Posts Tagged ‘renal cancer’

Liver Toxicity halts Clinical Trial of IAP Antagonist for Advanced Solid Tumors

Writer/Curator Stephen J. Williams, Ph.D.

A recent press release on FierceBiotech reported the FDA had put a halt on a phase 1 study for advanced refractory solid tumors and lymphomas of Curis Inc. oral inhibitor of apoptosis (IAP) antagonist CUDC-427.  The FDA placed the trial on partial clinical hold following reports of a death of a patient from severe liver failure.  The single-agent, dose escalation Phase 1 study was designed to determine the maximum tolerated dose and recommended doses for a Phase 2 trial. The press release can be found at:

According to the report one patient with breast cancer that had metastasized to liver, lungs, bone, and ovaries developed severe hepatotoxicity as evidenced by elevated serum transaminase activities (AST and ALT) and hyper-billirubinemia.  Serum liver enzyme activities did not attenuate upon discontinuation of CUDC-427.  This was unlike prior experience to the CUDC-427 drug, in which decreased hepatic function was reversed upon drug discontinuation.  The patient died from liver failure one month after discontinuation of CUDC-427.

It was noted that no other patient had experienced such a serious, irreversible liver dysfunction.

Although any incidence of hepatotoxicity can be cause for concern, the incidence of IDIOSYNCRATIC IRREVERSIBLE HEPATOTOXICITY warrants a higher scrutiny.

Four general concepts can explain toxicity profiles and divergences between individuals:

  1. Toxicogenomics: Small differences in the genetic makeup between individuals (such as polymorphisms (SNP) could result in differences in toxicity profile for a drug.  This ais a serious possibility as only one patient presented with such irreversible liver damage
  2. Toxicodynamics:  The toxicologic effect is an extension of the pharmacologic mechanism of action (or  lack thereof: could there have been alternate signaling pathways activated in this patient or noncanonical mechanism)
  3. Toxicokinetic:  The differences in toxicological response due to differences in absorption, distribution, metabolism, excretion etc. (kinetic parameters)
  4. Idiosyncratic: etiology is unknown; usually a minority of adverse effects


Since there is not enough information to investigate toxicogenomic or toxicokinetic mechanisms for this compound, the rest of this post will investigate the possible mechanisms of hepatotoxicity due to IAP antagonists and clues from other clinical trials which might shed light on a mechanism of toxicity (toxicodynamic) or idiosyncratic events.

Therefore this post curates the current understanding of drug-induced liver injury (DILI), especially focusing on a type of liver injury referred to as idiosyncratic drug-induced liver injury (IDILI) in the context of:

  1. Targeted and newer chemotherapies such as IAP antagonists
  2. Current concepts of mechanisms of IDILI including:

i)        Inflammatory responses provoked by presence of disease

ii)      Cellular stresses, provoked by disease, uncovering NONCANONICAL toxicity pathways

iii)    Pharmacogenomics risk factors of IDILI

Eventually this post aims to stimulate the discussion: 

  • Given inflammation, genetic risk factors, and cellular stresses (seen in clinical setting) have been implicated in idiosyncratic drug-induced liver injury from targeted therapies, should preclinical hepatotoxicity studies also be conducted in the presence of the metastatic disease?
  • Does inflammation and cellular stress from clinical disease unmask NONCANONICAL pharmacologic and/or toxicological mechanisms of action?

Classification of types of Cellular Liver injury:  A listing of types of cellular injury is given for review

I.     Hepatic damage after Acute Exposure

A. Cytotoxic (Necrotic):  irreversible cell death characterized by loss of cell membrane integrity, intracellular swelling, nuclear shrinkage (pyknosis) and eventual cytoplasmic breakdown of nuclear DNA (either by a process known as karyolysis or karyorhexus) localized inflammation as a result of release of cellular constituents.  Intracellular ATP levels are commonly seen in necrotic death.  Necrosis, unlike apoptosis, does not require a source of ATP.  A nice review by Yoshihide Tsujimoto describing and showing (by microscopy) the  differences between apoptosis and necrosis can be found here.

B. Cholestatic:  hepatobiliary dysfunction with bile stasis and accumulation of bile salts.  Cholestatic injury can result in lipid (particularly cholesterol) accumulation in cannicular membranes resulting in decreased permeability of the membrane, hyperbillirubinemia and is generally thought to result in metabolic defects.

