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Breast Cancer Extratumor Microenvironment has Effect on Progression

Posted in Biochemical pathways, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Informatics, Cell Biology, Signaling & Cell Circuits, Clinical & Translational, Clinical Diagnostics, Computational Biology/Systems and Bioinformatics, Curation, Cytoskeleton, Diagnostics and Lab Tests, Disease Biology, Gene Regulation, Genome Biology, Genomic Endocrinology, Human Immune System in Health and in Disease, Lipid metabolism, Metabolism, Metabolomics, Micronutrients, Mutagenesis, tagged breast cancer, extratunot microenvironment, metastasis on February 20, 2016| Leave a Comment »

Breast Cancer Extratumor Microenvironment has Effect on Progression

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Tumor Microenvironment Diversity Predicts Breast Cancer Outcomes

GEN News Highlights   Feb 17, 2016   http://www.genengnews.com/gen-news-highlights/tumor-microenvironment-diversity-predicts-breast-cancer-outcomes/81252378/

 

Intratumor heterogeneity, it is known, can complicate cancer treatments. Now it appears the same may be true of tumor microenvironment heterogeneity. According to a new study from the Institute of Cancer Research (ICR), London, breast cancers that develop within an “ecologically diverse” breast cancer microenvironment are particularly likely to progress and lead to death.

The study took an unusual approach: It combined ecological scoring methods with genome-wide profiling data. This approach, besides showing clinical utility in the evaluation of breast cancer outcomes, demonstrated that even so contextual a discipline as genomics can benefit from being placed within a larger context. In this case, the context is essentially Darwinian, albeit at a small scale.

Natural selection is typically studied at the level of ecosystems consisting of animals and plants. In the current study, however, it was assessed at the level of the tumor microenvironment, which consists of cancer cells, immune system lymphocytes, and stromal cells.

The ICR scientists, led by Yinyin Yuan, Ph.D., presented their work February 16 in the journal PLoS Medicine, in an article entitled “Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology–Genomic Integration Analysis.” The article describes how the scientists developed a tumor ecosystem diversity index (EDI), a scoring system that indicates the degree of microenvironmental heterogeneity along three spatial dimensions in solid tumors. EDI scores take account of “fully automated histology image analysis coupled with statistical measures commonly used in ecology.”

“[EDI] was compared with disease-specific survival, key mutations, genome-wide copy number, and expression profiling data in a retrospective study of 510 breast cancer patients as a test set and 516 breast cancer patients as an independent validation set,” wrote the authors. “In high-grade (grade 3) breast cancers, we uncovered a striking link between high microenvironmental heterogeneity measured by EDI and a poor prognosis that cannot be explained by tumor size, genomics, or any other data types.”

By using the EDI, the ICR team was able to identify several particularly aggressive subgroups of breast cancer. In fact, the EDI was a stronger predictor of survival than many established markers for the disease.

The ICR researchers also looked at the EDI in addition to genetic factors. For example, the researchers found that the prognostic value of EDI was enhanced with the addition of TP53 mutation status. By integrating EDI data and genome-wide profiling data, the researchers identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors. These tumors, which showed high microenvironmental diversity, substratified patients into poor prognostic groups.

“Our findings show that mathematical models of ecological diversity can spot more aggressive cancers,” said Dr. Yuan. “By analyzing images of the environment around a tumor based on Darwinian natural selection principles, we can predict survival in some breast cancer types even more effectively than many of the measures used now in the clinic.

“In the future, we hope that by combining cell diversity scores with other factors that influence cancer survival, such as genetics and tumor size, we will be able to tell apart patients with more or less aggressive disease so we can identify those who might need different types of treatment.”

“This ingenious study…teaches us a valuable lesson,” added Paul Workman, Ph.D., chief executive of the ICR. “[We] should always remember that cancer cells are not developing and growing in isolation, but are part of a complex ecosystem that involves normal human cells, too. By better understanding these ecosystems, we aim to create new ways to diagnose, monitor and treat cancer.”

 

Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology–Genomic Integration Analysis

Rachael Natrajan, Heba Sailem, Faraz K. Mardakheh, Mar Arias Garcia, Christopher J. Tape, Mitch Dowsett, Chris Bakal, Yinyin Yuan    

 

    Feb 16, 2016   http://dx.doi.org:/10.1371/journal.pmed.1001961

 

Background

The intra-tumor diversity of cancer cells is under intense investigation; however, little is known about the heterogeneity of the tumor microenvironment that is key to cancer progression and evolution. We aimed to assess the degree of microenvironmental heterogeneity in breast cancer and correlate this with genomic and clinical parameters.

Methods and Findings

We developed a quantitative measure of microenvironmental heterogeneity along three spatial dimensions (3-D) in solid tumors, termed the tumor ecosystem diversity index (EDI), using fully automated histology image analysis coupled with statistical measures commonly used in ecology. This measure was compared with disease-specific survival, key mutations, genome-wide copy number, and expression profiling data in a retrospective study of 510 breast cancer patients as a test set and 516 breast cancer patients as an independent validation set. In high-grade (grade 3) breast cancers, we uncovered a striking link between high microenvironmental heterogeneity measured by EDI and a poor prognosis that cannot be explained by tumor size, genomics, or any other data types. However, this association was not observed in low-grade (grade 1 and 2) breast cancers. The prognostic value of EDI was superior to known prognostic factors and was enhanced with the addition of TP53 mutation status (multivariate analysis test set, p = 9 × 10⁻⁴, hazard ratio = 1.47, 95% CI 1.17–1.84; validation set, p = 0.0011, hazard ratio = 1.78, 95% CI 1.26–2.52). Integration with genome-wide profiling data identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors with high microenvironmental diversity that also substratified patients into poor prognostic groups. Limitations of this study include the number of cell types included in the model, that EDI has prognostic value only in grade 3 tumors, and that our spatial heterogeneity measure was dependent on spatial scale and tumor size.

Conclusions

To our knowledge, this is the first study to couple unbiased measures of microenvironmental heterogeneity with genomic alterations to predict breast cancer clinical outcome. We propose a clinically relevant role of microenvironmental heterogeneity for advanced breast tumors, and highlight that ecological statistics can be translated into medical advances for identifying a new type of biomarker and, furthermore, for understanding the synergistic interplay of microenvironmental heterogeneity with genomic alterations in cancer cells.

Background

The human body contains millions of cells, all of which grow, divide, and die in an orderly fashion to build tissues during early life and to replace worn-out or dying cells and repair injuries during adult life. Sometimes, however, normal cells acquire genetic changes (mutations) that allow them to divide uncontrollably and to move around the body (metastasize), resulting in cancer. Because any cell in the body can acquire the mutations needed for cancer development, there are many types of cancer. For example, breast cancer, the most common cancer in women, begins when the cells in the breast that normally make milk become altered. Moreover, different types of cancer progress and evolve differently—some cancers grow quickly and kill their “host” soon after diagnosis, whereas others can be successfully treated with drugs, surgery, or radiotherapy. The behavior of individual cancers depends both on the characteristics of the cancer cells within the tumor and on the interactions between the cancer cells and the normal stromal cells (the connective tissue cells of organs) and other cells (for example, immune cells) that surround and feed cancer cells (the tumor microenvironment).

