Archive for the ‘Imaging-based Cancer Patient Management’ Category

Breast Cancer: Genomic profiling to predict Survival: Combination of Histopathology and Gene Expression Analysis

Posted in Bio Instrumentation in Experimental Life Sciences Research, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, CANCER BIOLOGY & Innovations in Cancer Therapy, Computational Biology/Systems and Bioinformatics, Genome Biology, Genomic Testing: Methodology for Diagnosis, Imaging-based Cancer Patient Management, Medical and Population Genetics, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Pharmacogenomics, Population Health Management, Genetics & Pharmaceutical, Proteomics, tagged breast cancer, gene expression, genome, Image analysis, Science Translational Medicine on December 24, 2012| 9 Comments »

Breast Cancer: Genomic profiling to predict Survival: Combination of Histopathology and Gene Expression Analysis

Reporter: Aviva Lev-Ari, PhD, RN

• 

Some assays that gauge cancer-related signatures can’t factor in tissue architecture, while other assessments that are good at gauging tissue architecture, provide mostly qualitative tumor data. To reconcile these differences, researchers led by Yinyin Yuan of Cancer Research UK decided to combine histopathological and gene expression analysis to show that quantitative image analysis of the cellular environment inside tumors can bolster the ability of genomic profiling to predict survival in breast cancer patients. This approach, too, though, has its limitations.

For instance, molecular assays that gauge cancer-related signatures are challenged by their inability to factor in tissue architecture and the results are confounded by genomic information from the different types of cells inside the tumor other than cancer cells. Meanwhile, traditional histopathological assessments are good at gauging tissue architecture and differentiating cellular heterogeneity, but mostly provide qualitative tumor data and are too time consuming to be applied in large-scale studies.

Recognizing these weaknesses, researchers led by Yinyin Yuan of Cancer Research UK decided to combine histopathological and gene expression analysis to show that quantitative image analysis of the cellular environment inside tumors can bolster the ability of genomic profiling to predict survival in breast cancer patients. “All technologies have some sort of weakness. That’s why when we combined two types of assays — image and microarray — we get a more reliable readout,” Yuan says.

As they report in Science Translational Medicine, Yuan and her colleagues gathered histopathological information from hematoxylin and eosin-stained images as well as gene expression and copy-number variation data on a discovery set of 323 samples and on a validation set of 241 samples from patients with estrogen receptor-negative breast cancer. Using the discovery sample set, the investigators developed an image-processing method to differentiate the cells inside tumor samples as cancerous, lymphocytic, or stromal. They then tested this technique on the validation sample.

Once Yuan and colleagues had an accurate picture of the types of cells in the tumor samples, they used image analysis to correct copy-number data — as it is influenced by cellular heterogeneity — and developed an algorithm to determine patients’ HER2 status better than copy-number analysis can.

Using the image-processing method, the researchers stratified the discovery and validation sample sets into lymphocytic infiltration-high and lymphocytic infiltration-low groups — as past studies have suggested that high lymphocytic infiltration is linked to better patient outcomes.

When the image analysis was compared to the pathological scores of the samples, the discovery set showed no difference in patient outcomes, but the assessments disagreed with regard to the outcomes of the lymphocytic infiltration-low group in the validation cohort.

Hypothesizing that integrating the gene expression signatures and quantitative image analysis would improve survival prediction, the study investigators combined them. “The gene expression classifier had 67 percent cross-validation accuracy in predicting disease-specific deaths, the image-based classifier had 75 percent, and the integrated classifier reached 86 percent,” the study authors write.

Finally, Yuan and her colleagues applied the image analysis to develop a quantitative score that determines whether specific types of cells are tightly clustered — a high score — or are randomly scattered — a low score. In stromal cells, this approach could discern that breast cancer patients with a high or low score had a “significantly better outcome” than patients whose scores fell in the medium range.

Ultimately, Yuan and her colleagues show that their image processing avoids the biases of manual pathological assessments and accurately quantifies cellular composition and tissue architecture not accounted for by molecular tests. The researchers’ computational approach is also faster than traditional pathological techniques. “These two sets of samples can be done in a day,” Yuan says.
According to the study authors, the limitation of the image processing technique is, of course, that it requires matched molecular and image data.

Turna Ray is the editor of GenomeWeb’s Pharmacogenomics Reporter. She covers pharmacogenomics, personalized medicine, and companion diagnostics. E-mail her here or follow her GenomeWeb Twitter account at @PGxReporter.

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http://www.genomeweb.com/clinical-genomics/integrated-approach

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Predicting Tumor Response, Progression, and Time to Recurrence

Posted in Bio Instrumentation in Experimental Life Sciences Research, Biological Networks, Biomarkers & Medical Diagnostics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Disease Biology, Gene Regulation and Evolution, Genome Biology, Genomic Testing: Methodology for Diagnosis, Imaging-based Cancer Patient Management, Signaling & Cell Circuits, Small Molecules in Development of Therapeutic Drugs, Technology Transfer: Biotech and Pharmaceutical, tagged Bhatia, Biology, Cancer - General, DNA, DNA Adduct, Dror Nir, medicine, Methylation, microarray, Prosvue PSA, Remission, research, silencing, Treatment Response on December 20, 2012| 7 Comments »

A perspective on where we are on carcinogenesis, cancer variability and predictors of time to recurrence and future behavior

Author: Larry H Bernstein, MD, FCAP

Word Cloud by Daniel Menzin

I.     Background

In “Tumor Imaging and Targeting: Predicting Tumor Response to Treatment: Where we stand? “ ( Dec 13, 2012) Dr. Ritu Saxena  attempts to integrate three posts and to embed all comments made to all three papers, allowing the reader a critically thought compilation of evidence-based medicine and scientific discourse.

