Coloring the world with biomarkers
Sandeep Singhal, Ph.D., is an associate professor in both the UND School of Medicine & Health Sciences Department of Pathology and the university’s School of Electrical Engineering & Computer Science. Originally from India, Singhal arrived at UND in 2019 and has since brought a number of research grants to the university for his efforts in bioinformatics and artificial intelligence. As he describes below, researchers studying cancer today are finding important differences in cancer biology related to racial and ethnic diversity. His research is focused on identifying the key elements to understanding intrinsic and extrinsic factors that have a major impact on cancer diagnoses and which may affect patient response to treatment.
Interview conducted and edited for space by Brian James Schill
Thank you for your time, Dr. Singhal. Your focus is cancer and specifically biomarkers, yes? Help me understand how you got into that line of work.
My career path is a long journey that I covered in different continents of the world and tried to shape my career as a life-long learner. I have completed my bachelor’s degree and two master degrees (Master of Science and Master of Technology) from highly respected institutes in India. After my master degrees, I started my career as a software engineer and worked for a few industries at management level. At a certain point, I realized I wasn’t so enthusiastic about this kind of work and wanted to learn more, so I started my education again and moved to Europe as a Ph.D. Scholar at the Jules Bordet Institute, Université libre de Bruxelles, Belgium, which is very well-known Institute in the area of the breast cancer. There I employed my computational skills, including data-science, machine learning, and artificial intelligence approaches, to develop computational solutions to breast cancer treatment management. I was involved in developing cancer biomarkers, which play an important role in deciding the treatment option for breast cancer patients and providing more targeted treatment with the least toxicity.
So, what exactly is a biomarker and what role do biomarkers play in health management?
A biomarker can be anything that we can measure as an indicator of changes in the biological process between the “normal” human being and the person who is moving towards the disease or going through some treatment. It can be as simple as blood pressure or it can be a glucose level which we use to track diabetes. Or, it can be more complicated. We can look at a gene, a set of genes, or proteins in a biological sample such as blood, urine, or tissue, to help us identify whether a certain person is moving towards a certain disease, especially cancer. A biomarker may be a genetic variant, single gene, transcript, protein, or set of such molecules whose condition and quantity is related to the risk, incidence, or advancement of the disease. Cancer biomarkers can be divided into two types: biomarkers produced by cancer cells such as specific antigens of tumors; and antigens accompanying cancer produced by normal cells as a result of their response to pathological changes in the environment.
This sounds a lot like what we call translational research here at UND, where discoveries on the laboratory bench translate to bedside treatments for patients. So how might these biomarkers you mentioned be used in this bench-to-bedside strategy?
Clinical applications of biomarkers are extensive. They can be used as tools for disease risk assessment, screening and early detection of disease, accurate diagnosis, patient prognosis, prediction of response to therapy, and disease surveillance and monitoring response. My research is mainly focused on cancer biomarkers, especially breast cancer. To improve cancer treatment management, prognostic biomarkers are critically needed to aid physicians in deciding treatment strategies in a personalized fashion. With the help of biomarkers, clinicians stratifying patients and guiding treatment individualization can help produce improved outcomes. If the person is diagnosed with cancer, biomarkers can help clinicians know what subtype of cancer they have – what kind of treatment will work best? For example, the Oncotype DX and MammaPrint assay are genomic tests that have been widely used clinically to predict the recurrence risk of patients with estrogen-receptor-positive (ER+) breast cancer. In the Oncotype DX genomic marker, the expression levels of 16 marker genes and 5 control genes are measured to calculate a recurrence score that can be used to stratify patients into three prognostic groups with high-, intermediate-, and low-risk. It has been shown that high-risk patients are more likely to benefit from and should be treated with adjuvant chemotherapy, whereas low-risk patients do not benefit from chemotherapy and should thus not be treated with it to avoid side effects.
Part of your work includes exploring racial disparities in cancer diagnosis and treatment. Help me understand the impact that race and ethnicity have in diagnosis and treatment of cancer and the disparities we see in outcomes.
