cancer metabolic pathways | Leaders in Pharmaceutical Business Intelligence (LPBI) Group

Advent of genomics have revolutionized how we diagnose and treat lung cancer
We are currently needing to understand the driver mutations and variants where we can personalize therapy
PD-L1 and other checkpoint therapy have not really been used in pediatric cancers even though CAR-T have been successful
The incidence rates and mortality rates of pediatric cancers are rising
Large scale study of over 700 pediatric cancers show cancers driven by epigenetic drivers or fusion proteins. Need for transcriptomics. Also study demonstrated that we have underestimated germ line mutations and hereditary factors.
They put together a database to nominate patients on their IGM Cancer protocol. Involves genetic counseling and obtaining germ line samples to determine hereditary factors. RNA and protein are evaluated as well as exome sequencing. RNASeq and Archer Dx test to identify driver fusions
PECAN curated database from St. Jude used to determine driver mutations. They use multiple databases and overlap within these databases and knowledge base to determine or weed out false positives
They have used these studies to understand the immune infiltrate into recurrent cancers (CytoCure)
They found 40 germline cancer predisposition genes, 47 driver somatic fusion proteins, 81 potential actionable targets, 106 CNV, 196 meaningful somatic driver mutations

Tuesday, June 23

12:00 PM – 12:30 PM EDT

Awards and Lectures

NCI Director’s Address

Norman E Sharpless, Elaine R Mardis

DETAILS

Introduction: Elaine Mardis

NCI Director Address: Norman E Sharpless

They are functioning well at NCI with respect to grant reviews, research, and general functions in spite of the COVID pandemic and the massive demonstrations on also focusing on the disparities which occur in cancer research field and cancer care
There are ongoing efforts at NCI to make a positive difference in racial injustice, diversity in the cancer workforce, and for patients as well
Need a diverse workforce across the cancer research and care spectrum
Data show that areas where the clinicians are successful in putting African Americans on clinical trials are areas (geographic and site specific) where health disparities are narrowing
Grants through NCI new SeroNet for COVID-19 serologic testing funded by two RFAs through NIAD (RFA-CA-30-038 and RFA-CA-20-039) and will close on July 22, 2020

Tuesday, June 23

12:45 PM – 1:46 PM EDT

Virtual Educational Session

Immunology, Tumor Biology, Experimental and Molecular Therapeutics, Molecular and Cellular Biology/Genetics

Tumor Immunology and Immunotherapy for Nonimmunologists: Innovation and Discovery in Immune-Oncology

This educational session will update cancer researchers and clinicians about the latest developments in the detailed understanding of the types and roles of immune cells in tumors. It will summarize current knowledge about the types of T cells, natural killer cells, B cells, and myeloid cells in tumors and discuss current knowledge about the roles these cells play in the antitumor immune response. The session will feature some of the most promising up-and-coming cancer immunologists who will inform about their latest strategies to harness the immune system to promote more effective therapies.

Judith A Varner, Yuliya Pylayeva-Gupta

Introduction

Judith A Varner

New techniques reveal critical roles of myeloid cells in tumor development and progression

Different type of cells are becoming targets for immune checkpoint like myeloid cells
In T cell excluded or desert tumors T cells are held at periphery so myeloid cells can infiltrate though so macrophages might be effective in these immune t cell naïve tumors, macrophages are most abundant types of immune cells in tumors
CXCLs are potential targets
PI3K delta inhibitors,
Reduce the infiltrate of myeloid tumor suppressor cells like macrophages
When should we give myeloid or T cell therapy is the issue

Judith A Varner

Novel strategies to harness T-cell biology for cancer therapy

Positive and negative roles of B cells in cancer

Yuliya Pylayeva-Gupta

New approaches in cancer immunotherapy: Programming bacteria to induce systemic antitumor immunity

Tuesday, June 23

12:45 PM – 1:46 PM EDT

Virtual Educational Session

Cancer Chemistry

Chemistry to the Clinic: Part 2: Irreversible Inhibitors as Potential Anticancer Agents

There are numerous examples of highly successful covalent drugs such as aspirin and penicillin that have been in use for a long period of time. Despite historical success, there was a period of reluctance among many to purse covalent drugs based on concerns about toxicity. With advances in understanding features of a well-designed covalent drug, new techniques to discover and characterize covalent inhibitors, and clinical success of new covalent cancer drugs in recent years, there is renewed interest in covalent compounds. This session will provide a broad look at covalent probe compounds and drug development, including a historical perspective, examination of warheads and electrophilic amino acids, the role of chemoproteomics, and case studies.

Benjamin F Cravatt, Richard A. Ward, Sara J Buhrlage

Discovering and optimizing covalent small-molecule ligands by chemical proteomics

Benjamin F Cravatt

Multiple approaches are being investigated to find new covalent inhibitors such as: 1) cysteine reactivity mapping, 2) mapping cysteine ligandability, 3) and functional screening in phenotypic assays for electrophilic compounds
Using fluorescent activity probes in proteomic screens; have broad useability in the proteome but can be specific
They screened quiescent versus stimulated T cells to determine reactive cysteines in a phenotypic screen and analyzed by MS proteomics (cysteine reactivity profiling); can quantitate 15000 to 20,000 reactive cysteines
Isocitrate dehydrogenase 1 and adapter protein LCP-1 are two examples of changes in reactive cysteines they have seen using this method
They use scout molecules to target ligands or proteins with reactive cysteines
For phenotypic screens they first use a cytotoxic assay to screen out toxic compounds which just kill cells without causing T cell activation (like IL10 secretion)
INTERESTINGLY coupling these MS reactive cysteine screens with phenotypic screens you can find NONCANONICAL mechanisms of many of these target proteins (many of the compounds found targets which were not predicted or known)

Electrophilic warheads and nucleophilic amino acids: A chemical and computational perspective on covalent modifier

The covalent targeting of cysteine residues in drug discovery and its application to the discovery of Osimertinib

Richard A. Ward

Cysteine activation: thiolate form of cysteine is a strong nucleophile
Thiolate form preferred in polar environment
Activation can be assisted by neighboring residues; pKA will have an effect on deprotonation
pKas of cysteine vary in EGFR
cysteine that are too reactive give toxicity while not reactive enough are ineffective

Accelerating drug discovery with lysine-targeted covalent probes

Tuesday, June 23

12:45 PM – 2:15 PM EDT

Virtual Educational Session

Molecular and Cellular Biology/Genetics

Virtual Educational Session

Tumor Biology, Immunology

Metabolism and Tumor Microenvironment

This Educational Session aims to guide discussion on the heterogeneous cells and metabolism in the tumor microenvironment. It is now clear that the diversity of cells in tumors each require distinct metabolic programs to survive and proliferate. Tumors, however, are genetically programmed for high rates of metabolism and can present a metabolically hostile environment in which nutrient competition and hypoxia can limit antitumor immunity.

Jeffrey C Rathmell, Lydia Lynch, Mara H Sherman, Greg M Delgoffe

T-cell metabolism and metabolic reprogramming antitumor immunity

Jeffrey C Rathmell

Introduction

Jeffrey C Rathmell

Metabolic functions of cancer-associated fibroblasts

Mara H Sherman

Tumor microenvironment metabolism and its effects on antitumor immunity and immunotherapeutic response

Greg M Delgoffe

Multiple metabolites, reactive oxygen species within the tumor microenvironment; is there heterogeneity within the TME metabolome which can predict their ability to be immunosensitive
Took melanoma cells and looked at metabolism using Seahorse (glycolysis): and there was vast heterogeneity in melanoma tumor cells; some just do oxphos and no glycolytic metabolism (inverse Warburg)
As they profiled whole tumors they could separate out the metabolism of each cell type within the tumor and could look at T cells versus stromal CAFs or tumor cells and characterized cells as indolent or metabolic
T cells from hyerglycolytic tumors were fine but from high glycolysis the T cells were more indolent
When knock down glucose transporter the cells become more glycolytic
If patient had high oxidative metabolism had low PDL1 sensitivity
Showed this result in head and neck cancer as well
Metformin a complex 1 inhibitor which is not as toxic as most mito oxphos inhibitors the T cells have less hypoxia and can remodel the TME and stimulate the immune response
Metformin now in clinical trials
T cells though seem metabolically restricted; T cells that infiltrate tumors are low mitochondrial phosph cells
T cells from tumors have defective mitochondria or little respiratory capacity
They have some preliminary findings that metabolic inhibitors may help with CAR-T therapy

Obesity, lipids and suppression of anti-tumor immunity

Lydia Lynch

Hypothesis: obesity causes issues with anti tumor immunity
Less NK cells in obese people; also produce less IFN gamma
RNASeq on NOD mice; granzymes and perforins at top of list of obese downregulated
Upregulated genes that were upregulated involved in lipid metabolism
All were PPAR target genes
NK cells from obese patients takes up palmitate and this reduces their glycolysis but OXPHOS also reduced; they think increased FFA basically overloads mitochondria
PPAR alpha gamma activation mimics obesity

Tuesday, June 23

12:45 PM – 2:45 PM EDT

Virtual Educational Session

Clinical Research Excluding Trials

The Evolving Role of the Pathologist in Cancer Research

Long recognized for their role in cancer diagnosis and prognostication, pathologists are beginning to leverage a variety of digital imaging technologies and computational tools to improve both clinical practice and cancer research. Remarkably, the emergence of artificial intelligence (AI) and machine learning algorithms for analyzing pathology specimens is poised to not only augment the resolution and accuracy of clinical diagnosis, but also fundamentally transform the role of the pathologist in cancer science and precision oncology. This session will discuss what pathologists are currently able to achieve with these new technologies, present their challenges and barriers, and overview their future possibilities in cancer diagnosis and research. The session will also include discussions of what is practical and doable in the clinic for diagnostic and clinical oncology in comparison to technologies and approaches primarily utilized to accelerate cancer research.