Dr. Dror Nir authored a post on October 16th titled “Knowing the tumor’s size and location, could we target treatment to THE ROI by applying imaging-guided intervention?” The article attracted over 20 comments from readers including researchers and oncologists debating the following issues:

imaging technologies in cancer

• tumor size, and
• tumor response to treatment.

The debate lead to several new posts authored by:

Dr. Bernstein’s (What can we expect of tumor therapeutic response),

Dr. Saxena, the Author of this post’s, (Judging ‘tumor response’-there is more food for thought) and

Dr. Lev-Ari’s post on Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS) http://pharmaceuticalintelligence.com/2012/12/01/personalized-medicine-cancer-cell-biology-and-minimally-invasive-surgery-mis/#comment-5269

The post was a compilation of the views of authors representing different specialties including research and medicine. In medicine: Pathology, Oncology Surgery and Medical Imaging, are represented.

Dror Nir added a fresh discussion in “New clinical results supports Imaging-guidance for targeted prostate biopsy” based on a study of “Artemis”, a system that is adjunct to ultrasound and performs 3D Imaging and Navigation for Prostate Biopsy by Eigen (a complementary post to “Imaging-guided biopsies: Is there a preferred strategy to choose?”).

Image fusion is the process of combining multiple images from various sources into a single representative image. Ultrasound is the imaging modality used to guide Artemis in performing the biopsies. In this study MRI is used to overcome the “blindness” regarding tumor location.  This supports the detection reliability issue made in his “ Imaging-guided biopsies: Is there a preferred strategy to choose?” and  “Fundamental challenge in Prostate cancer screening.”

This makes the case that In the future, MRI-ultrasound fusion for lesion targeting is likely to result in fewer and more accurate prostate biopsies than the present use of systematic biopsies with ultrasound guidance alone.   Nevertheless, we haven’t completed the case for prediction of recurrence, even if we may eliminate the unnecessary consequences of radical prostatectomy.

Let’s look a little further. A discussion opens up more questions for discussion. I just read an interesting related article. The door has opened  wider.

II.               Novel technology to detect cancer in early stages

A. nanoparticles

Researchers have developed novel technology to detect the tumors in the body in early stages with the help of nanoparticles .( Nature Biotechnology).

Cancer cells produce many of the proteins that could be used as biomarkers to detect the cancer in the body but the amount of these proteins is not up to the mark or they may get diluted in the body of the patients making it nearly impossible to detect them in early stages.

This new technology has been developed by the researchers from MIT . Nanoparticles (brown) coated with peptides (blue) cleaved by enzymes (green) at the disease site. Peptides than come into the urine to be detected by mass spectrometry. (Credit: Justin H. Lo/MIT)

In this technology, nanoparticles will interact with the tumor proteins helping to make thousands of biomarkers secreted by the cancer cells. We had this ‘aha’ moment: What if you could deliver something that could amplify the signal?”

• Scientists administered ‘synthetic biomarkers’ having peptides bonded to the nanoparticles and
• the particles interact with the protease enzymes often found in large quantities in cancer cells

as they help them to cut the proteins normally holding the cells in place and to spread in other parts of the body.

Researchers found that the proteases break down hundreds of peptides from the nanoparticles and release them in the bloodstream. These peptides are then excreted in the urine, where the process of mass spectrometry could help to detect such peptides.

These “Synthetic biomarkers” perform three functions in vivo:

1. they target sites of disease,
2. sample dysregulated protease activities and
3. emit mass-encoded reporters into host urine (for multiplexed detection by MS).

According to Bhatia, this biomarker amplification technology could also be used to manage the advancement of the disease and to check the response of the tumors to the drugs.

Reference:

Kwong, G., von Maltzahn, G., Murugappan, G., Abudayyeh, O., Mo, S., Papayannopoulos, I., Sverdlov, D., Liu, S., Warren, A., Popov, Y., Schuppan, D., & Bhatia, S. (2012). Mass-encoded synthetic biomarkers for multiplexed urinary monitoring of disease Nature Biotechnology  http://dx.doi.org:/10.1038/nbt.2464

IIB    Synthetic Nucleosides

J Gong and SJ Sturla published “A Synthetic Nucleoside Probe that Discerns a DNA Adduct from Unmodified DNA” in JACS Communications on web 4/03/2007).  They state that biologically reactive chemicals alkylate DNA and induce structural modifications in the form of covalent adducts that can persist, escape repair, and serve as templates for polymerase-mediated DNA synthesis. Therefore, correlating chemical structures and quantitative levels of adducts with toxicity is essential for targeting specific agents to carcinogenesis.

• DNA adducts are formed at exceedingly low levels.
• Minor lesions may have greater biological impact than more abundant products.
• New molecular approaches for addressing specific low-abundance adducts are needed

They describe the first example of a synthetic nucleoside that may serve as the chemical basis for a probe of a bulky carcinogen-DNA adduct

IIC.  MicroRNAs caused by DNA methylation

Another molecular approach “ A microRNA DNA methylation signature for human cancer metastasis” was published in PNAS [2008;105(36):13556-13561)] by A Lujambio , Calin GA, Villanueva A et al.

