Vaccines Without Vaccinations Won’t End the Pandemic
COVID-19 vaccine development has advanced at a record-setting pace, thanks to our nation's longstanding support for basic vaccine science coupled with massive public and private sector investments.
Yet, policymakers aren't according anywhere near the same level of priority to investments in the social, behavioral, and data science needed to better understand who and what influences vaccination decision-making. "If we want to be sure vaccines become vaccinations, this is exactly the kind of work that's urgently needed," says Dr. Bruce Gellin, President of Global Immunization at the Sabin Vaccine Institute.
Simply put: it's possible vaccines will remain in refrigerators and not be delivered to the arms of rolled-up sleeves if we don't quickly ramp up vaccine confidence research and broadly disseminate the findings.
According to the most recent Gallup poll, the share of U.S. adults who say they would get a COVID-19 vaccine rose to 58 percent this month from 50 percent in September, with non-white Americans and those ages 45-65 even less willing to be vaccinated. While there is still much we don't understand about COVID-19, we do know that without high levels of immunity in the population, a return to some semblance of normalcy is wishful thinking.
Research from prior vaccination campaigns such as H1N1, HPV, and the annual flu points us in the right direction. Key components of successful vaccination efforts require 1) Identifying the concerns of particular segments of the population; 2) Tailoring messages and incentives to address those concerns, and 3) Reaching out through trusted sources – health care providers, public health departments, and others in the community.
Research during the H1N1 flu found preparing people for some uncertainty actually improved trust, according to Dr. Sandra Crouse Quinn, professor and chair, Family Science, University of Maryland. Dr. Crouse Quinn's research during that period also underscored the need to address the specific vaccine concerns of racial and ethnic groups.
The stunning scientific achievement of COVID-19 vaccines anticipated to be ready in record time needs to be backed up by an equally ambitious and evidence-based effort to build the public's confidence in the vaccines.
Data science has provided crucial insight about the social media universe. Dr. Neil Johnson, a scientist at George Washington University, found that despite having fewer followers, anti-vaccination pages are more numerous and growing faster than pro-vaccination pages. They are more often linked to in discussions on other Facebook pages – such as school parent associations – where people are undecided about vaccination.
We've learned about building vaccine confidence from earlier campaigns. Now, however, we are faced with a unique and challenging set of obstacles to unpack quickly: How do we communicate the importance of eventual COVID-19 vaccines to Americans in light of the muddled-to-poor messaging from political leaders, the weaponizing of relatively simple public health recommendations, the enormous disproportionate toll on people of color, and the torrent of online misinformation? We urgently need data reflective of today's circumstances along with the policy to ensure it is quickly and effectively disseminated to the public health and clinical workforce.
Last year prompted in part by the measles outbreaks, Reps. Michael C. Burgess (R-TX) and Kim Shrier (D-WA), both physicians, introduced the bipartisan Vaccines Act to develop a national surveillance system to monitor vaccination rates and conduct a national campaign to increase awareness of the importance of vaccines. Unfortunately, that legislation wasn't passed. In response to COVID-19, Senate HELP Committee Ranking member Patty Murray (D-WA) has sought funds to strengthen vaccine confidence and combat misinformation with federally supported communication, research, and outreach efforts. Leading experts outside of Congress have called for this type of research, including the Sabin-Aspen Vaccine Science Policy Institute. Most recently, the National Academy of Sciences, in its report regarding the equitable distribution of the COVID-19 vaccine, included as one of its recommendations the need for "a rapid-response program to advance the science behind vaccine confidence."
Addressing trust in vaccination has never been as challenging nor as consequential. The stunning scientific achievement of COVID-19 vaccines anticipated to be ready in record time needs to be backed up by an equally ambitious and evidence-based effort to build the public's confidence in the vaccines. In its remaining days, the Trump Administration should invest in building vaccine confidence with current resources, targeting efforts to ensure COVID vaccines reduce rather than exacerbate racial and ethnic health disparities. Congress must also act to provide the additional research and outreach resources needed as well as pass the Vaccines Act so we are better prepared in the future.
If we don't succeed, COVID-19 will continue wreaking havoc on our health, our society, and our economy. We will also permanently jeopardize public trust in vaccines – one of the most successful medical interventions in human history.
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.