Jorge S Reis-Filho, Thomas J Fuchs, David L Rimm, Jayanta Debnath

DETAILS

Tuesday, June 23

12:45 PM – 2:45 PM EDT

High-dimensional imaging technologies in cancer research

David L Rimm

Using old methods and new methods; so cell counting you use to find the cells then phenotype; with quantification like with Aqua use densitometry of positive signal to determine a threshold to determine presence of a cell for counting
Hiplex versus multiplex imaging where you have ten channels to measure by cycling of flour on antibody (can get up to 20plex)
Hiplex can be coupled with Mass spectrometry (Imaging Mass spectrometry, based on heavy metal tags on mAbs)
However it will still take a trained pathologist to define regions of interest or field of desired view

Introduction

Jayanta Debnath

Challenges and barriers of implementing AI tools for cancer diagnostics

Jorge S Reis-Filho

Implementing robust digital pathology workflows into clinical practice and cancer research

Jayanta Debnath

Invited Speaker

Thomas J Fuchs

Founder of spinout of Memorial Sloan Kettering
Separates AI from computational algothimic
Dealing with not just machines but integrating human intelligence
Making decision for the patients must involve human decision making as well
How do we get experts to do these decisions faster
AI in pathology: what is difficult? =è sandbox scenarios where machines are great,; curated datasets; human decision support systems or maps; or try to predict nature
1) learn rules made by humans; human to human scenario 2)constrained nature 3)unconstrained nature like images and or behavior 4) predict nature response to nature response to itself
In sandbox scenario the rules are set in stone and machines are great like chess playing
In second scenario can train computer to predict what a human would predict
So third scenario is like driving cars
System on constrained nature or constrained dataset will take a long time for commuter to get to decision
Fourth category is long term data collection project
He is finding it is still finding it is still is difficult to predict nature so going from clinical finding to prognosis still does not have good predictability with AI alone; need for human involvement
End to end partnering (EPL) is a new way where humans can get more involved with the algorithm and assist with the problem of constrained data
An example of a workflow for pathology would be as follows from Campanella et al 2019 Nature Medicine: obtain digital images (they digitized a million slides), train a massive data set with highthroughput computing (needed a lot of time and big software developing effort), and then train it using input be the best expert pathologists (nature to human and unconstrained because no data curation done)
Led to first clinically grade machine learning system (Camelyon16 was the challenge for detecting metastatic cells in lymph tissue; tested on 12,000 patients from 45 countries)
The first big hurdle was moving from manually annotated slides (which was a big bottleneck) to automatically extracted data from path reports).
Now problem is in prediction: How can we bridge the gap from predicting humans to predicting nature?
With an AI system pathologist drastically improved the ability to detect very small lesions

Virtual Educational Session

Epidemiology

Cancer Increases in Younger Populations: Where Are They Coming from?

Incidence rates of several cancers (e.g., colorectal, pancreatic, and breast cancers) are rising in younger populations, which contrasts with either declining or more slowly rising incidence in older populations. Early-onset cancers are also more aggressive and have different tumor characteristics than those in older populations. Evidence on risk factors and contributors to early-onset cancers is emerging. In this Educational Session, the trends and burden, potential causes, risk factors, and tumor characteristics of early-onset cancers will be covered. Presenters will focus on colorectal and breast cancer, which are among the most common causes of cancer deaths in younger people. Potential mechanisms of early-onset cancers and racial/ethnic differences will also be discussed.

Stacey A. Fedewa, Xavier Llor, Pepper Jo Schedin, Yin Cao

Cancers that are and are not increasing in younger populations

Stacey A. Fedewa

Early onset cancers, pediatric cancers and colon cancers are increasing in younger adults
Younger people are more likely to be uninsured and these are there most productive years so it is a horrible life event for a young adult to be diagnosed with cancer. They will have more financial hardship and most (70%) of the young adults with cancer have had financial difficulties. It is very hard for women as they are on their childbearing years so additional stress
Types of early onset cancer varies by age as well as geographic locations. For example in 20s thyroid cancer is more common but in 30s it is breast cancer. Colorectal and testicular most common in US.
SCC is decreasing by adenocarcinoma of the cervix is increasing in women’s 40s, potentially due to changing sexual behaviors
Breast cancer is increasing in younger women: maybe etiologic distinct like triple negative and larger racial disparities in younger African American women
Increased obesity among younger people is becoming a factor in this increasing incidence of early onset cancers

Press Coverage

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on NCI Activities: COVID-19 and Cancer Research 5:20 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM

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Tumor Ammonia Recycling: How Cancer Cells Use Glutamate Dehydrogenase to Recycle Tumor Microenvironment Waste Products for Biosynthesis

Posted in Breast Cancer - impalpable breast lesions, Cancer - General, CANCER BIOLOGY & Innovations in Cancer Therapy, cancer metabolism, Cancer-Immune Interactions, Glioblastoma, Immunology, Inflammasome, Metabolic Immuno-Oncology, Oxidative phosphorylation, tumor microenvironment, Warburg effect, tagged alpha ketoglutarate, ammonia metabolism, biomass, Biosynthesis, breast cancer, cancer metabolic pathways, cancer metabolism, ER positive breast cancer, estrogen receptor positive cancer, glutaminolysis, IDH1 and IDH2, metabolic reprogramming, tumor microenvironment, Warburg Effect on February 13, 2019| Leave a Comment »

Tumor Ammonia Recycling: How Cancer Cells Use Glutamate Dehydrogenase to Recycle Tumor Microenvironment Waste Products for Biosynthesis

Reporter: Stephen J. Williams, PhD

A feature of the tumorigenic process is the rewiring of the metabolic processes that provides a tumor cell the ability to grow and thrive in conditions of limiting nutrients as well as the ability to utilize waste products in salvage pathways for production of new biomass (amino acids, nucleic acids etc.) required for cellular growth and division ^1-8. A Science article from Spinelli et al. ⁹ (and corresponding Perspective article in the same issue by Dr. Chi V. Dang entitled Feeding Frenzy for Cancer Cells ¹⁰) describes the mechanism by which estrogen-receptor positive (ER+) breast cancer cells convert glutamine to glutamate, release ammonia into the tumor microenvironment, diffuses into tumor cells and eventually recycle this ammonia by reductive amination of a-ketoglutarate by glutamate dehydrogenase (GDH) to produce glutamic acid and subsequent other amino acids needed for biomass production. Ammonia can accumulate in the tumor microenvironment in poorly vascularized tumor. Thus ammonia becomes an important nitrogen source for tumor cells.

Mammalian cells have a variety of mechanisms to metabolize ammonia including

Glutamate synthetase (GS) in the liver can incorporate ammonia into glutamate to form glutamine
glutamate dehydrogenase (GDH) converts glutamate to a-ketoglutarate and ammonia under allosteric regulation (discussed in a post on this site by Dr. Larry H. Berstein; subsection Drugging Glutaminolysis)
the reverse reaction of GDH, which was found to occur in ER+ breast cancer cells, a reductive amination of a-ketoglutarate to glutamate¹¹, is similar to the reductive carboxylation of a-ketoglutarate to citrate by isocitrate dehydrogenase (IDH) for fatty acid synthesis (IDH is overexpressed in many tumor types including cancer stem cells ^12-15), and involved in immune response and has been developed as a therapeutic target for various cancers. IDH mutations were shown to possess the neomorphic activity to generate the oncometabolite, 2-hydroxyglutarate (2HG) ^16-18. With a single codon substitution, the kinetic properties of the mutant IDH isozyme are signiﬁcantly altered, resulting in an obligatory sequential ordered reaction in the reverse direction ¹⁹.

In the Science paper, Spinelli et al. report that ER+ breast cancer cells have the ability to utilize ammonia sources from their surroundings in order to produce amino acids and biomass as these ER+ breast cancer cells have elevated levels of GS and GDH with respect to other breast cancer histotypes.

GDH was elevated in ER+ luminal cancer cells and the quiescent epithelial cells in organoid culture

However proliferative cells were dependent on transaminases, which transfers nitrogen from glutamate to pyruvate or oxaloacetate to form a-ketoglutarate and alanine or aspartate. a-ketoglutarate is further metabolized in the citric acid cycle.