C. Lipid Peroxidation: free radical generation producing peroxide of cellular lipids, generally resulting in a cytotoxic cell death

II.     Hepatic damage after Chronic Exposure

A. Chirrotic: Chronic morphologic alteration of the liver characterized by the presence of septae of collagen distributed throughout the major portion of the liver; Forms fibrous sheaths altering hepatic blood flow, resulting in a necrotic process with scar tissue; Alteration of hepatic metabolic systems.

B. Carcinogenesis

III. Idiosyncratic Drug Induced Liver Injury

The aforementioned mechanisms of hepatotoxicity are commonly referred to as the “intrinsic” (or end target-organ) toxicity mechanisms.  Idiosyncratic drug-induced liver injury (IDILI) is not well understood but can be separated into allergic and nonallergic reactions.  Although the risk of acute liver failure associated with idiosyncratic hepatotoxins is low (about 1 in ten thousand patients) there are more than 1,000 drugs and herbal products associated with this type of toxic reaction. Idiosyncratic drug induced liver failure usually gets a black box warning from the FDA. Idiosyncratic drug-induced liver injury differs from “intrinsic” toxicity in that IDILI:

  • Happens in a minority of patients (susceptible patients)
  • Not reproducible in animal models
  • Not dose-dependent
  • Variable time of onset
  • Variable liver pathology (not distinctive lesions)
  • Not related to drug’s pharmacologic mechanism of action (trovafloxacin IDILI vs. levofloxacin)

A great review in Perspectives in Pharmacology written by Robert Roth and Patricia Ganey at Michigan State University explains these differences between intrinsic and idiosyncratic drug-induced hepatotoxicity[1] (however authors do note that there are many similarities between the two mechanisms).    It is felt that drug sensitivity (allergic) and inflammatory responses (nonallergic) may contribute to the occurrence of IDILI.  For instance lipopolysaccharide (LPS) form bacteria can potentiate acetaminophen toxicity.  In fact animal models of IDILI have been somewhat successful:

  • co-treatment of rats and mice with nontoxic doses of trovafloxacin (casues IDILI in humans) and LPS resulted in marked hepatotoxicity while no hepatotoxicity seen with levofloxacin plus LPS[2]
  • correlates well with incidence of human IDILI (adapted from a review Inflammatory Stress and Idiosyncratic Hepatotoxicity: Hints from Animal Models (in Pharmacology Reviews)[3].  Idiosyncratic injury damage has been reported for diclofenac, halothane, and sulinac.  These drugs also show hepatotoxicity in the LPS model for IDILI.
  • Roth and Ganey suggest the reason why idiosyncratic hepatotoxicity is not seen  in most acute animal toxicity studies is that, in absence of stress/inflammation  IDILI occurrence is masked by lethality but stress/inflammation shifts increases sensitivity to liver injury at a point before lethality is seen


Figure.  Idiosyncratic toxic responses of the liver.    In the absence of stress and/or genetic factors, drug exposure may result in an idiosyncratic liver injury (IDILI) at a point (or dose) beyond the therapeutic range and lethal exposure for that drug.  Preclinical studies, usually conducted at sublethal doses, would not detect DILI .  Stress and/or genetic factors sensitize the liver to toxic effects of the drug (synergism) and DILI is detected at exposure levels closer to therapeutic range.  Note IDILI is not necessarily dose-dependent but cellular stress (like ROS or inflammation) may expose NONCANONICAL mechanisms of drug action or toxicity which result in IDILI. Model adapted from Roth and Ganey.

What Stress factors contribute to IDILI?

Various stresses including inflammation from bacterial, viral infections ,inflammatory cytokines  and stress from reactive oxygen (ROS) have been suggested as mechanisms for IDILI.

  1. Inflammation/Cytokines (also discussed in other sections of this post):  Inflammation has long been associated with human cases of DILI.    Many cytokines and inflammatory mediators have been implicated including TNFα, IL7, TGFβ, and IFNϒ (viral infection) leading some to conclude that serum measurement of cytokines could be a potential biomarker for DILI[4].  In addition, ROS (see below) is generated from inflammation and also considered a risk factor for DILI[5].
  2. Reactive Oxygen (ROS)/Reactive Metabolites: Oxidative stress, either generated from reactive drug metabolites or from mitochondrial sources, has been shown to be involved in apoptotic and necrotic cell death.  Both alterations in the enzymes involved in the generation of and protection from ROS have been implicated in increased risk to DILI including (as discussed further) alterations in mitochondrial superoxide dismutase 2 (SOD2) and glutathione S-transferases.  Both ROS and inflammatory cytokines can promote JNK signaling, which has been implicated in DILI[6].

