Microarray Provides Three Genomic Guides To Breast Cancer Treatment Decisions
Main Category: Breast CancerAlso Included In: Genetics; Endocrinology; Women's Health / Gynecology
Article Date: 07 Sep 2007 - 0:00 PDT
Three genomic tests separately predict the likelihood that a patient's breast cancer will reoccur after surgery without additional treatment, and the cancer's vulnerability to chemotherapy or hormone therapy, researchers at The University of Texas M. D. Anderson Cancer Center report at the first American Society of Clinical Oncology ASCO Breast Cancer Symposium Sept. 7 - 8 in San Francisco.
Each predictor - of prognosis, of sensitivity to chemotherapy and sensitivity to hormone therapy is independent of the others, providing unique information to physicians and patients considering treatment options, says W. Fraser Symmans, M.D., professor in M. D. Anderson's Department of Pathology.
"Existing genomic tests for breast cancer provide information about future risk in general, but not the likely benefit of each treatment option separate from a patient's overall prognosis if no treatment followed surgery. It is important to independently assess these three variables," Symmans says.
Symmans and Lajos Pusztai, M.D., Ph.D., associate professor in M. D. Anderson's Department of Breast Medical Oncology will present two research updates on the genomic predictors, which can be reported from a single microarray analysis of a needle biopsy of a patient's breast cancer.
Symmans will present results from two studies involving 960 patients validating a 200-gene index that predicts a patient's response to hormone-suppressing therapy. About 70 percent of breast cancers express the estrogen receptor (ER), indicating that their growth is fueled to some extent by the female hormone estrogen. Anti-estrogen therapies such as tamoxifen only benefit about half of these patients. The challenge is to predict exactly who will be helped and who should seek additional treatment.
In the two studies the Sensitivity to Endocrine Therapy (SET) Index score predicted distant relapse free survival among 453 patients who received tamoxifen for five years. The index did not predict prognosis among 507 patients who did not receive hormone therapy. "We believe this is the first genomic test to predict sensitivity to hormone therapy independent of a patient's prognosis if no post-surgical treatment is received," Symmans says.
"A patient with ER-positive breast cancer probably still would choose to receive hormonal therapy, but better understanding of their cancer's sensitivity to endocrine therapy would help patients and their doctors decide on a treatment strategy," Symmans notes.
Pusztai will present a poster showing what the three predictors reported in two groups of breast cancer patients. "These three predictors were developed and validated separately, now we've put them together for the intended purpose - to provide all the necessary information for physicians and patients to decide on the best therapy or combination of therapies for breast cancer from a single assay," Pusztai says.
-- A 76-gene prognostic test that indicates whether a patient is at high or low risk of the cancer recurring after surgery developed by investigators at Erasmus University (Rotterdam, Netherlands) and Veridex LLC.
-- A 30-gene predictor of the cancer's sensitivity to chemotherapy developed by M.D. Anderson investigators.
-- The 200-gene index (SET) of sensitivity to hormone (endocrine) therapy developed by M.D. Anderson in collaboration with Nuvera Biosciences Inc.
The ASCO poster describes gene expression profiles analyzed from 198 patients with stage 1 or stage 2 breast cancer that had not spread to the lymph nodes and who had not been given chemotherapy or endocrine therapy after surgery.
Among the 198 patients, 55 were predicted to be at relatively low risk that the cancer would return. Of those low-risk patients, 21 were predicted to have cancer vulnerable to chemotherapy and 16 were predicted to have tumors susceptible to endocrine therapy. Two had cancers sensitive to both therapies.
Of the 143 patients predicted to have a high risk of recurrence, the analysis predicted 109 had cancer unlikely to respond to endocrine therapy, 64 were predicted to be insensitive to chemotherapy, and 38 were predicted to be unlikely to respond to both therapies.
Ultimately, Pusztai says, the predictors will help guide the decision whether to follow surgery with chemotherapy, endocrine therapy, both, or neither. A planned prospective clinical trial at M. D. Anderson will use these predictors to select treatment options for new patients.
"Let's say a new patient has a needle biopsy performed, and the microarray analysis of the tumor's gene expression predicts she is at low risk of recurrence and also has cancer that is insensitive to both chemo- and endocrine therapies; in this cases the best option is relatively clear; surgery alone," Pusztai explains. "However, it is important to know the sensitivity of the cancer to chemo- or endocrine therapies independent of the risk of recurrence alone. For example, a person even with low risk for cancer recurrence might elect to receive further therapy if her cancer is known to be highly susceptible to treatment."
