China’s Xi Writes Thank-You Letter to Bill Gates for Virus Help – Chinese President Xi Jinping expressed his sincere gratitude to Bill and Melinda Gates for their foundation’s support to China after the outbreak of the novel coronavirus @ Novel Technology Could Significantly Reduce Coronavirus Diagnostic Time & NIH Scientists Identify Atomic Structure of Novel Coronavirus Protein @ Lab Rat Chat @ TAU, Harvard Researchers Build Human ´Body-On-Chips´ To Better Predict Drug Response @ Imperial researchers in race to develop a coronavirus vaccine & Transplant device keeps hearts alive for 24 hours @ Single-cell analyses reveal increased intratumoral heterogeneity after the onset of therapy resistance in small-cell lung cancer – Nature Cancer @ The Price of DNA Sequencing Dropped From $2.7 Billion to $300 in Less Than 20 Years – Nebula Genomics is offering access to your entire genetic makeup for less than the price of an Apple Watch @ Links and Images

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Pathol Res Pract. 2012 Jul 15;208(7):377-81. doi: 10.1016/j.prp.2012.04.006. Epub 2012 Jun 8.

The influence of physical activity in the progression of experimental lung cancer in mice

Renato Batista Paceli 1Rodrigo Nunes CalCarlos Henrique Ferreira dos SantosJosé Antonio CordeiroCassiano Merussi NeivaKazuo Kawano NagaminePatrícia Maluf Cury


GRUPO_AF1GROUP AFA1 – Aerobic Physical Activity – Atividade Física Aeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

GRUPO AFAN 1GROUP AFAN1 – Anaerobic Physical ActivityAtividade Física Anaeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

GRUPO_AF2GROUP AFA2 – Aerobic Physical ActivityAtividade Física Aeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

GRUPO AFAN 2GROUP AFAN 2 – Anaerobic Physical ActivityAtividade Física Anaeróbia´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

Slides – mestrado´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto



Avaliação da influência da atividade física aeróbia e anaeróbia na progressão do câncer de pulmão experimental – Summary – Resumo´´My´´ Dissertation Faculty of Medicine of Sao Jose do Rio Preto


Lung cancer is one of the most incident neoplasms in the world, representing the main cause of mortality for cancer. Many epidemiologic studies have suggested that physical activity may reduce the risk of lung cancer, other works evaluate the effectiveness of the use of the physical activity in the suppression, remission and reduction of the recurrence of tumors. The aim of this study was to evaluate the effects of aerobic and anaerobic physical activity in the development and the progression of lung cancer. Lung tumors were induced with a dose of 3mg of urethane/kg, in 67 male Balb – C type mice, divided in three groups: group 1_24 mice treated with urethane and without physical activity; group 2_25 mice with urethane and subjected to aerobic swimming free exercise; group 3_18 mice with urethane, subjected to anaerobic swimming exercise with gradual loading 5-20% of body weight. All the animals were sacrificed after 20 weeks, and lung lesions were analyzed. The median number of lesions (nodules and hyperplasia) was 3.0 for group 1, 2.0 for group 2 and 1.5-3 (p=0.052). When comparing only the presence or absence of lesion, there was a decrease in the number of lesions in group 3 as compared with group 1 (p=0.03) but not in relation to group 2. There were no metastases or other changes in other organs. The anaerobic physical activity, but not aerobic, diminishes the incidence of experimental lung tumors.

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China’s Xi Writes Thank-You Letter to Bill Gates for Virus Help

By Linly Lin21 de fevereiro de 2020 21:19 BRTCoronavirus: A Cell Biologist Explains How a Virus Mutates and SpreadsUnmuteCoronavirus: A Cell Biologist Explains How a Virus Mutates and Spreads





Chinese President Xi Jinping expressed his sincere gratitude to Bill and Melinda Gates for their foundation’s support to China after the outbreak of the novel coronavirus.

Xi thanked the foundation for its “generous support” provided at a critical juncture, in a letter to Bill Gates posted by the official Xinhua news agency.

Xi complimented the foundation’s early efforts in the global cause of fighting against the virus and said he was grateful for a letter from Gates expressing support for the Chinese people.

“Unity and cooperation is the most powerful weapon” in the battle against the virus, Xi said in the letter, indicating his support for the Gates Foundation’s coordination with Chinese agencies on the ground.

The foundation earlier this month raised its commitment to provide up to $100 million for the global response to the outbreak. The foundation said that part of the donation will be used to acceleration the detection, isolation and treatment of the coronavirus in China and other countries that have confirmed cases.UP NEXT

Coronavirus Latest: Virus May Be ‘Disease X’

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The Coronavirus May Be ‘Disease X’ Health Experts Warned About

By Jason Gale21 de fevereiro de 2020 22:58 BRT Updated on 

  •  Picture is emerging of an unpredictable, enigmatic pathogen
  •  SARS-like lung inflammation seen in severe Covid-19 cases

AI being used to fight coronavirus





The World Health Organization cautioned years ago that a mysterious “disease X” could spark an international contagion. The new coronavirus, with its ability to quickly morph from mild to deadly, is emerging as a contender.

From recent reports about the stealthy ways the so-called Covid-19 virus spreads and maims, a picture is emerging of an enigmatic pathogen whose effects are mainly mild, but which occasionally — and unpredictably — turns deadly in the second week. In less than three months, it’s infected almost 78,000 people, mostly in China, and killed more than 2,300. Emerging hot spots in South Korea, Iran and Italy have stoked further alarm.

“Whether it will be contained or not, this outbreak is rapidly becoming the first true pandemic challenge that fits the disease X category,” Marion Koopmans, head of viroscience at Erasmus University Medical Center in Rotterdam, and a member of the WHO’s emergency committee, wrote Wednesday in the journal Cell.

The disease has now spread to more than two dozen countries and territories. Some of those infected caught the virus in their local community and have no known link to China, the U.S. Centers for Disease Control and Prevention said.More FromPrognosisIran Reports Sixth Death; Britons on Cruise Return: Virus UpdateItaly Races to Contain Spread of Virus in North After Two DeathsAlmost All Patients in South Korean Psychiatric Ward Have VirusIn South Korea, Opaque Sect Draws Scrutiny With Virus SpikeREAD MORE FROM PROGNOSIS

“We are not seeing community spread here in the United States yet, but it’s very possible — even likely — that it may eventually happen,” Nancy Messonnier, director of the CDC’s National Center for Immunization and Respiratory Diseases, told reporters Friday.

Unlike SARS, its viral cousin, the Covid-19 virus replicates at high concentrations in the nose and throat akin to the common cold, and appears capable of spreading from those who show no, or mild, symptoms. That makes it impossible to control using the fever-checking measures that helped stop SARS 17 years ago.

Spreading Surreptitiously

A cluster of cases within a family living in the Chinese city of Anyang is presumed to have begun when a 20-year-old woman carried the virus from Wuhan, the outbreak’s epicenter, on Jan. 10 and spread it while experiencing no illness, researchers said Friday in the Journal of the American Medical Association.

Five relatives subsequently developed fever and respiratory symptoms. Covid-19 is less deadly than SARS, which had a case fatality rate of 9.5%, but appears more contagious. Both viruses attack the respiratory and gastrointestinal tracts, via which they can potentially spread.

Read More: How the Novel Coronavirus Can (Maybe) Infect You

Food Shopping at Produce Market In Shanghai as Chinese Government Says China Is Entering 'Most Critical Time' in Fighting Disease
A woman wearing a protective mask and a poncho pushes a bicycle past a fresh produce market in Shanghai.Photographer: Qilai Shen/Bloomberg

While more than 80% of patients are reported to have a mild version of the disease and will recover, about one in seven develops pneumonia, difficulty breathing and other severe symptoms. About 5% of patients have critical illness, including respiratory failure, septic shock and multi-organ failure.

“Unlike SARS, Covid-19 infection has a broader spectrum of severity ranging from asymptomatic to mildly symptomatic to severe illness that requires mechanical ventilation,” doctors in Singapore said in a paper in the same medical journal Thursday. “Clinical progression of the illness appears similar to SARS: patients developed pneumonia around the end of the first week to the beginning of the second week of illness.”

Unpredictable Illness

Older adults, especially those with chronic conditions, such as hypertension and diabetes, have been found to have a higher risk of severe illness. Still, “the experience to date in Singapore is that patients without significant co-morbid conditions can also develop severe illness,” they said.

Li Wenliang, the 34-year-old ophthalmologist who was one of the first to warn about the coronavirus in Wuhan, died earlier this month after receiving antibodies, antivirals, antibiotics, oxygen and having his blood pumped through an artificial lung.

Update: Li Wenliang is currently in critical condition. His heart reportedly stopped beating at around 21:30. He was then given treatment with ECMO(extra-corporeal membrane oxygenation).— Global Times (@globaltimesnews) February 6, 2020

The doctor, who was in good health prior to his infection, appeared to have a relatively mild case until his lungs became inflamed, leading to the man’s death two days later, said Linfa Wang, who heads the emerging infectious disease program at Duke-National University of Singapore Medical School.

A similar pattern of inflammation noted among Covid-19 patients was observed in those who succumbed to the 1918 “Spanish flu” pandemic, said Gregory A. Poland, the Mary Lowell Leary emeritus professor of medicine, infectious diseases, and molecular pharmacology and experimental therapeutics at the Mayo Clinic in Rochester, Minnesota.

“Whenever, you have an infection, you have a battle going on,” Poland said in a phone interview Thursday. “And that battle is a battle between the damage that the virus is doing, and the damage the body can do when it tries to fight it off.”

Mild Symptoms

Doctors studying a 50-year-old man who died in China last month found Covid-19 gave him mild chills and dry cough at the start, enabling him to continue working. But on his ninth day of illness, he was hospitalized with fatigue and shortness of breath, and treated with a barrage of germ-fighting and immune system-modulating treatments.

He died five days later with lung damage reminiscent of SARS and MERS, another coronavirus-related outbreak, doctors at the Fifth Medical Center of PLA General Hospital in Beijing said in a Feb. 16 study in the Lancet medical journal. Blood tests showed an over-activation of a type of infection-fighting cell that accounted for part of the “severe immune injury” he sustained, the authors said.

Medical personnel walk among patients with mild symptoms of the COVID-19 coronavirus in a temporary Hospital set up in a sports stadium in Wuhan on Feb. 18.Source: AFP via Getty Images

Controversially, he had been given 80 milligrams twice daily of methylprednisolone, an immune-suppressing corticosteroid drug that’s in common use in China for severe cases, though has been linked to “prolonged viral shedding” in earlier studies of MERS, SARS and influenza, according to the WHO.

The patient’s doctors recommended corticosteroids be considered alongside ventilator support for severely ill patients to prevent a deadly complication known as acute respiratory distress syndrome.

He was given at least double what would typically be recommended for patients with the syndrome and other respiratory indications, said Reed Siemieniuk, a general internist and a health research methodologist at McMaster University in Hamilton, Ontario. Based on what was observed with MERS, the drug may delay viral clearance in Covid-19 patients, he said.