Different sets of miRNAs are usually deregulated in different cancers, and some miRNAs are aberrantly methylated and silenced, causing tumorigenesis. The authors

• identified aberrantly methylated and silenced miRNAs that are cancer-specific
• using miRNA microarray techniques.

Functional analyses for the selected genes proved that these miRNAs act on C-MYC, E2F3, CDK6 and TGIF2, resulting in metastasis through aberrant methylation of the miRNAs. The authors suggest that these may be applicable to advance research in the clinical setting.

III.              New methods require advanced mathematical prediction methods

A.  First Case …ProsVue PSA

One of the most elegant papers I have seen in several years  has been published in Clinical Biochemistry (CLB–12-00159), by Mark J. Sarnoa¹ and Charles S. Davis². [¹Vision Biotechnology Consulting, 19833 Fortuna Del Este Road, Escondido, CA 92029, USA (mjsarno@att.net), ²CSD Biostatistics, Inc., San Diego, CA, 4860 Barlows Landing Cove, San Diego, CA 92130, USA (chuck@csdbiostat.com)]

Robustness of ProsVue™ linear slope for prediction of prostate cancer recurrence: Simulation studies on effects of analytical imprecision and sampling time variation.
Keywords: ProsVue, slope, prostate cancer, random variates.
Financial support for the investigation was provided by Iris Molecular Diagnostics

Abstract: Objective: The ProsVue assay measures

• serum total prostate-specific antigen (PSA) over three time points post-radical prostatectomy and
• calculates rate of change expressed as linear slope. Slopes ≤2.0 pg/ml/month are associated with reduced risk for prostate cancer recurrence.

However, an indicator based on measurement at multiple time points, calculation of slope, and relation of slope to a binary cutpoint may be subject to effects of analytical imprecision and sampling time variation.

They performed simulation studies to determine the presence and magnitude of such effects.

Design and Methods: Using data from a two-site precision study and a multicenter retrospective clinical trial of 304 men, they carried out simulation studies to assess whether analytical imprecision and sampling time variation can drive misclassification of patients with stable disease or classification switching for patients with clinical recurrence.

Results:

• Analytical imprecision related to expected PSA values in a stable disease population results in ≤1.2% misclassifications.
• For recurrent populations, an analysis taking into account correlation between sampling time points demonstrated that classification switching across the 2.0 pg/ml/month cutpoint occurs at a rate ≤11%.
• Lastly, sampling time variation across a wide range of scenarios results in 99.7% retention of proper classification for stable disease patients with linear slopes up to the 75th percentile of the distribution.

Conclusions:

• These results demonstrate the robustness of the ProsVue assay and the linear slope indicator.
• Further, these simulation studies provide a potential framework for evaluation of future assays that may rely on the rate of change principle

The ProsVue Assay has been cleared for commercial use by the US Food and Drug Administration (FDA) as “a prognostic marker in conjunction with clinical evaluation as an aid in

• identifying those patients at reduced risk for recurrence of prostate cancer for the eight year period following prostatectomy.”

The assay measures

1. serum total prostate specific antigen (PSA) in post-RP samples and
2. calculates rate of change of PSA over the sampling period,

expressing the outcome as linear slope. The assay is novel in at least a few respects.

• the assay is optimized to identify patients at reduced risk for recurrence.

In order to demonstrate efficacy for this indication, the assay employs the immuno-polymerase chain reaction (immuno-PCR) to achieve sensitivity

• an order of magnitude lower than existing “ultrasensitive” PSA assays.

The improved sensitivity allows quantification of PSA at levels exhibited in stable disease (<5 pg/ml), which have been historically below the

measurement range of ultrasensitive assays.

Secondly, the assay is the first to receive clearance based on

• linear slope of tumor marker concentration versus time post-surgery.
• Specifically, PSA is measured in three samples taken between 1.5 and 20 months post-RP and
• the slope calculated using simple least squares regression.
• The calculated slope is compared to a threshold of 2.0 pg/ml/month with values at or below the threshold associated with reduced risk for PCa recurrence.

Does analytical imprecision present a potential risk for misclassification by driving errors in the calculated slope that result in classification switching?  Since excursions of precision can occur as point sources in single sampling points or in cumulative effect from the three sampling points, the question is worthy of consideration. They carried out studies

• to address these questions specific to ProsVue and also
• provide a potential framework for evaluation of future assays.
• Similarly, does variation in the time at which samples are taken drive errors resulting in classification switching?

Both questions require evaluating the robustness of the ProsVue Assay and are properly presented for clinical chemists and physicians evaluating use of the assay in clinical practice. Furthermore, since future diagnostic assays may employ the rate of change principle, it is important to develop statistical methods to evaluate effects of variation.

The point is that more sophisticated methods are needed to measure scarce analytes associated with risk for eventual clinical events.

• Accurate measurement at post-RP levels to identify patients with reduced risk of recurrence represents a new development.
• Furthermore, measurement of PSA at multiple time points and calculation of rate of change using linear regression extends application of the analyte markedly beyond traditional use.

Such use presents certain questions of variation effects.

