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Oncologists’ Guide to Genomics

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

Friday, May 20, 2022

Patients diagnosed with a type of brain tumor survived for longer when they were treated aggressively with surgery, radiation, and chemotherapy. But far from suggesting that more treatment always leads to better survival, the study by UC San Francisco (UCSF) underscores the critical role of genomic profiling in diagnosing and grading brain tumors.

In the study, UCSF researchers followed 38 patients with a tumor type that was reclassified by the World Health Organization in November 2021 from a Grade 2 or 3 glioma, to a “glioblastoma, IDH-wildtype, CNS WHO Grade 4," based on its molecular features. The new more accurate diagnosis comes from genomic profiling in which the DNA alterations that drive tumor growth are identified. The previous diagnosis was determined by traditional microscopic comparisons between cancer cells and normal cells.

All patients underwent genomic sequencing using the UCSF500 Cancer Gene Panel and were offered treatment consistent with a conventional glioblastoma, the deadliest and most common adult brain tumor. The length of their survival was compared with a retrospective cohort of 130 patients with the same tumor type, whose treatment regimens were more conservative, in line with the previous tumor classification.

The first group of patients survived an average of 24 months, while the second group survived an average of 16 months, the researchers reported in their study, appearing in the online issue of Neuro-Oncology (2022; doi: 10.1093/neuonc/noac089).

“The study shows that genomic profiling resulted in more aggressive patient management that ultimately led to improved clinical outcomes, compared to the biologically matched historical patient cohort," said senior author, David Solomon, MD, PhD, Assistant Professor in the UCSF Department of Pathology, investigator at the UCSF Brain Tumor Center, and a principal investigator of the UCSF Glioblastoma Precision Medicine Program.

Research Findings
In studying the MRIs of the first group of patients, the researchers found that 33 of the 38 had imaging features suggestive of a lower-grade glioma, while the remaining five had imaging features suggestive of conventional glioblastoma, such as peripheral enhancement and dead tissue. While both groups were histologically and molecularly indistinguishable, the first set, so-called early/evolving glioblastoma, may have “underlying biologic differences" in the immune micro-environment and an intact blood-brain barrier that may affect treatment efficacy.

The UCSF research was prompted by a landmark 2015 study by the Cancer Genome Atlas Research Network that performed genome-wide analyses on 293 Grade 2 and 3 gliomas. The researchers identified a subset of patients, approximately one in five, who lacked an IDH mutation, a molecular biomarker known to be associated with better outcomes. The genomic profile of this subset, which was categorized as IDH-wildtype glioma, mirrored that of the UCSF patients. The patients in this subset were on average 50 years old at diagnosis, about 5-10 years older than the patients with IDH-mutant gliomas.

“While these patients did not have the traditional histological hallmarks of conventional glioblastoma, they shared the same molecular features and had similar survival," said Solomon, who has pioneered genomic profiling for diagnostic classification and treatment of brain tumors at UCSF since 2015, and has co-authored more than 50 publications on brain tumor molecular pathogenesis.

The results of this study not only led to the treatment objective of the UCSF study, but it also solved a conundrum that had bedeviled neuro-oncologists for years: why some Grade 2 and 3 glioma patients progressed slowly and survived for several years, while others advanced rapidly and died within a year or two.

“Historically, we've relied on what pathologists see under the microscope to guide treatment, which can be subjective to the pathologist's experience and the size of the sample collected from the neurosurgeon, said co-author Jennie Taylor, MD, MPH, a neuro-oncologist with the UCSF Department of Neurological Surgery who treats adult brain tumor patients. “For patients with tumors that are not surgically accessible, this may mean undersampling of a tumor, which could lead to a patient being misdiagnosed, undertreated, and ineligible for clinical trial enrollment."

Future research may further extend the survival of patients with IDH-wildtype glioblastoma, said Taylor, who is also affiliated with the UCSF Weill Institute for Neurosciences. “Integrating genomic profiling into the pathology report, which takes a small quantity of brain tumor cells, means that doctors can be more confident in their treatment recommendations, enabling more patients to take part in clinical trials."