Figure 1. Reductive amination and transamination reactions of glutamic acid. Source http://www.biologydiscussion.com/organism/metabolism-organism/incorporation-of-ammonia-into-organic-compounds/50870

Spinelli et al. showed GDH is necessary for ammonia reductive incorporation into a-ketoglutarate and also required for ER+ breast cancer cell growth in immunocompromised mice.

In addition, as commented by Dr. Dang in his associated Perspectives article, (quotes indent)

The metabolic tumor microenvironment produced by resident cells, such as fibroblasts and macrophages, can create an immunosuppressive environment ²⁰. Hence, it will be of great interest to further understand whether products such as ammonia could affect tumor immunity or induce autophagy (end quote indent)

Figure 2. Tumor ammonia recycling. Source: From Chi V. Dang Feeding Frenzy for cancer cells. Rights from RightsLink (copyright.com)

Metabolic recycling of ammonia via glutamate dehydrogenase supports breast cancer biomass

Jessica B. Spinelli¹,², Haejin Yoon¹, Alison E. Ringel¹, Sarah Jeanfavre², Clary B. Clish², Marcia C. Haigis¹ *

1. ¹Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA. 2. ²Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

* ↵*Corresponding author. Email: marcia_haigis@hms.harvard.edu

Science 17 Nov 2017:Vol. 358, Issue 6365, pp. 941-946 DOI: 10.1126/science.aam9305

Abstract

Ammonia is a ubiquitous by-product of cellular metabolism; however, the biological consequences of ammonia production are not fully understood, especially in cancer. We found that ammonia is not merely a toxic waste product but is recycled into central amino acid metabolism to maximize nitrogen utilization. In our experiments, human breast cancer cells primarily assimilated ammonia through reductive amination catalyzed by glutamate dehydrogenase (GDH); secondary reactions enabled other amino acids, such as proline and aspartate, to directly acquire this nitrogen. Metabolic recycling of ammonia accelerated proliferation of breast cancer. In mice, ammonia accumulated in the tumor microenvironment and was used directly to generate amino acids through GDH activity. These data show that ammonia is not only a secreted waste product but also a fundamental nitrogen source that can support tumor biomass.

References

1 Strickaert, A. et al. Cancer heterogeneity is not compatible with one unique cancer cell metabolic map. Oncogene 36, 2637-2642, doi:10.1038/onc.2016.411 (2017).

2 Hui, S. et al. Glucose feeds the TCA cycle via circulating lactate. Nature 551, 115-118, doi:10.1038/nature24057 (2017).

3 Mashimo, T. et al. Acetate is a bioenergetic substrate for human glioblastoma and brain metastases. Cell 159, 1603-1614, doi:10.1016/j.cell.2014.11.025 (2014).

4 Sousa, C. M. et al. Erratum: Pancreatic stellate cells support tumour metabolism through autophagic alanine secretion. Nature 540, 150, doi:10.1038/nature19851 (2016).

5 Sousa, C. M. et al. Pancreatic stellate cells support tumour metabolism through autophagic alanine secretion. Nature 536, 479-483, doi:10.1038/nature19084 (2016).

6 Commisso, C. et al. Macropinocytosis of protein is an amino acid supply route in Ras-transformed cells. Nature 497, 633-637, doi:10.1038/nature12138 (2013).

7 Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57-70 (2000).

8 Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646-674, doi:10.1016/j.cell.2011.02.013 (2011).

9 Spinelli, J. B. et al. Metabolic recycling of ammonia via glutamate dehydrogenase supports breast cancer biomass. Science 358, 941-946, doi:10.1126/science.aam9305 (2017).

10 Dang, C. V. Feeding frenzy for cancer cells. Science 358, 862-863, doi:10.1126/science.aaq1070 (2017).

11 Smith, T. J. & Stanley, C. A. Untangling the glutamate dehydrogenase allosteric nightmare. Trends in biochemical sciences 33, 557-564, doi:10.1016/j.tibs.2008.07.007 (2008).

12 Metallo, C. M. et al. Reductive glutamine metabolism by IDH1 mediates lipogenesis under hypoxia. Nature 481, 380-384, doi:10.1038/nature10602 (2011).

13 Garrett, M. et al. Metabolic characterization of isocitrate dehydrogenase (IDH) mutant and IDH wildtype gliomaspheres uncovers cell type-specific vulnerabilities. Cancer & metabolism 6, 4, doi:10.1186/s40170-018-0177-4 (2018).

14 Calvert, A. E. et al. Cancer-Associated IDH1 Promotes Growth and Resistance to Targeted Therapies in the Absence of Mutation. Cell reports 19, 1858-1873, doi:10.1016/j.celrep.2017.05.014 (2017).

15 Sciacovelli, M. & Frezza, C. Metabolic reprogramming and epithelial-to-mesenchymal transition in cancer. The FEBS journal 284, 3132-3144, doi:10.1111/febs.14090 (2017).

16 Dang, L. et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 462, 739-744, doi:10.1038/nature08617 (2009).

17 Gross, S. et al. Cancer-associated metabolite 2-hydroxyglutarate accumulates in acute myelogenous leukemia with isocitrate dehydrogenase 1 and 2 mutations. The Journal of experimental medicine 207, 339-344, doi:10.1084/jem.20092506 (2010).

18 Ward, P. S. et al. The common feature of leukemia-associated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting alpha-ketoglutarate to 2-hydroxyglutarate. Cancer cell 17, 225-234, doi:10.1016/j.ccr.2010.01.020 (2010).

19 Rendina, A. R. et al. Mutant IDH1 enhances the production of 2-hydroxyglutarate due to its kinetic mechanism. Biochemistry 52, 4563-4577, doi:10.1021/bi400514k (2013).

20 Zhang, X. et al. IDH mutant gliomas escape natural killer cell immune surveillance by downregulation of NKG2D ligand expression. Neuro-oncology 18, 1402-1412, doi:10.1093/neuonc/now061 (2016).

Metabolic Response Heterogeneity

Posted in Anticancer Resistance, Biological Networks, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Curation, tagged anticancer drug resistance, Basal metabolic rate, cancer metabolic pathways, cancer metabolism, tumor heterogeneity on October 19, 2015| Leave a Comment »

Metabolic Response Heterogeneity

Larry H Bernstein, MD, FCAP, Curator

LFBI

The Prognostic Significance of Metabolic Response Heterogeneity in Metastatic Colorectal Cancer

PLoS One. 2015; 10(9): e0138341.

Published online 2015 Sep 30. doi: 10.1371/journal.pone.0138341

PMCID: PMC4589397

Alain Hendlisz,^1,* Amelie Deleporte,¹ Thierry Delaunoit,² Raphaël Maréchal,³ Marc Peeters,⁴ Stéphane Holbrechts,⁶Marc Van den Eynde,⁷ Ghislain Houbiers,⁹ Bertrand Filleul,² Jean-Luc Van Laethem,³ Sarah Ceyssens,⁵ Anna-Maria Barbuto,⁶ Renaud Lhommel,⁸ Gauthier Demolin,⁹ Camilo Garcia,¹⁰ Hazem El Mansy,^{1,2,3,4,5,6,7,8,9,10} Lieveke Ameye,¹¹ Michel Moreau,¹¹ Thomas Guiot,¹⁰ Marianne Paesmans,¹¹ Martine Piccart,¹ and Patrick Flamen¹⁰

Daniele Santini, Editor

Author information ► Article notes ► Copyright and License information ►

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Abstract

Background Tumoral heterogeneity is a major determinant of resistance in solid tumors. FDG-PET/CT can identify early during chemotherapy non-responsive lesions within the whole body tumor load. This prospective multicentric proof-of-concept study explores intra-individual metabolic response (mR) heterogeneity as a treatment efficacy biomarker in chemorefractory metastatic colorectal cancer (mCRC).

Methods Standardized FDG-PET/CT was performed at baseline and after the first cycle of combined sorafenib (600mg/day for 21 days, then 800mg/day) and capecitabine (1700 mg/m²/day administered D1-14 every 21 days). MR assessment was categorized according to the proportion of metabolically non-responding (non-mR) lesions (stable FDG uptake with SUV-max decrease <15%) among all measurable lesions.

Results Ninety-two patients were included. The median overall survival(OS) and progression-free survival (PFS) were 8.2months (95%CI:6.8–10.5) and 4.2months (95%CI:3.4–4.8) respectively. In the 79 assessable patients, early PET-CT showed no metabolically refractory lesion in 47%, a heterogeneous mR with at least one non-mR lesion in 32%, and a consistent non-mR or early disease progression in 21%. On exploratory analysis, patients without any non-mR lesion showed a significantly longer PFS (HR 0.34; 95% CI: 0.21–0.56, P-value 0.02) compared to the other patients. The proportion of non-mR lesions within the tumor load did not impact PFS/OS.

Conclusion The presence of at least one metabolically refractory lesion is associated with a poorer outcome in advanced mCRC patients treated with combined sorafenib-capecitabine. Early detection of treatment-induced mR heterogeneity may represent an important predictive efficacy biomarker in mCRC.

