Every cancer patient deserves their own equation: Kristin Swanson at TEDxUChicago 2014

Summary

In this talk, a mathematician shares a personal story about losing several family members, including their father, to cancer. This personal experience inspired them to apply mathematics to cancer treatment, specifically focusing on improving clinical decisions for individual patients. The speaker highlights how mathematical models can help tailor treatments to unique patients, rather than relying on generalized clinical trial results. They illustrate the potential of these models to better predict tumor growth, treatment success, and improve patient outcomes, advocating for more personalized and precise cancer care.

Takeaways

• 🧑‍⚕️ The speaker shares personal experiences of losing family members, including their father, to cancer, highlighting the widespread impact of the disease.
• 📊 The speaker emphasizes that cancer is a significant global issue, and everyone can likely relate to knowing someone affected by it.
• ➕ The speaker believes math can contribute to finding solutions for cancer, despite the complexity of the disease.
• 👨‍👧‍👦 The speaker's love for math began at a young age, influenced by their father, who played math games with them during long road trips.
• 💔 The speaker's father was diagnosed with lung cancer at age 62, which deeply impacted the speaker and led them to focus on cancer research.
• 📚 The speaker pursued a PhD in applied mathematics, studying under Jim Murray, a pioneer in mathematical biology, to explore the intersection of math and biology.
• 👩‍🔬 Attending tumor boards, the speaker learned how physicians make complex treatment decisions based on clinical trials and patient data.
• 🏥 Clinical trials typically focus on the 'median patient,' but the speaker argues that every patient is unique and should not be treated as a statistic.
• 🔬 The speaker's research focuses on glioblastoma, an aggressive brain cancer, and how math models can help predict tumor behavior and improve treatment plans.
• 💡 The speaker advocates for using mathematical models to better individualize cancer treatments, potentially improving patient outcomes and avoiding unnecessary suffering.

Q & A

What personal experience did the speaker have with cancer?

The speaker’s father was diagnosed with lung cancer when the speaker was 20 years old. The speaker was heavily involved in his care during the last seven months of his life, which motivated the speaker to study the intersection of math and biology to contribute to cancer research.

How did the speaker’s background in mathematics influence their approach to cancer research?

The speaker’s background in mathematics, cultivated by their father during childhood, gave them the tools to approach cancer as a complex system. This mathematical approach became the foundation for developing models to understand and improve cancer treatments.

What is the role of mathematical biology in cancer research, according to the speaker?

Mathematical biology helps understand the complexity of cancer as a system. It allows researchers to model tumor growth, cell behavior, and treatment responses, providing more personalized and effective strategies for cancer treatment.

What frustrations did the speaker experience while dealing with their father's cancer treatment?

The speaker felt powerless and frustrated by their inability to contribute to medical decisions and understand the medical conversations. Despite their mathematical background, they struggled to grasp the complexity of cancer treatment decision-making at the time.

Why does the speaker believe the solution to cancer involves math?

The speaker believes math is essential because it allows for better modeling of the complexity of cancer. Mathematical tools can simulate how tumors grow, how they respond to treatments, and predict outcomes, moving beyond generalized clinical trial results to more personalized patient care.

What is the concept of ‘treating to the median’ in cancer treatment, and what is its limitation?

‘Treating to the median’ refers to basing treatment decisions on the average outcomes from clinical trials, which may not reflect the individual patient's response. The limitation is that every patient is different, and treating them based on a median approach can overlook those who fall outside the typical response curve.

How does the speaker suggest cancer treatment should be individualized?

The speaker suggests using mathematical models to predict how a specific patient's tumor will behave and respond to treatment. This would allow doctors to tailor treatment plans more precisely to the individual patient rather than relying solely on generalized data from clinical trials.

What example does the speaker give of a case where traditional clinical trials might mislead treatment decisions?

The speaker gives an example of a 66-year-old patient with a brain tumor whose tumor grew during treatment, which would typically be considered a failure. However, the patient lived for five years, much longer than expected, suggesting that despite the tumor growth, the treatment was effective in derailing the tumor's course.

What is the role of MRI in cancer treatment decision-making, according to the speaker?

