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Prostate Cancer: Combined Biopsy Strategy Makes for More Accurate Diagnosis

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Doctor consult
Credit: iStock

Last year, nearly 175,000 American men were diagnosed with prostate cancer [1]. Most got the bad news after a blood test or physical exam raised concerns that warranted a biopsy of the prostate, a walnut-sized gland just below the bladder.

Traditional biopsies sample tissue from 12 systematically placed points within the prostate that are blind to tumor locations. Such procedures have helped to save many lives, but are prone to missing or misclassifying prostate cancers, which has led doctors both to over and under treat their patients.

Now, there may be a better approach. In a study of more than 2,000 men, NIH researchers and their colleagues recently found that combining the 12-point biopsy with magnetic resonance imaging (MRI)-targeted biopsy during the same session more accurately diagnoses prostate cancer than either technique alone [2].

The findings address a long-standing challenge in prostate cancer diagnostics: performing a thorough prostate biopsy to allow a pathologist to characterize as accurately as possible the behavior of a tumor. Some prostate tumors are small, slow growing, and can be monitored closely without treatment. Other tumors are aggressive and can grow rapidly, requiring immediate intervention with hormonal therapy, radiation, or surgery.

But performing a thorough prostate biopsy can run into technical difficulties. The 12-point biopsy blindly samples tissue from across the prostate gland, but it can miss a cancer by not probing in the right places.

Several years ago, researchers at the NIH Clinical Center, Bethesda, MD, envisioned a solution. They’d use specially designed MRI images of a man’s prostate to guide the biopsy needle to areas in the prostate that look suspicious and deserve a closer look under a microscope.

Through a cooperative research-and-development agreement, NIH and the now- Florida-based Philips Healthcare created an office-based, outpatient prostate biopsy device, called UroNav, that was later approved by the Food and Drug Administration. The UroNav system relies on software that overlays MRI images highlighting suspicious areas onto real-time ultrasound images of the prostate that are traditionally used to guide biopsy procedures.

The new technology led to a large clinical study led by Peter Pinto, a researcher with NIH’s National Cancer Institute. The study results, published in 2015, found that the MRI-targeted approach was indeed superior to the 12-point biopsy at detecting aggressive prostate cancers [3].

But some doctors had questions about how best to implement the UroNav system and whether it could replace the 12-point biopsy. The uncertainty led to a second clinical study to nail down more answers, and the results were just published in The New England Journal of Medicine.

The research team enrolled 2,103 men who had visible prostate abnormalities on an MRI. Once in the study, each man underwent both the 12-point blind biopsy and the MRI-targeted approach—all in a single office visit. Based on this two-step approach, 1,312 people were diagnosed with prostate cancer. Of that total, 404 men had evidence of aggressive cancer, and had their prostates surgically removed.

The researchers then compared the diagnoses from each approach alone versus the two combined. The data showed that the combined biopsy found 208 cancers that the standard 12-point biopsy alone would have missed. Adding the MRI-targeted biopsy also helped doctors find and sample the more aggressive cancers. This allowed them to upgrade the diagnosis of 458 cancers to aggressive and in need of more full treatment.

Combining the two approaches also led to more accurate diagnoses. By carefully analyzing the 404 removed prostates and comparing them to the biopsy results, the researchers found the 12-point biopsy missed the most aggressive cancers about 40 percent of the time. But the MRI-targeted approach alone missed it about 30 percent of the time. Combined, they did much better, underestimating the severity of less than 15 percent of the cancers.

Even better, the combined biopsy missed only 3.5 percent of the most aggressive tumors. That’s compared to misses of about 17 percent for the most-aggressive cancers for the 12-point biopsy alone and about 9 percent for MRI-targeted biopsy alone.

