Pull at Cancer’s Levers
Curator: Larry H. Bernstein, MD, FCAP
Driving Cancer Immunotherapy
The Stakes in Immuno-Oncology Are Too High for Researchers to Pull at Cancer’s Levers Blindly. Researchers Need a System.
Within the past decade or so, a revolutionary idea has emerged in the minds of scientists, physicians, and medical experts. Instead of using man-made chemicals to treat cancer, let us instead unleash the power of our own bodies upon the malignancy.
This idea is the inspiration behind cancer immunotherapy, which is, according to most experts, a therapeutic approach that involves training the immune system to fight off cancer. In the words of one expert, cancer immunotherapy means “taking the immune system’s inherent properties and turbo charging those to fight cancer.”
Cancer immunotherapy technologies are being developed to accomplish
- Enhance the molecular targeting of cancer cells
- Report the rate of killing by specific immune agents
- Direct immune cells toward tumor destruction.
Since its inception, the field has evolved, and it continues to do so. It began with in vivo investigations of tumor growth and development, and it progressed through laboratory investigations of cellular morphology and survival curves. And now it is adopting pathway analysis to guide therapeutic development and improve patient care.
To begin to understand cancer immunotherapy, one must understand how the immune system targets tumor cells. One of the prominent adaptive components of the immune system is the T cell, which responds to perceived threats through the massive increase in clonal T cells targeted in some way toward the diseased cell or pathogen.
The T-Cell Repertoire
Adaptive Biotechnologies’ immunoSEQ Assay, a high-throughput research platform for immune system profiling, is designed to generate sequencer-ready libraries using highly optimized primer sets in a multiplex PCR format that targets T- and B-cell receptor genes. This image depicts how the assay’s two-step PCR process can be used to quantify the clonal diversity of immune cells.
Immunologists call this process VJD rearrangement. It happens during T-lymphocyte development and affects three gene regions, the variable (V), the diversity (D), and the joining (J) regions. This rearrangement of the genetic code allow for the structural diversity in T-cell receptors responsible for antigenic specificity including antigenic targets on tumor cells. In the case of cancer, specificity is complicated because the tumor is actually part of the body itself, one of the reasons cancers naturally evade detection.
The specificity problem would always hinder attempts to goad the immune system into attacking cancer, scientists realized, unless technologies emerged that could efficiently track the clonal diversity of T cells inside patients. Existing technologies, such as spectratyping, were inadequate. In 2007, when Dr. Robins and his collaborators began developing the technology, only 10,000 T-cell receptor sequences had been reported in all the literature using older methodologies.
“The immunology field of the time had no connection with high-throughput sequencing,” notes Dr. Robins, recalling his days as a computational biologist for the Fred Hutchinson Cancer Research Center. “It became clear that instead of using this old technology to look at T-cell receptors, we could just directly sequence them—if we could amplify them correctly.”
With its first experiment, Dr. Robins’ team ended up with six million T-cell receptor sequences. “Our approach,” Dr. Robins modestly suggests, “kind of changed the scale of what we were able to do.” The team went on to develop advanced multiplex sequencing technology, doing work that essentially started the field of immune sequencing. “Previously,” maintains Dr. Robins, “no one had ever been able to quantitatively do a multiplex PCR.”
Adaptive Biotechnologies’ product, the ImmunoSEQ® assay, uses several hundred primer pairs to quantify the clonal diversity of T cells. Using this technology, researchers and clinicians can focus on T-cell clones that are expanded specifically in or near a tumor or that are circulating in the blood stream.
“You obviously can’t get a serial sample of the tumor,” explains Dr. Robins, “but you can get serial samples of blood,” allowing for immune cell repertoire tracking during the progression of a disease. The technology is already being used to assess leukemias in the clinic, directly tracking the leukemia itself based on the massive clonal expansion of a single cancerous B or T cell.
Eventually, Dr. Robins’ team hopes to monitor serial changes in T cell clones before, during, and after therapeutic intervention. The team has even developed a tumor infiltrating lymphocyte (TIL) assay to examine clones that are attracted to tumors.
Circulating Tumor Cells
“Years ago, they were just interested in what was happening in the tumor,” says Daniel Adams, senior research scientist at Creatv MicroTech. “Now people have realized that the immune system is reacting to the tumor.”
Scientists such as Adams have been tracking tumor cells and tumor-modified stromal cells, as well as components of the non-adaptive immune system, directly within the bloodstream to examine changes that occur over time.