Why Was This Study Done?

Although recent studies have highlighted the importance of the tumor microenvironment for disease-related outcomes, little is known about how the heterogeneity of the tumor microenvironment—the diversity of non-cancer cells within the tumor—affects outcomes. Mathematical modeling suggests that tumors with heterogeneous and homogeneous microenvironments have different growth patterns and that heterogeneous microenvironments are more likely to be associated with aggressive cancers than homogenous microenvironments. However, the lack of methods to quantify the spatial variability and cellular composition across solid tumors has prevented confirmation of these predictions. Here, the researchers develop a computational system for quantifying microenvironmental heterogeneity in breast cancer based on tumor morphology (shape and form) in histological sections (tissue samples taken from tumors that are examined microscopically). They then use this system to analyze the associations between clinical outcomes, molecular changes, and microenvironmental heterogeneity in breast cancer.

What Did the Researchers Do and Find?

The researchers used automated image analysis and statistical analysis to develop the ecosystem diversity index (EDI), a numerical measure of microenvironmental heterogeneity in solid tumors. They compared the EDI with prognosis (likely outcome), key mutations, genome-wide copy number (tumor cells often contain abnormal numbers of copies of specific genes), and expression profiling data (the expression of several key proteins is altered in tumors) in a test set of 510 samples from patients with breast cancer and in a validation set of 516 additional samples. Among high-grade breast cancers (grade 3 cancers; the grade of a cancer indicates what the cells look like; high-grade breast cancers have a poor prognosis), but not among low-grade breast cancers (grades 1 and 2), a high EDI (high microenvironmental heterogeneity) was associated with a poor prognosis. Specifically, patients with grade 3 tumors and a high EDI had a ten-year disease-specific survival rate of 51%, whereas the remaining patients with grade 3 tumors had a ten-year survival rate of 70%. Notably, the combination of a high EDI with specific DNA alterations—mutations in a gene called TP53 and loss of genes on Chromosomes 4p14 and 5q13—improved the accuracy of prognosis among patients with grade 3 breast cancer and stratified them into subgroups with disease-specific five-year survival rates of 35%, 9%, and 32%, respectively.

What Do These Findings Mean?

These findings establish a method for measuring the spatial heterogeneity of the microenvironment of solid tumors and suggest that the measurement of tumor microenvironmental heterogeneity can be coupled with information about genomic alterations to provide an accurate way to predict outcomes among patients with high-grade breast cancer. The association between EDI, specific genomic alterations, and outcomes needs to be confirmed in additional patients. However, these findings suggest that microenvironmental heterogeneity might provide an additional biomarker to help clinicians identify those patients with advanced breast cancer who have a particularly bad prognosis. The ability to identify these patients is important because it will help clinicians target aggressive treatments to individuals with a poor prognosis and avoid the overtreatment of patients whose prognosis is more favorable. Finally, and more generally, these findings describe a new way to investigate the interactions between the tumor microenvironment and genomic alterations in cancer cells.

Additional Information

This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001961.

• The US National Cancer Institute provides comprehensive information about cancer and its development (in English and Spanish), including detailed information about breast cancer and an online booklet for patients
• Cancer Research UK, a not-for-profit organization, provides information about cancer, including detailed information about breast cancer and a science blog on the tumor microenvironment
• Breast Cancer Now is a not-for-profit organization that provides up-to-date information about breast cancer (in English and Spanish)
• The UK National Health Service Choices website has information and personal stories about breast cancer; the not-for-profit organization Healthtalkonline also provides personal stories about dealing with breast cancer
• Wikipedia has a page about the tumor microenvironment (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)

Citation: Natrajan R, Sailem H, Mardakheh FK, Arias Garcia M, Tape CJ, Dowsett M, et al. (2016) Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology–Genomic Integration Analysis.
PLoS Med 13(2): e1001961.     http://dx.doi.org:/10.1371/journal.pmed.1001961

http://journals.plos.org/plosmedicine/article/figure/image?size=large&id=info:doi/10.1371/journal.pmed.1001961.g001

Fig 1. In silico tumor dissection pipeline for quantifying spatial diversity in the tumor ecosystem. (A) Flow diagram depicting the overall study design. (B) Schematic of our pipeline for quantifying spatial diversity in pathological samples. H&E sections are morphologically classified and divided into regions to be spatially scored. The number of clusters k in the regional scores is indicative of the number of sub-populations of cell types in the tumor regions. (C) Examples of tumor regions with low and high diversity scores using the Shannon diversity index, accounting for cancer cells (outlined in green), lymphocytes (blue), and stromal cells (red). Cell classification is automated by image analysis. (D) The 3-D landscape of cell diversity scores on an example H&E section; the x- and y-axes are the geometric axes of the image, and the z-axis is cell diversity computed on a region-by-region basis. (E) The distribution of regional scores in a tumor from the METABRIC study with two regional clusters identified using Gaussian mixture clustering (grey shading: histogram; dashed black line: density; solid black lines: mixture components/clusters).

http://journals.plos.org/plosmedicine/article/figure/image?size=medium&id=info:doi/10.1371/journal.pmed.1001961.g002

Fig 2. Application of EDI to 1,026 breast tumors from the METABRIC study. (A) The frequencies of EDI scores in breast tumors. (B) H&E staining, distribution of classified cells (green: cancer; blue: lymphocyte; red: stromal cells), and the heatmap of regional diversity scores for a tumor with the highest EDI score (EDI = 5). (C) Representative regions from each of the clusters k1–k5 are shown in a tumor with EDI = 5, with cluster k1 having the lowest diversity score and k5 the highest. By mapping regional clusters to the H&E image, we can begin to interpret these clusters with different cell diversity. We observed predominantly cancer cells in k1, increasingly more stromal cells and ductal in situ carcinoma cells (DCIS) in k2, and a vessel in k3. Cluster k4 features extensive stromal lymphocytes between ductal in situ carcinoma components, while k5 shows tumor-infiltrating lymphocytes (TIL) associated with invasive carcinoma cells.

