Aggressive T-cell lymphoma is a rare and devastating form of blood cancer with a very low five-year survival rate. Patients often relapse after receiving initial therapy, making it especially challenging for clinicians to keep this destructive disease in check.

In a new study, researchers from MIT, in collaboration with researchers involved in the PETAL consortium at Massachusetts General Hospital, identified a practical and powerful prognostic marker that could help clinicians identify high-risk patients early, and potentially tailor treatment strategies to improve survival.

The team found that, when patients relapse within 12 months of initial therapy, their chances of survival decline dramatically. For these patients, targeted therapies might improve their chances for survival, compared to traditional chemotherapy, the researchers say.

According to their analysis, which used data collected from thousands of patients all over the world, the finding holds true across patient subgroups, regardless of the patient’s initial therapy or their score in a commonly used prognostic index.

A causal inference framework called Synthetic Survival Controls (SSC), developed as part of MIT graduate student Jessy (Xinyi) Han’s thesis, was central to this analysis. This versatile framework helps to answer “when-if” questions — to estimate how the timing of outcomes would shift under different interventions — while overcoming the limitations of inconsistent and biased data.

The identification of novel risk groups could guide clinicians as they select therapies to improve overall survival. For instance, a clinician might prioritize early-phase clinical trials over canonical therapies for this cohort of patients. The results could inform inclusion criteria for some clinical trials, according to the researchers.

The causal inference framework for survival analysis can also be applied more broadly. For instance, the MIT researchers have used it in areas like criminal justice to study how structural factors drive recidivism.

“Often we don’t only care about what will happen, but when the target event will happen. These when-if problems have remained under the radar for a long time, but they are common in a lot of domains. We’ve shown here that, to answer these questions with data, you need domain experts to provide insight and good causal inference methods to close the loop,” says Devavrat Shah, the Andrew and Erna Viterbi Professor in Electrical Engineering and Computer Science at MIT, a member of Institute for Data, Systems and Society (IDSS) and of the Laboratory for Information and Decision Systems (LIDS), and co-author of the study.

Shah is joined on the paper by many co-authors, including Han, who is co-advised by Shah and Fotini Christia, the Ford International Professor of the Social Sciences in the Department of Political Science and director of IDSS; and corresponding authors Mark N. Sorial, a clinical pharmacist and investigator at the Dana-Farber Cancer Institute, and Salvia Jain, a clinician-investigator at the Massachusetts General Hospital Cancer Center, founder of the global PETAL consortium, and an assistant professor of medicine at Harvard Medical School. The research appears today in the journal Blood.

Estimating outcomes

The MIT researchers have spent the past few years developing the Synthetic Survival Control causal inference framework, which enables them to answer complex “when-if” questions when using available data is statistically challenging. Their approach estimates when a target event happens if a certain intervention is used.

In this paper, the researchers investigated an aggressive cancer called nodal mature T-cell lymphoma, and whether a certain prognostic marker led to worse outcomes. The marker, TTR12, signifies that a patient relapsed within 12 months of initial therapy.

They applied their framework to estimate when a patient will die if they have TTR12, and how their survival trajectory would be different if they do not have this prognostic marker.

“No experiment can answer that question because we are asking about two outcomes for the same patient. We have to borrow information from other patients to estimate, counterfactually, what a patient’s survival outcome would have been,” Han explains.

Answering these types of questions is notoriously difficult due to biases in the available observational data. Plus, patient data gathered from an international cohort bring their own unique challenges. For instance, a clinical dataset often contains some historical data about a patient, but at some point the patient may stop treatment, leading to incomplete records.

In addition, if a patient receives a specific treatment, that might impact how long they will survive, adding to the complexity of the data. Plus, for each patient, the researchers only observe one outcome on how long the patient survives — limiting the amount of data available.

Such issues lead to suboptimal performance of many classical methods.

The Synthetic Survival Control framework can overcome these challenges. Even though the researchers don’t know all the details for each patient, their method stitches information from multiple other patients together in such a way that it can estimate survival outcomes.

