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Oncologists’ Guide to Genomics

Stay current on the latest trends in genomics and molecular diagnostics for oncology.

Thursday, December 20, 2018

While many cells in our bodies can accumulate oncogenic mutations, the majority of these events do not lead to tumor formation as these abnormal cells are eliminated by defense mechanisms. Instead, tumors arise when a mutation happens in a particular cell type that is uniquely sensitive to it. Identifying such cancer cells of origin is essential to properly target cancer.

For example, mutations in the retinoblastoma (RB) tumor suppressor gene, which normally blocks abnormal cell growth and division, gives rise to retinoblastoma. It arises in specialized retinal cells called cone cells, which collect light. Why this kind of cancer always starts in cone cells is unknown. But if scientists can get a clearer, more accurate view of the downstream effects of RB mutations in cone cells versus other cells in the retina, they may identify unique therapeutic targets that can prevent or treat retinoblastoma with laser-like precision.

But studying the effects of gene mutations in specific cell types is easier said than done. It is nearly impossible to collect pure samples made up of just one cell type. Instead, scientists often have to use a bulk sample prepared from an entire tissue.

"This only gives a sort of average picture of gene expression in individual cells since it pools thousands of cells, some of which may be unwanted no matter how much the sample is purified. The results of examining the effects of RB mutations using these kinds of samples don't accurately represent how an RB mutation affects gene expression in a particular cell type," said Maxim Frolov, Professor of Biochemistry and Molecular Genetics in the University of Illinois at Chicago (UIC) College of Medicine.

Single-Cell RNA Sequencing

Single-cell RNA sequencing lets researchers study gene expression in individual cells, eliminating the problem of contaminated cell samples and averaging. Frolov's laboratory adapted a technology called Drop-seq, which allows researchers to isolate and genetically sequence single cells. Drop-seq can sequence thousands of individual cells at the same time.

Frolov and his graduate student, Majd Ariss, assembled a Drop-seq instrument to isolate cells of the eye in developing fruit flies, which the lab uses as a model system. Then they were able to study gene expression changes caused by mutations in the RB gene in thousands of different cells in the eye compared with gene expression in cells with normal copies of the RB gene. Their results are published in Nature Communications (2018; doi:10.1038/s41467-018-07540-z).

"Since this is the first time single-cell RNA sequencing has been performed in cells of the fruit fly eye, we had to create a comprehensive map or cell atlas, accurately describing gene expression in each cell type in the normal eye. We then relied on this atlas to determine how an RB mutation affects gene expression of each cell type in the eye," Frolov explained.

Key Findings

Their analysis of eye cells with an RB mutation revealed a distinctive but small population of cells where the mutation altered gene expression and changed cell metabolism. The metabolic change sensitized the cells to apoptosis or self-induced cell death. The propensity of cells with mutations in the RB gene to undergo apoptosis is a well-known phenomenon and is eventually overcome through additional mutations during the development of cancer, which is characterized by out-of-control cell growth and division—the opposite of apoptosis.

"The metabolic changes we observed in RB mutant cells make them vulnerable in ways that might be exploited with therapeutic approaches before additional mutations hit the same cell, making them resistant to cell death," Frolov said. "Since these effects were limited to such a small group of cells, they were previously missed when whole RB mutant eye tissue was analyzed."

The Drop-seq platform took Ariss more than 3 months to build. He painstakingly followed instructions contained in a 40-page manual to generate his first single-cell RNA sequencing dataset.

"Nobody guided us on how to do single-cell sequencing as we were the first at UIC to do it," said Ariss, who is the first author on the paper.

Their Drop-seq instrument is the only one of its kind at UIC.

"It is a truly revolutionary technology that promises to shed new light on the origin of cancer and why certain cancers originate in certain cell types and not others. Only now can we begin to investigate why and how," Frolov said. "For the past year and a half, we performed over a hundred experiments and generated transcriptomes of more than a hundred thousand cells from fruit fly organs, mouse tumors, and human cell lines. We hope that more UIC researchers will use it going forward."