Fig 3. Reproducibility, stability, and independence of the EDI-high group in 507 grade 3 breast tumors.

http://journals.plos.org/plosmedicine/article/figure/image?size=large&id=info:doi/10.1371/journal.pmed.1001961.g003

Fig 3. Reproducibility, stability, and independence of the EDI-high group in 507 grade 3 breast tumors. (A) Kaplan–Meier curves of disease-specific survival to illustrate the prognosis of EDI-high samples compared to other grade 3 samples in two independent patient cohorts. Shown below the graph are the number of patients (the number of disease-specific events) per group for EDI-low (grey) and EDI-high (red). (B) Agreement of the EDI subtyping between 100% data and resampling with progressively fewer tumor regions in 200 repeats. (C) Distribution of known subtypes in grade 3 tumors stratified by EDI; asterisks mark subtypes enriched in the EDI-high group. (D) Kaplan–Meier curves illustrating the duration of disease-specific survival according to tumor size (left) and improvement of stratification with the addition of EDI information (right).

Accumulating evidence suggests that the interactions of cancer cells and stromal cells within their microenvironment govern disease progression, metastasis, and, ultimately, the evolution of therapeutic resistance [1–3]. Recent reports have highlighted the significance of the contribution of stromal gene expression and morphological structure as powerful prognostic determinants for a number of tumor types, emphasizing the importance of the tumor microenvironment in disease-related outcomes [4–7]. In breast cancer, a number of studies have demonstrated the prognostic correlation of individual cell types, including the immune cell infiltrate that predicts response to therapy [8–10], and the high percentage of tumor stroma that predicts poor prognosis in triple-negative disease but good prognosis in estrogen receptor (ER)–positive disease [11,12]. Nevertheless, different types of cells coexist with varying degrees of heterogeneity within a tumor. This fundamental feature of human tumors and the combinatorial effects of cell types have been largely ignored, and the collective implications for clinical outcome remain elusive. Consistent observations from mathematical models have highlighted that tumors with diverse microenvironments show growth patterns dramatically different from those of tumors with homogeneous environments [13] and are more likely to be associated with aggressive cancer phenotypes [2] that select for cell migration and eventual metastasis by allowing cancer cells to evolve more rapidly [14]. These observations highlight the need to understand the collective physiological characteristics and heterogeneity of tumor microenvironments. However, there is a lack of methods to quantify the high spatial variability and diverse cellular composition across different solid tumors. Moreover, the interplay of genomic alterations in cancer cells and microenvironmental heterogeneity and its subsequent role in treatment response have not been explored. Our aims were (i) to develop a computational system for quantifying microenvironmental heterogeneity based on tumor morphology in routine histological sections, (ii) to define the clinical implications of microenvironmental heterogeneity, and (iii) to integrate this histologybased index with RNA gene expression and DNA copy number profiling data to identify molecular changes associated with microenvironmental heterogeneity.

The Ecosystem Diversity Index To characterize the tumor ecosystem based on cell compositions, we developed a new index to be used in conjunction with our image analysis tool [16]. First, we used our automated morphological classification method [16] to identify and classify cells into cancer, lymphocyte, or stromal cell classes in H&E sections (Fig 1B). We next divided sections into smaller spatial regions and quantified the diversity of the tumor ecosystem in a tumor region j using the Shannon diversity index: dj ¼ Sm i pi logpi ; ð1Þ where m is the number of cell types and pi is the proportion of the ith cell type (Fig 1B and 1C). A high value of the Shannon diversity index dj reports a heterogeneous environment populated by many cell types, whilst a low value indicates a homogeneous environment (Fig 1C). Compared to other methods such as the Simpson index, the Shannon diversity index accounts for rare species and, hence, is less dominated by main species [17]. Subsequently, we derived the ecosystem diversity index (EDI) by applying unsupervised clustering that identifies the optimum number of clusters in the dataset in an unbiased manner, in order to group tumor regions and quantify the degree of spatial heterogeneity. Let D = d1,d2,…,dn be the Shannon index for n regions in a tumor. We used Gaussian mixture models to fit data D: D SK k¼1okNðmk; s2 kÞ: ð2Þ where μk, ,s2 k, and ωk are the mean, variance, and weight of a Gaussian distribution k, and K is the number of clusters. The Bayesian information criterion was then used to select the best number of clusters K [18]. We used K = 1–5 as the range of K to avoid small EDI groups (S1 Text). The final value of K thus is a measurement of heterogeneity and the score of EDI for a tumor.

Fig 5. The relationship between ecological heterogeneity and cancer genomic aberrations in 507 grade 3 tumors. (A) Genome-wide copy number aberrations in grade 3 breast tumors and genomic coordinates of genes with copy number aberrations enriched in the EDI-high group. Lengths of black lines denote level of enrichment significance with copy number gains (above the horizontal line) or losses (below the horizontal line). (B) Kaplan–Meier curves illustrating the duration of disease-specific survival in grade 3 breast cancer patients according to copy number loss of the 4p14 region (left) and the EDI-high group with additional information of 4p14 copy number loss (right). (C) Kaplan–Meier curves illustrating the duration of disease-specific survival according to copy number loss of the 5q13 region (left) and the EDI-high group with additional information of 5q13 copy number loss (right).

http://dx.doi.org:/10.1371/journal.pmed.1001961.g005

This study has a number of limitations. The motivation for our computational development was to use a data-driven model and measure the degree of spatial heterogeneity in tumor pathological specimens. In this model, only three major cell types in breast tumors were considered. Further sub-classification of the different types of stromal and immune cells by immunohistochemistry may add additional discriminatory value to our model. For dissecting spatial heterogeneity, we chose to use square regions with equal sizes. We found that EDI was correlated with the size of the region chosen for calculation of the Shannon diversity index, and as such the spatial heterogeneity is scale dependent. This phenomenon has been well described in a number of studies in ecology that show that a scale needs to be chosen that is appropriate for the ecological process under study [38,39], further highlighting the analogy between tumor studies and ecology. Similar to the recent observation that breast cancer subclonal heterogeneity is correlated with tumor size [35], we also found an association between microenvironmental heterogeneity and tumor size; hence, EDI may have more limited value in smaller tumors. However, small tumors were present in the EDI-high group, and addition of EDI within tumors grouped by size further stratified their prognosis. We found that EDI was prognostic only in grade 3 tumors in our study, which could be a limitation, given the possible discordance in grading between pathologists.

The identification of additional biomarkers in subgroups of patients that identify them as high risk is important for patient management and to avoid overtreatment for low-risk patients. We envision that the use of our measure of microenvironment heterogeneity, together with key genomic alterations, will enable the diagnosis of patients at very high risk of relapse and facilitate the enrollment of these patients into additional clinical trials for novel therapies or treatment intensification. Our novel computational approach provides a fully automated tool that is relatively easy to implement. Integration of this measure with genomic profiling provides additional prognostic information independent of known clinical parameters. The results of this study highlight the possibility of a grade-3-specific prognostic tool that may aid in further classification of high-grade breast cancer patients beyond standard assays such as ER and HER2 status.

 

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Is the Warburg effect an effect of deregulated space occupancy of methylome?

