renal cell carcinoma. Cancer 117(4):758-767, 2011.

Robert C, Ribas A, Wolchok JD, Hodi FS, Hamid O, Kefford R, Weber JS, Joshua AM, Hwu WJ, Gangadhar TC, Patnaik A, Dronca R, Zarour H, Joseph RW, Boasberg P, Chmielowski B, Mateus C, Postow MA, Gergich K, Elassaiss-Schaap J, et al. Anti-programmed-death-receptor-1 treatment with pembrolizumab in ipilimumab-refractory advanced melanoma: a randomised dose-comparison cohort of a phase 1 trial. Lancet 384(9948):1109-1117, 2014.

Robert C, Thomas L, Bondarenko I, O’day S, M DJ, Garbe C, Lebbe C, Baurain JF, Testori A, Grob JJ, Davidson N, Richards J, Maio M, Hauschild A, Miller WH Jr, Gascon P, Lotem M, Harmankaya K, Ibrahim R, Francis S, et al. Ipilimumab plus dacarbazine for previously untreated metastatic melanoma. N Engl J Med 364(26):2517-2526, 2011.

Sheridan C. Cautious optimism surrounds early clinical data for PD-1 blocker. Nat Biotechnol30(8):729-730, 2012.

Tarhini AA, Cherian J, Moschos SJ, Tawbi HA, Shuai Y, Gooding WE, Sander C, Kirkwood JM. Safety and efficacy of combination immunotherapy with interferon alfa-2b and tremelimumab in patients with stage IV melanoma. J Clin Oncol 30(3):322-328, 2012.

Topalian SL, Drake CG, Pardoll DM. Targeting the PD-1/B7-H1(PD-L1) pathway to activate anti-tumor immunityCurr Opin Immunol 24(2):207-212, 2012a.

Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, Mcdermott DF, Powderly JD, Carvajal RD, Sosman JA, Atkins MB, Leming PD, Spigel DR, Antonia SJ, Horn L, Drake CG, Pardoll DM, Chen L, Sharfman WH, Anders RA, Taube JM, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med 366(26):2443-2454, 2012b.

Waterhouse P, Penninger JM, Timms E, Wakeham A, Shahinian A, Lee KP, Thompson CB, Griesser H, Mak TW. Lymphoproliferative disorders with early lethality in mice deficient in Ctla-4. Science270(5238):985-988, 1995.

Weber JS, Kähler KC, Hauschild A. Management of immune-related adverse events and kinetics of response with ipilimumab. J Clin Oncol 30(21):2691-2697, 2012.

Weber JS, Minor DR, D’Angelo S, Hodi FS, Gutzmer R, Neyns B, Hoeller C, Khushalani NI, Miller WH, Grob J, Lao C, Linette G, Grossmann K, Hassel J, Lorigan P, Maio M, Sznol M, Lambert A, Yang A, Larkin J. A phase 3 randomized, open-label study of nivolumab (anti-PD-1; BMS-936558; ONO-4538) versus investigator’s choice chemotherapy (ICC) in patients with advanced melanoma after prior anti-CTLA-4 therapy. ESMO Annual Meetings. Abstract #LBA3_PR. 2014.

Westin JR, Chu F, Zhang M, Fayad LE, Kwak LW, Fowler N, Romaguera J, Hagemeister F, Fanale M, Samaniego F, Feng L, Baladandayuthapani V, Wang Z, Ma W, Gao Y, Wallace M, Vence LM, Radvanyi L, Muzzafar T, Rotem-Yehudar R, et al. Safety and activity of PD1 blockade by pidilizumab in combination with rituximab in patients with relapsed follicular lymphoma: a single group, open-label, phase 2 trial. Lancet Oncol 15(1):69-77, 2014.

Wolchok JD, Kluger H, Callahan MK, Postow MA, Rizvi NA, Lesokhin AM, Segal NH, Ariyan CE, Gordon RA, Reed K, Burke MM, Caldwell A, Kronenberg SA, Agunwamba BU, Zhang X, Lowy I, Inzunza HD, Feely W, Horak CE, Hong Q, et al. Nivolumab plus ipilimumab in advanced melanoma.N Engl J Med 369(2):122-133, 2013.

Yang JC, Hughes M, Kammula U, Royal R, Sherry RM, Topalian SL, Suri KB, Levy C, Allen T, Mavroukakis S, Lowy I, White DE, Rosenberg SA. Ipilimumab (anti-CTLA4 antibody) causes regression of metastatic renal cell cancer associated with enteritis and hypophysitis. J Immunother30(8):825-830, 2007.

Zou W, Chen L. Inhibitory B7-family molecules in the tumour microenvironment. Nat Rev Immunol8(6):467-477, 2008.

[Discovery Medicine; ISSN: 1539-6509; Discov Med 18(101):341-350, December 2014.Copyright © Discovery Medicine. All rights reserved.]

 

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1:45PM 11/12/2014 – 10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston

REAL TIME Coverage of this Conference by Dr. Aviva Lev-Ari, PhD, RN – Director and Founder of LEADERS in PHARMACEUTICAL BUSINESS INTELLIGENCE, Boston http://pharmaceuticalintelligence.com

 

1:45 p.m. Panel Discussion – Oncology

Oncology

There has been a remarkable transformation in our understanding of the molecular genetic basis of cancer and its treatment during the past decade or so. In depth genetic and genomic analysis of cancers has revealed that each cancer type can be sub-classified into many groups based on the genetic profiles and this information can be used to develop new targeted therapies and treatment options for cancer patients. This panel will explore the technologies that are facilitating our understanding of cancer, and how this information is being used in novel approaches for clinical development and treatment.

