Spencer Feldman

Marissa is a “GenXer” living as a “flattie” with metastatic breast cancer since March 2014. Read Marissa's blogs 

Cancer may not define me, but it’s there in everything I do. By all outward appearances, I look like any other person. My body tells a very different story. I feel cancer’s presence daily in my numb scars with their reddish squiggles from radiation snaking across my flat chest wall. I can see it in my protruding PowerPort. I feel it in the neuropathy in the tips of my fingers and toes from the cumulative effects of chemo. It affects my taste buds and my joints. I am reminded by every pill I swallow.

In March 2014, I was diagnosed with metastatic breast cancer. I had a lump that I had noticed and hoped it would go away on its own. It didn’t. My arm swelled up and was incredibly painful, prompting a visit to an urgent care. I was referred to the local emergency room to have an ultrasound to check for blood clots and scheduled for a mammogram the following day. A DVT blood clot was found on the ultrasound, and I was admitted for five days. I never made it to that mammogram appointment. I started chemo on my fifth day in the hospital before being discharged.

That week of my life was truly awful. I went from normal, or what I thought of as a healthy 38-year-old, to a terminal cancer patient in five days. On top of a devastating and life-altering diagnosis, my last surviving grandfather was dying in a nearby hospital. Any control I had over my life and health was gone.

I finished up four rounds of the “red devil” chemo, Adriamycin (doxorubicin), and Cytoxan (cyclophosphamide) and was put on tamoxifen. As my hair was beginning to grow back, I was starting to notice nodules appearing on the side of my breast and under my arm. My oncologist brushed me off, dismissing my concerns. When it was determined I was stage 4, the standard of care dictated a mastectomy, and radiation was no longer an option. All treatments would be palliative.

Then came a stroke of good luck. I was reassigned to a new oncologist who thought outside the box and immediately took charge by ordering a scan which showed the cancer that shrunk with the chemo was now coming back. He switched my treatment and ultimately put me back on chemo. He also insisted I have a mastectomy with no reconstruction, even though this is not standard of care. Shortly after my 40th birthday, I had my mastectomy.

My metastatic sites initially were in my liver and an adnexal mass. In 2017, we thought my cancer had flipped from hormone positive to triple negative after receiving the results from a biopsy of a nodule. My oncologist at that time thought I would be a good candidate for a trial. Although it turned out my cancer had not mutated, I just had one rogue nodule which disqualified me for the trial. Instead, I qualified for a hysterectomy to remove the mass on my ovaries that was actively growing.

In the past 11 years since my diagnosis, I have been on numerous types of treatments, some lasting only a couple of months and some lasting years. I have been treated more aggressively than the majority of patients with metastatic breast cancer. Even after my mastectomy, I continued to have nodules appear from time to time, letting us know it was time to move on to a different treatment. In 2019, I did 38 rounds of radiation to the chest wall, which has put a stop to those recurrences for now.

After radiation in early 2020, the cancer returned to my lungs. Fortunately, we knew I had a somatic BRCA2 mutation discovered by the Foundation One testing of the tumor removed during my hysterectomy. I was running out of oral treatment options at this point, having already been on nine previous lines. We decided to try the PARP inhibitor Lynparza (olaparib). I was offered a lung biopsy to confirm the cancer hadn’t mutated. I declined and took a leap of faith that I still had the somatic mutation, which apparently, I do. I have now been on Lynparza for more than five years, a treatment most people stop after two years.

I am a metastatic misfit. My treatment from the beginning has been very different from the standard of care that I see other patients with stage 4 experiencing. I don’t fit in anywhere, and it’s hard. I struggle with finding my purpose in life and I feel very much alone in this diagnosis. Yet while every cancer story is unique and as different as I might feel, I do think we all share one common goal: We are not alone in our hope for a future with more time, better treatment options, and someday, maybe, a cure.

For more news on cancer updates, research and education, don’t forget to 

Articles

A personal narrative details the journey of living with metastatic breast cancer, highlighting the physical and emotional challenges faced. The patient describes the initial diagnosis, treatment experiences, and the impact of cancer on daily life. Despite deviations from standard care, including a mastectomy and various treatments, the patient has managed to survive beyond typical expectations. The story underscores the uniqueness of each cancer journey and the shared hope among patients for improved treatments and a potential cure, emphasizing the importance of personalized care and ongoing research.

Diagnosed in 2014, I share my unique aggressive treatment journey over 11 years, highlighting a common hope for more time and better treatments.

My 11-Year Metastatic Breast Cancer Story Defied the Standard Path

Pausing treatment during vaccination did not improve immunity in patients with chronic lymphocytic leukemia and should not be recommended: © stock.adobe.com.

