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

Posted in Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, BioSimilars, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Prevention: Research & Programs, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, FDA Regulatory Affairs, Genetics & Innovations in Treatment, Genome Biology, Health Economics and Outcomes Research, Health Law & Patient Safety, Human Immune System in Health and in Disease, Liver & Digestive Diseases Research, Personalized and Precision Medicine & Genomic Research, tagged AAV, adverse effects, anti-cancer therapy, Apoptosis, Aviva Lev-Ari, Cancer - General, DILI, Drug development, drug induced liver injury, Food and Drug Administration, health, hepatoxicity, IAP, idiosyncratic, inflammation, inhibitor of apoptosis, novartis, renal cancer, SMA, Spinal muscular atrophy, toxicology, Zolgensma on November 25, 2013| 1 Comment »

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

Curator: Stephen J. Williams, Ph.D.

UPDATED 8/12/2022

Athough not related to IAP Antagonists this update does report 2 deaths from IDILI or idiosynchratic drug induced liver injury from a gene therapy trial using an AAV (adeno associated virus) targeting the disease spinal muscular atrophy. Please see below after reading about IDILI.

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:

http://www.fiercebiotech.com/press-releases/curis-reports-third-quarter-2013-financial-results-and-provides-cudc-427-de.

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:

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
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)
Toxicokinetic: The differences in toxicological response due to differences in absorption, distribution, metabolism, excretion etc. (kinetic parameters)
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:

Targeted and newer chemotherapies such as IAP antagonists
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.

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].
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].

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:

Uridine Diphosphate Glucuronosyltransferase 2B7 (UGT2B7): variant increased susceptibility to diclofenac-induced DILI
Adenosine triphosphate-binding cassette C2 (ABCC2) variant ABCC-24CT increased susceptibility to diclofenac-induced DILI
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

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
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:

Extrinsic pathway: initiated by TNFα and death ligand FasLigand; involves caspase-8; process inhibited by IAP1/2
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 (wikidot.com)

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

Canonical: Canonical pathway can be initiated (for example in inflammation) when TNF-α binds its receptors activating death domains (TRADD)
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 http://www.bu.edu/nf-kb/gene-resources/target-genes/ .

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: http://www.slideshare.net/RashaHaggag/toxicities-of-targeted-therapies

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

UPDATED 8/12/2022

Zolgensma Gene Therapy Linked to 2 Deaths in SMA Patients, Novartis Reports

August 12, 2022

Victoria Johnson

Source: https://www.cgtlive.com/view/zolgensma-gene-therapy-linked-to-2-deaths-in-sma-patients-novartis-reports

The 2 deaths, due to acute liver failure, occurred in patients treated in Kazakhstan and Russia.

Two children with spinal muscular atrophy (SMA) have died after being treated with onasemnogene abeparvovec (Zolgensma; Novartis) from acute liver failure, a known safety risk of the therapy.¹

Novartis has updated the FDA and other regulatory agencies in countries that Zolgensma is approved in, including Russia and Kazakhstan, where the deaths occurred. The company will also update the labeling of Zolgensma to include the deaths.

“While this is important safety information, it is not a new safety signal and we firmly believe in the overall favorable risk/benefit profile of Zolgensma, which to date has been used to treat more than 2300 patients worldwide across clinical trials, managed access programs, and in the commercial setting,” Novartis said in an emailed statement to BioPharma Dive.²

Zolgensma’s labeling includes the risk of liver injury and instructs clinicians to assess liver function before treatment and to manage liver enzyme counts with steroid treatment. The 2 deaths occurred 5 to 6 weeks after the one-time infusion and 1 to 10 days after corticosteroid treatment was tapered, according to an initial report from Stat News.¹

An FDA advisory committee meeting that took place last fall identified risks of adeno associated virus (AAV) gene therapies including, specifically, Zolgensma.² The committee recommended caution, but nothing that would hinder gene therapy development.

Zolgensma, which was approved in the US in May 2019, just recently demonstrated further positive data from SPR1NT (NCT03505099), a phase 3 multicenter, single-arm trial on its effect in presymptomatic children with SMA in 2 articles published in Nature Medicine.^3,4

All children in both the type 1 and type 2 cohorts achieved the ability to independently sit and most achieved other age-appropriate milestones including standing and walking. None of the children in the study required respiratory support or nutritional support, and there were no serious treatment-related adverse events observed.

