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


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


“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.




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.


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Targeted Therapy for Triple Negative Breast Cancer

Curator: Larry H. Bernstein, MD, FCAP



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.

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).


  • 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



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.

Cytogenetic locations: 3p25.3 , 11q13.3
Matching terms: lindau, disease, von, hippellindau, hippel
  • Birt-Hogg-Dube syndrome,
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.
Cytogenetic locations: 3p25.3 3p25.3 3q21.1 8q24.13 12q24.31 17p11.2 17q12 
Matching terms: renal, familial, papillary, carcinoma, cell


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)
 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)
 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
 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
 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
 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
 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
 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
 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):


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].


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
Clinical setting RR (%) PFS (months) OS (months)
VEGF-Targeted Therapy

Bevacizumab +

649 First-line 31 vs. 12 10.2 vs. 5.5
23.3 vs. 21.3
*CALBG 90206

Bevacizumab +

732 First-line 25.5 vs. 13 8.4 vs. 4.9
18.3 vs. 17.4
Sunitinib vs.
750 First-line 47 vs. 12 11 vs. 5
26.4 vs. 21.8

Sorafenib vs.

903 Second-line


10 vs. 2 5.5 vs. 2.8
Pazopanib vs.
435 First line/second line


30 vs. 3 9.2 vs. 4.2
22.9 vs. 20.5

Axitinib vs.
sorafenib [269]

723 Second line

(post-sunitinib, cytokine,
bevacizumab or

19 vs. 9
6.7 vs. 4.7
Not reported
mTOR-Targeted Therapy
vs. Tem + IFNa
vs. IFNa[249]
624 First line, ≥ 3 poor risk
9 vs. 5 3.8 vs. 1.9 for
10.9 vs. 7.3 for
Everolimus vs.
placebo [274]
410 Second line
(post sunitinib and/or
2 vs. 0 4.9 vs. 1.9


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
Frequency of
Among RCC
Tumors (%)
CD8+ T cell
Patients with
HLA Class I
CD4+ T cell
Patients with
HLA Class II
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
> 90 B7 NR 120
(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
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;



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
References found in Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.
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
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:


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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.

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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.

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[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


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.


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


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




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Summary to Metabolomics

Summary 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?

 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.


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).


“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.


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.


“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.



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.


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).


  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


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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Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing[1]

Curator and Reporter: Stephen J. Williams, Ph.D.

Genomic instability is considered a hallmark and necessary for generating the mutations which drive tumorigenesis. Multiple studies had suggested that there may be multiple driver mutations and a plethora of passenger mutations driving a single tumor.  This diversity of mutational spectrum is even noticed in cultured tumor cells (refer to earlier post Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell).  Certainly, intratumor heterogeneity has been a concern to clinicians in determining the proper personalized therapy for a given cancer patient, and has been debated if multiple biopsies of a tumor is required to acquire a more complete picture of a tumor’s mutations.  In the New England Journal of Medicine, lead author Dr. Marco Gerlinger in the laboratory of Dr. Charles Swanton of the Cancer Research UK London Research Institute, and colleagues reported the results of a study to determine if intratumoral differences exist in the mutational spectrum of primary and metastatic renal carcinomas, pre- and post-treatment with the mTOR (mammalian target of rapamycin) inhibitor, everolimus (Afinitor®)[1].

The authors compared exome sequencing of multiregion biopsies from four patients with metastatic renal-cell carcinoma who had been enrolled in the Personalized RNA Interference to Enhance the Delivery of Individualized Cytotoxic and Targeted Therapeutics clinical trial of everolimus (E-PREDICT) before and after cytoreductive surgery.

Biopsies taken:

  • Multiregion spatial biopsy of primary tumor (representing 9 regions of the tumor)
  • Chest-wall metastases
  • Perinephric metastases
  • Germline DNA as control

Multiple platforms were used to determine aberrations as follows:

  1. Illumina Genome Analyzer IIx and Hiseq: for sequencing and mutational analysis
  2. Illumina Omni 2.5: for SNP (single nucleotide polymorphism)-array-based allelic imbalance detection for chromosomal imbalance and ploidy analysis
  3. Affymetrix Gene 1.0 Array: for mRNA analysis

A phylogenetic reconstruction of all somatic mutations occurring in primary disease and associated metastases was  performed to determine the clonal evolution of the metastatic disease given the underlying heterogeneity of the tumor.  Basically the authors wanted to know if the mutational spectra of one metastasis could be found in biopsies taken from the underlying primary tumor or if the mutational landscape of metastases had drastically changed.


Multiregion exon-capture sequencing of DNA from pretreatment biopsy samples of the primary tumor, chest wall metastases, and perinephrous metastasis revealed 128 mutations classified as follows:

  • 40 ubiquitous mutations
  • 59 mutations shared by several but not all regions
  • 29 mutations unique to specific regions
  • 31 mutations shared by most primary tumor regions
  • 28 mutations shared by most metastatic regions

The authors mapped these mutations out with respect to their location, in order to determine how the metastatic lesions evolved from the primary tumor, given the massive heterogeneity in the primary tumor.  Construction of this “phylogenetic tree” (see Merlo et. al[2]) showed that the disease evolves in a branched not linear pattern, with one branch of clones evolving into a metastatic disease while another branch of clones and mutations evolve into the primary disease.

One of the major themes of the study is shown by results that an average of 70 somatic mutations were found in a single biopsy (a little more than just half of all tumor mutations) yet only 34% of the mutations in multiregion biopsies were detected in all tumor regions.

This indicated to the authors that “a single biopsy was not representative of the mutational landscape of the entire bulk tumor”. In addition, microarray studies concluded that gene-expression signatures from a single biopsy would not be able to predict outcome.

