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Next-Generation Technologies for Next-Generation Cancer Models (NGT)

NCI's Next generation technology for next generation cancer models program

NCI's Next-Generation Technology for Next-Generation Cancer Models Program

About the Program

The primary goal of the Next-Generation Technologies for Next-Generation Cancer Models (NGT) program (RFA CA-19-055) is to facilitate the use of the Human Cancer Models Initiative’s (HCMI) next-generation cancer models (NGCMs) by the research community.

The new tools and broader use of NGCMs will contribute to the progress in understanding the important pathways in cancer initiation, progression and metastasis, identification of mechanisms of resistance, novel therapeutic targets, development of diagnostic or predictive biomarkers, and other aspects relevant to precision oncology.

Protocols, knowledge, and materials developed by the program will be shared broadly and expeditiously with the research community.


Three Centers at Broad Institute, Dana-Farber Cancer Institute, and Massachusetts Institute of Technology are part of the NGT program. The descriptions of the Centers below are adapted from the abstracts of the Centers' proposals.

Broad Institute

Principal Investigator: John G. Doench, Ph.D.

Advanced Tools for HCMI Model Genetic Perturbation and Metastasis Characterization

The Center plans to develop genome editing vector systems and high-throughput screening methods suitable for slowly proliferating HCMI NGCMs and to develop in vivo characterization methods to predict their metastatic potential. Standard approaches to genome editing (involving first creating cell lines expressing Cas9 stably and then introducing guide RNAs) will not work for slowly proliferating cells often growing in 3D. Therefore, development of an all-in-one genome editing vector systems will make it possible to bring the power of genome editing to HCMI models. In functional genomics screens using CRISPR-Cas9, the standard viability readouts of “drop-out” screens involve the growth of cells over many population doublings. However, for slowly proliferating HCMI models, alternative readouts will be required for efficient screening. Therefore, the institution will use short-term single cell RNA sequencing (scRNA-seq) methods that will serve as surrogate readouts for long-term viability.In addition, by using HCMI models’ associated clinical data, the researchers plan to investigate more complex, physiologically relevant questions such as metastatic potential.

The team will use multiplexed Cas 12/gRNA gene editing system for these genetic perturbation studies and develop methods to determine organ-specific metastatic potential for NGCMs. The Center plans to create a public resource of the metastasis map (MetMap) for at least 50 HCMI NGCMs. All data, protocols, and reagents will be made publicly available. Importantly, throughout the project, all cell models will be rigorously monitored for evidence of genetic and epigenetic drift. At the conclusion of the proposed project, the Center will generate a set of tools and data that will help propel the future of cancer precision medicine based on NGCMs.

Dana-Farber Cancer Institute

Principal Investigator: William C. Hahn, M.D., Ph.D.

Development and Implementation of Multiplex Methods to Understand the Biology and Heterogeneity of Patient-Derived Cancer Models

The Center will develop genome scale informatics methods as well as high-throughput approaches to profile genetic characteristics of the patient-derived NGCMs developed by the HCMI and study their responses to small molecule or genetic perturbations and their sensitivity to drugs. In addition, they will use innovative MIX-Seq (Multiplexed Interrogation of gene eXpression through single-cell RNA Sequencing) and computational methods to interrogate cell state plasticity and heterogeneity in these models.

These studies will allow the cancer research community to perform both high- and low-throughput analyses in NGCMs and provide deep insight into the stability and phenotypes represented by these models.

The Center will develop and implement a highly multiplexed method to screen NGCMs with both small molecules and genetic reagents. These studies will provide a powerful approach to interrogating HCMI models at high throughput.

The team will build on the preliminary studies that indicate that pancreatic NGCMs exhibit heterogeneity and rapid shifts in expressed phenotypes when compared to the originating tumor. Using newly developed sequencing technology, they will interrogate the dynamics of these state changes and assess the degrees of heterogeneity in these models.

Additionally, the group will build on Project Achilles and the DepMap to create and implement an optimized genome scale CRISPR-Cas9 library that permits the systematic genetic interrogation of genetic dependencies in NGCMs.

These studies will create new methods that permit rigorous evaluation of HCMI models as well as the discovery of novel biomarkers and therapeutic targets. More broadly, these studies will provide critical proof of principle that these methods can be used by others to study specific phenotypes in NGCMs.

Massachusetts Institute of Technology

Principal Investigators: Timothy K. Lu, M.D., Ph.D., Ömer H. Yilmaz, M.D., Ph.D., and Bonnie Berger, Ph.D.

Developing High-Throughput Genetic Perturbation Strategies for Single Cells in Cancer Organoids

The Center plans to develop innovative experimental and computational platforms to address the complexity of heterogeneous cancers that are resistant to chemotherapy and that frequently recur or metastasize using NGCMs developed by HCMI. They will develop a set of tools based on multidisciplinary innovations combining Synthetic Biology, Cancer Organoid Technology, and Bioinformatics.

The Synthetic Tools to Annotate Reporter Organoids for Cancer Heterogeneity and Recurrence Development (StarOrchard) include:

  • Synthetic Promoter Activated Recombination of Kaleidoscopic Organoids (SPARKO)
  • Combinatorial Genetics En Masse (CombiGEM)
  • Single-cell RNA sequencing panorama (Scanorama)

The SPARKO tool allows annotation of heterogeneous cancer populations within living cells via fluorescent protein expression libraries to make multicolored tumor organoids.

CombiGEM can rapidly identify potential therapeutic targets via large-scale, massively parallel, and unbiased combinatorial genetic screens. CombiGEM will be used to assemble a library of barcoded genetic libraries of perturbations, created using pair-wise guide RNA-mediated CRISPR system to investigate novel synthetic lethality and identify therapeutic targets.

Scanorama is an efficient tool that integrates large datasets of single-cell transcriptomics via sophisticated bioinformatics algorithms. Scanorama can integrate large quantity of scRNA-seq datasets from diverse cell types, tumor types, and experimental perturbations; find nearest neighbors among other datasets; and group similar cell types together in panoramic fashion.

The StarOrchard tools focus on barcoding strategies to enable accurate tracking and analysis of individual tumor cells that harbor distinct genetic aberrations, and substantially expand the utility of the NGCMs for cancer mechanistic investigations or therapeutic discovery. These will be applied to a large number and variety of NGCMs to optimize experimental protocols for the research community.

Data Sharing

The Next-Generation Technologies for Next-Generation Cancer Models (NGT) program is one of NCI’s Cancer Moonshot℠ research projects. All data generated from the program will be released in concordance with NCI Cancer Moonshot Public Access and Data Sharing Policy.

The release of data to the scientific community is intended to maximize the translational impact of these findings. The data will be accessible through the program data access page when it becomes available.


  • McFarland JM, Paolella BR, Warren A, et al. Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action. Nat Commun 2020; 11(4296). [Abstract]
  • Narayan A, Berger B, and Cho H. Assessing single-cell transcriptomic variability through density-preserving data visualization. Nat Biotechnol 2021; 39(6):765–744. [PubMed Abstract]

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