Wednesday, November 6th, 2019

Time	Event
11:29a	MIT report provides guidance on climate-related financial disclosures An MIT white paper released today outlines a series of recommendations on how companies, particularly those in the oil and gas industry, can use scenario analysis to effectively disclose risks and opportunities they face as a result of global climate change. The report, “Climate-Related Financial Disclosure Disclosures: The Use of Scenarios,” was organized by the Office of the Vice President for Research and drafted by a team of MIT faculty and staff members. It builds on insights gained from a workshop held at MIT last year, which included representatives from oil and gas companies, credit rating agencies, investment firms, and nongovernmental organizations, along with academics and other entities engaged in the production of global climate scenarios. “This report, and the workshop it grew out of, are part of MIT’s ongoing efforts under our Plan for Action on Climate Change,” says Vice President for Research Maria T. Zuber. “A key element of the plan is a strategy of engaging with a wide variety of sectors to accelerate the world’s transition away from carbon emitting energy sources.” Financial disclosures that include an examination of risk factors that could impact a company’s operations, facilities, and financial performance are an essential tool to provide guidance to potential investors and lenders, credit rating agencies, and insurers. For these disclosures to be useful, however, they must be prepared using comparable methods and consistent approaches. In 2017, the Task Force on Climate-related Financial Disclosures (TCFD), established by the G20 Financial Stability Board, provided a guiding framework and set of recommendations to promote that kind of consistency. However, the use of scenario analysis to describe the resilience of a company’s strategy, as recommended by the TCFD, still represents a significant challenge for companies. MIT, with its extensive experience in analysis of climate futures, saw this as an opportunity to shed some light on the task. “The point was to engage with industry to help all of the different stakeholders get on the same page about the scenarios they use,” says Erik Landry, research associate in the Office of the Vice President for Research and lead author of the report. “Once a common understanding is reached, then at least we are all working on the same problem.” The report aims to advance the state of scenario-based disclosures of climate-related risks and opportunities by promoting a better understanding of the underlying scenarios. It is meant to help oil and gas companies produce more useful scenario-based disclosures, help the financial community better evaluate such disclosures, and enable a dialogue that would help scenario producers make their scenarios more relevant to company-level climate-related risk assessment. Henry Jacoby, the William F. Pounds Professor of Management, Emeritus, in the MIT Sloan School of Management and a member of the MIT working group, says, “Most climate scenarios were developed to study the implications of specific policies or technological developments, not to assess near-term financial risks in a particular industry,” so the report tries to outline ways such scenarios can be applied usefully to this new task. “We’re trying to tweak the tools to fit the purposes we’re trying to use them for,” he adds. In this case, a major aim is to help financial decision-makers make more informed decisions about where best to allocate resources, potentially in ways aligned with a low-carbon transition. Many different groups produce such scenarios, the report points out. One widely used set of scenarios is that issued annually by the International Energy Agency. But several other organizations, including the Integrated Assessment Modeling Consortium, the International Renewable Energy Agency, and the Organization for Economic Cooperation and Development, also produce scenarios, each one taking a somewhat different approach. The producers of these climate scenarios “all differ in their modeling methodologies, and one or another may be more relevant to some sectors,” Landry says. “It is important for financial decision-makers to be aware of the underlying assumptions being made about the future.” Arguably, the strength of using scenarios lies in their range of possible futures they can explore, from “business as usual” scenarios to those in which global temperature rise is limited to 2 degrees Celsius, and beyond. Scenarios involve various assumptions about technology and policy. Some assumptions that go into these scenarios are relatively easy to quantify, such as whether or not a carbon price is implemented, and if so how much it is and how it increases over time. Other factors have greater inherent uncertainties, such as the expected rate of improvement in energy production and storage technologies, the development and scalability of carbon capture and sequestration, or social factors such as how quickly people