MRI scans are used to provide detailed images of tumors before and after treatment. Physicians use these images to assess the tumor's size and shape, plan surgeries, and tailor radiation doses. However, the speaker argues that MRI alone may not tell the full story of a tumor's behavior and response to therapy.

What drives the speaker’s commitment to improving cancer treatment through mathematics?

The speaker is driven by personal loss, including the death of their father and other family members to cancer. Their work is further inspired by the colleagues and friends in their lab who have faced cancer. They are motivated to ensure that every patient receives treatment tailored to their unique condition.

Outlines

00:00

👨‍👩‍👧‍👦 Family Ties and Personal Loss to Cancer

The speaker introduces their family members who passed away from cancer, emphasizing that many in the audience likely share similar experiences. They explain that their personal connection to cancer began with their father, who was a Vietnam War veteran and engineer, diagnosed with lung cancer at age 62. The speaker recounts the emotional and logistical challenges of caring for their father while balancing university studies in math and physics, ultimately feeling powerless in the face of medical decisions they couldn’t fully comprehend.

05:01

📊 The Role of Clinical Trials in Cancer Treatment Decisions

This section delves into how clinical trials guide treatment decisions in tumor boards. Physicians rely on clinical trials that focus on median outcomes to determine the best therapy for cancer patients. However, the speaker highlights the complexity of individual patient responses, stressing that clinical trials provide general trends (median survival rates), but not all patients respond in the same way. The speaker uses their own family’s experiences to illustrate the diversity of cancer outcomes, where one brother lived longer than expected while another died earlier.

10:03

🧠 Studying Brain Tumors: A Deeper Look into Glioblastoma

The speaker shifts focus to their research on glioblastoma, a highly aggressive and uniformly fatal type of brain cancer. They explain how tumor boards discuss individual patient cases, with medical teams trying to find personalized treatment plans. MRI scans play a critical role in this process, giving detailed images of the tumor's location and response to treatment. The speaker emphasizes that even though median survival is low, each patient is unique, which makes cancer treatment a combination of scientific precision and the art of medicine.

15:05

📉 The Complexities of Cancer Growth and Treatment Response

The speaker discusses the limitations of judging a treatment's success based on tumor size alone. In one case, although a patient’s tumor grew during treatment (usually indicating failure), the patient lived much longer than expected, defying median statistics. This suggests that understanding cancer is more complex than just tracking tumor size, as factors like individual cell behavior and treatment impact on tumor growth trajectories are crucial. The speaker calls for a more nuanced approach to treatment decisions, focusing on how therapies deflect tumor growth rather than simply stopping it.

Keywords

💡Cancer

Cancer is a disease characterized by the uncontrolled division of abnormal cells in the body. In the video, the speaker highlights the personal impact of cancer, as several members of their family, including their father and brothers, passed away due to the disease. The theme of cancer is central to the talk, focusing on both the personal toll it takes and the scientific approach to finding better solutions for treatment.

💡Mathematics

Mathematics is the study of numbers, quantities, shapes, and patterns. In this context, the speaker discusses the application of mathematics to cancer research. The speaker’s conviction is that mathematical models can be used to better understand and treat cancer, moving beyond traditional approaches in medical research.

💡Clinical trials

Clinical trials are research studies that test new medical treatments in patients to evaluate their effectiveness. The speaker explains that clinical trials provide the 'median' patient data used by physicians to make treatment decisions. However, the video critiques the reliance on median outcomes, suggesting that individualized approaches, aided by mathematics, could offer more tailored and effective treatments.

💡Tumor board

A tumor board is a meeting where doctors from different specialties discuss the best treatment options for individual cancer patients. The speaker describes their experience attending these meetings and highlights the collaborative decision-making process, where various specialists come together to determine the best course of action for a patient.

💡Median patient

The 'median patient' refers to the statistical middle of a group of patients, typically used in clinical trials to represent an average outcome. In the video, the speaker criticizes the over-reliance on treating patients based on the median result, noting that every cancer patient is different and should be treated according to their unique situation, not just the average.

