Sanju Sinha Archives - Sanford Burnham Prebys

Tag: Sanju Sinha

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AI-driven cancer prediction tool makes NIH director’s highlights for 2024

AuthorScott LaFee
Date

January 3, 2025

illustration of AI driven data

On April 18, 2024, first author Sanju Sinha, PhD, an assistant professor in the Cancer Molecular Therapeutics Program at Sanford Burnham Prebys, and colleagues published details about a new artificial intelligence-powered tool called PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology).

PERCEPTION was proof-of-concept that AI could be used to predict a cancer’s treatment responses from bulk RNA. Sinha and colleagues built AI models for 44 drugs approved by the FDA and found that their tool “predicted the success of targeted treatments against cell lines with a high degree of accuracy.”

The paper was among six specifically highlighted by Monica Bertagnolli, MD, in her blog as director of the National Institutes of Health.

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How cancer cells change as they metastasize

AuthorScott LaFee
Date

December 9, 2024

cancer metastasis

Most cancer deaths are caused by metastasis, but how cancer cells and tumors modify themselves and spread from their origins to other parts of the body remains largely a mystery — and fundamentally challenging.

In a new paper published December 6, 2024 in Science Advances, study co-author Sanju Sinha, PhD, assistant professor in the Cancer Molecular Therapeutics Program at Sanford Burnham Prebys, and colleagues, investigate whether primary and metastatic tumors more closely resemble the tissues of origin or target tissues in terms of gene expression.

Their findings suggest movement and evolution, providing a comprehensive transcriptome-wide view of the processes through which cancer tumors adapt to their metastatic environments before and after metastasis.

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Coding clinic

AuthorGreg Calhoun
Date

August 6, 2024

Programming in a Petri Dish - AI series graphic

Rapidly evolving computational tools may unlock vast archives of untapped clinical information—and help solve complex challenges confronting healthcare providers

The wealth of data stored in electronic medical records has long been considered a veritable treasure trove for scientists able to properly plumb its depths.  

Emerging computational techniques and data management technologies are making this more possible, while also addressing complicated clinical research challenges, such as optimizing the design of clinical trials and quickly matching eligible patients most likely to benefit.  

Scientists are also using new methods to find meaning in previously published studies and creating even larger, more accessible datasets.  

“While we are deep in the hype cycle of artificial intelligence [AI] right now, the more important topic is data,” says Sanju Sinha, PhD, an assistant professor in the Cancer Molecular Therapeutics Program at Sanford Burnham Prebys. “Integrating data together in a clear, structured format and making it accessible to everyone is crucial to new discoveries in basic and clinical biomedical research.” 

Sinha is referring to resources such as the  St. Jude-Washington University Pediatric Cancer Genome Project, which makes available to scientists whole genome sequencing data from cancerous and normal cells for more than 800 patients.

Medulloblastoma tumor cells with hundreds of circular DNA pieces

The Chavez lab uses fluorescent markers to observe circular extra-chromosomal DNA elements floating in cancer cells. Research has shown that these fragments of DNA are abundant in solid pediatric tumors and associated with poor clinical outcomes. Image courtesy of Lukas Chavez.

The Children’s Brain Tumor Network is another important repository for researchers studying pediatric brain cancer, such as Lukas Chavez, PhD, an assistant professor in the Cancer Genome and Epigenetics Program at Sanford Burnham Prebys. 

“We have analyzed thousands of whole genome sequencing datasets that we were able to access in these invaluable collections and have identified all kinds of structural rearrangements and mutations,” says Chavez. “Our focus is on a very specific type of structural rearrangement called circular extra-chromosomal DNA elements.” 

Circular extra-chromosomal DNA elements (ecDNA) are pieces of DNA that have broken off normal chromosomes and then been stitched together by DNA repair mechanisms. This phenomenon leads to circular DNA elements floating around in a cancer cell.  

Sanju Sinha, PhD

Sanju Sinha, PhD, is an assistant professor in the Cancer Molecular
Therapeutics Program at  Sanford Burnham Prebys.

“We have shown that they are much more abundant in solid pediatric tumors than we previously thought,” adds Chavez. “And we have also shown that they are associated with very poor outcomes.” 

To help translate this discovery for clinicians and their patients, Chavez is testing the use of deep learning AI algorithms to identify tumors with ecDNA by analyzing the biopsy slides that are routinely created by pathologists to diagnose brain cancer. 