Oncology

Opening Speaker & Moderator:

Lynda Chin, M.D.
Department Chair, Department of Genomic Medicine
MD Anderson Cancer Center     @MDAnderson   #endcancer

  • Who pays for personalized medicine?
  • potential of Big data, analytics, Expert systems, so not each MD needs to see all cases, Profile disease to get same treatment
  • business model: IP, Discovery, sharing, ownership — yet accelerate therapy
  • security of healthcare data
  • segmentation of patient population
  • management of data and tracking innovations
  • platforms to be shared for innovations
  • study to be longitudinal,
  • How do we reconcile course of disease with personalized therapy
  • phenotyping the disease vs a Patient in wait for cure/treatment

Panelists:

Roy Herbst, M.D., Ph.D.    @DrRoyHerbstYale

Ensign Professor of Medicine and Professor of Pharmacology;
Chief of Medical Oncology, Yale Cancer Center and Smilow Cancer Hospital     @YaleCancer

Development new drugs to match patient, disease and drug – finding the right patient for the right Clinical Trial

  • match patient to drugs
  • partnerships: out of 100 screened patients, 10 had the gene, 5 were able to attend the trial — without the biomarker — all 100 patients would participate for the WRONG drug for them (except the 5)
  • patients wants to participate in trials next to home NOT to have to travel — now it is in the protocol
  • Annotated Databases – clinical Trial informed consent – adaptive design of Clinical Trial vs protocol
  • even Academic MD can’t read the reports on Genomics
  • patients are treated in the community — more training to MDs
  • Five companies collaborating – comparison of 6 drugs in the same class
  • if drug exist and you have the patient — you must apply personalized therapy

 

Lincoln Nadauld, M.D., Ph.D.
Director, Cancer Genomics, Huntsman Intermountain Cancer Clinic @lnadauld @intermountain

  • @Stanford, all patients get Tumor profiles Genomic results, interpretation – deliver personalized therapy
  • Outcomes from Genomics based therapies
  • Is survival superior
  • Targeted treatment – Health economic impact is cost lower or not for same outcome???
  • genomic profiling of tumors: Genomic information changes outcome – adverse events lower
  • Path ways and personalized medicine based on Genomics — integration not yet been worked out

Question by Moderator: Data Management

  • Platform development, clinical knowledge system,
  • build consortium of institutions to share big data – identify all patients with same profile

 

 

 

 

See more at  http://personalizedmedicine.partners.org/Education/Personalized-Medicine-Conference/Program.aspx#sthash.qGbGZXXf.dpuf

@HarvardPMConf

#PMConf

@SachsAssociates

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Introduction to Metabolomics


Introduction to Metabolomics

Author and Curator: Larry H. Bernstein, MD, FCAP 

This concludes a long step-by-step journey into rediscovering biological processes from the genome as a framework to the remodeled and reconstituted cell through a number of posttranscription and posttranslation processes that modify the proteome and determine the metabolome.  The remodeling process continues over a lifetime. The process requires a balance between nutrient intake, energy utilization for work in the lean body mass, energy reserves, endocrine, paracrine and autocrine mechanisms, and autophagy.  It is true when we look at this in its full scope – What a creature is man?

http://masspec.scripps.edu/metabo_science/recommended_readings.php
 Recommended Readings and Historical Perspectives

Metabolomics is the scientific study of chemical processes involving metabolites. Specifically, metabolomics is the “systematic study of the unique chemical fingerprints that specific cellular processes leave behind”, the study of their small-molecule metabolite profiles.[1] The metabolome represents the collection of all metabolites in a biological cell, tissue, organ or organism, which are the end products of cellular processes.[2] mRNA gene expression data and proteomic analyses reveal the set of gene products being produced in the cell, data that represents one aspect of cellular function. Conversely, metabolic profiling can give an instantaneous snapshot of the physiology of that cell. One of the challenges of systems biology and functional genomics is to integrate proteomic, transcriptomic, and metabolomic information to provide a better understanding of cellular biology.

The term “metabolic profile” was introduced by Horning, et al. in 1971 after they demonstrated that gas chromatography-mass spectrometry (GC-MS) could be used to measure compounds present in human urine and tissue extracts. The Horning group, along with that of Linus Pauling and Arthur B. Robinson led the development of GC-MS methods to monitor the metabolites present in urine through the 1970s.

Concurrently, NMR spectroscopy, which was discovered in the 1940s, was also undergoing rapid advances. In 1974, Seeley et al. demonstrated the utility of using NMR to detect metabolites in unmodified biological samples.This first study on muscle highlighted the value of NMR in that it was determined that 90% of cellular ATP is complexed with magnesium. As sensitivity has improved with the evolution of higher magnetic field strengths and magic angle spinning, NMR continues to be a leading analytical tool to investigate metabolism. Efforts to utilize NMR for metabolomics have been influenced by the laboratory of Dr. Jeremy Nicholson at Birkbeck College, University of London and later at Imperial College London. In 1984, Nicholson showed 1H NMR spectroscopy could potentially be used to diagnose diabetes mellitus, and later pioneered the application of pattern recognition methods to NMR spectroscopic data.

In 2005, the first metabolomics web database, METLIN, for characterizing human metabolites was developed in the Siuzdak laboratory at The Scripps Research Institute and contained over 10,000 metabolites and tandem mass spectral data. As of September 2012, METLIN contains over 60,000 metabolites as well as the largest repository of tandem mass spectrometry data in metabolomics.

On 23 January 2007, the Human Metabolome Project, led by Dr. David Wishart of the University of Alberta, Canada, completed the first draft of the human metabolome, consisting of a database of approximately 2500 metabolites, 1200 drugs and 3500 food components. Similar projects have been underway in several plant species, most notably Medicago truncatula and Arabidopsis thaliana for several years.