change their energy-related choices. One recommendation the report makes is for oil and gas companies to compare their own scenarios to “reference scenarios,” or credible scenarios that are commonly used and understood by many stakeholders. While companies may want to include their own specific scenarios based on the unique characteristics of their own facilities and supply chains, making clear exactly how their scenarios differ from a reference case enables investors to assess each company by its own merits, while also retaining a level of comparability between companies. “Insofar as companies disclose clearly and transparently what scenarios they use and what assumptions go into them, they can let investors to do their jobs and see how they compare,” Landry says. It also calls for companies to be complete in their descriptions of how their strategies are resilient in the face of a changing climate and a low carbon transition. This includes addressing both where the company’s vulnerabilities lie and their degree of preparedness. For audiences evaluating such descriptions, it’s important to “be wary of general claims of resilience that are not visibly grounded in clear, consistent, and transparent use of scenarios,” Landry says. While this report focuses on the climate-related disclosures by the oil and gas industry, the authors believe that the principles it outlines should also be very applicable to many other industries such as manufacturing, commercial transportation, or agriculture. With more useful disclosures, financial decision-makers can make choices that not only promote their own interests, but also encourage the advancement of more sustainable business models. The report was produced by MIT’s Working Group on Climate-Related Scenarios, which in addition to Landry and Jacoby included Louis Carranza and Sergey Paltsev of the MIT Energy Initiative, James Gomes of the Office of the Vice President for Research, and Donald Lessard and Bethany Patten of the MIT Sloan School of Management. (Comment on this)
12:10p	Machine learning shows no difference in angina symptoms between men and women The symptoms of angina — the pain that occurs in coronary artery disease — do not differ substantially between men and women, according to the results of an unusual new clinical trial led by MIT researchers. The findings could help overturn the prevailing notion that men and women experience angina differently, with men experiencing “typical angina” — pain-type sensations in the chest, for instance — and women experiencing “atypical angina” symptoms such as shortness of breath and pain-type sensations in the non-chest areas such as the arms, back, and shoulders. Instead, it appears that men and women’s symptoms are largely the same, say Karthik Dinakar, a research scientist at the MIT Media Lab, and Catherine Kreatsoulas of the Harvard T.H. Chan School of Public Health. Dinakar and his colleagues presented the results of their HERMES angina trial at the European Society of Cardiology’s annual congress in September. Their research is one of the first clinical trials accepted at the prestigious conference to use machine learning techniques, which were used to characterize the full range of symptoms experienced by individual patients and to capture nuances in how they described their symptoms in a natural language exchange. The trial included 637 patients in the United States and Canada who had been referred for their first coronary angiogram, the gold-standard test to diagnose coronary artery disease. After analyzing the language expressed in recorded conversations between physicians and patients and in interviews with patients, the researchers found that almost 90 percent of women and men reported chest pain as a symptom. Women reported significantly more angina symptoms than men, but the machine learning algorithms identified nine clusters of symptoms, such as “chest sensations and physical limitations” and “non-chest area and associated symptoms” where there were no significant differences among men and women with blockages in their heart. “This work, showing no real differences between women and men in chest pain, goes against the dogma and will shake up the field of cardiology,” says Deepak L. Bhatt, executive director of Interventional Cardiovascular Programs at Brigham and Women’s Hospital and professor of medicine at Harvard Medical School, a co-author of the study. “It is also exciting to see an application of machine learning in health care that actually worked and isn’t just hype,” he adds. “This sophisticated machine learning study suggests, alongside several other recent more conventional studies, that there may be fewer if any differences in symptomatic presentation of heart attacks in women compared to men,” says Philippe Gabriel Steg, a professor of cardiology at Université Paris- Diderot and director of the Coronary Care Unit of Hôpital Bichat in Paris, France. “This has important consequences in the organization of care for patients with suspected heart attacks, in whom diagnostic