Dr. Neil Kaplowitz suggested that we:

“develop a unifying hypothesis that involves underlying genetic or acquired mitochondrial abnormalities as a major determinant of susceptibility for a number of drugs that target mitochondria and cause DILI. The mitochondrial hypothesis, implying gradually accumulating and initially silent mitochondrial injury in heteroplasmic cells which reaches a critical threshold and abruptly triggers liver injury, is consistent with the findings that typically idiosyncratic DILI is delayed (by weeks or months), that increasing age and female gender are risk factors and that these drugs are targeted to the liver and clearly exhibit a mitochondrial hazard in vitro and in vivo. New animal models (e.g., the Sod2(+/-) mouse) provide supporting evidence for this concept. However, genetic analyses of DILI patient samples are needed to ultimately provide the proof-of-concept”[7].

Clin Infect Dis. 2004 Mar 38(Supplement 2) S44-8, Figure 1

Clin Infect Dis. 2004 Mar 38(Supplement 2) S44-8, Figure 3

Figures. Mechanisms of Drug-Induced Liver Injury and Factors related to the occurrence of  DILI (used with permission from Oxford Press; reference [7])

To this end, Dr. Brett Howell and other colleagues at the Hamner-UNC Institute for Drug Safety Sciences (IDSS) developed an in-silico model of DILI ( the DILISym™ model)which is based on  depletion of cellular ATP and reactive metabolite formation as indices of DILI.

Have there been Genetic Risk Factors identified for DILI?

Candidate-gene-associated studies (CGAS) have been able to identify several genetic risk factors for DILI including:

  1. Uridine Diphosphate Glucuronosyltransferase 2B7 (UGT2B7): variant increased susceptibility to diclofenac-induced DILI
  2. Adenosine triphosphate-binding cassette C2 (ABCC2) variant ABCC-24CT increased susceptibility to diclofenac-induced DILI
  3. Glutathione S-transferase (GSTT1): patients with a double GSTT1-GSTM1 null genotype had a significant 2.7 fold increased risk of DILI from nonsteroidal anti-inlammatory agents, troglitazone and tacrine.  GSTs are involved in the detoxification of phase 1 metabolites and also protect against cellular ROS.

Although these CGAS confirmed these genetic risk factors,  Stefan Russman suggests a priori genome-wide association studies (GWAS) might provide a more complete picture of genetic risk factors for DILI as CGAS is limited due to

  1. Candidate genes are selected based on current mechanisms and knowledge of DILI so genetic variants with no known knowledge of or mechanistic information would not be detected
  2. Many CGAS rely on analysis of a limited number of SNP and did not consider intronic regions which may control gene expression

A priori GWAS have the advantage of being hypothesis-free, and although they may produce a high number of false-positives, new studies of genetic risk factors of ximelagatran, flucioxaciliin and diclofenac-induced liver injury are using a hybrid approach which combines the whole genome and unbiased benefits of GWAS with the confirmatory and rational design of CGAS[8-10].

Even though idiosyncratic DILI is rare, the severity, unpredictable onset, and unknown etiology and risk factors have prompted investigators such as Stefan Russmann from University Hospital Zurich and Ignazio Grattagliano from University of Bari to suggest:

Identification of risk factors for rare idiosyncratic hepatotoxicity requires special networks that contribute to data collection and subsequent identification of environmental as well as genetic risk factors for clinical cases of idiosyncratic DILI[11].

Therefore, a DILI network project (DILIN) had been developed to collect samples and detailed genetic and clinical data on IDILI cases from multiple medical centers.  The project aims to identify the upstream and downstream genetic risk factors for IDILI[12].  Please see a SlideShare presentation here of the goals of the DILI network project.

Drs Colin Spraggs and Christine Hunt had reviewed possible genetic risk factors of DILI seen with various tyrosine kinase inhibitors (TKIs) including Lapatinib (Tykerb/Tyverb©, a dual inhibitor of  HER2/EGFR heterodimer) and paopanib (Votrient©; a TKI that targets VEGFR1,2,3 and PDGFRs)[13].