Their results indicate that analytical imprecision in the range of concentrations exhibited in patients at reduced risk for recurrence (the focus of the assay) presents no significant risk of misclassification.

• Classification switching in this population occurs at a frequency of ≤1.2%.
• Slopes for recurrent patients and clinical classification are substantively insensitive to analytical variation even in a subpopulation of recurrent patients with slowly rising PSA values.
• Sampling time variation negligibly affects clinical classification for stable disease patients with slopes at and below the 75th percentile.

Table 1. Side-effects and effects on recovery of treatments for newly diagnosed prostate cancer. The Prostate Brachytherapy Advisory Group: http://www.prostatebrachytherapyinfo.net (Photo credit: Wikipedia)

_____________________________________________________________________________________________________

IIIB. Other interesting developments are going to need further development and validation.

For instance, research has been published online in the journal Cancer Cell, reports a cellular component that is involved in mobility of cancer to other body parts and inhibition of which could increase the tumor formation. These investigators worked on various animal models including chicken, zebrafish and mouse, and patient samples and have found a cellular component; Prrx1 that stops the cancer cells from staying in organs.  Epithelial-mesenchymal transition (EMT) is the process that is required by the cancer cells to spread to other organs. This process helps the cells to become mobile and move with the bloodstream. These cells must lose their mobility before attaching to other body parts.

In the final analysis the cells have to lose the component Prrx1 to lose mobility and to become stationary. Researchers wrote, “Prrx1 loss reverts EMT & induces stemness, both required for metastatic colonization.”  Consequently,  Prrx1 has to be turned off for these cells to group together to form other tumours.” It has been found that the tumors with elevated levels of Prrx1 cannot form new tumors.

IIIC.  PXR and AhR Nuclear Receptor Activation

• The primary mechanism of cytochrome P450 induction is via increased gene transcription which typically occurs through nuclear receptor activation.
• The most common nuclear receptors involved in the induction of drug metabolizing enzymes include the pregnane X receptor (PXR), the aryl hydrocarbon receptor (AhR), and the constitutive androstane receptor (CAR) which are known to regulate CYP3A4, CYP1A2 and CYP2B6, respectively.
• An industry survey of current practices and recommendations (Chu et al., (2009) Drug Metab Dispos 37: 1339-1354) indicates 64% of survey respondents routinely use nuclear receptor transactivation assays to assess the potential of test compounds to cause enzyme induction
• ‘Because reporter assays are relatively high throughput and cost effective, they can be a valuable tool in drug discovery.’(Chu V, Einolf HJ, Evers R, Kumar G, et.  (2009) Drug Metab Dispos 37; 1339-1354)
• Luciferase reporter gene assay

No cytotoxicity was observed for any of six compounds at the concentration range tested with the exception of troglitazone for which cytotoxicity was observed at the highest concentration of 50μM.  This data point was excluded in this instance and not used for calculating the Emax or EC50.

In brief, CAR and PXR regulate distinct but overlapping sets of target genes, which include certain phase 1 P450 enzymes (e.g., CYP2B, CYP3A, and CYP2C), phase II conjugation enzymes such as UDP glucuronosyltransferase UGT1A1 and sulfotransferase SULT2A, and phase III transporters such as P-glycoprotein (MDR-1). The AhR receptor has been shown to regulate the expression of CYP1A.

Will this be combined with the other methods for drug selection and prediction of drug free survival?

I have mentioned an improved molecular assay of PSA at the pcg/ml level that is approved for use with an acceptable linear prediction of survival for 8 years post radical prostatectomy.  Then there is a report of a method of measuring nanoparticles in urine, to amplify the signal detected by mass spectrometry. This new technology has been developed by the researchers from MIT and led by Sangeeta Bhatia at MIT. (Novel technology to detect cancer in early stages, Nature Biotechnology, Dec 16, 2012).  There is still another recent report about using gene expression profiles to predict breast cancer, and a number of articles have shown variability in breast cancer types.   I view with reservations until I can see long term predictions of prognosis.

IIID.  Prediction of Breast Cancer Metastasis by Gene Expression Profiles

The report in Cancer Informatics (http://www.la-press.com; open access)  by M Burton, M Thomassen, Q Tan, and TA Kruse is “Prediction of Breast Cancer Metastasis by Gene Expression Profiles: A Comparison of Metagenes and Single Genes.”   The authors state “The diversity of microarray platforms has made the full validation of gene expression  profiles across studies difficult and, the classification accuracies are rarely validated in multiple independent datasets. The individual genes between such lists may not match, but genes with comparable function are included across gene lists. However,  genes can be grouped together as metagenes (MGs) based on common characteristics such as pathways, regulation, or genomic location. Such MGs might be used as features in building a predictive model applicable for classifying independent data.”

Microarray gene expression analysis has in several previous studies been applied to elucidate the relation between clinical outcome and gene expression patterns in breast cancer and has demonstrated improvement of recurrence prediction. In some studies, genes in such profiles might be fully or partially missing in the test data used for validation due to the choice of microarray platform or the presence of missing values associated with a given probe.