“Corticosteroids could cause more harm than good because of that risk,” Siemieniuk said in an interview. “I wouldn’t want to let a patient die without trying steroids, but I would wait until patients were extremely ill.”(Updates death toll in second paragraph.)Have a confidential tip for our reporters?
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Immunology & Microbiologyfrom TECHNOLOGY NETWORKS

Novel Technology Could Significantly Reduce Coronavirus Diagnostic Time

NEWS   Feb 17, 2020 | Original story from Bar-Ilan UniversityNovel Technology Could Significantly Reduce Coronavirus Diagnostic Time

Credit: CDC, Unsplash Read Time: 2 min

Diagnosing coronavirus takes approximately one hour using current methods. A new technology, based on a combination of optics and magnetic particles, can rapidly test 100 samples of patients potentially infected with the virus and reduce the diagnostic time to approximately 15 minutes.

The time it currently takes to diagnose coronavirus poses one of the greatest challenges in treating infected patients and increases the risk of exposure.  Using the new technology, developed by Dr. Amos Danielli of the Alexander Kofkin Faculty of Engineering, saliva tests can be analyzed within 15 minutes.  The technology has already been proven to reduce the diagnostic time of Zika virus and is currently being used in the Ministry of Health’s central virology laboratory at Tel Hashomer Hospital.

Dr. Danielli’s lab has developed a technology for sensitive detection of virus-specific RNA sequences by attaching the virus’ RNA to a fluorescent molecule that emits light when illuminated by a laser beam. At very low concentrations of RNA, the signal emitted is so low that existing devices cannot detect it. “If we think of the saliva of a corona patient filling an entire room, then this laser beam can be compared to the size of a fist and at low concentrations of virus RNA, there might be only 2–3 fluorescent molecules within that fist,” explains Danielli.  Adding magnetic particles to the solution enables them to adhere to the fluorescent molecules.  This enables a greater concentration of fluorescent molecules and a much more accurate measurement.

Two main goals guided Dr. Danielli in developing this technology – simplifying the diagnostic process and making it more accurate. “This development relies on the use of two small electromagnets, which are magnets powered by an electric current. By properly positioning them, we were able to create a strong magnetic field and collect all the thousands of fluorescent molecules from the entire solution and aggregate them inside the laser beam, thereby multiplying the signal strength by several orders of magnitude. But that’s not all.  Instead of pumping the solution we alternately operate the electromagnets, once on the left and once on the right, moving the molecules from side to side, in and out of the laser beam.  As they pass through the laser beam they become illuminated. When they exit the light beam they are no longer illuminated. This flickering allows us, without any additional procedures, to accurately determine whether a person has been exposed to coronavirus.”

The high sensitivity of the platform and its ease of operation facilitate its use in point of care applications where resources are limited. To provide doctors with an alternative method for accurate detection, Dr. Danielli’s group is also collaborating with European universities to identify antibodies that the immune system produces against coronavirus.

While Dr. Danielli develops kits to identify various diseases, such as the Zika and coronavirus, MagBiosense, a medical device company, is developing a device the size of a home coffee machine that will be based on Danielli’s technology. Currently, Dr. Danielli is searching for an investor to accelerate the development of the coronavirus kit, so it can rapidly be introduced in hospitals.

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.


New Biomarkers for Diagnosis and Prognosis of Lung Cancer

New Biomarkers for Diagnosis and Prognosis of Lung CancerNEWS

Research conducted in the SCBI of the UMA shows, using bioinformatic approaches, that the expression of repetitive DNA regions changes when healthy cells become cancerous. The researchers have identified new biomarkers for the diagnosis, prognosis and even treatment of lung cancer.READ MORE 

Targeting Turncoat Immune Cells To Tackle Cancer

Targeting Turncoat Immune Cells To Tackle CancerNEWS

A newly published study has identified a mechanism by which regulatory T cells, which suppress immune responses, adapt their metabolism to thrive in the tumor microenvironment. This mechanism, is exclusively engaged by regulatory T cells (Tregs) that reside in tumors and could be disrupted to boost the effects of cancer immunotherapy.READ MORE 

Antibiotic Resistance Calls Future of Cancer Treatment Into Question

Antibiotic Resistance Calls Future of Cancer Treatment Into QuestionNEWS

A new study has offered a snapshot of the threat that rising antibiotic resistance poses to the treatment of cancer.READ MORE 

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NIH Scientists Identify Atomic Structure of Novel Coronavirus Protein

NIAID Now | February 19, 2020

The atomic-level structure of the SARS-CoV-2 spike protein in its prefusion conformation. The receptor binding domain, the part of the spike that binds to the host cell, is colored green.

The atomic-level structure of the SARS-CoV-2 spike protein in its prefusion conformation. The receptor binding domain, the part of the spike that binds to the host cell, is colored green.Credit:Credit: UT Austin, McLellan Lab

NIAID scientists working with investigators from the University of Texas at Austin (UT) identified the atomic structure of an important protein on the surface of the novel coronavirus (SARS-CoV-2, formerly called 2019-nCoV). The findings appear in the peer-reviewed journal Science. The authors note that the findings will aid in the design of candidate vaccines and the development of treatments for COVID-19, the disease caused by the new virus, which was first identified in China in December 2019.

Like other coronaviruses, SARS-CoV-2 particles are spherical and have mushroom-shaped proteins called spikes protruding from their surface, giving the particles a crown-like appearance. The spike binds and fuses to human cells, allowing the virus to gain entry. However, coronavirus infection can be prevented or slowed if this process is disrupted.

Scientists in China shared the genome of a SARS-CoV-2 virus isolate to a global database, which NIAID and UT experts used to start their work determining the spike structure. The spike undergoes a massive rearrangement as it fuses the virus and cell membranes. The researchers confirmed that the original spike stabilized in its prefusion conformation is more likely to preserve targets for infection-blocking antibodies induced by a vaccine.

Importantly, the new data supports NIAID’s approach to a gene-based vaccine for COVID-19 and will also be useful in other vaccine approaches including protein-based vaccines and other nucleic acid or vector-based delivery approaches. NIAID scientists designed the stabilized spike antigen based on previous knowledge obtained from studying other coronavirus spike structures. NIAID and the biotechnology company Moderna, based in Cambridge, Massachusetts, are developing a messenger RNA (mRNA) vaccine, which directs the body’s cells to express the spike in its prefusion conformation to elicit an immune response.   

The new research also confirms that the structure of the SARS-CoV-2 spike is very similar to that of the coronavirus responsible for the global outbreak of severe acute respiratory syndrome in 2003 that was eventually contained (known as SARS-CoV). However, despite the similarities, the paper shows that some monoclonal antibodies developed to target SARS-CoV do not bind to the new coronavirus, indicating that antibodies that recognize the SARS-CoV from 2003 will not necessarily be effective in preventing or treating COVID-19, the disease caused by the new virus.

Recent reports show that the novel virus and SARS-CoV also bind to the same receptor on the host cell. However, NIAID and UT scientists determined that SARS-CoV-2 binds more easily to this receptor as compared to SARS-CoV, which could potentially explain why SARS-CoV-2 appears to spread more efficiently from human-to-human. However, more data is needed to investigate this possibility, the authors note.

This research was supported by the NIAID Intramural Research Program and a NIAID grant to the University of Texas at Austin (R01-AI127521).


D. Wrapp, N Wang et al. Cryo-EM Structure of the 2019-nCoV Spike in the Prefusion Conformation. Science DOI: 10.1126/science.abb2507 (2020)

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TAU, Harvard Researchers Build Human ‘Body-On-Chips’ To Better Predict Drug Response

By NoCamels Team February 03, 2020

Human Organs on chips. Screenshot of a video by the Wyss Institute at Harvard University

Health NewsTechnology NewsHuman Organs on chips. Screenshot of a video by the Wyss Institute at Harvard University

biotech breakthrough? A team of over 50 researchers at Harvard University and Tel Aviv University (TAU) have successfully built human “organs-on-chips” that they say will allow scientists to better predict human responses to drugs during trials as a way to speed up drug development, and may offer alternatives to some animal testing.

A total of eight microchips were created to recapitulate the build and functions of living human organs – including the lungs, liver, intestines, kidneys, skin, bone marrow, brain, and the blood-brain barrier. The scientists also built an automated instrument to fluidically link up to 10 “organ chips” to create what they called “a functional human Body-on-Chips platform.

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The chips and the instrument, called the “Interrogator,” were the subject of two new studies published last week in the science journal Nature Biomedical Engineering. The research was conducted by scientists from Harvard’s Wyss Institute for Biologically Inspired Engineering whose founding director Professor Donald Ingber first developed the “organ-on-a-chip” concept a decade ago, and Tel Aviv University’s Department of Biomedical Engineering and Sagol School of Neuroscience led by Dr. Ben Maoz, a former Technology Development Fellow at the Wyss Institute.

In the first study, titled “Robotic fluidic coupling and interrogation of multiple vascularized organ chips,” the scientists presented the modular Body-on-Chips platform and the Interrogator which can culture the microchips and transfer fluids to mimic normal human blood flow between the different organs of our body. The interrogator enabled the team to culture, perfuse, and link the living human cultured tissues in a multi-Organ Chip system, as well as add and sample the medium in a fully programmable way using the device’s robotic liquid transfer capabilities, while continuously monitoring tissue integrity, according to an explanation of the process provided in a release by the Wyss Institute.

The second study, “Quantitative prediction of human pharmacokinetic responses to drugs via fluidically coupled vascularized organ chips,” saw the scientists put the Interrogator to use and apply a new computational model they developed to two different sets of three organ chips – Gut, Liver and Kidney, and Liver, Kidney, and Bone Marrow – linked to each other to test two drugs: nicotine and cisplatin, a common chemotherapy medication.

Interrogator linked chips. Photo: Wyss Institute at Harvard University
Interrogator linked chips. Photo: Wyss Institute at Harvard University

The organs were also linked to a central arterio-venous (AV) fluid mixing reservoir that “helped recapitulate life-like blood and drug exchange between the individual organs, while also providing a way to carry out blood sampling that would mimic blood drawing from a peripheral vein.”

In this study, the researchers accurately modeled the oral uptake of nicotine, which is being investigated as an oral drug for neurodegenerative and inflammatory bowel diseases, and the intravenous uptake of cisplatin, and their first passage through relevant organs with highly quantitative predictions of human pharmacokinetic (involving the quantification of its absorption, distribution, metabolism, and excretion) and pharmacodynamic (involving effects the drug produces on its target organs) parameters.

Dr. Ben Maoz. Photo: Yonatan Tzur via Tel Aviv University
Dr. Ben Maoz. Photo: Yonatan Tzur via Tel Aviv University

“The resulting calculated maximum nicotine concentrations, the time needed for nicotine to reach the different tissue compartments, and the clearance rates in the Liver Chips in our in vitro-based in silico model mirrored closely what had been measured in patients,” said Dr. Maoz in a TAU statement.

As for the cisplatin portion of the study, “the analysis recapitulates the pharmacodynamic effects of cisplatin in patients, including a decrease in numbers of different blood cell types and an increase in markers of kidney injury,” said co-first author Anna Herland, Ph.D., who worked on Ingber’s team. “In addition, the in vitro-to-in vivo translation capabilities of the system produced quantitative information on how cisplatin is metabolized and cleared by the liver and kidney, which will make it suitable for more refined predictions of drug absorption, distribution, metabolism, excretion and toxicity.”

Study implications

Both studies highlighted three key recognitions: animal models do not effectively predict drug responses in humans given the fundamental interspecies differences; this leads to high failure rates in clinical trials that test new drugs for their safety and efficacy in humans; drug development is an already highly expensive, arduous, and lengthy process with just 13.8 percent of all tested drugs demonstrating ultimate clinical success and obtaining approval by the Food and Drug Administration (FDA), according to estimated cited by both Harvard and TAU.