From a compilation of studies:

  • Elevation in serum bilirubin during treatment with lapatinib and pazopanib are associated with UGT1A1 polymorphism related to Gilbert’s syndrome (a clinically benign syndrome)
  • Anecdotal evidence shows that polymorphisms of lapatinib and pazopanib metabolizing enzymes may contribute to differences seen in onset of DILI
  • Pazopanib-induced elevations of ALT correlate with HFE variants, suggesting alterations in iron transport may predispose to DILI
  • Strong correlations between lapatinib-induced DILI and class II HLA locus suggest inflammatory stress response important in DILI

Note that these clinical findings were not evident from the preclinical tox studies. According to the European Medicines Agency assessment report for Tykerb states: “the major findings in repeat dose toxicity studies were attributed to lapatinib pharmacology (epithelial effect in skin and GI system.  The toxic events occurred at exposures close to the human exposure at the recommended dose.  Repeat-dose toxicity studies did not reveal important safety concerns than what would be expected from the mode of action”.

However, it should be noted that in high dose repeat studies in mice and rats, severe lethality was seen with hematologic, gastrointestinal toxicities in combination with altered blood chemistry parameters and yellowing of internal organs.

IAP Antagonists, Mechanism of Action, and Clinical Trials:

A few IAP antagonists which are in early stage development include:

  • Norvatis IAP Inhibitor LCL161: at 2012 San Antonia Breast Cancer Symposium, a phase 1 trial in triple negative breast cancer showed promising results when given in combination with paclitaxel.
  • Ascenta Therapeutics IAP inhibitor AT-406 in phase 1 in collaboration with Debiopharm S.A. showed antitumor efficacy in xenograft models of breast, pancreatic, prostate and lung cancer. The development of this compound is described in a paper by Cai et. al.

National Cancer Institute sponsored trials using antagonists of IAPs include

  • Phase II Study of Birinapant for Advanced Ovarian, Fallopian Tube, and Peritoneal Cancer (NCI-12-C-0191). Principle Investigator: Dr. Christina Annunziata. See the protocol summary. More open trials for this drug are located here.  Closed trials including safety studies can be found here.
  • A Phase 1 non-randomized dose escalation study to determine maximum tolerated dose (MTD) and characterize the safety for the TetraLogic compound TL32711 had just been completed. Results have not been published yet.
  • Closed Clinical trials with the IAP antagonist HGS1029 in advanced solid tumors determined that weekly i.v. administration of HGS1029 reported a safety issue for primary outcome measures

A great review on IAP proteins and their role as regulators of apoptosis and potential targets for cancer therapy [14] can be found as a part of a Special Issue in Experimental Oncology “Apoptosis: Four Decades Later”.  Human IAPs (inhibitors of apoptosis) consist of eight proteins involved in cell death, immunity, inflammation, cell cycle, and migration including:

In general, IAP proteins are directly involved in inhibiting apoptosis by binding and directly inhibiting the effector cysteine protease caspases (caspase 3/7) ultimately responsible for the apoptotic process [15].  IAPs were actually first identified in baculoviral genomes because of their ability to suppress host-cell death responses during viral infection [16]. IAP proteins are often overexpressed in cancers [17].

Apoptosis is separated into two pathways, defined by the initial stress or death signal and the caspases involved:

  1. Extrinsic pathway: initiated by TNFα and death ligand FasLigand;  involves caspase-8; process inhibited by IAP1/2
  2. Intrinsic pathway: initiated by DNA damage, irradiation, chemotherapeutics; mitochondrial pathway involving caspase 9 and cytochrome c release from mitochondria; mitochondria also releases SMAC/DIABLO, which binds and inhibits XIAP (XIAP inhibits the Intrinsic apoptotic pathway.



Intrinsic and Extrinsic pathways of apoptosis. Figure photocredit (

The Curis IAP antagonist (and others) is a SMAC small molecule mimetic. It is interesting to note [18, 19] that IAP antagonists can result in death by

  • Apoptosis: an IAP antagonist in presence of competent TNFα signaling
  • Necrosis: seen with IAP inhibitors in cells with altered TNFα signaling or with presence of caspase inhibitors

IAPs are also involved in the regulation of signaling pathways such as:

NF-ΚB signaling pathway

NF-ΚB is a “rapid-acting” transcription factor which has been found to be overexpressed in various cancers.  Under most circumstances NF-ΚB translocation to the nucleus results in transcription of genes related to cell proliferation and survival.  NF-ΚB signaling is broken down in two pathways

  1. Canonical:  Canonical pathway can be initiated (for example in inflammation) when TNF-α binds its receptors activating  death domains (TRADD)
  2. Noncanonical: since requires new protein synthesis takes longer than canonical signaling.  Can be initiated by other TNF like ligands like CD40

IAP1/2 is a negative regulator of the noncanonical NF-ΚB signaling pathway by promoting proteosomal degradation of the TRAF signaling complex. A wonderfully annotated list of NF-ΚB target genes can be found on the Thomas Gilmore lab site at Boston University at .