Pausing Bruton's tyrosine kinase inhibitors (BTKi) therapy around the time of vaccination was not beneficial for immunity and should not be recommended in clinical practice for patients with chronic lymphocytic leukemia currently undergoing such therapy, according to study findings published in 

“This randomised trial found no benefit in pausing BTKi therapy around the time of vaccination with a SARS-CoV-2 booster,” wrote lead study author Dr. Jonathan A Cook and colleagues. “As such, in clinical practice, individuals should not be recommended to pause their BTKi therapy to enhance immunity to vaccination.

Cook works in the Oxford Clinical Trials Research Unit at University of Oxford, Oxford, UK.

Between Oct. 10, 2022, and June 8, 2023, 99 individuals (72% male, 28% female; 90% White) were randomly assigned to pause (51%) or continue (49%) their BTKi therapy and were observed for 12 weeks. Three weeks after vaccination, the geometric mean anti-spike-RBD-specific antibody level was 218.8 U/mL (SD 122.9) in those who continued treatment and 153.4 U/mL (SD 103.2) in those who paused. This indicates that, on average, patients who continued their BTKi therapy had higher antibody levels three weeks after vaccination compared with those who paused treatment.

The geometric mean ratio was 1.104, which means that the antibody level in those who continued was about 10.4% higher than in the those who paused. One serious side effect occurred: a participant in the pause group died from COVID-19 infection two months after randomization.

A serious side effect which occurred during the 12-week follow-up was the death of one participant in the pause group from COVID-19 infection two months after randomization.

“The results highlight the importance of testing treatment interventions in a powered, randomised setting but also the need for more fundamental science to consider the drivers of the heterogeneous responses observed,” wrote Cook. “This vulnerable group continues to be at risk from infections and necessary precautions should be adopted where available, such as monoclonal antibody prophylaxis.

Chronic lymphocytic leukemia is the most common type of leukemia and is linked to significant immunosuppression. Although BTKis have transformed treatment, according to the study, they impair vaccine-induced immunity.

Furthermore, the data showed that both study groups had strong cellular immune responses that improved after vaccination, suggesting the immune system can recognize and respond to newer variants despite changes in the virus.

Pausing treatment did not affect quality of life, and most patients followed their doctor’s advice, with only two patients restarting early. Some individuals reported temporary lymph node swelling, which resolved without medical help by 12 weeks. Vaccination remains important, especially before starting continuous therapy, though how these findings apply to other vaccines like shingles is unclear, according to study authors.

Additionally, most patients received an mRNA vaccine targeting the original COVID-19 strain, though a few received a protein-based version. Since fewer patients enrolled than expected and vaccine types varied, differences in antibody responses cannot be ruled out, as per the study.

This study included adults with chronic lymphocytic leukemia on BTKi therapy for at least 12 months. Patients either paused treatment for 3 weeks around their COVID-19 booster or continued as usual. Most received a bivalent mRNA vaccine. The main goal was to measure antibody levels 3 weeks after vaccination.

The primary outcome was the anti-spike RBD-specific antibody titer three weeks after vaccination, analyzed by intention to treat after trial completion.

“A 3-week pause versus continued Bruton tyrosine kinase inhibitor use during COVID-19 vaccination in individuals with chronic lymphocytic leukemia (IMPROVE trial): a randomised, open-label, superiority trial,” by Dr. Jonathan A Cook, et al., The Lancet.

For more news on cancer updates, research and education, don’t forget to 

Pausing Bruton’s tyrosine kinase inhibitors (BTKi) therapy during COVID-19 vaccination in chronic lymphocytic leukemia patients does not enhance immunity and is not recommended. A study involving 99 participants showed higher antibody levels in those who continued BTKi therapy compared to those who paused. The trial highlighted the importance of randomized testing and the need for further research on immune response variability. Despite BTKi therapy impairing vaccine-induced immunity, both groups exhibited strong cellular immune responses post-vaccination. Quality of life remained unaffected by pausing treatment.

Pausing treatment during vaccination did not improve immunity in patients with chronic lymphocytic leukemia and should not be recommended, a trial found.

Pausing BTKi for Vaccine Did Not Boost Immunity in CLL

Dr. Zafar Sayed discuss the importance of recognizing Head and Neck Cancer Awareness Month and highlights early symptoms of the disease to be aware of.

Recognizing Symptoms During Head and Neck Cancer Awareness Month

April serves as Head and Neck Cancer Awareness Month and aims to address unmet needs within the landscape. According to the Oral Cancer Foundation, recognizing this awareness month is important because approximately 58,500 people will be newly diagnosed with oral cancer this year and 132 individuals will be newly diagnosed with oral cancer every day in the United States.

Dr. Zafar Sayed sat down for an interview with CURE to discuss the importance of recognizing Head and Neck Cancer Awareness Month, as well as highlights early symptoms of the disease.

