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cancer study

Ramana Davuluri

By Daniel Dunaief

Ramana Davuluri feels like he’s returning home.

Davuluri first arrived in the United States from his native India in 1999, when he worked at Cold Spring Harbor Laboratory. After numerous other jobs throughout the United States, including as Assistant Professor at Ohio State University and Associate Professor and Director of Computational Biology at The Wistar Institute in Philadelphia, Davuluri has come back to Long Island. 

As of the fall of 2020, he became a Professor in the Department of Biomedical Informatics and Director of Bioinformatics Shared Resource at Stony Brook Cancer Center.

“After coming from India, this is where we landed and where we established our life. This feels like our home town,” said Davuluri, who purchased a home in East Setauket with his wife Lakshmi and their six-year-old daughter Roopavi.

Although Davuluri’s formal training in biology ended in high school, he has applied his foundations in statistics, computer programming and, more recently, the application of machine learning and deep algorithms to the problems of cancer data science, particularly for analyses of genomic and other molecular data.

Davuluri likens the process of the work he does to interpreting language based on the context and order in which the words appear.

The word “fly,” for example, could be a noun, as in an insect at a picnic, or a verb, as in to hop on an airplane and visit family for the first time in several years.

Interpreting the meaning of genetic sentences requires an understanding not only of the order of a genetic code, but also of the context in which that code builds the equivalent of molecular biological sentences.

A critical point for genetic sequences starts with a promoter, which is where genes become active. As it turns out, these areas have considerable variability, which affects the genetic information they produce.

“Most of the genetic variability we have so far observed in population-level genomic data is present near the promoter regions, with the highest density overlapping with the transcription start site,” he explained in an email.

Most of the work he does involves understanding the non-coding portion of genomes. The long-term goal is to understand the complex puzzle of gene-gene interactions at isoform levels, which means how the interactions change if one splice variant is replaced by another of the same gene.

“We are trying to prioritize variants by computational predictions so the experimentalists can focus on a few candidates rather than millions,” Davuluri added.

Most of Davuluri’s work depends on the novel application of machine learning. Recently, he has used deep learning methods on large volumes of data. A recent example includes building a classifier based on a set of transcripts’ expression to predict a subtype of brain cancer or ovarian cancer.

In his work on glioblastoma and high grade ovarian cancer subtyping, he has applied machine learning algorithms on isoform level gene expression data.

Davuluri hopes to turn his ability to interpret specific genetic coding regions into a better understanding not only of cancer, but also of the specific drugs researchers use to treat it.

He recently developed an informatics pipeline for evaluating the differences in interaction profiles between a drug and its target protein isoforms.

In research he recently published in Scientific Reports, he found that over three quarters of drugs either missed a potential target isoform or target other isoforms with varied expression in multiple normal tissues.

Research into drug discovery is often done “as if one gene is making one protein,” Davuluri said. He believes the biggest reason for the failure of early stage drug discovery resides in picking a candidate that is not specific enough.

Ramana Davuluri with his daughter Roopavi. Photo by Laskshmi Davuluri

Davuluri is trying to make an impact by searching more specifically for the type of protein or drug target, which could, prior to use in a clinical trial, enhance the specificity and effectiveness of any treatment.

Hiring Davuluri expands the bioinformatics department, in which Joel Saltz is chairman, as well as the overall cancer effort. 

Davuluri had worked with Saltz years ago when both scientists conducted research at Ohio State University.

“I was impressed with him,” Saltz said. “I was delighted to hear that he was available and potentially interested. People who are senior and highly accomplished bioinfomaticians are rare and difficult to recruit.”

Saltz cited the “tremendous progress” Davuluri has made in the field of transcription factors and cancer.

Bioinformatic analysis generally doesn’t take into account the way genes can be interpreted in different ways in different kinds of cancer. Davuluri’s work, however, does, Saltz said.

Developing ways to understand how tumors interact with non-tumor areas, how metastases develop, and how immune cells interact with a tumor can provide key advances in the field of cancer research, Saltz said. “If you can look at how this plays out over space and time, you can get more insights as to how a cancer develops and the different part of cancer that interact,” he said.

When he was younger, Davuluri dreamt of being a doctor. In 10th grade, he went on a field trip to a nearby teaching hospital, which changed his mind after watching a doctor perform surgery on a patient.

Later in college, he realized he was better in mathematics than many other subjects.

Davuluri and Lakshmi are thrilled to be raising their daughter, whose name is a combination of the words for “beautiful” and “brave” in their native Telugu.

As for Davuluri’s work, within the next year he would like to understand variants. 

“Genetic variants can explain not only how we are different from one another, but also our susceptibility to complex diseases,” he explained. With increasing population level genomic data, he hopes to uncover variants in different ethnic groups that might provide better biomarkers.