Everolimus therapy did not change the mutational landscape.  Interestingly, allelic composition and ploidy analyses revealed an extensive intratumor heterogeneity, with ploidy heterogeneity in two of four tumors and 26 of 30 tumor samples containing divergent allelic-imbalances.  This strengthens the notion that multiple clones with diverse genomic instability exist in various regions of the tumor.

 The intratumor heterogeneity reveals a convergent tumor evolution with associated heterogeneity in target function

Genes commonly mutated in clear cell carcinoma[3, 4] (and therefore considered the prevalent driver mutations for renal cancer) include:

Only VHL mutations were found in all regions of a given tumor, however there were three distinct SETD2 mutations (frameshift, splice site, missense) which were located in different regions of the tumor.

SETD2 trimethylates histones at various lysine residues, such as lysine residue 36 (H3K36).  The trimethylation of H3K36 is found on many actively transcribed genes.  Immunohistochemistry showed trimethylated H3K36 was reduced in cancer cells but positive in most stromal cells and in SETD2 wild-type clear-cell carcinomas.

Interestingly most regions of the primary tumor, except one, contained a kinase-domain activating mutation in mTOR.  Immunohistochemistry analysis of downstream target genes of mTOR revealed that mTOR activity was enhanced in regions containing this mutation.  Therefore the intratumoral heterogeneity corresponded to therapeutic activity, leading to the impression that a single biopsy may result in inappropriate targeted therapy.   Additional downstream biomarkers of activity confirmed both the intratumoral heterogeneity of mutational spectrum as well as an intratumoral heterogeneity of therapeutic-target function.

The authors conclude that “intratumor heterogeneity can lead to underestimation of the tumor genomics landscape from single tumor biopsies and may present major challenges to personalized-medicine and biomarker development”.

In an informal interview with Dr. Swanton, he had stressed the importance of performing these multi-region biopsies and the complications that intratumoral heterogeneity would present for personalized medicine, biomarker development, and chemotherapy resistance.

Q: Your data clearly demonstrates that multiple biopsies must be done to get a more complete picture of the tumor’s mutational landscape.  In your study, what percentage of the tumor would be represented by the biopsies you had performed?

Dr. Swanton: Realistically this is a very difficult question to answer, the more biopsies we sequence, the more we find, in the near term it may be very difficult to ever formally address this in large metastatic tumours

Q:  You have very nice data which suggest that genetic intratumor heterogeneity complicates the tumor biomarker field? do you feel then that quests for prognostic biomarkers may be impossible to attain?

Dr. Swanton: Not necessarily although heterogeneity is likely to complicate matters

Identifying clonally dominant lesions may provide better drug targets

Predicting resistance events may be difficult given the potential impact of tumour sampling bias and the concern that in some tumours a single biopsy may miss a relevant subclonal mutation that may result in resistance

Q:  Were you able to establish the degree of genomic instability among the various biopsies?

Dr. Swanton:  Yes, we did this by allelic imbalance analysis and found that the metastases were more genomically unstable than the primary region from which the metastasis derived

Q: I was actually amazed that there was a heterogeneity of mTOR mutations and SETD2 after everolimus therapy?   Is it possible these clones obtained a growth advantage?

Dr. Swanton: We think so yes, otherwise we wouldn’t identify recurrent mutations in these “driver genes”

Dr. Swanton will present his results at the 2013 AACR meeting in Washington D.C. (http://www.aacr.org/home/scientists/meetings–workshops/aacr-annual-meeting-2013.aspx)

The overall points of the article are as follows:

  • Multiple biopsies of primary tumor and metastases are required to determine the full mutational landscape of a patients tumor
  • The intratumor heterogeneity will have an impact on the personalized therapy strategy for the clinician


  • Metastases arising from primary tumor clones will have a greater genomic instability and mutational spectrum than the tumor from which it originates


  • Tumors and their metastases do NOT evolve in a linear path but have a branched evolution and would complicate biomarker development and the prognostic and resistance outlook for the patient

A great video of Dr. Swanton discussing his research can be viewed here


Everolimus: an inhibitor of mTOR

The following information was taken from the New Medicine Oncology Database (http://www.nmok.net)




Approved/Filed Indications

Novartis PharmaCurrent as of: August 30, 2012 Generic Name: Everolimus
Brand Name: Afinitor
Other Designation: RAD001, RAD001C
RAD001, an ester of the macrocytic immunosuppressive agent sirolimus (rapamycin), is an inhibitor of mammalian target of rapamycin (mTOR) kinase.Administration Route: intravenous (IV) • PO • solid organ transplant
• renal cell carcinoma (RCC), metastatic after failure of treatment with sunitinib, sorafenib, or sunitinib plus sorafenib
• renal cell carcinoma, advanced, refractory to treatment with vascular endothelial growth factor (VEGF)-targeted therapy
• treatment of progressive neuroendocrine tumors (NET) of pancreatic origin (PNET) in patients with inoperable, locally advanced or metastatic disease