Tuesday, November 20, 2018

A combined evaluation of common variants with small effects and rare predisposing mutations among young female childhood cancer survivors may further stratify this high-risk population for subsequent breast cancer risk (Clin Cancer Res 2018; doi:10.1158/1078-0432.CCR-18-1775).

Female childhood cancer survivors have an increased risk of developing subsequent breast cancer compared with the general population. This increased risk has largely been attributed to cancer treatment regimens, such as chest irradiation and/or exposure to high-dose chemotherapeutic agents. Current screening of this population relies on treatments and doses used to treat childhood cancer, explained study author Zhaoming Wang, PhD, Associate Member in the Department of Epidemiology and Cancer Control at the St. Jude Children's Research Hospital in Memphis.

Wang previously found that survivors of childhood cancer have an increased risk of subsequent breast cancer if the survivors carry pathogenic or likely pathogenic (P/LP) mutations, such as mutations to the BRCA1 gene.

"Our current study attempts to investigate the contributions to the risk of subsequent breast cancer by considering the full picture of breast cancer genetic susceptibility, which includes common genetic variants with small effects (polygenic determinants) in addition to P/LP mutations (monogenic determinants)," Wang explained.

Study Specifics

Wang and colleagues utilized information from the St. Jude Lifetime Cohort Study by analyzing whole-genome sequencing data for 1,133 female cancer survivors of European ancestry. Of these survivors, 47 developed one or more subsequent breast cancers.

The researchers constructed a polygenic risk score (PRS) for individual survivors by calculating the weighted sum of 170 common breast cancer risk alleles present in each survivor's genome. Investigators also evaluated the presence of P/LP mutations in 11 breast cancer predisposition genes. Relative rates of subsequent breast cancer incidence were estimated.

Following multivariable analysis, the researchers found that survivors in the highest PRS quintile had 2.7 times the risk of subsequent breast cancer compared to survivors in the lowest quintile. Survivors treated with chest irradiation had even higher risk; those in the highest PRS quintile treated with radiation had three times the risk of subsequent cancer compared to those in lowest quintile treated with radiation.

Survivors who carried P/LP mutations had 21.8 times increased risk for subsequent breast cancer compared with those who didn't have P/LP mutations. Survivors treated with chest irradiation and who carried P/LP mutations had 10.3 times increased risk for subsequent breast cancer compared with those who didn't have P/LP mutations treated with chest irradiation.

"The PRS can identify individuals with high breast cancer risk that do not carry known pathogenic mutations," Wang noted. "Our results indicate that both polygenic determinants and large-effect rare mutations (monogenic determinants) contribute to the risk of subsequent breast cancer independently."

Notably, PRS was significantly associated with risk of subsequent breast cancer only in women less than 45 years old. "Our data supports the hypothesis that genetic risk factors play a more important role in the development of subsequent breast cancers in younger women," Wang said. "However, this observed age-specific association could be partly due to the smaller sample size of older survivors in our study."

P/LP mutations were defined as mutations to the following breast cancer predisposition genes: BRCA1, BRCA2, TP53, PTEN, CDH1, STK11, NF1, PALB2, ATM, CHEK2, and NBN.

"Our findings suggest that polygenic screening can inform personalized breast cancer surveillance in female childhood cancer survivors," said Wang. "This method can be utilized in the clinical setting to enhance the identification of high-risk survivors to enable the early detection and potential prevention of subsequent breast cancer."

In addition, "Our results indicate that personalized breast cancer surveillance strategies for survivors should incorporate prior exposure to specific anti-cancer treatments, the presence of P/LP mutations, and the cumulative presence of small-effect common variants, as represented by a polygenic risk score."