NF-ΚB has been considered a possible target for chemotherapeutic development however Drs. Veronique Baud and Michael Karin have pondered the utility of IAP antagonists as a good target in their review: Is NF-ΚB a good target for cancer therapy?: Hopes and pitfalls [20].  The authors discuss issues such that IAP antagonism induced both the classical and noncanonical NF-ΚB pathway thru NIK stabilization, resulting in stabilization of NF-ΚB signaling and thereby undoing any chemotherapeutic effect which would be desired.

AKT signaling

IAPs have been shown to interact with other proteins including a report that SIAP regulates AKT activity and caspase-3-dependent cleavage during cisplatin-induced apoptosis in human ovarian cancer cells and could be another mechanism involved in cisplatin resistance[21].   In addition there have been reports that IAPs can regulate JNK and MAPK signaling.

Therefore, IAPs are involved in CANONICAL and NONCANONICAL pathways.

IAPs can Regulate Pro-Inflammatory Cytokines

A recent 2013 JBC paper [22]showed that IAPs and their antagonists can regulate spontaneous and TNF-induced proinflammatory cytokine and chemokine production and release

  • IAP required for production of multiple TNF-induced proinflammatory mediators
  • IAP antagonism decreased TNF-mediated production of chemokines and cytokines
  • But increased spontaneous release of chemokines

In addition Rume Damgaard and Mads Gynd-Hansen have suggested that IAP antagonists may be useful in treating inflammatory diseases like Crohn’s disease as IAPs regulate innate and acquired immune responses[23].

Toxicity profiles of IAP antagonists

NOTE: In a paper in Toxicological Science from 2012[24], Rebecca Ida Erickson form Genentech reported on the toxicity profile of the IAP antagonist GDC-0152 from a study performed in dogs and rats. A dose-dependent toxicity profile from i.v. administration was consistent with TNFα-mediated toxicity with

  • Elevated plasma cytokines and an inflammatory leukogram
  • Increased serum transaminases
  • Inflammatory infiltrate and apoptosis/necrosis in multiple tissues

In a related note, a similar type of fatal idiosyncratic hepatotoxicity was reported in a 62 year-old man treated with the Raf kinase inhibitor sorafenib for renal cell carcinoma[25]: Fatal case of sorafenib-associated idiosyncratic hepatotoxicity in the adjuvant treatment of a patient with renal cell carcinoma; Case Report  in BMC Cancer.

At week four after initiation of sorafenib treatment, the patient noticed increasing fatigue, malaise, gastrointestinal discomfort and abdominal rash.  Although treatment was discontinued, jaundice developed and blood test revealed an acute hepatitis with

  • Elevated serum ALT
  • Elevated serum alkaline phosphatase
  • Increased prothrombin time
  • Increased LDH

…elevated levels seen in the case with the aforementioned IAP antagonist.  Autopsy revealed

  • Lobular hepatitis
  • Mononuclear cell infiltrate
  • Hepatocyte necrosis

These findings are in line with a drug-induced inflammation and IDILI. In addition to hepatotoxicity, renal insufficiency developed in this patient. The authors had suggested the death was probably due to “an idiosyncratic allergic reaction to sorafenib manifesting as hepatotoxicity with associated renal impairment”.  The authors also noted that genome wide association studies of idiosyncratic drug-induced liver injury support involvement of major histocompatibility complex (MHC) polymorphisms[26].  MHC involvement has also been associated with lapatanib and pazopanib hepatotoxicity [27, 28].

Curis has been involved in another novel oncology therapeutic, a first in class.

Last year Roche and Genentech had won approval for a Hedgehog pathway inhibitor vismodegib for treatment of advanced basal cell carcinoma (reported at FierceBiotech©). Vismodegib was initially developed in collaboration with Curis, Inc.  The hedgehog signaling pathway, which controls the function of Gli factors (involved in stem cell differentiation), is overactive in advanced basal cell carcinoma as well as other cancer types.