💡Tumor growth

Tumor growth refers to the increase in size or spread of a tumor in the body. The speaker discusses how tumor growth is often used to evaluate whether a treatment is effective. However, they challenge the assumption that any growth is automatically a sign of failure, using an example of a patient whose tumor grew but who still lived significantly longer than expected.

💡Mathematical biology

Mathematical biology is a field that uses mathematical techniques to solve biological problems. In this context, the speaker was introduced to this field while researching sperm motility and later applied these principles to cancer research, believing that mathematical models can help doctors better predict cancer progression and treatment outcomes.

💡MRI

Magnetic Resonance Imaging (MRI) is a medical imaging technique used to visualize detailed internal structures, including tumors. The speaker discusses how MRI images are used by doctors to guide cancer treatment decisions, such as determining the size of a tumor and its response to therapy. They emphasize that while MRI provides a clear image of the tumor, mathematical models could offer additional insight into the tumor’s behavior.

💡Personalized medicine

Personalized medicine refers to tailoring medical treatment to the individual characteristics of each patient. The speaker advocates for this approach, arguing that mathematical tools can help doctors move beyond 'median' patient data and create more personalized treatment plans that take into account the unique biology of each patient’s cancer.

💡Complex systems

Complex systems are systems with many interconnected parts that interact in dynamic ways. The speaker compares cancer to a complex system, where multiple factors such as tumor cells, treatment responses, and patient biology interact. They argue that mathematics can be used to model these systems and predict how they will behave, much like it is used to predict weather patterns or financial markets.

Highlights

The speaker's family has been significantly impacted by cancer, with multiple members losing their lives to the disease.

Cancer is a widespread problem, and many people can relate to knowing family, friends, or colleagues who have battled the disease.

The speaker emphasizes that while the problem of cancer is well known, the solution to it might involve mathematics.

The speaker's early love for mathematics was sparked by solving math puzzles with their father, who was an engineer and a pilot.

After their father was diagnosed with lung cancer, the speaker was deeply involved in his care, commuting back and forth while finishing a degree in mathematics and physics.

The speaker expresses frustration with being unable to contribute to medical decisions and not understanding the discussions around their father's cancer treatment.

This frustration led the speaker to pursue a PhD in applied mathematics, focusing on the intersection of mathematics and cancer biology.

The speaker worked with Jim Murray, a leading figure in mathematical biology, to bridge the gap between math and clinical decision-making.

Tumor boards, where physicians from different specialties collaborate on patient care, are a key part of cancer treatment, but their decisions are often based on median results from clinical trials.

The speaker notes that clinical trials often group patients with the same diagnosis, but there is a broad spectrum of outcomes, and treating based on the median can be misleading.

The speaker's research focuses on modeling tumor growth and response to therapy, using mathematical models to predict individual patient outcomes rather than relying on average results.

An example is given of a patient with a brain tumor (glioblastoma) who, despite tumor growth, lived five years due to the therapy keeping the tumor off its natural growth course.

The speaker highlights the potential of using mathematical models to guide personalized treatment decisions, extending or adjusting therapies based on predicted individual outcomes.

Mathematical tools can help physicians simulate different treatment options, offering a more tailored approach to cancer care rather than relying solely on clinical trial averages.

The speaker concludes by emphasizing the uniqueness of each patient and the need for individualized equations to optimize cancer treatment.