Limitations of the study include a relatively young cohort of childhood cancer survivors. Additionally, analysis was restricted to survivors of European ancestry; follow-up studies should be conducted within other non-European ethnic groups.

Monday, October 22, 2018

Despite months of aggressive treatment involving surgery and chemotherapy, about 85 percent of women with high-grade widespread ovarian cancer will have a recurrence of their disease. This leads to further treatment, but never to a cure. About 15 percent of patients, however, do not have a recurrence. Most of those women remain disease-free for years. Recently, researchers have identified an independent prognostic factor—cancer/testis antigen 45 (Cell 2018;175(1):159).

CT45 is associated with extended disease-free survival for women with advanced ovarian cancer. The team of physicians and scientists found that patients with high levels of CT45 in their tumors lived more than seven times as long as patients who lacked sufficient CT45. Data from long-term survivors averaged 2,754 days (7.5 years), compared to only 366 days for patients who had little or no CT45.

Cancer Proteomics

The study authors attribute their discovery to the emerging field of multi-level cancer proteomics. The researchers relied on minute pieces of tissue acquired from the University of Chicago ovarian cancer tissue bank, which has been following patient outcomes for 20 years.

They used pieces of these samples to isolate, identify, and characterize thousands of proteins. The most interesting of those proteins was CT45. They determined that higher levels of this biomarker were closely linked to treatment success and excellent patient outcomes.

"We believe this is the first example of mass spectrometry-based proteomics leading to the discovery of a prognostic and functionally important cancer biomarker," said co-lead author Ernst Lengyel, MD, PhD, an ovarian cancer specialist and Chairman of the Department of Obstetrics and Gynecology at the University of Chicago.

"Our goal was to find reliable biomarkers that could predict treatment response," said the study's co-lead author Matthias Mann, PhD, Chairman at the Max-Planck Institute. The team quantified more than 9,000 proteins and "identified CT45 as an independent prognostic factor for patients with high-grade serous ovarian cancer."

"Using mass spectrometry, we can identify, for the first time, almost all of the proteins in the tumor tissue of the patients," Mann said. "Our highly sensitive methods now enable us to profile thousands of proteins simultaneously, allowing us to search for the proteins that are critical to the disease by comparing the tissue samples."

It was encouraging for the research team to find the first significant biomarker in tissues from ovarian cancer patients who responded to platinum-based chemotherapy. "CT45," Lengyel said, "was completely unknown until then."

Study Details

To validate their initial findings, the researchers studied tissues collected from more than 200 patients from the University of Chicago. They found no CT45 in 82 of those patient samples, but they found high levels in 42 patients, all of whom had much longer disease-free survival.

A larger study, using sequence data from The Cancer Genome Atlas, confirmed their initial results, leading to their conclusion that "CT45 expression is a novel prognostic indicator for advanced stage high-grade serous ovarian cancer."

Since little was known about CT45's role in cellular functions, the study authors tried to understand the molecular mechanisms that improved responses to chemotherapy. They found that the standard chemotherapy for ovarian cancer, carboplatin, caused DNA damage, particularly in tumor cells expressing high levels of CT45. This lead to cell death in tissue culture and tumor reduction in treated mice.

"We suspect that CT45 plays a major role in the response of tumors to carboplatin. This gives us hope that future strategies that activate CT45 expression in the tumor could make it more sensitive to carboplatin treatment," said Marion Curtis, PhD, a postdoctoral scholar in the Lengyel laboratory.

They also found two peptides from CT45-positive ovarian cancer cells that stimulated a solid immune response against the cancer. T cells collected from a CT45-positive patient with high-grade ovarian cancer were able to kill cancer cells in vitro "in a dose-dependent manner."

"We have evidence that tumor-specific expression of CT45 stimulates the patient's immune system to fight the cancer, as would a virus or bacterial-infected cells," Lengyel added. "Our long-term goal is to find new ways to improve patient outcomes based on these exciting insights."