“To solve this massive preclinical bottleneck problem, we need to become much more effective at setting the stage for drugs that are truly promising and rule out others that for various reasons are likely to fail in people,” Professor Ingber said in a university statement.

Gut-on-a-chip interrogator. Photo: Wyss Institute at Harvard University
Gut-on-a-chip interrogator. Photo: Wyss Institute at Harvard University

“The modularity of our approach and availability of multiple validated Organ Chips for a variety of tissues for other human Body-on-Chip approaches now allows us to develop strategies to make realistic predictions about the pharmacology of drugs much more broadly,” he added. “Its future use could greatly increase the success rates of Phase I clinical trials.”

Dr. Maoz said the scientists hope their research will help “bridge the gap on current limitations in drug development by providing a practical, reliable, relevant system for testing drugs for human use.”

There is also the question of animal welfare and what the Harvard announcement called the “increasing ethical concerns relating to the use of animal studies.”

Ingber said the team hopes that their “demonstration that this level of biomimicry is possible using Organ Chip technology will garner even greater interest from the pharmaceutical industry so that animal testing can be progressively reduced over time.”

The Organ Chip technology is licensed by a Wyss Institute-launched startup company, Emulate, which is now further developing and commercializing the technology and automated instruments “to bring these important research tools to biotechnology, pharmaceutical, cosmetics, and chemical companies as well as academic institutions and hospitals for personalized medicine,” the institute said.


Ingber and his team at the Wyss Institute first developed the human “Organ-on-a-Chip” model for the lung, in a study published in Science magazine in 2010. The chips for the additional organs were developed over much of the last decade with teams of collaborators, including the Israeli scientists.

The Organ Chips themselves are made of a clear flexible polymer and contains two parallel hollow channels separated by a porous membrane that allows them to communicate. One channel is lined with cells from a specific human organ or organ structure, the other one is lined with cells presenting a blood vessel.

“Mechanical forces can be applied to mimic the physical microenvironment of living organs, including breathing motions in lung and peristalsis-like deformations in the intestine,” according to an explainer post.

The Organ Chips “are essentially living, three-dimensional cross-sections of major functional units of whole living organs” and “present an ideal microenvironment to study molecular- and cellular-scale activities that underlie human organ function and mimic human-specific disease states, as well as identify new therapeutic targets in vitro.”

“We took a game-changing advance in microengineering made in our academic lab, and in just a handful of years, turned it into a technology that is now poised to have a major impact on society,” Ingber has said.



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Imperial researchers in race to develop a coronavirus vaccine

by Ryan O’Hare03 February 2020

Professor Robin Shattock, from the Department of Infectious Disease, standing in his laboratory

A microbiology lab at Imperial’s St Mary’s campus is at the centre of a scientific race to develop a vaccine against a global viral outbreak.

This article was updated on 5 February 2019

Led by Professor Robin Shattock, from the Department of Infectious Disease, the Imperial team is one of only a handful of research groups in the world currently working to create a viable vaccine against the novel coronavirus.

Since emerging at the end of last year, the virus (called ‘2019-nCoV’) has spread beyond the limits of Wuhan city in China’s Hubei province, causing hundreds of deaths in China and leading to confirmed cases in 24 countries around the world – including two in the UK.

According to Professor Shattock, the difference between this vaccine effort and that of previous outbreaks, such as the SARS outbreak in 2002, is that a vaccine could potentially be produced much faster than conventional methods.

“We have the technology to develop a vaccine with a speed that’s never been realised before,” he explains.

Professor Shattock added: “We have successfully generated our novel coronavirus vaccine candidate in the lab – just 14 days from getting the genetic sequence to generating the candidate in the lab. This will go into the first animal experiments on Monday [10th February].

“If this work is successful, and if we secure further funding, the vaccine could enter into clinical studies (with human participants) in early Summer.”

Imperial’s Ryan O’Hare spoke to Professor Shattock to find out more about the virus at the centre of the outbreak and the road to developing a vaccine.

Listen to the full interview.

Article text (excluding photos or graphics) © Imperial College London.

Photos and graphics subject to third party copyright used with permission or © Imperial College London.


Ryan O’Hare
Communications and Public AffairsClick to expand or contract


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Transplant device keeps hearts alive for 24 hours

Tom Whipple, Science Editor, SeattleFriday February 14 2020, 12.01am GMT, The TimesPlay Video

A machine the size of a suitcase that can keep a heart alive outside the body for 24 hours could increase the number of organs available for transplant, potentially saving thousands of lives.

The device, which has been demonstrated on pig hearts, quadruples the maximum time a heart can be prevented from deteriorating while a transplant recipient is found.

It is awaiting approval for human trials, and if it can be shown to work in people then it would buy surgeons more time to match donors to patients.

It is estimated that in the US as many as three quarters of viable hearts are rejected because they cannot be used in time. In the UK, travel time is less of an issue, but the NHS