“We have already done the genomic analysis, and we are now turning our attention to the histopathological images to see how much of the genomic information can be predicted from these images,” says Chavez. “Our hope is that we can identify tumors that have ecDNA by evaluating the images without having to go through the genomic sequencing process.”  

Currently, this approach serves only as a clinical biomarker of a challenging prognosis, but Chavez believes it can also be a diagnostic tool—and a game changer for patients.  

“I’m optimistic that in the future we will have drugs that target these DNA circles and improve the therapeutic outcome of patients,” says Chavez.  

“Once medicine catches up, we need to be able to find the patients and match them to the right medicine,” says Chavez. “We’re not there yet, but that’s the goal.” 

Chavez is also advancing his work as scientific director of the Pediatric Neuro-Oncology Molecular Tumor Board at Rady Children’s Hospital in San Diego.  

“Recently, it has been shown that new sequencing technologies coupled with machine learning tools make it possible to compress the time it takes to sequence and classify types of tumors from days or weeks to about 70 minutes,” says Chavez. “This is quick enough to take that technology into the operating room and use a surgical biopsy to classify a tumor.  

“Then we could get feedback to the surgeon in real time so that more or less tissue can be removed depending on if it is a high- or low-grade tumor—and this could dramatically affect patient outcomes.  

“When I talk to neurosurgeons, they are always in a pickle between trying to be aggressive to reduce recurrence risk or being conservative to preserve as much cognitive function and memory as possible for these patients.  

“If the surgeon knows during surgery that it’s a tumor type that’s resistant to treatment versus one that responds very favorably to chemotherapy, radiation or other therapies, that will help in determining how to strike that surgical balance.” 

Lukas Chavez, PhD

Lukas Chavez, PhD, is an assistant professor in the Cancer Genome and Epigenetics Program at Sanford Burnham Prebys.

Artist’s rendering X-shaped chromosomes floating in a cell

Artist’s rendering of X-shaped chromosomes floating in a cell alongside circular extra-chromosomal DNA elements.

Rady Children’s Hospital has also contributed to the future of genomic and computational medicine through BeginNGS, a pilot project to complement traditional newborn health screening with genomic sequencing that screens for approximately 400 genetic conditions. 

“The idea is that if there is a newborn baby with a rare disease, their family often faces a very long odyssey before ever reaching a diagnosis,” says Chavez. “By sequencing newborns, this program has generated success stories, such as identifying genetic variants that have allowed the placement of a child on a specific diet to treat a metabolic disorder, and a child to receive a gene therapy to restore a functional immune system.”

Programming in a Petri Dish, an 8-part series

How artificial intelligence, machine learning and emerging computational technologies are changing biomedical research and the future of health care

  • Part 1 – Using machines to personalize patient care. Artificial intelligence and other computational techniques are aiding scientists and physicians in their quest to prescribe or create treatments for individuals rather than populations.
  • Part 2 – Objective omics. Although the hypothesis is a core concept in science, unbiased omics methods may reduce attachments to incorrect hypotheses that can reduce impartiality and slow progress.
  • Part 3 – Coding clinic. Rapidly evolving computational tools may unlock vast archives of untapped clinical information—and help solve complex challenges confronting health care providers.
  • Part 4 – Scripting their own futures. At Sanford Burnham Prebys Graduate School of Biomedical Sciences, students embrace computational methods to enhance their research careers.
  • Part 5 – Dodging AI and computational biology dangers. Sanford Burnham Prebys scientists say that understanding the potential pitfalls of using AI and other computational tools to guide biomedical research helps maximize benefits while minimizing concerns.
  • Part 6 – Mapping the human body to better treat disease. Scientists synthesize supersized sets of biological and clinical data to make discoveries and find promising treatments.
  • Part 7 – Simulating science or science fiction? By harnessing artificial intelligence and modern computing, scientists are simulating more complex biological, clinical and public health phenomena to accelerate discovery.
  • Part 8 – Acceleration by automation. Increases in the scale and pace of research and drug discovery are being made possible by robotic automation of time-consuming tasks that must be repeated with exhaustive exactness.
Institute News

Objective omics

AuthorGreg Calhoun
Date

August 1, 2024

Programming in a Petri Dish - AI series graphic

Although the hypothesis is a core concept in science, unbiased omics methods may reduce attachments to incorrect hypotheses that can reduce impartiality and slow progress

Biological techniques that study the entire landscape of a sample’s genes or proteins—genomics or proteomics, respectively—help scientists discover new results without becoming too narrowly focused on what they predicted would happen. Although some scientists pursuing studies with this wider lens have been accused of going on “fishing expeditions,” many researchers counter that they now are able to investigate their hypotheses without missing other important results.