The clinical implications from this study "could be significant," the authors noted. Expression of CT45 improves the efficacy of platinum-based chemotherapy, and potentially immunotherapy, for patients with advanced stage ovarian cancer. "CT45 may be particularly relevant to long-term survival," they added.

"This study," researchers concluded, also "highlights the power of clinical cancer proteomics to identify targets for chemo and immunotherapy, define their mechanisms, and contribute to the development of effective cancer therapies."

Friday, September 21, 2018

By Joel Diamond, MD, FAAP

Few advances in recent decades have the potential to change health care practice as significantly as genomics and precision medicine. Not surprisingly, oncology is proving to be the standard bearer in the charge.

In recent years, and certainly since the human genome was mapped in the early 2000s, uptake and progress in clinical practice have been swift:

  • Oncologists have greater insight into disease mechanisms, which supports targeted diagnosis and treatment.
  • Rather than viewing cancer as a site-specific disease, physicians now can base clinical decision-making on molecular classifications.
  • These considerations allow treatment to be individualized so therapeutic benefit is achieved faster.
  • Cancer specialists are equipped to prospectively evaluate risk, and likewise can detect the disease and intervene earlier.

The picture is bright and promises to get only brighter as advances in the science of genomics accelerate.

Bringing Genomics Into Workflow

Yet the industry faces one significant barrier: Bringing the value of genomic data into the oncology workflow so physicians can access and use the information at the point of care. Forward-looking health care leaders are seeking an informatics strategy as the basis for ensuring both clinicians and patients are able to leverage the full value of genomic results.

Three factors contribute significantly to this obstacle:

1. Lack of standardized nomenclature. Despite tremendous inroads made by health care IT professionals and policy makers, the industry lacks standardized nomenclature to make information meaningful and useful. Oncology itself presents its own set of challenges. Naming conventions for specific cancers vary significantly. Cancer staging is not reflected consistently across clinical IT software. Other considerations such as toxicity and disease recurrence likewise are not integrated uniformly.

Efforts to standardize genomic nomenclature are even more immature—and vocabularies around molecular immunotherapy lag further still. This is due, in part, to the rapid pace of scientific discovery. The health care industry struggles to understand terms that are already in use, while emerging vocabularies around new concepts like proteomics have yet to be undefined. While it is never easy to "issue" a medical vocabulary (consider the lingering strain surrounding the move from ICD-9 to ICD-10), the task is nevertheless critical for oncology to fully exploit genomics and precision medicine at the point of care.

2. Too much paper and too little foresight. Integrating genomic information into the EHR, where it is readily available for the clinician, is critical if oncologists are to fully benefit from precision medicine.

Right now, precision medicine is in danger of taking a giant step backwards when it comes to electronic data sharing. Incredible amounts of genomic information continue to be communicated via paper, a practice that simply is not sustainable. Obvious inefficiencies aside, paper-based documents greatly limit how the information can be accessed and how it can be leveraged. Genomic information must be shared as discrete data so it can be mined and applied across treatment and research activities, and so it can be referenced and factored as physicians follow patients longitudinally.

Oncology thought leaders increasingly talk about the value of application platforms like SMART on FHIR—and they are, without a doubt, a necessary next step. But, alone, they are insufficient in propelling the industry where it needs to go. We have likewise learned from the past that waiting for government consensus on industry IT standards requires patience. The wheels of progress turn slowly.

Instead, oncology and other specialists can achieve "speed to value" by considering a different informatics strategy, including implementation of existing vendor-neutral solutions that consume genomic data from any knowledge base or source, and deliver it directly into any EHR.

3. Data silos. Health care is in danger of building another generation of data silos around genomics, repeating the mistake made when other clinical information systems emerged. Already, some health systems are exploring disease-specific precision medicine technologies, such as those available to oncology departments. This may prove to be a shortsighted strategy, however.