It may take time for doctors to change how they detect prostate cancer in their practices. But the findings show that combining both approaches will significantly improve the accuracy of diagnosing prostate cancer. This will, in turn, help to reduce risk of suboptimal treatment (too much or too little) by allowing doctors and patients to feel more confident in the biopsy results. That should come as good news now and in the future for the families and friends of men who will need an accurate prostate biopsy to make informed treatment decisions.


[1] Cancer State Facts: Prostate Cancer. National Cancer Institute Surveillance, Epidemiology, and End Results Program.

[2] MRI-targeted, systematic, and combined biopsy for prostate cancer diagnosis. Ahdoot M, Wilbur AR, Reese SE, Lebastchi AH, Mehralivand S, Gomella PT, Bloom J, Gurram S, Siddiqui M, Pinsky P, Parnes H, Linehan WM, Merino M, Choyke PL, Shih JH, Turkbey B, Wood BJ, Pinto PA. N Engl J Med. 2020 Mar 5;382(10):917-928.

[3] Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer. Siddiqui M, Rais-Bahrami, George AK, Rothwax J, Shakir N, Okoro C, Raskolnikov D, Parnes HL, Linehan WM, Merino MJ, Simon RM, Choyke PL, Wood BJ, and Pinto PA. JAMA. 2015 January 27;313(4):390-397.


Prostate Cancer (National Cancer Institute/NIH)

Video: MRI-Targeted Prostate Biopsy (YouTube)

Pinto Lab (National Cancer Institute/NIH)

NIH Support: National Cancer Institute; NIH Clinical Center

A Global Look at Cancer Genomes

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Cancer Genomics

Cancer is a disease of the genome. It can be driven by many different types of DNA misspellings and rearrangements, which can cause cells to grow uncontrollably. While the first oncogenes with the potential to cause cancer were discovered more than 35 years ago, it’s been a long slog to catalog the universe of these potential DNA contributors to malignancy, let alone explore how they might inform diagnosis and treatment. So, I’m thrilled that an international team has completed the most comprehensive study to date of the entire genomes—the complete sets of DNA—of 38 different types of cancer.

Among the team’s most important discoveries is that the vast majority of tumors—about 95 percent—contained at least one identifiable spelling change in their genomes that appeared to drive the cancer [1]. That’s significantly higher than the level of “driver mutations” found in past studies that analyzed only a tumor’s exome, the small fraction of the genome that codes for proteins. Because many cancer drugs are designed to target specific proteins affected by driver mutations, the new findings indicate it may be worthwhile, perhaps even life-saving in many cases, to sequence the entire tumor genomes of a great many more people with cancer.

The latest findings, detailed in an impressive collection of 23 papers published in Nature and its affiliated journals, come from the international Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. Also known as the Pan-Cancer Project for short, it builds on earlier efforts to characterize the genomes of many cancer types, including NIH’s The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC).

In these latest studies, a team including more than 1,300 researchers from around the world analyzed the complete genomes of more than 2,600 cancer samples. Those samples included tumors of the brain, skin, esophagus, liver, and more, along with matched healthy cells taken from the same individuals.

In each of the resulting new studies, teams of researchers dug deep into various aspects of the cancer DNA findings to make a series of important inferences and discoveries. Here are a few intriguing highlights:

• The average cancer genome was found to contain not just one driver mutation, but four or five.

• About 13 percent of those driver mutations were found in so-called non-coding DNA, portions of the genome that don’t code for proteins [2].

• The mutations arose within about 100 different molecular processes, as indicated by their unique patterns or “mutational signatures.” [3,4].

• Some of those signatures are associated with known cancer causes, including aberrant DNA repair and exposure to known carcinogens, such as tobacco smoke or UV light. Interestingly, many others are as-yet unexplained, suggesting there’s more to learn with potentially important implications for cancer prevention and drug development.

• A comprehensive analysis of 47 million genetic changes pieced together the chronology of cancer-causing mutations. This work revealed that many driver mutations occur years, if not decades, prior to a cancer’s diagnosis, a discovery with potentially important implications for early cancer detection [5].