Similarly, a patient with highly endocrine sensitive cancer that is resistant to chemotherapy could avoid potentially toxic chemotherapy. Even individuals who are at high risk of recurrence and show genomic signs of low sensitivity to chemo and endocrine therapies could benefit from this knowledge; they might choose to participate in clinical trials with novel drugs.
The researchers' poster also covers genomic analysis of another 40 patients who received paclitaxel/FAC chemotherapy before surgery. Of those, 14 were predicted at low risk or recurrence (were they treated with surgery alone), four of whom (28 percent) had a complete pathologic response - no sign of cancer - supporting the investigators' claim that some low-risk individuals are highly responsive to chemotherapy. The remaining 26 were predicted to be at high risk of recurrence, four of whom had a complete pathologic response (15 percent). Eight of the high-risk patients had cancer that was predicted to be vulnerable to endocrine therapy.
This research was funded by grants from the National Cancer Institute and the Breast Cancer Research Foundation and the Commonwealth Foundation for Cancer Research. It was conducted in collaboration with investigators at M.D. Anderson Cancer Center led by Pusztai and Symmans; the Institute Jules Bordet (Brussels, Belgium) lead by Christos Sotiriou, M.D., Ph.D.; Erasmus University (Rotterdam, Netherlands) lead by Jan Klijn, M.D., Ph.D., and John Foekens, Ph.D; scientists of Veridex LLC (San Diego) lead by Yixin Wang, Ph.D.; and Nuvera Biosciences Inc. (Woburn, Mass.) lead by Christos Hatzis, Ph.D. Nuvera Biosciences is a start-up company launched by M. D. Anderson that has licensed the chemotherapy and endocrine therapy predictor technology.
University of Texas M. D. Anderson Cancer Center
1515 Holcombe Blvd., Box 229
Houston, TX 77030
United States
http://www.mdanderson.org
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Personalized Cancer Treatment Integrating Promising Insights And Methods
posted by Gregory D. Pawelski on 25 Sep 2007 at 10:29 pmGenetic profiles are able to help doctors determine which patients will probably develop cancer, and those who will most likely relapse. However, it cannot be suitable for specific treatments for individual patients.
The Microarray is a device that measures differences in gene sequence, gene expression or protein expression in biological samples. Microarrays may be used to compare gene or protein expression under different conditions, such as cells found in cancer.
It would be more advantageous to sort out what's the best "profile" in terms of which patients benefit from this drug or that drug. Can they be combined? What's the proper way to work with all the new drugs? If a drug works extremely well for a certain percentage of cancer patients, identify which ones and "personalize" their treatment. If one drug or another is working for some patients then obviously there are others who would also benefit. But, what's good for the group (population studies) may not be good for the individual.
Patients would certainly have a better chance of success had their cancer been chemo-sensitive rather than chemo-resistant, where it is more apparent that chemotherapy improves the survival of patients, and where identifying the most effective chemotherapy would be more likely to improve survival above that achieved with "best guess" empiric chemotherapy through clinical trials.
Gene expression assays can be either probing for the specific RNA messengers (messenger RNA) or it can mean looking for the proteins themselves. Many have hoped that molecular tests would hold the key to success, particularly as more specific drugs are designed to hit the molecular changes that are responsible for the uncontrolled growth of cancer cells. Like testing breast cancer for the presence of hormone receptors and over-expression of growth factor receptors. However, most drugs cannot be looked at in this way.
It may be very important to zero in on different genes and proteins. However, when actually taking the "targeted" drugs, do the drugs even enter the cancer cell? Once entered, does it immediately get metabolized or pumped out, or does it accumulate? In other words, will it work for every patient?
All the validations of this gene or that protein provides us with a variety of sophisticated techniques to provide new insights into the tumorigenic process, but if the "targeted" drug either won't "get in" in the first place or if it gets pumped out/extruded or if it gets immediately metabolized inside the cell, it just isn't going to work.
To overcome the problems of heterogeneity in cancer and prevent rapid cellular adaptation, oncologists are able to tailor chemotherapy in individual patients. This can be done by testing "live" tumor cells to see if they are susceptible to particular drugs, before giving them to the patient. DNA microarray work will prove to be highly complementary to the parellel breakthrough efforts in targeted therapy through cell function analysis.
As we enter the era of "personalized" medicine, it is time to take a fresh look at how we evaluate new medicines and treatments for cancer. More emphasis should be put on matching treatment to the patient, through the use of individualized pre-testing.
Upgrading clinical therapy by using drug sensitivity assays measuring "cell death" of three dimensional microclusters of "live" fresh tumor cell, can improve the situation by allowing more drugs to be considered. The more drug types there are in the selective arsenal, the more likely the system is to prove beneficial.
Source: Cell Function Analysis
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