Trial Registration ClinicalTrials.gov NCT01290926

Background

Tumoral heterogeneity is a major determinant of resistance in solid tumors. FDG-PET/CT can identify early during chemotherapy non-responsive lesions within the whole body tumor load. This prospective multicentric proof-of-concept study explores intra-individual metabolic response (mR) heterogeneity as a treatment efficacy biomarker in chemorefractory metastatic colorectal cancer (mCRC).

Methods

Standardized FDG-PET/CT was performed at baseline and after the first cycle of combined sorafenib (600mg/day for 21 days, then 800mg/day) and capecitabine (1700 mg/m²/day administered D1-14 every 21 days). MR assessment was categorized according to the proportion of metabolically non-responding (non-mR) lesions (stable FDG uptake with SUVmax decrease <15%) among all measurable lesions.

Results

Ninety-two patients were included. The median overall survival (OS) and progression-free survival (PFS) were 8.2 months (95% CI: 6.8–10.5) and 4.2 months (95% CI: 3.4–4.8) respectively. In the 79 assessable patients, early PET-CT showed no metabolically refractory lesion in 47%, a heterogeneous mR with at least one non-mR lesion in 32%, and a consistent non-mR or early disease progression in 21%. On exploratory analysis, patients without any non-mR lesion showed a significantly longer PFS (HR 0.34; 95% CI: 0.21–0.56, P-value <0.001) and OS (HR 0.58; 95% CI: 0.36–0.92, P-value 0.02) compared to the other patients. The proportion of non-mR lesions within the tumor load did not impact PFS/OS.

Conclusion

The presence of at least one metabolically refractory lesion is associated with a poorer outcome in advanced mCRC patients treated with combined sorafenib-capecitabine. Early detection of treatment-induced mR heterogeneity may represent an important predictive efficacy biomarker in mCRC.

Trial Registration

ClinicalTrials.gov NCT01290926

Introduction

The development of new therapeutics for solid tumors is currently strained by increasing regulatory demands to better define subpopulations bearing resistant diseases in order to spare patients from useless toxicities and the society from unaffordable costs in case of ineffective treatments.

Tumor heterogeneity through the existence of resistant subclones (genetic drift) or local environmental factors is nowadays accepted as a major determinant of treatment resistance. However, sensitive biomarkers of tumoral heterogeneity are lacking.[1–3] Current response assessment methods using morphology (RECIST using MRI/CT) or metabolism (PERCIST using FGD-PET/CT) do not allow the description of tumor heterogeneity because dichotomization of response (versus non-response) requires summing of measurements or the selection of the one single most representative lesion.[4] Moreover most of the new biological therapies render response evaluation even more challenging by the infrequency of tumor shrinkage.[5–8]

Imaging tumour metabolism using ¹⁸F-Fluorodeoxyglucose positron emission tomography coupled with computed tomography (FDG-PET/CT) allows rapid identification of treatment-refractory lesions with a high negative predictive value (NPV).[9–14] FDG-PET is currently central in the international recommendations for response assessment for Hodgkin’s disease and aggressive non-Hodgkin’s lymphoma, in which medical conditions it is used commonly as a basis for therapeutic decisions. [14–17] In contrast, solid tumors are frequently more refractory to treatment and reveal smaller and slower changes in FDG uptake under therapy leading to the existence of different criteria for metabolic response assessment at the lesion as well as at the patient level.[18,19] This ongoing discussion explain why metabolic imaging has still not acquired a biomarker status in solid tumors.

Metabolic imaging provides a whole-body quantitative assessment of treatment-induced changes in tumoral glycolysis early after treatment initiation, before any morphological changes are observed. It has therefore the potential to detect tumoral heterogeneity by revealing how distinct tumor sites behave in response to treatment.

Several trials suggest meaningful clinical activity of combined sorafenib-capecitabine in metastatic breast and colorectal cancer. However the significant toxicity of the combination renders its use practically incompatible with a palliative setting, further underscoring the need to identify a sensitive biomarker for patient selection.[20,21] Preliminary reports in lung and renal cancer suggest that FDG-PET-based metabolic response assessment could be used as a predictive biomarker of sorafenib.[22,23]

The trial is a proof-of-concept study designed to explore intra-individual mR heterogeneity as a prognostic biomarker for this combination of a biological and a cytotoxic agent in mCRC.

Material and Methods

Belgian competent authorities and ethical committees of the 6 participating centres approved the study (EudraCT 2010-023695-91, clinicaltrials.gov NCT01290926), designed as a prospective multicentric single-arm phase II, with one-stage accrual.

Patients with histologically proven unresectable metastatic CRC failing all standard treatments but not necessarily bevacizumab were eligible. Exclusion criteria were contraindications for capecitabine and sorafenib, ECOG performance status (PS) > 1, age < 18 years, and cerebral metastasis. Normal organ and bone marrow function, a life expectancy >12 weeks, and a signed informed consent were required.

Both drugs were given orally on an outpatient basis: sorafenib 200mg in the morning and 400 mg in the evening every day for the first cycle, then 400 mg twice a day every day; capecitabine 850 mg/m² twice a day on days 1 to 14, every 21 days. One cycle was defined as a 21-day period. Adverse events were reported according to the National Cancer Institute Criteria, version 3.0 (http://ctep.cancer.gov/protocolDevelopment/electronic_applications/docs/ctcaev3.pdf). Study medications were to be stopped at disease progression or when unacceptable toxicity occurred. RECIST 1.1-radiological response was assessed locally every two cycles (6weeks). Patients were followed until objective disease progression and every 3 months thereafter for survival assessment.

FDG-PET/CT Imaging

For the FDG-PET/CT, patients were referred to one of the 5 participating PET/CT centres, previously approved for participation based on FDG-PET phantom imaging study for quality’s central assessment [24]. An independent academic molecular imaging core laboratory (OriLab) centralized all FDG-PET/CT images through anonymized CD-Rom transfers, checked image’s quality, DICOM headers, compliance to the Standard Procedures Imaging Manual and imaging case report forms.

Baseline FDG-PET/CT was performed within 7 days preceding chemotherapy initiation and repeated under the same technical and patient conditions on day (D)21 (range D19-D23), with D1 as the first day of chemotherapy administration. Prior to FDG injection, fasting ≥ 6 hours and glycemia levels <120 mg/dL for non-diabetic patients, and <180 mg/dL for diabetic patients were required. Short-acting insulin use on the day of FDG-PET/CT was not allowed.

The PET/CT was initiated 60 to 90 minutes after intravenous injection of 3.7 to 7.4 MBq/kg FDG, optimized for body weight. Similar FDG activity (+/-15%) and time window (+/- 15 min) were used for the second PET/CT.

Whole body scanning with a low dose CT (without intravenous or oral contrast, from proximal femur to skull) was performed, immediately followed by the PET acquisition. Imaging acquisition and reconstruction remained stable over the whole study period. The second FDG-PET/CT was strictly blinded to the investigators, and was not added to the patient’s (electronic) medical records.

The standard uptake value (SUV) of FDG used was the lean body mass-based maximal SUV value within the lesion (SUVmax, g/ml).

All FDG-PET/CT images were analysed in batches using the same software (PETVcar version 4.6, General Electric, USA) and display techniques. Two senior nuclear medicine physicians (PF, CG) performed independent mR analyses using a predefined 3-step methodology.[13] First, on the baseline PET/CT, target lesions were identified according to the following criteria: transaxial diameter (measured on the CT of the PET/CT) > 15 mm, intense FDG uptake (> 2 x normal liver parenchym uptake) and with an unequivocally neoplastic basis. Each target lesion was then classified as non-responding (decrease of SUVmax on second PET-CT<15%) or responding. Second, the patients were classified according to the lesional distribution of mR; class I: absence of any metabolically non-responding lesion, class II: a minor part of whole body tumour load shows a non-response, class III: major part of whole body target tumour load does not respond, and, class IV: all target lesions are non-responding, or presence of a progressive lesion (progression defined as >25% increase of SUVMax, or appearance of a new lesion). (Fig 1) Finally, different methods of patient response dichotomization (metabolic responders versus non-responders) were explored.

Fig 1

Classes of metabolic responses. Class1: no metabolic unresponsive lesion; Class2: minority of unresponsive lesions among whole body target tumour load; Class3: majority of whole body target tumour load does not respond; Class4: all target lesions are non-responding,or, presence of progressive lesions [progression defined as > 25% increase of FDG up take on second PET or appearance of a new lesion] http://dx.doi.org:/10.1371/journal.pone.0138341.g001

Classes of metabolic responses.

Statistical considerations

A first co-primary objective defined the minimal clinical activity necessary to explore the negative predictive value of metabolic response imaging on OS as a survival rate at 6 months > 30% according to the existing literature on chemorefractory CRC. To reject the null hypothesis that the 6 month-OS rate would be <30% using a binomial distribution, a 1-sided test with α = 0.025 and a power of 90% in case of a true 6 months-OS ≥ 50% was used, requiring a sample size of 66 eligible patients followed for at least 6 months. An intent-to-treat (ITT) approach was used.