“I am a major proponent of omics, and especially unbiased omics,” says Sanju Sinha, PhD, an assistant professor in the Cancer Molecular Therapeutics Program at Sanford Burnham Prebys. “If someone now doesn’t show me unbiased results, it deeply bothers me. If every experiment only shows results from one pathway, it’s concerning and increases my skepticism about the study.”

An omics approach differs from traditional hypothesis-driven research in that it includes a comprehensive perspective about the phenomenon a scientist is studying and what might be causing it.

Sinha Lab

The Sinha Lab

“Unbiased omics look at the global picture of how everything is changing,” explains Sinha. “If you’re looking at genetic factors, you present all 20,000 genes and how they change, rather than just one pathway and maybe 10 genes.”

Sanju Sinha, PhD

Sanju Sinha, PhD, is an assistant professor in the Cancer Molecular
Therapeutics Program at  Sanford Burnham Prebys.

This method reflects the dynamic nature of biomedical research.

“Biomedical research is currently experiencing a period of accelerating and metamorphic discoveries fueled by unparalleled technologies that generate enormous amounts of data that, in turn, spur and spawn avenues of new inquiry and questions previously unimagined,” says David A. Brenner, MD, president and CEO of Sanford Burnham Prebys.

“An effective and successful biomedical researcher in the 21st century requires input from different disciplines that previously were not part of standard practice or the scientific method.”

Sinha agrees. “People used to work in small silos. They could work on the same biological pathway for 30 years.” The new model, he said, is quickly shifting to more multidisciplinary, team-based science where experts from many fields collaborate to make the most of new technology and the rich data it can provide.

Some teams employing these omics approaches have been criticized for conducting aimless studies due to the lack of traditional hypotheses. Sinha is quick to defend against these claims.

“I don’t mind these so-called fishing expeditions. I like to say that there are only two kinds of science: applied science and not-yet-applied science. Fishing expeditions are valuable if the data is made available and other scientists can make discoveries with it for years to come.”

“We should remember that fishing expeditions in biomedical research have done a great service to humanity.”

The hypothesis is not an endangered species destined to be replaced by unbiased omics approaches. On the contrary, omics experiments can often be kick-starters that help scientists generate new hypotheses to explore.

A team of scientists at Sanford Burnham Prebys and their collaborators are using an omics technique called resistomics to develop a new class of antibiotics effective against a drug-resistant pathogen.

In a paper published on January 3, 2024 in Nature, a multi-institutional team including  Andrei Osterman, PhD, a professor in the Immunity and Pathogenesis Program at Sanford Burnham Prebys, with colleagues at  Roche—the Swiss-based pharmaceutical/healthcare company—and others, describe a novel class of small-molecule-tethered macrocyclic peptide (MCP) antibiotics with potent antibacterial activity against carbapenem-resistant  Acinetobacter baumannii  (CRAB).

The World Health Organization and the Centers for Disease Control and Prevention have both categorized multidrug-resistant  A. baumannii as a top-priority pathogen and public health threat.

In the study, Osterman and colleagues applied an experimental evolution approach to help identify the drug target (the LPS transporter complex) of a new class of antibiotics—a macrocyclic peptide called Zosurabalpin—and elucidate the dynamics and mechanisms of acquired drug resistance in four distinct strains of A. baumannii. 

Andrei Osterman, PhD

Andrei Osterman, PhD, is a professor in the Immunity and Pathogenesis program at Sanford Burnham Prebys.

They used an integrative workflow that employs continuous bacterial culturing in an “evolution machine” (morbidostat) followed by time-resolved, whole-genome sequencing and bioinformatics analysis to map resistance-inducing mutations. 

“This comprehensive mapping of the drug-resistance landscape yields valuable insights for a variety of practical applications,” says Osterman, “from therapy optimization via genomics-based assessment of drug resistance/susceptibility of bacterial pathogens to a rational development of novel drugs with minimized resistibility potential.”