When the UCSF500 Cancer Gene Panel was first implemented in 2015, around 5-10 percent of patients had to have their pathological diagnosis amended following genomic testing, Solomon said. In pediatrics, the figure was even higher, affecting six of the first 31 patients who were tested, according to a 2016 study. Today, genomic sequencing is routinely carried out for all adult and pediatric brain tumors at UCSF to ensure accurate diagnosis and optimal care. It is usually covered by both private and public insurers.

A preliminary pathological diagnosis following microscopic review is made after tumor resection or biopsy. A final diagnosis integrating the molecular findings comes about 3 weeks later, but radiation or chemotherapy may be initiated earlier if recommended.

Sometimes the final diagnosis is sobering, and sometimes it is joyful—such as the child whose earlier microscopic evaluation had pointed toward a Grade 4 tumor, which was corrected to Grade 1 tumor after genomic testing, said Solomon. “There's no question that we need a definitive diagnosis to provide the most appropriate treatment plan. That might mean more treatment and it might mean less." 

Wednesday, April 20, 2022

The groundbreaking NCI-MATCH trial continues to offer treatment opportunities for adult patients with relapsed/refractory cancers. A new treatment arm (Z1M) is evaluating the immunotherapy combination of relatlimab and nivolumab. It is for patients whose tumors have DNA mismatch repair deficiency (dMMR) and LAG-3 expression and have progressed after anti-PD-1/PD-L1 immunotherapy.

In addition, researchers continue to seek patients with BRAF mutations to assess the combination of dabrafenib and trametinib, both targeted therapies (Arm H). NCI-MATCH already showed the activity of this combination across a broad range of cancer types in a first cohort of patients and is now seeking additional patients to confirm these initial positive results. Together, the two treatment arms seek to enroll 85 patients.

"These two highly focused arms illustrate the ability of precision medicine trials to be nimble in response to positive findings, and to develop positive efficacy signals into studies that have the potential to change clinical practice," said Peter J. O'Dwyer, MD, Group Co-Chair of the ECOG-ACRIN Cancer Research Group, which is co-leading the trial with the National Cancer Institute.

NCI-Molecular Analysis for Therapy Choice (NCI-MATCH or EAY131) is the first and largest basket trial in precision medicine for cancer. Its primary aim is to establish whether patients with advanced cancer derive clinical benefit from treatments that target a molecular feature regardless of where cancer occurs in the body. The trial assesses a range of nearly 40 unique molecular targets and corresponding drugs. These matches were selected based on early evidence of potential effectiveness. However, most of the data are from trials in single cancer types.

Each NCI-MATCH arm and every patient who participates provides valuable information on potential cancer treatments. So far, nearly 1,200 patients have received treatment representing multiple cancer types. Most have uncommon or rare cancers—diseases other than breast, colorectal, non-small cell lung, or prostate cancer.

Most NCI-MATCH treatment arms are now closed to patient enrollment and are in follow-up. Multiple journals have published results for individual treatment arms. Others are to come on a rolling basis. The publications contain patient outcomes, detailed genomic analyses, and conclusions drawn from this information.

As researchers delve into the genomic features of each patient, they are uncovering valuable new information on responsive versus unresponsive tumors, especially in rare cancers where there are often no effective treatments. Signals of effectiveness will merit future examination in other trials beyond NCI-MATCH. They are also discovering how drugs that target a specific mutation perform in patients with other co-existing mutations. Such discoveries may reveal opportunities to explore new drug combinations.

“The emergence of these two combination arms to represent the culmination of research in NCI-MATCH is no accident and has arisen organically from the aggregated results of the trial to date," O'Dwyer noted.

Future Precision Medicine
At the time of development, the size and scope of the NCI-MATCH trial had never before been attempted. For the NCI and ECOG-ACRIN, it was an enormous undertaking to develop the infrastructure for a national precision medicine trial across all cancers. Ultimately, NCI-MATCH is a demonstration of leadership and cooperation among the NCI, the NCI Clinical Trials Network cooperative groups, pharmaceutical and biotechnology companies, genomic testing laboratories, participating clinical sites, and physicians.

