In this review, we have recently summarized achievements in tumor-suppressor gene therapy with a focus on the p53 gene. Abstract Tumor-suppressor genes play pivotal roles in maintaining genome integrity and in regulating cell proliferation, differentiation, and apoptosis.

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A 4 th -order polynomial fit to the residual bias corrected lesion sizes most effectively Methods. b , The random barcode exhibited a high-degree of randomness across the intended nucleotides.

c , Number of lesions called per mouse using Tuba-seq. Numbers of tumors above two different cell number cutoffs and are shown as the average number of tumors per mouse ± the standard deviation.

KT mice transduced with a high titer 6. There was no statistically significant difference in the number of tumors observed per capsid at either cell cutoff suggesting that barcode diversity is still not limited above half a million tumors and that small tumors are not caused by tumor crowding.

Mice of the same genotype, but different viral titers, cluster together, suggesting that size profile differences are determined primarily by tumor genotype, not viral titer.

e, f , Lesion sizes are not dramatically affected by differences in read depth. The barcode region from the tumor-bearing lungs of an individual mouse was sequenced at very high depth and then randomly down-sampled to typical read depth.

e The tumor size distributions of the full x-axis and downsampled y-axis data sets were very similar, indicating that our analysis parameters are unbiased by, and fairly robust to, read depth. f The percentile calculations are also reproducible upon downsampling.

This allowed us to quantify the variation in DADA2-called tumor sizes with six replicates within each mouse. Tumor size distributions are reproducibly called when using all tumors from each mouse and when using each subset of tumors with a given sgID.

The size of the tumors at the indicated percentiles are plotted for KT left , KLT middle , and KPT right mice. Each dot represents the value of a percentile calculated using tumors within a single sgID.

Percentiles are represented in grey-scale. The six replicate percentile values of tumor size with differing sgIDs are difficult to distinguish since their strong correlation means that markers for each sgID are highly overlapping.

All mice were homozygous for the R26 LSL-Tomato allele to determine the frequency of homozygous inactivation. Tomato-negative tumors are outlined with dashed lines. c , Immunohistochemistry for Tomato protein uncovered Tomato-positive Pos , Tomato-mixed Mixed , and Tomato-negative Neg tumors.

Tumors are outlined with dashed lines. Percent of Tomato positive, mixed, and negative tumors is shown with the number of tumors in each group indicated in brackets. Lung lobes are outlined with white dashed lines. Normal lung weight is indicated by the dotted red line.

Each dot represents a mouse and the bar is the mean. Hsp90 shows loading. a , Schematic of the experiment to assess the in vitro cutting efficiency of each sgRNA by transducing Cas9- expressing cells with lentiviral vectors carrying each individual sgRNA.

We tested three individual sgRNAs for each targeted loci and we report the cutting efficiency of the best sgRNA. b , Cutting efficiency of the best sgRNA for each targeted tumor suppressor.

Cutting efficiency was assessed by Sanger sequencing and TIDE analysis software Brinkman et al. Acids Res. Cells were harvested 48 hours after transduction, genomic DNA was extracted, the 14 targeted regions were PCR amplified, and the products were Illumina sequenced. b , Summary of data from published studies in which these tumor suppressor genes were inactivated in the context of Kras G12D -driven lung cancer models.

c , Each vector has a unique sgID and was diversified with random barcodes. The sgID for each of the vectors and the estimated number of barcodes associated with each sgRNA is indicated. The percent of each vector in the pool deviated only slightly from the expected representation of each vector red dashed line.

a , Histology confirms that KT mice have hyperplasias and small tumors, while KT;Cas9 mice have much larger tumors. Viral titer is indicated. Relative tumor size at the indicated percentiles represents merged data from 10 mice, normalized to the average of sg Inert tumors.

Percentiles that are significantly different from sg Inert are in color. c , Estimates of mean tumor size, assuming a lognormal tumor size distribution, showed expected minor variability in KT mice.

Bonferroni-corrected, bootstrapped p-values are shown. d , Tumor sizes at the indicated percentiles for each sgRNA relative to the average of sg Inert -containing tumors at the same percentiles.