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ARTICLES of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 2Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 3The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA. 4Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 5Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 6Department of Oncological Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, UT, USA. 7Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 8Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 9These authors contributed equally: C. Allison Stewart, Carl M. Gay, Yuanxin Xi. *e-mail: lbyers@mdanderson.orgSmall-cell lung cancer (SCLC) is a high-grade neuroendocrine lung carcinoma notable for early dissemination and robust, albeit transient, responses to frontline platinum-based therapy that are rapidly followed by refractory relapses. Initial responses occur in more than 60% of patients. However, following relapse, approved therapies are effective in <20% of patients, underscoring a dramatic shift towards resistance. As a result, the median survival for these patients is only 12 months1. Improved molecular under-standing of SCLC has translated into only modest clinical improve-ments and, consequently, the National Cancer Institute identified SCLC as a recalcitrant malignancy with urgent areas of research needs, including identifying and targeting novel vulnerabilities, developing xenograft models and defining mechanisms underlying rapid therapeutic resistance2.Tissue sampling of SCLC tumors typically occurs only at the time of diagnosis and is often limited to scant cells acquired via fine-nee-dle aspiration. Patients rarely undergo surgical resection at any time or post-relapse biopsy. Several groups have illustrated the utility of engineering xenograft models of SCLC using circulating tumor cells (CTCs)3–5. SCLC CTCs represent metastatic cell populations and their abundance reflects the burden of disease6. SCLC CTC-derived xenograft (CDX) models faithfully reflect the molecular landscape and clinical response of the original patient tumors3,4,7. In addi-tion to CDX generation, liquid biopsies from patients with SCLC provide a valuable resource for performing protein or transcriptional profiling of circulating tumor and immune cells. Furthermore, veni-puncture is a minimally invasive procedure enabling valuable access to tumor cells at instances in SCLC’s natural history that were previ-ously elusive, including post-relapse or longitudinally4.SCLC was once considered a molecularly homogeneous malig-nancy; however, recent analyses led to the classification of molecu-lar subtypes based on intertumoral heterogeneity in the expression of ASCL1, NEUROD1 (refs. 8,9), POU2F3 (ref. 10), NKX2-1 (ref. 11), Notch pathway genes12, MYC family genes and neuroendocrine genes among others13. Often, the predominant subtype mediates specific therapeutic vulnerabilities, as in the relative sensitivity of MYC/NEUROD1-driven SCLC to Aurora kinase inhibitors9,11. Intratumorally, heterogeneity is evidenced in genetically engineered mouse models of SCLC by the juxtaposition of persistent neuro-endocrine cells and non-neuroendocrine, chemoresistant cells12. These findings suggest that the coexistence of transcriptionally het-erogeneous tumor cell populations with distinct vulnerabilities and resistance mechanisms could underlie the ability of SCLC to evolve rapidly from chemosensitive to overwhelmingly chemoresistant. Single-cell analyses reveal increased intratumoral heterogeneity after the onset of therapy resistance in small-cell lung cancerC. Allison Stewart1,9, Carl M. Gay1,9, Yuanxin Xi2,9, Santhosh Sivajothi3, V. Sivakamasundari3, Junya Fujimoto4, Mohan Bolisetty3, Patrice M. Hartsfield1, Veerakumar Balasubramaniyan5, Milind D. Chalishazar6, Cesar Moran7, Neda Kalhor7, John Stewart7, Hai Tran1, Stephen G. Swisher8, Jack A. Roth8, Jianjun Zhang1, John de Groot5, Bonnie Glisson1, Trudy G. Oliver6, John V. Heymach1, Ignacio Wistuba4, Paul Robson3, Jing Wang2 and Lauren Averett Byers 1*The natural history of small-cell lung cancer (SCLC) includes rapid evolution from chemosensitivity to chemoresistance, although mechanisms underlying this evolution remain obscure due to the scarcity of post-relapse tissue samples. We gen-erated circulating tumor cell (CTC)-derived xenografts from patients with SCLC to study intratumoral heterogeneity (ITH) via single-cell RNA sequencing of chemosensitive and chemoresistant CTC-derived xenografts and patient CTCs. We found globally increased ITH, including heterogeneous expression of therapeutic targets and potential resistance pathways, such as epithelial-to-mesenchymal transition, between cellular subpopulations following treatment resistance. Similarly, serial profil-ing of patient CTCs directly from blood confirmed increased ITH post-relapse. These findings suggest that treatment resis-tance in SCLC is characterized by coexisting subpopulations of cells with heterogeneous gene expression leading to multiple, concurrent resistance mechanisms. These findings emphasize the need for clinical efforts to focus on rational combination therapies for treatment-naïve SCLC tumors to maximize initial responses and counteract the emergence of ITH and diverse resistance mechanisms.NATURE CANCER | CANCERHowever, the exploration of such intratumoral heterogeneity (ITH) requires genomic and/or expression analysis of large numbers of cells, which in SCLC has been hindered by the lack of biopsy speci-mens and animal models of relapsed disease. Overall, many ques-tions remain regarding the scale and evolution of ITH in SCLC and how it contributes to clinical outcomes.To investigate the contribution of heterogeneity to therapeutic resistance, we generated CDX models from patients with SCLC that mimic patient tumor genomics and response to chemotherapy. We performed baseline single-cell RNA sequencing (RNA-Seq) analy-ses of platinum-sensitive and -resistant CDX models, as well as lon-gitudinal single-cell RNA-Seq analyses of CDX models and patient CTCs over the course of therapy. With the onset of resistance in each model, we observed not only increases in ITH, but the emer-gence of distinct cellular populations defined by established drug resistance gene signatures. To confirm this relationship between ITH and resistance, platinum-sensitive CDX models were subjected to extended treatment with cisplatin chemotherapy or DNA damage response inhibitors until relapse. Single-cell RNA-Seq confirmed that untreated tumors were molecularly homogeneous, while relapse was associated with increased ITH and distinct variations in the expression of therapeutic targets or epithelial-to-mesenchymal transition (EMT) genes between cellular populations. Longitudinal single-cell profiling of CTCs directly from patient blood before, during and after platinum relapse confirmed increased ITH post-relapse accompanying unique gene expression patterns within spe-cific cell populations that were reflected in a paired CDX. These findings suggest that, in response to treatment, SCLC develops increasing transcriptional ITH marked by concurrent, diverse resistant cell clusters. Clinically, these findings underscore the importance of maximizing and maintaining the initial response in platinum-sensitive SCLC tumors, and highlight the intrinsic tran-scriptional fluidity underlying the profound treatment resistance of SCLC following initial therapy.ResultsSingle-cell transcriptomic analysis of CDXs. We generated CDX models from patients with SCLC at various treatment milestones. From these, we selected models to represent a range of expected platinum responses. The predicted platinum-sensitive models included those derived from patients (MDA-SC39, MDA-SC68 and HCI-008) who were treatment naïve and another (MDA-SC4) who had received only a single cycle of platinum-based chemotherapy. Meanwhile, predicted platinum-resistant models included those derived from patients (MDA-SC16, MDA-SC49 and MDA-SC55) following relapse post-frontline platinum therapy and another (MDA-SC75) who, while treatment-naïve, had minimal response to frontline platinum chemotherapy (Fig. 1a). As expected3,4, these tumors were histologically consistent with SCLC and with the diagnostic immunohistochemistry (IHC) performed on the corresponding patient tumors (see patient IHC data in Extended Data Fig. 1a,b). Interestingly, spontaneous leptomeningeal disease developed and was detected in mice with MDA-SC39 flank tumors (Fig. 1b). This observation provides histological evidence of spon-taneous leptomeningeal disease in SCLC xenograft models and is consistent with the presence of central nervous system metastasis in the patient (Extended Data Fig. 1c).Consistent with the patients’ clinical histories, MDA-SC4, MDA-SC39 and MDA-SC68 were extremely sensitive to cispla-tin in vivo, while MDA-SC16 and MDA-SC49 were resistant and MDA-SC75 exhibited an intermediate response (stable disease; Fig. 1c). Additionally, CDX tumors were genomically consistent with SCLC, including universal loss of TP53 and frequent loss of RB1 (Extended Data Fig. 1d). Tumor genomics were stably preserved across multiple passages (Extended Data Fig. 1e). Of note, for patient MDA-SC49, a patient-derived xenograft (PDX) model was derived from a malignant pleural effusion on the same day blood was collected for CTCs, yielding paired CDX and conventional PDX models for this patient. These models are genomically, transcriptionally and pro-teomically (Extended Data Fig. 1a,f,g) similar and match what is known of the patient from whom they were derived (for example, loss of TTF1, thyroid transcription factor-1, in Extended Data Fig. 1b).To investigate ITH, CDX tumors (n = 1–3 per model) were dis-sociated, sorted for human cells to remove any potential mouse contribution and subjected to single-cell RNA-Seq analysis (>3,500 cells per model; Fig. 1d). The sequenced cells specifically represent the cancer cell population, as confirmed by expression of neuroen-docrine markers (UCHL1, NCAM1, SYP and CHGA) in sequenced cells from all models (Extended Data Fig. 2a). As with other can-cers14, clustering of cells was primarily driven by the patient/CDX of origin (intertumoral heterogeneity; Fig. 1e). In general, the exis-tence of such intertumoral heterogeneity is representative of SCLC and lung cancers15,16. Furthermore, intertumoral heterogeneity between patients has previously been shown in single-cell profiling of tumor biopsies14.The ITH is not driven by individual tumors, as single-cell RNA-Seq analysis of replicate samples reveals admixing of cells from dis-tinct, biological CDX replicates from the same model and passage grown in different mice (Extended Data Fig. 2b). Consistent with SCLC and corresponding patient tumor IHC, all CDXs contained large numbers of cells expressing neuroendocrine-specific genes (NCAM1, SYP and CHGA), while one CDX exhibited loss of NKX2-1 and high MYC (confirmed by whole-exome sequencing)—a feature observed in ~20% of SCLC tumors17 (Fig. 1f). Expression of these SCLC marker genes, as with other genes investigated, shows vari-ability in expression within cells from each model.Classifying SCLC molecular subtypes via single-cell RNA-Seq. Using the single-cell RNA-Seq data, we first established the molecular subtypes of the CDX models based on their expression of both neu-roendocrine and non-neuroendocrine markers, ASCL1/NEUROD1/POU2F3/YAP1 transcription factors, MYC family members and EMT score in CDXs10,16,18,19. Single-cell RNA-Seq analyses revealed that CDXs are predominantly neuroendocrine (Extended Data Fig. 2c). Abundant expression of ASCL1 was detected in both platinum-sensi-tive (MDA-SC4s, MDA-SC39s, MDA-SC68s and HCI-008s) and plat-inum-resistant CDXs (MDA-SC16r, MDA-SC55r and MDA-SC75r), while one platinum-resistant model (MDA-SC49r) expressed high NEUROD1 (Fig. 1g and Extended Data Fig. 2d). No CDX contained large populations of cells expressing POU2F3 or YAP1.As MYC family members are amplified in ~20% of SCLCs19 and play a role in tumor propagation and drug resistance in SCLC and other tumors9,11,20, we investigated whether the expression of MYC, MYCL and MYCN was enriched in our CDX tumors. MYC was expressed by a moderate number of cells in two CDXs (MDA-SC39s and MDA-SC49r), while MYCL was expressed in three CDXs (MDA-SC4s, MDA-SC49r and MDA-SC55r) and MYCN was abun-dantly expressed in only MDA-SC68s (Fig. 1h and Extended Data Fig. 2e). Notably, we also witnessed evidence invivo of aggressive behavior imparted by MYC activation, as in MDA-SC39s, wherein we observed spontaneous leptomeningeal metastasis (Fig. 1b). This is consistent with a known role for MYC in driving central nervous system metastatic potential21,22.Like MYC family genes, EMT is associated with treatment resis-tance and metastasis23,24. SCLC is a primarily epithelial malignancy, with high expression of epithelial genes (CDH1, EPCAM and so on) in most CDXs at the single-cell level. However, three CDX models (MDA-SC39s, HCI-008s and MDA-SC49r) have a rela-tively higher proportion of cells expressing the mesenchymal gene encoding vimentin (VIM) and elevated EMT scores, consistent with epithelial-to-mesenchymal transformation (Extended Data Fig. 2f). As with MYC family gene expression, we observed that EMT score NATURE CANCER | CANCER1234567123456788a bcMaximum % baseline change1,00070050020010020–30–100t-SNE 1dt-SNE 2DissociateCDX tumorSelecthuman cellsRemove mousecellsDroplet-basedsingle-cellRNA-SeqMDA-SC4sMDA-SC39sMDA-SC68sHCI-008sMDA-SC16rMDA-SC49rMDA-SC55rMDA-SC75rCDXet-SNE 2t-SNE 1fCHGANCAM1NKX2-1SYPNCAM1 expressionSYP expressionCHGA expressionNKX2-1 expressiongNEUROD1ASCL1POU2F3YAP1ExpressionMYC1.50MYCL2.50MYCN1.750t-SNE 2t-SNE 2t-SNE 2t-SNE 1 t-SNE 1t-SNE 1hKi67 NCAM CHGA SYP TTF140200–20–4040200–20–4040200–20–4040200–20–404321043210864206420250–25–50250–25–50250–25–50250–25–50250–25–50250–25–500642MDA-SC4MDA-SC39MDA-SC68HCI-008MDA-SC16MDA-SC49MDA-SC55MDA-SC755 10Months15 25Platinum-based txNo txOther systemic txImmunotherapyCDXgeneratedDeathFig. 