strategies probably need to be similar in women and men,” adds Steg, who was not involved with the MIT study. Lensing offers a new look The idea of applying machine learning to cardiology came when Catherine Kreatsoulas, then a Fulbright fellow and heart and stroke research fellow at the Harvard School of Public Health, met Dinakar after a talk in 2014 by noted linguist Noam Chomsky. An interest in language drew them both to the talk, and Kreatsoulas in particular was concerned about the differences in the way men and women express their symptoms, and how physicians might be understanding — or misunderstanding — the way men and women speak about their heart attack symptoms. In the United States and Canada, 90 percent of cardiologists are male, and Kreatsoulas thought, “‘could this be a potential case of ‘lost in translation?’,” she says. Kreatsoulas also was concerned that doctors might be misdiagnosing or underdiagnosing female patients — as well as men who didn’t express “typical” angina symptoms — “because doctors have this frame, given their years of medical training in cardiology, that men and women have different symptoms,” Dinakar explains. Dinakar thought a machine learning framework called “lensing” that he had been working on for crisis counseling might offer a new way of understanding angina symptoms. In its simplest form, lensing acknowledges that different participants bring their own viewpoint or biases to a collective problem or conversation. By developing algorithms that include these different lenses, researchers can retrieve a more complete picture of the data provided by real-world conversations. “When we train machine learning models in situations like the heart disease diagnosis, it is important for us to capture, in some way, the lens of the physician and the lens of the patient,” says Dinakar. To accomplish this, the researchers audio-recorded two clinical interviews, one of patients describing their angina symptoms in clinical consult interviews with physicians and one of patient-research assistant conversations “to capture in their own natural words their descriptions of symptoms, to see if we could use methods in machine learning to see if there are a lot of differences between women and men,” he says. In a typical clinical trial, researchers treat “symptoms as check boxes” in their statistical analyses, Dinakar notes. “The result is to isolate one symptom from another, and you don’t capture the entire patient symptomatology presentation — you begin to treat each symptom as if it’s the same across all patients,” says Dinakar. “Further, when analyzing symptoms as check boxes, you rarely see the complete picture of the constellation of symptoms that patients actually report. Often this important fact is compensated for poorly in traditional statistical analysis,” Kreatsoulas says. Instead, the lensing model allowed the scientists “to represent each patient as a unique fingerprint of their symptoms, based on their natural language,” says Dinakar. Seeing patients in this way helped to uncover clusters of symptoms that could be compared in men and women, leading to the conclusion that there were few differences in symptoms between these two groups of patients. "The terms ‘typical’ and ‘atypical’ angina should be abandoned, as they do not correlate with disease and may perpetuate stereotypes based on sex," Dinakar and his colleagues conclude. Helping doctors think deeper The goal of clinical trials like the HERMES trial is not to “replace cardiologists with an algorithm,” says Dinakar. “It’s just a more sophisticated way of doing statistics and bringing them to bear on an urgent problem like this.” In the medical realm, the unique lens of each patient and physician might typically be thought of as “bias” in the pejorative sense — data that should be ignored or tossed out of an analysis. But the lensing algorithms treat these biases as information that can provide a more complete picture of a problem or reveal a new way of considering a problem. In this case, Dinakar said, “bias is information, and it helps us to think deeper. It’s very important that we capture that and try to represent that the best we can.” Although machine learning in medicine is often seen as a way to “brute force” through problems, like identifying tumors by applying image recognition software and predictive algorithms, Dinakar hopes that models like lensing will help physicians break down “ossified” frames of thinking across medical challenges. Dinakar and Kreatsoulas are now applying the machine learning models in a clinical trial with neuro-gastroenterology researchers at Massachusetts General Hospital to compare physician lenses in diagnosing diseases such as functional gastrointestinal disease and irritable bowel syndrome. “Anything we can do in statistics or machine learning in medicine to help break down an ossified frame or broken logic and help both providers and patients think deeper in my opinion is a win,” he says. (Comment on this)
