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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

Seeing the Cytoskeleton in a Whole New Light

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It’s been 25 years since researchers coaxed a bacterium to synthesize an unusual jellyfish protein that fluoresced bright green when irradiated with blue light. Within months, another group had also fused this small green fluorescent protein (GFP) to larger proteins to make their whereabouts inside the cell come to light—like never before.

To mark the anniversary of this Nobel Prize-winning work and show off the rainbow of color that is now being used to illuminate the inner workings of the cell, the American Society for Cell Biology (ASCB) recently held its Green Fluorescent Protein Image and Video Contest. Over the next few months, my blog will feature some of the most eye-catching entries—starting with this video that will remind those who grew up in the 1980s of those plasma balls that, when touched, light up with a simulated bolt of colorful lightning.

This video, which took third place in the ASCB contest, shows the cytoskeleton of a frequently studied human breast cancer cell line. The cytoskeleton is made from protein structures called microtubules, made visible by fluorescently tagging a protein called doublecortin (orange). Filaments of another protein called actin (purple) are seen here as the fine meshwork in the cell periphery.

The cytoskeleton plays an important role in giving cells shape and structure. But it also allows a cell to move and divide. Indeed, the motion in this video shows that the complex network of cytoskeletal components is constantly being organized and reorganized in ways that researchers are still working hard to understand.

Jeffrey van Haren, Erasmus University Medical Center, Rotterdam, the Netherlands, shot this video using the tools of fluorescence microscopy when he was a postdoctoral researcher in the NIH-funded lab of Torsten Wittman, University of California, San Francisco.

All good movies have unusual plot twists, and that’s truly the case here. Though the researchers are using a breast cancer cell line, their primary interest is in the doublecortin protein, which is normally found in association with microtubules in the developing brain. In fact, in people with mutations in the gene that encodes this protein, neurons fail to migrate properly during development. The resulting condition, called lissencephaly, leads to epilepsy, cognitive disability, and other neurological problems.

Cancer cells don’t usually express doublecortin. But, in some of their initial studies, the Wittman team thought it would be much easier to visualize and study doublecortin in the cancer cells. And so, the researchers tagged doublecortin with an orange fluorescent protein, engineered its expression in the breast cancer cells, and van Haren started taking pictures.

This movie and others helped lead to the intriguing discovery that doublecortin binds to microtubules in some places and not others [1]. It appears to do so based on the ability to recognize and bind to certain microtubule geometries. The researchers have since moved on to studies in cultured neurons.

This video is certainly a good example of the illuminating power of fluorescent proteins: enabling us to see cells and their cytoskeletons as incredibly dynamic, constantly moving entities. And, if you’d like to see much more where this came from, consider visiting van Haren’s Twitter gallery of microtubule videos here:


[1] Doublecortin is excluded from growing microtubule ends and recognizes the GDP-microtubule lattice. Ettinger A, van Haren J, Ribeiro SA, Wittmann T. Curr Biol. 2016 Jun 20;26(12):1549-1555.


Lissencephaly Information Page (National Institute of Neurological Disorders and Stroke/NIH)

Wittman Lab (University of California, San Francisco)

Green Fluorescent Protein Image and Video Contest (American Society for Cell Biology, Bethesda, MD)

NIH Support: National Institute of General Medical Sciences

Giving Thanks for Biomedical Research

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This Thanksgiving, Americans have an abundance of reasons to be grateful—loving family and good food often come to mind. Here’s one more to add to the list: exciting progress in biomedical research. To check out some of that progress, I encourage you to watch this short video, produced by NIH’s National Institute of Biomedical Imaging and Engineering (NIBIB), that showcases a few cool gadgets and devices now under development.

Among the technological innovations is a wearable ultrasound patch for monitoring blood pressure [1]. The patch was developed by a research team led by Sheng Xu and Chonghe Wang, University of California San Diego, La Jolla. When this small patch is worn on the neck, it measures blood pressure in the central arteries and veins by emitting continuous ultrasound waves.

