Hidden cancer cells : now scientists can make them stand out from the crowd


Cancer cells are masters at avoiding detection, but a new system developed by Yale scientists can make them stand out from the crowd and help the immune system spot and eliminate tumors that other forms of immunotherapies might miss, the researchers report Oct. 14 in the journal Nature Immunology.

The new system reduced or eliminated melanoma and triple-negative breast and pancreatic tumors in mice, even those located far from the primary tumor source, the researchers report.

“This is an entirely new form of immunotherapy,” said Sidi Chen, assistant professor of genetics and senior author of the study.

Immunotherapy has revolutionized the treatment of cancer but existing therapies don’t work on all patients or not at all against some cancers.

Existing therapies sometimes fail to recognize all molecular disguises of cancer cells, rendering them less effective.

To address those shortcomings, Chen’s lab developed a new system that weds viral gene therapy and CRISPR gene-editing technology.

Instead of finding and editing pieces of DNA and inserting new genes, the new system – called Multiplexed Activation of Endogenous Genes as Immunotherapy (MAEGI) – launches a massive hunt of tens of thousands of cancer-related genes and then acts like a GPS to mark their location and amplify the signals.

MAEGI marks the tumor cells for immune destruction, which turns a cold tumor (lacking immune cells) into a hot tumor (with immune cells).

It is the molecular equivalent of dressing tumor cells in orange jump suits, allowing the immune system police to quickly find and eradicate the deadly cells, Chen said.

“And once those cells are identified, the immune system immediately recognizes them if they show up in the future,” Chen said.

The new system in theory should be effective against many cancer types, including those currently resistant to immunotherapy, he said.

Upcoming studies will optimize the system for simpler manufacturing and prepare for clinical trials in cancer patients.

More information: Multiplexed activation of endogenous genes by CRISPRa elicits potent antitumor immunity, Nature Immunology (2019). DOI: 10.1038/s41590-019-0500-4 , https://nature.com/articles/s41590-019-0500-4

Journal information: Nature Immunology
Provided by Yale University

Identifying driver mutations in cancer is notoriously difficult. To date, recurrence of a mutation in patients remains one of the most reliable markers of mutation driver status.

However, some mutations are more likely to occur than others due to differences in background mutation rates arising from various forms of infidelity of DNA replication and repair machinery, endogenous, and exogenous mutagens. We calculated nucleotide and codon mutability to study the contribution of background processes in shaping the observed mutational spectrum in cancer.

We developed and tested probabilistic pan-cancer and cancer-specific models that adjust the number of mutation recurrences in patients by background mutability in order to find mutations which may be under selection in cancer. We showed that mutations with higher mutability values had higher observed recurrence frequency, especially in tumor suppressor genes.

This trend was prominent for nonsense and silent mutations or mutations with neutral functional impact. In oncogenes, however, highly recurring mutations were characterized by relatively low mutability, resulting in an inversed U-shaped trend.

Mutations not yet observed in any tumor had relatively low mutability values, indicating that background mutability might limit mutation occurrence. We compiled a dataset of missense mutations from 58 genes with experimentally validated functional and transforming impacts from various studies. We found that mutability of driver mutations was lower than that of passengers and consequently adjusting mutation recurrence frequency by mutability significantly improved ranking of mutations and driver mutation prediction.

Even though no training on existing data was involved, our approach performed similarly or better to the state-of-the-art methods.


Cancer development and progression is associated with accumulation of mutations. However, only a small fraction of mutations identified in a patient is responsible for cellular transformations leading to cancer.

These so-called drivers characterize molecular profiles of tumors and could be helpful in predicting clinical outcomes for the patients. One of the major problems in cancer research is prioritizing mutations.

Recurrence of a mutation in patients remains one of the most reliable markers of its driver status. However, DNA damage and repair processes do not affect the genome uniformly, and some mutations are more likely to occur than others.