As late as mid-2010, metabolomics was still considered an “emerging field”. Further, it was noted that further progress in the field depended in large part, through addressing otherwise “irresolvable technical challenges”, by technical evolution of mass spectrometry instrumentation.

Metabolome refers to the complete set of small-molecule metabolites (such as metabolic intermediates, hormones and other signaling molecules, and secondary metabolites) to be found within a biological sample, such as a single organism. The word was coined in analogy with transcriptomics and proteomics; like the transcriptome and the proteome, the metabolome is dynamic, changing from second to second. Although the metabolome can be defined readily enough, it is not currently possible to analyse the entire range of metabolites by a single analytical method. The first metabolite database(called METLIN) for searching m/z values from mass spectrometry data was developed by scientists at The Scripps Research Institute in 2005. In January 2007, scientists at the University of Alberta and the University of Calgary completed the first draft of the human metabolome. They catalogued approximately 2500 metabolites, 1200 drugs and 3500 food components that can be found in the human body, as reported in the literature. This information, available at the Human Metabolome Database (www.hmdb.ca) and based on analysis of information available in the current scientific literature, is far from complete.

Each type of cell and tissue has a unique metabolic ‘fingerprint’ that can elucidate organ or tissue-specific information, while the study of biofluids can give more generalized though less specialized information. Commonly used biofluids are urine and plasma, as they can be obtained non-invasively or relatively non-invasively, respectively. The ease of collection facilitates high temporal resolution, and because they are always at dynamic equilibrium with the body, they can describe the host as a whole.

Metabolites are the intermediates and products of metabolism. Within the context of metabolomics, a metabolite is usually defined as any molecule less than 1 kDa in size.
A primary metabolite is directly involved in the normal growth, development, and reproduction. A secondary metabolite is not directly involved in those processes.  By contrast, in human-based metabolomics, it is more common to describe metabolites as being either endogenous (produced by the host organism) or exogenous. Metabolites of foreign substances such as drugs are termed xenometabolites. The metabolome forms a large network of metabolic reactions, where outputs from one enzymatic chemical reaction are inputs to other chemical reactions.

Metabonomics is defined as “the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification”. The word origin is from the Greek μεταβολή meaning change and nomos meaning a rule set or set of laws. This approach was pioneered by Jeremy Nicholson at Imperial College London and has been used in toxicology, disease diagnosis and a number of other fields. Historically, the metabonomics approach was one of the first methods to apply the scope of systems biology to studies of metabolism.

There is a growing consensus that ‘metabolomics’ places a greater emphasis on metabolic profiling at a cellular or organ level and is primarily concerned with normal endogenous metabolism. ‘Metabonomics’ extends metabolic profiling to include information about perturbations of metabolism caused by environmental factors (including diet and toxins), disease processes, and the involvement of extragenomic influences, such as gut microflora. This is not a trivial difference; metabolomic studies should, by definition, exclude metabolic contributions from extragenomic sources, because these are external to the system being studied.

Toxicity assessment/toxicology. Metabolic profiling (especially of urine or blood plasma samples) detects the physiological changes caused by toxic insult of a chemical (or mixture of chemicals).

Functional genomics. Metabolomics can be an excellent tool for determining the phenotype caused by a genetic manipulation, such as gene deletion or insertion. Sometimes this can be a sufficient goal in itself—for instance, to detect any phenotypic changes in a genetically-modified plant intended for human or animal consumption. More exciting is the prospect of predicting the function of unknown genes by comparison with the metabolic perturbations caused by deletion/insertion of known genes.

Nutrigenomics is a generalised term which links genomics, transcriptomics, proteomics and metabolomics to human nutrition. In general a metabolome in a given body fluid is influenced by endogenous factors such as age, sex, body composition and genetics as well as underlying pathologies. The large bowel microflora are also a very significant potential confounder of metabolic profiles and could be classified as either an endogenous or exogenous factor. The main exogenous factors are diet and drugs. Diet can then be broken down to nutrients and non- nutrients.

http://en.wikipedia.org/wiki/Metabolomics

Jose Eduardo des Salles Roselino

The problem with genomics was it was set as explanation for everything. In fact, when something is genetic in nature the genomic reasoning works fine. However, this means whenever an inborn error is found and only in this case the genomic knowledge afterwards may indicate what is wrong and not the completely way to put biology upside down by reading everything in the DNA genetic as well as non-genetic problems.

Coordination of the transcriptome and metabolome by the circadian clock PNAS 2012

Coordination of the transcriptome and metabolome by the circadian clock PNAS 2012

analysis of metabolomic data and differential metabolic regulation for fetal lungs, and maternal blood plasma

conformational changes leading to substrate efflux.img

conformational changes leading to substrate efflux.img

The cellular response is defined by a network of chemogenomic response signatures.

The cellular response is defined by a network of chemogenomic response signatures.

Dynamic Construct of the –Omics

Dynamic Construct of the –Omics

 genome cartoon

genome cartoon

central dogma phenotype

central dogma phenotype

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Cancer Metastasis

Author: Tilda Barliya PhD

Metastasis, a complex process that involves the spread of tumor cells, accounts for more than 90%of cancer-related mortality (1,2). A metastatic tumor cell has a treacherous journey to go through:

  • local invasion and intravasation
  • survival in the circulation
  • homing and extravasation into the parenchyma of distant organs
  • adaptation to the new environment
  • outgrowth of secondary lesions

Although tumor cells that are shed from the primary tumor disseminate throughout the body, they tend to colonize select organs, with characteristically different periods of latency and efficiency depending on tumor type or subtype (2).