Sayed is an assistant professor of surgery (otolaryngology) and a part of the Head and Neck Cancers Program at the Yale School of Medicine, located in New Haven, Connecticut, and specializes in otolaryngology surgery with a focus on head and neck surgical oncology at Yale Cancer Center, Yale Medicine.

The head and neck are so important to us as human beings. Almost everything we think of as being part of the human experience comes from this area: your eyesight, your sense of smell, your ability to speak, your facial movements and emotion. It's such a critical area that when we talk about cancers of this spot, we are so concerned with maintaining function. Therefore, we want to catch tumors early, and that's why raising public awareness is so important.

The head and neck tend to be a spot that can really be damaged by ultraviolet radiation, so making sure folks are aware to check for skin cancers in this area is key. Tobacco and alcohol are risk factors for the development of mouth and voice box cancers; throat cancers are now mostly human papillomavirus related. We, as head and neck surgeons, also deal a lot with thyroid cancer, which has also seen a rise in incidence. There are many different tumors that affect this area, and it's just important to know what to look for so we can catch them early and hopefully stave off any major issues.

The evolution in treatment, at least on the surgical side, has been to try to make things a little less invasive. Many of the surgeries we used to do decades ago, even before I was a physician, would involve placing tracheostomy tubes (breathing tubes in the neck) and feeding tubes (something called a G-tube, a gastrostomy tube, was very common). What we're doing with newer techniques is trying to limit the number of patients who need those measures long term. With some of the newer surgeries, you may need a tracheostomy or a feeding tube, but usually, that will be temporary, and by the time you go home, a lot of that has been removed so you can resume function. So, that is where a lot of our work is focused

There are also some studies looking at how much of a margin around the cancer we need to resect, how much we need to take. We have found, especially in the oropharynx, around tonsil tumors and invasive tongue tumors, that the margin can be a bit smaller for HPV tumors compared to what we've classically needed to do for tobacco-related cancers. So, the combination of less invasive techniques and decreasing the amount of normal tissue that needs to be removed has been really helpful over the last few years.

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An AI model may help identify patients with myelofibrosis at high risk of early death after transplant, according to an expert: © stock.adobe.com.

A machine learning model may help improve transplant risk assessment for patients with myelofibrosis by more accurately identifying those at high-risk of early death, according to an expert.

Dr. Adrián Mosquera, a hematologist at the University Hospital of Santiago de Compostela in Spain, shared valuable insights during an interview with CURE in which he highlighted the development of the machine learning model. According to Mosquera, this model is designed to enhance transplant risk assessment for patients with myelofibrosis.

CURE: How does the machine learning model outperform the current models and predict and transplant outcomes?

: There were some previous efforts. The conventional statistical method used in medicine for a long time was more or less like performing traditional Cox regression, and based on that strategy, patients were classified as high risk, low risk, and intermediate risk, or similar categories.

The issue is that despite this being a valid approach that could somewhat stratify patients, we still had too many patients in the intermediate risk group who behaved as high risk. So, if 30% of patients die early due to toxicity, and you can only identify 8% of them, you are clearly underperforming.

The approach then was to use artificial intelligence. Let's generate not a rule-of-thumb-based score, but a quantitative score, and then try to find the optimal point where we can cut and clearly identify these high-risk patients. To our surprise, that threshold was somewhere between the 25% and 30% higher-risk patients, and these patients did actually have very poor survival. So, you have to be very careful when considering an allogeneic stem cell transplantation in this patient profile, because the likelihood of the one-year death rate is 40%, and the two-year death rate goes over 50%, and that's significant.

What inspired the development of the machine learning model for transplant risk assessment in patients with myelofibrosis?

We've been working on several artificial intelligence modeling projects in hematology for a long time, and I always encounter the same kind of question. This is interesting because when you go to the bank to get credit or something you need to pay off over time, you pay a lot of attention to all the payments you need to make and the interest rates. You want to know a lot about how you will face any difficulties with payment you may have.

However, in medicine, we are not as accustomed to using data-driven approaches to balance decision-making in something far more important than the economy — human health. Our commitment here is bringing advancements in big data and artificial intelligence to address important unmet medical needs, which can sometimes be improved with the implementation of data-driven approaches. This is particularly focused on areas where we have to make difficult decisions that can lead either to disease duration, prolonged disease remission, or, in the worst case, toxic death.

How do key patient characteristics, such as age and comorbidities, factor into the model's predictions?

Well, these characteristics actually perform well. We initially analyzed the whole dataset, which included more than 5,000 patients with long follow-up. This is a significant advantage of the EBMT registry, which is probably the best in the world for conducting this kind of research. We had over 52 potentially prognostic variables available for analysis. We initially fed all those variables into the model, and we obtained an initial model that performed quite well.