“We can’t go back in to re-biopsy the patient every year, or every time there is a recurrence,” says Adams, “It’s just not feasible.”
That is why Creatv MicroTech, with locations in Maryland and New Jersey, has developed the CellSieve, a mechanical cell filter. The CellSieve, which improves on older technology through better polymers and engineering, isolates circulating tumor cells (CTCs) and stromal cells in order to capture them for further clinical analysis.
Isolation, culture and expansion of cells isolated on CellSieve™. (A) MCF-7 cells spiked into vacutainers, isolated by filtration and cultured on CellSieve for 2-3 weeks. A 3 dimensional cluster attributed to this cell line is seen on the filter. (green=anti-cytokeratin, blue=DAPI) (B) PANC-1 cells spiked into vacutainers, isolated by filtration and grown on CellSieve for 2-3 weeks. PANC-1 is seen growing as a monolayer on the filter. (C) SKBR3 cells are spiked into blood, filtered by CellSieve. The CTCs are identified by presence of anti-cytokeratin and anti-EpCAM, and absence of anti-CD45. After CTCs are counted, cells are subtyped by HER2 FISH. (D) SKBR3 cells are spiked into vacutainers, isolated by filtration and grown on CellSieve for 2-3 weeks. Expanded colonies were directly analyzed as a whole colony and as individual cells, molecularly by HER2/CR17 FISH probes. (E) Circulating stromal cell, e.g. a 70 µm giant cancer associated macrophage can be identified for clinical use, myeloid marker in red. (F) A cell of interest can be identified and restained with immunotherapeutic biomarkers, e.g. PD-L1 (green) and PD-1 (purple). (G) After filtration, cells were identified with histopathological stains (e.g. H&E) for cytological analysis. (H) After H&E, external cell structures were analyzed by SEM. [Creatv MicroTech].
“As a patient goes through therapy, the patient’s resistance builds, and the cancer recurs in different subpopulations,” states Adams. “And after a few years, the original tumor mass is no longer applicable to what is growing in the patient farther down the road.”
Although CTCs are exceedingly rare in the bloodstream, with just one or two in every 5 to 10 mL of blood, and although these cells have a very low viability, the surviving CTCs have a high prognostic value.
“We looked at 30 to 40 breast cancer patients over two years,” reports Adams. “And we showed that if you have a dividing CTC, you have a 90% chance of dying in two years and a 100% chance of dying within two and half years.”
Furthermore, the immune system response can be tracked, says Adams, by examining stromal cells, which can also be collected with the CellSieve filtration device. That is, these cells can be collected serially. Much recent evidence supports the conclusion that stromal cells in the tumor environment co-evolve with the tumor, suggesting that stromal marker changes reflect tumor changes.
“There is this plethora of stromal cells and tumor cells out there in the circulation for you to look at,” declares Adams. “Once the cells are isolated, you can subject them to pathological approaches, biomarker approaches, or molecular approaches—or all of the above.”
A MicroTech Creatv study published in the Royal Society of Chemistry showed the efficacy of following up CTC isolation with techniques such as fluorescence in situ hybridization (FISH), histopathological analysis, and cell culture.
Diverse mechanisms are at play in cancer biology. Our understanding of these mechanisms contributes to a couple of virtuous cycles. It strengthens and is strengthened by diagnostic approaches, such as immune- and tumor-cell monitoring. The same could be said of therapeutic approaches. Cancer biology will inform and be informed by cancer immunotherapies such as adoptive cell transfer. To maintain the virtuous cycle, however, it will be necessary to conduct in vitro testing.
“There is no doubt that immunotherapy is going to play a major role in the treatment of cancer,” says Brandon Lamarche, Ph.D., technical communicator and scientist at ACEA Biosciences. “Regardless of what the route is, what is going to have to happen in terms of the research area is that you need an effective cell-killing assay.”
ACEA Biosciences, a San Diego-based company, has developed a microtiter plate that is coated with gold electrodes across 75% of the well bottoms. When the microtiter plates are placed in the company’s xCELLigence plate reader, the electrodes enable the detection of changes in cell morphology and viability through electrical impedance.
“The instrument provides a weak electric potential to the electrodes on the plate, so you get electrons flowing between these electrodes,” explains Dr. Lamarche. Researchers can then apply reagents or non-adherent immune cell suspensions to adherent cancer cells and examine the effect.
