Technologies
Recommendation J:
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D
evelop innovative new technologies in the areas of imaging, instrumentation (e.g., multidimensional single cell imaging and molecular analysis technologies and intra-tumoral microdosing devices), biological models (e.g., patient-derived tissues and 3-dimensional organlike cultures and humanized mouse models), and computational platforms (e.g., integrated multiscale data systems) that interconnect molecular, cellular, tissue, and patient-derived information. Development of these new technologies would greatly enable researchers and physicians to develop and select effective, individualized therapy strategies for patients.What is the problem?
A long-standing challenge in translational cancer research is the lack of dependable instrumentational, biological, and computational platforms on which to test anticancer agents and accurately predict success in patients. Recent technological advances have made it possible to test multiple combinations of therapeutics in a variety of systems that more closely mimic the complexity of tumors at different stages of disease progression. These advances have the potential to more accurately predict clinical outcomes when combined with powerful high-throughput genomics, proteomics, and imaging technologies.
Testing and prioritization of drug candidates at the preclinical and early clinical stages are typically conducted in cancer cell lines (i.e., cells grown in culture flasks or engrafted onto immune-compromised host mice) or genetically engineered mouse models that focus on individual or narrow groupings of tumor-associated genetic mutations. While these
pre-clinical models have been extremely informative about basic cancer biology, they generally do not recapitulate all aspects of disease progression or the heterogeneity of cell types that form the tumor mass and its surrounding tissues. Consequently, these models have limitations in their potential for testing therapeutics, despite significant progress in expanding the number of human cancer cell lines and mouse models of human disease. Many cancer patients
develop resistance or relapse to standard of care for unknown reasons. Establishment of preclinical cancer models that better recapitulate the complexities of tumor biology,
including the immune components, and highly lethal metastatic programs would facilitate the development of more effective therapies.
Adding to the challenges, computational complexities in informatics have hampered efforts to integrate large biomedical data sets containing genomic (e.g., The Cancer Genome Atlas), proteomic (e.g., Human Protein Atlas), histological (at the cellular/tissue level), and clinical (patient) information. Multiscale integration of data collected using different mouse and cell line repositories, drug libraries, and analytical tools is equally challenging. Collectively, this has created significant roadblocks for basic researchers and clinicians in their efforts to deposit, retrieve, combine, and analyze cancer-relevant data derived from humans and laboratory systems that would make it possible to accurately model and predict the
effectiveness of different treatment regimens. This has also slowed down the translational pipeline of turning different drug combinations into clinically available therapies.
What are the challenges?
Tumors are highly heterogeneous at the cellular, molecular, and genetic levels; however, sophisticated technologies for characterizing this heterogeneity and for testing drug
combinations have simply not been available. The same holds true in biomedical informatics, where the diverse nature of the data and methods used to generate them has made it
difficult to harmonize and consolidate data into functional, interoperable units that are readily accessible to basic and translational researchers.
Nevertheless, the field has matured significantly in recent years, and new technologies offer hope for circumventing these problems. For example, newly engineered devices designed to dispense “micro” doses of drugs directly into an intact tumor (either separately or in varying combinations) are now emerging and have been tested successfully in animal models. These technologies, if widely implemented, could revolutionize the way drug combinations are tested and tailored for each individual patient. Other technologies have been developed to target cancer drugs more selectively to cancer cells or to deliver new forms of cancer therapies (e.g. inhibitory RNAs) to tumors.
Parallel advances in patient-derived organoid and tissue-slice platforms have enabled the development of companion 3-dimensional systems that recapitulate the original architectural and molecular diversity found in different subtypes of cancers and are therefore suitable for fast and efficient pharmacologic screening. Breakthroughs in the design of mouse models that have been engrafted with patient-derived tumors and/or functional human immune systems also hold potential to enable basic and translational researchers to include important cancer-related architectural complexities and cellular heterogeneity in their testing systems.