“The robust data from both the 2- and 3-copy SPR1NT cohorts are being published together for the first time, further supporting the significant and clinically meaningful benefit of Zolgensma in presymptomatic babies with SMA,” Shephard Mpofu, MD, SVP, chief medical officer, Novartis Gene Therapies, said in a previous statement.⁵ “When treated with Zolgensma prior to the onset of symptoms, not only did all 29 patients enrolled in SPR1NT survive, but were thriving—breathing and eating on their own, with most even sitting, standing, and walking without assistance.”

REFERENCE

1. Silverman E. Novartis reports two children died from acute liver failure after treatment with Zolgensma gene therapy. STAT. August 11, 2022. https://www.statnews.com/pharmalot/2022/08/11/novartis-zolgensma-liver-failure-gene-therapy-death/

2. Pagliarulo N. Novartis reports deaths of two patients treated with Zolgensma gene therapy. BioPharma Dive. August 12, 2022. https://www.biopharmadive.com/news/novartis-zolgensma-patient-death-liver-injury/629542/

3. Strauss KA, Farrar MA, Muntoni F, et al. Onasemnogeneabeparvovec for presymptomatic infants with two copies of SMN2 at risk for spinal muscular atrophy type 1: the Phase III SPR1NT trial. Nat Med. Published online June 17, 2022. doi:10.1038/s41591-022-01866-42

4. Strauss KA, Farrar MA, Muntoni F, et al. Onasemnogeneabeparvovec for presymptomatic infants with three copies of SMN2 at risk for spinal muscular atrophy: the Phase III SPR1NT trial. Nat Med. Published online June 17, 2022.doi: 10.1038/s41591-022-01867-3

5. Novartis announces Nature Medicine publication of Zolgensma data demonstrating age-appropriate milestones when treating children with SMA presymptomatically. News release. Novartis. June 17, 2022. https://firstwordpharma.com/story/5597735

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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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Metabolomics and renal cancer

Posted in Biomarkers & Medical Diagnostics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Prevention: Research & Programs, Chemical Biology and its relations to Metabolic Disease, Metabolomics, Nutrition, Population Health Management, Nutrition and Phytochemistry, tagged carcinoma, Kidney, Metabolite, Metabolomics, renal cancer on August 5, 2013| 2 Comments »

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:

http://www.europeanurology.com/article/S0302-2838(12)01352-8/fulltext

http://www.ncbi.nlm.nih.gov/pubmed/19845817

http://www.ncbi.nlm.nih.gov/pubmed/18072195

http://www.ncbi.nlm.nih.gov/pubmed/17123452

http://www.ncbi.nlm.nih.gov/pubmed/20464042

http://www.ncbi.nlm.nih.gov/pubmed/21930086

https://www.google.com.tw/url?sa=t&rct=j&q=&esrc=s&source=web&cd=8&cad=rja&ved=0CGwQFjAH&url=http%3A%2F%2Fjournals.sfu.ca%2Fcuaj%2Findex.php%2Fjournal%2Farticle%2Fdownload%2F665%2F466&ei=-ub8UbD0JcfolAXeyYGYBw&usg=AFQjCNFIzdti065pBEGKlEDkBDCPA4IwUw&sig2=scgq8qT9ffKa65MxAvpuNg&bvm=bv.50165853,d.dGI

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Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

Posted in Biological Networks, Gene Regulation and Evolution, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Prevention: Research & Programs, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Computational Biology/Systems and Bioinformatics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Genome Biology, Genomic Testing: Methodology for Diagnosis, Health Economics and Outcomes Research, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, tagged Aviva Lev-Ari, Biology, Cancer - General, Cancer Research UK, Cancer stem cell, carcinoid, DNA, Eukaryotic, everolimus, Evolution, Exome sequencing, gene expression, genetics, genome sequence, Mammalian target of rapamycin, medicine, mTOR, neuroendocrine tumor, New England Journal of Medicine, ovarian cancer, Personalized medicine, renal cancer, research, Targeted therapy on April 10, 2013| 13 Comments »

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

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

UPDATED 3/11/2023

The updates to this article are an expansion of the original article on 4/10/2013 on tumor heterogeneity in renal cancer, extended to other solid tumor types.

