Peritoneal carcinomatosis from ovarian cancer

gdpawel
gdpawel Member Posts: 523 Member
in Ovarian Cancer #1
Peritoneal carcinomatosis from ovarian cancer: chemosensitivity test and tissue markers as predictors of response to chemotherapy

Chiara Arienti (1), Anna Tesei (1), Giorgio M Verdecchia (2), Massimo Framarini (2), Salvatore Virzì (3), Antonio Grassi (3), Emanuela Scarpi (1), Livia Turci (1), Rosella Silvestrini (1), Dino Amadori (1) and Wainer Zoli (1)

Author Affiliations

(1) Biosciences Laboratory, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (I.R.S.T.), Meldola, Italy

(2) Department of Surgery and Advanced Cancer Therapies, Morgagni-Pierantoni Hospital, Forlì, Italy

(3) Department of Surgery, Bentivoglio Hospital, Bologna, Italy

Abstract

Background:

Platinum-based regimens are the treatments of choice in ovarian cancer, which remains the leading cause of death from gynecological malignancies in the Western world. The aim of the present study was to compare the advantages and limits of a conventional chemosensitivity test with those of new biomolecular markers in predicting response to platinum regimens in a series of patients with peritoneal carcinomatosis from ovarian cancer.

Methods:

Fresh surgical biopsy specimens were obtained from 30 patients with primary or recurrent peritoneal carcinomatosis from ovarian cancer. ERCC1, GSTP1, MGMT, XPD, and BRCA1 gene expression levels were determined by Real-Time RT-PCR. An in vitro chemosensitivity test was used to define a sensitivity or resistance profile to the drugs used to treat each patient.

Results:

MGMT and XPD expression was directly and significantly related to resistance to platinum-containing treatment (p = 0.036 and p = 0.043, respectively). Significant predictivity in terms of sensitivity and resistance was observed for MGMT expression (75.0% and 72.5%, respectively; p = 0.03), while high predictivity of resistance (90.9%) but very low predictivity of sensitivity (37.5%) (p = 0.06) were observed for XPD. The best overall and significant predictivity was observed for chemosensitivity test results (85.7% sensitivity and 91.3% resistance; p = 0.0003).

Conclusions:

The in vitro assay showed a consistency with results observed in vivo in 27 out of the 30 patients analyzed. Sensitivity and resistance profiles of different drugs used in vivo would therefore seem to be better defined by the in vitro chemosensitivity test than by expression levels of markers.

Journal of Translational Medicine 2011, 9:94 doi:10.1186/1479-5876-9-94

http://www.translational-medicine.com/content/9/1/94

Comments

  • gdpawel
    gdpawel Member Posts: 523 Member
    #2
    Cancer Cytometrics More Accurate than Molecular Gene Testing
    Clinical Trial Finds Personalized Cancer Cytometrics More Accurate than Molecular Gene Testing

    In the first head-to-head clinical trial comparing gene expression patterns with Personalized Cancer Cytometric testing (also known as “functional tumor cell profiling” or “chemosensitivity testing”), Personalized Cancer Cytometrics was found to be substantially more accurate.

    In a clinical trial involving ovarian cancer patients, patterns of gene expression identified through molecular gene testing were compared with results of Personalized Cancer Cytometric testing (in which whole, living cancer cells are exposed to candidate chemotherapy drugs). Four different genes were included in the molecular part of the study. The four genes were selected as those which researchers believe to have the greatest likelihood of accurately predicting individual patient response to specific anti-cancer drugs.

    Study Results:

    For two of the genes studied, there was no significant correlation between gene expression pattern and patient response. In other words, results for these genes were found to be meaningless. For the third gene studied, there was a 75% correlation between expression and patient response. This means that the gene was 75% accurate when it came to identifying an active drug for that patient. For the fourth gene studied, the accuracy in identifying an active drug was only 25%. In marked contrast, Personalized Cancer Cytometric testing was found by the researchers to be 90% accurate in identifying active drugs for ovarian cancer patients in this study.