“The depth and breadth of expertise among the investigators and staff involved in NCI-MATCH is unprecedented and includes hundreds of translational scientists, clinical oncologists, community practitioners, and research personnel all with deep experience in clinical trials," O'Dwyer said.

NCI-MATCH has built a knowledge base and laid the groundwork for future precision medicine initiatives in several areas that are defined in several publications, and summarized here:

  • Established the proportion of relapsed/refractory cancers of various histologies that may respond to drugs targeted to molecular alterations (J Natl Cancer Inst 2020; https://doi.org/10.1093/jnci/djz245)
  • Set benchmarks for the utility of next-generation sequencing in clinical trials (J Clin Oncol 2020; doi: 10.1200/JCO.19.03010)
  • Demonstrated the broad interest among physicians (e.g., the trial is open at more than 1,100 hospitals and cancer centers nationwide)
  • Developed a pathway for patients to enter the trial using standard tumor gene tests ordered by physicians to guide clinical care (no new biopsy needed for eligibility determination), and designated nearly 30 commercial and academic laboratories to participate
  • Proved that tumor testing by many laboratories is an effective way to identify patients, evidenced by a high concordance rate between external lab assays and the central assay
  • For rare cancers, showed that a precision medicine trial across all cancers is an effective way to study uncommon diseases, by exceeding expectations in the enrollment of these patients
  • Defined “precision" (Curr Probl Cancer 2017; https://doi.org/10.1016/j.currproblcancer.2017.02.001)
  • Developed a central assay platform with a commercial partner
  • Set considerations for central assays in future trials (J Mol Diagn 2017; https://doi.org/10.1016/j.jmoldx.2016.10.007)
  • Established a validated computational platform (MATCHbox) for treatment allocation
  • Validated immunohistochemical assays for integral biomarkers (Clin Cancer Res 2018; https://doi.org/10.1158/1078-0432.CCR-17-1597)

"NCI-MATCH is influencing how cancer clinical trials will be designed and conducted in the future," said Lyndsay Harris, MD, a medical oncologist at the NCI, Associate Director of the NCI's Cancer Diagnosis Program, and NCI study co-chair for the overall NCI-MATCH trial.

Patients With dMMR & LAG-3 Expression
Arm Z1M is evaluating the two-drug immunotherapy combination of relatlimab plus nivolumab in patients whose cancer has progressed after treatment with anti-PD-1/PD-L1 immunotherapy. To be eligible, patients must have dMMR on genomic testing and tumors that express LAG-3.

"Multiple trials have shown exciting and compelling responses for patients who have DNA mismatch repair deficiency who are treated with PD-1 inhibitors, including pembrolizumab and nivolumab. But, unfortunately, half of the patients won't respond or will progress when they are on therapy," said lead investigator Nilofer Azad, MD from Johns Hopkins University. However, recent data suggest that LAG-3 inhibition may restore anti-PD-1 sensitivity.

Patients with melanoma are not eligible for Arm Z1M. The exclusion is based on the Phase III RELATIVITY-047 trial results, which found that progression-free survival increased significantly when relatlimab was added to nivolumab in patients with advanced melanoma. The FDA is conducting a priority review of the relatlimab-nivolumab combination to treat patients with unresectable or metastatic melanoma.

BRAF-Mutated Cancers
Arm H is investigating the combination of the selective BRAF inhibitor dabrafenib and the MEK1/2 inhibitor trametinib in patients whose tumors harbor BRAF V600E or BRAF V600K mutations. A promising efficacy signal in the first group of NCI-MATCH patients to receive this treatment led to the opening of an expansion cohort. The initial study met its primary endpoint with an overall objective response rate of 38 percent in a heavily pre-treated cohort of 17 distinct tumor types—several rare—with BRAF mutations (J Clin Oncol 2020; doi: 10.1200/JCO.20.00762).

"NCI-MATCH's Arm H shows promising activity outside of currently approved FDA indications," said lead researcher April K.S. Salama, MD, from Duke University. “So far, the data show that BRAF/MEK combination therapy has widespread activity across multiple cancer types whose tumors harbor BRAF mutations. Therefore, we hope to identify 50 more patients for Arm H to define the broad applicability of these positive findings."