The second co-primary objective was the prognostic value of mR classification. Based on a previous study,[13] and anticipating a 95% eligibility rate, a 50% early PET/CT non-responders rate, and a hazard ratio (HR) around 0.385 for comparison between the survival distributions, 54 events were needed for a 90% power and a two-sided logrank test at the 2.5% level.

Because the mR rate monitored during the study was higher than expected, the number of events to be observed was increased to 62. This decision was taken without changing the HR to be detected and without estimating this HR during study conduct.

Secondary objectives were to describe PFS, objective response rate and toxicity and to determine the predictive value of early MR on PFS.

For the first co-primary objective, the 6 month-OS, median (m)OS and mPFS were calculated from the patient’s inclusion. For the second co-primary objective, the predictive value assessment of mR on OS and PFS was done from the time of the second FDG-PET/CT on patients having undergone the second FDG-PET/CT in order to control for guarantee-time bias.[25] PFS was calculated up to the time of disease progression or death, whichever occurred first. Kaplan-Meier estimates were used to characterise PFS and OS, and the log-rank test to investigate comparisons between survival curves. Cox’s proportional hazards model was used to calculate HR and their 95% CI

The multivariate analysis was performed using Cox’s proportional hazard model. Variables with a univariate P-value < 0.20 were considered as possible predictors in the multivariate model. We performed stepwise forward selection of variables, i.e. forward selection but at each step variables already in the model could be dropped if their associated p-value became >0.05. To verify the final model, also backward selection of variables was performed on all variables with univariate p-value<0.20, resulting in the same set of variables.[26]

All statistical analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC, USA) and GraphPad Prism 6 software.

Patients found with an early metabolic progressive disease (class IV) were not excluded from the statistical analyses as the objectives of the paper were to show the predictive value of early metabolic response on OS and PFS, which implies the necessity of an intent-to-treat analysis. The event “progression” in the definition of PFS is moreover a radiological progression. Patients belonging to class IV do not meet this definition of radiological progression, which remains an event to be predicted.

Results

Between February and October 2011, 97 consecutive patients were enrolled in 6 clinical centres. The CONSORT diagram details the reasons for considering 5 patients as ineligible, excluding them from all analysis (Fig 2). The eligible patients (N = 92), median age 63 (range 28–83), male/female ratio of 54/46, PS 0 (55%) or 1(45%) received a median of 5 (range 0–44+) cycles of sorafenib-capecitabine after an history of a median of 3 (range 1–6) prior therapeutic lines including bevacizumab in 55% of patients. Codons 12–13 KRAS mutations were present in 52%.

Fig 2

Consort Diagram. http://dx.doi.org:/10.1371/journal.pone.0138341.g002

Toxicity (Table 1)

Table 1

Most important (>10%) side effects in the 88 patients who received treatment according to Common Toxicity Criteria CTC3.0. http://dx.doi.org:/10.1371/journal.pone.0138341.t001

Patients presented a median of 7 (Q1 = 4, Q3 = 9) different adverse reactions during therapy. All but one patient experienced at least one toxicity of any grade, of whom 61.4% with at least one grade III-IV. Grade III-IV side effects were mainly fatigue (21.6%), hand-foot skin reactions (HFSR) (15.9%), and diarrhoea (12.5%). No toxic death was observed. Toxicity led to dose modifications in 63.6% and therapy discontinuation in 5.7% of cases.

Survival data and radiological response

The mOS and mPFS were 8.2 months (95% CI: 6.8–10.5) and 4.2 months (95% CI: 3.4–4.8) respectively. The OS rate at 6 months was 71% (65/92) (95% CI: 61%-79%), significantly higher than the 30% minimal efficiency level predefined in the statistical plan (p-value <0.001), meeting the clinical co-primary endpoint.

According to RECIST, partial response was observed in 7/92 patients (7.6%, 95%CI 2.2–13.0). In the 79 assessable patients, disease control at first evaluation (partial responses and stable diseases according to RECIST) was noted in 32/37 (80%) of the patients with consistent mR versus 24/42 (57%) in other patients (p-value 0.006) (Table 2).

Table 2

RECIST1.1 versus Metabolic Response classes in patients for whom both mR and RECIST assessment of response are available. http://dx.doi.org:/10.1371/journal.pone.0138341.t002

Metabolic response analysis

MR data were available for 79 patients: 37 (46.8%) were classified as class I; 14 (17.7%) as class II; 11 (13.9%) as class III; and 17 (21.5%) as class IV. Within Class IV, 8 patients (10%) showed early metabolic disease progression.

Patients without any metabolically non-responding lesions (Class I) performed better than patients with heterogeneous responses (Class II and III) or with a consistent non-response or progressive disease (Class IV). The difference between the four classes is statistically significant for mPFS (p-value <0.001) but not for mOS (p-value = 0.13). (Fig 3A and 3B)

Fig 3

PFS* (A) and OS* (B) distribution according to the 4 classes of metabolic response. Class1: no metabolic unresponsive lesion; Class2: minority of unresponsive lesions among whole body target tumour load; Class3: majority of whole body target tumour load does not respond; Class 4: all target lesions are nonresponding, or, presence of progressive lesions [progression defined as >25% increase of FDG uptake on second PET, or appearance of a new lesion].*from date of the second FDG PET-CT.

Two classifications were considered for reporting response in a dichotomized way according to mR heterogeneity among lesions: classes (I and II) versus classes (III and IV),[13] and classes (I) versus classes (II+III+IV). The first compares outcome according to the dominance of non-mR lesions within the tumor load, the second according to the consistence of mR (Table 3, Fig 4). “Using the “dominance” classification to define early metabolic non response, the second co-primary objective, which was to identify a prognostic value on survival for early metabolic assessment, was not met while it was successful to discriminate patients according to their outcome using the exploratory “consistence” classification.“Five of the 42 patients (12%) with at least one non-responding lesion remained free of disease progression at 6 months, versus 15 of the 37 class I patients (41%) (p-value 0.005).

http://dx.doi.org;/10.1371/journal.pone.0138341.g003

Table 3 Correlation of mPFS and mOS with Dominance and Consistency of metabolic response. http://dx.doi.org:/10.1371/journal.pone.0138341.t003

Fig 4 PFS and OS distribution according to the dichotomized mR classifications. http://dx.doi.org:/10.1371/journal.pone.0138341.g004

Multivariate analysis after stepwise variable selection of age, PS, number of previous chemotherapy lines, bevacizumab pretreatment, sex, Body Mass Index (BMI), HFSR occurrence and mR retained the absence of metabolically resistant lesion (class I) as the only variable significantly correlated with both mOS and mPFS (Table 4).

Table 4 Univariate and multivariate analysis for OS^¶ and PFS^¶. http://dx.doi.org:/10.1371/journal.pone.0138341.t004

Discussion

Tumoral heterogeneity, described as the coexistence of genomically different subclones within a patient tumor load or to local environmental aspects, is recognized as a major determinant of resistance to treatment in solid tumors.[1–3] However, interlesional tumor heterogeneity in metastatic setting is not covered by current response assessment methods because of the analysis’ methodology performing averaging of responses among lesions. This prospective multicentric proof-of-concept study explored interlesional mR heterogeneity as a biomarker of treatment resistance in advanced solid tumors.

As previously reported in several solid tumors, FDG-PET/CT response assessment after one therapy cycle allows a rapid identification of non-responding lesions/patients, fulfilling the necessary conditions to become potentially a good predictive biomarker, which is crucial to avoid useless toxicity.[4,9–12,22,27] Moreover, significant progresses and implementation of standardized methodology for FDG-PET/CT imaging, including homogenization of imaging procedures and patient’s preparation, quality control and independent central analysis, now allows its use in multicentric trials.[24,27,28]

Studying tumoral heterogeneity requires assessing the response of the whole baseline metastatic tumor load without restriction in number nor site. However, existing morphological (WHO, RECIST) and metabolic (EORTC, PERCIST) response assessment methods do not take into account this response heterogeneity because they only consider a limited number of operator-selected target lesions and/or perform summing or averaging of response variables.[4,19,29,30] Moreover, being classically performed late during treatment, these assessment criteria measure response, while from a clinical point of view, it is the presence of non-response that triggers the need for treatment adaptation. For this, based on prior research, in order to optimize the negative predictive value (NPV) of mR assessment, a 15% cut-off value of SUVmax decrease instead of the standard 25–30% response cut-off value was chosen.[18,31] Such low cut-off value maximally avoids unjustified denial to a potentially active treatment regimen.

With regard to the characterization of response heterogeneity among lesions, this study adopted a multistep descriptive procedure. First, a lesion-by-lesion response analysis of all measurable lesions on baseline FDG-PET/CT without restriction of their number was performed applying the 15% cut-off for non-response. Then, a patient-based 4-class classification was applied, describing the presence and proportion of metabolically non-responding lesions among the whole-body tumor load.[13]

Using such methodology, 22% of the patients showed overall treatment resistance of whom 10% showed early metabolic disease progression at 3 weeks. This observation indicates the importance of performing the baseline FDG-PET/CT as close as possible before the start of the tested drug administration, because rapid disease progression during this timeframe could lead to false negative mR assessment.