Transcripts

this is my

brother my aunt my mother-in-law my

other brother and my

father these are some of my family

members who have lost their life with

cancer now the sad fact is that the

numbers tell us that each one of you in

this audience also can generate a

similar list of people around you family

friends colleagues who have battled

cancer I don't have to convince you that

the problem of cancer is a big big one

but I might have to convince you of my

conviction that the solution involves

math so my

introduction

to my introduction to cancer involved my

father he was an engineer and a pilot he

served three tours in Vietnam before

retiring he retired shortly after I was

born so what that meant for me is in his

retirement he became an antique dealer

so that meant I spent many many moons

many hours on the road trans traveling

from antique show to antique show we uh

I by the time I was in I think third

grade I had traveled to 48 different

states so on those long Journeys we had

to do something and one of the things we

often did was play play games

specifically Math Games my father would

teach me well if x + y is 5 and x * Y is

6 what are X and Y I was like 6 years

old so uh needless to say that interest

and enthusiasm for mathematics started

early unfortunately when my father was

62 when I was 20 uh my father was

diagnosed with lung

cancer uh I was very involved with my

father's care um unfortunately he died 7

months after after he was

diagnosed but being involved in his care

um meant that I was commuting back and

forth from tane University where I was

finishing my my bachelor's degree in

math and

physics so in traveling back and forth I

would spend all this time with my father

and his dying months and I would spend a

lot of time with his Physicians trying

to figure out what the next best step

was for my father now at the same time I

also happened to be doing research at

tan with some lovely mathematical

biologists who were doing doing some

amazing work on sperm motility in tubes

but it is mathematics meeting biology I

don't think you can dispute that right

so those two things brought together an

opportunity for me said well my father

is sitting here I could watch him over

those sad seven months watch him die and

I watched all those decisions being made

by The Physician about what is the next

best choice for my dad should we do this

therapy should we do that therapy is he

failing this therapy should we move on

to another

therapy and I remember being so

powerless and frustrated in the fact

that I couldn't contribute to those

conversations and worse yet I couldn't

understand what the conversation was

happening it was like a man with a

bubble above his head with an equation

and it just didn't add up to me and I

was a mathematician or a virging one at

that so while I was doing this research

at tane on sperm motility and tubes I

could see that maybe there was an

opportunity for me to bring math the

mathematics my father taught me to the

biology of cancer and contribute to the

world that to improve the life of my

father and others like him so yes I'm a

mathematician and I study

cancer so a month after my father was

died um I uh was admitted to a PhD

program and Applied Mathematics I chose

this specific program because it had the

world leader what I would argue as the

father of the father of the field of

mathematical biology his name was Jim

Murray he had just moved a few years

earlier from the center for Center for

mathematical biology at Oxford

um and so he was the perfect person to

introduce me and to tutor me in the in

the field of the interface between

mathematics and

biology at the same time I was very

interested at the interface between

mathematics and the clinic and how

Physicians were interacting with

patients and how I could create this

opportunity for adding math to our

understanding of cancer so I began

attending tumor board so tumor boards

are totally interesting unique

environment right so each they're

basically often a large table group of

Physicians around this table and they're

all from different areas so there's

Radiologists there's physi physicist um

radiation

biologist there's radiologist there's a

oncologist there's surgeons uh there's

chemotherapist you name it they're all

around the table and they're all making

the best decision possible they're all

collaborating to figure out what the

best decision possible is for that

individual patient but what's also

interesting kind of like the bubble

above the head of the Physicians I was

telling you earlier that couldn't decode

what I learned was that the the Baseline

decisions that were being made were a

function of a simple answer clinical

trials so each physician has in their

back pocket some knowledge of the result

of how how a patient um how patient

should do give if I give them this

therapy ver versus the next therapy and

the format of those clinical

trials are something like this you group

get a group of homogeneous patients

together homogeneous in the sense that

we call them the same diagnosis and I

use air quotes liberally in this

particular context so they have the same

diagnosis they're extremely similar in

some way and we want to introduce a new

therapy to them and we want to compare

that to some standard approach what we

call the standard of care so these

physician these patients are then given

some therapy and the net result is a

survival curve so at the end of the day

the key measure of this of this outcome

of this trial is this curve so the curve

has little steps on it each step is

actually a patient dying and so you get

a a sense of the degree to which this

therapy is working summarized by one

thing this dot in the middle the Green

Dot that's the median patient that's the

that's the average result that is the

result that's in the pockets of the

physicians at the clinical Tri at the