    Discussion:

    Molecular testing – that is, testing for gene expression patterns – is widely studied and heavily promoted as a method to identify effective chemotherapy drugs for individual cancer patients. However, most studies of molecular testing carried-out to date show only modest correlation or no correlation between test results and actual patient response. In other words, much work remains to be done before molecular gene testing can be regarded as an accurate tool for chemotherapy selection. And yet in this, first ever, head-to-head study of test accuracy, Personalized Cancer Cytometrics was found to be highly accurate when it came to identifying effective drugs.

    Comparing this study with previous studies:

    Although this was the first head-to-head trial, the accuracy levels found in this trial for Personalized Cancer Cytometric testing are strikingly consistent with those documented in dozens of previous studies, published by respected cancer researchers around the world. In those studies, as in this one, extremely high levels of correlation (in other words, high levels of test accuracy) were found for Personalized Cancer Cytometrics.

    Arienti et al. Peritoneal carcinomatosis from ovarian cancer: chemosensitivity test and tissue markers as predictors of response to chemotherapy. Journal of Translational Medicine 2011, 9:94.
  • carolenk
    carolenk Member Posts: 907 Member
    #3
    gdpawel said:

    Cancer Cytometrics More Accurate than Molecular Gene Testing
    Clinical Trial Finds Personalized Cancer Cytometrics More Accurate than Molecular Gene Testing

    In the first head-to-head clinical trial comparing gene expression patterns with Personalized Cancer Cytometric testing (also known as “functional tumor cell profiling” or “chemosensitivity testing”), Personalized Cancer Cytometrics was found to be substantially more accurate.

    In a clinical trial involving ovarian cancer patients, patterns of gene expression identified through molecular gene testing were compared with results of Personalized Cancer Cytometric testing (in which whole, living cancer cells are exposed to candidate chemotherapy drugs). Four different genes were included in the molecular part of the study. The four genes were selected as those which researchers believe to have the greatest likelihood of accurately predicting individual patient response to specific anti-cancer drugs.

    Study Results:

    For two of the genes studied, there was no significant correlation between gene expression pattern and patient response. In other words, results for these genes were found to be meaningless. For the third gene studied, there was a 75% correlation between expression and patient response. This means that the gene was 75% accurate when it came to identifying an active drug for that patient. For the fourth gene studied, the accuracy in identifying an active drug was only 25%. In marked contrast, Personalized Cancer Cytometric testing was found by the researchers to be 90% accurate in identifying active drugs for ovarian cancer patients in this study.

    Discussion:

    Molecular testing – that is, testing for gene expression patterns – is widely studied and heavily promoted as a method to identify effective chemotherapy drugs for individual cancer patients. However, most studies of molecular testing carried-out to date show only modest correlation or no correlation between test results and actual patient response. In other words, much work remains to be done before molecular gene testing can be regarded as an accurate tool for chemotherapy selection. And yet in this, first ever, head-to-head study of test accuracy, Personalized Cancer Cytometrics was found to be highly accurate when it came to identifying effective drugs.

    Comparing this study with previous studies:

    Although this was the first head-to-head trial, the accuracy levels found in this trial for Personalized Cancer Cytometric testing are strikingly consistent with those documented in dozens of previous studies, published by respected cancer researchers around the world. In those studies, as in this one, extremely high levels of correlation (in other words, high levels of test accuracy) were found for Personalized Cancer Cytometrics.

    Arienti et al. Peritoneal carcinomatosis from ovarian cancer: chemosensitivity test and tissue markers as predictors of response to chemotherapy. Journal of Translational Medicine 2011, 9:94.

    Thanks, Greg, for your
    Thanks, Greg, for your continued support.

    (((hugs)))

    Carolen
  • gdpawel
    gdpawel Member Posts: 523 Member
    #4
    carolenk said:

    Thanks, Greg, for your
    Thanks, Greg, for your continued support.

    (((hugs)))

    Carolen

    The different genes studied in the genetic testing
    The different genes that were studied in the molecular part of the above study were ERCC1, GSTP1, MGMT, XPD and BRCA1. These are putative drug resistance genes. ERCC and XPD are response elements for CDDP repair. BRCA1 is also a response element for DNA damage and part of FANC gene family (a genomic fidelity function).