On the other hand, after one treatment cycle, 32% of the patients showed heterogeneous metabolic responses combining resistant with potentially responding lesions (Class II and III). Of these, 18% showed non-mR in the minor, while 14% showed a non-mR in the major part of the tumor load. The proportion of heterogeneous response observed in this study is considerable, confirming earlier observation in an independent mCRC patient group treated with chemotherapy, where heterogeneity of mR was described in 67% of patients.[13] Other comparisons are impossible because information about heterogeneity is lacking in most available literature, which apply dichotomization to response assessment.[32–34]

Indeed, for clinical decision-making, the response assessment is generally reported dichotomously, because clinicians have to decide whether to continue or adapt the initiated treatment. Such information-reducing response reporting may only be adequate in case of homogeneous mR, but blurs useful information in case of response heterogeneity.

Outcome analysis in this study indicated that mPFS and mOS are comparable in patients bearing one or more metabolically resistant lesion. Only patients without any resistant lesion (class I) seemed to have a better outcome (mPFS and mOS) compared to all others. Therefore it seems that the presence but not the number/proportion of non-responding lesions is the most important prognostic determinant. Moreover, its value is reinforced by a multivariate analysis showing absence of any metabolically treatment resistant lesion as an independent prognostic factor for both PFS and OS.

A valid assessment of a predictive biomarker requires a significant level of activity of the regimen under study. This was achieved, as 71% of the included patients were still alive at 6 months, which was significantly higher than the minimal activity predefined in the study design. ITT analysis of the 92 eligible patients showed a mPFS of 4.2 months and a mOS of 8.2 months respectively, suggesting an overall beneficial effect for this drug combination compared to recent historical data with 2 months mPFS and 4–6 months mOS in the same clinical setting.[6,31,35–37]

Moreover, this study confirms the need for an effective predictive response biomarker for a sorafenib-containing regimen, because of the high toxicity profile together with the poor sensitivity of morphology-based imaging (CT/MRI) for detecting responses (only 8% of partial response according to RECIST) during treatment.[7,8,38]

A major application of standardized metabolic imaging is expected in early drug development (phase I-II) for two reasons: (i) as FDG-PET response analysis seems to be correlated with prognosis, it provides a rapid appraisal of the new drug activity even in small patient populations, and (ii) image-guided biopsies of resistant lesions could identify the molecular basis of treatment resistance by demonstrating genomic or epigenomic heterogeneity.

In this study for instance, half (47%) of the patients didn’t demonstrate any resistant lesion, indicating a remarkable activity level for such a heavily pre-treated patients population, unsuspected by classical morphological imaging.

Furthermore, in the metastatic setting, FDG-PET/CT may provide a tool for the identification of patients with one or very few metastatic sites resisting to treatment for whom the continuation of unchanged therapy carries a grim prognosis. This raises the potential of adding locoregional ablative treatments guided by the imaging of metabolic response, in order to achieve homogeneity of disease control and restore prognosis. If the current observation is confirmed by an ongoing multicentric trial, (clinicaltrials.gov NCT01929616), randomized prospective trials using early FDG-PET/CT response assessment as an interventional tool for targeting locoregional therapy (eg. surgery, radioembolization, radiofrequency ablation) will be justified.

Finally, in the absence of randomized data based on PET response, it remains to be proven whether the presence of metabolically non-responding lesions is a biomarker identifying more heterogeneous diseases with intrinsically a worse prognosis, or a genuine therapeutic predictive tool for a given treatment.

Conclusions

Metabolic response assessment allows the early identification of treatment-resistant tumor sites. The presence of metabolically refractory lesions seems to negatively impact overall treatment outcome whatever their number, adding to the mounting evidence that tumour heterogeneity is a key element in cancer management.

Early assessment of mR heterogeneity is a potentially powerful predictive biomarker enabling the personalization of anticancer treatments by increasing their cost-effectiveness and sparing useless toxicities.

Go to:

Supporting Information

S1 Protocol

Study protocol.

(PDF)

Click here for additional data file.^{(1.1M, pdf)}

S1 TREND Checklist

TREND Checklist.

(PDF)

Click here for additional data file.^{(1.3M, pdf)}

References

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Grothey A, Hedrick EE, Mass RD, Sarkar S, Suzuki S, Ramanathan RK, et al. (2008) Response-independent survival benefit in metastatic colorectal cancer: a comparative analysis of N9741 and AVF2107.J Clin Oncol 26: 183–189. doi: 10.1200/JCO.2007.13.8099[PubMed]
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Llovet JM, Ricci S, Mazzaferro V, Hilgard P, Gane E, Blanc JF, et al. (2008) Sorafenib in advanced hepatocellular carcinoma. N Engl J Med 359: 378–390. doi: 10.1056/NEJMoa0708857[PubMed]
Awada A, Gil T, Whenham N, Van Hamme J, Besse-Hammer T, Brendel E, et al. (2011) Safety and pharmacokinetics of sorafenib combined with capecitabine in patients with advanced solid tumors: results of a phase 1 trial. J Clin Pharmacol 51: 1674–1684. doi: 10.1177/0091270010386226[PubMed]
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Genomics and Epigenetics: Genetic Errors and Methodologies – Cancer and Other Diseases

Posted in Academic Publishing, Anticancer Resistance, Biochemical pathways, Biological Networks, Cancer Screening, Cell Biology, Chemical Biology and its relations to Metabolic Disease, Disease Biology, Enzymes and isoenzymes, Gene Regulation, Genome Biology, Genomic Expression, Genomic Testing: Methodology for Diagnosis, Immunodiagnostics, Innovations, Kinase, Loss of function gene, Metabolism, Metabolomics, Methods, Methylation, Pentose monophosphate shunt, Personalized and Precision Medicine & Genomic Research, Phosphorylase, Proteins, Proteomics, Pyridine nucleotides, tagged cancer metabolic pathways, dysmetabolism, Epigenetics, genome expression, genomics on March 25, 2015| Leave a Comment »

2.0 Genomics and Epigenetics: Genetic Errors and Methodologies – Cancer and Other Diseases

Writer and Curator: Larry H Bernstein, MD, FCAP

This is the second article in a series concerning genomic expression, The first of which was concerned with the expanded technologies in use for study of genomic expression. This portion will also cover more of genetic errors as well as methodologies, but not all examples are in the realm of cancer.

I shall start with a New York Times editorial on July 24, 2015 by Angelina Jolie Pitt on her experience with BRCA1 gene and her family history. It is very instructive on how she worked through her experience.

http://www.nytimes.com/2015/03/24/opinion/angelina-jolie-pitt-diary-of-a-surgery.html?

Two years ago she was found to have a positive test for BRCA1, carrying an 87 percent risk for breast cancer and a 50 percent risk for ovarian cancer. At that time she had a preventive mastectomy. The decision was not easy, but it also brought into consideration that her mother and grandmother both died of breast cancer. She did not have an oophorectomy at that time because on considering the advice of medical experts, she would have been left with no estrogen support. She wanted to delay her early vegetative senescence. She has reached the age of 39 years and on the advice of medical expert opinion, she proceeded with salpingo-oophorectomy, at age 39 years, a decade before her mother had developed cancer. But her delay was to allow her to recover and adjust emotionally to her ongoing situation, with a remaining risk for ovarian cancer.

She tested negative for CA-125^1-5 at this time prior to surgery. But the CA-125 test could well be negative with early onset ovarian cancer. It may be considered a better test for following treatment than for early diagnosis. Her choice was to sacrifice early menopause to the ability to live through her childrens’ childhood development. This was a well thought out decision. In addition, there were abnormal inflammatory markers that were not specific for cancer rsik, but were worth taking into account. The procedure itself was simpler than the mastectomy.

http://static01.nyt.com/images/2015/03/23/opinion/23op-ed/23op-ed-master315.jpg

2.1 CA-125 and Ovarian Cancer

2.1.1 lmmunoradiometric Assay of CA 125 in Effusions: Comparison with Carcinoembryonic Antigen

Marguerite M. Pinto, MD,‘ Larry H. Bernstein, MD,* Dennis A. Brogan, MPH, MT

and Elaine Criscuolo, CT(ASCP) CMIACS

The levels of CA 125 antigen were measured in 167 effusions from 150 patients using radioimmunoassay, and the results compared with the levels of carcinoembryonic antigen (CEA) in the fluids. The results indicate that an elevated fluid CA 125 level (>14,000 U/ml-68,000 U/ml) and a negative fluid CEA level (4 ng/ml) is suggestive of serous and endometrioid carcinoma of ovary, and adenocarcinoma of the endometrium and fallopian tube. Alternatively, an elevated fluid CEA level (14 ng/ml-600 ng/ml) and a negative CA 125 level (20-5000 U/ml) is seen in metastatic carcinomas of breast, lung, gastrointestinal tract, and mucinous ystadenocarcinoma. Lymphomas, melanomas, and benign effusions are negative for both antigens. The combined use of CEA and CA 125 antigen in fluids is useful in the differential diagnosis of adenocarcinoma of unknown primary. Cancer 59:218-222, 1987.