Importantly, their method is robust to specific modeling assumptions, making it broadly applicable in practice. 

The power of prognostication

The researchers’ analysis revealed that TTR12 patients consistently had much greater risk of death within five years of initial therapy than patients without the marker. This was true no matter the initial therapy the patients received or which subgroup they fell into.

“This tells us that early relapse is a very important prognosis. This acts as a signal to clinicians so they can think about tailored therapies for these patients that can overcome resistance in second-line or third-line,” Han says.

Moving forward, the researchers are looking to expand this analysis to include high-dimensional genomics data. This information could be used to develop bespoke treatments that can avoid relapse within 12 months.

“Based on our work, there is already a risk calculation tool being used by clinicians. With more information, we can make it a richer tool that can provide more prognostic details,” Shah says.

They are also applying the framework to other domains.

For instance, in a paper recently presented at the Conference on Neural Information Processing Systems, the researchers identified a dramatic difference in the recidivism rate among prisoners of different races that begins about seven months after release. A possible explanation is the different access to long-term support by different racial groups. They are also investigating individuals’ decisions to leave insurance companies, while exploring other domains where the framework could generate actionable insights.

“Partnering with domain experts is crucial because we want to demonstrate that our methods are of value in the real world. We hope these tools can be used to positively impact individuals across society,” Han says.

This work was funded, in part, by Daiichi Sankyo, Secure Bio, Inc., Acrotech Biopharma, Kyowa Kirin, the Center for Lymphoma Research, the National Cancer Institute, Massachusetts General Hospital, the Reid Fund for Lymphoma Research, the American Cancer Society, and the Scarlet Foundation.

National Institutes of Health (NIH) Director Jay Bhattacharya visited MIT on Friday, engaging in a wide-ranging discussion about policy issues and research aims at an event also featuring Rep. Jake Auchincloss MBA ’16 of Massachusetts.

The forum consisted of a dialogue between Auchincloss and Bhattacharya, followed by a question-and-answer session with an audience that included researchers from the greater Boston area. The event was part of a daylong series of stops Bhattacharya and Auchincloss made around Boston, a world-leading hub of biomedical research.

“I was joking with Dr. Bhattacharya that when the NIH director comes to Massachusetts, he gets treated like a celebrity, because we do science, and we take science very seriously here,” Auchincloss quipped at the outset.

Bhattacharya said he was “delighted” to be visiting, and credited the thousands of scientists who participate in peer review for the NIH. “The reason why the NIH succeeds is the willingness and engagement of the scientific community,” he said.

In response to an audience question, Bhattacharya also outlined his overall vision of the NIH’s portfolio of projects.

“You both need investments in ideas that are not tested, just to see if something works. You don’t know in advance,” he said. “And at the same time, you need an ecosystem that tests those ideas rigorously and winnows those ideas to the ones that actually work, that are replicable. A successful portfolio will have both elements in it.”

MIT President Sally A. Kornbluth gave opening remarks at the event, welcoming Bhattacharya and Auchincloss to campus and noting that the Institute’s earliest known NIH grant on record dates to 1948. In recent decades, biomedical research at MIT has boomed, expanding across a wide range of frontier fields.

Indeed, Kornbluth noted, MIT’s federally funded research projects during U.S. President Trump’s first term include a method for making anesthesia safer, especially for children and the elderly; a new type of expanding heart valve for children that eliminates the need for repeated surgeries; and a noninvasive Alzheimer’s treatment using sound and light stimulation, which is currently in clinical trials.

“Today, researchers across our campus pursue pioneering science on behalf of the American people, with profoundly important results,” Kornbluth said.

“The hospitals, universities, startups, investors, and companies represented here today have made greater Boston an extraordinary magnet for talent,” Kornbluth added. “Both as a force for progress in human health and an engine of economic growth, this community of talent is a precious national asset. We look forward to working with Dr. Bhattacharya to build on its strengths.”

