Ovarian Cancer Tumors Can Grow For Ten Years Or More Before Being Detected By Today’s Blood Tests

A new mathematical model developed by Stanford University School of Medicine scientists finds that ovarian cancer tumors can grow for 10 years or longer before currently available blood tests will detect them.

A new mathematical model developed by Stanford University School of Medicine scientists indicates that tumors can grow for 10 years or longer before currently available blood tests will detect them. The analysis, which was restricted to ovarian cancer tumors but is broadly applicable across all solid tumor types, was published online November 16 in Science Translational Medicine.

“The study’s results can be viewed as both bad and good news,” said Sanjiv “Sam” Gambhir, M.D., Ph.D., professor and chair of radiology and the study’s senior author. Sharon Hori, Ph.D., a postdoctoral scholar in Dr. Gambhir’s laboratory, is the lead study author.

The mathematical model developed by Dr. Sam Gambhir’s lab shows that it would be possible to detect tumors years before they grow big enough to metastasize if researchers can develop the right biomarkers.

The bad news, as explained by Dr. Gambhir, is that by time a tumor reaches a detectable size using today’s available blood tests, it is likely to have metastasized to other areas of the body, making it much more deadly than if it had been caught earlier. “The good news is that we have, potentially, 10 or even 20 years to find the tumor before it reaches this size, if only we can improve our blood-based methods of detecting tumors,” said Dr. Gambhir. “We think our mathematical model will help guide attempts to do that.”

The study advances previous research about the limits of current detection methods. For instance, it is strikingly consistent with a finding reported two years ago by Stanford biochemistry professor Patrick Brown, M.D., Ph.D., that current ovarian cancer tests could not detect tumors early enough to make a significant dent in the mortality rate. There is a push to develop more-sensitive diagnostic tests and find better biomarkers, and Dr. Gambhir’s new model could be an essential tool in this effort. For the first time, the new model connects the size of a tumor with blood biomarker levels being shed by that tumor.

To create their model, Drs. Gambhir and Hori used mathematical models originally developed to predict the concentration of drugs injected into the blood. The investigators linked these to additional models of tumor cell growth.

Tumors do not secrete drugs, but they can shed telltale molecules into surrounding tissue, from which those substances, known as “biomarkers,” diffuse into the blood. Some biomarkers may be made predominantly by tumor cells.  These substances can be measured in the blood as proxies for a tumor.

Some biomarkers are in wide use today. One is the well-known PSA (prostate specific antigen) for prostate cancer. Another example of a biomarker is CA-125 (cancer antigen 125) for ovarian cancer. But these and other currently used blood tests for cancer biomarkers were not specifically developed for early detection, and are generally more effective for relatively noninvasive monitoring of the progress of a late-stage tumor or tumor response to treatment. That is, rising blood levels of the substance may indicate that the tumor is growing, while declining levels may indicate possible tumor shrinkage.

Both CA-125 and PSA are also produced, albeit in smaller amounts, by healthy tissue, complicating efforts to detect cancer at an early stage when the tumor’s output of the biomarker is relatively low.

The new mathematical model employs separate equations, each governing the movement of a biomarker from one compartment into the next. Into these equations, one can plug known values — such as how fast a particular type of tumor grows, how much of the biomarker a tumor cell of this type sheds per hour, and the minimum levels of the biomarker that must be present in the blood for a currently available assay to detect it.

As a test case, Drs. Gambhir and Hori chose CA-125, a well-studied biomarker which is shed into the blood by ovarian cancer tumors. Ovarian cancer is a notorious example of a condition for which early detection would make a significant difference in survival outcomes.

CA-125 is a protein made almost exclusively by ovarian tumor cells. The well-known pharmacokinetics, metabolic fates (typical amounts secreted by an ovarian cell), typical ovarian tumor growth rates, and other properties of CA-125 make the biomarker an excellent candidate for “road testing” with Gambhir and Hori’s model. CA-125 is by no means the ideal biomarker, said Dr. Gambhir, while noting that it can still be used to better understand the ideal properties of biomarkers for early ovarian cancer detection.