Moreover, mutational probability (mutability) varies with the cancer type. We developed models that adjust the number of mutation recurrences in patients by cancer-type specific background mutability in order to prioritize cancer mutations. Using a comprehensive experimental dataset, we found that mutability of driver mutations was lower than that of passengers, and consequently adjusting mutation recurrence frequency by mutability significantly improved ranking of mutations and driver mutation prediction.


Cancer is driven by changes at the nucleotide, gene, chromatin, and cellular levels. Somatic cells may rapidly acquire mutations, one or two orders of magnitude faster than germline cells [1]. The majority of these mutations are largely neutral (passenger mutations) in comparison to a few driver mutations that give cells the selective advantage leading to their proliferation [2]. Such a binary driver-passenger model can be adjusted by taking into account additive pleiotropic effect of mutations [34]. Mutations might have different functional consequences in various cancer types and patients, they can lead to activation or deactivation of proteins and dysregulation of a variety of cellular processes. This gives rise to high mutational, biochemical, and histological intra- and inter-tumor heterogeneity that may explain the resistance to therapies and complicates the identification of driving events in cancer [56].

Point DNA mutations can arise from various forms of infidelity of DNA replication and repair machinery, endogenous, and exogenous mutagens [69]. There is an interplay between processes leading to DNA damage and those maintaining genome integrity. The resulting mutation rate can vary throughout the genome by more than two orders of magnitude [1011] due to many factors operating on local and global scales [1214]. Many studies support point mutation rate dependence on the local DNA sequence context for various types of germline and somatic mutations [9111315]. For both germline and somatic mutations, local DNA sequence context has been identified as a dominant factor explaining the largest proportion of mutation rate variation [1016]. Additionally, differences in mutational burden between cancer types suggest tissue type and mutagen exposure as important confounding factors contributing to tumor heterogeneity.

Assessing background mutation rate is crucial for identifying significantly mutated genes [1718], sub-gene regions [1920], mutational hotspots [2122], or prioritizing mutations [23]. This is especially important considering that the functional impact of the majority of changes observed in cancer is poorly understood, in particular for rarely mutated genes [24]. Despite this need, there is a persistent lack of quantitative information on per-nucleotide and per-codon background rates in various cancer types and tissues.

There are many computational methods that aim to detect driver genes and fewer methods trying to rank mutations with respect to their potential carcinogenicity. As many new approaches to address this issue have been developed [25] [26], it still remains an extremely difficult task. As a consequence, many driver mutations, especially in oncogenes, are not annotated as high impact or disease related [27] even though cancer mutations harbor the largest proportion of harmful variants [28].

In this study we utilize probabilistic models that estimate background mutability per nucleotide or codon substitution to rank mutations and help distinguish driver from passenger mutations. The mutability concept has been used in many evolutionary and cancer studies (although it has been estimated in different ways) and is defined as a probability to obtain a nucleotide or codon substitution based on the underlying background processes of mutagenesis and repair that are devoid of cancer selection component affecting a specific genomic (or protein) site. The mutability can be calculated using background models (mutational profiles), mutational signatures or mutations motifs that are constructed under the assumption that vast majority of cancer context-dependent mutations have neutral effects, while only a small number of these mutations in specific sites are under positive or negative selection. To assure this, we removed all recurrent mutations as these mutations might be under selection in cancer. Mutational profiles are calculated by sampling the frequency data on types of mutations and their trinucleotide (for nucleotide mutations) and pentanucleotide (for codon substitutions) contexts regardless of their genomic locations. These models can be used to estimate the expected mutation rate in a given genomic site as a result of different local or long-range context-dependent mutational processes.

In this paper we try to decipher the contribution of background DNA mutability in the observed mutational spectrum in cancer for missense, nonsense, and silent mutations. We compiled a set of cancer driver and neutral missense mutations with experimentally validated impacts collected from multiple studies and used this set to verify our approach and compare it with other existing methods. Our approach has been implemented online as part of the MutaGene web-server and as a stand-alone Python package: https://www.ncbi.nlm.nih.gov/research/mutagene/gene.


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