Dr. Lamarche asserts that the xCELLigence system overcomes problems that bedevil competing cell-killing assays. These problems include leaky and radioactive labels, such as chromium 51, and assays that can only provide users with an endpoint for cell killing. “With xCELLigence,” he insists, “you’re getting the full spectrum of what’s happening, and there’s all kinds of subtleties in the cell-killing curves that are very informative in terms of the biology.”
ACEA would like to see the xCELLigence system become the new standard in cell-killing assays from standard research to clinical testing on patient tumors. Dr. Lamarche envisions a day when patient tumor cells are quickly screened with therapeutic scenarios to determine the most efficacious killing option. “xCELLigence technology,” he suggests, “enables you to quickly sample a broad spectrum of conditions with a very simple workflow.”
Bioinformatics of Immuno-Oncology
From monitoring to treatment modalities, the field of cancer immunotherapy is aided by bioinformatics-minded data-mining experts, such as the analysts at Thompson-Reuters who are compiling data archives and applying advanced analytics to find new targets. “Essentially,” says Richard Harrison Ph.D., the company’s chief scientific officer for the life science division, “for every stage within pharmaceutical drug development, we have a database associated with that.”
The analysts at Thompson-Reuters curate and compile databases such as MedaCore and Cortellis, which they provide to their clients to help them with their research and clinical studies. “We can take customer data, and using our tools and our pathway maps, we can help them understand what their data is telling them,” explains Dr. Harrison.
Matt Wampole, Ph.D., a solutions scientist at Thompson-Reuters, spends his days reaching out and working with customers to help them understand and better use the company’s products. “Bench researchers,” he points out, “don’t necessarily know what is upstream of whatever expression change might be leading to a particular change in regulation.” Dr. Wampole indicates that he is part of a “solution team” that aids clients in determining important signaling cascades, regulators, and so on.
“We have a group of individuals who are very ‘skilling’ experts in the field,” Dr. Wampole continues, “including experts in the areas such as biostatistics, data curation, and data analytics. These experts help clients identify models, stratify patients, understand mechanisms, and look into disease mechanisms.”
Dr. Harrison sums up the Thompson-Reuters approach as follows: “We look for master regulators that can serve as both targets and biomarkers.” By examining the gene signatures from both the patient and from curated datasets, in the case of cancer immunotherapy, they hope to segregate patients according to what drugs will work best for them.
“We are working with a number of pharmaceutical companies to put our approach into practice for clinical trials,” informs Dr. Harrison. The approach has already been applied in several studies, including one that used data analysis of cell lines to help predict drug response in patients. Another study helped stratify glioblastoma patients.
Deciphering the Cancer Transcriptome
A Rogue’s Gallery of Malignant Outliers May Hide in Transcriptome Profiles That Emphasize Averages
The key link between genomic instability and cancer progression is transcriptome dynamics. The shifts in transcriptome dynamics that contribute to cancer evolution may come down to statistical outliers. [iStock/zmeel]
In recent years, scientists have adopted a gene-centric view of cancer, a tendency to see each malignant transformation as the consequence of alterations in a discrete number of genes or pathways. These alterations are, fortunately, absent from healthy cells, but they pervert malignant cells.
The gene-centric view takes in molecular landscapes illuminated by genomic and transcriptomic technologies. For example, genomes can be cost-effectively sequenced within hours. Such capabilities have made it possible to interrogate associations between genotypes and phenotypes for increasing numbers of conditions, and to collect data from progressively larger patient groups.
As genomic and transcriptomic technologies rise, they reveal much—but much remains hidden, too. Perhaps these technologies are less like the sun and more like the proverbial streetlight, the one that narrows our searches because we’re inclined to stay in the light, even though what we hope to find may lie in the shadows.
“Each individual study that looks at the cancer transcriptome is impressive and tells a convincing story, but if we put several high-quality papers together, there are very few genes that overlap,” says Henry H. Heng, Ph.D., professor of molecular medicine, genetics, and pathology at Wayne State University. “This shows that something is wrong.”
One of the major observations in Dr. Heng’s lab is that the intra- and intertumor cellular heterogeneity results in nearly every cancer cell having a unique, distinct karyotype, that is, an important but often ignored genotype. “Biological systems need a lot of heterogeneity,” notes Dr. Heng. “People like to think that this is noise, but heterogeneity is a fundamental buffer system for biological function to be achievable. Moreover, it is the key agent for cellular adaptation.”