Jason Sheltzer. Photo by ©Gina Motisi, 2018/CSHL

By Daniel Dunaief

A diagnosis of cancer brings uncertainty and anxiety, as a patient and his or her family confront a new reality. But not all cancers are the same and not all patients are the same, making it difficult to know the severity of the disease.

As doctors increasingly focus on individual patient care, researchers are looking to use a wealth of information available through new technology to assist with everything from determining cancer risks, to making early diagnoses, to providing treatment and aftercare.

Jason Sheltzer, a fellow at Cold Spring Harbor Laboratory, and his partner Joan Smith, a senior software engineer at Google, have sought to use the genetic fingerprints of cancer to determine the likely course of the disease.

By looking at genes from 20,000 cancer patients, Sheltzer and Smith found that a phenomenon called copy number variation, in which genes add copies of specific long or short sequences, is often a good indicator of the aggressiveness of the cancer. Those cancers that have higher copy number variation are also likely to be the most aggressive. They recently published their research in the journal eLife.

While the investigation, which involved work over the course of four years, is in a preliminary stage, this kind of prognostic biomarker could offer doctors and patients more information from which to make decisions about treatment. It could also provide a better understanding of the course of a disease, as copy number variation changes as cancer progresses.

“The main finding is simply that copy number variation is a much more potent prognostic biomarker than people had realized,” Sheltzer said. “It appears to be more informative than mutations in most single genes.”

Additionally, despite having data from those thousands of patients, Sheltzer and Smith don’t yet know if there’s a tipping point, beyond which a cancer reaches a critical threshold.

Some copy number changes also were more problematic than others. “Our analysis suggested that copy number alterations affecting a few key oncogenes and tumor suppressors seemed to be particularly bad news for patient prognosis,” Sheltzer said, adding that they weren’t able to do a clinical follow-up to determine how genes changed as the cancer progressed. 

“Hopefully, we can follow up this study, where we can do a longitudinal analysis,” he said.

Joan Smith. Photo by Seo-young Silvia Kim

Smith, who has written computer code for Twitter, Oracle and now Google, wrote code that’s specific to this project. “The analysis we’ve done here is new and is on a much more significant scale than the analysis we did in the past,” she said.

Within the paper, Smith was able to reuse parts of code that were necessary for different related experiments. Some of the reusable code cleaned up the data and provided a useful format, while some of the code searched for genetic patterns.

“There is considerable refinement that went into writing this code, and into writing code in general,” she explained in an email.

Smith has a full-time job at Google, where she has to clear any work she does with Sheltzer with the search engine. Before publication, she sent the paper to Google for approval. She works with Sheltzer “on her personal time,” and her efforts have “nothing to do with Google or Google Tools.”

The search engine company “tends to be supportive of employees doing interesting and valuable external work, as long as it doesn’t make use of any Google confidential information or Google owned resources,” including equipment supplies or facilities, she explained in an email.

The phenomenon of copy number variation occurs frequently in people in somatic cells, including those who aren’t battling a deadly disease Sheltzer said. “People in general harbor a lot of normal copy number variation,” he added.

Indeed, other types of repetitive changes in the genome have played a role in various conditions.

Some copy number variations, coupled with deletions, can be especially problematic. A tumor suppressor gene called P53, which is widely studied in research labs around the world, can accumulate copy number variations.

“Patients who have deletions in P53 tend to accumulate more copy number alterations than patients who don’t,” Sheltzer said. “A surprising result from our paper is that copy number variation goes above and beyond P53 mutations. You can control for P53 status and still find copy number variations that act as significant prognostic biomarkers.”

The copy number variations Sheltzer and Smith were examining were affecting whole genes, of about 10,000 bases or longer.

“We think that is because cancers are Darwinian,” explained Sheltzer. “The cells are competing against one another to grow the fastest and be the most aggressive. If a cancer amplifies one potent oncogene, it’s good for the cancer. If the cancer amplifies 200 others, it conveys a fitness penalty in the context of cancer.”

Smith is incredibly pleased to have the opportunity to contribute her informatics expertise to Sheltzer’s research, bringing together skill sets that are becoming increasingly important to link as technology makes it possible to accumulate a wealth of data in a much shorter term and at considerably lower expense.

Smith has a physics degree from MIT and has been in the technology world ever since.

“It’s been super wonderful and inspiring to get to do both” technology work and cancer research, she said.

The dynamic scientific duo live in Mineola. They chose the location because it’s equidistant between their two commutes, which are about 35 minutes. When they are not working, the couple, who have been together for eight years and have been collaborating in their research for almost all of them, enjoy biking, usually between 30 to 60 miles at a time.

Sheltzer greatly appreciates Smith’s expertise in using computer programs to mine through enormous amounts of data.

They are working on the next steps in exploring patient data.