To overcome the obstacles, these authors propose that individual genes could be considered part of a larger network such that their expression being controlled by the expression level of other genes or that a group of genes belong to a specific pathway performing a well-defined task. These genes may be controlled by the same transcription factor or located in the same chromosomal region. In fact these groupings have been collected in public databases (the Kyoto Encyclopedia of Genes and Genomes (KEGG), the Molecular Signature Database (MsigDB), the Gene Ontology database (GO)). This could be upregulation or deregulation of pathways associated with metastasis. Metastasis progressionas well as tumor grading (in breast cancer) are associated with accumulated mutations in several genes, leading to amplification or inactivation of genes.

Several studies have defined metagene/gene modules derived from microarray data using various methods such as penalized matrix decomposition which clusters similar genes but without similar expression profiles – hierarchical clustering, correlation, or combining a priori protein-protein interactions with microarray gene expression data defining interaction networks as features. Few studies have attempted to use such predefined gene sets for prediction models.

Their study compared the performance of either metagene- or single gene-based feature sets and classifiers using random forest and two support vector machines for classifier building. The performance within the same dataset, feature set validation performance, and validation performance of entire classifiers in strictly independent datasets were assessed by 10 times repeated 10-fold cross validation, leave-one-out cross validation, and one-fold validation, respectively. To test the significance of the performance difference between MG- and SG-features/classifiers, we used a repeated down-sampled binomial test approach.

They found MG- and SG-feature sets are transferable and perform well for training and testing prediction of metastasis outcome in strictly independent data sets, both between different and within similar microarray platforms.  Further, The study showed that MG- and SG-feature sets perform equally well in classifying independent data. Furthermore, SG-classifiers significantly outperformed MG-classifier when validation is conducted between datasets using similar platforms, while no significant performance difference was found when validation was performed between different platforms.

• The MG- and SG-classifiers had similar performance when conducting classifier validation in independent data based on a different microarray platform.
• The latter was also true when only validating sets of MG- and SG-features in independent datasets, both between and within similar and different platforms.

This study appears to be unique in the same way that the PCa prediction study is unique in that genome-based expression patterns are used to classify and predict metastatic potential.

These studies have the potential to materialize into practice changing behavior.

IIIE. Colon Cancer and Treatment Recurrence

Cancer scientists led by Dr. John Dick at the Princess Margaret Cancer Centre have found a way to follow single tumour cells and observe their growth over time. By using special immune-deficient mice to propagate human colorectal cancer, they found that genetic mutations, regarded by many as the chief suspect driving cancer growth, are only one piece of the puzzle. The team discovered that biological factors and cell behaviour — not only genes — drive tumour growth, contributing to therapy failure and relapse. The findings are published December 13 online ahead of print in Science, are “a major conceptual advance in understanding tumour growth and treatment response” according to Dr. Dick.

[1] only some cancer cells are responsible for keeping the cancer growing.

[2] these kept the cancer growing for long time periods (up to 500 days of repeated tumour transplantation)

[3] a class of propagating cancer cells that could lie dormant before being activated.

[4] the mutated cancer genes were identical for all of these different cell behaviours.

[5] given chemotherapy the long-term propagating cells were generally sensitive to treatment, but dormant cells were not killed by drug treatment.

[6] these became activated andpropagated new tumour.

IV. Related References

Diagnostic efficiency of carcinoembryonic antigen and CA125 in the cytological evaluation of effusions.
M M Pinto, L H Bernstein, R A Rudolph, D A Brogan, M Rosman
Arch Pathol Lab Med 1992; 116(6):626-631 ; ICID: 825503

Medically significant concentrations of prostate-specific antigen in serum assessed.
L H Bernstein, R A Rudolph, M M Pinto, N Viner, H Zuckerman
Clin Chem 1990; 36(3):515-518 ; ICID: 825497

Entropy and Information Content of Laboratory Test Results
R T Vollmer
Am J Clin Pathol.  2007;127(1):60-65.

Abstract

This article introduces the use of information theoretic concepts such as entropy, S, for the evaluation of laboratory test results, and it offers a new measure of information, 1 – S,
which tells us just how far toward certainty a laboratory test result can predict a binary outcome. The derived method is applied to the serum markers troponin I and
prostate-specific antigen and to histologic grading of HER-2/neu staining, to cytologic diagnosis of cervical specimens, and to the measurement of tumor thickness in malignant
melanoma. Not only do the graphic results provide insight for these tests, they also validate prior conclusions. Thus, this information theoretic approach shows promise for
evaluating and understanding laboratory test results.

A map of protein-protein interactions involving calmodulin. Protein-protein interactions are both numerous and incredibly complex, and they can be mapped using the Database of
Interacting Proteins (DIP). This image depicts a DIP map for the protein calmodulin. The interactions with the most confidence are drawn with wider connecting lines. This diagram
highlights one level of complexity involved in understanding the downstream effects of gene regulation and expression.