Applying their equations to CA-125, Drs. Gambhir and Hori determined that an ovarian cancer tumor would need to reach a size of approximately 1.7 billion cells, or the volume of a cube with a 2-centimeter edge, before the currently available CA-125 blood test could reliably detect it. At typical tumor-growth rates, it would take a single cancer cell approximately 10.1 to 12.6 years of development to become a tumor containing 1.7 billion cells.

The model further calculated that a biomarker otherwise equivalent to CA125 — but shed only by ovarian tumor cells — would allow reliable detection within 7.7 years, while the tumor’s size would be that of a tiny cube about one-sixth of an inch high.

In the last decade, many potential new biomarkers for different forms of cancers have been identified. There’s no shortage of promising candidates — six for lung cancer alone, for example. But validating a biomarker in large clinical trials is a long, expensive process. So it is imperative to determine as efficiently as possible which, among many potential tumor biomarkers, is the best prospective candidate.

“This [mathematical] model could take some of the guesswork out of it,” Gambhir said. He also stated:

“It [the mathematical model] can be applied to all kinds of solid cancers and prospective biomarkers as long as we have enough data on, for instance, how much of it a tumor cell secretes per hour, how long the biomarker can circulate before it’s degraded and how quickly tumor cells divide. We can tweak one or another variable — for instance, whether a biomarker is also made in healthy tissues or just the tumor, or assume we could manage to boost the sensitivity of our blood tests by 10-fold or 100-fold — and see how much it advances our ability to detect the tumor earlier on.”

There are new detection technologies capable of detecting biomarkers at concentrations as low as a few hundred molecules per milliliter (1-cubic centimeter) of blood. In 2009, Dr. Gambhir and his colleagues reported on one such developing technology: “magneto-nanosensors” that can detect biomarkers with a 100-fold greater sensitivity than current methods.

Better biomarker detection alone might allow ovarian cancer tumor detection at the 9-year point, said Gambhir.

A second priority is to come up with new and better biomarkers. “It’s really important for us to find biomarkers that are made exclusively by tumor cells,” Dr. Gambhir said.

Under the right conditions (a highly sensitive assay measuring levels of a biomarker that is shed only by cancer cells), Gambhir stated, the model predicts that a tiny tumor with a volume equivalent to a cube less than one-fifteenth of an inch (or 1.7 millimeters) on a side could be detected.

Dr. Gambhir is also the Virginia and D.K. Ludwig Professor in Cancer Research and director of the Molecular Imaging Program at Stanford, the director of the Canary Center at Stanford for Cancer Early Detection, and a member of the Stanford Cancer Institute.

The study was funded by the Canary Foundation and the National Cancer Institute.

Sources:

• Hori S, Gambhir S, Mathematical Model Identifies Blood Biomarker–Based Early Cancer Detection Strategies and Limitations. Sci. Transl. Med. 3, 109ra116 (2011).

• New model establishes guidelines for earlier cancer detection, Press Release, Stanford School of Medicine, November 16, 2011.

New Study Shows Four-Year Window for Early Detection of Ovarian Cancer

A new study by Howard Hughes Medical Institute researchers shows that most early stage ovarian tumors exist for years at a size that is a thousand times smaller than existing tests can detect reliably.  But the researchers say their findings also point to new opportunities for detecting ovarian cancer—a roughly four-year window during which most tumors are big enough to be seen with a microscope, but have not yet spread.

Tiny Early-Stage Ovarian Tumors Define Early Detection Challenge

Currently available tests detect ovarian cancer when it is about the size of the onion in the photograph. To reduce ovarian cancer mortality by 50 percent, an early detection test would need to be able to reliably detect tumors the size of the peppercorn. (Photo Source: Patrick O. Brown, Howard Hughes Medical Institute Investigator, Research News Release, July 28, 2009)

A new study by Howard Hughes Medical Institute researchers shows that most early stage ovarian tumors exist for years at a size that is a thousand times smaller than existing tests can detect reliably.