Marker Designation
Gene Location

Marker Description


5’-AMP-activated Protein Kinase (AMPK)AMPK beta 1 (beta1 non-catalytic subunit) • HAMPKb (beta1 non-catalytic subunit) • MGC17785 (beta1 non-catalytic subunit) • AMPK2 (alpha1 catalytic subunit) • PRKAA (alpha1 catalytic subunit) • AMPK alpha 1 (alpha1 catalytic subunit) • AMPKa1 ( AMPK is a member of a metabolite-sensing protein kinase family found in all eukaryotes. It functions as a cellular fuel sensor and its activation strongly suppresses cell proliferation in non-malignant cells and cancer cells. AMPK regulates the cell cycle by upregulating the p53-p21 axis and modulating the TSC2-mTOR (mammalian target of rapamycin) pathway. The AMPK signaling network contains a number of tumor suppressor genes including LKB1, p53, TSC1 and TSC2, and modulates growth factor signaling involving proto-oncogenes including PI3K, Akt and ERK. AMPK activation is therefore therapeutic target for cancer (Motoshima H, etal, J Physiol, 1 Jul 2006; 574(Pt 1): 63–71).AMPK is a protein serine/threonine kinase consisting of a heterotrimeric complex of a catalytic alpha subunit and regulatory ß and gamma subunits. AMPK is activated by increased AMP/ATP ratio, under conditions such as glucose deprivation, hypoxia, ischemia and heat shock. It is also activated by several hormones and cytokines. AMPK inhibits ATP-consuming cellular events, protein synthesis, de novo fatty acid synthesis, and generation of mevalonate and the downstream products in the cholesterol synthesis pathway (Motoshima H, etal, J Physiol, 1 Jul 2006; 574(Pt 1): 63–71). – ovarian cancer
– brain cancer
– liver cancer
– leukemia
– colon cancer
CREB regulated transcription coactivator 2 (CRTC2)TOR complex 2 (TORC2, mTORC2) • RP11-422P24.6 • transducer of regulated cAMP response element-binding protein (CREB)2 • transducer of CREB protein 2 • TOR1Location: 1q21.3 The mammalian target of rapamycin (mTOR) exists in two complexes, TORC1 and TORC2, which are differentially sensitive to rapamycin. cAMP response element-binding protein (CREB) regulated transcription coactivator 2 (CRTC2) or TORC2 is a multimeric kinase composed of mTOR, mLST8, mSin1, and rictor. The complex is insensitive to acute rapamycin exposure and functions in controlling cell growth and actin cytoskeletal assembly.TORC2 controls gene silencing, telomere length maintenance, and survival under DNA-damaging conditions. It is primaily located in the cytoplasm but also shuttles into the nucleus (Schonbrun M, etal, Mol Cell Biol, Aug 2009;29(16):4584-94). – brain cancer
Hypoxia inducible factor 1 alpha (HIF1A)HIF1-alpha (HIF-1 alpha) • HIF-1A • PASD8 • MOP1 • bHLHe78Location: 14q21-q24 The alpha subunit of the hypoxia inducible factor 1 (HIF-1alpha) is a 826 amino acid antigen consisting of a basic helix-loop-helix (bHLH)-PAS domain at its N-terminus. HIF-1alpha is rapidly degraded by the proteasome under normal conditions, but is stabilized by hypoxia resulting in the transactivation of several proangiogenic genes. HIF-1alpha is responsible for inducing production of new blood vessels as needed when tumors outgrow existing blood supplies. HIF-1alpha serves as a transcriptional factor that regulates gene expression involved in response to hypoxia and promotes angiogenesis.HIF-1alpha is a proangiogenic transcription factor induced primarily by tumor hypoxia that is critically involved in tumor progression, metastasis and overall tumor survival. HIF-1alpha functions as a survival factor that is required for tumorigenesis in many types of malignancies, and is expressed in a majority of metastases and late-stage tumors. HIF-1alpha is overexpressed in brain, breast, colon, endometrial, head and neck, lung, ovarian, and pancreatic cancer, and is associated with increased microvessel density and/or VEGF expression – prostate cancer
– bladder cancer
– nasopharyngeal cancer
– head and neck cancer
– kidney cancer
– pancreatic cancer
– endometrial cancer
– breast cancer
Mammalian target of rapamycin (mTOR)FK506 binding protein 12-rapamycin associated protein 1 • RAFT1 • FK506 binding protein 12-rapamycin associated protein 2 • FRAP • FRAP1 • FRAP2 • RAPT1 • FKBP-rapamycin associated protein • FKBP12-rapamycin complex-associated protein 1 • rapamycin target protein • TOR • FLJ44809 • MTORC1 • MTORC2 • RPTOR • RAPTOR • KIAA1303 • mammalian target of rapamycin complex 1Location: 1p36.22 The mammalian target of rapamycin (mTOR) is a large serine/threonine protein (Mr 300,000) having heat repeats, and protein-protein interaction domains at its amino terminus, and a protein kinase domain at its carboxy terminus. mTOR is a member of the phosphoinositide 3-kinase (PI3K)-related kinase (PIKK) family and a central modulator of cell growth. It regulates cell growth, proliferation and survival by impacting on protein synthesis and transcription. mTOR is present in two multi-protein complexes, a rapamycin-sensitive complex, TOR complex 1 (TORC1), defined by the presence of Raptor and a rapamycin insensitive complex, TOR complex 2 (TORC2), with Rictor, Protor and Sin1. Rapamycin selectively inhibits mTORC1 by binding indirectly to the mTOR/Raptor complex via FKBP12, resulting in inhibition of p70S6kinase but not the mTORC2 substrate AKTSer473. Selective inhibition of p70S6K attenuates negative feedback loops to IRS1 and TORC2 resulting in an increase in pAKT which may limit the activity of rapamycin.In a hypoxic environment the increase in mass of solid tumors is dependent on the recruitment of mitogens and nutrients. As a function of nutrient levels, particularly essential amino acids, mTOR acts as a checkpoint for ribosome biogenesis and cell growth. Ribosome biogenesis has long been recognized in the clinics as a predictor of cancer progression; increase in size and number of nucleoli is known to be associated with the most aggressive tumors and a poor prognosis. In bacteria, ribosome biogenesis is independently regulated by amino acids and energy charge. The mTOR pathway is controlled by intracellular ATP levels, independent of amino acids, and mTOR itself is an ATP sensor (Kozma SC, etal, AACR02, Abs. 5628). – breast cancer
– pancreatic cancer
– multiple myeloma
– liver cancer
– brain cancer
– prostate cancer
– kidney cancer
– lymphoma
Signal transducer and activator of transcription 3 (STAT3)Stat-3 • acute-phase response factor (APRF) • FLJ20882 • HIESLocation: 17q21 Signal transducer and activator of transcription 3 (STAT3) is a member of the STAT protein family. STAT3, plays a critical role in hematopoiesis. STAT3 is located in the cytoplasm and translocated to the nucleus after tyrosine phosphorylation. In response to cytokines and growth and other activation factors, STAT family members are phosphorylated by the receptor associated kinases and then form homo- or heterodimers, which translocate to the cell nucleus where they act as transcription activators. – multiple myeloma
– hematologic malignancy
– lymphoma
Sonic hedgehog homolog (SHH)Shh • HHG1 • HHG-1 • holoprosencephaly 3 (HPE3) • HLP3 • SMMCILocation: 7q36 Sonic hedgehog, a secreted hedgehog ligand, is a human homolog of the Drosophila segment polarity gene hedgehog, cloned by investigators at Harvard University (Marigo V, etal, Genomics, 1 Jul 1995;28 (1):44-51).The mammalian sonic hedgehog (Shh) pathway controls proliferation of granule cell precursors in the cerebellum and is essential for normal embryonic development. Shh signaling is disrupted in a variety of malignancies. – pancreatic cancer
– CNS cancer