Furthermore, improved high-throughput multiplexed imaging technologies, like single-cell mass cytometry and multidimensional fluorescence microscopy, now allow for more than 30 to 100 different proteins to be monitored simultaneously at subcellular resolution.
These systems can be used to determine the location of cells (and proteins within the cell) relative to its neighbors in the same tissue, and to elucidate how these cellular and molecular configurations influence its functional characteristics. Similarly, these high-throughput technologies are useful for the isolation and characterization of DNA and RNA from individual cells—and with them the power to profile in great depth thousands of single cells simultaneously. Continued development of these powerful technologies will expand our ability to visualize tumor architecture.
Beyond these, new technologies need to be developed for patient imaging, such as
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probes, and immunoPET. It will be important to develop methods that allow different
imaging methods to be combined, such as histology, immunohistochemistry, and molecular analyses of fixed tissues. Collectively, these and many other technological advances in the bioinformatics and meta-data analysis sectors will, once they become standard use, further enhance the translational potential of medicines for personalized treatment of patients.
Why is this important?
The broad implementation of emerging technologies that mimic the complexities of tumors will enable the basic and clinical oncology communities to make more informed and
predictive decisions on how to successfully treat each individual patient, regardless of the cancer type or stage at the time of diagnosis. Cancer, by definition, is a highly heterogeneous disease—even for tumor types that may fall under the same classification. Unfortunately, this heterogeneity leads to standard of care therapies that benefit some but not all patients.
This inherent complexity of cancers is exacerbated by the fact that not only are tumor cells within a single tumor mass likely to be genetically heterogeneous, but they also interact with multiple cell types, such as fibroblasts, endothelial cells, and immune cells, that increase the diversity of the tumor biology and response to therapy. Having the capabilities to tap into integrated biomedical informatics platforms is critically important, as the combined data can better guide researchers and clinicians into productive research and therapeutic directions.
Similarly, access to technologies that enable the isolation of cancer cells directly from patients for 3-dimensional organoid development and/or allow the testing of drug combinations directly on patients’ tumors with microdosing devices can accelerate progress in cancer therapeutics by focusing on systems that preserve tumor integrity and complexity.
What will it achieve?
Under the Cancer Moonshot, implementation of this recommendation will create an incentive for biomedical manufacturers and researchers to develop powerful new technologies
that help researchers and physicians deliver smarter, more effective therapies tailored to patients’ individual tumors. Access to these emerging technologies will also accelerate the discovery and translational pipelines of drug development and galvanize the pharmaceutical, biotechnology, technology, and government sectors in an effort to establish a centralized and comprehensive data sharing system conducive to the exchange of information and resources within virtual communities.
Determining the best treatment for each cancer patient can be a difficult task. To make that decision, clinicians rely on information such as the tumor type and stage, the presence of certain prognostic markers, and specific genetic characteristics of the tumor. Nevertheless, it is not always possible to determine if a patient will be responsive to a specific therapeutic agent, and in some cases, several rounds of varied treatment strategies are required to find one that is effective in a given patient.
An emerging possibility to more efficiently determine the most effective therapeutic agent is to use the patient’s own tumor to safely and simultaneously test several candidate drugs. A major advantage of this strategy is that it has the potential to be completely personalized to each individual patient. A number of new technologies have advanced this strategy, and more early research on the potential uses of
these innovative technologies needs to be conducted.
Patient-derived organoids are one new technology that could allow for rapid,
individualized therapeutic testing. Organoids are spheres of cells grown in a
three-dimensional format in the laboratory. They can be developed from pieces of healthy or tumor tissue obtained from patients, for example, during a routine biopsy. Early research shows that results from laboratory tests with patient-derived organoids closely mirror the patient’s actual clinical response. Therefore, it will be important to determine whether organoids can be rapidly developed from patients and used to test candidate therapies.
Developing organoids for every cancer patient may not be feasible, but it should be possible to establish an organoid resource in which every possible tumor type would be represented.