Steven Paget’s century-old ‘seed and soil’ hypothesis (2, 9) likened tumor cells to ‘seeds’ that are systemically distributed, but that only inhabit particular environments, or ‘soils’, which are supportive to their sustained growth. Understanding the molecular complexity of this process is difficult and we’ll try to unravel some of the pathogenesis and cellular basis that support the metastatic process.

Progression models:

There are two major tumor progression models (2) :

  • Linear –  primary tumour cells undergo successive rounds of mutation and selection35, giving rise to a biologically heterogeneous cellular population in which a subset of malignant clones have accumulated genetic alterations, necessary for metastasis.
  • Parallel –  tumor cells may disseminate very early in malignant progression, colonize multiple secondary sites at different times and ultimately accumulate genetic changes independently from those incurred by the primary tumor.

While both theories are possible, the linear model is validated by both clinical evidence and animal models, the parallel model is mainly based on animal models and still under investigation for clinical clues.

Meera Saxena. Molecular Oncology
Volume 7, Issue 2 , Pages 283-296, April 2013

Drivers of metastasis

During the past few years several methods and studies have been used to find and correlate between a specific gene and it’s homing target.

These genes, which were found using next-generation sequencing and their equivalents, were also validated their actual functional consequences.

Figure 1 (Meera Saxena et al) represent some of the genes that were associated with organ-specific translocation (additional genes were recently identified and included in table 1 – Sethi N et all). Herein, we generally show the gene to organ-specific homing, yet we will not discuss each and every one of them.  An example of specific gene to organ will be further discussed in detail in follow up article.

Signalling pathways in cancer metastasis have been extensively studied at the level of individual proteins or as a linear cascade of proteins but they have been less frequently evaluated through a network approach (2). Understanding the different variables in the gene-metastasis network may be crucial for drug development.

For example;  the drug–gene–phenotype Connectivity Map approach was successfully used to identify the mTOR inhibitor rapamycin as an effective agent for overcoming dexamethasone resistance in acute lymphoblastic leukaemia (2, 4).

Microenvironment

“Non-neoplastic stromal cells have a function in the development of tumor metastasis. Stromal cells as important regulators of metastasis through their ability to influence cancer cell functions such as chemotaxis and invasion, as well as microenvironment properties. It should not come as a surprise that tumor angiogenesis was among the initial findings that supported a role for stromal cells in cancer metastasis; the poor vascular integrity of newly synthesized blood vessels within the tumour allows for the escape of malignant cells with the potential of distant spread” (2). Such cells include:

  • Tumour-associated macrophages,
  • Leukocytes and other immune cells,
  • Mesenchymal cells that reside in breast tissue
  • Mesenchymal cells and neuroendocrine cells

Although some of the molecular pathway was discovered, the molecular components that facilitate communication between tumour cells and individual stromal cells of the primary tumor have yet to be fully understood.

Circulating Tumor cells (CTC)

“Essential to cancer metastasis is the ability of primary tumor cells to enter the vasculature and to use these fluid ‘highways’ as a means to reach distant organs”. Tight vascular wall barriers, unfavorable conditions for survival in distant organs, and a rate-limiting acquisition of organ colonization functions are just some of the impediments to the formation of distant metastasis (2,5,6 ).

Despite their clear prognostic importance, the diagnostic value of CTCs is largely unknown and fairly unexplored. Research challenges both in detection and interpretation render their ability to  be clinically accepted. Additional research is needed to fully explore CTCs’ potential in to predict clinical response to therapy would also help to guide disease management.

Colonization

The colonization and outgrowth of tumor cells in a secondary organ is often considered the rate-limiting, as well as the most poorly delineated, step in the metastatic cascade. Understanding the functional involvement of the tumor stromal cells of the secondary site may be crucial to understanding their ability to colonize.

The pre-metastatic niche model shows that, preceding the arrival of  disseminated tumour cells (DTCs), bone marrow-derived haematopoietic stem cells are mobilized by tumour-derived factors and are recruited to the secondary site where they negotiate a more hospitable microenvironment to foster the survival and expansion of metastatic lesions. Inflammatory cytokines have emerged as crucial mediators of the pre-metastatic niche and self-seeding and include IL-6, SRC and NF-kB.

After surviving the adjustment to the secondary site, tumor cells must sustain their growth to develop overt metastases. Developmental pathways have emerged as important players in tumor progression and metastasis. These include: transforming growth factor-β (TGFβ), bone morphogenetic protein (BMP), WNT and Hedgehog.  These genes will trigger additional genes that will affect downstream steps of the colonization process.

Clinical Aspect

“As most metastatic cancers are inoperable, systemic treatments using chemotherapeutic or targeted therapy is often the only option to slow tumor growth or to relieve metastasis-associated morbidity”.  Genes and pathways that have crucial roles in primary tumour growth and metastasis are ideal targets for therapeutic inventions. One example is the oncogenic BRAF:  potent inhibitors of mutant BRAF, had initial clinical results which suggest dramatic efficacy in the treatment of metastatic malignant melanoma.  It is important to keep in mind that many cancers develop resistance to BRAF inhibitor and require used of next-generation drugs. More so,  the mechanism of resistance will be discussed elsewhere.

A sound framework of normal homeostatic mechanisms can improve our ability to understand and target tumor–stromal interactions in metastasis.

Summary:

“Despite recognizing the devastating consequences of metastasis, we are not yet able to effectively treat cancer that has spread to vital organs” .  Despite our increasing knowledge about metastatic colonization, we still hold little understanding of how metastatic tumour cells behave as solitary disseminated entities. Understanding the genomics of metastatic cancer cells and the complexity of the metastasis process will enable us to develop a better target-therapeutic drugs.