To capture the degree of genomic heterogeneity at the genome level and its impact on cancer cell growth, Dr. Heng and colleagues performed serial dilutions to isolate single mouse ovarian surface epithelial cells that had undergone spontaneous transformation. Spectral karyotyping revealed that within a short timeframe each of these unstable cells exhibited a very distinct karyotype. In these unstable cells, cloning at the level of the karyotype was not possible.
Stable cells exhibited a normal growth distribution, i.e., no subset of stable cells contributed disproportionately to the overall growth of the cell population. In contrast, unstable cell populations showed a non-normal growth distribution, with few cells contributing most to the cell population’s growth. For example, a single unstable colony contributed more than 70% to the cell population’s growth. This finding suggests that although average profiles can be used to describe non-transformed cells, they cannot be taken to represent the biology of malignant cells.
“Most people who study the transcriptome want to get rid of the noise, but the noise is in fact the strategy that cancer uses to be successful,” explains Dr. Heng. “Each individual cancer cell is very weak but together the entity becomes very robust.”
In a recent model that Dr. Heng and colleagues proposed, system inheritance visualizes chromosomes not merely as the vehicle for transmitting genetic information, but as the genetic network organizer that shapes the physical interactions between genes in the three-dimensional space. Based on this model, individual genes represent parts of the system. The same genes can be reorganized to form different systems, and chromosomal instability becomes more important than the contribution of individual genes and pathways to cancer biology.
The vital link between genomic instability and cancer progression is transcriptome dynamics, and the shifts in those dynamics that contribute to cancer evolution may come down to statistical outliers.
“Transcriptome studies rarely focus on single-cell analyses, which means important outliers are frequently ignored,” declares Dr. Heng. “This preoccupation with uninformative averages explains why we have learned so little despite having examined so many transcriptomes.”
Chimeras and Fusion Genes
“Our focus is on chimeric RNA molecules,” says Laising Yen, Ph.D, assistant professor of pathology at Baylor College of Medicine. “This category of RNAs is very special because their sequences come from different genes.”
In a study that was designed to capture chimeric RNAs in prostate cancer, Dr. Yen and his colleagues performed high-throughput sequencing of the transcriptomes from human prostate cancer samples. “We found far more chimeric RNAs, in terms of abundance, and a number of species that are not seen in normal tissue,” reports Dr. Yen. This approach identified over 2,300 different chimeric RNA species. Some of these chimeras were present in prostate cancer cell lines, but not in primary human prostate epithelium cells, which points toward their relevance in cancer.
“Most of these chimeric RNAs do not have a genomic counterpart, which means that they could be produced by trans-splicing,” explains Dr. Yen. During trans-splicing, individual RNAs are generated and trans-spliced together as a single RNA, which provides a mechanism for generating a chimera.
“The other possibility is that in cancer cells, where gene–gene boundaries are known to become broken, chimeras can be formed by cis-splicing from a very long transcript that encodes several neighboring genes located on the same chromosome,” informs Dr. Yen. Chimeric RNAs formed by either of these two mechanisms can potentially translate into fusion proteins, and these aberrant proteins may have oncogenic consequences.
Another effort in Dr. Yen’s laboratory focuses on chromosomal aberrations in ovarian cancer. One of the hallmarks of ovarian cancer is the high degree of genomic rearrangement and the increased genomic instability.
“When we looked at ovarian cancers, we did not find as many chimeric RNAs,” notes Dr. Yen. “But we found many fusion genes.” Gene fusions, similarly to chimeric RNAs, increase the diversity of the cellular proteome, which could be used selectively by cancer cells to increase their rates of proliferation, survival, and migration.
A recent study in Dr. Yen’s lab identified BCAM-AKT2, a recurrent fusion gene that is specific and unique to high-grade serous ovarian cancer. BCAM-AKT2 is the only fusion gene in this malignancy that was proved to be translated into a fusion kinase in patients, which points toward its functional significance and potential therapeutic value.
“Recurrent fusion genes, which are repeatedly found in many patients in precisely made forms, indicate that there is a reason that they are present,” concludes Dr. Yen. “This might have important therapeutic implications.”
“We contributed to a study of tumor gene expresssion that we are currently revisiting because so much more data has become available,” says Barbara Stranger, Ph.D., assistant professor, Institute for Genomics and Systems Biology, University of Chicago. “The data is being processed in homogenized analytic pipelines, and we can look at many more tumor types across the Cancer Genome Atlas than a few years ago.”