Programming in a Petri Dish, an 8-part series

How artificial intelligence, machine learning and emerging computational technologies are changing biomedical research and the future of health care

  • Part 1 – Using machines to personalize patient care. Artificial intelligence and other computational techniques are aiding scientists and physicians in their quest to prescribe or create treatments for individuals rather than populations.
  • Part 2 – Objective omics. Although the hypothesis is a core concept in science, unbiased omics methods may reduce attachments to incorrect hypotheses that can reduce impartiality and slow progress.
  • Part 3 – Coding clinic. Rapidly evolving computational tools may unlock vast archives of untapped clinical information—and help solve complex challenges confronting health care providers.
  • Part 4 – Scripting their own futures. At Sanford Burnham Prebys Graduate School of Biomedical Sciences, students embrace computational methods to enhance their research careers.
  • Part 5 – Dodging AI and computational biology dangers. Sanford Burnham Prebys scientists say that understanding the potential pitfalls of using AI and other computational tools to guide biomedical research helps maximize benefits while minimizing concerns.
  • Part 6 – Mapping the human body to better treat disease. Scientists synthesize supersized sets of biological and clinical data to make discoveries and find promising treatments.
  • Part 7 – Simulating science or science fiction? By harnessing artificial intelligence and modern computing, scientists are simulating more complex biological, clinical and public health phenomena to accelerate discovery.
  • Part 8 – Acceleration by automation. Increases in the scale and pace of research and drug discovery are being made possible by robotic automation of time-consuming tasks that must be repeated with exhaustive exactness.
Institute News

Using machines to personalize patient care

AuthorGreg Calhoun
Date

July 30, 2024

Programming in a Petri Dish - AI series graphic

Artificial intelligence (AI) and other computational techniques are aiding scientists and physicians in their quest to create treatments for individuals rather than populations

The Human Genome Project captured the public’s imagination with its global quest to better understand the genetic blueprint stored on the DNA within our cells. The project succeeded in delivering the first-ever sequence of the human genome while foreshadowing a future for medicine once considered to be science fiction. The project presaged the possibility that health care could be personalized based on clues within a patient’s unique genetic code.

Chavez lab

The Chavez Lab

While many more people have undergone genetic testing through consumer genealogy and health services such as 23andMe and Ancestry than through health care systems, genomic sequencing has influenced clinical care in some specialties. Personalized medicine—also known as precision medicine or genomic medicine—has been especially helpful for people suffering from rare diseases that historically have been difficult to diagnose and treat.

Scientists at Sanford Burnham Prebys are employing new technologies and expertise to test ways to improve diagnoses and customize treatments for many diseases based on unique characteristics within tumors, blood samples and other biopsies.

AI and other computational techniques are enabling patient samples to be rapidly analyzed and compared to data from vast numbers of individuals who have been treated for the same condition. Physicians can use AI and other tools to identify subtypes of cancers and other conditions, as well as improve selection of eligible candidates for clinical trials.

“I think we’ve gotten a lot better at precision diagnostics,” says Lukas Chavez, PhD, an assistant professor in the Cancer Genome and Epigenetics Program at Sanford Burnham Prebys. “In my work at Rady Children’s Hospital in cancer, we can characterize a tumor based on mutations, including predicting how quickly different tumors will spread. What we too often lack, however, are better treatment approaches or medicines. That will be the next generation of precision medicine.”

Sanju Sinha, PhD, an assistant professor in the Cancer Molecular Therapeutics Program at Sanford Burnham Prebys, is developing projects to help bridge the gap between precision diagnostics and treatment. He is partnering with the National Cancer Institute on a first-of-its-kind computational tool to systematically predict patient response to cancer drugs at single-cell resolution.

A study published in the journal  Nature Cancer discussed how the tool, called PERCEPTION, was successfully validated by predicting the response to individual therapies and combination treatments in three independent published clinical trials for multiple myeloma, breast and lung cancer.

Lukas Chavez, PhD

Lukas Chavez, PhD, is an assistant professor in the Cancer Genome and Epigenetics Program at Sanford Burnham Prebys.

In each case, PERCEPTION correctly stratified patients into responder and non-responder categories. In lung cancer, it even captured the development of drug resistance as the disease progressed, a notable discovery with great potential.