But the researchers say their findings also point to new opportunities for detecting ovarian cancer—a roughly four-year window during which most tumors are big enough to be seen with a microscope, but have not yet spread.

“Our work provides a picture of the early events in the life of an ovarian tumor, before the patient knows it’s there,” says Howard Hughes Medical Institute researcher Patrick O. Brown. “It shows that there is a long window of opportunity for potentially life-saving early detection of this disease, but that the tumor spreads while it is still much too small to be detected by any of the tests that have been developed or proposed to date.”

According to the American Cancer Society, some 15,000 women in the United States and 140,000 women worldwide die from ovarian cancer each year. The vast majority of these deaths are from cancers of the serous type, which are usually discovered only after the cancer has spread.

“Instead of typically detecting these cancers at a very advanced stage, detecting them at an early stage would be enormous in terms of saving lives,” says Brown, who is at Stanford University School of Medicine. Early detection would enable surgeons to remove a tumor before it spreads, he adds.

The article—co-authored by Chana Palmer of the Canary Foundation, a nonprofit organization focused on early cancer detection—was published July 28, 2009, in the open access journal PLoS Medicine.

“Like almost everything with cancer … the more closely you look at the problem, the harder it looks,” Brown says. “That’s not to say that I don’t believe it’s a solvable problem. It’s just a difficult one.” — Patrick O. Brown, M.D. Ph.D.

Patrick O. Brown, M.D. Ph.D., Howard Hughes Medical Institute Investigator, Stanford Univ. School of Medicine

“Like almost everything with cancer … the more closely you look at the problem, the harder it looks,” Brown says. “That’s not to say that I don’t believe it’s a solvable problem. It’s just a difficult one.”

In the quest to develop early detection methods for ovarian cancer, Brown says, science hasn’t had a firm grasp on its target. So he and Palmer took advantage of published data on ovarian tumors to generate a better understanding of how the cancer progresses in its earliest stages.

The team analyzed data on serous-type ovarian tumors that were discovered when apparently healthy women at high genetic [BRCA1 gene mutation] risk for ovarian cancer had their ovaries and fallopian tubes removed prophylactically. Most of the tumors were microscopic in size; they were not detected when the excised tissue was examined with the naked eye.

The analysis uncovered a wealth of unexplored information. Thirty-seven of the early tumors had been precisely measured when they were excised – providing new details about the size of the tumors when they were developing prior to intervention, Brown says. By extrapolating from this “occult” size distribution to the size distribution of larger, clinically evident tumors, the researchers were able to develop a model of how the tumors grew and progressed. “We are essentially trying to build a story for how these tumors progress that fits the data,” Brown explains.

Among the study’s findings:

• Serous ovarian tumors exist for at least four years before they spread.

• The typical serous cancer is less than three millimeters across for 90 percent of this “window of opportunity for early detection.”

• These early tumors are twice as likely to be in the fallopian tubes as in the ovaries.

• To cut mortality from this cancer in half, an annual early-detection test would need to detect tumors five millimeters in diameter or less – about the size of a black peppercorn and less than a thousandth the size at which these cancers are typically detected today.

Brown’s lab is now looking for ways to take advantage of that window of opportunity to detect the microscopic tumors and intervene before the cancer spreads.

One strategy the laboratory is pursuing is to examine tissues near the ovaries, in the female reproductive tract, for protein or other molecular markers that could signify the presence of cancer. Brown says answering another question might also prove helpful: whether there is any reliable flow of material from the ovaries and fallopian tubes through the uterus and cervix into the vagina—material that might be tested for a specific cancer marker.