Related article

• Nanoparticles Enable Earlier Cancer Diagnosis (genesisnanotech.wordpress.com)
• Nanoparticles amplify cancer tumor signals, making them much easier to detect (nextbigfuture.com)
• New nano technology may enable earlier cancer diagnosis (smarteconomy.typepad.com)
• MIT-Harvard Center of Cancer Nanotechnology Excellence: (genesisnanotech.wordpress.com)
• Breakthrough study finds cancer cells hide from treatment (thestar.com)
• Imaging and targeting the tumor & predicting tumor response: Where we stand? (pharmaceuticalintelligence.com)
• Residual Tumor Cells That Drive Disease Relapse after Chemotherapy Do Not Have Enhanced Tumor Initiating Capacity
  (plosone.org)
• Recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes in serous endometrial tumors
  (pharmaceuticalintelligence.com)
• What can we expect of tumor therapeutic response? (pharmaceuticalintelligence.com)
• Exome sequencing of serous endometrial tumors shows recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase
  complex genes (pharmaceuticalintelligence.com)
•  Nanotechnology: Detecting and Treating metastatic cancer in the lymph node
• Phillips, T. (2008) The role of methylation in gene expression. Nature Education 1(1)
• Morgan, H., et al. Activation-induced cytidine deaminase deaminates 5-methylcytosine in DNA and is expressed in pluripotent tissues.
  Journal of Biological Chemistry 2004;279: 52353–52360. http://dx.doi.org:/10.1074/jbc.M407695200
• Suzuki, M., & Bird, A. DNA methylation landscapes: Provocative insights from epigenomics. Nature Reviews Genetics 2008; 9: 465–476
  http://dx.doi.org:/10.1038/nrg2341
• Tamaru, H., & Selker, E. A histone H3 methyltransferase controls DNA methylation in Neurospora crassa. Nature 2001; 414: 277–283.
  doi:10.1038/35104508
•  Hoopes, L. (2008) Genetic diagnosis: DNA microarrays and cancer. Nature Education 1(1)
• Tibshirani, R., et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000; 430: 503–511.
  http://dx.doi.org:/10.1038/35000501
• Campbell, A. M., et al. Make microarray data with known ratios. CBE Life Sciences Education 2007; 6: 196–197.
•  Adams, J. (2008) The complexity of gene expression, protein interaction, and cell differentiation. Nature Education 1(1)
• Stumpf, M. P. H., et al. Estimating the size of the human interactome. Proceedings of the National Academy of Sciences 2008; 105:
  6959–6964. http://dx.doi.org:/10.1073/pnas.0708078105
• Torres-Padilla, M. E., et al. Histone arginine methylation regulates pluripotency in the early mouse embryo. Nature 2007; 445:
  214–218. http://dx.doi.org:/10.1038/nature05458
• Nunes Amaral, L. A. A truer measure of our ignorance. Proceedings of the National Academy of Sciences 2008; 105: 6795–6796.
  http://dx.doi.org:/10.1073/pnas.0802459105

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Tumor Imaging and Targeting: Predicting Tumor Response to Treatment: Where we stand?

Posted in Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, CANCER BIOLOGY & Innovations in Cancer Therapy, Imaging-based Cancer Patient Management, Interviews with Scientific Leaders, Medical Devices R&D Investment, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, tagged antitumor therapy, breast cancer, Cancer - General, cancer genetic marker, Colorectal cancer, Conditions and Diseases, European Association for Research and Treatment of Cancer, free margin, GI cancer, HIFU, histological analysis, histopathology, imaging-based therapy, immunohistochemical, laser capture microdissection, lymph nodes, lymphatic vessel, magnetic resonance, Magnetic resonance imaging, minimally invasive surgery, MIS, MR, NCI, Personalized medicine, Prostate cancer, recurrence of cancer, RO-resection, ROC analysis, ROI, stroma, Sunnybrook Health Sciences Centre, surrogate marker, transurethral ultrasound therapy, tumor response, tumor size on December 13, 2012| 15 Comments »

Tumor Imaging and Targeting: Predicting Tumor Response to Treatment: Where we stand?

Author and curator: Ritu Saxena, Ph.D.

Article ID #9: Tumor Imaging and Targeting: Predicting Tumor Response to Treatment: Where we stand?. Published on 12/13/2012

WordCloud Image Produced by Adam Tubman

 

This post attempts to integrate three posts and to embed all comments made to all three papers, allowing the reader a critically thought compilation of evidence-based medicine and scientific discourse.

Dr. Dror Nir authored a post on October 16^th titled “Knowing the tumor’s size and location, could we target treatment to THE ROI by applying imaging-guided intervention?” The article attracted over 20 comments from readers including researchers and oncologists debating the following issues:

• imaging technologies in cancer
• tumor size, and
• tumor response to treatment.

The debate lead to several new posts authored by:

• Dr. Bernstein’s (What can we expect of tumor therapeutic response),
• Dr. Saxena, the Author of this post’s, (Judging ‘tumor response’-there is more food for thought) and
• Dr. Lev-Ari’s post on Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS) http://pharmaceuticalintelligence.com/2012/12/01/personalized-medicine-cancer-cell-biology-and-minimally-invasive-surgery-mis/#comment-5269

This post is a compilation of the views of authors representing different specialties including research and medicine. In medicine: Pathology, Oncology Surgery and Medical Imaging, are represented.

Dr. Nir’s post talked about an advanced technique developed by the researchers at Sunnybrook Health Sciences Centre, University of Toronto, Canada for cancer lesions’ detection and image-guided cancer treatment in the specific Region of Interest (ROI). The group was successfully able to show the feasibility and safety of magnetic resonance imaging (MRI) – controlled transurethral ultrasound therapy for prostate cancer in eight patients.