Posted in Anaerobic Glycolysis, Cancer - General, CANCER BIOLOGY & Innovations in Cancer Therapy, Curation, Genomic Expression, Oxidative phosphorylation, Pyruvate Kinase, Warburg effect, tagged anaerobic and aerobic gycolysis, ascorbate, Cancer - General, dihydroascorbate, glutamine, lactate dehydrogenase reaction, Warburg Effect on February 15, 2016| Leave a Comment »

Is the Warburg effect an effect of deregulated space occupancy of methylome?

Larry H. Bernstein and Radoslov Rosov, co-curation

LPBI

UPDATED

8/20/2024

Editor Correction:  One of the co-curators names on this article was misspelled. The co-curators should be Larry H. Bernstein and  Radislov Rosov.

Therefore the correction version of the article title and authors should read

Is the Warburg effect an effect of deregulated space occupancy of methylome?

Larry H. Bernstein and Radoslov Rosov, co-curation

The editors of LPBI Group apologize for the confusion.

 

It would appear that pyruvate is directly used by cancer cell machinery for sustaining genome independence, and that CRISP-Cas9 system is essentially a modified CAD protein for making small bases.

¹³C-labeled biochemical probes for the study of cancer metabolism with dynamic nuclear polarization-enhanced magnetic resonance imaging

Lucia Salamanca-Cardona and Kayvan R. Keshari

Cancer & Metabolism 2015; 3:9          http://dx.doi.org:/10.1186/s40170-015-0136-2

In recent years, advances in metabolic imaging have become dependable tools for the diagnosis and treatment assessment in cancer. Dynamic nuclear polarization (DNP) has recently emerged as a promising technology in hyperpolarized (HP) magnetic resonance imaging (MRI) and has reached clinical relevance with the successful visualization of [1-¹³C] pyruvate as a molecular imaging probe in human prostate cancer. This review focuses on introducing representative compounds relevant to metabolism that are characteristic of cancer tissue: aerobic glycolysis and pyruvate metabolism, glutamine addiction and glutamine/glutamate metabolism, and the redox state and ascorbate/dehydroascorbate metabolism. In addition, a brief introduction of probes that can be used to trace necrosis, pH changes, and other pathways relevant to cancer is presented to demonstrate the potential that HP MRI has to revolutionize the use of molecular imaging for diagnosis and assessment of treatments in cancer.

 

Since the hallmark discovery of the Warburg effect in cancer cells in the 1920s, it has been widely accepted that the metabolic properties of cancer cells are vastly different from those of normal cells [1]. Starting from the observation that many cancerous (neoplastic) cells have higher rates of glucose utilization and lactate production, the development of tools and methods to correlate specific cellular metabolic processes to different types of cancer cells has received increased research focus [2, 3]. Several imaging techniques are currently in use for this purpose, including radiography, scintigraphy, positron emission tomography (PET), single-photon emission computed tomography (SPECT), and magnetic resonance (MR) [4, 5].

For more than 30 years, MR has been a revolutionary diagnostic tool, used in a wide range of settings from the central nervous system to cardiomyopathies and cancers. MR imaging (MRI) can outline molecular and cellular processes with high spatial resolution. Typically, MRI of body tissues is achieved via contrast visualization of the protons (¹H) of water, which are present in high abundance in living systems. This can be extended to MR spectroscopy (MRS), which can further differentiate between less abundant, carbon-bearing, biological metabolites in vivo utilizing ¹Hs of these compounds [6, 7]. However, despite its usefulness in imaging whole body tissues, ¹H MRS has low spectral resolution and poor sensitivity for these less abundant metabolites. In addition,¹³C MRS is increasingly difficult, in comparison to ¹H MRS, in that both the gyromagnetic ratio (approximately 25 % of ¹H) and natural abundance (1.1 % of ¹H) are significantly lower, making the detection of carbon-bearing compounds difficult [8, 9]. The low spectral resolution of ¹H MRS for metabolites can be addressed by using ¹³C-enriched compounds, and with this direct ¹³C MRS, metabolic processes can be traced, utilizing enriched tags on specific carbons in a given metabolite [10]. While enrichment of molecules in ¹³C can also moderately address the sensitivity limitation of MRS, recent work in hyperpolarization (HP) provides a means of dramatically increasing sensitivity and enhancing signals, well beyond that of the equilibrium state obtained via MRS. [11, 12]. The focus of this review will be the introduction of this approach in the setting of cancer metabolism, delineating probes of interest, which have been applied to study metabolic processes in vivo.

Obtaining a hyperpolarized probe

In MR, a desired target is placed in a magnetic field where the nuclear spins of molecules are aligned with or against the direction of the magnetic field. The nuclear spins have thus different energies, and an MR signal is detected upon relaxation of nuclear spins of higher energy. At thermal equilibrium, the number of spins aligned with the magnetic field nearly equals the number of spins opposing the direction of the magnetic field. Thus, at thermal equilibrium, spin polarization is in the order of >0.0005 % resulting in a limited signal. Signal increases on the order of 100,000-fold can be achieved by hyperpolarizing the system via the redistribution of the spin population levels found at equilibrium [10, 13]. There are several techniques that have been used to achieve hyperpolarization of various nuclei: spin-exchange optical pumping of ³He and ¹²⁹Xe, parahydrogen-induced polarization (PHIP), and dissolution dynamic nuclear polarization (DNP) [11,14, 15]. Both PHIP and DNP techniques can polarize biologically relevant nuclei like ¹³C and ¹⁵N, although there is a wider range of molecules that can be targeted for hyperpolarization using dissolution DNP [14, 16–18].

The goal of DNP is the transfer of polarization from highly polarized unpaired electron spins to the nuclear spins of a desired target compound. This is achieved by applying an external magnetic field to a free-radical agent in order to polarize electron spins, followed by saturating the electron spin resonance via microwave irradiation in order to obtain polarization transfer. The free-radical agent is generally a stable organic compound that is compatible with aqueous buffers, which are used as solvent in order to obtain a homogeneous distribution of the radical [13]. Nearly 100 % of the electrons on the free-radical agent are polarized when the free-radical/solvent mixture is subjected to high magnetic fields (≥3.3 T) followed by rapid freezing to 1 K using liquid helium in order to obtain a sample frozen to an amorphous state, which is necessary for retention and transfer of polarization [18]. For biological applications, after transfer of electron spin polarization to the nuclei of interest has occurred, the preparation must exist in solution, which can be achieved utilizing a dissolution process in which the solid sample is rapidly melted via injection of a hot solvent, typically a biologically compatible buffer, into the frozen sample [13]. The dissolution process results in a liquid sample at room temperature, while still preserving the enhanced polarization obtained by the microwave irradiation of the frozen sample [8]. Additionally, the use of chelating agents (e.g., EDTA) with the solvent to eliminate trace metals and more recently the use of gadolinium (Gd) chelates with the DNP sample have been used to further enhance and retain polarization in the liquid sample, albeit with caution over potential toxic effects when applied in vivo and the potential for loss of hyperpolarization due to T ₁ shortening [11, 19, 20]. More in-depth exploration of the technical aspects of probe development has been previously reviewed [8, 11].