However, the problem is that in clinical routine, at least at this stage of technological development, you cannot input 52 variables into a calculator. You have to simplify this somehow to make it easy for clinicians to use and adopt this technology in their clinical routine. So, we then set out to identify the variables that contained most of the independent prognostic value, and we derived a 10-variable base model which contained the same prognostic accuracy but with fewer variables—specifically, 42 fewer. This model is actually pretty much aligned with what we know about prognostication in allogeneic stem cell transplantation.

So, there are a few variables related to demographics, particularly age. Then we have a few variables related to comorbidity and prior treatment. Following that, we have a few variables related to myelofibrosis stage—how aggressive is the myelofibrosis at the moment allogeneic stem cell transplantation is prescribed? And finally, we have a few variables related to transplant type, donor type, and conditioning regimen for allogeneic stem cell transplantation. This makes a lot of sense because it takes into consideration variables that we know are involved in different layers of complexity that our patients have.

What impact could this tool have on the decision-making process for clinicians and patients regarding allergenic hematopoietic cell transplant?

I think it has a big impact, and this is not only my belief but that of our collaborators as well. For myelofibrosis, allogeneic stem cell transplantation is the only potentially curative therapy we have. However, thanks to advancements in pharmacology, there is a growing number of drugs being developed for this disease. We know that although allogeneic stem cell transplantation might be curative, many patients do not fare well.

It's particularly regrettable when you recommend an allogeneic stem cell transplantation and the patient starts to experience significant complications, like graft-versus-host disease (GVHD). This can lead not only to early mortality but also to a poor quality of life. So, it doesn't mean that you cannot perform allogeneic stem cell transplantations in these patients, but they have the opportunity to make a better-informed decision, to discuss it more thoroughly with their healthcare team and the patient, and perhaps even to develop potential risk-mitigating strategies.

So, it will have an impact, and we expect this impact will be in the best interest of all patients. Furthermore, there are several similar projects being planned now that actually follow the same guidelines, but for other conditions like myelodysplastic syndromes (MDS) and chronic myelomonocytic leukemia (CMML).

What are potential next steps in refining the model and incorporating additional disease factors into future versions?

I would argue that the next step is to apply the same technology to other existing data, even if it comes from different disease subgroups, like MDS and CMML, as I mentioned previously. Our approach aims to provide efficiency to the health system, offering a fast way to proceed. However, I agree with you. When you analyze the results of our paper, although the strategy is sound and we have attractive results, we still see that a significant portion of the predictability of the mortality variable is not explained by baseline variables, at least the clinical ones.

So, we need to take a step forward and incorporate other technologies. We are discussing including molecular testing of tumors, as well as imaging from bone marrow, and definitely also some generative AI tools that we are developing, which aim to better classify or estimate frailty in patients.

This is a very important aspect because we tend to overlook it. Frailty estimation is often very subjective, and there is a significant need to objectify it. While there are scores being used, they are not always well-applied, and there is a need to facilitate the implementation and reproducibility of these scores. This is an area where large language models are being developed, at least within our team, with that specific purpose.

How do you envision this open access tool being integrated into daily clinical practice,

It seems it will be integrated. You know, we've been using risk models throughout our professional lives, and they are often suboptimal. In my experience, you only tend to use these risk models when you actually have actionable information that can guide you to make a better decision for a patient.

In this case, I think it's going to be used quite frequently because in myelofibrosis, we are never entirely sure about how much the disease risk is balanced with the toxicity of the transplant. We often make decisions based on our experience and some indirect data from the literature.

However, this data-driven approach will probably enhance or even reinforce the decisions that some of our colleagues might have already considered. So, it is valuable, and I think it's going to be used quite frequently. Furthermore, it's a kind of breakthrough in the way it is actually moving or changing paradigms in data management for a large international society like the EBMT, shifting towards AI-driven agreement approaches that are very much needed in the transplantation process.

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A machine learning model has been developed to improve transplant risk assessment for myelofibrosis patients by accurately identifying high-risk individuals. Traditional methods often misclassify patients, leading to suboptimal outcomes. The new model uses artificial intelligence to provide a quantitative score, identifying high-risk patients more effectively. Key patient characteristics, such as age and comorbidities, are incorporated into a simplified 10-variable model. This tool aims to enhance decision-making for allogeneic stem cell transplantation, potentially improving patient outcomes and informing similar projects for other hematological conditions.

An AI model may help identify patients with myelofibrosis at high risk of early death after transplant, according to an expert. 

Spencer Feldman

My 11-Year Metastatic Breast Cancer Story Defied the Standard Path

Pausing BTKi for Vaccine Did Not Boost Immunity in CLL

Recognizing Symptoms During Head and Neck Cancer Awareness Month

AI Tool May Better Predict Transplant Risk in Myelofibrosis

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