 

References:

1. Naure Review: Cancer: focus on metastasis. http://www.nature.com/nrc/focus/metastasis/index.html

2. Nilay Sethi and Yibin Kang. Unravelling the complexity of metastasis — molecular understanding and targeted therapies. Nature Reviews Cancer 2011; 11:732- 748. http://www.nature.com/nrc/journal/v11/n10/abs/nrc3125.html

3. Meera Saxena and Gerhard Christophor. Rebuilding cancer metastasis in the mouse. Molecular Oncology 2013, 7(2):283-296. http://www.moloncol.org/article/S1574-7891(13)00033-1/abstract

4. Lamb, J. et al. The Connectivity Map: using geneexpression signatures to connect small molecules, genes, and disease. Science 2006 313, 1929–1935. http://www.sciencemag.org/content/313/5795/1929.short

5. Chiang AC and Massagué J. Molecular basis of metastasis. N Engl J Med. 2008 Dec 25;359(26):2814 23 ;http://www.ncbi.nlm.nih.gov/pubmed/19109576

6. By: Ritu Saxena PhD. In focus: Circulating Tumor Cells. https://pharmaceuticalintelligence.com/2013/06/24/in-focus-circulating-tumor-cells/

7.   Davies, H. et al. Mutations of the BRAF gene in human cancer. Nature 2002, 417, 949–954. http://www.nature.com/nature/journal/v417/n6892/full/nature00766.html

8. Arozarena, I. et al. Oncogenic BRAF induces melanoma cell invasion by downregulating the cGMP specific phosphodiesterase PDE5A. Cancer Cell 19, 45–57 (2011). http://www.ncbi.nlm.nih.gov/pubmed/21215707

9. Isaiah J. Fidler. The pathogenesis of cancer metastasis: the ‘seed and soil’ hypothesis revisited. Nature Review Cancer. 2003 June. 3(6):453-8. http://www.ncbi.nlm.nih.gov/pubmed/12778135

10. Christoph A. Klein. Parallel progression of primary tumours and metastases.   Nat Rev Cancer. 2009 Apr;9(4):302-12  http://www.ncbi.nlm.nih.gov/pubmed/19308069 http://prometheus.fmrp.usp.br/biocelmolcancer/Klein.pdf

 

Other related articles published on this Open Access Scientific Journal, include the following:

I. By: Ritu Saxena PhD. In focus: Circulating Tumor Cells. https://pharmaceuticalintelligence.com/2013/06/24/in-focus-circulating-tumor-cells/

II. By: Ritu Saxena PhD. Scientists use natural agents for prostate cancer bone metastasis treatment. https://pharmaceuticalintelligence.com/2012/09/17/natural-agents-for-prostate-cancer-bone-metastasis-treatment/

III. By: Prabodh Kandala, PhD. All Cancer Cells Are Not Created Equal: Some Cell Types Control Continued Tumor Growth, Others Prepare the Way for Metastasis. https://pharmaceuticalintelligence.com/2012/05/17/all-cancer-cells-are-not-created-equal-some-cell-types-control-continued-tumor-growth-others-prepare-the-way-for-metastasis/

IV. By: Aviva Lev-Ari PhD RN. MIT Scientists Identified Gene that Controls Aggressiveness in Breast Cancer Cells. https://pharmaceuticalintelligence.com/2013/07/03/mit-scientists-identified-gene-that-controls-aggressiveness-in-breast-cancer-cells/

V. By: Demet Sag PhD CRA, GCP.  The Magic of the Pandora’s Box : Epigenetics and Stemness with Long non-coding RNAs (lincRNA). https://pharmaceuticalintelligence.com/2013/06/30/the-magic-of-the-pandoras-box-epigenetics-and-stemmness-with-long-non-coding-rnas-lincrna/

 

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Author/Curator: Ritu Saxena, PhD

For several decades, research efforts have focused on targeting progression of cancer cells in primary tumors. Primary tumor cell targeting strategies include standard chemotherapy and immunotherapy and modulation of host microenvironment including tumor vasculature. However, cancer progression is comprised of both primary tumor growth and secondary metastasis (Langley RR and Fidler IJ. Tumor cell-organ microenvironment interactions in the pathogenesis of cancer metastasis. Endocr Rev. 2007 May;28(3):297-321; http://www.ncbi.nlm.nih.gov/pubmed/17409287). Owing to the property of unilimited cell division, cells in primary tumor increase rapidly in number and density and are able to favorably influence their microenvironment. Metastasis, on the other hand, depends on the ability of cancer cells to disseminate, circulate, adapt to the harsh environment and seed in different organs to establish secondary tumors. Although tumor cells are shed into the circulation in large numbers since early stages of tumor formation, few tumor cells can survive and proceed to overt metastasis. (Husemann Y et al. Systemic spread is an early step in breast cancer. Cancer Cell. 2008 Jan;13(1):58-68; http://www.ncbi.nlm.nih.gov/pubmed/18167340). Tight vascular wall barriers, unfavorable conditions for survival in distant organs, and a rate-limiting acquisition of organ colonization functions are just some of the impediments to the formation of distant metastasis (Chiang AC and Massagué J. Molecular basis of metastasis. N Engl J Med. 2008 Dec 25;359(26):2814-23; http://www.ncbi.nlm.nih.gov/pubmed/19109576).

It has been hypothesized that metastasis is initiated by a subpopulation of circulating tumor cells (CTC) found in the blood of patients. Therefore, understanding the function of CTC and targeting the CTC is gaining attention as a possible therapeutic avenue in carcinoma treatment.

