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Measuring Cancer Cell Death to Optimize Treatment Selection

Cell death is the most important measure in a cancer laboratory. An in vitro tumor explant testing platform in combination with a repository of test results…

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This article was originally published by GEN Genetic Engineering and Biotechnology News

Treatments for cancer patients generally follow standard clinical protocols that have the highest response rates or survival rates on average, but each patient presents a unique etiology. A new paradigm is emerging that offers oncologists the opportunity to predict how cancers in the body might respond to specific drugs or their combinations, before patients receive chemotherapy, opening new avenues for personalized therapy with optimal likelihood of success.

Robert Nagourney, MD, a practicing oncologist in Los Angeles, and his team at the Nagourney Cancer Institute have developed a functional profiling protocol that measures how cancer cells respond to a variety of drugs before patients are treated with them. [Robert Nagourney]

Over the past two decades, Robert Nagourney, MD, a practicing oncologist in Los Angeles, and his team at the Nagourney Cancer Institute, have developed a laboratory technique for generating functional profiles that measure how cancer cells respond to a variety of drugs. Nagourney and his team claim this approach is more powerful than genomic testing offered at most centers.

“At the individual level, responders are 100% responsive and non-responders are 0% responsive. What patients are looking for is to know, to the best of their knowledge, where they fit into the response expectations,” says Nagourney.

The technique, called EVA/PCD (Ex Vivo Analysis of Programmed Cell Death) and developed by Nagourney’s team, assesses which drugs cause cancer cells to die and is gaining popularity in the treatment of a variety of cancers, particularly advanced-stage pancreatic cancers.

In addition, the team has compiled a large database of over 10,000 human cancer studies that use the EVA/PCD protocol in breast, ovarian, lung, pancreatic, and other types of cancers. Using the database in clinical decision-making, they report a two-fold increase in clinical response and improved one-year survival in over 2,500 published patient outcomes.

The database allows categorizing average patients into those above and below average with performance characteristics of about 80% sensitivity and 80% specificity. “Using this approach, our objective response rates improve by a factor of 2.04, p<0.001, and our one-year survival is higher by 1.44-fold, p=0.02,” says Nagourney.

Key methods in cancer research

According to Nagourney, phosphoproteomics, metabolomics, and primary cultures of human tumors are the three top methodological advances in cancer research, diagnostics, and therapeutics. Commenting on the importance of focusing on protocols and detailed methodologies, Nagourney says, “We have spent an enormous amount of time on developing resources, diagnostics, and drugs, but I think we have given short shrift to technologies that connect these developments to clinical therapy. Methodology that turns gee-whiz science into practical utility has been lacking.”

Genetic abnormalities that lead to cancer result in molecular anomalies that could serve as diagnostic, prognostic, and predictive biomarkers for the disease. Identifying these biomarkers in different tumorigenic pathways makes it possible for clinicians to select the most appropriate therapy for each patient. “When talking of [method-based] breakthroughs—phosphoproteomics, epigenomics (Peter Jones’ or Steven Baylin’s work), or other developments critical in advancing [cancer] therapeutics—my bent is to say those technologies that more closely approximate phenotypes are closest to the biological behavior of a cell.”

Phosphoproteomics is a promising method that helps identify biomarkers to diagnose disease progression and assess therapeutic efficacy, and new targets that can be drugged to treat cancer. Functional, physical, and chemical interactions among proteins are often orchestrated in time and space through post-translational phosphorylation. Therefore, understanding the interconnected gamut of phosphorylated proteins provides key signatures of cancer that can be used to develop a more granular understanding of specific disease phenotypes and determine optimal and personalized treatment paradigms.

“I am a great believer in metabolomics. Our laboratory includes mass spectrometry capabilities because we believe [the metabolome] is a very good approximation of phenotype,” says Nagourney. “Human tumor primary cultures offer the ultimate phenotypic expression in real time.”

Penetrating problems

Nagourney began his career studying hematologic malignancies. His early papers focused on the ability to induce programmed cell death in primary suspension cultures and predict the outcomes of childhood leukemia.

“I did the first work on a drug called 2-chlorodeoxyadenosine. We discovered it was curative for hairy-cell leukemia. The beauty of it was that you had a primary culture—a human tissue that gave you an immediate handle on how a cell responds to stress and the appropriate drug or drug-combination inducing stress that led to cell death,” says Nagourney.

However, when Nagourney moved from Scripps to UC Irvine and attempted to apply the same method to predicting outcomes in solid tumors, he ran into obstacles.

“When you make the jump from leukemias, which are single-cell suspensions, to solid tumors, all that lovely data is pulled apart. Disaggregated cellular systems were not predictive of solid tumor biology. We had to go back and explore what constituted a good solid tumor model. We changed our disaggregation, enzymatic, and density gradient technologies, and began to turn our pure little cultures into dirty cultures—clusters and aggregates. When we began to study solid tumors in aggregates, the predictive validity began to approach that of leukemia data.”