Evaluating Tumor Evolution via Genomic Profiling of Individual Tumor Spheroids in a Malignant Ascites

Scientific Reports volume 8, Article number: 12724 (2018) Cite this article

Abstract

Epithelial ovarian cancer (EOC) is a silent but mostly lethal gynecologic malignancy. Most patients present with malignant ascites and peritoneal seeding at diagnosis. In the present study, we used a laser-aided isolation technique to investigate the clonal relationship between the primary tumor and tumor spheroids found in the malignant ascites of an EOC patient. Somatic alteration profiles of ovarian cancer-related genes were determined for eight spatially separated samples from primary ovarian tumor tissues and ten tumor spheroids from the malignant ascites using next-generation sequencing. We observed high levels of intra-tumor heterogeneity (ITH) in copy number alterations (CNAs) and single-nucleotide variants (SNVs) in the primary tumor and the tumor spheroids. As a result, we discovered that tumor cells in the primary tissues and the ascites were genetically different lineages. We categorized the CNAs and SNVs into clonal and subclonal alterations according to their distribution among the samples. Also, we identified focal amplifications and deletions in the analyzed samples. For SNVs, a total of 171 somatic mutations were observed, among which 66 were clonal mutations present in both the primary tumor and the ascites, and 61 and 44 of the SNVs were subclonal mutations present in only the primary tumor or the ascites, respectively. Based on the somatic alteration profiles, we constructed phylogenetic trees and inferred the evolutionary history of tumor cells in the patient. The phylogenetic trees constructed using the CNAs and SNVs showed that two branches of the tumor cells diverged early from an ancestral tumor clone during an early metastasis step in the peritoneal cavity. Our data support the monophyletic spread of tumor spheroids in malignant ascites.

Conclusion

In this study, we performed genome-wide sequence analysis of the primary tumor and the associated tumor spheroids in the malignant ascites of an EOC patient. We analyzed genetic heterogeneity in the primary tumor and tumor spheroids through multi-region sequencing and the laser-aided cell isolation technique¹¹. From the sequencing data, we discovered clonal or subclonal somatic CNAs and SNVs, based on which we constructed phylogenetic trees and inferred the evolutionary history of tumor cells in the patient. As a result, we found that the tumor cells in the malignant ascites were an independent lineage from the primary tumor. The phylogenetic analysis showed that the lineage branched before the evolution of the cancer cells at the primary tissues, which suggests that analyzing malignant ascites might be used to detect ovarian cancer or metastasis in the early stage. In summary, the genetic plasticity and similarity between a primary tumor and associated tumor spheroids are still not clear, and yet, the nature of the similarity may have profound implications for both tumor progression and therapeutic outcomes in ovarian cancer. Therefore, future prospective studies profiling the genomic information of primary ovarian tumors, distant metastatic tumors, and tumor spheroids to determine the direction of tumor evolution and metastasis of ovarian cancer are warranted.

Tumor evolution and intratumor heterogeneity of an epithelial ovarian cancer investigated using next-generation sequencing

BMC Cancer volume 15, Article number: 85 (2015) Cite this article

Abstract

Background

The extent to which metastatic tumors further evolve by accumulating additional mutations is unclear and has yet to be addressed extensively using next-generation sequencing of high-grade serous ovarian cancer.

Methods

Eleven spatially separated tumor samples from the primary tumor and associated metastatic sites and two normal samples were obtained from a Stage IIIC ovarian cancer patient during cytoreductive surgery prior to chemotherapy. Whole exome sequencing and copy number analysis were performed. Omental exomes were sequenced with a high depth of coverage to thoroughly explore the variants in metastatic lesions. Somatic mutations were further validated by ultra-deep targeted sequencing to sort out false positives and false negatives. Based on the somatic mutations and copy number variation profiles, a phylogenetic tree was generated to explore the evolutionary relationship among tumor samples.

Results

Only 6% of the somatic mutations were present in every sample of a given case with TP53 as the only known mutant gene consistently present in all samples. Two non-spatial clusters of primary tumors (cluster P1 and P2), and a cluster of metastatic regions (cluster M) were identified. The patterns of mutations indicate that cluster P1 and P2 diverged in the early phase of tumorigenesis, and that metastatic cluster M originated from the common ancestral clone of cluster P1 with few somatic mutations and copy number variations.