Considerations in probe selection and current research

The usefulness of a molecule for hyperpolarized MRS is dependent on the polarization lifetime of the nucleus of interest, and this property is determined by the spin-lattice relaxation constant (T₁) [21]. Dipolar coupling, the magnetic field range, and molecular size can also affect the T₁ of a given nucleus. In general, high magnetic fields and large molecular weights decrease the T₁. Dipole-dipole coupling of ¹³C with ¹H is common in biologically relevant molecules, and it shortens relaxation times; therefore, carbon atoms directly bound to ¹H are generally not useful as probes for HP. For example, all carbons present in glucose (an important substrate in cancer cells) have relaxation times shorter than 2 s [22]. On the other hand, carbonyl carbons of biologically relevant molecules generally have T₁’s above 20 s even at high magnetic fields like [1-¹³C] pyruvic acid, which has relaxation times of 67, 48, and 44 s at 3, 11.7, and 14.1 T, respectively [23–25]. Even carbons that are less oxidized than carbonyls, like the hemi-ketal in [2-¹³C] fructose have T₁’s one order of magnitude higher than glucose carbons. Short spin-lattice relaxation times can sometimes be increased by deuterium enrichment of the sample. With this technique, protons that are directly bound to carbons are exchanged for deuterium atoms which results in the reduction of dipole-dipole relaxation, further preserving the hyperpolarized state [26]. This has resulted in increased T₁’s of ¹³C nuclei in molecules such as glucose (T₁ increased from 2 s to 10–14 s), providing the possibility of utilizing them in future metabolic studies [27–29]. Despite the effect of deuterium enrichment, research efforts have largely focused on developing carbonyl-bearing molecules as molecular imaging probes. The focus of this review is to introduce representative compounds relevant to metabolism that are characteristic of cancer tissue and have been applied in the work of multiple groups: aerobic glycolysis, glutamine addiction, and the redox state.

Pyruvate and aerobic glycolysis

Of particular interest to cancer metabolism is the increased conversion of glucose to lactate as a result of modulated aerobic glycolysis. This process, also known as the Warburg effect, is characteristic of many tumors with altered metabolism where pyruvate generated from glucose metabolism via glycolysis is preferentially converted to lactate by lactate dehydrogenase (LDH) as opposed to entering the tricarboxylic acid cycle [1]. With this phenotype, cancer cells show a preference for lactate fermentation even in the presence of oxygen, thus bypassing oxidative respiration for ATP generation. Because of this, pyruvate has been the preferred probe for HP MRS research since it is an intermediate metabolite in pathways characteristic of aberrant metabolism in cancer cells, including increased lactate production as a result of aerobic glycolysis where detection of HP pyruvate-derived lactate can be used as a marker for cancer and response to treatment [30, 31] as well as an intermediate in amino acid metabolism (e.g., interconversion to alanine via transamination with glutamate) (Fig. 1). In addition, as mentioned before, carbonyl carbons in pyruvate have long relaxation times where even the methyl carbon can have T₁’s above 50 s after deuterium enrichment [32]. The interconversion of pyruvate to lactate has been exploited for MRI by using [1-¹³C] pyruvate and detecting the accumulation of increased lactate in cancerous tissue as compared to surrounding benign tissue. Increased conversion of pyruvate to lactate and alanine has been demonstrated to precede MYC-driven tumorigenesis by using HP [1-¹³C] pyruvate in murine models [33]. Furthermore, in the same study, a decrease in the flux of alanine was observed at the tumor stage while a decrease in lactate conversion was indicative of tumor regression [33]. In transgenic adenocarcinoma of mouse prostate (TRAMP) models, in vivo studies using HP [1-¹³C] pyruvate demonstrated that hyperpolarized pyruvate and its metabolic products can be used non-invasively and with high specificity to obtain a profile of the histologic grade of prostate cancers [34]. In vivo imaging following hyperpolarized pyruvate has also been used to evaluate the role of glutaminase and LDH in human lymphoma models [35] as well as to elucidate metabolism of pyruvate in breast cancer [36] and renal cell carcinoma with treatment [30, 37].

http://static-content.springer.com/image/art%3A10.1186%2Fs40170-015-0136-2/MediaObjects/40170_2015_136_Fig1_HTML.gif

Flux of hyperpolarized [1-¹³C] pyruvate to [1-¹³C] lactate in prostate regions. a MR image from patient with prostate cancer showing regions of cancerous tissue and surrounding normal tissue. b–d Localized dynamic hyperpolarized [1-¹³C]pyruvate and [1-¹³C]lactate spectral from voxels overlapping the contralateral region of prostate (turquoise), a region of prostate cancer (yellow), and a vessel outside the prostate (green). Adapted with permission from ref. [43]

Early work that utilized HP pyruvate to assess the response of tumors to treatment was conducted in mice xenografted with EL-4 lymphoma cells and treated with etoposide, a topoisomerase inhibitor that causes rapid cell death [38, 39]. In this study, tumor necrosis was correlated to a decrease in the flux of hyperpolarized lactate which was suggested to be due to a decrease in NAD+ and NADH in the intracellular pool as well as loss of LDH function. More recently, HP [1-¹³C] pyruvate has been used as a biomarker to evaluate early response to radiation therapy in glioma tumors by observing a decrease in hyperpolarized lactate suggested to be a result of changes in tumor perfusion which can be detected between 24 and 96 h following treatment [40, 41]. HP [1-¹³C] pyruvate has also been used to detect early response to temozolomide (TMZ) treatment on human glioblastoma rat models [42]. The study successfully showed for the first time detection of response to TMZ therapy 1 day after TMZ administration. The continued reports on using HP pyruvate as an imaging probe for assessing treatment response indicate the potential of the compound to become a standard in the field. Moreover, these studies demonstrate the usefulness of HP [1-¹³C] pyruvate as a tool for early assessment of therapy response, which can improve treatment selection at the clinical level. Pyruvate has also been validated as a metabolic imaging marker for use in humans [43]. In a two-phase study, patients with biopsy-proven prostate cancer of various histological grades were injected with HP [1-¹³C] pyruvate. In the first phase, a maximum dose level was determined to establish pharmacological safety of the HP probe while still injecting enough pyruvate to allow visualization. This addressed one of the major challenges faced in translating HP MRI to clinical applications: the potential toxicity of compounds that must be injected into patients. In the second phase, metabolism of pyruvate was visualized in real time and differences in the ratio of [1-¹³C] lactate to [1-¹³C] pyruvate between identified cancerous regions and normal tissue regions were successfully observed (Fig. 1a–d). [1-¹³C] lactate in regions that did not contain tumor was not detected, confirming previous biopsy and preclinical studies that demonstrated low flux of [1-¹³C] pyruvate to lactate and low concentrations of lactate in benign prostate tissues [44, 45]. Preliminary results indicated the possibility of detecting previously unobserved cancerous regions by HP [1-¹³C] pyruvate, later confirmed to be Gleason 4+3 cancer by biopsy, though further investigation into the relationship between grade and metabolism in prostate cancer patients is needed. While there are challenges associated with translation to clinical use for HP [1-¹³C] pyruvate, the first in human study demonstrated the feasibility of hyperpolarization technology as a safe diagnostic tool and provides the potential for utilizing this approach in preclinical models with direct translation to the clinic.