The discussion occurred amid uncertainty about future science funding levels and pending changes in the NIH’s grant-review processes. The NIH has announced a “unified strategy” for reviewing grant applications that may lead to more direct involvement in grant decisions by directors of the 27 NIH institutes and centers, along with other changes that could shift the types of awards being made.

Auchincloss asked multiple questions about the ongoing NIH changes; about 10 audience members from a variety of institutions also posed a range of questions to Bhattacharya, often about the new grant-review process and the aims of the changes.

“The unified funding strategy is a way to allow institute direcors to look at the full range of scoring, including scores on innovation, and pick projects that look like they are promising,” Bhattacharya said in response to one of Auchincloss’ queries.

One audience member also emphasized concerns about the long-term effects of funding uncertainties on younger scientists in the U.S.

“The future success of the American biomedical enterprise depends on us training the next generation of scientists,” Bhattacharya acknowledged.

Bhattacharya is the 18th director of the NIH, having been confirmed by the U.S. Senate in March. He has served as a faculty member at Stanford University, where he received his BA, MA, MD, and PhD, and is currently a professor emeritus. During his career, Bhattacharya’s work has often examined the economics of health care, though his research has ranged broadly across topics, in over 170 published papers. He has also served as director of the Center on the Demography and Economics of Health and Aging at Stanford University.

Auchincloss is in his third term as the U.S. Representative to Congress from the 4th district in Massachusetts, having first been elected in 2020. He is also a major in the Marine Corps Reserve, and received his MBA from the MIT Sloan School of Management.

Ian Waitz, MIT’s vice president for research, concluded the session with a note of thanks to Auchincloss and Bhattacharya for their “visit to the greater Boston ecosystem which has done so much for so many and contributed obviously to the NIH mission that you articulated.” He added: “We have such a marvelous history in this region in making such great gains for health and longevity, and we’re here to do more to partner with you.”

Many organizations are taking actions to shrink their carbon footprint, such as purchasing electricity from renewable sources or reducing air travel.

Both actions would cut greenhouse gas emissions, but which offers greater societal benefits?

In a first step toward answering that question, MIT researchers found that even if each activity reduces the same amount of carbon dioxide emissions, the broader air quality impacts can be quite different.

They used a multifaceted modeling approach to quantify the air quality impacts of each activity, using data from three organizations. Their results indicate that air travel causes about three times more damage to air quality than comparable electricity purchases.

Exposure to major air pollutants, including ground-level ozone and fine particulate matter, can lead to cardiovascular and respiratory disease, and even premature death.

In addition, air quality impacts can vary dramatically across different regions. The study shows that air quality effects differ sharply across space because each decarbonization action influences pollution at a different scale. For example, for organizations in the northeast U.S., the air quality impacts of energy use affect the region, but the impacts of air travel are felt globally. This is because associated pollutants are emitted at higher altitudes.

Ultimately, the researchers hope this work highlights how organizations can prioritize climate actions to provide the greatest near-term benefits to people’s health.

“If we are trying to get to net zero emissions, that trajectory could have very different implications for a lot of other things we care about, like air quality and health impacts. Here we’ve shown that, for the same net zero goal, you can have even more societal benefits if you figure out a smart way to structure your reductions,” says Noelle Selin, a professor in the MIT Institute for Data, Systems, and Society (IDSS) and the Department of Earth, Atmospheric and Planetary Sciences (EAPS); director of the Center for Sustainability Science and Strategy; and senior author of the study.

Selin is joined on the paper by lead author Yuang (Albert) Chen, an MIT graduate student; Florian Allroggen, a research scientist in the MIT Department of Aeronautics and Astronautics; Sebastian D. Eastham, an associate professor in the Department of Aeronautics at Imperial College of London; Evan Gibney, an MIT graduate student; and William Clark, the Harvey Brooks Research Professor of International Science at Harvard University. The research was published Friday in Environmental Research Letters.

A quantification quandary

Climate scientists often focus on the air quality benefits of national or regional policies because the aggregate impacts are more straightforward to model.