2:00p	New pathway for lung cancer treatment MIT cancer biologists have identified a new therapeutic target for small cell lung cancer, an especially aggressive form of lung cancer with limited options for treatment. Lung cancer is the leading cause of cancer-associated mortality in the United States and worldwide, with a five-year survival rate of less than 20 percent. But of the two major sub-types of lung cancer, small cell and non-small cell, small cell is more aggressive and has a much poorer prognosis. Small cell lung cancer tumors grow quickly and metastasize early, resulting in a five-year survival rate of about 6 percent. “Unfortunately, we haven’t seen the same kinds of new treatments for small cell lung cancer as we have for other lung tumors,” says Tyler Jacks, director of the Koch Institute for Integrative Cancer Research at MIT. “In fact, patients are treated today more or less the same way they were treated 40 or 50 years ago, so clearly there is a great need for the development of new treatments.” A study appearing in the Nov. 6 issue of Science Translational Medicine shows that small cell lung cancer cells are especially reliant on the pyrimidine biosynthesis pathway and that an enzyme inhibitor called brequinar is effective against the disease in cell lines and mouse models. Jacks is the senior author of this study. Other MIT researchers include Associate Professor of Biology and Koch Institute member Matthew Vander Heiden, and co-lead authors postdoc researcher Leanne Li and graduate student Sheng Rong Ng. Roadblock for cell replication Researchers in the Jacks lab used CRISPR to screen small cell lung cancer cell lines for genes that already have drugs targeting them, or that are likely to be druggable, in order to find therapeutic targets that can be tested more quickly and easily in a clinical setting. The group found that small cell lung cancer tumors are particularly sensitive to the loss of a gene encoding dihydroorotate dehydrogenase (DHODH), a key enzyme in the de novo pyrimidine biosynthesis pathway. Upon discovering that the sensitivity involved a metabolic pathway, the researchers sought the collaboration of the Vander Heiden lab, experts in normal and cancer cell metabolism who were already conducting studies on the role of pyrimidine metabolism and DHODH inhibitors in other cancers. Pyrimidine is one of the major building blocks of DNA and RNA. Unlike healthy cells, cancer cells are constantly dividing and need to synthesize new DNA and RNA to support the production of new cells. The investigators found that small cell lung cancer cells have an unexpected vulnerability: Despite their dependence on the availability of pyrimidine, this synthesis pathway is much less active in small cell lung cancer cells than in other types of cancer cells examined in the study. Through inhibiting DHODH, they found that small cell lung cancer cells were not able to produce enough pyrimidine to keep up with demand. When researchers treated a genetically engineered mouse model of small cell lung cancer tumors with the DHODH inhibitor brequinar, tumor progression slowed down and the mice survived longer than untreated mice. Similar results were observed for small cell lung cancer tumors in the liver, a frequent site of metastasis in patients. In addition to mouse model studies, the researchers tested four patient-derived small cell lung cancer tumor models and found that brequinar worked well for two of these models — one of which does not respond to the standard platinum-etoposide regimen for this disease. “These findings are noteworthy because second-line treatment options are very limited for patients whose cancers no longer respond to the initial treatment, and we think that this could potentially represent a new option for these patients,” says Ng. Shorter pathway to the clinic Brequinar has already been approved for use in patients as an immunosuppressant, and there has been some preclinical research showing that brequinar and other DHODH inhibitors may be effective for other types of cancers. “We’re excited because our findings could provide a new way to help small cell lung cancer patients in the future,” says Li. “While we still have a lot of work to do before brequinar can be tested in the clinic as a therapy for small cell lung cancer, we’re hopeful that this might happen more quickly now that we’re starting with a drug that is known to be safe in humans.” Next steps for the researchers include optimizing the therapeutic efficacy of DHODH inhibitors and combining them with other currently available treatment options for small cell lung cancer, such as chemotherapy and immunotherapy. To help clinicians tailor treatments to individual patients, researchers will also work to identify biomarkers for tumors that are susceptible to this therapy, and investigate resistance mechanisms in tumors that do not respond to this treatment. The research was funded, in part, by the MIT Center for Precision Cancer Medicine and the Ludwig Center for Molecular Oncology at MIT. (Comment on this)

<< Previous Day

2019/11/06 [Calendar]

Next Day >>