1.         Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P et al: Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. The New England journal of medicine 2012, 366(10):883-892.

2.         Merlo LM, Pepper JW, Reid BJ, Maley CC: Cancer as an evolutionary and ecological process. Nature reviews Cancer 2006, 6(12):924-935.

3.         Varela I, Tarpey P, Raine K, Huang D, Ong CK, Stephens P, Davies H, Jones D, Lin ML, Teague J et al: Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature 2011, 469(7331):539-542.

4.         Dalgliesh GL, Furge K, Greenman C, Chen L, Bignell G, Butler A, Davies H, Edkins S, Hardy C, Latimer C et al: Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes. Nature 2010, 463(7279):360-363.

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AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo

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Genomics in Medicine- Tomorrow’s Promise

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CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease – Part IIC

CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics and Computational Genomics – Part IIB

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Directions for Genomics in Personalized Medicine

LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2

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Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @ http://pharmaceuticalintelligence.com

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Larry H Bernstein, MD, FCAP
Pharmaceutical Intelligence

UPDATED 4/23/2020:  New Design for Phase 1 pediatric oncology trials to expedite dose escalation studies.

Clinical Trials Revisited


Cancer Clinical Trials of Tomorrow

Advances in genomics and cancer biology will alter the design of human cancer studies

By Tomasz M. Beer | April 1, 2013   The Scientist
We stand on the cusp of significant change in the fundamental structure of cancer clinical trials, as the emphasis begins to shift from large-scale studies of relatively unselected patients to smaller studies testing more narrowly targeted therapies in molecularly characterized populations.
The previous (and still current) generation of trials established the cancer treatment standards used today. Trials that demonstrated the value of combination chemotherapy in the adjuvant treatment of breast cancer are an excellent example. Meticulous development of treatment regimens through Phase 1 and Phase 2 trials, followed by large-scale comparisons of the new regimens to established treatment protocols, have defined the modern practice of oncology for the last 4 decades. Future cancer clinical trials will be very different from those of the past, adopting a more personalized, sometimes called “precision,” approach.
It is, of course, not entirely true that past clinical trials did not include efforts to target treatments to the right patients. Where possible, targeted therapies are already being implemented. Using the presence of endocrine receptors to guide endocrine therapy for breast cancer was one of the first forays into molecular selection of patients. Unfortunately, the ability to select subgroups of patients for study has been severely curtailed by a still-limited knowledge of human cancer biology.
This is rapidly changing, however, thanks to advances in genomics and comprehensive cancer biology research over the last decade. Large-scale efforts, such as The Cancer Genome Atlas, are comprehensively defining many of the crucial molecular characteristics of human malignancies by illuminating genetic alterations that are clinically and biologically important, and which, by virtue of their functional roles, are viable targets for cancer treatment. At the same time, the ability to design small-molecule inhibitors of specific cancer targets is rapidly accelerating. In 2011, two new agents exemplified the power of these trends: crizotinib was approved for the treatment of lung cancers that harbor a specific mutation in the ALK gene, and vemurafenib was approved for the treatment of melanomas with a specific BRAF mutation. In both cases, the drugs were approved along with companion diagnostic tests that identify patients with the target mutation, who are therefore likely to benefit from treatment.

Smaller, more precise trials ahead

Clinical trials are being transformed by these trends. It will not happen overnight, as the knowledge of cancer biology and the availability of targeted agents are uneven. Unselected populations of patients will still be studied, but it is inevitable that there will be a rise in the number of trials that incorporate molecular tumor testing prior to treatment, with treatment selection informed by the molecular features of each individual’s cancer. Such personalized trials have the potential to yield better outcomes by increasing the probability of response and to employ less toxic therapies by increasingly targeting cancer-specific functions, rather than normal proliferative functions.
To the extent that targeted therapies will prove more effective when given to selected patients, clinical trials should get dramatically smaller. Trial size is largely driven by how effective the treatment is expected to be, so fewer participants are needed when the therapeutic benefit is larger. But the promise of smaller trials will not to be universal; for example, when two targeted agents are compared to one another in the same molecularly selected population, the differences in efficacy may be small and larger trials will be required.
As approaches to cancer treatment advance, there will need to be continual engagement with patients and with cancer survivors.
Furthermore, smaller trials may not necessarily move faster or be easier to complete, as they will require the “right patients,” who may be hard to find. Many of the mutations that represent promising targets are present in a minority of tumors. Today, molecular characterization of tumors is often done as part of the screening process for each trial. Many, and sometimes most, of the patients prove ineligible, making this approach frustrating and difficult to carry out. A better avenue of attack would be to make comprehensive molecular characterization of tumors a routine part of establishing a patient’s eligibility for a range of therapies. With the plummeting cost of genomic analysis, one can envision a day in the near future when a complete cancer genome (and perhaps other molecular evaluations) becomes a standard component of an initial diagnostic evaluation. Patients will be armed with molecular information about their own tumors, and thus able to make more-informed decisions about standard and investigational therapies that match the mutations driving their cancer.

