NCI has announced several funding opportunities that align with the Cancer Moonshot.
See Funding OpportunitiesThis recommendation supports the development, adaption, and validation of emerging technologies that have the potential to transform cancer research and/or clinical care. Successfully implementing this recommendation will allow researchers working on the other Cancer Moonshot recommendations to leverage the most cutting-edge technological innovations and achieve their ambitions.
Projects aligned with this recommendation will focus on enhancing experimental and analytical capabilities to address the complexities of cancer. Many technologies are also being developed within projects from other recommendations.
Ultimately, the goal is to use these new enabling technologies to improve cancer research across the continuum of care.
Integration and Validation of Emerging Technologies to Accelerate Cancer Research
These exploratory projects are advancing the development and validation of new enabling technologies and tools that could lead to new capabilities in basic and clinical cancer research. These projects are focused on: enhancing experimental and analytical tools to understand the complexities of cancer, developing new technologies to advance cancer diagnosis, designing predictive models of cancer progression and responses to treatment, and generating new approaches to improve cancer-related data quality. Investigators in this program are also validating technology to ensure that new approaches could be readily adopted by the cancer research community.
Highlights from this initiative include the development of:
Patient-Derived Xenografts Development Network (PDXNet)
Patient-derived xenografts (PDXs) are models of cancer where tissue from a cancer patient’s tumor is implanted in a mouse. These models are emerging as an important approach for translational cancer research. PDXs simulate human tumor biology and can be used to identify the optimal combination therapy for groups of precisely defined cancer subtypes.
However, PDX models are often developed as isolated collections, leading to a lack of standardization as well as issues validating and replicating the results from experiments using these models. Also, isolated collections of PDX models are often too small to reflect the diversity of patient tumors found in large-scale clinical trials.
PDXNet is addressing these research challenges. PDX Development and Trial Centers (PDTCs) have engaged in collaborative research projects to show that PDX tumor models retain the genetic characteristics of the primary human tumor and that PDX drug responses and sequencing results are reproducible across diverse experimental protocols. This establishes the potential for multisite preclinical studies using PDX tumor models that could inform clinical trials.
The Minority (M)-PDTCs are developing PDXs that model tumors in racially and ethnically diverse populations that can be used to test cancer treatments. These (M)-PDTCs aim to advance our understanding of disparities observed in cancer treatment outcomes among racially and ethnically diverse populations.
Along with investigators developing PDX models, the Patient-Derived Models Repository (PDMR) at the Frederick National Laboratory for Cancer Research plays an important role in PDXNet. Over 270 PDX models, representing 33 different cancer types, are available by request through the repository.
More information about the network can be found at the PDXNet website.
Data Visualization + Cancer
In support of the broader goals of the Cancer Moonshot to accelerate the pace of discovery and share resources, a new effort was initiated to improve tools and approaches for visualizing the immense and complex data associated with Cancer Moonshot initiatives. Introducing truly novel tools should involve leveraging creative approaches developed for other fields. NCI is bringing together diverse experts from a variety of fields to form interdisciplinary teams around innovative ideas and develop these ideas into novel pilot projects with strong potential.
A series of virtual workshops were organized to draw disparate groups together, coupled with facilitated brainstorming activities. These activities highlighted several areas where improved user experience, user-focused design, game design, and data visualization can aid cancer researchers, clinicians, and others in navigating complex cancer data. Key areas of need include engaging patients, providing a better patient experience, visualizing multidimensional data, visualizing data in context, and enabling collaborations.
Activities to Promote Technology Research Collaborations (APTRC) for Cancer Research
There are two unique NCI programs related to APTRC that focus on supporting technology development for advancing cancer research. The Innovative Molecular Analysis Technologies (IMAT) program supports innovative data-generating platforms and methods, while the Informatics Technologies for Cancer Research (ITCR) program supports data processing and visualization technologies.
NCI is accelerating the development of new enabling cancer technologies by leveraging expertise in IMAT and ITCR through multidisciplinary collaborations between investigators in these programs. These collaborative projects bring together complementary technology platforms and approaches to enhance their capabilities for studies of cancer.
For example, one collaborative team developed an integrated pipeline to identify clinically relevant genetic variants to inform precision treatment. The method, OpenCAP, is a novel approach that uses an open-source database to guide probe development for targeted sequencing to identify mutations relevant to cancer.
Novel Technologies to Facilitate Research Using Next-Generation Patient-Derived Cancer Models
Research projects in this program are developing technology tools to accelerate and enhance studies across the spectrum of cancer research using advanced next-generation human cancer models, including 3D organoids and conditionally reprogrammed cells. These projects are specifically advancing cancer models developed as part of the Human Cancer Models Initiative (HCMI).
The technology tools being examined by investigators of the program include new laboratory methods and reagents for screening studies and computational approaches for data analysis from experiments using next-generation cancer models.