    GSTP1 is a detoxifying enzyme associated with thiol conjugation (alkylator resistance) while MGMT is the specific enzyme associated with the removal of temozolomide residues from DNA base pairs.

    What the investigators did was to examine the "Target Now" types of targets and compare clinical responses against the results with functional analyses, establishing that when one measures the biology of the disease it provides a more robust prediction of response. The "driver" term is less operative as these genes are not causative of the disease but causative of drug resistance.
  • gdpawel
    gdpawel Member Posts: 523 Member
    #5
    gdpawel said:

    The different genes studied in the genetic testing
    The different genes that were studied in the molecular part of the above study were ERCC1, GSTP1, MGMT, XPD and BRCA1. These are putative drug resistance genes. ERCC and XPD are response elements for CDDP repair. BRCA1 is also a response element for DNA damage and part of FANC gene family (a genomic fidelity function).

    GSTP1 is a detoxifying enzyme associated with thiol conjugation (alkylator resistance) while MGMT is the specific enzyme associated with the removal of temozolomide residues from DNA base pairs.

    What the investigators did was to examine the "Target Now" types of targets and compare clinical responses against the results with functional analyses, establishing that when one measures the biology of the disease it provides a more robust prediction of response. The "driver" term is less operative as these genes are not causative of the disease but causative of drug resistance.

    How Genetics Could Transform Ovarian Cancer Treatment?
    By Dr. R. Stephanie Huang
    University of Chicago Department of Medicine

    As a pharmacist, Dr. R. Stephanie Huang’s main concern is how drugs affect patients. Currently, Dr. Huang is exploring how genetics determine why some people respond to certain types of chemotherapy while others do not. For example, one-quarter of women with ovarian cancer develop a platinum resistant cancer. The reason that some people, or some tumors, are resistant to platinum therapies may have its roots in genetics. This area of personalized medicine is expanding as genetic sequencing technology becomes faster and cheaper.

    When studying the effect of genetics on anti-cancer treatments, two types of genetic variations need to be considered. The first, germline variations, encompass a person’s genetic code including any heritable mutations. The second, somatic variations, are mutations that are not heritable, such as genetic changes caused by exposure to chemicals. Dr. Huang’s research is focusing on the germline genetic variations, since these may predispose an individual to various treatment-related toxicities. Furthermore, since all cancer cells derive from normal cells, certain germline variations could be linked to both the likelihood of developing ovarian cancer and response to treatments.

    “One challenge is to differentiate between what we know and what we can do about it. I’m working to make genetics useful in terms of making fully informed decision about what works for each patient,” says Dr. Huang.

    Dr. Huang recently published a paper looking for differences among individuals in their normal DNA code, known as germline genetic variations, that predicted platinum sensitivity in women with ovarian cancer. To do this, she utilized hundreds of cell lines (specific cells bred in a laboratory and able to duplicate indefinitely so that they can be used for research purposes) evaluated by the International HapMap Consortium. This international collaboration is aimed at obtaining all common genetic variations in the world’s human populations and making them publicly available for research. Each of the cell lines is derived from an individual’s blood; information about their ethnicity is captured as well.

    The cell lines were treated in her lab with various chemotherapeutic agents to determine each individual’s sensitivity to drugs. Dr. Huang then did genetic analyses between each person’s genomic code and his or her sensitivity to the drug. Her goal was to identify germline genetic variations that could be used to predict chemotherapy response. These cell-based findings were then evaluated in 400 women with ovarian cancer who had undergone the related drug treatment. She found that there may be some genetic variations that affect overall survival, progression free survival or response to chemotherapy. Unfortunately, the data was not able to be validated in a larger scale analysis, but leaves the door open for continued research.

    The Huang lab is also examining the differences in germline variations and somatic mutations to see which of those are cancer-related. To do so, Dr. Huang and her collaborators are utilizing data from the Cancer Genome Atlas (TCGA).

    TCGA is a multi-year, multi-cancer study funded by the National Cancer Institute to understand the genetics of certain cancers, including ovarian. Preliminary ovarian cancer results were released earlier this year, showing that there are multiple genetic mutations in each case of ovarian cancer, and that each tumor may contain a unique combination of mutations. This poses a significant challenge for individualized ovarian cancer management.