Glutamine metabolism

Glutamine is an amino acid that plays an important cellular role as nitrogen donor in the form of an amide group for purine and pyrimidine biosynthesis, leaving a glutamate molecule in the process although glutamine can also be converted to glutamate by glutaminase in a reaction independent of nucleotide biosynthesis. Glutamate is the primary nitrogen donor for the biosynthesis of non-essential amino acids. Transaminases catalyze the transfer of the amine group from glutamate to α-ketoacids to synthesize alanine, aspartate (precursor for asparagine), serine (precursor for glycine and cysteine), ornithine (precursor for arginine), and proline which is derived from the glutamate carbon backbone. Glutamine is considered a non-essential amino acid as it can be recycled from glutamate and ammonia in a reaction catalyzed by glutamine synthetase; however, some cancer cells show increase consumption of glutamine and are unable to grow in the absence of exogenous glutamine [46, 47]. This metabolic characteristic of cells to require exogenous glutamine for growth has been termed “glutamine addiction” and has generated extensive research interest as an indicator of development of cancerous tissues [48]. In particular to the field of HP MRI, the conversion rate of glutamine to glutamate (Fig. 2) was explored in hepatocellular carcinoma (HCC) using a [5-¹³C] glutamine probe (Fig. 2) [49]. Using the ratio between [5-¹³C] glutamine and [5-¹³C] glutamate, it was demonstrated that HCC cells convert glutamine at a higher rate than normal cells supporting the notion of glutamine addiction. One important aspect of this study was the choice of [5-¹³C] glutamine as a probe as opposed to [1-¹³C] glutamine, which has a longer T₁ (16.1 vs. 24.6 s at 9.4 T) [49, 50]. [5-¹³C] glutamine was selected because the chemical shift change obtained from [1-¹³C] in glutamine and glutamate is far too small, which could prevent proper identification and quantification of the peaks. This highlights the importance of understanding not only the target compound to be hyperpolarized but also the metabolic products to be detected and their resulting spectra in MR. This is further emphasized with studies that demonstrate the usefulness of [1-¹³C] glutamine as a source for [1-¹³C] glutamate in order to follow the metabolism of α-ketoglutarate to observe the metabolic state of the TCA cycle in transformed cells [51]. Furthermore, [1-¹³C] α-ketoglutarate has been hyperpolarized and used to visualize other metabolic events involving [1-¹³C] glutamate such as mutations in IDH1 expression in glioma tumors and pathways dependent on hypoxia-inducible factor (HIF) [51–53]. More recently, [5-¹³C] glutamine has been used to visualize the metabolism of liver cancer in vivo and in vitro, as well as the treatment response of prostate cancer cells in vitro [54]. Based on the promise of glutamine as a biomarker for cancer diagnosis and treatment response, extending the spin-lattice relaxation time of the [5-¹³C] glutamine has been researched and successfully accomplished. The facile synthesis of [5-¹³C-4-²H₂] glutamine has been reported, and its study showed that by relying on the effect of deuterium enrichment to lessen dipolar coupling effects, the T₁ of [5-¹³C] glutamine could be increased from approximately 15 to 30 s [55]. Visualization of real-time conversion of glutamine to glutamate in SF188 cells was achieved using this probe, demonstrating the promise of [5-¹³C-4-²H₂] glutamine as a probe for molecular imaging of metabolic events in real time. Further investigation of this probe applied to in vivo preclinical models will lay the foundation for its clinical translational potential in the future.

http://static-content.springer.com/image/art%3A10.1186%2Fs40170-015-0136-2/MediaObjects/40170_2015_136_Fig2_HTML.gif

Metabolism of [5-¹³C] glutamine to [5-¹³C] glutamate. a Time-dependent spectral data following conversion of [5-¹³C] glutamine to [5-¹³C] glutamate. The signals are from ¹³C-enriched [5-¹³C]glutamate at 181.5 ppm and [5-¹³C]glutamine at 178.5 ppm and from natural abundance ¹³C label in [1-¹³C]glutamate at 175.2 ppm and [1-¹³C]glutamine at 174.7 ppm. b Plot of the ratio of the signal intensities of [5-¹³C]glutamate/[5-¹³C]glutamine showing the ratio in hepatoma cells (shaded circle), cell lysate (square), and control (triangle). These results demonstrated that hepatoma cancer cells convert glutamine to glutamine at a higher rate than normal cells. Adapted with permission from ref. [49]

Dehydroascorbate as a redox sensor

Reactive oxygen species (ROS) like the hydroxyl radical, superoxide, and hydrogen peroxide have been shown to cause DNA damage and can lead to mutations that transform normal cells into cancerous cells [56]. The reduction/oxidation (redox) state, which is dependent on the balance between oxidizing equivalents like ROS and reducing cofactors, can provide insight into the physiological condition of the cell with respect to potential cancer transformations. Furthermore, the presence of ROS in tissue has been implicated to be a factor in developing resistance to radiation therapies [57]. During oxidative stress (i.e., when there is an increase in ROS), redox homeostasis is maintained by the action of antioxidant compounds, such as ascorbate (or vitamin C, VitC), which can scavenge for ROS and reduce the compounds to rid the cells of damaging agents [58]. In this process, ascorbate that is available to cells in high concentrations can be oxidized to dehydroascorbate (DHA) while reducing ROS. DHA can then be transported into the cell where DHA is reduced back to ascorbate resulting in a process of recycling ascorbate and DHA (Fig. 3) [59]. In this sense, the ratio of DHA to ascorbate can be used as a molecular marker to investigate the redox state and thus the physiological state of tissues. Additionally, conversion of DHA to ascorbate can be enzymatically catalyzed in an NADPH-dependent manner or via oxidation of glutathione (GSH) to glutathione sulfide (GSSG); thus, visualization of ascorbate/DHA metabolism offers a method for probing in vivo metabolism of NADPH as well as determination of GSSG to GSH ratio, both of which have been implicated to be indicators of oxidative stress in the cells, particularly for neurodegenerative, cardiovascular, and cancer diseases [60–62]. Hyperpolarized [1-¹³C] DHA was successfully used in murine models to detect increased reducing capacity in prostate cancer with the purpose of developing a non-invasive, early diagnostic tool for improving selection of treatment therapies [62, 63]. DHA demonstrates a relatively long T₁ at clinically relevant field strengths (>50 s at 3 T) and adequate chemical shift separation between it and its metabolic product ascorbate (δ = 3.8 ppm). Increased reduction of HP [1-¹³C] DHA to ascorbate was observed in tumor tissue compared to normal tissue as well as other metabolic organs (Fig. 3). This was additionally demonstrated in lymphoma cells, further supporting the potential for using DHA as a probe in living systems [64]. A following study validated these results, and the correlation between increased DHA reduction and glutathione was established in vivo, thus showing the utility of [1-¹³C] DHA as a molecular imaging probe to detect events that go beyond the direct metabolism of DHA [63]. Notwithstanding the potential of HP DHA as a diagnostic probe, the toxicity of DHA remains to be validated. Earlier studies on mammalian cells showed DHA toxicity starting at 10 mM, while a study carried on rats demonstrated neurological effects of DHA starting at injections of 50 mg/kg [65, 66]. However, as outlined above, successful use of DHA injections in rats and mice for hyperpolarization has been demonstrated without reported side effects on the animals. More research is needed to determine the parameters regarding the toxicity of DHA in larger animal models using pure formulations to assess its potential for clinical trials. Further work in DHA could demonstrate its applicability for the study of ROS and redox changes in model systems.