Sanju Sinha, PhD

Sanju Sinha, PhD, is an assistant professor in the Cancer Molecular
Therapeutics Program at Sanford Burnham Prebys.

“The ability to monitor the emergence of resistance is the most exciting part for me,” says Sinha. “It has the potential to allow us to adapt to the evolution of cancer cells and even modify our treatment strategy.”

While PERCEPTION is not yet ready for clinics, Sinha hopes that widespread adoption of this technology will generate more data, which can be used to further develop and refine the technology for use by health care providers.

In another project, Sinha is focused on patients being treated for potential cancers that may never progress into dangerous conditions warranting treatment and its accompanying side effects.

“Many women who are diagnosed with precancerous changes in the breast seek early treatment,” says Sinha. “Most precancerous cells never lead to cancer, so it may be that as many as eight of 10 women with this diagnosis are being overtreated, which is a huge issue.”

To try and counter this phenomenon, Sinha is training AI models on images of biopsied samples in conjunction with multi-omics sequencing data. His team’s goal is to develop a tool capable of predicting which patients’ cancers would progress based on the imaged samples alone.

“In the field of precancer, insurance does not cover the cost of computing this omics data,” says Sinha. “Health care systems do routinely generate histopathological slides from patient biopsies, so we feel that a tool leveraging these images could be a scalable and accessible solution.”

If Sinha’s team is successful, an AI tool integrated into clinics would predict whether precancerous cells would progress within the next 10 years to guide treatment decisions and how patients are monitored.

“With precision medicine, our hope is not to just treat patients with better drugs, but also to make sure that patients are not unnecessarily treated and made to bear needless costs and side effects that disrupt their quality of life.”

Programming in a Petri Dish, an 8-part series

How artificial intelligence, machine learning and emerging computational technologies are changing biomedical research and the future of health care

  • Part 1 – Using machines to personalize patient care. Artificial intelligence and other computational techniques are aiding scientists and physicians in their quest to prescribe or create treatments for individuals rather than populations.
  • Part 2 – Objective omics. Although the hypothesis is a core concept in science, unbiased omics methods may reduce attachments to incorrect hypotheses that can reduce impartiality and slow progress.
  • Part 3 – Coding clinic. Rapidly evolving computational tools may unlock vast archives of untapped clinical information—and help solve complex challenges confronting health care providers.
  • Part 4 – Scripting their own futures. At Sanford Burnham Prebys Graduate School of Biomedical Sciences, students embrace computational methods to enhance their research careers.
  • Part 5 – Dodging AI and computational biology dangers. Sanford Burnham Prebys scientists say that understanding the potential pitfalls of using AI and other computational tools to guide biomedical research helps maximize benefits while minimizing concerns.
  • Part 6 – Mapping the human body to better treat disease. Scientists synthesize supersized sets of biological and clinical data to make discoveries and find promising treatments.
  • Part 7 – Simulating science or science fiction? By harnessing artificial intelligence and modern computing, scientists are simulating more complex biological, clinical and public health phenomena to accelerate discovery.
  • Part 8 – Acceleration by automation. Increases in the scale and pace of research and drug discovery are being made possible by robotic automation of time-consuming tasks that must be repeated with exhaustive exactness.
Institute News

The Cancer Letter covers collaboration between Sanford Burnham Prebys and the National Cancer Institute to precisely prescribe cancer drugs

AuthorGreg Calhoun
Date

May 14, 2024

Sanju Sinha, PhD, headshot

The May 10 issue of The Cancer Letter details a recent publication explaining the investigation of a new AI tool that may be able to match cancer drugs more precisely to patients.

The Cancer Letter—a news organization and weekly publication based in Washington, D.C., that focuses on cancer research and clinical care—included an article in its May 10 issue about a partnership between scientists at Sanford Burnham Prebys and the National Cancer Institute (NCI).

Authored by Sanju Sinha, PhD, assistant professor in the Cancer Molecular Therapeutics Program at Sanford Burnham Prebys, and the NCI’s Eytan Ruppin, MD, PhD, the “Trials & Tribulations” feature describes a first-of-its-kind computational tool to systematically predict patient response to cancer drugs at single-cell resolution. The study regarding this new tool was published on April 18, 2024, in the journal  Nature Cancer.