1 | SCLC CDXs mimic patient disease at the single-cell transcriptional level and by platinum response. a, Schematic of patient clinical courses, including the time points at which blood was collected for CDX generation (red triangles). Solid and dashed bars indicate treatment and are drawn to scale. tx, treatment. b, Histological images of leptomeningeal disease detected in the brain of MDA-SC39 (arrow) at different magnifications (top), including characterization of standard SCLC markers (bottom). The presence of leptomeningeal disease was detected in one of five mice whose brains were examined. Scale bars: 1 mm (left), 100 µm (top right) and 10 µm (bottom right). CHGA, chromogranin A; NCAM, neural cell adhesion molecule; SYP, synaptophysin; TTF1, thyroid transcription factor-1. c, Waterfall plot of the maximum change in tumor from baseline following the treatment of CDXs with cisplatin. MDA-SC4s, MDA-SC39s, MDA-SC68s and HCI-008s were platinum sensitive, while MDA-SC16r, MDA-SC49r, MDA-SC55r and MDA-SC75r were platinum resistant (see legend to the right of f). d, Schematic showing the method for performing single-cell RNA-Seq on CDXs with individual cell clusters indicated on in the t-SNE visualization on the right. e, t-SNE analysis of eight CDXs. Cells from each CDX were more similar to themselves than to other models. f, Violin plots indicating the range of expression of NCAM1, SYP, CHGA and NKX2-1 (TTF1) in single cells from each CDX. Each dot represents one cell and the violin curves represent the density of cells at different expression levels. g, Expression patterns of ASCL1, NEUROD1, POU2F3 and YAP1 genes within each CDX. h, t-SNE feature plots showing the heterogeneity of expression of MYC, MYCL and MYCN in all CDXs (identified by the color coding in e). In e–g, n = 2,000 cells.NATURE CANCER | CANCERis not uniform across cells within a single tumor, suggesting that more complex transcriptional programs also demonstrate ITH. For example, in the platinum-resistant model MDA-SC49r, we observed significant fractions of high and low MYC-expressing cells, high and low MYCL expressing cells, and mesenchymal and epithelial cells (Fig. 1h and Extended Data Fig. 2e,f). Observations such as these suggested that ITH may underlie the capacity for concur-rent, seemingly unrelated resistance mechanisms within individual tumors and that global increases in ITH may accompany the onset of therapeutic resistance.Baseline ITH in CDX models. To estimate the degree of ITH in our models, we identified genes with highly variable expression within individual CDXs after correcting for cell cycle. To quantitate het-erogeneity, an ITH score was calculated for each CDX tumor. We define the ITH score as the average distance between the normal-ized expression profiles of each cell and all other cells in the sam-ple and found higher ITH scores in the platinum-resistant CDXs (P < 2.2 × 10−16; Fig. 2a). ITH score selects first for the most highly variable genes, followed by comparison in principle component space, introducing some limitations to this approach. As an alterna-tive measure of ITH, we also calculated the dispersion between cells within the t-distributed stochastic neighbor embedding (t-SNE) plot for each tumor and similarly found greater dispersion in the gene expression profiles of the platinum-resistant CDXs (P = 0.05; Fig. 2b). Using t-SNE analysis for each individual CDX, we deter-mined the optimum number of clusters in each model (Extended Data Fig. 3a). We discovered between two and three distinct single-cell clusters in each platinum-sensitive model, whereas between four and nine single-cell clusters were observed in platinum-resistant models (platinum sensitive versus resistant: P < 0.01; Fig. 2c and Extended Data Fig. 3b). We observed that MDA-SC75r, although derived after minimal exposure to platinum, possessed high ITH and cluster number. Notably, the patient from whom this tumor was derived exhibited denovo platinum resistance.Unsupervised analyses were performed to determine whether ASCL1-driven platinum-resistant CDX models shared common signaling genes or potential regulatory mechanisms and found that the majority of genes differentially expressed between platinum-sensitive and -resistant CDXs were only detectable in a single CDX rather than commonly upregulated in two or more resistant models (Fig. 2d). Next, we performed gene set enrichment analysis (GSEA) to assess gene expression pathways associated with specific cell clusters in each CDX (Fig. 2e). As expected, platinum-resistant CDX mod-els showed increased variance (Extended Data Fig. 3c; P = 2.9 × 10−6; n = 21 pathways) and complexity with upregulation of multiple, unique, resistance-associated pathways specific to cellular subpopu-lations. While many of these pathways have previously been shown to be associated with platinum resistance in SCLC (MYC, mamma-lian target of rapamycin, EMT, WNT and so on)4,5,23,25, coexistence within a single tumor is a key finding. Platinum-sensitive SCLC does exhibit ITH with regard to gene expression (that is, MYC family expression), EMT, ITH score and pathway analysis, which may allow it to diversify rapidly to an even more heterogeneous state. Additionally, inferred copy-number analysis was performed to com-pare clusters in the resistant CDXs, and copy-number variation was similar between clusters within a CDX. This supports that ITH is driven by transcriptional changes and not genomic or copy-number changes (Extended Data Fig. 4a,b). These results suggest that not only are the mechanisms of cisplatin resistance distinct between tumors but, even within a single tumor, targeting multiple path-ways (for example, MYC and EMT) may be necessary to overcome platinum resistance in SCLC.Transcriptional diversity between subpopulations. Comparing gene expression between baseline platinum-sensitive and -resis-tant CDXs failed to detect any single gene or gene pair that clearly defined resistance or ITH, suggesting more complex molecular pro-cesses. GSEA clarified some of this complexity by identifying path-ways associated with resistance that are druggable and in clinical investigation (for example, mitotic spindle, DNA repair/E2F1, EMT and so on), leading us to focus specifically on therapeutic target genes (Aurora kinase genes, PARP1, CHEK1 and so on) and EMT-associated genes. Little intercluster variation in expression among these genes was detectable in the treatment-naïve CDXs (Fig. 3a). In contrast, platinum-resistant CDXs showed significant diversity in expression of either therapeutic targets or EMT between clusters from a single CDX. Many SCLC therapeutic targets are part of the DNA repair pathway26 and a similar pattern of expression was detected for genes associated with DNA repair (Extended Data Fig. 4c). As expected, clusters with high MYC expression tended to have low BCL2 expression and vice versa27. To highlight a few genes at the single-cell level, AURKA, AURKB and DLL3 (each a putative thera-peutic target in SCLC) exhibited elevated expression only within specific clusters from platinum-resistant MDA-SC16r, MDA-SC55r and MDA-SC75r (Fig. 3b). As AURKA and AURKB encode targets of Aurora kinase inhibitors, the heterogeneity of their expression among the platinum-resistant models may have important implica-tions for depth of response to this class of agents.Increased expression of these target-encoding genes was not detectable in clusters of treatment-naïve models or MDA-SC49r (Extended Data Fig. 4d). In the case of MDA-SC49r, cells exhibited increased but varying expression of EMT-associated genes, includ-ing VIM, among clusters (Fig. 3c). The VIM-high clusters (clusters 1–3) also expressed high levels of NFIB—a gene associated with disease dissemination in both patients with SCLC and GEM mod-els28–30. Unexpectedly, variation in the expression of either DNA repair or EMT genes was mutually exclusive (Fig. 3d). Overall, this suggests that resistant SCLC is characterized by diverse expression of putative resistance mechanisms and targets.Fig. 2 | Platinum-resistant disease is associated with increased ITH. a, Platinum-resistant CDXs exhibited higher ITH scores than platinum-sensitive CDXs, as determined by two-sided Wilcoxon rank-sum test. No adjustments were made for multiple comparisons (P < 2.2 × 10−16; n = 2,000 cells per CDX). b, Gene expression dispersion for platinum-sensitive and platinum-resistant CDXs by two-sided Wilcoxon rank-sum test (P = 0.05; n = 8 CDX models). c, t-SNE visualization of cell subpopulations from individual CDXs (n = 2,000 per CDX). d, Expression heatmap of common and model-specific differential genes for ASCL1-driven sensitive CDXs (MDA-SC4s, MDA-SC39s and MDA-SC68s) and resistant CDXs (MDA-SC16r, MDA-SC55r and MDA-SC75r). The differential genes were identified by the intersection of upregulated genes in resistant CDXs compared with each sensitive CDX. Genes commonly upregulated in at least two resistant CDXs (GRP, TCF4 and HES6), or specifically in MDA-SC16r (CDKN2A, NKX2-1, STAT1, TOP2A, NFIB and NEAT1), MDA-SC55r (ASCL2, KDM1A and MALAT1) or MDA-SC75r (PGAM2 and NBL1) were identified. The statistical cutoffs were set to adjusted P < 0.05 (two-sided Wilcoxon rank-sum test) and log2[fold change] > 0.7 (n = 6 CDX models). e, GSEA with normalized enrichment score (NES) and false discovery rate (FDR) q values for hallmark gene sets associated with clusters in all eight CDXs. No statistical method was used to predetermine sample size. Statistical significance was determined by GSEA Kolmogorov–Smirnov test. P values were adjusted for multiple comparisons. AKT, protein kinase B; IL, interleukin; JAK, Janus kinase; mTOR, mammalian target of rapamycin; mTORC1, mTOR complex 1; NF-κB, nuclear factor κB; PI3k, phosphoinositide 3-kinase; ROS, reactive oxygen species; STAT, signal transducer and activator of transcription; TGF-β, transforming growth factor β; TNF-α, tumor necrosis factor α. In a and b, center lines show median values, box limits indicate the 25th and 75th percentiles and whiskers extend 1.5× the interquartile range from the 25th and 75th percentiles.NATURE CANCER | CANCERSingle-cell transcriptomic profiling of patient CTCs. Levels of CTCs in SCLC are among the highest of all solid tumors3,31–34. To determine whether CTC quality and number in patients with SCLC are sufficient to sequence directly, we collected blood from patient MDA-SC55 before platinum–etoposide treatment (diagnosis), at maximum response (responding) and following relapse (relapsed) (Fig. 4a). CTC abundance reflected the patient’s clinical course, with the greatest number of CTCs found in the treatment-naïve (84 CTCs) and relapsed samples (627 CTCs), compared with maximum response (one CTC) (Fig. 4a). CTCs were isolated for CDX genera-tion at all three time points, but a model was generated only follow-ing relapse (MDA-SC55r CDX).PlatinumsensitivePlatinumresistantITH scorePlatinum sensitivePlatinum resistantt-SNE 2 t-SNE 2t-SNE 2 t-SNE 2t-SNE 2 t-SNE 2t-SNE 2 t-SNE 2t-SNE 1t-SNE 1t-SNE 1t-SNE 1t-SNE 1t-SNE 1t-SNE 1t-SNE 1abcDispersionPlatinumsensitivePlatinumresistantMDA-SC4sMDA-SC39sMDA-SC68sHCI-008sMDA-SC16rMDA-SC49rMDA-SC55rMDA-SC75rCDXPlatinum sensitive Platinum resistantCommongenesMDA-SC16rMDA-SC55rMDA-SC75rdE2F targetsPI3K/AKT/mTORG2/M checkpointMitotic spindleMYC targets v1DNA repairROSMYC targets v2mTORC1ApoptosisHypoxiaIL-2/STAT5EMTp53IL-6/JAK/STAT3TNF-α via NF-κBAngiogenesisHedgehogWnt/β-cateninNOTCHTGF-βCluster 12 12 12123 12341234 12345678 123456789MDA-SC4sMDA-SC39sMDA-SC68sHCI-008sMDA-SC16rMDA-SC49rMDA-SC55rMDA-SC75rPlatinum sensitive Platinum resistantNESe18161412068100. MDA-SC49rMDA-SC68s HCI-008sMDA-SC55r40200–20–4040200–20–4040200–20–4040200–20–4040200–20–4040200–20–4040200–20–4040200–20–4040200–20–4040200–20–4040200–20–4040200–20–4040200–20–4040200–20–4040200–20–4040200–20–400–242Expression1032–1–2FDR0.250.1<0.01Cluster341278569NATURE CANCER | CANCERTherapeutic targets/biomarker expressionUndergo EMTNaïve RelapseAURKA expressionAURKAAURKB expressionAURKBMDA-SC55rAURKA expressionAURKAMDA-SC75rAURKB expressionAURKBMDA-SC16rAURKA expressionAURKAAURKB expressionAURKBDLL3 expressionDLL3DLL3 expressionDLL3DLL3 expressionDLL3abcVIM expressionVIMdNFIB expressionNFIBCluster121212 1234 123412345678 123456789123MDA-SC4sMDA-SC39sMDA-SC68sHCI-008sMDA-SC16rMDA-SC49rMDA-SC55rMDA-SC75rPlatinum sensitive Platinum resistantTherapeutic targetsEMTZEB2VIMTWIST1ZEB1EMT scoreMYCBCL2TOP2BKDM1ADLL3TOP1EMT scoreNormalizedexpressionPARP1TOP2AAURKBAURKACHEK1EZH2VEGFAClusterClusterClusterClusterClusterClusterClusterClusterClusterClusterCluster43210432104321043210321032103210321032103210642032341214321432143214 3214769853214765832147698532147658321476985321476581.–0.5–1.0Fig. 3 | Platinum resistance is associated with heterogeneous expression of therapeutic targets or EMT-related genes within specific clusters. a, Expression of specific therapeutic targets and EMT-related genes, and EMT scores in the clusters from all eight CDXs. Little variation was detected in clusters from platinum-sensitive CDXs. b, Violin plots of the expression of several therapeutic targets (AURKA, AURKB and DLL3) within clusters from three platinum-resistant CDXs (MDA-SC16r, MDA-SC55r and MDA-SC75r). For MDA-SC16r, n = 438, 557, 554 and 451 cells for clusters 1–4, respectively. For MDA-SC55r, n = 271, 216, 360, 365, 384, 190, 148 and 66 cells for clusters 1–8, respectively. For MDA-SC75r, n = 109, 69, 228, 453, 233, 187, 296, 207 and 218 cells for clusters 1–9, respectively. c, Violin plots of VIM and NFIB expression within individual clusters from MDA-SC49r. Each dot represents one cell, and the violin curves represent the density of the cells at different expression levels (n = 683, 317, 652, 348 cells for clusters 1–4, respectively). d, Schematic indicating that changes in gene expression in the platinum-sensitive cells give rise to variation in either therapeutic targets or EMT-related genes.NATURE CANCER | CANCERFollowing single-cell RNA-Seq profiling, t-SNE analysis clas-sified the major cell types present in the samples. Based on gene expression, we determined that six clusters comprised non-CTCs (for example, white or red blood cells), while two were composed of CTCs (Fig. 4b). To verify the identification of CTCs compared with non-CTCs, the expression of established SCLC neuroendocrine markers, including UCHL1, NCAM1, SYP and CHGA, was evalu-ated (Fig. 4c). We further evaluated the percentage of cells express-ing mesenchymal and epithelial genes (as defined in the EMT signature) and those associated with expression of the major SCLC transcription factors ASCL1, NEUROD1 and POU2F3 (Extended Data Fig. 5) and found the highest percentage of cells expressing epithelial, neuroendocrine and lineage-specific genes in the CTC clusters. Marker genes were pooled due to small numbers of CTCs sequenced and fewer genes detected per cell compared with CDXs.Among the CTC populations, t-SNE analysis identified five unique CTC clusters (Fig. 4d), while the CDX cells comprised eight clusters (Fig. 2b). The majority of treatment-naïve CTCs (62%) were present in cluster 1, with the remaining clusters composed of mostly relapsed CTCs (Fig. 4e). The ITH scores of relapsed CTC and CDX cells were higher than those of cells at diagnosis (P = 3.0 × 10−17; Fig. 4f). CTCs and CDX cells derived from the same liquid biopsy were