The dilemma of defining the Region of Interest for imaging-based therapy

Dr. Bernstein, one of the authors at Pharmaceuticalintelligence.com, a Fellow of the American College of Pathology, reiterated the objective of the study stating that “Their study’s objective was to prove that using real-time MRI guidance of HIFU treatment is possible and it guarantees that the location of ablated tissue indeed corresponds to the locations planned for treatment.” He expressed his opinion about the study by bringing into focus a very important issue i.e., given the fact that the part surrounding the cancer tissue is in the transition state, challenge in defining a ROI that could be approached by imaging-based therapy. Regarding the study discussed, he states – “This is a method demonstration, but not a proof of concept by any means.  It adds to the cacophany of approaches, and in a much larger study would prove to be beneficial in treatment, but not a cure for serious prostate cancer because it is unlikely that it can get beyond the margin, and also because there is overtreatment at the cutoff of PSA at 4.0. I think that the pathologist has to see the tissue, and the standard in pathology now is for any result that is cancer, two pathologists or a group sitting together should see it. It’s not an easy diagnosis.”

“The crux of the matter in terms of capability is that the cancer tissue, adjacent tissue, and the fibrous matrix are all in transition to the cancerous state. It is taught to resect leaving “free margin”, which is better aesthetically, and has had success in breast surgery. The dilemma is that the patient may return, but how soon?” concludes Dr. Larry.

Dr. Nir responded, “The philosophy behind lumpectomy is preserving quality of life. It was Prof. Veronesi (IEO) who introduced this method 30 years ago noticing that in the majority of cases; the patient will die from something else before presenting recurrence of breast cancer. It is well established that when the resection margins are declared by a pathologist (as good as he/she could be) as “free of cancer”, the probability of recurrence is much lower than otherwise. He explains further, “The worst enemy of finding solutions is doing nothing while using the excuse of looking for the “ultimate solution.” Personally, I believe in combining methods and improving clinical assessment based on information fusion. Being able to predict, and then timely track the response to treatment is a major issue that affects survival and costs!

In this discussion my view is expressed, below.

• The paper that discusses imaging technique had the objective of finding out whether real-time MRI guidance of treatment was even possible and if yes, whether the treatment could be performed in accurate location of the ROI? The data reveals they were pretty successful in accomplishing their objective and of course that gives hope to the imaging-based targeted therapies.

• Whether the ROI is defined properly and if it accounts for the real tumor cure, is a different question. Role of pathologists and the histological analysis and what they bring to the table cannot be ruled out, and the absence of a defined line between the tumor and the stromal region in the vicinity is well documented. However, that cannot rule out the value and scope of imaging-based detection and targeted therapy. After all, it is seminal in guiding minimally invasive surgery.

• As another arm of personalized medicine-based cure for cancer, molecular biologists at MD Anderson have suggested molecular and genetic profiling of the tumor to determine genetic aberrations on the basis of which matched-therapy could be recommended to patients.

• When phase I trial was conducted, the results were encouraging and the survival rate was better in matched-therapy patients compared to unmatched patients. Therefore, every time there is more to consider when treating a cancer patient and who knows a combination of views of oncologists, pathologists, molecular biologists, geneticists, surgeons would device improvised protocols for diagnosis and treatment. It is always going to be complicated and generalizations would never give an answer. Smart interpretations of therapies – imaging-based or others would always be required!

To read additional comments, including those from Dr. Williams, Dr. Lev-Ari, refers to:

Knowing the tumor’s size and location, could we target treatment to THE ROI by applying imaging-guided intervention? Author and Reporter: Dror Nir, Ph.D.

Dr. Lev-Ari in her paper linked three fields that bear weight in the determination of Tumor Response to Therapy:

• Personalized Medicine
• Cancer Cell Biology, and
• Minimally Invasive Surgery (MIS)

Her objectives were to address research methodology, the heterogeneity innate to Cancer Cell Biology and Treatment choice in the Operating Room — all are related to the topic at hand: How to deliver optimal care with least invasive intervention course.

Any attempt aimed at approaching this desirable result, called Personalized Medicine,  involves engagement in three strategies:

• prediction of Patient’s reaction to Drug induction
• design of Clinical Trials to validate drug efficacy on small subset of patients predicted to react favorable to drug regimen, increasing validity and reliability
• Genetical identification of patients at no need to have a drug administered if non sensitivity to the drug has been predicted

These method are to be applied to a list of 56 leading Cancer types.

While the executive task of the clinician remains to assess the differentiation in Tumor Response to Treatment, pursuit of  individualized histopathology, as well as tumor molecular, genetic and functional characteristics has to take into consideration the “total” individual patients’ characteristics: age, co-morbidities, secondary risks and allergies to drugs.

In Dr. Lev-Ari’s paper Minimally Invasive Treatment (MIT) is compared with Minimally Invasive Surgery (MIS) applied for tumor resection.  In many cases MIS is not the right surgical decision, yet, it is applied for a corollary of patient-centered care considerations. At present, facing the unknown of the future behavior of the tumor as its response to therapeutics bearing uncertainty related to therapy outcomes.

Forget me not – says the ‘Stroma’

Dr. Brücher, the author of review on tumor response criteria, expressed his views on the topic. He remembers that 10 years ago, every cancer researcher stated – “look at the tumor cells only – forget the stroma”. However, the times have changed, “now, everyone knows that it is a system we are looking at, and viewing and analyzing only tumor cells is really not enough.”

He went on to state “if we would be honest, we would have to declare that all data, which had been produced 8-13 years ago, dealing with laser capture microdissection, would need a rescrutinization, because the influence of the stroma was ‘forgotten’.”