The findings represent a big step toward cataloging all the major cancer-causing mutations with important implications for the future of precision cancer care. And yet, the fact that the drivers in 5 percent of cancers continue to remain mysterious (though they do have RNA abnormalities) comes as a reminder that there’s still a lot more work to do. The challenging next steps include connecting the cancer genome data to treatments and building meaningful predictors of patient outcomes.

To help in these endeavors, the Pan-Cancer Project has made all of its data and analytic tools available to the research community. As researchers at NIH and around the world continue to detail the diverse genetic drivers of cancer and the molecular processes that contribute to them, there is hope that these findings and others will ultimately vanquish, or at least rein in, this Emperor of All Maladies.


[1] Pan-Cancer analysis of whole genomes. ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Nature. 2020 Feb;578(7793):82-93.

[2] Analyses of non-coding somatic drivers in 2,658 cancer whole genomes. Rheinbay E et al; PCAWG Consortium. Nature. 2020 Feb;578(7793):102-111.

[3] The repertoire of mutational signatures in human cancer. Alexandrov LB et al; PCAWG Consortium. Nature. 2020 Feb;578(7793):94-101.

[4] Patterns of somatic structural variation in human cancer genomes. Li Y et al; PCAWG Consortium. Nature. 2020 Feb;578(7793):112-121.

[5] The evolutionary history of 2,658 cancers. Gerstung M, Jolly C, Leshchiner I, Dentro SC et al; PCAWG Consortium. Nature. 2020 Feb;578(7793):122-128.


The Genetics of Cancer (National Cancer Institute/NIH)

Precision Medicine in Cancer Treatment (NCI)

ICGC/TCGA Pan-Cancer Project

The Cancer Genome Atlas Program (NIH)

NCI and the Precision Medicine Initiative (NCI)

NIH Support: National Cancer Institute, National Human Genome Research Institute

Working to Improve Immunotherapy for Lung Cancer

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Lung Cancer Immunotherapy
Credit: Xiaodong Zhu, Fred Hutchinson Cancer Research Center, Seattle

For those who track cancer statistics, this year started off on a positive note with word that lung cancer deaths continue to decline in the United States [1]. While there’s plenty of credit to go around for that encouraging news—and continued reduction in smoking is a big factor—some of this progress likely can be ascribed to a type of immunotherapy, called PD-1 inhibitors. This revolutionary approach has dramatically changed the treatment landscape for the most common type of lung cancer, non-small cell lung cancer (NSCLC).

PD-1 inhibitors, which have only been available for about five years, prime one component of a patient’s own immune system, called T cells, to seek and destroy malignant cells in the lungs. Unfortunately, however, only about 20 percent of people with NSCLC respond to PD-1 inhibitors. So, many researchers, including the team of A. McGarry Houghton, Fred Hutchinson Cancer Research Center, Seattle, are working hard to extend the benefits of immunotherapy to more cancer patients.

The team’s latest paper, published in JCI Insight [2], reveals that one culprit behind a poor response to immunotherapy may be the immune system’s own first responders: neutrophils. Billions of neutrophils circulate throughout the body to track down abnormalities, such as harmful bacteria and malignant cells. They also contact other parts of the immune system, including T cells, if help is needed to eliminate the health threat.

In their study, the Houghton team, led by Julia Kargl, combined several lab techniques to take a rigorous, unbiased look at the immune cell profiles of tumor samples from dozens of NSCLC patients who received PD-1 inhibitors as a frontline treatment. The micrographs above show tumor samples from two of these patients.

In the image on the left, large swaths of T cells (light blue) have infiltrated the cancer cells (white specks). Interestingly, other immune cells, including neutrophils (magenta), are sparse.

In contrast, in the image on the right, T cells (light blue) are sparse. Instead, the tumor teems with other types of immune cells, including macrophages (red), two types of monocytes (yellow, green), and, most significantly, lots of neutrophils (magenta). These cells arise from myeloid progenitor cells in the bone marrow, while T cells arise from the marrow’s lymphoid progenitor cell.