similar with respect to SCLC molecular subtypes. Both CTC and CDX cells expressed similar levels of CHGA and ASCL1 (Fig. 4g); however, MYCL and NFIB were both expressed at lower levels in the CDX (Fig. 4h). This suggests that these genes were expressed by metastatic cells in the circulation but not required in the pri-mary tumor, which supports the role of Mycl and Nfib in SCLC dissemination in GEM models20,29,30. Variations in the expression of therapeutic targets were detectable in relapsed CTCs and CDXs derived from the same patient, including MYC, BCL2, KDM1A, TOP1, TOP2A and VEGFA (Fig. 4i). Few of the therapeutic target genes are expressed by the CTCs at diagnosis. Overall, the transcrip-tional diversity between cellular subpopulations in relapsed CTCs and CDXs highlights the complexity of treatment resistance within patients with SCLC.Single-cell analysis of CDXs developing treatment resistance. To determine whether relapsed SCLC features observed in CDXs derived from different patients, such as increasing ITH score and transcriptional diversity between clusters, could be recapitulated by treating a platinum-sensitive CDX model with cisplatin chemo-therapy invivo, a treatment-naïve platinum-sensitive CDX (MDA-SC68s) was treated with vehicle or cisplatin until relapse (Fig. 5a) and single-cell profiling was performed. Following the onset of treatment resistance in the cisplatin-treated tumor, transcriptional differences were observed between the vehicle and cisplatin-treated MDA-SC68 cells based on t-SNE clustering (Fig. 5b,c). Cells from the vehicle-treated MDA-68 cells clustered separately from the cisplatin-relapsed cells, with greater ITH in the cisplatin-treated tumor compared with the vehicle-treated tumor. The number of clusters increased from three in the vehicle-treated tumor (Fig. 2b) to five in the cisplatin-treated tumor (Fig. 5d). The ITH score was also significantly higher in the cisplatin-treated tumors (P < 2.2 × 10−16; Fig. 5e). To determine whether transcriptional diversity in either therapeutic targets or EMT genes was detectable between the paired tumors after the onset of cisplatin resistance, we compared expression between vehicle and cisplatin-treated clus-ters (Fig. 5f). Unbiased principal component analysis revealed that the first component was associated with EMT score specifically in the cisplatin-treated tumor (Fig. 5g, left) and that cells with an elevated EMT score were located within cluster 3 (Fig. 5g, right). Accordingly, VIM was upregulated and EPCAM was downregulated within this population (Extended Data Fig. 6a,b). Interestingly, the onset of platinum resistance was associated with a decrease in ASCL1-expressing cells (P < 0.0001), with no change in NEUROD1 expression (Fig. 5h). DLL3 expression was decreased specifically within the EMT cluster (Fig. 5h,i). This suggests that in MDA-SC68s the relatively platinum-sensitive ASCL1-positive cells were replaced by mesenchymal cells following platinum treatment. These findings are consistent with a recent study observing decreased Achaete-scute homologue 1 (ASCL1) protein expression in chemotherapy-relapsed tumors from a cohort of patients with SCLC25.To determine whether the emergence of new clusters also occurs with targeted therapies, another platinum-sensitive CDX (MDA-SC4s) was treated with a poly (ADP-ribose) polymerase (PARP) inhibitor (PARPi; talazoparib) or checkpoint kinase 1 (CHK1) inhibitor (CHKi; prexasertib) until relapse developed and single-cell profiling was performed. The CDX selected was initially sensitive to both DNA damage response (DDR) inhibitors (Fig. 6a). Pooled t-SNE analysis of vehicle, PARPi- and CHKi-relapsed samples (n = 2–3 individuals each) identified three clusters (Fig. 6b). The majority of untreated cells (>99%), as well as subsets of PARPi-relapsed cells (20%) and CHKi-relapsed cells (32%) lay in cluster 1. In contrast, the two additional clusters were treatment specific, containing almost exclusively either cells from the PARPi-relapsed tumors (98% of cells in cluster 3) or cells from CHKi-relapsed tumors (97% of cluster 2) (Fig. 6b,c), suggesting that unique resis-tance mechanisms occur in response to specific therapies. A greater ITH score was calculated in the PARPi- or CHKi-relapsed samples compared with untreated ones (P = 7.5 × 10−132 and P = 4.4 × 10−65, respectively; Fig. 6d). Thus, while PARP1 and CHK1 are both targets of interest in SCLC and lead to DNA damage, the emergent tran-scriptional profiles during resistance to the inhibitors are distinct.DiscussionSCLC liquid biopsies are a noninvasive collection method that facilitates both serial and post-relapse tissue sampling and contain Fig. 4 | Serial single-cell RNA-Seq analysis of patient CTCs revealed similar transcriptional heterogeneity to a paired CDX. a, Top left: schematic of the clinical course of patient MDA-SC55 at the time blood was collected for CTC analysis and CDX generation. Right: patient MDA-SC55 body scans performed at the time blood was collected (red: lung primary tumor; yellow: liver metastases). Bottom left: CTC numbers at the time of collection. These samples were collected from one patient along the course of treatment and represent an isolated collection at a specific time point. b, t-SNE plot of pooled MDA-SC55 cells at diagnosis, responding and relapsed time points revealing eight separate clusters. Clusters 2 and 7 were identified as CTCs (circled). RBCs, red blood cells. c, t-SNE plot of cells expressing neuroendocrine genes to identify CTCs. d, Left: t-SNE plots of all MDA-SC55 CTCs by time point (green: at diagnosis; brown: at relapse). Right: t-SNE visualization of CTC cell clusters from all time points (n = 712 cells). e, Contribution of cells from the diagnosis or relapsed time points within each of the CTC clusters. f, CTCs and CDX cells from the relapsed time point have a higher ITH score by Kruskal–Wallis test than CTCs collected at diagnosis (diagnosis CTCs versus relapsed CTCs and diagnosis CTCs versus relapsed CDX: P = 3.0 × 10−17; diagnosis CTCs: n = 84 cells). Center lines show median values, box limits indicate the 25th and 75th percentiles and whiskers extend 1.5× the interquartile range from the 25th and 75th percentiles. g, Violin plots of CHGA and ASCL1 expression in the relapsed CTCs and CDX cells. h, Violin plots depicting decreased expression of MYCL and NFIB in the CDX cells compared with CTCs at relapse (two-sided Wilcoxon rank-sum test; P < 2.2 × 10−16 for both). FC, fold change. In g and h, each dot represents one cell, and the violin curves represent the density of the cells at different expression levels. i, Expression patterns of therapeutic targets in clusters from the CTCs and CDXs following relapse. CTCs were normalized separately from CDX cells. NA, not applicable. In b and c, n = 2,719 cells. In f–h, n = 627 relapsed CTCs and n = 2,000 relapsed CDX cells.NATURE CANCER | CANCERsufficient CTCs for the generation of CDXs, as well as for direct single-cell profiling. CDX models mirror patient disease by the expression of SCLC markers, sites of metastatic disease and plati-num response. Interestingly, one CDX developed spontaneous lep-tomeningeal disease from a primary flank tumor, which highlights the utility of these models for metastatic studies. These findings support the use of CDXs and/or CTCs for single-cell analysis to explore the role of increased ITH in SCLC treatment resistance.CDX single-cell transcriptional profiling revealed the expres-sion of SCLC neuroendocrine markers in a pattern consistent with DiagnosisResponding RelapsedabcNegativePositivet-SNE 2CTCsRBCst-SNE 2t-SNE 1t-SNE 1dt-SNE 2t-SNE 1t-SNE 1CTC diagnosisCTC relapsedefITH scoreCTCdiagnosisCTCrelapsedCDXrelapsedighASCL1 expressionASCL1CHGA expressionCHGAMYCL expressionMYCLNFIB expressionNFIBFC = 0.38FC = 0.40CTCnumber8451015Months251627Diagnosis Responding RelapsedPercentage of cellsCTC cluster CTC diagnosisCTC relapsed321451007550250181614121086040200–20–4040200–20–4040200–20–4040200–20–40NormalizedexpressionCTC relapsed CDX relapsedCTC diagnosis1.–10–20–3040200–2025–400–25200–20642064204206420Cluster34127856Cluster34125CTCrelapsedCDXrelapsedCTCrelapsedCDXrelapsedCTCrelapsedCDXrelapsedCTCrelapsedCDXrelapsedPlatinum-based txDiagnosisRespondingRelapsedNo txOther systemic txImmunotherapyCDXgenerationCTCcollectionDeathTherapeutic targetsMYCBCL2TOP2BKDM1ADLL3TOP1PARP1TOP2AAURKBAURKACHEK1EZH2VEGFACluster Cluster12345 12345678NANATURE CANCER | CANCERpatient and CDX IHC. The range in expression of SCLC-associated genes (for example, MYC and MYCL) and transcriptional programs (for example, EMT) among both platinum-sensitive and platinum-resistant models illustrates the complexity of SCLCs. This evolv-ing transcriptional complexity may exert profound influence on therapeutic responses. Consider, for example, the heterogene-ity in expression of DLL3, which is an inhibitory NOTCH ligand that is directly regulated by ASCL1 and highly expressed in SCLC. Several therapeutics targeting Delta-like ligand 3 (DLL3) are in clinical investigation for SCLC, including rovalpituzumab tesirine (antibody–drug conjugate)35, a chimeric antigen receptor T cell therapy and a bispecific Tcell engager (BiTE) program (Amgen)36. However, we find that DLL3 expression is variable, with DLL3-positive and -negative cells coexisting in many models. This hetero-geneity may contribute to resistance and the lower-than-expected response rates noted in some previous studies, despite selection for DLL3-high patients35,37. We also observe that DLL3 expression is dynamic and may disappear with treatment. This observation not only provides a potential mechanism of adaptive resistance to these agents, but highlights how the timing of targeted therapy admin-istration may impact response. We observed similar dynamic het-erogeneity in the expression of numerous other SCLC candidate targets, including AURKA/AURKB and PARP1. This implies that expression levels of biomarkers or therapeutic targets may be of less importance to patient response than uniformity in expression between cellular populations, which is a significantly more complex attribute to measure.However, the implications of transcriptional heterogeneity for therapeutic response in SCLC extend beyond variable expression of single target-encoding genes. Estimates of global transcriptional diversity within cell subsets on a gene-by-gene basis can be cal-culated as the ITH score, which revealed higher ITH in the plati-num-resistant CDXs compared with platinum-sensitive ones. This increased variability is transcriptional, as the genomic alterations remain static over time. The increased ITH associated with drug resistance was further confirmed invivo by analyses of CDXs over the course of cisplatin or DDR inhibitor therapy, as well as directly in CTCs from blood samples collected over a patient’s treatment course. The elevated ITH in platinum-resistant SCLC leads to con-current, but potentially druggable, resistance mechanisms that inter-fere with effective treatment, and suggests that maximally targeting treatment-naïve patients with relatively homogeneous tumors using frontline combination therapy may maximize response.Within all samples evaluated to date (n = 14), there is a great range of expression of virtually every gene, but this range of gene expression is not what defines ITH. ITH, as we define it, is repre-sented by the diversity of each cell’s gene expression pattern (ITH score) and broadly illustrated by the number of cell populations (clusters) with distinct or diverse gene expression patterns. Within a single sample, up to nine unique cell populations were identified, each with their own characteristic transcriptional programs, often including putative resistance mechanisms. These transcriptional programs, including those that may drive resistance, are not uni-formly distributed throughout a tumor following relapse; instead, they coexist with other programs. In other words, a heterogeneous, relapsed tumor with multiple transcriptionally defined clusters may possess an equal number of transcriptional mediators of resistance.Single-cell profiling of CTCs collected longitudinally allows monitoring of the transcriptional fluidity to gain a better under-standing of the onset of treatment resistance. In liquid biopsies, the lowest numbers of CTCs were found during maximum treatment response and the highest number were found following relapse. The expression of SCLC molecular subtyping genes was similar between CTCs and a CDX derived from the same liquid biopsy. However, genes associated with tumor propagation and metastases (NFIB and MYCL) were expressed at lower levels in CDXs compared with CTCs directly sequenced from the same patient. The cells were all derived from the same patient at the same time and represent a similar malignancy based on the subtyping, but also may show evi-dence of divergence (metastatic CTCs versus primary tumor CDX). Similarly, we found subtle differences in protein expression between xenografts generated from pleural fluid and CTCs collected from the same patient at the same time. Future studies will investigate paired specimens derived from a single patient with SCLC (biop-sies (primary versus metastatic sites), CTCs, PDXs, CDXs and so on) at the single-cell level to further characterize longitudinal tran-scriptional changes. Overall, the transcriptional heterogeneity of CTC and CDX cell clusters is comparable, but not identical. This further highlights that serial sampling may be important to capture dynamic changes in the expression of therapeutic targets over a patient’s treatment course.One limitation of the present study is the scarcity of archival tis-sue samples from patients with SCLC to permit comparisons of ITH between patient tumor biopsies, CDXs and CTCs. In the future, as SCLC xenograft libraries and banking of patient samples expands, it will be possible to explore how ITH is influenced by specific SCLC transcriptional subtypes (for example, ASCL1, NEUROD1 and POU2F3). Furthermore, longitudinal samples from single patients pre- and post-treatment may resolve whether there is evidence for transcriptional subtype switching or selection that underlies shifts in treatment sensitivity. Additional efforts could compare single-cell data from multiple sites of disease in the same patient to investigate whether distant metastatic sites recapitulate the ITH of the primary tumor site or reflect only a fraction of the primary tumor.While SCLC begins as a relatively homogeneous disease with exquisite sensitivity to chemotherapy, it quickly relapses as a virtu-ally pan-resistant entity. These analyses offer improved resolution of the natural history of SCLC as it endures