New challenges

The road to personalized and targeted treatment strategies will offer new challenges. For rare targets that are present in a minority of cases across many different types of cancers, one will have to consider clinical trials that include a number of different cancers. There are many design pitfalls to such trials, chiefly the additional clinical and molecular heterogeneity introduced by the inclusion of more than one cancer type. Despite these challenges, it will inevitably make sense in some settings to select patients who share a particular tumor biology, regardless of the tissue of origin.
Another major challenge is how to combine targeted therapies to improve clinical outcomes. To date, targeted therapies have not been able to cure advanced solid tumors. Clinical benefits, while sometimes quite impressive when compared to marginally effective treatments, still fall far short. It stands to reason that redundant survival and growth pathways enable tumors to overcome therapies that inhibit a single target. The simultaneous inhibition of relevant redundant pathways may yield dramatically better results, but will also dramatically increase the complexity of molecularly personalized clinical trials.
As approaches to cancer treatment advance, there will need to be continual engagement with patients and with cancer survivors. Fewer than 5 percent of adult cancer patients participate in a clinical trial. To carry out meaningful clinical trials in the future, that number must increase. This will be most important for treatments that target relatively rare mutations; a large number of potential volunteers will have to be screened to identify a sufficient number who harbor the relevant target. To succeed, we must partner with a much larger fraction of cancer patients.
Designing and executing future cancer clinical trials will not be easy, but physician-scientists are armed with a fast-growing body of omics-informed knowledge with which to surmount these hurdles.
Tomasz M. Beer is deputy director of the Knight Cancer Institute and a professor of medicine at Oregon Health & Science University in Portland. He is the coauthor of Cancer Clinical Trials: A Commonsense Guide to Experimental Cancer Therapies and Clinical Trials. Written for people living with cancer, the book is accompanied by a blog (www.cancer-clinical-trials.com) that seeks to disseminate knowledge about clinical trials.


tumor suppression, tumor heterogeneity, genetics & genomics, disease/medicine, clinical trials, chemotherapy, cancer genomics and cancer

UPDATED 4/23/2020:  New Design for Phase 1 pediatric oncology trials to expedite dose escalation studies.



Ushering in the next generation of precision trials for pediatric cancer

Steven G. DuBois, Laura B. Corson, Kimberly Stegmaier, Katherine A. Janeway

Science  15 Mar 2019:Vol. 363, Issue 6432, pp. 1175-1181 DOI: 10.1126/science.aaw4153



Cancer treatment decisions are increasingly based on the genomic profile of the patient’s tumor, a strategy called “precision oncology.” Over the past few years, a growing number of clinical trials and case reports have provided evidence that precision oncology is an effective approach for at least some children with cancer. Here, we review key factors influencing pediatric drug development in the era of precision oncology. We describe an emerging regulatory framework that is accelerating the pace of clinical trials in children as well as design challenges that are specific to trials that involve young cancer patients. Last, we discuss new drug development approaches for pediatric cancers whose growth relies on proteins that are difficult to target therapeutically, such as transcription factors.

Some terms from the bibliography:

3+3 design: A commonly used rule-based design for phase 1 clinical trials in which patients are enrolled in cohorts of three patients, and decisions to increase or decrease the dose level for the next three participants are based on toxicities observed in those three patients.


Basket trial: A precision oncology trial design in which patients with many different cancer types are enrolled, the tumor is tested for a set of biomarkers of interest, and then patients are assigned to one of several clinical trial subprotocols based on the presence of a biomarker corresponding to a particular molecularly targeted therapy.


Bayesian model–based trial designs: A broad class of trial designs that use data known before the trial as well as data obtained during the conduct of the trial to adapt trial parameters as more information becomes available

Continual reassessment method: One example of a Bayesian model–based trial design in which an initial mathematical model of the relationship between drug dose and probability of unacceptable toxicity is continually updated as new information becomes available to assign subsequent patients to a dose anticipated to have an unacceptable toxicity rate below a set rate.

First-in-child trial: The first clinical trial of a specific agent to include a pediatric population, traditionally considered patients <18 years of age.


Rolling 6 design: A variation of the 3+3 design in which up to six participants may be enrolled to a dosing cohort before enrollment pauses to assess toxicity.

Safety run-in: An initial component of a phase 2 or phase 3 trial in which a small group of patients are treated with a previously untested regimen to evaluate toxicity before opening the trial to a larger group of participants.

Umbrella trial: A precision oncology trial design in which patients with a specific cancer type are enrolled, tumor is tested for a set of biomarkers of interest, and then patients are assigned to one of several clinical trial subprotocols based on the presence of a biomarker corresponding to a particular molecularly targeted therapy.


In this review article, DuBois et al describe new paradigms for pediatric precision oncology trial design and how these designs should be contrasted with the old models and differentiate from the design for these types of trials in the adult.  As the genomic landscape of pediatric tumors is becoming clearer (12) the authors noticed two themes which are becoming evident:

  1. Pediatric cancers harbor certain genomic mutations rarely seen in adult cancers
  2. Pediatric cancers share some genomic alterations and mutational gene signatures with adult tumors

However there is only a small number of pediatric clinical trials to investigate if specific genetic mutations predict outcome to a given personalized therapy.

            Thus, there an urgent need for precision clinical trials in pediatric cancers.