Tuesday, March 22, 2022

Annual MRI screenings starting at ages 30-35 may reduce breast cancer mortality by more than 50 percent among women who carry certain genetic changes in three genes, according to a comparative modeling analysis published in JAMA Oncology (2022; doi:10.1001/jamaoncol.2021.6204).

The predictions involve pathogenic variants in ATM, CHEK2, and PALB2 genes—which collectively are as prevalent as the much-reported BRCA1/2 gene mutations. Authors of the study contend that their findings support MRI screening in some of these women earlier than existing preventive-care guidelines propose.

“Screening guidelines have been difficult to develop for these women because there haven't been clinical trials to inform when to start and how to screen," said Kathryn Lowry, MD, Assistant Professor of Radiology at the University of Washington School of Medicine and the paper's lead author.

The work was a collaboration of the Cancer Intervention and Surveillance Modeling Network (CISNET), the Cancer Risk Estimates Related to Susceptibility (CARRIERS) consortium, and the Breast Cancer Surveillance Consortium. Using established breast cancer simulation models, the researchers input age-specific risk estimates provided by CARRIERS and recent published data for screening performance. The CARRIERS data involved more than 32,000 breast cancer patients and a similar number of patients who had no cancer.

“For women with pathogenic variants in these genes, our modeling analysis predicted a lifetime risk of developing breast cancer at 21-40 percent, depending on the variant," Lowry said. “We project that starting annual MRI screening at age 30-35, with annual mammography starting at age 40, will reduce cancer mortality for these populations of women by more than 50 percent."

The simulations compared the combined performance of mammography and MRI against mammography alone, and projected that annual MRI conferred significant additional benefit to these populations.

“We also found that starting mammograms earlier than age 40 did not have a meaningful benefit but increased false-positive screens," Lowry added.

Results from CISNET models have informed past guidelines, including the 2009 and 2016 U.S. Preventive Services Task Force recommendations for breast cancer screening in average-risk women.

“Modeling is a powerful tool to synthesize and extend clinical trial and national cohort data to estimate the benefits and harms of different cancer control strategies at population levels," said Jeanne Mandelblatt, MD, PhD, MPH, Professor at Georgetown Lombardi Comprehensive Cancer Center and a senior author on the paper.

The simulations in this study also predicted the volume of false-positive screening results and benign biopsies, per 1,000 women scanned, that would accompany the authors' recommendations for annual MRIs starting earlier. Those projections translate to about four false-positive screening results and 1-2 benign biopsies per woman over a 40-year screening span, the authors said.

To realize a benefit of cancer screening guidelines based on genetic susceptibility, a woman would need to know she carries an implicated gene variant before receiving a disease diagnosis. More often it's the case that a genetic test panel is administered after someone tests positive for cancer—too late to be of preventive value for the patient but potentially life-saving for blood relatives who could seek genetic testing.

“People understand very well the value of testing for variants in BRCA1 and BRCA2, the most common breast cancer predisposition genes. These results show that testing other genes, like ATM, CHEK2, and PALB2, can also lead to improved outcomes," said Mark Robson, MD, Chief of Breast Medicine Service at the Memorial Sloan Kettering Cancer Center and a senior author on the paper.

The researchers hope their analysis will aid the National Comprehensive Cancer Network (NCCN), the American Cancer Society, and other organizations that issue guidance for medical oncologists and radiologists.

“Overall what we're proposing is slightly earlier screening than what the current guidelines suggest for some women with these variants," said Allison Kurian, MD, MSc, Professor at the Stanford University School of Medicine and senior author on the paper. “For example, current NCCN guidelines recommend starting at age 30 for women with PALB2, and at 40 for ATM and CHEK2. Our results suggest that starting MRI at age 30-35 appears beneficial for women with any of the three variants."

Friday, February 18, 2022

A mutated gene affects growth of brain tumor cells in young adults, indicating sensitivity to a new treatment strategy, according to a team of researchers at the University of Michigan (UM) Rogel Cancer Center. These findings, recently published in Cell Reports, present possibilities for more effective therapies for glioma patients with this gene mutation (2022; doi: 10.1016/j.celrep.2021.110216).