    Dr. Huang is working with a team to use TCGA’s information in her research. She is looking at the difference between a person’s genetic code and that of their tumor to establish links between germline genetic variation and somatic mutations in the tumor. Then she can determine genome markers. For example, we know that germline mutations in the BRCA mutations increase a woman’s risk for breast and ovarian cancer. Those women are also more responsive to certain drugs, like PARP inhibitors.

    There may be other genetic markers that will help providers understand a woman’s sensitivity to drugs. In the future, each woman may receive a personalized cocktail of drugs intended to target her tumor’s specific mix of genetic mutations. Researchers like Dr. Huang are trying to determine which genetic variations respond best to a particular treatment.

    In order to validate any treatment protocols, researchers would need to find the exact genetic variation as well as understand the patient’s overall genetic makeup. Then researchers would collect the patient’s blood, establish cell lines and link the patient’s treatment, outcome, genetics and tumor genetics to determine which treatments will work best. Currently, Dr. Huang is doing this for toxicity to chemotherapy in head, neck and ovarian tumors, and is seeing some patterns.

    “The technology in genomic sequencing is fairly mature, but it’s going to get faster and cheaper,” says Dr. Huang. “We will soon have the ability to look at every individual’s genome. Our challenge is to translate scientific discovery into implementation, which takes a lot of data, and a lot of patient participation.” As this field evolves, so might treatment options for women with ovarian cancer.

    Source: Ovarian Cancer National Alliance Periodical of Progress Volume 5 October 2011
  • gdpawel
    gdpawel Member Posts: 523 Member
    #6
    gdpawel said:

    How Genetics Could Transform Ovarian Cancer Treatment?
    By Dr. R. Stephanie Huang
    University of Chicago Department of Medicine

    As a pharmacist, Dr. R. Stephanie Huang’s main concern is how drugs affect patients. Currently, Dr. Huang is exploring how genetics determine why some people respond to certain types of chemotherapy while others do not. For example, one-quarter of women with ovarian cancer develop a platinum resistant cancer. The reason that some people, or some tumors, are resistant to platinum therapies may have its roots in genetics. This area of personalized medicine is expanding as genetic sequencing technology becomes faster and cheaper.

    When studying the effect of genetics on anti-cancer treatments, two types of genetic variations need to be considered. The first, germline variations, encompass a person’s genetic code including any heritable mutations. The second, somatic variations, are mutations that are not heritable, such as genetic changes caused by exposure to chemicals. Dr. Huang’s research is focusing on the germline genetic variations, since these may predispose an individual to various treatment-related toxicities. Furthermore, since all cancer cells derive from normal cells, certain germline variations could be linked to both the likelihood of developing ovarian cancer and response to treatments.

    “One challenge is to differentiate between what we know and what we can do about it. I’m working to make genetics useful in terms of making fully informed decision about what works for each patient,” says Dr. Huang.

    Dr. Huang recently published a paper looking for differences among individuals in their normal DNA code, known as germline genetic variations, that predicted platinum sensitivity in women with ovarian cancer. To do this, she utilized hundreds of cell lines (specific cells bred in a laboratory and able to duplicate indefinitely so that they can be used for research purposes) evaluated by the International HapMap Consortium. This international collaboration is aimed at obtaining all common genetic variations in the world’s human populations and making them publicly available for research. Each of the cell lines is derived from an individual’s blood; information about their ethnicity is captured as well.

    The cell lines were treated in her lab with various chemotherapeutic agents to determine each individual’s sensitivity to drugs. Dr. Huang then did genetic analyses between each person’s genomic code and his or her sensitivity to the drug. Her goal was to identify germline genetic variations that could be used to predict chemotherapy response. These cell-based findings were then evaluated in 400 women with ovarian cancer who had undergone the related drug treatment. She found that there may be some genetic variations that affect overall survival, progression free survival or response to chemotherapy. Unfortunately, the data was not able to be validated in a larger scale analysis, but leaves the door open for continued research.