room in the tumor board so they couldn't

possibly carry this whole curve around

them the reality is you look at this and

you say okay well we're talking the

median patient that's where the

terminology treating to the median comes

from from treating to the mean but now

we all know we all know in this room

that everybody's different my father was

here he died very early compared to when

he should have by the median my brother

was here and my other brother was way

out on the table tail he lived three

years when he would really shouldn't

have so we know there's a diversity we

know there's Shades of Gray everybody is

different they're all part of the

rainbow and the reality is how do you

match those things together how do you

use information about individual

patients and combine that with median

understanding of a clinical trial and so

that's where our tools sort of interface

so my research began into a specific

type of cancer known as Goma

specifically the most aggressive of

those

gastas uh I was working with this

amazing neuropathologist Buster alard um

he was a great Menor mentor and friend

in this

area so my research into this disease uh

began began um at this tumor board where

I would learn all about what the

clinical decisions were being made but

what's interesting and what's horrible

about this disease is it's a horrible

disease it's a very bad disease patients

typically live 15

months so the median survival for those

patients it's 15 months and it's also

considered uniformly fatal the term is

used in the first sentence of several

any article you would pull up on this

topic but we all know and the Physicians

all know that it's still just part of a

rainbow that each patient is part of a

rainbow this rainbow so now let's fast

forward to a tumor board for the

specific case of a of primary brain

tumors gomas gly

blastoma so each of these patients are

being discussed by The Physician they're

trying to figure out what the best plan

is for that particular patient knowing

in their back pocket what the green

patient's going to do what that median

patient should be expected to

do so the Physicians are often trying to

F to fight for that individual patient

right they're often coming to the table

going hey we know this patient is part

of a rainbow we just don't know where in

the rainbow it is where in the rainbow

this patient it will will lie and so

there's lots of indicators for figuring

that out and so this is kind of where

the science of biology meets the art of

medicine it's figuring out where these

this interface might be so one of the

tools that Physicians use in the case of

T brain tumors is Mr so here is an MRI

three-dimensional reconstruction of an

MRI we're going to scroll from the

bottom of the brain all the way up

you'll see it outlined in red the tumor

and you're going to scroll back down and

rebuild the threedimensional region that

is the tumor now this is exquisitely

detailed right it's amazingly beautiful

right it's all math and physics

ironically

underneath but what's interesting is

Physicians have to use these images to

figure out how to tailor their therapy

Choice given their knowledge of the

clinical trial that tells me the median

patient is expected to live 15 months

right these images are used by surgeons

to figure out how to approach the tumor

there are radiation therapists that

figure out how to um sculpt their dose

for radiation therapy they're

also also uh used by uh Physicians to

understand response to therapy and so

that's the case I would like you to

think about first so in this case you

see the tumor and there's a brain

there's a tumor you can at least see the

tumor so this is good okay so the tumor

um prior to treatment treatment is

introduced post treatment there is

another there is a a post- treatment

response another another image so what's

interesting about this is that if you

have um a physician you're sitting here

going well look prior to treatment this

tumor was this size after treatment was

another size at the intervening time

there was some sort of treatment in the

decision is at the second time point

should we change therapy the clinical

trial says we should this patient should

have an improved survival of whatever

amount by because of the median patient

outcome but not necessarily for um this

particular patient so do we do we make

the next what's the next choice for this

particular patient so clinically it is

determined that this would be considered

a

failure the tumor grew it got bigger as

a function of therapy so it's a failure

but I'm going to challenge you to

reconsider that choice reconsider the

possibility that this may not be a

failure I'm going to give you a little

insight this particular patient was a

66y old male who ended up living five

years with a

Goma so if that's a failure I'm not sure

I'm not sure what is so now let's go

back to our our MRI and think about the

Physicians are sitting there talking

about these two images the image the

tumor grew grew through therapy

therefore we should change treatment but

also we have these Exquisite detail of

this image and we have an Exquisite

understanding of what's underlying this

underlying the image that we see

specifically in this case we know if we

were to zoom

in we could if we had the tool for every

patient if we could zoom in we would

find individual cells migrating

individual cells proliferating within

this tumor you would see them um growing

and migrating and responding potentially

to the therapy that's being applied to

them so what's interesting is although

we have this Exquisite image that can't

quite get down to the Single Cell detail

what you understand about the tumor is

that it's a complex interacting system

this complex interacting system of

agents this complex system is start of

at the basis of a lot of what we do

scientifically in the world right the