Though the immune profiles of some tumor samples were tough to classify, the researchers found that most fit neatly into two subgroups: tumors showing active levels of T cell infiltration (like the image on the left) or those with large numbers of myeloid immune cells, especially neutrophils (like the image on the right). This dichotomy then served as a reliable predictor of treatment outcome. In the tumor samples with majority T cells, the PD-1 inhibitor worked to varying degrees. But in the tumor samples with predominantly neutrophil infiltration, the treatment failed.

Houghton’s team has previously found that many cancers, including NSCLC, actively recruit neutrophils, turning them into zombie-like helpers that falsely signal other immune cells, like T cells, to stay away. Based on this information, Houghton and colleagues used a mouse model of lung cancer to explore a possible way to increase the success rate of PD-1 immunotherapy.

In their mouse experiments, the researchers found that when PD-1 was combined with an existing drug that inhibits neutrophils, lung tumors infiltrated with neutrophils were converted into tumors infiltrated by T cells. The tumors treated with the combination treatment also expressed genes associated with an active immunotherapy response.

This year, January brought encouraging news about decreasing deaths from lung cancer. But with ongoing basic research, like this study, to tease out the mechanisms underlying the success and failure of immunotherapy, future months may bring even better news.


[1] Cancer statistics, 2020. Siegel RL, Miller KD, Jemal A. CA Cancer J Clin. 2020 Jan;70(1):7-30.

[2] Neutrophil content predicts lymphocyte depletion and anti-PD1 treatment failure in NSCLC. Kargl J, Zhu X, Zhang H, Yang GHY, Friesen TJ, Shipley M, Maeda DY, Zebala JA, McKay-Fleisch J, Meredith G, Mashadi-Hossein A, Baik C, Pierce RH, Redman MW, Thompson JC, Albelda SM, Bolouri H, Houghton AM. JCI Insight. 2019 Dec 19;4(24).

[3] Neutrophils dominate the immune cell composition in non-small cell lung cancer. Kargl J, Busch SE, Yang GH, Kim KH, Hanke ML, Metz HE, Hubbard JJ, Lee SM, Madtes DK, McIntosh MW, Houghton AM. Nat Commun. 2017 Feb 1;8:14381.


Non-Small Cell Lung Cancer Treatment (PDQ®)–Patient Version (National Cancer Institute/NIH)

Spotlight on McGarry Houghton (Fred Hutchinson Cancer Research Center, Seattle)

Houghton Lab (Fred Hutchinson Cancer Research Center)

NIH Support: National Cancer Institute

Insurance Status Helps Explain Racial Disparities in Cancer Diagnosis

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Diverse human hands
Credit: iStock/jmangostock

Women have the best odds of surviving breast cancer if their disease is caught at an early stage, when treatments are most likely to succeed. Major strides have been made in the early detection of breast cancer in recent years. But not all populations have benefited equally, with racial and ethnic minorities still more likely to be diagnosed with later-stage breast cancer than non-Hispanic whites. Given that recent observance of Martin Luther King Day, I thought that it would be particularly appropriate to address a leading example of health disparities.

A new NIH-funded study of more than 175,000 U.S. women diagnosed with breast cancer from 2010-2016 has found that nearly half of the troubling disparity in breast cancer detection can be traced to lack of adequate health insurance. The findings suggest that improving insurance coverage may help to increase early detection and thereby reduce the disproportionate number of breast cancer deaths among minority women.

Naomi Ko, Boston University School of Medicine, has had a long interest in understanding the cancer disparities she witnesses first-hand in her work as a medical oncologist. For the study published in JAMA Oncology, she teamed up with epidemiologist Gregory Calip, University of Illinois Cancer Center, Chicago [1]. Their goal was to get beyond documenting disparities in breast cancer and take advantage of available data to begin to get at why such disparities exist and what to do about them.