Several reviews have described numerous ongoing and recently completed trials however most are phase 1 dose escalation trials including basket trials and umbrella trials but based on previous data from adult trials using the same precision drug.  For example, pediatric trials involving the TRK inhibitor laratrectinib in tumors harboring a NTRK fusion gene or a pediatric crizotinib trial for pediatric glioblastomas having an ALK fusion protein have shown great success yet most of the early phase 1 work was based on adults or carried out in a way that does not take advantage of the new regulatory framework designed to expedite new drugs for adult precision medicines.

Speeding up the early phase trials in pediatric cancers: new trial design paradigms

Dose escalation phase I trials have, traditionally been the starting point for clinical development of new pediatric anticancer drugs however these first in child trials have seriously lagged their adult counterparts by many years.  These trials relied on the standard 3 x 3  or rolling six trial design, and doses escalated until a pediatric MTD  (maximum tolerated dose) was achieved.  In recent years new precision medicine pediatric trial design has been adopted to expedite the process, based on the fundamental shift in thinking that many new oncology agents will not have a true MTD when tested in adults.

Doses in phase 1 trials for targeted therapies like those in precision medicine are usually escalated based on considerations other than toxicity, like pharmacodynamics or biomarker analysis.  A pediatric phase 1 dose escalation trial may require more subjects than an adult trial.  But

although these newer approaches to early-phase trial design more efficiently establish a pediatric dose, they do little to advance our understanding of with patients are most likely to benefit from a new therapy.

Thus the need for good biomarkers to be included early on in these initial trial designs.  For example, Dana Farber’s first in child clinical trial NCT03654716, a Phase 1 Study of the Dual MDM2/MDMX Inhibitor ALRN-6924 in Pediatric Cancer (as a possible treatment for resistant (refractory) solid tumor, brain tumor, lymphoma or leukemia), are reducing the time children are waiting for entry into a trial, as unselected patients can enroll and the biomarker, increased MDM2 expression is used to determine those patients who go on to phase 2 dose escalation. In other cases, such as NCI Children’s Oncology Group basket trials, they have completely supplanted formal phase 1 trial design and instead incorporated molecularly targeted therapies based on adult doses but adjusted for patient size.  The use of combinations with traditional therapies in trial design is also helping to speed up the process for enrollment.  The authors also suggest that tumor profiling is pertinent however should be put in trial design so the costs to patients can be covered by the trial funds.


Figure 1Fig. 1 Evolution of precision trials for pediatric cancer.

Illustration: Kellie Holoski/Science

Source: Ushering in the next generation of precision trials for pediatric cancer BY STEVEN G. DUBOIS, LAURA B. CORSON, KIMBERLY STEGMAIER, KATHERINE A. JANEWAY SCIENCE 15 MAR 2019 : 1175-1181 https://science.sciencemag.org/content/363/6432/1175


  1. S. N. Gröbner, B. C. Worst, J. Weischenfeldt, I. Buchhalter, K. Kleinheinz, V. A. Rudneva, P. D. Johann, G. P. Balasubramanian, M. Segura-Wang, S. Brabetz, S. Bender, B. Hutter, D. Sturm, E. Pfaff, D. Hübschmann, G. Zipprich, M. Heinold, J. Eils, C. Lawerenz, S. Erkek, S. Lambo, S. Waszak, C. Blattmann, A. Borkhardt, M. Kuhlen, A. Eggert, S. Fulda, M. Gessler, J. Wegert, R. Kappler, D. Baumhoer, S. Burdach, R. Kirschner-Schwabe, U. Kontny, A. E. Kulozik, D. Lohmann, S. Hettmer, C. Eckert, S. Bielack, M. Nathrath, C. Niemeyer, G. H. Richter, J. Schulte, R. Siebert, F. Westermann, J. J. Molenaar, G. Vassal, H. Witt, B. Burkhardt, C. P. Kratz, O. Witt, C. M. van Tilburg, C. M. Kramm, G. Fleischhack, U. Dirksen, S. Rutkowski, M. Frühwald, K. von Hoff, S. Wolf, T. Klingebiel, E. Koscielniak, P. Landgraf, J. Koster, A. C. Resnick, J. Zhang, Y. Liu, X. Zhou, A. J. Waanders, D. A. Zwijnenburg, P. Raman, B. Brors, U. D. Weber, P. A. Northcott, K. W. Pajtler, M. Kool, R. M. Piro, J. O. Korbel, M. Schlesner, R. Eils, D. T. W. Jones, P. Lichter, L. Chavez, M. Zapatka, S. M. Pfister, ICGC PedBrain-Seq Project, ICGC MMML-Seq Project, The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018). 10.1038/nature25480pmid:29489754


2.  X. Ma, Y. Liu, Y. Liu, L. B. Alexandrov, M. N. Edmonson, C. Gawad, X. Zhou, Y. Li, M. C. Rusch, J. Easton, R. Huether, V. Gonzalez-Pena, M. R. Wilkinson, L. C. Hermida, S. Davis, E. Sioson, S. Pounds, X. Cao, R. E. Ries, Z. Wang, X. Chen, L. Dong, S. J. Diskin, M. A. Smith, J. M. Guidry Auvil, P. S. Meltzer, C. C. Lau, E. J. Perlman, J. M. Maris, S. Meshinchi, S. P. Hunger, D. S. Gerhard, J. Zhang, Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours. Nature 555, 371–376 (2018). 10.1038/nature25795pmid:29489755

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Acute Lymphoblastic Leukemia and Bone Marrow Transplantation

Author, Editor: Tilda Barliya PhD

Acute lymphoblastic leukemia (ALL) is a malignant disorder of lymphoid progenitor cells  was  previously discussed for the genetic origin and the prognostic factors used in clinical trials (1). We will now  focus on the treatment options with emphasis on the bone marrow transplantation (2).