He added, “the surgeon looks at the ‘free margin’ in a kind of reductionable model, the pathologist is more the control instance. I personally see the pathologist as ‘the control instance’ of surgical quality. Therefore, not the wish of the surgeon is important, the objective way of looking into problems or challenges. Can a pathologist always state if a R0-resection had been performed?”

What is the real RO-resection?

There have been many surrogate marker analysis, says Dr. Brücher, and that a substantially well thought through structured analysis has never been done: mm by mm and afterwards analyzing that by a ROC analysis. For information on genetic markers on cancer, refer to the following post by Dr. Lev-Ari’s: Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS)

He also stated that there is no gold standard to compare the statistical ROC analysis to. Often it is just declared and stated but it is still not clear what the real RO-resection is?

He added, “in some organs it is very difficult and we all (surgeons, pathologists, clinicians) that we always get to the limit, if we try interpreting the R-classification within the 3rd dimension.”

Dr. Brücher explains regarding resectability classification, “If lymph nodes are negative it does not mean, lymph nodes are really negative. For example, up to 38% upper GI cancers have histological negative lymph nodes, but immunohistochemical positive lymph nodes. And, Stojadinovic et al have also shown similar observations at el in colorectal cancer. So the 4th dimension of cancer – the lymph nodes / the lymphatic vessel invasion are much more important than just a TNM classification, which unfortunately does often not reflect real tumor biology.”

The discussion regarding the transition state of the tumor surrounding tissue and the ‘free margin’ led to a bigger issue, the heterogeneity of tumors.

Dr. Bernstein quoted a few lines from the review article titled “Tumor response criteria: are they appropriate?, authored by Dr Björn LDM Brücher et al published in Future Oncology in 2012.

• Tumor heterogeneity is a ubiquitous phemomenon. In particular, there are important differences among the various types of gastrointestinal (GI) cancers in terms of tumor biology, treatment response and prognosis.
• This forms the principal basis for targeted therapy directed by tumor-specific testing at either the gene or protein level. Despite rapid advances in our understanding of targeted therapy for GI cancers, the impact on cancer survival has been marginal.
• Can tumor response to therapy be predicted, thereby improving the selection of patients for cancer treatment?
• In 2000, the NCI with the European Association for Research and Treatment of Cancer, proposed a replacement of 2D measurement with a decrease in the largest tumor diameter by 30% in one dimension. Tumor response as defined would translate into a 50% decrease for a spherical lesion
• We must rethink how we may better determine treatment response in a reliable, reproducible way that is aimed at individualizing the therapy of cancer patients.
• We must change the tools we use to assess tumor response. The new modality should be based on empirical evidence that translates into relevant and meaningful clinical outcome data.
• This becomes a conundrum of sorts in an era of ‘minimally invasive treatment’.
• Integrated multidisciplinary panel of international experts – not sure that that will do it.

Dr. Bernstein followed up by authoring a separate post on tumor response. His views on tumor response criteria have been quoted in the following paragraphs:

Can tumor response to therapy be predicted?

The goal is not just complete response. Histopathological response seems to be related post-treatment histopathological assessment but it is not free from the challenge of accurately determining treatment response, as this method cannot delineate whether or not there are residual cancer cells. Functional imaging to assess metabolic response by 18-fluorodeoxyglucose PET also has its limits, as the results are impacted significantly by several variables:

• tumor type
• sizing
• doubling time
• anaplasia?
• extent of tumor necrosis
• type of antitumor therapy and the time when response was determined.

The new modality should be based on individualized histopathology as well as tumor molecular, genetic and functional characteristics, and individual patients’ characteristics, a greater challenge in an era of ‘minimally invasive treatment’.

This listing suggests that for every cancer the following data has to be collected (except doubling time). If there were five variables, the classification based on these alone would calculate to be very sizable based on Eugene Rypka’s feature extraction and classification.

But looking forward, time to remission and disease free survival are additionally important. Treatment for cure is not the endpoint, but the best that can be done is to extend the time of survival to a realistic long term goal and retain a quality of life.

For detailed discussion on the topic of tumor response and comments refer to the following posts:

What can we expect of tumor therapeutic response?

Author: Larry H. Bernstein, MD, FCAP

Judging ‘Tumor response’-there is more food for thought

Reporter: Ritu Saxena, Ph.D.

Additional Sources:

Research articles:

Brücher BLDM  et al. Tumor response criteria: are they appropriate? Future Oncol. August Vol. 8, No. 8, Pages 903-906 (2012).

Brücher BLDM, Piso P, Verwaal V et al. Peritoneal carcinomatosis: overview and basics. Cancer Invest.30(3),209–224 (2012).

Brücher BLDM, Swisher S, Königsrainer A et al. Response to preoperative therapy in upper gastrointestinal cancers. Ann. Surg. Oncol.16(4),878–886 (2009).

Miller AB, Hoogstraten B, Staquet M, Winkler A. Reporting results of cancer treatment. Cancer47(1),207–214 (1981).

Therasse P, Arbuck SG, Eisenhauer EA et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J. Natl Cancer Inst.92(3),205–216 (2000).

Brücher BLDM, Becker K, Lordick F et al. The clinical impact of histopathological response assessment by residual tumor cell quantification in esophageal squamous cell carcinomas. Cancer106(10),2119–2127 (2006).

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