Over the years, Nagourney and his team have moved from studying “clean” single cell suspensions to messier cultures that closely approximate the native state of cancer tissue and contain all elements of the in vivo tumor microenvironment, including stroma, intercellular messengers, inflammatory cells, vascular elements, and cytokines. “We began to work in micro-aggregates or spheroids. Now we might call them explants.”

Heterogenous primary tumor cultures, although more representative of reality, were accompanied by new challenges. “For example, many drugs do not permeate multiple levels of cells. You have to adjust the aggregate size so that you get diffusion throughout the tissue aggregate. If the cell clusters are too large, larger drugs like doxorubicin, cannot permeate. There is no vasculature, but you have aggregates that still have their vascular communications intact.”

In the absence of blood vessels infiltrating primary tumor explants cultured in vitro, ensuring proper diffusion is essential.

“Once we optimized the right size and dimensions of the tumor [explants] we achieved appropriate permeation of the drugs into the microenvironment and began to get nice dose response curves. That’s the technology we now use,” explains Nagourney.

Heterogeneity of cellular architecture in tumors in vivo suggests that the location of specific cancer cells in primary spheroid cultures and, in particular, the degree to which their location ex vivo mimics in vivo geographic orientations, determines how predictive the assay results might be. Nagourney says, “We seek to injure [cancer] cells and induce cell death under conditions that mimic or recreate the in vivo condition. It’s not a perfect reproduction. It would be interesting to examine, for example, how might the proximity to the blood vessels influence drug sensitivity. We have not gone down to that level of granularity.”

In his ex vivo assays to gauge the response of smaller drugs, Nagourney often finds cell death across the entire spheroidal explant population. Monoclonal antibody studies using spheroids are a different matter, however, since these are large molecules and do not permeate into the interior of explants.

“We developed a technique that we used to study cetuximab in its original evaluations before it was approved. We were examining cetuximab alone and in combination with small molecule TKIs (tyrosine kinase inhibitors) for EGFR (epidermal growth factor receptor) and made some interesting discoveries based on our onion peeling approach. We could see layers of cells that were exposed to the monoclonals peeling off as they died, leaving a central viable core. That’s about as far as we’ve extended the granularity of assessing cell distributions,” says Nagourney.

The platform is developed to test small molecules that permeate into explants. Nagourney’s team is currently working to apply the technology to test the efficacy of larger drugs and cell therapies.

“We are getting very close to having a methodology that uses this platform to examine checkpoint inhibitors,” says Nagourney. This method assesses cytolytic effects of activated T cells. The challenge in maintaining patient-derived tumor explant cultures in a state as close as possible to their native state is that they cannot be propagated, amplified, or sub-cultured.

“One of my concerns for groups who are doing propagated 3D spheroids is that they will not be as predictive,” says Nagourney. “What we have to convince people is that these are primary culture explants in their native state.” The team is currently using the approach to study the cytolytic activity of tumor-infiltrating lymphocytes (TILs).

Nagourney is hesitant to use commercially available 3D organoid culture systems to study cancer, although he recognizes the burgeoning interest in using such models in testing drug toxicity. “My problem with tumor organoids that are propagated from cellular materials that then grow into 3D cultures is that they are for the most part monocultures, which are not representative of the actual state of affairs in human tissue. We are concerned that as these technologies grow in the cancer field, they may not be predictive.”

Explant studies in tumor biology have gone through a lot of fits and starts, since their inception in the 1950s. “It went through a period when people were propagating cells in 2D, doing clonogenic assays and thymidine incorporation. That didn’t work and people ran away from the field in the 80s and early 90s.”

Two fundamental advances renewed success in the face of prior failures. The first was the recognition of programmed cell death as a therapeutic goal, where cancer cells aren’t just prevented from growing and dividing but are induced to die. The second is the development of 3D heterogenous culture systems.

Nagourney’s team does not limit its histological and metabolomic analyses to studying apoptotic markers of cell death. “Apoptosis is only one form of cell death—there is necroptosis, ferroptosis, and autophagic changes. These are not captured if you simply look at caspase activation,” explains Nagourney. “In hematologic tumors, the caspase3,7 activity was a good correlate. In solid tumors, it is not.”

Nagourney prefers global assessments of cell viability to techniques such as BH3 loading that forces cell death through apoptosis. “A cell dying in culture by apoptosis that in the body would not die by apoptosis will give false results,” says Nagourney. His team captures both apoptotic and non-apoptotic cell death by assaying mitochondrial function, ATP content, and histochemical staining to assess morphological attributes.

“Our original work was largely morphologic,” notes Nagourney. “The delayed loss of membrane integrity, visible under the microscope with histological stains and counterstains, remains the team’s most reliable measure for assaying cell death, particularly in the context of drug development.”

In addition, Nagourney’s team uses luminometer-based assessments of ATP content and MTT and XTT endpoints to assay mitochondrial succinate dehydrogenase activity as a metabolic measure of cell death.

Orthogonal validation

“We tend to do a number of different measures as we run these dose response curves so that we get different angles on what is causing cell death. Morphologically you can often distinguish an apoptotic cell, which has characteristic features, from a more autophagic cell and a necroptotic cell.”