According to the National Cancer Institute (NCI), the treatment of childhood ALL usually has 3 phases (3a):

  1. Induction Therapy: The goal is to kill leukemia cells in both the blood and the bone marrow and induce a remission.
  2. Consolidation/Intensification Therapy: It begins once the leukemia is in remission. The goal is to kill any remaining leukemia cells that may not be active but may regrow and cause relapse.
  3. Maintenance Therapy: The goal is to kill any remaining leukemia cells that may regrow and cause relapse. In this phase the different cancer treatments are usually been given at lower doses than those in the previous phases.

Four types of cancer treatment are used:

  • Chemotherapy – The way the chemotherapy is given depends on the child’s risk group. Children with high-risk ALL receive more anticancer drugs, higher doses of anticancer drugs, and receive treatment for a longer time than children with standard-risk ALL.. The full list of approved drug (3b)
  • Radiation Therapy– is a cancer treatment that uses high-energy x-rays or other types of radiation to kill cancer cells or keep them from growing. There are two types of radiation therapy. External radiation therapy uses a machine outside the body to send radiation toward the cancer. Internal radiation therapy uses a radioactive substance sealed in needles, seeds, wires, or catheters  that are placed directly into or near the cancer. External radiation therapy may be used to treat childhood ALL that has spread, or may spread, to the brain and spinal cord.
  • Chemotherapy with stem cell transplantation – A method inwhich stem cells (immature blood cells) are removed from the blood or bone marrow of a donor. After the patient receives treatment, the donor’s stem cells are given to the patient through an infusion. These reinfused stem cells grow into (and restore) the patient’s blood cells. Stem cell transplant is rarely used as initial treatment for children and teenagers with ALL. It is used more often as part of treatment for ALL that relapses
  • Targeted TherapyTyrosine Kinase Inhibitors (TKIs) are targeted therapy drugs that block the enzyme, tyrosine kinase, which causes stem cells to become more white blood cells or blasts than the body needs. For example, imatinib mesylate (Gleevec) is a TKI used in the treatment of children with Philadelphia chromosome-positive ALL. However, because patients can develop resistance to these drugs, new tyrosine kinase inhibitors are being investigated. For example, nilotinib (AMN-107) is being studied for patients with Philadelphia chromosome positive ALL who are resistant to imatinib

Bone Marrow or Peripheral Blood Stem cell Transplant for ALL

Stem cell transplants (SCT) offer a way for doctors to use high doses of chemo. Although the drugs destroy the patient’s bone marrow, transplanted stem cells can restore the bone marrow’s ability to make blood. Stem cells for a transplant come from either the blood or from the bone marrow. Bone marrow transplants were more common in the past, but they have largely been replaced by peripheral blood stem cell transplant (PBSCT).

Types of Transplants (4).

The stem cells can come from either the patient (an autologous transplant) or from a matched donor (an allogeneic transplant).

  • Allogeneic stem cell transplant: In an allogeneic transplant, the stem cells come from someone else – usually a donor whose tissue type is a very close match to the patient’s. The donor may be a brother or sister if they are a good match. Less often, an unrelated donor may be found. An allogeneic transplant is the preferred type of transplant for ALL when it is available.
  • “Mini-transplant”: “mini-transplant” (also called a non-myeloablative transplant or reduced-intensity transplant), where they get lower doses of chemo and radiation that do not destroy all the cells in their bone marrow. They then are given the donor stem cells. These cells enter the body and form a new immune system, which sees the leukemia cells as foreign and attacks them (a graft-versus-leukemia effect). This is not a standard treatment for ALL, and is being studied to find out how useful it may be.
  • Autologous stem cell transplant: In an autologous transplant, a patient’s own stem cells are removed from his or her bone marrow or blood. They are frozen and stored while the person gets treatment (high-dose chemo and/or radiation). The stem cells are then given back to the patient after treatment.

One problem with autologous transplants is that it is hard to separate normal stem cells from leukemia cells in the bone marrow or blood samples. Even after treating the stem cells in the lab to try to kill or remove any leukemia cells, there is the risk of returning some leukemia cells with the stem cell transplant

Stem cell transplants and side effects (4):

Early side effects: Early side effects are much the same as those caused by any other type of high-dose chemo, such as nausea, vomiting, loss of appetite, mouth sores, and hair loss. Because of the high doses of chemo used, these can sometimes be severe.

Infection resulting from a weakened immune system is the most common side effect. Because the stem cell procedure is done more swiftly, the risk period is shorter than with bone marrow transplantation. The risk for infection is most critical during the first 6 weeks following the transplant, but it takes 6 – 12 months post-transplant for a patient’s immune system to fully recover. Immune systems of patients with graft-versus-host disease can take even longer to function normally. Low red cell count and platelet counts are also early-side effects that when happens are treated with blood transfusion.

A rare but serious side effect of stem cell transplant is called veno-occlusive disease of the liver (VOD). In this disease, the high doses of chemo given for the transplant damage the liver. Symptoms include weight gain (from fluid collecting), liver swelling, and yellowing of the skin and eyes (jaundice). When severe, it can lead to liver failure, kidney failure, and even death.

Long-term side effects: Some side effects can last for a long time, or may not happen until years after the transplant. These long-term side effects can include the following:

  • Acute/Chronic Graft-versus-host disease (GVHD), which occurs only in a donor transplant
  • Organ damage:  lungs ( shortness of breath), ovaries (infertility and loss of menstrual period), thyroid, eyes (cataract), bone etc.
  • Developing another type of leukemia or other cancer several years later.

ALL (and AML), Bone Marrow transplant and Clinical Trials

Back in the early 80’s, chemotherapy was shown to cure a substantial portions of patients with ALL. Yet some patients had high risk of relapse when treated using conventional regimens, due to patient- and disease-related variables.  Bone marrow transplantation (BMT) was found to have encouraging results depending on the circumstances, yet the relative role between chemo and BMT to high-risk patients was controversial.