Despite science’s broad understanding of cancer at a molecular level, it has been challenging to identify simple molecular markers that signal the presence of early disease. One current blood marker, CA-125, has proven useful in monitoring later-stage ovarian cancer, but it has not been helpful for early detection. So Brown’s lab is also looking for biomarkers that are present only in ovarian tumors and not in healthy cells, instead of relying on tests that look for unusually high levels of a molecule that is part of normal biology (like CA-125).

The researchers are doing extensive sequencing of all messenger RNA molecules (which carry information for the production of specific proteins) in ovarian cancer cells, searching for evidence of proteins in these cells that would never be found in non-cancer cells. These variant molecules could be produced as a result of chromosome rearrangements—when the genome is cut and spliced in unusual ways—in ovarian cancers. “It’s a long shot,” says Brown, “but it’s important enough to try.”

Source: Tiny Early-Stage Ovarian Tumors Define Early Detection Challenge, Research News, Howard Hughes Medical Institute, July 29, 2009 [summarizing Brown PO, Palmer C, 2009 The Preclinical Natural History of Serous Ovarian Cancer: Defining the Target for Early Detection. PLoS Med 6(7): e1000114. doi:10.1371/journal.pmed.1000114].

Stanford Researchers Harness Nanoparticles To Track Cancer Cell Changes

“A new imaging technology could give scientists the ability to simultaneously measure as many as 100 or more distinct features in or on a single cell. In a disease such as cancer, that capability would provide a much better picture of what’s going on in individual tumor cells. A Stanford University School of Medicine team led by Cathy Shachaf, PhD, an instructor in microbiology and immunology, has for the first time used specially designed dye-containing nanoparticles to simultaneously image two features within single cells. … In a study published April 15 in the online journal PLoS-ONE, the Stanford team was able to simultaneously monitor changes in two intracellular proteins that play crucial roles in the development of cancer. Successful development of the new technique may improve scientists’ ability not only to diagnose cancers-for example, by determining how aggressive tumors’ constituent cells are-but to eventually separate living, biopsied cancer cells from one another based on characteristics indicating their stage of progression or their degree of resistance to chemotherapeutic drugs….”

“STANFORD, Calif. – The more dots there are, the more accurate a picture you get when you connect them. A new imaging technology could give scientists the ability to simultaneously measure as many as 100 or more distinct features in or on a single cell. In a disease such as cancer, that capability would provide a much better picture of what’s going on in individual tumor cells.

Catherine Shachaf, Instructor, Microbiology & Immunology - Baxter Laboratory, Stanford School of Medicine

A Stanford University School of Medicine team led by Cathy Shachaf, PhD, an instructor in microbiology and immunology, has for the first time used specially designed dye-containing nanoparticles to simultaneously image two features within single cells. Although current single-cell flow cytometry technologies can do up to 17 simultaneous visualizations, this new method has the potential to do far more. The new technology works by enhancing the detection of ultra-specific but very weak patterns, known as Raman signals, that molecules emit in response to light.

In a study published April 15 in the online journal PLoS-ONE, the Stanford team was able to simultaneously monitor changes in two intracellular proteins that play crucial roles in the development of cancer. Successful development of the new technique may improve scientists’ ability not only to diagnose cancers-for example, by determining how aggressive tumors’ constituent cells are-but to eventually separate living, biopsied cancer cells from one another based on characteristics indicating their stage of progression or their degree of resistance to chemotherapeutic drugs. That would expedite the testing of treatments targeting a tumor’s most recalcitrant cells, said Shachaf, a cancer researcher who works in a laboratory run by the study’s senior author, Garry Nolan, PhD, associate professor of microbiology and immunology and a member of Stanford’s Cancer Center.

Cancer starts out in a single cell, and its development is often heralded by changes in the activation levels of certain proteins. In the world of cell biology, one common way for proteins to get activated is through a process called phosphorylation that slightly changes a protein’s shape, in effect turning it on.

Two intracellular proteins, Stat1 and Stat6, play crucial roles in the development of cancer. The Stanford team was able to simultaneously monitor changes in phosphorylation levels of both proteins in lab-cultured myeloid leukemia cells. The changes in Stat1 and Stat6 closely tracked those demonstrated with existing visualization methods, establishing proof of principle for the new approach.