treatment and confirm Fig. 5 | Increased ITH and emergence of cell populations with EMT signatures occur following cisplatin relapse. a, Tumor growth for MDA-SC68s vehicle-receiving or cisplatin-treated mice (n = 1 per treatment). Tumors were collected when the tumor volume reached approximately 1,000 mm2. b, Pooled t-SNE plot of the MDA-SC68s vehicle-receiving and cisplatin-treated tumors in combination with the seven other CDXs. c, t-SNE visualization of the MDA-SC68s vehicle-receiving and cisplatin-treated CDXs (see legend in a). d, t-SNE plot of cell clusters in cisplatin-relapsed MDA-SC68s cells. e, Cisplatin-treated cells have an increased ITH score compared with cells receiving vehicle (two-sided Wilcoxon rank-sum test, P < 2.2 × 10−16). Center lines show median values, box limits indicate the 25th and 75th percentiles and whiskers extend 1.5× the interquartile range from the 25th and 75th percentiles. f, Expression of specific therapeutic targets and EMT-related genes in the clusters from vehicle-receiving or cisplatin-relapsed MDA-SC68s cells. g, Left: principal component analysis of MDA-SC68s identified the first component to be associated with EMT score in cisplatin-treated cells, but not in vehicle-receiving cells. Right: violin plots of the EMT scores of individual cells within each cluster indicate that cells with the highest EMT score were located in cluster 3 of the MDA-SC68s cisplatin-treated tumor. For MDA-SC68 receiving vehicle, n = 733, 704, 563 cells for clusters 1–3, respectively. For MDA-SC68 receiving cisplatin, n = 635, 489, 71, 467 and 338 cells for clusters 1–5, respectively. h, Violin plots of ASCL1, NEUROD1 and DLL3 expression within the clusters from MDA-SC68 vehicle-receiving and cisplatin-treated tumors. ASCL1 and DLL3 were expressed at lower levels in the cisplatin-treated sample (P < 0.0001 for each). For MDA-SC68 receiving vehicle, n = 733, 704 and 563 cells for clusters 1–3, respectively. For MDA-SC68 receiving cisplatin, n = 635, 489, 71, 467 and 338 cells for clusters 1–5, respectively. Each dot represents one cell, and violin curves represent the density of the cells at different expression levels. i, t-SNE visualization of DLL3 expression in vehicle-receiving and cisplatin-treated cells. In b–e, and i, n = 2,000 cells each.NATURE CANCER | CANCERthat, in its untreated state, the majority of SCLC cells are transcrip-tionally similar. However, even in this untreated state, individual cells already hint at the potential to pivot transcriptionally to more chemoresistant states, as tumors conceal small populations of cells with evidence of chemoresistant lineages. In post-treatment mod-els, SCLC quickly diversifies in a manner that requires single-cell t-SNE 2t-SNE 1ITH scoreVehicle Cisplatint-SNE 2t-SNE 1Tumor volume (mm3)abdcefDaysCluster ClusterVehicle CisplatinZEB2VIMTWIST1ZEB1BCL2TOP2BKDM1ADLL3TOP1PARP1TOP2AAURKBAURKACHEK1EZH2VEGFANormalizedexpressionEMT scorePrincipal component 2Principal component 2Principal component 1Principal component 1VehicleCisplatin1–15VehicleCisplatin123 1234EMT scoreCluster ClusterEMT scorehNEUROD1 expressionNEUROD1VehicleCisplatinClusterASCL1 expressionASCL1VehicleCisplatin5123 12345123 1234 5123 1234DLL3 expressionDLL3VehicleCisplatingMDA-SC4sMDA-SC39sMDA-SC68sHCI-008sMDA-SC16rMDA-SC49rMDA-SC55rMDA-SC75rCDXit-SNE 2t-SNE 2VehicleCisplatint-SNE 1t-SNE 130DLL3t-SNE 2t-SNE 140200–20–4040200–20–4040200–20–403020100–10–20–30222018161412108040200–20–404013245Cisplatin12345200–20–4063217127494235 56141,2001,0008006004002000VehicleCisplatin1.–2420–2420632103210420100–10–20–30200–2020100–10–20–3030200–20100–10100–1020100–10–20–30–40 0Therapeutic targetsEMT123 12345NATURE CANCER | CANCERresolution to appreciate fully. Not only are consensus markers of chemosensitivity rapidly downregulated, but multiple subsets of transcriptionally unique SCLC cells emerge within a single tumor, including the upregulation of diverse, concurrent mechanisms of resistance. This conclusion highlights the potential benefit of diver-sifying combination treatment strategies to maximize frontline responses before the emergence of heterogeneity that renders the disease essentially untargetable. Supporting this notion, the recently reported IMpower133 and CASPIAN clinical trials found improved outcomes38, despite similar response rates, with the addition of the immune checkpoint inhibitors to frontline chemotherapy in SCLC1. Our data suggest that the window of therapeutic vulnerability in SCLC is short; thus, approaches featuring aggressive deployment of diverse strategies in the frontline and maintenance settings may be critical to conquering this devastating disease.MethodsCDX and PDX model generation. Patients diagnosed with SCLC at the University of Texas MD Anderson Cancer Center were selected on the basis of extensive-stage disease irrespective of age, gender or other clinical criteria. All patients had a confirmed pathologic diagnosis of SCLC and classified (although not selected for) previous treatment. Included patients ranged from 49–90 years in age and included six females and two males. These patients gave informed consent to Institutional Review Board-approved protocol LAB10-0442 (Evaluation of blood-based test for the detection of CTCs and circulating proteins and microRNAs and molecular analysis for polymorphisms and mutations) and blood was collected. One vial of blood was collected for isolation and banking of plasma and peripheral blood mononuclear cells for use as a normal control. Ten milliliters of blood collected in abt-SNE 2t-SNE 2t-SNE 1t-SNE 1cPercentage of cells123Cluster123Vehicle CHKiPARPiITH scoredTumor volume (mm3)DaysVehiclePARPiCHKiPARPiCHKiVehicleMDA-SC39sMDA-SC68sHCI-008sMDA-SC16rMDA-SC49rMDA-SC55rMDA-SC75rCDXMDA-SC4s3503002502001501005002174828142351422201816141210010080604020040200–20–4040200–2020–400–20–4040200–20–40Fig. 6 | Resistance to DNA-damaging targeted therapies resulted in the emergence of new, therapy-specific clusters. a, Tumor growth for MDA-SC4s vehicle-receiving, PARPi-treated (talazoparib) and CHKi-treated (prexasertib) mice (n = 3 mice per treatment). Tumors were collected when the tumor volume doubled from that at the onset of treatment. b, Left: pooled t-SNE plot of the MDA-SC4 vehicle-receiving, PARPi-relapsed and CHKi-relapsed cells in combination with all seven other CDXs (MDA-SC39s, MDA-SC68s, HCI-008s, MDA-SC16r, MDA-SC49r, MDA-SC55r and MDA-SC75r; n = 2,000 cells each). The emergence of unique clusters was detected following relapse to PARPi or CHKi. Right: t-SNE visualization of cell populations from MDA-SC4s vehicle-receiving, PARPi-relapsed and CHKi-relapsed tumors form three clusters. c, Percentage of cells from MDA-SC4 vehicle-receiving, PARPi-relapsed and CHKi-relapsed samples within the clusters. d, ITH score was higher in CHKi-relapsed (P = 4.4 × 10−65) and PARPi-relapsed (P = 7.5 × 10−132) samples compared with vehicle-receiving samples (two-sided Wilcoxon rank-sum test; n = 2,000 cells each). Center lines show median values, box limits indicate the 25th and 75th percentiles and whiskers extend 1.5× the interquartile range from the 25th and 75th percentiles.NATURE CANCER | CANCER 43. McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012). 44. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010). 45. Tong, P., Chen, Y., Su, X. & Coombes, K. R. SIBER: systematic identification of bimodally expressed genes using RNAseq data. Bioinformatics 29, 605–613 (2013). 46. Wang, J., Wen, S., Symmans, W. F., Pusztai, L. & Coombes, K. R. The bimodality index: a criterion for discovering and ranking bimodal signatures from cancer gene expression profiling data. Cancer Inform. 7, 199–216 (2009). 47. Byers, L. A. etal. An epithelial–mesenchymal transition gene signature predicts resistance to EGFR and PI3K inhibitors and identifies Axl as a therapeutic target for overcoming EGFR inhibitor resistance. Clin. Cancer Res. 19, 279–290 (2013). 48. R Core Development Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2015). 49. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010). 50. Koboldt, D. C. etal. VarScan: variant detection in massively parallel sequencing of individual and pooled samples. Bioinformatics 25, 2283–2285 (2009). 51. Kim, D. etal. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013). 52. Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015). 53. Dai, Z. etal. edgeR: a versatile tool for the analysis of shRNA-Seq and CRISPR–Cas9 genetic screens. F1000Res 3, 95 (2014). 54. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010). 55. Stuart, T. etal. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).AcknowledgementsWe thank the patients who participated in this study, as well as their families. We also thank M. Vasquez for obtaining consent from the patients, E. Roarty for scientific input and editing, and K. Ramkumar for general laboratory assistance. This work was supported by NIH/NCI Cancer Center Support Grant P30-CA016672 (to the Bioinformatics Shared Resource), NIH/NCI T32 Award CA009666 (to C.M.G.), The University of Texas Southwestern Medical Center and MD Anderson Cancer Center Special Program of Research Excellence (5 P50 CA070907), NIH/NCI award R01-CA207295 (to L.A.B.), NIH/NCI award U01-CA213273 (to J.V.H. and L.A.B.), NIH/NCI award U01 CA231844 (to T.G.O.), award P30CA042014 (to the Huntsman Cancer Institute), the Department of Defense award LC170171 (to L.A.B.), the ASCO Young Investigator Award (to C.M.G.), generous philanthropic contributions to The University of Texas MD Anderson Cancer Center Moon Shots Program (to J.V.H., J.W. and L.A.B.), The University of Texas MD Anderson Cancer Center Small Cell Lung Cancer Working Group, Abell Hangar Foundation Distinguished Professor Endowment (to L.A.B. and B.G.), The University of Texas MD Anderson Cancer Center Physician Scientist Award (to L.A.B.), The Hope Foundation SWOG/ITSC Pilot Program (to P.R. and L.A.B.), an Andrew Sabin Family Fellowship (to L.A.B.) and Rexanna’s Foundation for Fighting Lung Cancer (to J.V.H. and L.A.B.).Author contributionsC.A.S., C.M.G. Y.X. and L.A.B. conceived of the project, analyzed and interpreted the data and wrote the manuscript. S.S., V.S., V.B., P.R., J.Z., B.G., J.d.G., S.G.S., J.A.R., M.D.C., T.G.O. and J.V.H. contributed to acquiring the data. J.F., C.M., N.K., J.S. and I.W. performed the pathology review and analysis. M.B. and J.W. contributed to analysis and interpretation of the data. P.M.H. collected liquid biopsies from the patients. H.T. coordinated the patient protocols. P.R., J.Z., B.G., J.d.G., S.G.S., J.A.R., M.D.C., T.G.O. and J.V.H. provided administrative and/or material support. All authors contributed to the writing, reviewing and/or revising of the manuscript.Competing interestsL.A.B. serves on advisory committees for AstraZeneca, AbbVie, Genmab, BerGenBio, Pharma Mar SA, Sierra Oncology, Merck, Bristol-Myers Squibb, Genentech and Pfizer and has research support from AbbVie, AstraZeneca, Genmab, Sierra Oncology and Tolero Pharmaceuticals. J.V.H. serves on advisory committees for AstraZeneca, Boehringer Ingelheim, Exelixis, Genentech, GlaxoSmithKline, Guardant Health, Hengrui, Lilly, Novartis, Spectrum, EMD Serono and Synta, and has research support from AstraZeneca, Bayer, GlaxoSmithKline and Spectrum and royalties and licensing fees from Spectrum. Otherwise, there are no pertinent financial or non-financial conflicts of interest to report.Additional informationExtended data is available for this paper at information is available for this paper at and requests for materials should be addressed to L.A.B.Reprints and permissions information is available at’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.© The Author(s), under exclusive licence to Springer Nature America, Inc. 2020NATURE CANCER | CANCERARTICLESNATURE CANCERExtended Data Fig. 1 | CDXs exhibit common SCLC markers and mutations that are maintained over multiple generations. a, Histological analysis of CDX tumors are consistent with SCLC. Scale bar = 100 µM. b, Patient expression of NCAM and TTF1 by staff pathologist review of diagnostic sample matches CDXs. c, Presence of parenchymal brain metastasis, confirmed by staff neuroradiologist and treating physician review, in the cerebellum (indicated by dashed circle) of the patient from which MDA-SC39 was derived. d, Genomic alterations in CDXs. Top panel: mutation load; middle panel: somatic mutations and genomic gain/loss status; lower panel: type of base-pair substitution. e, Mutational status of common SCLC genes and others unique to each CDX are maintained over multiple CDX passages in three separate models. f, Expression heatmap for ASCL1- and NEUROD1-associated genes. b, CDX and PDX models derived from patient SC49 exhibit similar patterns of expression for common SCLC markers, including loss of TTF1 expression. These experiments were repeated in three independent tumors from each model. Scale bar = 100 µM.NATURE CANCER | CANCERARTICLESNATURE CANCERExtended Data Fig. 4 | CDX copy number and expression of DNA repair genes between clusters. a,b, Inferred copy number between clusters in MDA-SC16r (a) and MDA-SC49r (b). c, Expression heatmap of genes associated with DNA repair in all CDX clusters. d, Violin plots indicating range of expression of several therapeutic targets within individual clusters. AURKA, AURKB and DLL3 were relatively unchanged between clusters. MDA-SC4s: n=978, 1,022 cells for clusters 1-2; MDA-SC39s: n=1172, 828 cells for clusters 1-2; MDA-SC68s: n=733, 704, 563 cells for clusters 1-3; HCI-008s: n=596, 1,404 cells for clusters 1-2; MDA-SC49r: n=683, 317, 652, 348 cells for clusters 1-4. Each dot represents one cell and the violin curve represent the density of the cells at different expression levels.NATURE CANCER | CANCERARTICLESNATURE CANCERExtended Data Fig. 5 | Validation of CTC identification within a patient liquid biopsy by positive expression of epithelial, NE and SCLC genes. Percentage of cells expressing epithelial, NE genes (for example, UCHL1, NCAM1, SYP, and CHGA) or SCLC lineage-specific genes (for example, ASCL1, NEUROD1, etc.) in the CTC population and non-CTC populations.NATURE CANCER | CANCERARTICLESNATURE CANCERExtended Data Fig. 6 | Emergence of a mesenchymal cell cluster following cisplatin-treatment. Violin plot of VIM (a) and EXPCAM (b) expression in the clusters of MDA-SC68s vehicle and cisplatin-treated CDXs. MDA-SC68 vehicle: n=733, 704, 563 cells for clusters 1-3; MDA-SC68 cisplatin: n=635, 489, 71, 467, 338 cells for clusters 1-5. Each dot represents one cell and the violin curve represent the density of the cells at different expression levels Source data.NATURE CANCER | Note SupplementsFiguresMetrics/ 23 Add to Library PDF151%