Conclusions

Although a high level of intratumor heterogeneity was evident in high-grade serous ovarian cancer, our results suggest that transcoelomic metastasis arises with little accumulation of somatic mutations and copy number alterations in this patient.

Discussion

We have shown the range of genomic diversity at nucleotide, copy number and gene expression scales present in six patients with HGSC. These samples were resected prior to treatment, revealing intratumoural diversity in the tumours’ natural evolutionary state. Our results were consistent with recent reports 39 that considerable genomic diversity is measurable in HGSCs at the level of copy number. However, in some cases (cases 3 and 5) we observed large-scale copy number differences in spatially separated samples that were not evident at the level of mutational profiling, indicative of different mutational mechanisms operating independently in different parts of a tumour. Together, copy number and mutational profiling identified well-characterized mutations in genes such as PIK3CA, CTNNB1 and PDGFRB, as well as homozygous deletions in NF1, that were not present in all samples of the same case. With the exception of TP53, our data suggest that well-known, actionable driver mutations may only be partially represented if only a single sample is considered per patient. Our results establish that embedded within the context of extreme intra- and intertumour heterogeneity, TP53 mutation remains the most stable genomic feature in HGSC. However, as efforts towards precision medicine informed by mutational landscapes of tumours reach maturity, it will be imprudent to ignore the degree to which regional genomic variation is present in primary samples prior to any treatment-related selective pressures. Moreover, studies of evolution in cancer through analysis of serial biopsies (eg through pre- and post-treatment samples, or comparisons of primary and metastatic samples), will need to account for evidence of divergent profiles simply due to regional sampling bias.

Mutational profiling revealed for the first time that mutations beyond TP53 are present in FT lesions. In case 4, we used the full spectrum of mutations to establish that evolutionary trajectories of two histologically and karyotypically distinct tumours within a single patient arose from a common aetiology, with 26.2% of all mutations (including TP53) conserved in the tubal lesion. Our results indicate how histologically distinct cell populations in the same patient can be linked by common aetiology in the FT. In contrast to HGSC, endometrioid tumours are thought to develop from atypical ovarian endometriosis, with the FT merely acting as a conduit for endometrial epithelium to spread to the ovary (reviewed in 40). The possibility of the FT playing a causative role in rare cases of endometrioid carcinoma, or mixed endometrioid–serous carcinoma, has not yet been explored. Mutations in case 5 revealed the FT lesion to be a metastatic implant more closely related to a lymph node metastasis than to the ovarian samples. Thus, future investigations into aetiological underpinnings of HGSC will likely benefit from mutational comparison to extra-tubal sites to definitively establish the evolutionary origin of FT lesions.

We noted an extreme case of unexpected genomic conservation. In case 6, samples were obtained at primary surgery and after 42 months, with 21 cycles of multi-agent chemotherapy in between. The two samples exhibited near-identical genomic landscapes. Coupled with the long survivorship of this patient, this observation represents an intriguing anecdote. Larger studies will be needed to establish if stable clonal population structure is a feature of long survivorship.

Finally, as proof of concept, examination of plasma ctDNA suggested that representative mutations in the ancestral clone and mutations present in subclones could be detected in ctDNA, although sensitivity to detect mutations varied from patient to patient. This implies that clinical sensitivity analyses will need to be undertaken to establish the generalizability of ctDNA analysis to clonally diverse tumours.

In summary, our study unveils the extensive genomic diversity in primary, untreated, high-grade serous cancers of the ovary prior to treatment-related selection pressures, and illuminates highly individualized evolutionary trajectories that will require detailed consideration if curative therapeutic strategies are to be achieved.

Updated 3/04/2023 articles on specific tumor type intratumoral heterogeneity

Complex Patterns of Genomic Heterogeneity Identified in 42 Tumor Samples and ctDNA of a Pulmonary Atypical Carcinoid Patient

Source: Tamsin J. Robb, Peter Tsai, Sandra Fitzgerald, Paula Shields, Pascalene S. Houseman, Rachna Patel, Vicky Fan, Ben Curran, Rexson Tse, Jacklyn Ting, Nicole Kramer, Braden J. Woodhouse, Esther Coats, Polona Le Quesne Stabej, Jane Reeve, Kate Parker, Ben Lawrence, Cherie Blenkiron, Cristin G. Print; Complex Patterns of Genomic Heterogeneity Identified in 42 Tumor Samples and ctDNA of a Pulmonary Atypical Carcinoid Patient. Cancer Research Communications 3 January 2023; 3 (1): 31–42. https://doi.org/10.1158/2767-9764.CRC-22-0101