The protein coding gene ATRX is mutated in just over half of high-grade glioma young adult patients, most commonly in teenagers and adults up to 40 years old. Though some studies have given hints as to why ATRX is mutated in gliomas, many questions remain, and researchers have not been able to use medicines to target the mutation.

“We're still learning why it so often affects this population and how it affects response to targeted therapies," said Carl Koschmann, MD, Pediatric Neuro-Oncologist at UM Rogel Cancer Center and UM C.S. Mott Children's Hospital, researcher with the Chad Carr Pediatric Brain Tumor Center, and lead investigator on the study.

This research gave Koschmann and his team more insight into how ATRX-mutant operates in glioma cells, and its interactions with ATM inhibitors. They found that glioma cells with mutated ATRX have less amount and activity of the protein checkpoint kinase 1 (Chk1), which regulates the division of glioma cells. “It's basically a puppeteer protein," he said. “When you don't have enough Chk1, you have a dysregulated cell cycle."

Radiation generally stops cells from cycling and dividing, and healthy cells and glioma cells will use this time to heal their damaged DNA to maintain the strength of the cell. But those checks aren't in place with ATRX-mutated cells. After radiation, the mutated cells keep cycling and have limited ability to repair their DNA. This makes the cells more responsive to radiation, but instead of being eliminated completely, Koschmann and his team discovered that another checkpoint gene—checkpoint kinase 2 (Chk2)—“fills in" when Chk1 is silenced, enabling the mutated cells to survive the radiation to some degree.

With this knowledge, the team investigated how radiation sensitizers, medicines taken alongside radiation, would interact with ATRX-mutant cells and target this unique biology.

In a previous study, Koschmann and colleagues in the Castro/Lowenstein lab found radiation to be an effective treatment for glioma patients with a mutated ATRX gene. They hypothesized that incorporating ATM inhibitors, a class of radiation sensitizer, which prevented Chk2 from compensating for an inactive Chk1, would increase the efficacy of radiation therapy in mice lab samples.

“We were overwhelmed by the data," said Koschmann. “When we added ATM inhibitors to a standard course of radiation for mice with gliomas with mutated ATRX, we witnessed much longer survival rates—triple the survival rate than using only radiation therapy. We didn't see this in the glioma with non-mutated (wildtype) glioma. The ATM inhibitors basically turn off the only remaining checkpoint. The ATRX-mutated cells can't handle the damage."

Koschmann explained that the difficulty in treating brain tumors, unlike other types of cancer elsewhere in the body, lies in the blood-brain barrier, which only about 5 percent of drugs can cross. For Koschmann and his team, much of their investigation into ATM inhibitors centered around figuring out its ability to traverse that barrier. “We were surprised to see how well this kind of drug went to the brain and did what we needed it to do in the tumor cells," he stated.

While the study was conducted in the lab using mice, the team is hopeful these findings will be relevant outside the lab, too.

“For glioma patients with this mutation, this class of drugs wouldn't have been considered otherwise. With this data, we're making a case that the next round of clinical trials should use this kind of therapy for anyone with this mutation," Koschmann noted.

The team is now communicating with the manufacturer of the ATM inhibitors used in this research to see how to best incorporate these findings into a clinical trial. One is currently underway using a drug tested in this study, AZD1390, but may exclude the patient population most likely to have the ATRX mutation.

“Our hope is that the trial sponsor will either start a new trial or add an arm to their current trial that captures this population, because we think that's who will have the best response to this drug," Koschmann explained. He believes the success of this study lies in the collaboration between him and his fellow researchers.

“I'm a pediatric neuro-oncologist, but we have collaborators from neurosurgery and radiation oncology, pathology, and bioinformatics," he said. “This research is a really nice spread of all the various cancer researchers that contribute to brain tumor research in the Rogel Cancer Center."​

Wednesday, January 19, 2022

Researchers at the Francis Crick Institute, UCL, The Royal Marsden NHS Foundation Trust, and The Institute of Cancer Research, London have developed a computer model to analyze how the way in which tumors grow affects their genetic makeup. Using this new model, they have identified links between tumor growth and shape, and how quickly a patient's cancer might progress.