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The Price of DNA Sequencing Dropped From $2.7 Billion to $300 in Less Than 20 Years

Nebula Genomics is offering access to your entire genetic makeup for less than the price of an Apple Watch

Emily MullinFollowFeb 18 · 4 min read

A sequencing chromatograph showing a DNA sequence and a sample of DNA from the human genome mapping project.
Photo: Science & Society Picture Library/SSPL/Getty Images

GGenome sequencing was once impossibly expensive. The Human Genome Project, an international effort to decode the human genome that launched in 1990, took 13 years and an estimated $2.7 billion to complete. Then, in 2007, DNA pioneer James Watson became the first person to get his genome sequenced for less than $1 million. Since then, the cost of genome sequencing has been decreasing at a rate faster than Moore’s law.

Now, Nebula Genomics, a spinout of Harvard University co-founded by geneticist George Church, is launching an at-home whole genome sequencing test for less than the price of the latest Apple Watch. At $299, Nebula’s service provides a readout of a person’s entire genetic code.

Nebula’s sequencing is a much more comprehensive test than the ones offered by companies like 23andMe and Ancestry, which use a different technique called genotyping. Genotyping looks at only a small part of the genome. For instance, 23andMe’s $199 health and ancestry test reports on a handful of genetic variants associated with about a dozen health conditions. Sequencing looks at all of a person’s genes and their variants. It’s the difference between reading a few pages versus an entire book.

Nebula’s sequencing is a much more comprehensive test than the ones offered by companies like 23andMe and AncestryDNA.

In 2016, Boston-based Veritas Genetics, also co-founded by Church, was the first company to break the much-hyped $1,000 threshold for genome sequencing. Last year, Veritas slashed that price to $599, crediting automation for the drop. But at the end of 2019, the company abruptly shut down U.S. operations after what the startup called an “unexpected adverse financing situation.”

Today, Nebula is offering even cheaper genome sequencing — and is trying out a new business model. Most other at-home DNA testing companies rely on a one-time purchase. Customers have little reason to continue their relationship with these companies after they review their DNA results. To keep people coming back, Nebula is launching a subscription service that will provide weekly updates about new genetics studies and explain to users how these findings relate to their own DNA. To get these additional insights, customers can pay a monthly fee of $9.99 a month for a yearly subscription or $19.99 for a month-to-month subscription on top of the initial $299 for genome sequencing. Nebula customers also get an in-depth ancestry analysis.

“I think the direction in which the whole market is going is providing more comprehensive tests that go beyond ancestry,” says Dennis Grishin, co-founder and chief scientific officer of Nebula, referring to direct-to-consumer genetic testing in general.

In fact, genealogy company Ancestry is taking a similar approach. In October, it announced a new health screening test for $199 that will initially report on a handful of medical conditions. A biannual subscription fee of $49 gives users updates about their genetic information every few months. The test isn’t available yet, but Ancestry says it will launch sometime in 2020. At a time when the at-home genetic testing craze has hit a lull, these expanded services could spark new interest.

Nebula hopes to further differentiate itself by taking a different tack when it comes to privacy. Like other DNA testing companies, Nebula shares users’ data with research partners, such as pharmaceutical companies. But it offers customers more control over their genetic data than is typical. Ancestry and 23andMe have two options for sharing your data for research: a blanket consent that allows the companies to share your de-identified genetic data with any research partner that wants to use it and an option to not share your data at all. Nebula offers these options as well, plus a third one: Users can opt to receive a separate consent request every time a research partner wants to use their data.

At a time when the at-home genetic testing craze has hit a lull, these services could spark new interest.

“We try to make sure everything is very transparent, that people know what happens with their data, who uses it and for what purpose,” Grishin says. Nebula uses a blockchain to protect customers’ data and offers anonymous genome sequencing.

Whether there is a mass market for whole genome sequencing remains to be seen. Gillian Hooker, president of the National Society of Genetic Counselors, says one hurdle is that many people just haven’t heard of whole genome sequencing or are skeptical of how useful the results will be for managing their health. After all, scientists are still learning how to interpret the genome, and most doctors don’t yet know what to do with the results of whole genome sequencing tests.

“There is a small percentage of people who get genome sequencing who learn something that will change their health care,” Hooker says. For instance, a person might learn they have a genetic variant that raises their risk of cancer or heart disease, so a doctor might recommend more frequent screening for those conditions. Right now, most people don’t walk away with actionable information, she says. But that will likely change as scientists’ understanding of genetics evolves.

With the price getting increasingly cheaper, whole genome sequencing could soon replace the more limited genetic tests that dominate the market today.OneZero

The undercurrents of the future. A Medium publication about tech and science.



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Emily Mullin


Staff writer at OneZero, covering the intersection of biology and technology.




The undercurrents of the future. A Medium publication about tech and science.

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