Tumor evolution underlies many challenges facing precision oncology, and improving our understanding has the potential to improve clinical care. This study represents a rare opportunity to study tumor heterogeneity and evolution in a patient with an understudied cancer type. A patient with pulmonary atypical carcinoid, a neuroendocrine tumor, metastatic to 90 sites, requested and consented to donate tissues for research. 42 tumor samples collected at rapid autopsy from 14 anatomically distinct sites were analyzed through DNA whole-exome sequencing and RNA sequencing, and five analyzed through linked-read sequencing. Targeted DNA sequencing was completed on two clinical tissue biopsies and one blood plasma sample. Chromosomal alterations and gene variants accumulated over time, and specific chromosomal alterations preceded the single predicted gene driver variant (ARID1A). At the time of autopsy, all sites shared the gain of one copy of Chr 5, loss of one copy of Chr 6 and 21, chromothripsis of one copy of Chr 11, and 39 small variants. Two tumor clones (carrying additional variants) were detected at metastatic sites, and occasionally in different regions of the same organ (e.g., within the pancreas). Circulating tumor DNA (ctDNA) sequencing detected shared tumor variants in the blood plasma and captured marked genomic heterogeneity, including all metastatic clones but few private tumor variants. This study describes genomic tumor evolution and dissemination of a pulmonary atypical carcinoid donated by a single generous patient. It highlights the critical role of chromosomal alterations in tumor initiation and explores the potential of ctDNA analysis to represent genomically heterogeneous disease. Significance: DNA sequencing data from tumor samples and blood plasma from a single patient highlighted the critical early role of chromosomal alterations in atypical carcinoid tumor development. Common tumor variants were readily detected in the blood plasma, unlike emerging tumor variants, which has implications for using ctDNA to capture cancer evolution.

In this article Robb et. al analyzes the tumor evolution of variants and mutations over time in a single patient. Tumor samples were obtained from liquid biopsy as ctDNA, which is unlike the previous study in renal cancer, where multiple fine needle aspirates needed to be conducted to determine intratumoral heterogeneity with respect to spatial distribution within a solid tumor. In this current study the authors were able to study the evolution of tumor heterogeneity in a single patient.

Introduction

Tumor evolution underlies many of the most pressing challenges facing clinical precision oncology today, and a better understanding of this process has the potential to improve clinical care. We present a unique example of tumor evolution in an uncommon tumor type not previously featured in such studies, a pulmonary atypical carcinoid [an intermediate grade neuroendocrine tumor (NET)].

NETs arise from hormone-producing cells of the neuroendocrine system located throughout the body, and are highly heterogeneous, both genetically and pathologically. While once considered rare tumors (1) and thought to be indolent in nature (2), we now recognize that NETs have an age-adjusted incidence of 6.2 cases per 100,000 in the country where this study was undertaken, New Zealand (3), similar to the incidence of ovarian and cervical cancers (4). Pulmonary atypical carcinoids are mitotically active well-differentiated NETs, with one in five presenting with distant metastatic disease at diagnosis (5). They feature relatively few DNA variants, with recurrent variants occurring in chromatin remodeling genes such as MEN1 and ARID1A [reported in 25% and 10% of studied atypical carcinoids, respectively (6)]. In general, NETs feature few small DNA variants and often instead have large-scale chromosomal changes; for example, around 25% of pancreatic NETs lose a suite of 10 chromosomes, and a further 40% feature the loss of chromosome (Chr) 11 (7). Few genomic studies have been completed on pulmonary atypical carcinoids; however, comparative genomic hybridization studies identified recurrent deletions in Chr 11q (harboring MEN1) in around 60% of cases (8–10).

Tumor development is an evolutionary process with similarities to Darwinian natural selection, where tumor cells may be under multiple simultaneous “selective pressures” (11), including immune attack (12) and drug treatment (13). There are multiple debated models for the generation, propagation, and selection of variants throughout tumor development, including linear, branching, punctuated, and neutral evolution (11, 14), each of which may occur in different tumors or at different timepoints within the same patient. Tumor evolution has been associated with key clinical challenges, including cancer metastasis (15), immune evasion (12, 16), and drug resistance (13, 17, 18).