As cancer cells mutate, some gain an advantage through mutations which make them more likely to survive, divide, and create a group of “fitter" cells. This group may outcompete others to become dominant, for example, if they have evolved to survive in conditions where there is a low supply of nutrients or oxygen. This process of tumor evolution is highly complex and is impacted by many factors, including how the tumor is growing. But it is not fully understood.

In their study, published in Nature Ecology and Evolution, the scientists used their computer model to study two types of tumor growth in kidney cancers: one where growth is consistent throughout the tumor, the “volume growth model" and one where growth is restricted to the surface, the “surface growth model" (2021; doi: 10.1038/s41559-021-01586-x).

Two scenarios occurred in the volume growth model. In some cases, a single “fit" group of genetically related cancer cells arose in the tumor at an early stage. In others, the tumors did not develop a new “fit" group, but rather the original group of parental cancer cells remained dominant.

In the surface growth model, there was extensive genetic diversity with different groups of “fit" cells forming on the surface. The team suggests that this creates a competitive environment where different groups of cells are pushed to evolve more rapidly.

Xiao Fu, PhD, first author and postdoctoral training fellow in the Biomolecular Modelling lab at the Crick, noted that they've "taken two distinct growth models and identified stark differences in how tumors evolve over time and in space. This is difficult to do with real tissue as it requires repeatedly taking multiple biopsies from various parts of a tumor. These findings are just the start of what we hope to uncover with this model."

The researchers validated their model using data from 66 tumors analyzed through the TRACERx Renal study. By cross-referencing the model and this tumor data, they found that different rates of real-world tumor progression corresponded with different growth models. For example, tumors which rapidly progressed fitted with the volume growth model where one “fit" group of cells was present from early on. While cases which did not progress fitted with the volume growth model where the parental group of cells remained dominant.

The model also provided insights into how different types of growth impact the shape of tumors. Volume growth tumors grew outwards in a more consistent shape, while surface growth tumors showed bulges on the surface, where the “fitter" groups were growing.

Xiao added that "what's exciting is how this structural information could be used as a window into the evolution of a tumor. More research is needed, but it could be used to help determine what sort of growth a tumor is undergoing, for example, if radiological imaging of an early tumor shows bulges this means it's more likely to be undergoing surface growth. This information could help inform medical teams and treatment decisions."

The researchers also used their model to analyze the impact of necrosis, the death of tissue within the tumor, on its evolution. When necrosis was present under the surface growth model, the tumors quickly developed more “fit" groups of genetically distinct cells.

Paul Bates, PhD, paper author and group leader of the Biomolecular Modelling lab at the Crick stated that "computer simulations are extremely valuable in further our understanding of how tumors evolve over time. By developing these models and using them to analyze how cancers change, we hope to find periods in their evolution and growth where the cancer may be most vulnerable to treatments."

Samra Turajlic, PhD, author and group leader of the Crick's Cancer Dynamics Laboratory and consultant oncologist at The Royal Marsden NHS Foundation Trust, said that "the most important observations regarding cancer behavior are gleaned through analyses of patients' tumors because they reflect the time-scales and complexities of actual cancer evolution. However, every instance of cancer evolution in a patient is unique, cannot be rewound, and repeated, making it hard to predict how likely tumors are to go down certain paths.

"This is where mathematical modeling can be a powerful tool to help us understand how the patterns we observe in real tumors come about. Informed mathematical models combined with detailed clinical, molecular, histological and radiological data from real-life tumors can bring about critical insights that will translate into patient benefit."

Erik Sahai, PhD, author and group leader of the Tumor Cell Biology Laboratory at the Crick, noted that "technological advances mean that we now have more information than ever before about individual cancers, the challenge is to decode this for patient benefit. This requires highly talented people who can work across disciplines, engaging with both clinicians and 'basic' researchers. The set-up of the Crick enables and encourages these interactions, and it is gratifying to see the dividends in work like this."

The researchers will continue developing their model and using it to better understand tumor evolution.​