2.1.2 CA-125 in fine-needle aspirates of solid tumors: comparison with cytologic diagnosis and carcinoembryonic antigen (CEA) assay.

Marguerite M. Pinto, S Kotta

Diagnostic Cytopathology 03/1996; 14(2):121-5.
http://dx.doi.org:/10.1002/(SICI)1097-0339(199603)14:2<121::AID-DC4>3.0.CO;2-M

One hundred and twenty-two fine needle aspirates (FNA) from female patients were studied to determine whether CA-125 assay contributed to cytologic diagnosis and CEA assay. Cytologic examination was done on Papanicolaou-stained smears and cell blocks, CEA by EIA (Abbott Laboratory, > 5 ng/ml cutoff) and CA-125 by RIA (Abbott Laboratory, North Chicago, IL, > 66 mu/ml cutoff). Final diagnosis were correlated with histologic diagnosis when available, clinical, radiologic studies, and follow-up. Results: 29 benign, 93 malignant. Sensitivities and specificities: cytology, 91%, 100%; CEA: 59%, 86%; CA-125, 50%, 55%. CEA plus cytology sensitivity, 97%. CA-125 content was highest in endometrial/ovarian carcinoma (39,899 mu/ml) and < 5,000 mu/ml in other tumors and benign FNA in contrast to CEA which showed highest levels in carcinomas of colon, pancreas, and lung (> 280 ng/ml). While elevated CEA enhances the sensitivity of cytologic diagnosis of carcinomas of the colon, pancreas, and lung, low CEA and high CA-125 content supports an ovarian/endometrial primary.

2.1.3 Diagnostic efficiency of carcinoembryonic antigen and CA125 in the cytological evaluation of effusions.

Pinto MM, Bernstein LH, Rudolph RA, Brogan DA, Rosman M.
Arch Pathol Lab Med. 1992 Jun; 116(6):626-31.

In our previous study, the combination of the concentrations of carcinoembryonic antigen (CEA) and CA125 and the findings from cytological examination in 189 benign and malignant pleural and peritoneal effusions was useful in the diagnosis/classification of malignant effusions. Sensitivity of CEA (level, greater than 5 ng/mL) was 68%; specificity was 99% for the diagnosis of malignant effusions secondary to carcinoma of the lung, breast, gastrointestinal tract, and mucinous carcinoma of the ovary. Sensitivity of CA125 (level, greater than 5000 U/mL) was 85%; specificity was 96% for the diagnosis of malignant effusions in carcinoma of the ovary, fallopian tube, and endometrium. We now expanded the study to include 840 pleural and peritoneal effusions (benign, n = 520; malignant, n = 320) and analyzed the data by the statistical method of Rudolph and colleagues. Based on new cutoff values, ie, CEA level at 6.3 ng/mL and CA125 level at 3652 U/mL, the sensitivities for detection of malignant effusions secondary to carcinomas of the lung, breast, and gastrointestinal tract and mucinous carcinoma of the ovary varied between 75% and 100%; specificity was 98%. Sensitivity of CA125 for detection of malignant effusions from müllerian epithelial carcinoma was 71%; specificity was 99%. The elevated CEA fluid level alone helped to diagnose malignant effusions of the gastrointestinal tract in 54%, breast in 19%, and lung in 16%. The high CA125 fluid level was predictive of müllerian epithelial carcinoma. Adjunctive use of CEA and CA125 levels in fluid enhances the sensitivity of cytological diagnosis and may be predictive of the primary site in patients who present with carcinoma of an unknown primary source.

2.2 Carcinoembryonic antigen in diagnostics

2.2.1 Carcinoembryonic antigen content in fine needle aspirates of the lung. A diagnostic adjunct to cytology.

Pinto MM1, Ha DJ.
Acta Cytol. 1992 May-Jun; 36(3):277-82

Carcinoembryonic Antigen (CEA) was measured in 59 consecutive fine needle aspirates (FNAs) of the lung from 58 patients to determine if the CEA content would enhance the sensitivity of the cytologic diagnosis. Twenty-eight males and 30 females with tumors 1-40 cm in diameter were studied. Final diagnoses were correlated with the clinical history, radiologic studies, tissue (when available) and follow-up. Image-guided FNAs were performed by radiologists using a 22-gauge Chiba needle and 20-mL syringe with one to four passes per specimen. Cytologic examination included rapid assessment in the radiology suite and a final diagnosis in 24 hours. CEA was measured by enzyme immunoassay using monoclonal antibody. Nine benign aspirates and 50 malignant aspirates were diagnosed. The sensitivity of cytology was 86% and specificity, 100%. Using 5 ng/mL as the cutoff, the sensitivity of CEA for malignant aspirates was 50% and specificity, 90%. The combined sensitivity of CEA and cytology was 95%. The mean CEA in malignant aspirates was 131 ng/mL and in benign aspirates, 2.41. The highest mean CEA was seen in adenocarcinoma, 402.6 ng/mL. Lower CEA content was seen in epidermoid carcinoma (58.6 ng/mL), large cell carcinoma (8.09), oat cell carcinoma, metastatic carcinoma of the kidney and breast, thymoma and lymphoma (each less than 1 ng/mL). Elevated CEA alone was diagnostic in two aspirates of bronchioloalveolar carcinoma; carcinoma with an unknown primary source, three; and large cell carcinoma, one. The adjunctive use of CEA in FNAs of the lung enhances the sensitivity of the cytologic diagnosis.

2.2.2 Relationship between serum CA125 half life and survival in ovarian cancer

Table
Gupta and Lis Journal of Ovarian Research 2009 2:13
http://dx.doi.org:/10.1186/1757-2215-2-13

First Author, Year, Study Place	Data Collection	Study Design	Sample Size	RR/HR, (95% CI), P-Value
Riedinger JM, 2006, France	1988 to 1996	R	553	2.04 (1.58-2.63), < 0.0001
Gadducci A, 2004, Italy	1996 to2002	R	71	3.11 (1.22-7.98), 0.0181
Munstedt K, 1997, Germany	1987 to1994	R	85	0.6184
Gadducci A, 1995, Italy	1986 to1992	R	225	2.13 (1.23-3.68), 0.0073
Rosman M, 1994, Connecticut	1985 to 1989	R	51	3.6 (1.8-7.4), < 0.001
Yedema C A, 1993, Netherlands	1984 to 1990	R	60	9.17 (1.49-56.3), 0.01
Hawkins RE, 1989, London	NA	P	29	3.7 (0.7-20.1), 0.001;27.8 (4.0-193), 0.001

¹CA125 half-life was independent prognostic indicator for survival
²FIGO stage, tumor grade, residual disease, CA125
http://www.ovarianresearch.com/content/2/1/13/table/T6

3.3.0 DNA double strand breaks

2.3.1. Collaboration and competition – DNA double-strand break repair pathways

Kass EM, Jasin M
FEBS Letters 2010; 584:3703-3708
http://dx.doi.org:/jfebslet.2010.07.057

DNA double-strand breaks occur in replication and exogenous sources pose risk to genome stability. There are two pathways to repair. They are non-homologous end joining and homologous recombination. Both pathways cooperate and compete at double-strand break sites.

2.3.2 DNA Double-Strand Break Repair Inhibitors as Cancer Therapeutics

Srivastava M, Rashavan SC
Chem & Biol 2015 Jan; pp17-29
http://dx.doi.org:/10.1016/jchembiol.2014.11.013

Homologous recombination and non-homologous end joining are the two major repair pathways expressed in eukaryotes. For double-strand breaks, and the DSB repair gene is vulnerable to chemotherapy and radiation therapy, accounting for treatment resistance. Therefore, targeting DSB repair is attractive. Blocking the residual repair using inhibitors can potentiate treatment.

2.3.3 Animation published in DNA Repair: Helleday T, Lo J, van Gent DC, Engelward BP. DNA double-strand break repair: From mechanistic understanding to cancer treatment. DNA Repair. (14 Mar 2007)
2.3.3.1 http://web.mit.edu/engelward-lab/animations/DSBR.html

2.3.3.2 https://www.youtube.com/watch?v=eg8rpYFsqCA

2.3.4 Homology-dependent double strand break repair. Oxford Academic (Oxford University Press)

https://www.youtube.com/watch?v=86JCMM5kb2A

2.4.0 Managing DNA data sets

2.4.1 Bionimbus – a cloud for managing, analyzing and sharing large genomics datasets

The Bionimbus Protected Data Cloud (PDC) is a collaboration between the Open Science Data Cloud (OSDC) and the IGSB (IGSB,) the Center for Research Informatics (CRI), the Institute for Translational Medicine (ITM), and the University of Chicago Comprehensive Cancer Center (UCCCC). The PDC allows users authorized by NIH to compute over human genomic data from dbGaP in a secure compliant fashion. Currently, selected datasets from the The Cancer Genome Atlas (TCGA) are available in the PDC.

https://bionimbus-pdc.opensciencedatacloud.org/

2.4.1.2 Accounting for uncertainty in DNA sequencing data

O’Rawe JA, Ferson S, Lyon GJ
Trends in Genetics 2015 Feb; 31(2):61-66
http://dx.doi.org:/10.101/jtig.2014.12.002

This article reviews uncertainty in quantification in DNA sequency applications and sources of error propagation, and it proposes methods to account for errors and uncertainties.