Consider the disadvantages a precision oncology data silo might represent. Optimal use of genomics in the care of cancer patients is not limited to somatic data. Some cancer susceptibility is tied to germline data—clinicians may benefit from insights contained in epigenetic information as well, for example. In addition, pharmacogenomics supports better clinical decision-making by revealing how a patient might respond to specific medications (e.g., pain or anti-nausea drugs), and whether or not toxicity might be an issue. It also reveals non-cancerous comorbidities and how they are being addressed, which might impact the oncologist's recommended course of treatment.

Adaptive Clinical Trials

Bringing genomic information to the point of care via an intentional informatics infrastructure will enable oncology to make strides in other key areas as well.

Precision medicine holds the promise of dramatically changing clinical trial matching, for instance. Industry leaders are considering the fact that genomics may accelerate a move away from certain randomized clinical trials towards greater reliance upon adaptive trials. Consider the benefit to the patient—and the researcher—if matching began with biology rather than the drug.

When a treatment is intended for patients with specific markers, perhaps only those individuals should be included in the trial. The oncologist, at the point of care, can access the genomic data and refer only those patients most likely to respond to therapy. Why waste clinical resources, as well as the patient's time and possibly quality of life, if the oncologist already knows that the treatment is ineffective, based on biology? This, of course, means patient cohorts would be smaller, and researchers may also eliminate the need for control groups receiving only placebos.

Addressing Disparity

Disparity around which patients do or do not receive genetic testing is rapidly becoming a point of discussion and contention among industry thought leaders. EHRs, as an example, don't enable oncologists (or any physician) to identify at-risk patients easily or effectively. Critical information—like comprehensive family history—is often limited or incomplete and, even when present, typically not stored as discrete data integrated with the workflow and appropriate decision-support tools.

The result of this information gap was perhaps best articulated by Kevin S. Hughes, MD, FACS, Co-Director of the Avon Foundation for Comprehensive Breast Evaluation at Massachusetts General in Boston, in a recent article in the Journal of Clinical Oncology (2017;35:3789-3791): "Our problem, which desperately cries out for a solution, is that huge numbers of high-risk patients who could be identified by genetic testing are instead developing cancer and often dying of that disease."

Bringing access to genomic testing into the workflow would go a long way to save lives because "out of sight, out of mind" affects physicians just like all other segments of the population. If the opportunity to consider the option of genetic testing…and the functionality to order these tests…and the ability to review the results all presented themselves at the point of care, within the workflow, oncologists and all clinicians would constantly be reminded of their option to leverage genomics.

With access to genomics at the point of care, physicians could ensure every patient for whom testing is appropriate would have access.

Few doubt that genomics and precision medicine will soon be recognized as the standard of care in oncology and other specialties.  To reap its full value, however, clinicians must have meaningful access to all genomic and clinical information that could impact testing, diagnostic, and therapeutic decisions. Moreover, it must be available within the workflow and in formats that empower oncologists to leverage it to its fullest potential. To do that, we must evaluate our informatics strategy, and build a precision care platform to meet today's needs and accommodate the discoveries just over the horizon.

JOEL DIAMOND, MD, FAAP, is Adjunct Associate Professor of Biomedical Informatics at the University of Pittsburgh. He is a diplomat of the American Board of Family Practice and a fellow in the American Academy of Family Physicians. He cares for patients at Handelsman Family Practice in Pittsburgh.

Monday, August 20, 2018

Treating early-stage lung cancers with drugs that unleash the immune system's ability to attack malignant cells may hinder tumor growth and improve overall survival, according to new research by Weill Cornell Medicine and NewYork-Presbyterian investigators.