Organizations’ efforts to “go green” are much harder to quantify because they exist within larger societal systems and are impacted by these national policies.

To tackle this challenging problem, the MIT researchers used data from two universities and one company in the greater Boston area. They studied whether organizational actions that remove the same amount of CO2 from the atmosphere would have an equivalent benefit on improving air quality.

“From a climate standpoint, CO2 has a global impact because it mixes through the atmosphere, no matter where it is emitted. But air quality impacts are driven by co-pollutants that act locally, so where those emissions occur really matters,” Chen says.

For instance, burning fossil fuels leads to emissions of nitrogen oxides and sulfur dioxide along with CO2. These co-pollutants react with chemicals in the atmosphere to form fine particulate matter and ground-level ozone, which is a primary component of smog.

Different fossil fuels cause varying amounts of co-pollutant emissions. In addition, local factors like weather and existing emissions affect the formation of smog and fine particulate matter. The impacts of these pollutants also depend on the local population distribution and overall health.

“You can’t just assume that all CO2-reduction strategies will have equivalent near-term impacts on sustainability. You have to consider all the other emissions that go along with that CO2,” Selin says.

The researchers used a systems-level approach that involved connecting multiple models. They fed the organizational energy consumption and flight data into this systems-level model to examine local and regional air quality impacts.

Their approach incorporated many interconnected elements, such as power plant emissions data, statistical linkages between air quality and mortality outcomes, and aviation emissions associated with specific flight routes. They fed those data into an atmospheric chemistry transport model to calculate air quality and climate impacts for each activity.

The sheer breadth of the system created many challenges.

“We had to do multiple sensitivity analyses to make sure the overall pipeline was working,” Chen says.

Analyzing air quality

At the end, the researchers monetized air quality impacts to compare them with the climate impacts in a consistent way. Monetized climate impacts of CO2 emissions based on prior literature are about $170 per ton (expressed in 2015 dollars), representing the financial cost of damages caused by climate change.

Using the same method as used to monetize the impact of CO2, the researchers calculated that air quality damages associated with electricity purchases are an additional $88 per ton of CO2, while the damages from air travel are an additional $265 per ton.

This highlights how the air quality impacts of a ton of emitted CO2 depend strongly on where and how the emissions are produced.

“A real surprise was how much aviation impacted places that were really far from these organizations. Not only were flights more damaging, but the pattern of damage, in terms of who is harmed by air pollution from that activity, is very different than who is harmed by energy systems,” Selin says.

Most airplane emissions occur at high altitudes, where differences in atmospheric chemistry and transport can amplify their air quality impacts. These emissions are also carried across continents by atmospheric winds, affecting people thousands of miles from their source.

Nations like India and China face outsized air quality impacts from such emissions due to the higher level of existing ground-level emissions, which exacerbates the formation of fine particulate matter and smog.

The researchers also conducted a deeper analysis of short-haul flights. Their results showed that regional flights have a relatively larger impact on local air quality than longer domestic flights.

“If an organization is thinking about how to benefit the neighborhoods in their backyard, then reducing short-haul flights could be a strategy with real benefits,” Selin says.

Even in electricity purchases, the researchers found that location matters.

For instance, fine particulate matter emissions from power plants caused by one university are in a densely populated region, while emissions caused by the corporation fall over less populated areas.

Due to these population differences, the university’s emissions resulted in 16 percent more estimated premature deaths than those of the corporation, even though the climate impacts are identical.

“These results show that, if organizations want to achieve net zero emissions while promoting sustainability, which unit of CO2 gets removed first really matters a lot,” Chen says.

In the future, the researchers want to quantify the air quality and climate impacts of train travel, to see whether replacing short-haul flights with train trips could provide benefits.

They also want to explore the air quality impacts of other energy sources in the U.S., such as data centers.

This research was funded, in part, by Biogen, Inc., the Italian Ministry for Environment, Land, and Sea, and the MIT Center for Sustainability Science and Strategy.