http://static-content.springer.com/image/art%3A10.1186%2Fs40170-015-0136-2/MediaObjects/40170_2015_136_Fig3_HTML.gif

Determination of redox state by imaging of HP [1-¹³C] ascorbate (VitC) and [1-¹³C] dehydroascorbate (DHA). Oxidative stress caused by ROS (1.) can be alleviated by oxidation of ascorbate to DHA (2.), and recycling of DHA to ascorbate can occur indirectly with oxidation of glutathione (3.) or directly with oxidation of NADH (4.). The ratio of [ascorbate] to [DHA] has been successfully used in mice models as a biomarker to determine pH in vivo. Adapted with permission from ref. [62]

Other metabolic imaging probes

While the three probes discussed earlier are the most well studied in metabolic events that are characteristic of cancer cells in general, other molecules have been evaluated in their potential to be used as biomarkers. Hyperpolarized bicarbonate (H¹³CO₃⁻) has been successfully used to determine the pH in extracellular matrix of lymphoma tumors in mice, and a correlation between acidic environments and cancer was established [67]. The relaxation times for bicarbonate compounds at 3 T are between 34 and 50 s, which is enough time to detect the rapid conversion of H¹³CO₃⁻ and ¹³CO₂ catalyzed by carbonic anhydrase [23]. The attractive feature of this probe is based on how ubiquitous acidic extracellular environments are to a wide variety of diseases; thus, HP bicarbonate has the potential for clinical translation beyond cancer research, though extensive work will be necessary to generate a preparation which will result in an adequate dose for the clinic [68, 69]. More recently, the potential of α-ketoisocaproate (KIC) as a molecular probe for in vivo detection of branched chain amino acid transaminase (BCAT) has been explored. BCAT catalyzes the conversion of KIC to leucine, and its expression has been suggested to correlate to genetic characterization of certain tumors. In a pilot study, HP α-keto-[1-¹³C]-isocaproate was shown to have a T₁ of 100 s so its metabolism can be sensitively traced for over a minute after injection [70]. In the same study, metabolism of HP [1-¹³C] KIC to [1-¹³C] leucine by BCAT was observed in murine lymphoma tumor tissue but was absent in rat mammary adenocarcinoma with a correlation between BCAT expression and [1-¹³C] leucine signal detection [70]. Additionally, in the same models, [1-¹³C] pyruvate conversion to [1-¹³C] lactate and [1-¹³C] alanine was detected in both types of tumors. These findings show the promise of using [1-¹³C] KIC as a discriminative probe in addition to pyruvate in order to diagnose different types of cancer [71, 72]. Furthermore, the correlation between BCAT expression and [1-¹³C] leucine detection was also shown in rat brain tissue, confirming the usefulness of HP [1-¹³C] KIC in assessing BCAT activity in vivo [73]. Choline is another compound that has been evaluated as a molecular imaging probe since elevated choline and choline-derived metabolites have been correlated by ¹H-MRS imaging to cancer in the brain, breast, colon, cervix, and prostate [74–76]. Despite its potential as a global marker for cancer because of the long T₁ relaxation times that can be achieved with deuterium and ¹⁵N enrichment [77, 78], HP applications of ¹³C enriched choline are limited due to the small change in chemical shifts of choline and choline-derived metabolites as well as its potential toxicity [16, 79, 80]. It has been shown that choline toxicity occurs at doses of 53 mg/kg in mice, although a recent study successfully detected HP ¹³C choline in vivo without adverse effects in rats at doses of 50 mg/kg by using atropine to prevent cholinergic intoxication [81, 82] though metabolic products have been difficult to visualize in vivo. As mentioned earlier, the usefulness of glucose as a probe is limited due to the short relaxation times of all the carbons present in the molecule and although the T₁’s can be increased through deuterium enrichment, the lifetime of the probe remains a hurdle for clinical applications [27, 28]. Thus, fructose (a pentose analog of glucose) has been successfully used as an alternative to probe glycolytic pathways [83] in TRAMP models where differences in HP [2-¹³C] fructose uptake and metabolism was visualized in tumor regions compared to surrounding normal tissues. Like choline, the limiting factor in the usefulness of [2-¹³C] fructose for in vivo studies is in small chemical shifts between the metabolite and its phosphorylated product. Finally, tumor necrosis can be used as a measure of treatment response, particularly early necrosis. HP [1,4-,¹³C] malate has been visualized in lymphoma mice models after injection of HP [1,4-¹³C] fumarate [84]. In normal cells, fumarate has a slow rate of transport into the mitochondria; however, in necrotic cells where the mitochondrial membrane is degraded, fumarase has access to the HP fumarate and its ubiquitous cofactor, water, thus facilitating rapid conversion to malate. Preliminary studies have shown the potential for its use in animal models though further work is required to determine the necessary density of necrotic cells for detection and the timings required for adequate visualization in patients.