As an additional reference, the FDA National Center for Toxicological Research has developed THE LIVER TOXICITY KNOWLEDGE BASE (LTKB).

“The LTKB is a project designed to study drug-induced liver injury (DILI). Liver toxicity is the most common cause for the discontinuation of clinical trials on a drug, as well as the most common reason for an approved drug’s withdrawal from the marketplace. Because of this, DILI has been identified by the FDA’s Critical Path Initiatives as a key area of focus in a concerted effort to broaden the agency’s knowledge for better evaluation tools and safety biomarkers.”

A nice SlideShow of Toxicity of Targeted Therapies can be found here:

Also please note that ALL GENES in this article are linked to their GENECARD 


1.            Roth RA, Ganey PE: Intrinsic versus idiosyncratic drug-induced hepatotoxicity–two villains or one? The Journal of pharmacology and experimental therapeutics 2010, 332(3):692-697.

2.            Waring JF, Liguori MJ, Luyendyk JP, Maddox JF, Ganey PE, Stachlewitz RF, North C, Blomme EA, Roth RA: Microarray analysis of lipopolysaccharide potentiation of trovafloxacin-induced liver injury in rats suggests a role for proinflammatory chemokines and neutrophils. The Journal of pharmacology and experimental therapeutics 2006, 316(3):1080-1087.

3.            Deng X, Luyendyk JP, Ganey PE, Roth RA: Inflammatory stress and idiosyncratic hepatotoxicity: hints from animal models. Pharmacological reviews 2009, 61(3):262-282.

4.            Laverty HG, Antoine DJ, Benson C, Chaponda M, Williams D, Kevin Park B: The potential of cytokines as safety biomarkers for drug-induced liver injury. European journal of clinical pharmacology 2010, 66(10):961-976.

5.            Schwabe RF, Brenner DA: Mechanisms of Liver Injury. I. TNF-alpha-induced liver injury: role of IKK, JNK, and ROS pathways. American journal of physiology Gastrointestinal and liver physiology 2006, 290(4):G583-589.

6.            Seki E, Brenner DA, Karin M: A liver full of JNK: signaling in regulation of cell function and disease pathogenesis, and clinical approaches. Gastroenterology 2012, 143(2):307-320.

7.            Kaplowitz N: Drug-induced liver injury. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2004, 38 Suppl 2:S44-48.

8.            Kindmark A, Jawaid A, Harbron CG, Barratt BJ, Bengtsson OF, Andersson TB, Carlsson S, Cederbrant KE, Gibson NJ, Armstrong M et al: Genome-wide pharmacogenetic investigation of a hepatic adverse event without clinical signs of immunopathology suggests an underlying immune pathogenesis. The pharmacogenomics journal 2008, 8(3):186-195.

9.            Aithal GP, Ramsay L, Daly AK, Sonchit N, Leathart JB, Alexander G, Kenna JG, Caldwell J, Day CP: Hepatic adducts, circulating antibodies, and cytokine polymorphisms in patients with diclofenac hepatotoxicity. Hepatology 2004, 39(5):1430-1440.

10.          Daly AK, Aithal GP, Leathart JB, Swainsbury RA, Dang TS, Day CP: Genetic susceptibility to diclofenac-induced hepatotoxicity: contribution of UGT2B7, CYP2C8, and ABCC2 genotypes. Gastroenterology 2007, 132(1):272-281.

11.          Russmann S, Kullak-Ublick GA, Grattagliano I: Current concepts of mechanisms in drug-induced hepatotoxicity. Current medicinal chemistry 2009, 16(23):3041-3053.

12.          Fontana RJ, Watkins PB, Bonkovsky HL, Chalasani N, Davern T, Serrano J, Rochon J: Drug-Induced Liver Injury Network (DILIN) prospective study: rationale, design and conduct. Drug safety : an international journal of medical toxicology and drug experience 2009, 32(1):55-68.

13.          Spraggs CF, Xu CF, Hunt CM: Genetic characterization to improve interpretation and clinical management of hepatotoxicity caused by tyrosine kinase inhibitors. Pharmacogenomics 2013, 14(5):541-554.

14.          de Almagro MC, Vucic D: The inhibitor of apoptosis (IAP) proteins are critical regulators of signaling pathways and targets for anti-cancer therapy. Experimental oncology 2012, 34(3):200-211.