    The Huang lab is also examining the differences in germline variations and somatic mutations to see which of those are cancer-related. To do so, Dr. Huang and her collaborators are utilizing data from the Cancer Genome Atlas (TCGA).

    TCGA is a multi-year, multi-cancer study funded by the National Cancer Institute to understand the genetics of certain cancers, including ovarian. Preliminary ovarian cancer results were released earlier this year, showing that there are multiple genetic mutations in each case of ovarian cancer, and that each tumor may contain a unique combination of mutations. This poses a significant challenge for individualized ovarian cancer management.

    Dr. Huang is working with a team to use TCGA’s information in her research. She is looking at the difference between a person’s genetic code and that of their tumor to establish links between germline genetic variation and somatic mutations in the tumor. Then she can determine genome markers. For example, we know that germline mutations in the BRCA mutations increase a woman’s risk for breast and ovarian cancer. Those women are also more responsive to certain drugs, like PARP inhibitors.

    There may be other genetic markers that will help providers understand a woman’s sensitivity to drugs. In the future, each woman may receive a personalized cocktail of drugs intended to target her tumor’s specific mix of genetic mutations. Researchers like Dr. Huang are trying to determine which genetic variations respond best to a particular treatment.

    In order to validate any treatment protocols, researchers would need to find the exact genetic variation as well as understand the patient’s overall genetic makeup. Then researchers would collect the patient’s blood, establish cell lines and link the patient’s treatment, outcome, genetics and tumor genetics to determine which treatments will work best. Currently, Dr. Huang is doing this for toxicity to chemotherapy in head, neck and ovarian tumors, and is seeing some patterns.

    “The technology in genomic sequencing is fairly mature, but it’s going to get faster and cheaper,” says Dr. Huang. “We will soon have the ability to look at every individual’s genome. Our challenge is to translate scientific discovery into implementation, which takes a lot of data, and a lot of patient participation.” As this field evolves, so might treatment options for women with ovarian cancer.

    Source: Ovarian Cancer National Alliance Periodical of Progress Volume 5 October 2011

    This doesn't surprise me
    In the end, the genetic variations alone could not determine response to chemotherapy, overall survival and progressive-free survival.

    "The cell lines were treated in her lab with various chemotherapeutic agents to determine each individual’s sensitivity to drugs. Dr. Huang then did genetic analyses between each person’s genomic code and his or her sensitivity to the drug. Her goal was to identify germline genetic variations that could be used to predict chemotherapy response. These cell-based findings were then evaluated in 400 women with ovarian cancer who had undergone the related drug treatment. She found that there may be some genetic variations that affect overall survival, progression free survival or response to chemotherapy. Unfortunately, the data was not able to be validated in a larger scale analysis, but leaves the door open for continued research."

    Dr. Huang used cell-based findings to test effectiveness against tumors (now why would a researcher do that unless she believed they were accurate) but then could not correlate that to genetic profile? Other than that she used cell-lines instead of using fresh cells, this looks very much like the same results that happen in 2 large trials that found no association between the CYP2D6 genotype and the effectiveness of tamoxifen in preventing breast cancer recurrence (CYP2D6 polymorphism and the outcome of tamoxifen therapy), presented at the 33rd Annual San Antonio Breast Cancer Symposium (SABCS): Abstracts S1-7 and S1-8, December 9, 2010.

    Genotype (germline variations) does not equal phenotype. Genes do not operate alone within the cell but in an intricate network of interactions. The particular sequence of DNA that an organism possess (genotype) does not determine what bodily or behaviorial form (phenotype) the organism will finally display. Among other things, environmental influences can cause the suppression of some gene functions and the activation of others. Our knowledge of genomic complexity tells us that genes and parts of genes interact with other genes, as do their protein products, and the whole system is constantly being affected by internal and external environmental factors.

    The gene may not be central to the phenotype at all, or at least it shares the spotlight with other influences. Environmental tissue and cytoplasmic factors clearly dominate the phenotypic expression processes, which may in turn, be affected by a variety of unpredictable protein-interaction events. This view is not shared by all molecular biologists, who disagree about the precise roles of genes and other factors, but it signals many scientists discomfort with a strictly deterministic view of the role of genes in an organism's functioning.

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