Small Business Innovation Research (SBIR) and Small Business Technology Transfer Research (STTR) Grants and Contracts for Enabling Technologies
NCI supports grants and contracts with small businesses that are developing new enabling technologies for cancer research. The range of projects supported through these investments span the breadth of Cancer Moonshot. For example, active projects include the development of experimental models to study cancer disparities, new cancer detection technologies, immunotherapy manufacturing, and subcellular microscopy.
Evaluation of Prostate Specific Membrane Antigen (PMSA)-Based PET Imaging of High-Risk Prostate Cancer
At this time, there are limited ways to stratify high-risk prostate cancer patients. To address this issue, researchers with NCI’s Center for Cancer Research are investigating the clinical use positron emission tomography (PET) imaging on patients with high-risk prostate cancer. PET offers the opportunity to image prostate specific membrane antigen (PSMA), a protein that is expressed in prostate cancer tissue and associated with aggressive cancer. By comparing the PSMA PET scans with complication-free survival outcomes, the researchers hope to understand if PSMA imaging could be used to identify subsets of patients with high-risk prostate cancer and guide treatment decisions.
NCI Program for Natural Products Discovery (NPNPD)
NPNPD is advancing natural product research and the discovery of new molecules in nature that impact cancer's biological processes. The NCI Natural Products Repository of the NPNPD has over 326,000 extracts of plants, microbes, algae, and marine species, which are being used to generate more than a million research-ready, partially purified natural product samples.
The NPNPD collection of natural product extracts and partially purified samples is being used by researchers around the world who are performing drug screens. Once researchers identify an extract with potential anticancer activity, the NPNPD uses automated techniques to quickly identify and isolate the active compound for more detailed studies.
Cancer Technology Projects Awarded Cancer Moonshot Funding
Funding Opportunity | Project Title | Institution | Principal Investigator(s) |
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PDX Data Commons and Coordinating Center (PDCCC) for the PDX Development and Trial Centers Research Network (PDXNet) (U24) | Data Coordination Center for PDXNet | Jackson Laboratory | Chuang, Jeffrey Hsu-Min; Davis-Dusenbery, Brandi Nicole |
PDX Development and Trial Centers (PDTCS) (U54) | Washington University PDX Development and Trial Center | Washington University | Govindan, Ramaswamy; Ding, Li; Li, Shunqiang |
Rational Approaches to Melanoma Therapy | Wistar Institute | Herlyn, Meenhard F; Davies, Michael | |
University of Texas PDX Development and Trial Center | University of Texas MD Anderson Cancer Center | Roth, Jack; Meric-Bernstam, Funda | |
PDX Trial Center for Breast Cancer Therapy | University of Utah | Welm, Alana L; Lewis, Michael T; Welm, Bryan E | |
Minority-Patient Derived Xenograft (PDX) Development and Trial Center (PDTC) Network (U54) | Minority PDX Development and Trial Center: Baylor College of Medicine and MD Anderson Cancer Center Collaboration on Mechanistic Studies to Dissect and Combat Health Disparities in Cancer | Baylor College of Medicine | Mitsiades, Nicholas |
University of California Minority Patient-Derived Xenograft (PDX) Development and Trial Center (UCaMP) to Reduce Cancer Health Disparities | University of California at Davis | Pan, Chong-Xian; Carvajal Carmona, Luis Guillermo; Chen, Moon Shao-Chuang | |
Integration and Validation of Emerging Technologies to Accelerate Cancer Research (R33) | Advanced Cancer Classification via Single-Cell Electrophoretic Cytopathology | University of California Berkeley | Herr, Amy Elizabeth |
Development of Genetically Tractable, Driver Gene-syngeneic Brain Tumor Models for Pre-clinical Adoptive TCR-T Therapy | Duke University | Li, Qijing; Yan, Hai | |
High Precision Single Cell Genomes: Linear Amplification and Digital Haplotypes | Harvard University | Xie, Xiaoliang Sunney | |
In-Depth Proteome Mapping of the Tumor Microenvironment with Single-Cell Resolution | Battelle Pacific Northwest Laboratories | Kelly, Ryan T | |
Multi-Tracer Volumetric PET (MTV-PET) to Measure Tumor Glutamine and Glucose Metabolic Rates in a Single Imaging Session | University of Pennsylvania | Mankoff, David; Karp, Joel | |
Minimally Intrusive Colorectal Cancer Risk Stratification with Nanocytology: Targeting Underscreened Populations | Boston Medical Center | Roy, Hemant; Backman, Vadim | |
Precise DCE-MRI Assessment of Brain Tumors | University of Southern California | Nayak, Krishna S | |
Functional Microscale Organotypic Assays to Predict Patient Response to Anti-angiogenesis Therapies | University of Wisconsin-Madison |
Beebe, David J; Abel, E Jason; Cho, Steve Yoon-Ho; Huang, Wei; Kim, Kyungmann; Kyriakopoulos, Christos |
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Genome-wide Identification and Targeting of Resistance to Cancer Therapy | University of Maryland, College Park |
Ruppin, Eytan; Gutkind, J Silvio |
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Molecular Beacon Based Extracellular mRNA and Protein Detection for Early Cancer Diagnosis | Ohio State University | Lee, Ly James; Fleisher, Martin |