Disparities in breast cancer outcomes surely stem from a complicated mix of factors, including socioeconomic factors, culture, diet, stress, environment, and biology. Ko and Calip focused their attention on insurance, thinking of it as a factor that society can collectively modify.

Many earlier studies had shown a link between insurance and cancer outcomes [2]. It also stood to reason that broad differences among racial and ethnic minorities in their access to adequate insurance might drive some of the observed cancer disparities. But, Ko and Calip asked, just how big a factor was it?

To find out, they looked to the NIH’s Surveillance Epidemiology, and End Results (SEER) Program, run by the National Cancer Institute. The SEER Program is an authoritative source of information on cancer incidence and survival in the United States.

The researchers focused their attention on 177,075 women of various races and ethnicities, ages 40 to 64. All had been diagnosed with invasive stage I to III breast cancer between 2010 and 2016.

The researchers found that a higher proportion of women receiving Medicaid or who were uninsured received a diagnosis of advanced stage III breast cancer compared with women with health insurance. Black, American Indian, Alaskan Native, and Hispanic women also had higher odds of receiving a late-stage diagnosis.

Overall, their sophisticated statistical analyses traced up to 47 percent of the racial/ethnic differences in the risk of locally advanced disease to differences in health insurance. Such late-stage diagnoses and the more extensive treatment regimens that go with them are clearly devastating for women with breast cancer and their families. But, the researchers note, they’re also costly for society, due to lost productivity and escalating treatment costs by stage of breast cancer.

These researchers surely aren’t alone in recognizing the benefit of early detection. Last week, an independent panel convened by NIH called for enhanced research to assess and explore how to reduce health disparities that lead to unequal access to health care and clinical services that help prevent disease.


[1] Association of Insurance Status and Racial Disparities With the Detection of Early-Stage Breast Cancer. Ko NY, Hong S, Winn RA, Calip GS. JAMA Oncol. 2020 Jan 9.

[2] The relation between health insurance coverage and clinical outcomes among women with breast cancer. Ayanian JZ, Kohler BA, Abe T, Epstein AM. N Engl J Med. 1993 Jul 29;329(5):326-31.

[3] Cancer Stat Facts: Female Breast Cancer. National Cancer Institute Surveillance, Epidemiology, and End Results Program.


Cancer Disparities (National Cancer Institute/NIH)

Breast Cancer (National Cancer Institute/NIH)

Naomi Ko (Boston University)

Gregory Calip (University of Illinois Cancer Center, Chicago)

NIH Support: National Center for Advancing Translational Sciences; National Cancer Institute; National Institute on Minority Health and Health Disparities

Artificial Intelligence Speeds Brain Tumor Diagnosis

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Real time diagnostics in the operating room
Caption: Artificial intelligence speeds diagnosis of brain tumors. Top, doctor reviews digitized tumor specimen in operating room; left, the AI program predicts diagnosis; right, surgeons review results in near real-time.
Credit: Joe Hallisy, Michigan Medicine, Ann Arbor

Computers are now being trained to “see” the patterns of disease often hidden in our cells and tissues. Now comes word of yet another remarkable use of computer-generated artificial intelligence (AI): swiftly providing neurosurgeons with valuable, real-time information about what type of brain tumor is present, while the patient is still on the operating table.

This latest advance comes from an NIH-funded clinical trial of 278 patients undergoing brain surgery. The researchers found they could take a small tumor biopsy during surgery, feed it into a trained computer in the operating room, and receive a diagnosis that rivals the accuracy of an expert pathologist.

Traditionally, sending out a biopsy to an expert pathologist and getting back a diagnosis optimally takes about 40 minutes. But the computer can do it in the operating room on average in under 3 minutes. The time saved helps to inform surgeons how to proceed with their delicate surgery and make immediate and potentially life-saving treatment decisions to assist their patients.