weather in the evening news is all about

a complex system where data is

integrated about the current wind speeds

right are understanding the financial

markets there are people all populating

Wall Street that are all about

quantifying and integrating complex

information to understand the complex

system that is the World Market to make

some sort of

predictions so at that so at a base one

could use math to understand this

complexity and even if you're talking

really simply talking about Sumer cells

proliferating invading and responding to

therapy you can write down a really

simple

equation that said the point is is you

look at the words in this case we're

writing down that the cells are

migrating proliferating and respond to

therapy and then you go back to those

Exquisite MRIs that the Physicians were

dealing with in tumor board and you run

this forward in time thankfully you

still see the red tumor here so now

let's look at the same exact case of

that same exact patient but let's

imagine we had the opportunity and the

insight to understand what that tumor

was going to do without

treatment and here is the tumor evolving

in time you can see the untreated course

has not

changed untreated growth compared to the

legion

above now I ask you is this a

response turns out it is so one of the

problems in these clinical trials and

one of the problems with new therapies

that are studied is the fact that if the

tumor grows through therapy The

Physician changes course well what's

interesting is not all patients that

grow through therapy end up doing poorly

just like this patient this patient

lived five years okay but it turns out

in our studies of now hundreds of

patients going on thousands we've looked

at this sort of response the degree to

which the treatment deflects the tumor

off its course is a response that tells

you that that patient is going to live

longer right so this is a completely new

tool to think about when you're a

physician

and you're struggling with the

treatments in the intervening there's

the post treatment tumor grew okay let's

change course well this treatment

derailed this tumor off course for quite

quite significantly to so much so that

this patient actually ended up living

quite long and maybe they should have

continued on this therapy because it was

continuing to keep the tumor off course

keep the tumor derailed right so having

this out this this in your back pocket

is a completely new way to think about

not the median patient but uh but other

other patients but individualizing our

understanding of

patients so now imagine the case where

you've got a tumor you you patient shows

up at tumor board we're all sitting

around this table again and now you have

something equivalent of an iPad

app on that app you have options for

surgery radiation chemotherapy that's

because the surgeons have their have

their plan they have a whole

navigational system where they figure

out how they're going to approach the

tumor same thing thing for the radiation

oncologists they have a plan they're

going to input they're they're going to

they're going to have a plan they're

going to bring it to the table and the

chemotherapist they have a plan they're

going to bring it to the table now a lot

of those plans are based on

understanding of results of clinical

trials but what they don't know is

really how those things marry together

how those things add together right so

now let's imagine the surgical team

brings in their their plan and the

radiation oncology team brings in their

plan and they're going to apply them in

this way so roughly on day 25 we're

going to have the surgery of of a signif

of what whatever degree which I'll show

you in just a moment and then the

radiation oncology plan involves

sculpting the radiation dose so that it

salvages much as much as possible of the

normal normal brain tissue because you

don't want to radiate the normal brain

if you can all avoid it so now let's add

those things

together so in this case we're going to

we see the tumor tumor being reected

there's a large reection from the

surgery there's the radiation therapy

being introduced you you can see the

tumor being derailed sort of off course

during that and then the

recurrence so although this is not the

ideal plan ideal result right this is

what would this patient would actually

receive this is in fact the standard of

care for this particular patient this is

actually what this patient actually

ended up receiving in real life what you

can say is well now you're sitting at

tumor board and you can say well hey

I've got an opportunity here this

patient was significantly deflected off

of their growth curve their tumor was

significantly deflected off their growth

curve during radi ation let's just

extend that out a little bit or approach

it in a slightly different way or what

if we did that first and then did the

surgery later there's a lot of options

that can be played out that are not

practical currently because we focus so

much on the clinical trials of a median

patient this is a way in which you can

think about individualizing our

understanding a given patient's tumor

given patient's response to treatment

and capitalizing on that for that

patient so if my father's Physicians had

had tools like this in their hands

perhaps my dad would not have received

that last round of therapy that was so

painful to his quality of life perhaps

he would have received the next round of

a different therapy that was actually

more successful for him and so that is

the thing that that certainly drives

me this is my

father these are the faces of the folks

in my lab the colleagues of friends and

family of folks in my lab who have all

faced cancer and they are the people

that remind us every day that every

patient is unique and every patient

truly Des deserves their own equation

thank

[Applause]

you