CTCs

Figure: Circulating tumor cells in the metastatic cascade

(Image source: Chaffer CL and Weinberg RA. Science 2011,331, pp. 1559-1564; http://www.ncbi.nlm.nih.gov/pubmed/21436443)

Isolation of CTC

Initial methods relied on the difference in physical properties of cells. When spun in a centrifuge, different cells in the blood sample settle in separate layers based on their byoyancy, and CTC are found in the white blood cell fraction. Because CTC are generally larger than white blood cells, a size-based filter could be used to separate the cell types (Vona G, et al, Isolation by size of epithelial tumor cells : a new method for the immunomorphological and molecular characterization of circulating tumor cells. Am J Pathol, 2000 Jan;156(1):57-63; http://www.ncbi.nlm.nih.gov/pubmed/10623654).

Herbert A Fritsche, PhD, Professor and Chief, Clinical Chemistry, Department of Laboratory Medicine, The University of Texas, MD Anderson Cancer Center, demonstrated that the CTC can be captured using antibody labeled magnetic beads, either in positive or negative selection schema. After the circulating tumor cells are isolated, they may be characterized by immunohistochemistry and counted.  Alternatively, these cells may be characterized by gene expression analysis using RT-PCR. One of the CTC detection methods, Veridex Inc, Cell Search Assay, has been cleared by the US FDA for use as a prognostic test in patients with metastatic cancers of the breast, prostate and colon. This technology relies on the expression of epithelial cellular adhesion molecular (EpCAM) by epithelial cells and the isolation of these cells by immunomagnetic capture using anti-EpCAM antibodies.  Enriched CTC are identified by immunofluorescence. Martin Fleisher, PhD, Chair, Department of Clinical Laboratories, Memorial Sloan-Kettering Cancer Center discussed in a webinar at the biomarker symposia, Cambridge Healthtech Institute, that every new technology has shortcomings, and the reliance on cancer cells to express sufficient EpCAM to enable capture may affect the role of this technology in future clinical use. Heterogeneous downregulation of epithelial surface antigen in invasive tumor cells has been reported. Thus, alternative methods to detect CTC are being developed. These new methods include-

  1. Flow cytometry that sorts cells by size and surface antigen expression.
  2. CTC microchips that are designed to capture CTC as whole blood flows past EpCAM-coated mirco-posts.
  3. Enrichment by filtration using filters with a pore size of 7-8 µm, that permits smaller red blood cell, leukocytes, and platelets to pass, but captures CTC that have diameters of about 12-15 µm.

Better identification of CTC

Baccelli et al (2013) developed a xenograft assay and demonstrated that the primary human luminal breast cancer CTC contain metastasis-initiated cells (MICs) that give rise to bone, lung and liver metastases in mice. These MIC-containing CTC populations expressed EPCAM, CD44, CD47 and MET. It was observed that in a small cohort of patients with metastases, the number of CTC expressing markers EPCAM,CD44, CD47 and MET, but not of bulk EPCAM+ CTC, correlated with lower overall survival and increased number of metastasic sites. These data describe functional circulating MICs and associated markers, which may aid the design of better tools to diagnose and treat metastatic breast cancer. The findings were published in the Nature Biotechnology journal recently (Baccelli I, et al. Identification of a population of blood circulating tumor cells from breast cancer patients that initiates metastasis in a xenograft assay. Nature Biotechnology 2013 31, 539–544; http://www.ncbi.nlm.nih.gov/pubmed/23609047).

CTC as prognostic and predictive factor for cancer progression

Martin Fleisher, PhD states “detecting CTC in peripheral blood of patients with cancer has become a clinically relevant and important prognostic biomarker and has been shown to be a predictive biomarker post-therapy. But, key to the use of CTC as a biomarker is the technology designed to enrich cancer cells from peripheral blood.”

Since CTC isolation methods started being established, correlation studies between the cells and a patient’s disease emerged. In 2004, investigators at the Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center (Houston, TX) discovered that the CTC were associated with disease progression and survival in metastatic breast cancer. The clinical trial recruited 177 patients with measurable metastatic breast cancer for levels of CTC both before the patients were to start a new line of treatment and at the first follow-up visit. The progression of the disease or the response to treatment was determined with the use of standard imaging studies at the participating centers. Patients in a training set with levels of CTC equal to or higher than 5 per 7.5 ml of whole blood, as compared with the group with fewer than 5 CTC per 7.5 ml, had a shorter median progression-free survival (2.7 months vs. 7.0 months, P<0.001) and shorter overall survival (10.1 months vs. >18 months, P<0.001). At the first follow-up visit after the initiation of therapy, this difference between the groups persisted (progression-free survival, 2.1 months vs. 7.0 months; P<0.001; overall survival, 8.2 months vs. >18 months; P<0.001), and the reduced proportion of patients (from 49 percent to 30 percent) in the group with an unfavorable prognosis suggested that there was a benefit from therapy.  Thus, the number of CTC was found to be an independent predictor of progression-free survival and overall survival in patients with metastatic breast cancer (Cristofanilli M, et al, Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med. 2004 Aug 19;351(8):781-91; http://www.ncbi.nlm.nih.gov/pubmed/15317891).

Similar results have been observed in other cancer types, including prostate and colorectal cancer. The Cell Search System developed by Veridex LLC (Huntingdon Valley, PA) enumerated CTC from 7.5 mL of venous blood and was used to compare the outcomes from three prospective multicenter studies investigating the use of CTC to monitor patients undergoing treatment for metastatic breast, colorectal, or prostate cancer. Evaluation of CTC at anytime during the course of disease allowed assessment of patient prognosis and is predictive of overall survival (Miller MC, et al. Significance of Circulating Tumor Cells Detected by the CellSearch System in Patients with Metastatic Breast Colorectal and Prostate Cancer. J Oncol. 2010; http://www.ncbi.nlm.nih.gov/pubmed/20016752). In addition, the CTC test may permit the oncologist to make an early decision to discontinue first line therapy for metastatic breast cancer and pursue more aggressive alternative treatments.