Racial/ethnic disparities in cancer survival in the United States are well documented, but the underlying biology is not well explored. Therefore, people may experience the same disease differently because of a variety of lived experiences such as living conditions, as well as characteristics like race and ethnicity, age, and sex. In the growing technology era, it’s essential that clinical trials and research include people with all those possible variables so that all communities can benefit from scientific advances. As researchers, we try to find if and how the biological processes of all ethnicities and races are different, and how this can be measured by different biomarkers. A large population of African American and European American breast cancer patients, who contributed to our study, helped us to identify the role of racial diversity on different biomarkers. Based upon clinically-available data, American African women have a higher death rate from breast cancer in the United States compared to European American ethnicity. Therefore, our studies were aimed to evaluate trends in survival and response to treatment, by race, for women diagnosed with breast cancer. This disparity is greatest in hormone receptor–positive subtypes. We uncovered some biological factors underlying this disparity by comparing functional expression and prognostic significance of several biomarkers, including master transcriptional regulators of luminal differentiation. Our study shows, even within clinically homogeneous tumor groups, regulatory networks that drive mammary luminal differentiation reveal race-specific differences in their association with clinical outcome.
Understanding these biomarkers and their downstream effects will elucidate the intrinsic mechanisms that drive racial disparities in breast cancer survival. And certain minority groups in the U.S. tend to have higher rates of certain cancers, yes?
As per the National Cancer Institute report among both men and women, non-Hispanic blacks have the highest cancer death rates both overall and for most cancer types. Then it’s white, Asian or Pacific Islander, American Indian/ Alaska Native, and Hispanic/Latino men and women. Recent trends show cancer death rates among black people declined over time, but still remain higher than other racial and ethnic groups. According to a Centers for Disease Control and Prevention report in 2022, American Indian people are more likely to get certain cancers (including lung, colorectal, liver, stomach, and kidney cancers) compared to non-Hispanic white people in the United States. These reports clearly indicate a need to diversify oncological research to different populations along with novel strategies to enhance race/ethnicity data recording and reporting. Historically, all the biomarkers have been applied to all the population; most of them are not race- or ethnicity-specific. That’s why we always encourage more and more people to come forward and participate in clinical trials, so we can understand these different biological processes and build better diagnostic tools and treatments for people.
You have many national and international collaborations, including at Columbia University, Stony Brook University, University of Southern California, and so on. So how does that kind of collaboration help improve the research? I’m assuming your research benefits greatly from such collaboration?
Yes. As we know, the technology is developing very fast, and there is hardly any single institute that can control the entire domain. Inter-institutional collaboration helps to gain needed resources and expertise which plays an important role in developing and diversifying basic, biomedical, and clinical research effectively. Fortunately, we have been able to develop a team of experts – which includes a clinician, pathologist, microbiologist, lab technicians, and bioinformaticians – to combine different outcomes on a single platform, and then look at it from all possible dimensions and perspectives. Together with increasing treatment options for any given disease, there is a growing challenge of selecting the most appropriate treatment for each patient. Therefore, we are working on artificial intelligence-based approaches that substantially expand our understanding of the tumor macroenvironment. This digital-pathology approach helps us to identify the optimal treatment regimen based on patient profiles.
On that note, you have a joint appointment with the College of Engineering and Mines here at UND to do bioinformatics and data science. Give me a sense of how data science works within your health research.
Data science is currently a major component of clinical-translational research as we have large amounts of data which is freely available, and it is not yet fully explored. Currently, more than seven million biological samples are freely available on the National Institutes of Health (NIH) website. So that’s the area of research where we can test several biological hypotheses without spending a single penny. We are building a large in-house data platform together with analytical tools that can help researchers – even those who do not have training in the field of coding or data science. We have recently developed a cloud-based module with a Google team through an NIH grant. This is a multi-omics platform that we developed where researchers can evaluate genomic information together with the epigenomic information without writing any code or without having a high-performance computer – just using a Google cloud account. Researchers can simply upload their data and can perform the entire analysis (which is step-by-step guided) to generate the outcome.