The study suggests that immunotherapy that blocks PD-1—a protein that inhibits immune response to tumors—could be successfully incorporated into early-stage non-small cell lung cancer (NSCLC) treatment (JCI Insight 2018;3(13):e96836). Currently, the FDA has approved these immune checkpoint inhibitors only for late-stage NSCLC cases either alone or in combination with chemotherapy. Treatment is based on the proportion of tumor cells expressing PD-L1, a protein that together with PD-1 triggers the deactivation of the immune response. By initiating this therapy earlier in the disease course, clinicians can take advantage of patients' healthier immune systems before tumors progress or other treatments physically weaken patients.

"The standard of care for early-stage lung cancer is surgery, and removing a localized tumor has a therapeutic benefit. But 50 percent of patients relapse eventually," said senior author Vivek Mittal, PhD, Professor of Cell and Developmental Biology in Cardiothoracic Surgery, and Director of the Neuberger Berman Foundation Lung Cancer Laboratory at Weill Cornell Medicine. "If we could do something in the early stages to generate an active immune response, we might be able to prevent recurrence in the future."

NSCLC comprises about 80 percent of all lung cancers, with a 5-year survival rate of only 15-20 percent. About 20-25 percent of the 220,000 people diagnosed with NSCLC in the U.S. each year present with early-stage disease.

Checkpoint inhibitor drugs are designed to release the immune system's built-in brakes, known as checkpoints, which impede the immune system's ability to attack cancer cells.

"Integrating these drugs into the treatment of patients with early-stage lung cancer, preferably before surgery, can result in a powerful immune response that may persist even after the tumor is surgically removed," said co-senior author Nasser Altorki, MD, Chief of the Division of Thoracic Surgery at Weill Cornell Medicine and NewYork-Presbyterian/Weill Cornell Medical Center and the Gerald J. Ford-Wayne Isom Research Professor in Cardiothoracic Surgery at Weill Cornell Medicine. "This will hopefully reduce or prevent the chance of future recurrence of the cancer after surgery."

Focusing on Immune Cells

The study aimed at testing the utility of these drugs in early-stage NSCLC was completed in two parts. First, Mittal and his colleagues evaluated molecular components of the immune system in early-stage NSCLC by comparing tumor and healthy lung tissue samples taken from 20 NSCLC patients treated at Weill Cornell Medicine. All of the patients had been diagnosed with stages I through IIIA of the disease—considered early-stage cases—and hadn't yet undergone non-surgical treatment. The team found that tumor tissues contained large numbers of T cells, indicating an anti-tumor immune response.

However, that immune response was rendered ineffective when the checkpoint proteins, PD-1 on the T cells and PD-L1 on the tumor cells, interacted. This indicated immune suppression in patients—even at this early stage of cancer development.

Next, the researchers treated mice with lung cancer with immune checkpoint inhibitors. The lung cancer in these mice was driven by a mutation in the KRAS gene that occurs in about 30 percent of cases of human lung cancer. There are no FDA-approved targeted therapies currently available for these patients, Mittal noted. "Rodents treated with checkpoint inhibitors lived about 25 percent longer than those not treated with the drugs."

The team found that immune cells called CD4 T cells, which coordinate the immune response by signaling other T cells to collectively fight invaders, grew in number and activity with checkpoint inhibitor treatment. They discovered that treatment also increased the number of CD8 T cells, which directly kill tumor cells. They further showed that loss of both populations led to a diminished response to the drug and enhanced tumor progression. Their data demonstrated that these checkpoint inhibitors are affecting not only the CD8 T cells that do the killing, but also the CD4 T cells that coordinate the response. And more importantly, they work together, which has yet to be appreciated.

This dual approach yielded key insights about how checkpoint inhibitors influence immune system T cells, which recognize and fight invaders.

Few other scientists nationwide are examining the role of checkpoint inhibitors in early-stage NSCLC, Mittal said, and clinical trials in humans are already underway at Weill Cornell Medicine.

"For early-stage lung cancer patients, there's a lot of excitement to begin to look into the effectiveness of immune checkpoint inhibitors," he said. "We need to refine the use immunotherapies, preferably in combination with other therapies to hopefully stop the cancer from coming back."