It was believed that the factors which predict poor outcome with chemo do not adversely affect the transplant outcome, yet this assumption was not based on comparing similar predicting factors . More so, the prognostic factors for outcome after BMT were not well-defined and the optimal regimen for transplant was not agreed upon. Thus, researches aimed to identify the characteristics and factors affecting good outcome after transplantation for ALL in first and second remission.

For this, 690 patients with HLA-identical sibling receiving allogeneic BMT either after first or second complete remission (CR). Numerous factors were accounted for including; age, sex, donor-recipient sex match, chemo regimen and presence of GVHD.

Of the many factors evaluated, several were highly significant in BMT outcome:

  • GVHD – It may have both favorable and unfavorable effect on the outcome. On one hand it may reduce leukemia relapse but on the other hand it may increase transplant-related mortality.
  • Conditioning chemo regimens –  most chemo regimens had negative effects of the BTM outcome. By, since the study group included only a small number of patients and these studies were conducted before the new chemo types/regimes using high-does etoposide, this factor may need to be reevaluated.
  • Donor-recipient sex match –  This factor was found to be highly significant in female receiving donors from male-matched donors. These patients had higher risk of relapse and treatment failure. This was probably due to host sensitization to the H-Y antigens. This data is also needed to be handled with cautious due to the small number of patients.
  • Immune phenotype –  Blood cell type and leukocyte levels at the beginning of the treatment is a another crucial factor. Higher leukocyte levels and non-T cell phenotype resulted in adverse outcome which led to remission.
  • Patient age – Age did not play a role when comparing the outcome after first relapse, but was found to be more favorable for younger ages (<16) when comparing the outcome after second relapse.
  • First relapse – a failure of first therapy override any other variable. The medical situation ( on/off chemo) at the time of a first relapse is highly important.  If relapse occurred while OFF chemo, patients had better prognosis.

A recent study conducted by Wing Leung, M.D., Ph.D from St. Jude Children Hospital shows that that transplantation offers real hope of survival to patients with high-risk leukemia that is not curable with intensive chemotherapy. Bone marrow transplant survival more than doubled in recent years for young, high-risk leukemia patients who lacked genetically matched donors (5).

Five years after transplantation, survival was 65 percent for the 37 St. Jude patients with high-risk ALL treated at the hospital between 2000 and 2007, compared to 28 percent for the 57 St. Jude ALL patients who underwent treatment between 1991 and 1999. For AML patients, success rates grew from 34 % to 74%.

Dr. Leung explains that historically, transplant patients fared best and suffered fewer complications when the donors were relatives who carried the same six proteins on their white blood cells. Known as HLA proteins, they serve as markers to help the immune system distinguish between an individual’s healthy tissue and diseased cells that should be eliminated.

However, St. Jude investigators pioneered the use of haploidentical transplants (=partially genetically matched donors such as parents), demonstrating that careful matching of patients and donors and proper processing of the hematopoietic donor cells enhances the anti-cancer effect of transplantation without significantly increasing side effects.

The process involves careful testing and HLA screening of potential donors to identify the one whose immune system is likely to mount the most aggressive attack against remaining leukemia cells using specialized immune cells known as natural killer cells (5).

Dr. Leung further explains that the odds of finding a good haploidentical donor are 70 to 80 percent, compared to about a 25 percent chance of having a matched sibling donor, Leung said. The likelihood of finding a genetically identical, unrelated donor ranges from about 60 to 90 percent depending on the patient’s race or ethnicity.


Previous study have identified several factors that may affect the outcome of BMT in high-risk patients and included GVHD, blood count, chemo regimen prior to the transplantation, donor-sex matched and others. In a more recent study, however,  the results indicated that all patients with very high-risk leukemia should be considered as candidates for HCT  (Allogeneic hematopoietic cell transplantation) early in the course of diagnosis or relapse treatment, regardless of the availability of a matched donor or the intensity of prior chemotherapy. HLA typing, donor search, and transplant center referral should be performed as soon as possible. Patients with persistent minimal residual disease (MRD) or hematologic relapse while on therapy are also considered candidates for HCT in current protocols. There are several major differences between previous years study-analyses and this current one that needs to be taken into consideration before including or excluding each of them. [A]; 24% of the allogeneic HCTs in patients younger than 20 years worldwide were performed using cord blood grafts vs the previous bone marrow transplant procedure, [B] differences chemo-regimens between the previous and current years,  [C] different transplant approaches evolved simultaneously, and therefore it is difficult to conduct retrospective analyses and [D] matching in HLA-C was not required for unrelated donor HCTs before 2008 in several institutes and therefore outcomes after contemporary 8 of 8 loci-matched transplantations may even be better than those favorable rates reported.

The data reported within is highly important and may increase patients survival rates and increased quality of lives. It is therefore necessary that different clinical-trial centers will re-evaluate current protocols and consider this new approach.


1. Acute Lymphoblastic Leukemia (ALL) and Nanotechnology. Author Tilda Barliya PhD


2.  In Focus: Identity of Cancer Stem Cells. Author Ritu Saxena


3a. NCI: Childhood Acute Lymphoblastic Leukemia Treatment (PDQ®).


3b. Drugs Approved for Acute Lymphoblastic Leukemia (ALL)


4. American Cancer Society: Leukemia–Acute Lymphocytic Overview


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6. AJ Barrett, MM Horowitz, RP Gale, JC Biggs, BM Camitta, KA Dicke, E Gluckman, RA Good, RH Herzig, and MB Lee. Marrow transplantation for acute lymphoblastic leukemia: factors affecting relapse and survival. Blood August 1, 1989vol. 74 no. 2 862-871


7. Fujii H, Tradeau JD., Teachey DT., Fish JD., Grupp SA., Schlts KR and Reid GS. In vivo control of acute lymphoblastic leukemia by immunostimulatory CpG oligonucleotides. Blood 2007, 109: 2008-2013. 


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