Previously, Dr. Stranger and colleagues performed expression quantitative trait loci (eQTL) analyses to examine mRNA and miRNA expression in breast, colon, kidney, lung, and prostate cancer samples. This approach identified 149 known cancer risk loci, 42 of which were significantly associated with expression of at least one transcript.
Causal alleles are being prioritized using a fine-mapping strategy that integrated the eQTL analysis with genome-wide DNAseI hypersensitivity profiles obtained from ENCODE data. These analyses are focusing on capturing differences across tumors and on performing comparisons with normal tissue, and one of the challenges is the lack of normal tissue from the same patients.
“But still there is a lot of power in these analyses because they are based on large-scale genomic datasets. Also, these tumor datasets can be compared with large-scale normal tissue genomics datasets, such as the NIH’s Genotype-Tissue Expression (GTEx) project,” clarifies Dr. Stranger. “This helps us characterize differences between those tumors and normal tissue in terms of the genetics of gene regulation.”
An ongoing effort in Dr. Stranger’s laboratory involves elucidating how the effect of genetic polymorphisms is shaped by context. Stimulated cellular states, cell-type differences, cellular senescence, and disease are some of the contexts that are known to impact genetic polymorphisms.
“We have seen a lot of context specificity,” states Dr. Stranger. “Our observations suggest that a genetic polymorphism can have a specific effect in regulating a particular gene or transcript in one context, and another effect in another context.”
Another example of cellular context is sex, and an active area of investigation in Dr. Stranger’s lab proposes to dissect the manner in which sex differences shape the regulatory effects of genetic polymorphisms.
“Thinking about sex-specific differences is not very different from thinking about a different cellular environment,” notes Dr. Stranger.
The expression of specific transcription factors can be determined by sex; consequently, a polymorphism that interacts with a transcription factor may have functional outcomes that can be seen in only one of the sexes.
“There are gene-level and gene-splicing differences that we see in normal tissues between males and females, and we want to take the same approach and look at the cancer context to see whether the genetic regulation of gene expression and transcript splicing is different between individuals and whether it has a sex bias,” concludes Dr. Stranger. “Finally, we want to see how that differs in cancer relative to normal tissues.”
Early Clinical Impact
An increasing number of clinicians are adding the cancer transcriptome to their precision medicine program. They have found that the transcriptome is important in identifying clinically impactful results. [iStock/DeoSum]
“Over the last two years,” says Andrew Kung, M.D., Ph.D., chief of the Division of Pediatric Hematology, Oncology, and Stem Cell Transplantation at Columbia University Medical Center, “we have included the cancer transcriptome as part of our precision medicine program.” Dr. Kung and colleagues developed a clinical genomics test that includes whole-exome sequencing of tumors and normal tissue and RNA-seq of the tumor.
“Our results show that the transcriptome is very important in identifying clinically impactful results,” asserts Dr. Kung. “The technology has really moved from a research tool to real clinical application.” In fact, the test has been approved by New York State for use in cancer patients.
The data from transcriptome profiling has enabled identification of translocations, verification of somatic alterations, and assessment of expression levels of cancer genes. Dr. Kung and his colleagues are using genomic information for initial diagnosis and prognostic decisions, as well as the investigation of potentially actionable alterations and the monitoring of disease response.
To gain insight into gene-expression changes, transcriptome analysis usually compares two different types of tissues or cells. For example, analyses may attempt to identify differentially expressed genes in cancer cells and normal cells.
“In patients with cancer, we usually do not have access to the normal cell of origin, making it harder to identify the genes that are over- or under-expressed,” explains Dr. Kung. “Fortunately, the vast amounts of existing gene-expression data allow us to identify genes whose expression are most changed relative to models built on the expression data aggregated across large existing datasets.”
These genomic technologies were first used to augment the care of pediatric patients at Columbia. The technologies were so successful that they attracted philanthropic funding, which is being used to expand access to genomic testing to all children with high-risk cancer across New York City.
Other related articles published in this Open Access Online Scientific Journal include the following:
Immunotherapy in Combination, 2016 MassBio Annual Meeting 03/31/2016 8:00 AM – 04/01/2016 3:00 PM Royal Sonesta Hotel, Cambridge, MA
Live Press Coverage: Aviva Lev-Ari, PhD, RN
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