Other great technologies featured in the video include:

Laser-Powered Glucose Meter. Peter So and Jeon Woong Kang, researchers at Massachusetts Institute of Technology (MIT), Cambridge, and their collaborators at MIT and University of Missouri, Columbia have developed a laser-powered device that measures glucose through the skin [2]. They report that this device potentially could provide accurate, continuous glucose monitoring for people with diabetes without the painful finger pricks.

15-Second Breast Scanner. Lihong Wang, a researcher at California Institute of Technology, Pasadena, and colleagues have combined laser light and sound waves to create a rapid, noninvasive, painless breast scan. It can be performed while a woman rests comfortably on a table without the radiation or compression of a standard mammogram [3].

White Blood Cell Counter. Carlos Castro-Gonzalez, then a postdoc at Massachusetts Institute of Technology, Cambridge, and colleagues developed a portable, non-invasive home monitor to count white blood cells as they pass through capillaries inside a finger [4]. The test, which takes about 1 minute, can be carried out at home, and will help those undergoing chemotherapy to determine whether their white cell count has dropped too low for the next dose, avoiding risk for treatment-compromising infections.

Neural-Enabled Prosthetic Hand (NEPH). Ranu Jung, a researcher at Florida International University, Miami, and colleagues have developed a prosthetic hand that restores a sense of touch, grip, and finger control for amputees [5]. NEPH is a fully implantable, wirelessly controlled system that directly stimulates nerves. More than two years ago, the FDA approved a first-in-human trial of the NEPH system.

If you want to check out more taxpayer-supported innovations, take a look at NIBIB’s two previous videos from 2013 and 2018 As always, let me offer thanks to you from the NIH family—and from all Americans who care about the future of their health—for your continued support. Happy Thanksgiving!


[1] Monitoring of the central blood pressure waveform via a conformal ultrasonic device. Wang C, Li X, Hu H, Zhang, L, Huang Z, Lin M, Zhang Z, Yun Z, Huang B, Gong H, Bhaskaran S, Gu Y, Makihata M, Guo Y, Lei Y, Chen Y, Wang C, Li Y, Zhang T, Chen Z, Pisano AP, Zhang L, Zhou Q, Xu S. Nature Biomedical Engineering. September 2018, 687-695.

[2] Evaluation of accuracy dependence of Raman spectroscopic models on the ratio of calibration and validation points for non-invasive glucose sensing. Singh SP, Mukherjee S, Galindo LH, So PTC, Dasari RR, Khan UZ, Kannan R, Upendran A, Kang JW. Anal Bioanal Chem. 2018 Oct;410(25):6469-6475.

[3] Single-breath-hold photoacoustic computed tomography of the breast. Lin L, Hu P, Shi J, Appleton CM, Maslov K, Li L, Zhang R, Wang LV. Nat Commun. 2018 Jun 15;9(1):2352.

[4] Non-invasive detection of severe neutropenia in chemotherapy patients by optical imaging of nailfold microcirculation. Bourquard A, Pablo-Trinidad A, Butterworth I, Sánchez-Ferro Á, Cerrato C, Humala K, Fabra Urdiola M, Del Rio C, Valles B, Tucker-Schwartz JM, Lee ES, Vakoc BJ9, Padera TP, Ledesma-Carbayo MJ, Chen YB, Hochberg EP, Gray ML, Castro-González C. Sci Rep. 2018 Mar 28;8(1):5301.

[5] Enhancing Sensorimotor Integration Using a Neural Enabled Prosthetic Hand System


Sheng Xu Lab (University of California San Diego, La Jolla)

So Lab (Massachusetts Institute of Technology, Cambridge)

Lihong Wang (California Institute of Technology, Pasadena)

Video: Lihong Wang: Better Cancer Screenings

Carlos Castro-Gonzalez (Madrid-MIT M + Visión Consortium, Cambridge, MA)

Video: Carlos Castro-Gonzalez (YouTube)

Ranu Jung (Florida International University, Miami)

Video: New Prosthetic System Restores Sense of Touch (Florida International)

NIH Support: National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Diseases and Stroke; National Heart, Lung, and Blood Institute; National Cancer Institute; Common Fund

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