Conclusions

The application of hyperpolarized ¹³C imaging has been extensively investigated in preclinical models, and the successful demonstration of HP [1-¹³C] pyruvate in patients with prostate cancer has validated the potential of HP MRI as a safe diagnostic and treatment assessment tool. Application of other probes beyond pyruvate is still in its infancy, particularly because of the need to further study the currently developed models under conditions that are relevant to a clinical setting (i.e., lower magnetic fields) as well as to study the necessary parameters (probe toxicity dose limits, safety limits for rapid injection) to withstand the necessary hurdles to translation. Nevertheless, these vast research findings are promising and indicate an eventual translation to humans. Furthermore, there is a large variety of biologically relevant molecules that have the potential to be hyperpolarized (Fig. 4), and molecular imaging of metabolic events in real time using not only one single probe but a combination of relevant probes could become an invaluable tool in elucidating so far undiscovered metabolic and proteomic interactions that play a role in cancer development and treatment. This gives HP MRI the great potential to revolutionize current molecular imaging technologies.

http://static-content.springer.com/image/art%3A10.1186%2Fs40170-015-0136-2/MediaObjects/40170_2015_136_Fig4_HTML.gif

Metabolic pathways with compounds that can be used as molecular imaging probes for HP MRI. A wide variety of metabolic pathways have already been visualized or have the potential to be visualized using hyperpolarization technology that can be applied to different pathological states of the cell including cardiovascular disease and a large variety of cancers. 1. Metabolism of C1 (red dots) in pyruvate. Theasterisks on selected compounds represent enrichment of ¹³C in the second pass of pyruvate in TCA cycle. 2. Metabolism of C1 (brown dots) in DHA using a pool of NADPH derived from the pentose phosphate pathway. 3. Metabolism of C1 (blue dots) and C5 (green dots) of glutamine. 4. Metabolism of C1 and C4 (purple dots) of fumarate unrelated to TCA metabolites. 5. Metabolism of extracellular bicarbonate (gray dots). MTC1 monocarboxylate transporter 1, MTC4 monocarboxylate transporter 4,System ASC amino acid transporter, GLUTs glucose transporters, DCT dicarboxylate transporter, DHARdehydroascorbate reductase, GR glutathione reductase, GSH glutathione, GSSG glutathione disulfide,LDH lactate dehydrogenase, ALT alanine transaminase, CA carbonic anhydrase, PC pyruvate carboxylase,PDH pyruvate dehydrogenase, CS citrate synthase, GLS glutaminase, GLDH glutamate dehydrogenase,IDH isocitrate dehydrogenase, OGDC oxoglutarate dehydrogenase complex, SCS succinyl CoA synthetase, SQR succinate dehydrogenase, FH fumarate hydratase, MDH malate dehydrogenase, FUMfumarase. Cofactors have been omitted for brevity

Abbreviations

ALT:   alanine transaminase;   BCAT:  branched chain amino acid transaminase;   DHA:  dehydroascorbate;   DNP:  dynamic nuclear polarization;   EDTA:  ethylenediaminetetraacetic acid;   GSH:  glutathione;   GSSG:   glutathione sulfide;   HCC:  hepatocellular carcinoma;   HIF:  hypoxia-inducible factor;   HP:  hyperpolarized/hyperpolarization;   IDH:  isocitrate dehydrogenase;   KIC:  ketoisocaproate;   LDH:  lactate dehydrogenase;   MR: magnetic resonance;   MRI:  Magnetic resonance imaging;   MRS:  magnetic resonance spectroscopy;   NAD(H):  nicotinamide adenine dinucleotide;   NADP(H):  nicotinamide adenine dinucleotide phosphate;   PET:  positron emission tomography;   ROS:  reactive oxygen species;   SPECT:  single-photon emission computed tomography;   TRAMP:  transgenic adenocarcinoma of mouse prostate

References

1. Warburg O. On the origin of cancer cells. Science. 1956;123:309–14.View ArticlePubMed
2. Gatenby RA, Gillies RJ. Why do cancers have high aerobic glycolysis? Nat Rev Cancer. 2004;4(11):891–9.View ArticlePubMed
3. Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science. 2009;324:1029–33.View ArticlePubMed CentralPubMed
4. Shie P, Cardarelli R, Brandon D, Erdman W, AbdulRahim N. Meta-analysis: comparison of F-18 fluorodeoxyglucose-positron emission tomography and bone scintigraphy in the detection of bone metastases in patients with breast cancer. Clin Nucl Med. 2008;33:97–101.View ArticlePubMed
5. Frangioni JV. New technologies for human cancer imaging. J Clin Oncol. 2008;26:4012–21.View ArticlePubMed CentralPubMed
6. Castillo M, Kwock L, Mukherji SK. Clinical applications of proton MR spectroscopy. Am J Neuroradiol. 1996;17:1–16.PubMed
7. Barker PB, Bizzi A, De Stefano N, Gullapalli R, Lin DD. Clinical MR spectroscopy: techniques and applications. Cambridge University Press; 2009.
8. Comment A, Merritt ME. Hyperpolarized magnetic resonance as a sensitive detector of metabolic function. Biochemistry. 2014;53:7333–57.View ArticlePubMed
9. Rider OJ, Tyler DJ. Clinical implications of cardiac hyperpolarized magnetic resonance imaging. J Cardiov Magn Reson. 2013;15:93.View Article
10. Golman K, Olsson LE, Axelsson O, Månsson S, Karlsson M, Petersson JS. Molecular imaging using hyperpolarized 13C. Br J Radiol. 2003;76 Suppl 2:S118–S27.View ArticlePubMed

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sjwilliamspa commented on Is the Warburg effect an effect of deregulated space occupancy of methylome?

Is the Warburg effect an effect of deregulated space occupancy of methylome? Larry H. Bernstein and Radoslav Bozov, …

It would be an interesting figure, although I am not sure anyone has been able to measure it, is the spatial distribution of lactate and pyruvate over the tumor as a function of diffusion distance such as a heat map to see if pyruvate and lactate levels have a gradiant over a solid tumor. I am not sure it would but interesting to see where tumor cells, which undergo Warburg type metabolic phenotype actually exist, if it is a function of angiogenesis or a function of the proliferative capacity of cells in situ.

Response by LHB…

Radoslav Bozov has repeatedly referred to the real problem of space/time in the required experimental view that is intractable, as seen by Erwin Schroedinger.  It is confounded by
the restrictions imposed by research, and to an extent also the dilemma of location and velocity.

I think it is to an extent also inherent in the modern revelations of autophagy and apoptosis that were not part of the view in the mid 20th century.  However, the work of B. Chance led to a substantially better understanding of the hydride transfer in the NAD/NADH.  What is overlooked is the important role cited by NO Kaplan of NADPH/NADP vs NADH/NAD associated with synthetic and, alternatively, catabolic processes in the cell. What role the pyridine nucleotide transhydrogenase would play is anyones guess.   In any case the proliferation of malignant cells is dependent on NADPH.  This would limit the NAD/NADH related reactions. The effect in the cytoplasm is PYR –> LAC, with generation of NAD from NADH.  In addition, the type of isoenzyme favored should be consequential.  For instance, the M-type LDH does not form an abortive ternary complex LDH*NAD+*PYR. In addition, Bernstein, Everse and Grisham showed that in cancer there is an aberrant cytoplasmic MDH.

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