2.5.0 Linking Traits to Mechanisms and UPR response/proteostasis

2.5.1 Stress-Independent Activation of XBP1s and/or ATF6 Reveals –Three Linking traits based on their shared molecular mechanisms

Shoulders MD, Ryno LM, Genereux JC,…Wiseman BL
Cell Reports 2013 Apr; 3, pp 1279-1292
http://dx.doi.org:/10.1016/j.celrep.2013.03.024

The unfolded protein response (UPR) maintains ER proteostasis through the transcription factors XP1s and ATF6. This study measured orthogonal small molecule-mediated activation of transcription factors nXP1s and/or ATF6 using transcriptomics and quantitative proteomics. The finding is that three ER proteostasis environmants differentially influence

Folding
Traffiking, and
Degradation of destabilized ER client proteins

Without affecting endogenous proteome. The proteostasis network is remodeled with the potential for selective restoration of the aberrant ER proteostasis.

2.5.2 Biological and chemical approaches to diseases of proteostasis deficiency.

Powers ET, Morimoto RI, Dillin A, Kelly JW, Balch WE
Annu Rev Biochem. 2009; 78:959-91.
http://dx.doi.org:/10.1146/annurev.biochem.052308.114844

Many diseases appear to be caused by the misregulation of protein maintenance. Such diseases of protein homeostasis, or “proteostasis,” include loss-of-function diseases (cystic fibrosis) and gain-of-toxic-function diseases (Alzheimer’s, Parkinson’s, and Huntington’s disease). Proteostasis is maintained by the proteostasis network, which comprises pathways that control protein synthesis, folding, trafficking, aggregation, disaggregation, and degradation. The decreased ability of the proteostasis network to cope with inherited misfolding-prone proteins, aging, and/or metabolic/environmental stress appears to trigger or exacerbate proteostasis diseases. Herein, we review recent evidence supporting the principle that proteostasis is influenced both by an adjustable proteostasis network capacity and protein folding energetics, which together determine the balance between folding efficiency, misfolding, protein degradation, and aggregation. We review how small molecules can enhance proteostasis by binding to and stabilizing specific proteins (pharmacologic chaperones) or by increasing the proteostasis network capacity (proteostasis regulators). We propose that such therapeutic strategies, including combination therapies, represent a new approach for treating a range of diverse human maladies.

2.5.3 Extracellular Chaperones and Proteostasis

Amy R. Wyatt, Justin J. Yerbury, Heath Ecroyd, and Mark R. Wilson
Annual Review of Biochemistry 2013 Jun; 82: 295-322
http://dx.doi.org:/10.1146/annurev-biochem-072711-163904

There exists a family of currently untreatable, serious human diseases that arise from the inappropriate misfolding and aggregation of extracellular proteins. At present our understanding of mechanisms that operate to maintain proteostasis in extracellular body fluids is limited, but it has significantly advanced with the discovery of a small but growing family of constitutively secreted extracellular chaperones. The available evidence strongly suggests that these chaperones act as both sensors and disposal mediators of misfolded proteins in extracellular fluids, thereby normally protecting us from disease pathologies. It is critically important to further increase our understanding of the mechanisms that operate to effect extracellular proteostasis, as this is essential knowledge upon which to base the development of effective therapies for some of the world’s most debilitating, costly, and intractable diseases.

http://www.proteostasis.com/our-technology/proteostasis-network.html

http://www.proteostasis.com/images/stories/technology/illustration1.gif

2.6.0 Transcription

2.6.1 Looping Back to Leap Forward. Transcription Enters a New Era

Levine M, Cattoglio C, Tijan R
Cell 2014 Mar; 157: 13-22.
http://dx.doi.org:/10.1016/j.cell.2014.02.009

Organism complexity is not in gene number, but lies in gene regulation. The human genbome contains hundreds of thousands of enhancers, and genes are embedded in a milieu of enhancers . Proliferation of cis-regulatory DNAs is accompanied by complexity and functional diversity of transcription machinery recognizing distal enhancers and promotors, and high-order spatial organization. This article reviews the dynamic communication of remote enhancers with target promoters.

2.6.2 Activating gene expression in mammalian cells with promoter-targeted duplex RNAs.

Janowski BA, Younger ST, Hardy DB, Ram R, Huffman KE, Corey DR.
Nat Chem Biol. 2007 Mar; 3(3):166-73
http://dx.doi.org:/10.1038/nchembio860

The ability to selectively activate or inhibit gene expression is fundamental to understanding complex cellular systems and developing therapeutics. Recent studies have demonstrated that duplex RNAs complementary to promoters within chromosomal DNA are potent gene silencing agents in mammalian cells. Here we report that chromosome-targeted RNAs also activate gene expression. We have identified multiple duplex RNAs complementary to the progesterone receptor (PR) promoter that increase expression of PR protein and RNA after transfection into cultured T47D or MCF7 human breast cancer cells. Upregulation of PR protein reduced expression of the downstream gene encoding cyclooygenase 2 but did not change concentrations of estrogen receptor, which demonstrates that activating RNAs can predictably manipulate physiologically relevant cellular pathways. Activation decreased over time and was sequence specific. Chromatin immunoprecipitation assays indicated that activation is accompanied by reduced acetylation at histones H3K9 and H3K14 and by increased di- and trimethylation at histone H3K4. These data show that, like proteins, hormones and small molecules, small duplex RNAs interact at promoters and can activate or repress gene expression.
2.6.3 Tight control of gene expression in mammalian cells by tetracycline-responsive promoters.

M Gossen and H Bujard
Proc Natl Acad Sci U S A. 1992 Jun 15; 89(12): 5547–5551.

Control elements of the tetracycline-resistance operon encoded in Tn10 of Escherichia coli have been utilized to establish a highly efficient regulatory system in mammalian cells. By fusing the tet repressor with the activating domain of virion protein 16 of herpes simplex virus, a tetracycline-controlled transactivator (tTA) was generated that is constitutively expressed in HeLa cells. This transactivator stimulates transcription from a minimal promoter sequence derived from the human cytomegalovirus promoter IE combined with tet operator sequences. Upon integration of a luciferase gene controlled by a tTA-dependent promoter into a tTA-producing HeLa cell line, high levels of luciferase expression were monitored. These activities are sensitive to tetracycline. Depending on the concentration of the antibiotic in the culture medium (0-1 microgram/ml), the luciferase activity can be regulated over up to five orders of magnitude. Thus, the system not only allows differential control of the activity of an individual gene in mammalian cells but also is suitable for creation of “on/off” situations for such genes in a reversible way.

Diagrams of two regulatable gene expression systems.

http://www.intechopen.com/source/html/16788/media/image5.jpeg

schematic-representation-of-transgenic-mouse-breeding-scheme-h2b-gfp-mice-should-not-express-gfp-in-the-absence-of-a-tetracycline-regulatable-transactivator

http://openi.nlm.nih.gov/imgs/512/321/2408727/2408727_pone.0002357.g001.png

2.7.0 Epigenetics and Cancer

2.7.1 Epigenetics and cancer metabolism

Johnson C, Warmoes MO, Shen X, Locasale JW
Cancer Letters 2015; 356:309-314.
http://dx.doi.org:/10.1016/j.canlet.2013.09.043

Cancer is characterized by adaptive metabolic changes for proliferation and survival of the neoplastic cell, which is accompanied by dysfunctional metabolic enzyme changes in a specific nutrient supplied environment. The oncogenic change uses epigenetic level enzymes that catalyze posttranslational modifications of the DNA/histone expression with metabolites including cofactors and substrates for reactions. This interaction of epigenetics and metabolism provides new insights for anti-cancer therapy.

2.7.2 Cancer Epigenetics. From Mechanism to Therapy

Dawson MA, Konzarides T
Cell 2012 Jul; 150:12-27
http://dx.doi.org:/10.1016/j.cell.2012.06.013

Carcinogenesis requires all of the following:

DNA methylation
Histone modification
Nucleosome remodeling
RNA mediated targeting

This article reviews basic principles of epigenetic pathways that are dysregulated in carcinogenesis.

2.7.4 A subway review of cancer pathways

Hahn WC, Weinberg RA
Nature Reviews: Cancer
http://www.nature.com/nrc/poster/subpathways/index.html

Cancer arises from the stepwise accumulation of genetic changes that confer upon an incipient neoplastic cell the properties of unlimited, self-sufficient growth and resistance to normal homeostatic regulatory mechanisms. Advances in human genetics and molecular and cellular biology have identified a collection of cell phenotypes � the main destinations in the subway map below � that are required for malignant transformation1. Specific molecular pathways (subway lines) are responsible for programming these behaviours. Although the connections between cancer-cell wiring and function remain incompletely explored and specified � hence the many lines under construction � the broad outlines of the molecular circuitry of the cancer cell can now be sketched. Further advances in understanding these pathways and their interconnections will accelerate the development of molecularly targeted therapies that promise to change the practice of oncology.