Sequencing the cell-free DNA (cfDNA) in a patient’s blood plasma to identify ctDNA variants derived from tumor cells has been postulated to better represent the total disease burden (including potential tumor heterogeneity) than single tumor biopsies or resections (19, 20). However, we do not yet fully understand the detectability of ctDNA shed from different anatomic sites around a patient’s body—and early results suggest that some tumor sites may be more easily detected than others (21).

In this study, we completed multimodal genomic analysis on samples collected at autopsy from 41 metastatic tumor sites from a single patient with an uncommon pulmonary atypical carcinoid, alongside a blood plasma sample and clinical biopsies, to catalog driver genomic alterations and hypothesize their order of accumulation. We compared the genomic variants identified in the tumors with those detected in the patient’s blood plasma to consider how well the ctDNA analysis represented the genomic tumor heterogeneity, and in turn, infer metastatic sites that clinical ctDNA assays may poorly detect. A potential limitation, though, as stated by authors

“studies that analyze many tumors from a single patient, while revealing detailed and valuable insights about that patient’s cancer, cannot necessarily be generalized to other patients. Furthermore, all samples except for two clinical biopsies were collected at a single timepoint (at autopsy), limiting the extent of computational tumor evolution inferences that could be made, and the small size of these biopsies limited the scope of genomic assays that could be applied

In their conclusions they allude to the branched evolution that was seen with the renal cancer study:

Models of Tumor Evolution and Metastatic Dissemination

We can hypothesize a putative genomic evolution process (Fig. 7) that is consistent with the genomic heterogeneity observed at autopsy and in the two clinical biopsies. The early lung tumor represented by the diagnostic biopsy featured gain of Chr 5, loss of Chr 21 as well as nine small variants, none of which are known or predicted cancer drivers. Genomic features of the tumors collected at autopsy are consistent with further mutation after the lung biopsy was undertaken, followed by an evolutionary event such as a selective sweep, that fixed into the genomes of subsequent tumors a loss of function variant in ARID1A as well as 29 small non-driver variants and additional chromosomal alterations including Chr 11 chromothripsis and Chr 6 loss. There are plausible selective advantages to the tumor of each, including Chr 6 LOH of the HLA genes responsible for presenting peptides to T cells, hypothetically reducing the immune system’s ability to recognize neoantigens presented by the tumor cells (47). The data are consistent with subsequent branching events, including the accumulation of a second somatic “hit” to EPS8L2 (where the first was somatic LOH on Chr 11), before two parallel lineages developed, metastatic group 1 and 2 (Fig. 7). In metastatic group 1, some variants of unknown significance deserve further study. For instance, truncation of tumor suppressor ETV6 [a common fusion gene partner in breast and thyroid cancer (48)], following the LOH of a region of Chr 12 may be functionally significant given this gene’s role in development (49). The missense SLIT1 variant was the only additional protein-affecting variant carried by tumors in metastatic group 2 and may play a role in angiogenesis and migration (50). Interestingly, the two metastatic groups in the pancreas appear to have arisen by two independent seeding events. However, in the literature there is not overwhelming evidence for consistent genetic drivers of metastatic progression (51, 52) and we have no evidence in our data to suggest the additional genomic variants accumulated by each of the two metastatic groups (e.g., in EPS8L2, ETV6, and SLIT1) were necessary to drive their metastasis. Given a general propensity of cancers to metastasize to lung (53), it is possible that the presence of multiple metastatic groups in the lung may be the result of tertiary metastatic seeding events returning tumor cells that have evolved elsewhere to the site of the lung primary tumor.

4/10/2013

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:

Illumina Genome Analyzer IIx and Hiseq: for sequencing and mutational analysis
Illumina Omni 2.5: for SNP (single nucleotide polymorphism)-array-based allelic imbalance detection for chromosomal imbalance and ploidy analysis
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.

Results

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:

VHL: the Von Hippel–Lindau tumor suppressor
KDM5C: a lysine-specific demethylase 5C, commonly known as JARD1C
SETD2: a histone-lysine N-methyltransferase
MTOR

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

VIEW VIDEO

Everolimus: an inhibitor of mTOR

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

Developer	Designation	Description	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 Alias Gene Location	Marker Description	Indications
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

References:

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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.