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Researchers Map Tumor Suppressive Landscape of Lung Cancer in Mice Using CRISPR, Barcode Seq

NEW YORK (GenomeWeb) – Researchers at Stanford University and the Massachusetts Institute of Technology used a combination of tumor barcoding, CRISPR-Cas9-mediated genome editing, and ultra-deep barcode sequencing to interrogate pairwise combinations of mutations in tumor suppressor genes in autochthonous mouse models of human lung adenocarcinoma in order to determine the functional impact of these alterations.

In a new study published in Nature Genetics, the team mapped the tumor suppressive effects of 31 common lung cancer genotypes. "Cancer genome sequencing has catalogued many of these alterations; however, the combinatorial effects of these alterations on tumor growth is largely unknown," the authors wrote. "Cancer growth is largely the consequence of multiple, cooperative genomic alterations. Most putative drivers are altered in less than 10 percent of tumors, suggesting that these alterations may be inert, weakly beneficial, or beneficial only in certain genomic contexts."

The researchers aimed to identify context dependence and differential effect strengths of combinations of tumor suppressor mutations. They recently developed a method called Tuba-seq, which quantitatively measures the effect of different tumor suppressor gene alterations in parallel using tumor barcoding coupled with high-throughput barcode sequencing.

"Tuba-seq combines genetically engineered mouse models of lung adenocarcinoma with CRISPR-Cas9-mediated tumor suppressor inactivation, tumor barcoding, and deep sequencing of DNA barcodes. Because Tuba-seq measures the size of every tumor and is compatible with multiplexing tumor genotypes in individual mice, growth effects can be measured with unprecedented precision, sensitivity, and throughput," the researchers explained.

For this study, they used Tuba-seq to systematically analyze pairwise combinations of tumor suppressor alterations in vivo. They quantified the growth of oncogenic KrasG12D-driven lung tumors with 31 common tumor suppressor genotypes and then identified unexpected genetic interactions. Through that, the team found that the effects of most tumor suppressors are context-dependent and may explain several patterns of genetic alterations in human lung adenocarcinoma.

For example, the researchers noted that the tumor suppressor TP53 is inactivated in more than half of human lung adenocarcinomas. To determine the effect of Trp53 (p53) deletion on the growth suppressive effects of 10 other putative tumor suppressors, they initiated tumors in genome-edited mice targeting many common tumor suppressor genes. Their analysis found an altered spectrum of tumor suppressive effects for many of the genes they analyzed.

"The emergence of additional tumor suppressors in this background suggests that Trp53 deficiency potentiates subsequent tumor evolution. By enabling more mutations to be adaptive, Trp53 loss may decrease the predictability of tumor evolution and facilitate subsequent evolution, including the emergence of treatment resistance and metastatic disease," the authors wrote. "Coincident deletion of Trp53 not only enabled more alterations to be adaptive, but also significantly changed the magnitude of effect of tumor suppressor loss."

The researchers further confirmed the effect of coincident inactivation of Trp53 and Rb1 on lung cancer growth using conventional mouse models. The quantitatively different growth benefits of Rb1 inactivation in Trp53-proficient tumors compared to Trp53-deficient tumors also allowed them to look into whether changes in the fitness strength of a driver altered the frequency of its alterations in human lung adenocarcinomas. They found that co-occurrence of RB1 single nucleotide variants and copy number variants and TP53 alterations were enriched in human lung adenocarcinoma.

Notably, they added, despite an approximately fivefold enrichment in the co-occurrence of these two alterations, this interaction would be statistically insignificant in a naive survey of all potential pairwise driver interactions after correcting for multiple-hypothesis testing. This further illustrated that genetic interactions must be studied beyond co-occurrence, they said.

"Larger genomic screens could survey more putative drivers, interactions with other oncogenic events, or triplets of tumor suppressor alterations," the authors wrote. "Our barcoded and multiplexed in vivo genome-editing approach could be easily employed to interrogate these genetic interactions' impacts on therapeutic response, cell signaling, or metastatic progression."

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