As reported in Nature Medicine, researchers led by Daniel Orringer, NYU Langone Health, New York, and Todd Hollon, University of Michigan, Ann Arbor, took advantage of AI and another technological advance called stimulated Raman histology (SRH). The latter is an emerging clinical imaging technique that makes it possible to generate detailed images of a tissue sample without the usual processing steps.

The SRH technique starts off by bouncing laser light rapidly through a tissue sample. This light enables a nearby fiberoptic microscope to capture the cellular and structural details within the sample. Remarkably, it does so by picking up on subtle differences in the way lipids, proteins, and nucleic acids vibrate when exposed to the light.

Then, using a virtual coloring program, the microscope quickly pieces together and colors in the fine structural details, pixel by pixel. The result: a high-resolution, detailed image that you might expect from a pathology lab, minus the staining of cells, mounting of slides, and the other time-consuming processing procedures.

To interpret the SRH images, the researchers turned to computers and machine learning. To teach a computer, it must be fed large datasets of examples in order to learn how to perform a given task. In this case, they used a special class of machine learning called deep neural networks, or deep learning. It’s inspired by the way neural networks in the human brain process information.

In deep learning, computers look for patterns in large collections of data. As they begin to recognize complex relationships, some connections in the network are strengthened while others are weakened. The finished network is typically composed of multiple information-processing layers, which operate on the data to return a result, in this case a brain tumor diagnosis.

The team trained the computer to classify tissues samples into one of 13 categories commonly found in a brain tumor sample. Those categories included the most common brain tumors: malignant glioma, lymphoma, metastatic tumors, and meningioma. The training was based on more than 2.5 million labeled images representing samples from 415 patients.

Next, they put the machine to the test. The researchers split each of 278 brain tissue samples into two specimens. One was sent to a conventional pathology lab for prepping and diagnosis. The other was imaged with SRH, and then the trained machine made a diagnosis.

Overall, the machine’s performance was quite impressive, returning the right answer about 95 percent of the time. That’s compared to an accuracy of 94 percent for conventional pathology.

Interestingly, the machine made a correct diagnosis in all 17 cases that a pathologist got wrong. Likewise, the pathologist got the right answer in all 14 cases in which the machine slipped up.

The findings show that the combination of SRH and AI can be used to make real-time predictions of a patient’s brain tumor diagnosis to inform surgical decision-making. That may be especially important in places where expert neuropathologists are hard to find.

Ultimately, the researchers suggest that AI may yield even more useful information about a tumor’s underlying molecular alterations, adding ever greater precision to the diagnosis. Similar approaches are also likely to work in supplying timely information to surgeons operating on patients with other cancers too, including cancers of the skin and breast. The research team has made a brief video to give you a more detailed look at the new automated tissue-to-diagnosis pipeline.


[1] Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Hollon TC, Pandian B, Adapa AR, Urias E, Save AV, Khalsa SSS, Eichberg DG, D’Amico RS, Farooq ZU, Lewis S, Petridis PD, Marie T, Shah AH, Garton HJL, Maher CO, Heth JA, McKean EL, Sullivan SE, Hervey-Jumper SL, Patil PG, Thompson BG, Sagher O, McKhann GM 2nd, Komotar RJ, Ivan ME, Snuderl M, Otten ML, Johnson TD, Sisti MB, Bruce JN, Muraszko KM, Trautman J, Freudiger CW, Canoll P, Lee H, Camelo-Piragua S, Orringer DA. Nat Med. 2020 Jan 6.


Video: Artificial Intelligence: Collecting Data to Maximize Potential (NIH)

New Imaging Technique Allows Quick, Automated Analysis of Brain Tumor Tissue During Surgery (National Institute of Biomedical Imaging and Bioengineering/NIH)

Daniel Orringer (NYU Langone, Perlmutter Cancer Center, New York City)

Todd Hollon (University of Michigan, Ann Arbor)

NIH Support: National Cancer Institute; National Institute of Biomedical Imaging and Bioengineering

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