Genetic analysis of CTC

Additional studies have analyzed the genetic mutations that the cells carry, comparing the mutations to those in a primary tumor or correlating the findings to a patient’s disease severity or spread. In one study, lung cancer patients whose CTC carried a mutation known to cause drug resistance had faster disease progression than those whose CTC lacked the mutation. The investigators analyzed the evolutionary aspect of cancer progression and studied the precursor cells of metastases directly for the identification of prognostic and therapeutic markers. Single disseminated cancer cells isolated from lymph nodes and bone marrow of 107 consecutive esophageal cancer patients were analyzed by whole-genome screening which revealed that primary tumors and lymphatically and hematogenously disseminated cancer cells diverged for most genetic aberrations. Chromosome 17q12-21, the region comprising HER2, was identified as the most frequent gain in disseminated tumor cells that were isolated from both ectopic sites. Furthermore, survival analysis demonstrated that HER2 gain in a single disseminated tumor cell but not in primary tumors conferred high risk for early death (Stoecklein NH, et al. Direct genetic analysis of single disseminated cancer cells for prediction of outcome and therapy selection in esophageal cancer. Cancer Cell. 2008 May;13(5):441-53; http://www.ncbi.nlm.nih.gov/pubmed/18455127).

The abovementioned studies indicate that CTC blood tests have been successfully used to track the severity of a cancer or efficacy of a treatment. In conclusion, the evolution of the CTC technology will be critical in the emerging area of targeted therapy.  With the development and use of new technologies, the links between the genomic information and CTC could be explored and established for targeted therapy.

Challenges in CTC research

  1. Potential clinical significance of CTC has been demonstrated as early detection, diagnostic, prognostic, predictive, surrogate, stratification, and pharmacodynamic biomarkers. Hong B and Zu Y (2013) discuss that “the role of CTC as a disease marker may be unique in different clinical conditions and should be carefully interpreted. A good example is the comparison between the prognostic and predictive biomarkers. Both biomarkers employ progression-free survival and overall survival for data interpretation; however, the prognostic biomarker is independent of specific drug treatment or therapy, and used for the determination of outcomes before treatment, while the predictive biomarker is related to a particular treatment to predict the response. Furthermore, inconsistent results are increasingly reported among the various CTC assay methods, specifically pertaining to results for the CTC detection rate, patient positivity rate, and the correlation between the presence of CTC and survival rate (Hong B and Zu Y. Detecting circulating tumor cells: current challenges and new trends. Source. Theranostics. 2013 Apr 23;3(6):377-94; http://www.ncbi.nlm.nih.gov/pubmed/23781285).
  2. Heterogeneity in CTC along with several other technical factors contribute to discordance, including the changes in methodology, lack of reference standard, spectrum and selection bias, operator variability and bias, sample size, blurred clinical impact with known clinical/pathologic data, use of diverse capture antibodies from different sources, lack of awareness of the pre-analytical phase, oversimplification of the cytopathology process, use of dichotomous decision criteria, etc (Sturgeon C. Limitations of assay techniques for tumor markers. In: (ed.) Diamandis EP, Fritsche HA, Lilja H, Chan DW, Schwartz MK. Tumor markers: physiology, pathobiology, technology, and clinical applications. Washington, DC: AACC Press. 2002:65-82; Gion M and Daidone MG. Circulating biomarkers from tumour bulk to tumour machinery: promises and pitfalls. Eur J Cancer. 2004;40(17):2613-2622; http://www.ncbi.nlm.nih.gov/pubmed/15541962). Therefore, employing a standard protocol is essential in order to minimize a lot of inconsistencies and technical errors.
  3. CTC in a small amount of blood sample might not represent the actual CTC count in the whole blood. In fact, it has been reported that the Cell Search system might undercount the number of CTC. Nagrath et al (2007) have demonstrated that the average CTC number per mL of whole blood is approximately 79-155 in various cancers (Nagrath S, et al. Isolation of rare circulating tumous cells in cancer patients by microchip technology. Nature. 2007;450(7173):1235-1239; http://www.ncbi.nlm.nih.gov/pubmed/18097410). In addition, an investigative CellSearch Profile approach (for research use only) detected an approximately 30-fold higher number of the median CTC in the same paired blood samples (Flores LM, et al. Improving the yield of circulating tumour cells facilitates molecular characterisation and recognition of discordant HER2 amplification in breast cancer. Br J Cancer. 2010;102(10):1495-502; http://www.ncbi.nlm.nih.gov/pubmed/20461092). Such measurement discrepancies indicate that the actual CTC numbers in the blood of patients could be at least 30-100 fold higher than that currently reported by the only FDA-cleared CellSearch system.

Thus, although promising, the CTC technology faces several challenges both in detection and interpretation, which has resulted in its limited clinical acceptance and use. In order to prepare the CTC technology for future widespread clinical acceptance, a comprehensive guideline for all phases of CTC technology development was published by the Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium. The guidelines describe methods for interactive comparisons of proprietary new technologies, clinical trial designs, a clinical validation qualification strategy, and an approach for effectively carrying out this work through a public-private partnership that includes test developers, drug developers, clinical trialists, the FDA and the National Cancer Institute (NCI) (Parkinson DR, et al. Considerations in the development of circulating tumor cell technology for clinical use. J Transl Med. 2012;10(1):138; http://www.ncbi.nlm.nih.gov/pubmed/22747748).