15.          Deveraux QL, Takahashi R, Salvesen GS, Reed JC: X-linked IAP is a direct inhibitor of cell-death proteases. Nature 1997, 388(6639):300-304.

16.          Crook NE, Clem RJ, Miller LK: An apoptosis-inhibiting baculovirus gene with a zinc finger-like motif. Journal of virology 1993, 67(4):2168-2174.

17.          Tamm I, Kornblau SM, Segall H, Krajewski S, Welsh K, Kitada S, Scudiero DA, Tudor G, Qui YH, Monks A et al: Expression and prognostic significance of IAP-family genes in human cancers and myeloid leukemias. Clinical cancer research : an official journal of the American Association for Cancer Research 2000, 6(5):1796-1803.

18.          Laukens B, Jennewein C, Schenk B, Vanlangenakker N, Schier A, Cristofanon S, Zobel K, Deshayes K, Vucic D, Jeremias I et al: Smac mimetic bypasses apoptosis resistance in FADD- or caspase-8-deficient cells by priming for tumor necrosis factor alpha-induced necroptosis. Neoplasia 2011, 13(10):971-979.

19.          He S, Wang L, Miao L, Wang T, Du F, Zhao L, Wang X: Receptor interacting protein kinase-3 determines cellular necrotic response to TNF-alpha. Cell 2009, 137(6):1100-1111.

20.          Baud V, Karin M: Is NF-kappaB a good target for cancer therapy? Hopes and pitfalls. Nature reviews Drug discovery 2009, 8(1):33-40.

21.          Asselin E, Mills GB, Tsang BK: XIAP regulates Akt activity and caspase-3-dependent cleavage during cisplatin-induced apoptosis in human ovarian epithelial cancer cells. Cancer research 2001, 61(5):1862-1868.

22.          Kearney CJ, Sheridan C, Cullen SP, Tynan GA, Logue SE, Afonina IS, Vucic D, Lavelle EC, Martin SJ: Inhibitor of apoptosis proteins (IAPs) and their antagonists regulate spontaneous and tumor necrosis factor (TNF)-induced proinflammatory cytokine and chemokine production. The Journal of biological chemistry 2013, 288(7):4878-4890.

23.          Damgaard RB, Gyrd-Hansen M: Inhibitor of apoptosis (IAP) proteins in regulation of inflammation and innate immunity. Discovery medicine 2011, 11(58):221-231.

24.          Erickson RI, Tarrant J, Cain G, Lewin-Koh SC, Dybdal N, Wong H, Blackwood E, West K, Steigerwalt R, Mamounas M et al: Toxicity profile of small-molecule IAP antagonist GDC-0152 is linked to TNF-alpha pharmacology. Toxicological sciences : an official journal of the Society of Toxicology 2013, 131(1):247-258.

25.          Fairfax BP, Pratap S, Roberts IS, Collier J, Kaplan R, Meade AM, Ritchie AW, Eisen T, Macaulay VM, Protheroe A: Fatal case of sorafenib-associated idiosyncratic hepatotoxicity in the adjuvant treatment of a patient with renal cell carcinoma. BMC cancer 2012, 12:590.

26.          Daly AK: Drug-induced liver injury: past, present and future. Pharmacogenomics 2010, 11(5):607-611.

27.          Spraggs CF, Budde LR, Briley LP, Bing N, Cox CJ, King KS, Whittaker JC, Mooser VE, Preston AJ, Stein SH et al: HLA-DQA1*02:01 is a major risk factor for lapatinib-induced hepatotoxicity in women with advanced breast cancer. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2011, 29(6):667-673.

28.          Xu CF, Reck BH, Goodman VL, Xue Z, Huang L, Barnes MR, Koshy B, Spraggs CF, Mooser VE, Cardon LR et al: Association of the hemochromatosis gene with pazopanib-induced transaminase elevation in renal cell carcinoma. Journal of hepatology 2011, 54(6):1237-1243.