While the new technology so far has been used only to view cells on slides, it could eventually be used in a manner similar to flow cytometry, the current state-of-the-art technology, which lets scientists visualize single cells in motion. In flow cytometry, cells are bombarded with laser light as they pass through a scanning chamber. The cells can then be analyzed and, based on their characteristics, sorted and routed to different destinations within the cytometer.

Garry Nolan, Associate Professor, Microbiology & Immunology - Baxter Laboratory; Member, Bio-X; Member, Stanford Cancer Center, Stanford School of Medicine

Still, flow cytometry has its limits. It involves tethering fluorescent dye molecules to antibodies, with different colors tied to antibodies that target different molecules. The dye molecules respond to laser light by fluorescing-echoing light at exactly the same wavelength, or color, with which they were stimulated. The fluorescence’s strength indicates the abundance of the cell-surface features to which those dyes are now attached. But at some point, the light signals given off by multiple dyes begin to interfere with one another. It is unlikely that the number of distinct features flow cytometry can measure simultaneously will exceed 20 or so.

The new high-tech dye-containing particles used by the Stanford team go a step further. They give off not just single-wavelength fluorescent echoes but also more-complex fingerprints comprising wavelengths slightly different from the single-color beams that lasers emit. These patterns, or Raman signals, occur when energy levels of electrons are just barely modified by weak interactions among the constituent atoms in the molecule being inspected.

Raman signals are emitted all the time by various molecules, but they’re ordinarily too weak to detect. To beef up their strength, the Stanford team employed specialized nanoparticles produced by Intel Corp., each with its own distinctive signature. Intel has designed more than 100 different so-called COINs, or composite organic–inorganic nanoparticles: These are essentially sandwiches of dye molecules and atoms of metals such as silver, gold or copper whose reflective properties amplify a dye molecule’s Raman signals while filtering out its inherent fluorescent response. The signals are collected and quantified by a customized, automated microscope.

Shachaf anticipates being able to demonstrate simultaneous visualization of nine or 10 COIN-tagged cellular features in the near future and hopes to bring that number to 20 or 30, a new high, before long. ‘The technology’s capacity may ultimately far exceed that number,’ she added. Some day it could be used for more than 100 features. Meanwhile, another group outside Stanford, now collaborating with the Nolan group, has developed a prototype device that can detect Raman signals in a continuous flow of single cells, analogous to flow cytometry but with higher resolving power, Shachaf said.

The study was funded by the National Cancer Institute’s Center for Cancer Nanotechnology Excellence Focused on Therapy Response and by the Flight Attendant Medical Research Institute. Other Stanford contributors were researchers Sailaja Elchuri, PhD, and Dennis Mitchell of the Nolan lab; engineering and materials science graduate student Ai Leen Koh; and Robert Sinclair, PhD, professor of materials science and engineering.

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The Stanford University School of Medicine consistently ranks among the nation’s top 10 medical schools, integrating research, medical education, patient care and community service. For more news about the school, please visit http://mednews.stanford.edu. The medical school is part of Stanford Medicine, which includes Stanford Hospital & Clinics and Lucile Packard Children’s Hospital. For information about all three, please visit http://stanfordmedicine.org/about/news.html.”

Source: Stanford researchers harness nanoparticles to track cancer cell changes, by Bruce Goldman, News Release, Stanford School of Medicine, April 14, 2009.

Primary Citation:  Shachaf CM, Elchuri SV, Koh AL, Zhu J, Nguyen LN, et al. 2009  A Novel Method for Detection of Phosphorylation in Single Cells by Surface Enhanced Raman Scattering (SERS) using Composite Organic-Inorganic Nanoparticles (COINs). PLoS ONE 4(4): e5206. doi:10.1371/journal.pone.000520. For an Adobe Reader PDF copy of the study, CLICK HERE.