Reference:

  1. Langley RR and Fidler IJ. Tumor cell-organ microenvironment interactions in the pathogenesis of cancer metastasis. Endocr Rev. 2007 May;28(3):297-321; http://www.ncbi.nlm.nih.gov/pubmed/17409287
  2. Husemann Y et al. Systemic spread is an early step in breast cancer. Cancer Cell. 2008 Jan;13(1):58-68; http://www.ncbi.nlm.nih.gov/pubmed/18167340
  3. Chiang AC and Massagué J. Molecular basis of metastasis. N Engl J Med. 2008 Dec 25;359(26):2814-23; http://www.ncbi.nlm.nih.gov/pubmed/19109576
  4. Vona G, et al, Isolation by size of epithelial tumor cells : a new method for the immunomorphological and molecular characterization of circulating tumor cells. Am J Pathol, 2000 Jan;156(1):57-63; http://www.ncbi.nlm.nih.gov/pubmed/10623654
  5. Baccelli I, et al. Identification of a population of blood circulating tumor cells from breast cancer patients that initiates metastasis in a xenograft assay. Nature Biotechnology 2013 31, 539–544; http://www.ncbi.nlm.nih.gov/pubmed/23609047
  6. Cristofanilli M, et al, Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med. 2004 Aug 19;351(8):781-91; http://www.ncbi.nlm.nih.gov/pubmed/15317891
  7. Miller MC, et al. Significance of Circulating Tumor Cells Detected by the CellSearch System in Patients with Metastatic Breast Colorectal and Prostate Cancer. J Oncol. 2010; http://www.ncbi.nlm.nih.gov/pubmed/20016752
  8. Stoecklein NH, et al. Direct genetic analysis of single disseminated cancer cells for prediction of outcome and therapy selection in esophageal cancer. Cancer Cell. 2008 May;13(5):441-53; http://www.ncbi.nlm.nih.gov/pubmed/18455127
  9. Hong B and Zu Y. Detecting circulating tumor cells: current challenges and new trends. Source. Theranostics. 2013 Apr 23;3(6):377-94; http://www.ncbi.nlm.nih.gov/pubmed/23781285
  10. 10. Sturgeon C. Limitations of assay techniques for tumor markers. In: (ed.) Diamandis EP, Fritsche HA, Lilja H, Chan DW, Schwartz MK. Tumor markers: physiology, pathobiology, technology, and clinical applications. Washington, DC: AACC Press. 2002:65-82
  11. Gion M and Daidone MG. Circulating biomarkers from tumour bulk to tumour machinery: promises and pitfalls. Eur J Cancer. 2004;40(17):2613-2622; http://www.ncbi.nlm.nih.gov/pubmed/15541962
  12. Nagrath S, et al. Isolation of rare circulating tumous cells in cancer patients by microchip technology. Nature. 2007;450(7173):1235-1239; http://www.ncbi.nlm.nih.gov/pubmed/18097410
  13. Flores LM, et al. Improving the yield of circulating tumour cells facilitates molecular characterisation and recognition of discordant HER2 amplification in breast cancer. Br J Cancer. 2010;102(10):1495-502; http://www.ncbi.nlm.nih.gov/pubmed/20461092
  14. Chaffer CL and Weinberg RA. Science 2011,331, pp. 1559-1564; http://www.ncbi.nlm.nih.gov/pubmed/21436443

Other related articles on circulation cells as biomarkers published on this Open Access Scientific Journal, include the following:

Blood-vessels-generating stem cells discovered

Ritu Saxena, PhD

https://pharmaceuticalintelligence.com/2012/10/22/blood-vessel-generating-stem-cells-discovered/

Cardiovascular and circulating endothelial cells as BIOMARKERS for prediction of Disease progression risks

Statins’ Nonlipid Effects on Vascular Endothelium through eNOS Activation Curator, Author,Writer, Reporter: Larry Bernstein, MD, FCAP

Cardiovascular Outcomes: Function of circulating Endothelial Progenitor Cells (cEPCs): Exploring Pharmaco-therapy targeted at Endogenous Augmentation of cEPCs Author and Curator: Aviva Lev-Ari, PhD, RN

Vascular Medicine and Biology: Macrovascular Disease – Therapeutic Potential of cEPCs Curator and Author: Aviva Lev-Ari, PhD, RN

Repair damaged blood vessels in heart disease, stroke, diabetes and trauma: Cellular Reprogramming amniotic fluid-derived cells into Endothelial Cells

Reporter: Aviva Lev-Ari, PhD, RN

Stem cells in therapy

A possible light by Stem cell therapy in painful dark of Osteoarthritis” – Kartogenin, a small molecule, differentiates stem cells to chondrocyte, healthy cartilage cells Author and Reporter: Anamika Sarkar, Ph.D and Ritu Saxena, Ph.D.

Human embryonic pluripotent stem cells and healing post-myocardial infarctionAuthor: Larry H. Bernstein, MD

Stem cells create new heart cells in baby mice, but not in adults, study showsReporter: Aviva Lev-Ari, PhD, RN

Stem cells for the rescue of mitochondrial dysfunction in Parkinson’s diseaseReporter: Ritu Saxena, Ph.D.

Stem Cell Research — The Frontier is at the Technion in Israel Reporter: Aviva Lev-Ari, PhD, RN

Research articles by MA Gaballa, PhD

Harris DT, Badowski M, Nafees A, Gaballa MAThe potential of Cord Blood Stem Cells for Use in Regenerative Medicine. Expert Opinion in Biological Therapy 2007. Sept 7(9): 1131-22.

Furfaro E, Gaballa MADo adult stem cells ameliorate the damaged myocardium?. Human cord blood as a potential source of stem cells. Current Vascular Pharmacology 2007, 5; 27-44.

 

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