Scoring how cells appear to be dying from morphologic features gives the team a good handle on how a drug affects a cancer cell and the molecular signaling pathway that induces it to die.

Cancer cells change constantly to metastasize, evade therapy, and adapt to immune pressure. The U.K.’s TRACERx (TRAcking Cancer Evolution through therapy) project led by Charles Swanton, FRCP, PhD, at the Francis Institute, is tracking how cancer cells vary as they migrate from primary to metastatic lesions. The multi-million-pound initiative aims to improve diagnosis, better tailor therapies, and forestall recurrence. During the early stages of the disease, cancer cells express what Swanton described as “truncal neoantigens” that involve a select set of tumor suppressor genes such as p53 or KRAS.

“Over time, under conditions of stress tumor cells will develop new tricks that allow these stressed cells to survive new conditions. You might see variations of mutations that change the course of the therapy,” says Nagourney. In vitro assays reflect these changes in cancer progression observed in vivo and are equally predictive in testing metabolic and morphologic markers in primary and metastasized solid tumors.

“Most of our patients come to us at fairly advanced stages. Very often they have liver metastasis or extensive lymphadenopathy. We get a biopsy of that. At this stage, they do become somewhat more resistant. We often feel that since they share the truncal mutation and may have acquired new mutations, if we can find activity in those distant sites, it often characterizes the population as a whole—both primary and metastatic.”

Cancer cell death

Nagourney is a great believer in the work of John Reed. “His group suggests cancer is a disease of cell survival. Ultimately cancer is a cell that wants to stay alive,” says Nagourney. Although a cancer cell may proliferate before it is targeted by a drug, Nagourney is not interested in proliferation. He says, “The endpoint of cell death is the most important measure in a cancer laboratory because only a living cell can proliferate, and a dead cell cannot kill you.”

In a 2018 paper in Oncotarget and a 2021 paper in Gynecological Oncology, Nagourney’s team explored the metabolic features that enable cancer cells to stay alive and outsmart the immune system. “These can often be measured metabolically in the blood or in tissue culture media. The signatures correlate with drug resistance. There is a continuum from the state of a cell gaining a survival advantage, utilizing that survival advantage to remain alive to ultimately propagating and metastasizing. They use those same survival advantages to resist chemotherapy and other targeted therapies.”

These subtle abnormalities in cell biology that ultimately result in cancer are similar to inborn errors of metabolism, believes Nagourney. “Metabolic studies may for the first time give us a handle on the phenotype from a drop of blood. We’re extremely excited by the possibility of applying that in the clinical setting.”

Database

Nagourney’s team has compiled laboratory analyses of over 10,000 patients into a comprehensive proprietary database. The database enables the prediction of whether a patient is likely to respond to a specific treatment, with a high degree of accuracy. Through contracts with various nations, the institute offers the database as a service to patients around the world.

“For almost every type of cancer, if you ask me, is this degree of say, taxane or platinum likely to confer response? Based on the distributional analyses of the continuum of sensitivity and resistance, we can place every patient against their cohort,” points out Nagourney.

The database also enables in vitro Phase II trials. “It’s an excellent way for the pharmaceutical industry to have a bridge between, as I mentioned previously, gee-whiz science and practical utility,” he says.

Nagourney’s team has used the database to test the efficacy of a drug called gemcitabine for Eli Lily. Gemcitabine, a difluorodeoxycytidine originally developed as an antiviral, was repurposed for the treatment of sarcoma, ovarian, lung, lymphoma, and bladder cancers. “The laboratory results predicted virtually all of the ultimate FDA approvals.”

Toward precision

Despite robust predictive advantage, Nagourney laments that these technologies have been underestimated in clinical practice and decried in editorials.

“They continue to suggest these technologies need to prove they save lives. I have never seen a clinical trial where a genomic platform has been forced to show it saves lives,” he says. “What we prove is the performance characteristics of the test within the standards.”

Nagourney believes initial errors of reasoning that led to failed strategies of blocking cell proliferation, and disaggregating tumors into single cells instead of focusing on cell death, has created a bias against the technology. “Once you use this technology, it’s very hard to go back to clinical oncology as it is practiced.”

Nagourney believes guideline-driven medicine is increasingly on a collision course with precision medicine. “We better get on the right side of that,” says Nagourney, “because what we’re seeing is increasing standardization of therapies while patients are clamoring for individualized care.”

Nagourney celebrates the recent advances in genomics, transcriptomics, gene editing using CRISPR, epigenomics, and proteomics.

“But the ultimate measure of the human phenotype is cell biology studies,” he explains. That’s going to be so important to grasp the complexity of cell behavior. We need to be humble in our scientific pursuits so that we allow this extraordinary biological complexity, redundancy, and promiscuity of events to teach us. In my work, I get the answer—other people have to provide the question.”

Nagourney hopes the in vitro tumor explant testing platform in combination with the database could be the key to achieving personalized care so that cancer patients are treated based on the latest research optimized for their specific condition and not just what has been approved for a narrow, generalized indication.

The post Measuring Cancer Cell Death to Optimize Treatment Selection appeared first on GEN – Genetic Engineering and Biotechnology News.





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