Other articles on the site about Toxicology and Pharmacology of New Classes of Cancer Chemotherapy include:

FDA Guidelines For Developmental and Reproductive Toxicology (DART) Studies for Small Molecules

Gamma Linolenic Acid (GLA) as a Therapeutic tool in the Management of Glioblastoma

DNA Methultransferases – Implications to Epigenetic Regulation and Cancer Therapy Targeting: James Shen, PhD

Molecular Profiling in Cancer Immunotherapy: Debraj GuhaThakurta, PhD

AT13148 – A Novel Oral Multi-AGC Kinase Inhibitor Has Potent Antitumor Activity

Targeting Mitochondrial-bound Hexokinase for Cancer Therapy

Breast Cancer, drug resistance, and biopharmaceutical targets

Ubiquitin-Proteosome pathway, Autophagy, the Mitochondrion, Proteolysis and Cell Apoptosis: Part III

Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis


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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

The metabolite pool of cells and tissues represents the end result of metabolism determined by genetic, environmental and nutritional factors. The metabolic profile of biological systems is closely related to the individual phenotype and reflects the biological endpoint of a multitude of pathways and their interaction with any confounding stimuli. Cancer cells exhibit activation of specific metabolic pathways to compensate for their extremely high energy demands. Indeed increased glucose uptake and lactate production and decreased respiration are key phenomena of tumour cell metabolism. In particular, the generation of an acidic microenvironment through increased lactate production, even under aerobic conditions, may confer extracellular matrix degeneration and exert toxic effects on surrounding cell populations, while being harmless for the cancer cell itself. Thus, the metabolic adaptations may indeed be critical for the development of accelerated proliferation and the invasive growth of tumour cell populations. The molecular mechanisms underlying the metabolic hallmarks of cancer are still poorly understood, although genetic, epigenetic and environmental factors driving cancer development and progression will interact to determine the metabolic phenotype of tumour cells. Recent studies suggest that metabolic changes play a pivotal role in the biology of renal cell carcinoma – a tumour entity that is largely resistant to conventional chemo- and radiotherapy. The metabolic profile of renal tumours may thus serve as a reliable biomarker of malignant transformation and biological behaviour.

Recent advances in metabolic profiling technologies by providing quantitative measures of metabolite profiles from gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) based technology present the opportunity to apply this technique in human specimens. Global metabolic profiling has emerged as a promising approach to characterize the metabolite pool within a cell, tissue or bodily fluid under certain conditions, such as health or disease status. Metabolic profiling is applied to monitor the health to disease continuum and has the potential of increasing our understanding of the mechanisms of disease. Thus the characterization of the metabolic features in tumours is expected to provide a better understanding of the mechanisms of malignant transformation and progression and may lead to the identification of metabolic biomarkers for cancer detection and prognostication. However, comparative profiling of low molecular weight compounds, such as sugars, lipids and amino acids, in cancer as compared to the corresponding normal tissue is a rather unexplored area. The objective of this study was to characterize the key metabolic features of renal cell carcinoma using GC-TOF-MS and mutual information as well as decision tree-based data analysis.

Hypoxia is key in tumour cell behaviour. Hypoxia, via hypoxia inducible factor, plays a key role in the metabolic changes in the kidney cancer cell and influences different pathways. Pathways that use tyrosine kinases and mammalian target of rapamycin are well studied. Hypoxia-related effects on vascular epithelial growth factors and angiogenesis will influence the metabolic status of the cell significantly. This is the basis of inhibitor-type drugs or antibody blocking agents and the effect on the clinical course of renal cell carcinoma patients. Detailed information about metabolic changes is crucial to understanding these mechanisms more clearly. Treating renal cell carcinoma patients is not like treating one disease. Renal cell carcinoma has different morphologic entities with distinct differences in cytogenetic background. These differences should be reflected in the different approaches of diagnostic and therapeutic strategies. This consideration will help to increase the efficacy of novel agents and decrease unnecessary side-effects. Metabolomics explains the importance of explaining genetic changes and the functional outcome of the tumour cells. In addition, epidemiologic differences in incidence and prevalence in different parts of the world may help provide insight into the etiology of kidney cancer. Factors that may influence renal cancer likelihood (eg, obesity, antihypertensive therapy) may have an explanation in metabolomics.

Source References:,d.dGI

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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. (–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 (




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.

Other Articles related to this topic appeared on this Open Access Online Scientific Journal, including the following:

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

Genomics of bronchial epithelial dysplasia

Genomics in Medicine- Tomorrow’s Promise

Prostate Cancer: Androgen-driven “Pathomechanism” in Early-onset Forms of the Disease

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

Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell

Directions for Genomics in Personalized Medicine

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

Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1

Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @

In Focus: Targeting of Cancer Stem Cells

Modulating Stem Cells with Unread Genome: microRNAs

What can we expect of tumor therapeutic response?


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