The Cancer Letter was founded in 1973 and focuses its coverage on the development of cancer therapies, drug regulation, legislation, cancer research funding, health care finance and public health.

Institute News

NIH director highlights Sanford Burnham Prebys and National Cancer Institute project to improve precision oncology

AuthorGreg Calhoun
Date

May 9, 2024

Big data technology and data science illustration

The NIH director’s blog features a recent publication detailing the study of a new AI tool that may be able to match cancer drugs more precisely to patients.

Monica M. Bertagnolli, MD, director of the National Institutes of Health (NIH), highlighted a collaboration between scientists at Sanford Burnham Prebys and the National Cancer Institute (NCI) on the NIH director’s blog. Bertagnoli noted advances that have been made in precision oncology approaches using a growing array of tests to uncover molecular or genetic profiles of tumors that can help guide treatments. She also recognizes that much more research is needed to realize the full potential of precision oncology.

The spotlighted Nature Cancer study demonstrates the potential to better predict how patients will respond to cancer drugs by using a new AI tool to analyze the sequences of the RNA within each cell of a tumor sample. Current precision oncology methods take an average of the DNA and RNA in all the cells in a tumor sample, which the research team hypothesized could hide certain subpopulations of cells—known as clones—that are more resistant to specific drugs.  

Bertagnoli said, “Interestingly, their research shows that having just one clone in a tumor that is resistant to a particular drug is enough to thwart a response to that drug. As a result, the clone with the worst response in a tumor will best explain a person’s overall treatment response.” 

More of Bertagnoli’s thoughts on this collaboration between scientists at Sanford Burnham Prebys and the NCI are available on the NIH director’s blog. 

Sanju Sinha, PhD, assistant professor in the Cancer Molecular Therapeutics Program at Sanford Burnham Prebys, is the first author on the featured study. 

Institute News

From postdoc to PI, it’s a journey. Don’t forget to pack some support

AuthorSanju Sinha
Date

December 15, 2023

Sanju Sinha (right) with computational biologist Lihe Liu, PhD, a postdoc member of Sinha’s new lab

The journal Nature Cancer asked a dozen early-career investigators to share their thoughts and experiences about starting their own labs in 2023. Among them: Sanju Sinha, PhD, who joined Sanford Burnham Prebys in June. Below is his essay. You can read the rest here.

Don’t forget to pack support 

Against the backdrop of a world emerging from a pandemic, starting my laboratory in 2023 was a whirlwind of excitement and anxiety, against the backdrop of a world emerging from a pandemic.

The goal for my laboratory is to understand cancer initiation and use this knowledge to develop preventative therapies—a goal appreciated by many, yet understudied and underfunded. We are aiming to achieve this by developing computational techniques based on machine-learning and leveraging big data from various sources, such as healthy tissues, pre-cancerous lesions and tumors. This journey has taken several unexpected turns, with its fair share of delights and challenges.

One significant hurdle appeared early: hiring. I recall the advice I received: “Forget it, you can’t hire a postdoc as an early-stage laboratory.” This made me ponder—if I were to choose right now, would I pursue a postdoc? My immediate answer was no. It struck me then: the traditional postdoc route needed a revamp.

Determined to instigate change, I introduced a new role: computational biologist. This position, an alternative to a postdoc, was tailored for transitioning to industry and offered better pay. The response was staggering—more than 400 applications.

Now, I’m proud to lead a fantastic team of three computational biologists from whom I am continually learning. This experience taught me a valuable lesson: crafting roles that serve both the goals of the laboratory and the career aspirations of the applicants can make a world of difference. I urge new principal investigators to shatter norms and design roles that provide fair compensation and smooth industry transition—reflecting the reality of the current job market.

However, the path to establishing a new laboratory was not without setbacks. Rejection is common in this field. I have already experienced a grant rejection and, considering the average grant success rate, I am prepared for many more.

Amid these challenges, my support system proved to be my lifeline. I’m grateful to be part of Sanford Burnham Prebys, which has proved to be more than just a top biomedical research institution. It is a community that provides unparalleled support for early principal investigators through generous startup packages, administrative assistance, hiring and grant-writing guidance, and a network of compassionate peers and mentors.

Equally important is my personal support system—my family, partner and friends who remind me that there is life beyond science, helping me maintain my well-being. This balance, I have realized, is the most crucial tool for anyone on a similar journey—so do not forget to pack support for the ride.