Dotted line represents no change from Inert. e , Estimates of mean tumor size, assuming lognormality, identified sgRNAs with significant growth advantage in KT;Cas9 mice. h , Fold change in overall sgID representation in KT;Cas9 mice relative to KT mice ΔsgID Representation identified several sgRNAs that increase in representation, consistent with increased growth of tumors with inactivation of the targeted tumor suppressor genes.

The size relative to sg Inert -initiated tumors is indicated with dashed lines. Lung lobes are outlined with white dashed lines in the fluorescence dissecting scope images.

Each dot represents a mouse and the bars are the mean. Relative tumor size at the indicated percentiles is merged data from 8 and 3 mice, respectively, normalized to the average of sgInert tumors. Percentiles that are significantly larger than sgInert are in color. Power-law p-values are indicated.

Note that in this experimental setting only the very largest sg p53 initiated tumors are greater in size than the sgInert tumors. This is likely partially explained by the relatively poor cutting efficiency of sg p53 Supplementary Fig.

In-frame indels are shown in grey. c There is no preference for out of frame mutations in cells targeted in vitro. Collectively, these data are consistent with the tumor suppressive function of p53 and our data from KPT mice. a , The percent of reads containing indels at the targeted locus was normalized to the average percent of reads containing indels in 3 independent Neomycin loci.

This value is plotted versus the size of the 95 th percentile tumor for each sgRNA for three individual mice. We demonstrate a high frequency of indels in Setd2 , Lkb1 , and Rb1 consistent with selection for on-target sgRNA cutting. Each dot represents an sgRNA from a single mouse.

sg Neo dots are in black and all other dots are colored according to Figure 4b. Inframe mutations are shown in grey. Average and standard deviations for Neo was calculated by averaging all three mice and all three Neo target sites as a single group.

We detected no preference for inframe mutations in any of these genomic locations, suggesting that the distribution seen in the KT;Cas9 mice is most likely due to advantageous expansion of tumors with out-of-frame indels. Representative Sox9-negative and Sox9-positive tumors are shown.

Reprints and permissions. A quantitative and multiplexed approach to uncover the fitness landscape of tumor suppression in vivo. Nat Methods 14 , — Download citation. Received : 04 December Accepted : 19 April Published : 22 May Issue Date : 01 July Anyone you share the following link with will be able to read this content:.

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Skip to main content Thank you for visiting nature. nature nature methods articles article. Subjects Cancer genomics Cancer models. Abstract Cancer growth is a multistage, stochastic evolutionary process.

Access through your institution. Buy or subscribe. Change institution. Learn more. Figure 1: Tuba-seq combines tumor barcoding with high-throughput sequencing to allow parallel quantification of tumor sizes.

Figure 2: Tuba-seq precisely and reproducibly quantifies tumor sizes. Figure 3: Massively parallel quantification of tumor sizes enables probability distribution fitting across multiple genotypes. Figure 4: Rapid quantification of tumor-suppressor phenotypes using Tuba-seq and multiplexed CRISPR—Cas9-mediated gene inactivation.

Figure 5: Tuba-seq uncovers known and novel tumor suppressors with unprecedented resolution. Accession codes Primary accessions Gene Expression Omnibus GSE References Lawrence, M.

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Efficient systemic gene delivery systems will be required ultimately for treatment of metastatic disease. In this review, we have recently summarized achievements in tumor-suppressor gene therapy with a focus on the p53 gene.

Abstract Tumor-suppressor genes play pivotal roles in maintaining genome integrity and in regulating cell proliferation, differentiation, and apoptosis. Publication types Research Support, U. Tumor suppressor genes are present in all cells in our body.

When they are switched on, they prevent ourselves from growing and dividing. You can think of them as being like the brakes of a car. However, when a tumor suppressor gene is switched off, either because the cell mistakenly deletes it or mutates it, the brake is released and the cell may start to grow and divide uncontrollably and potentially drive the cell to turn into a cancer cell.

Breadcrumb Home About Genomics Educational Resources Talking Glossary of Genomic and Genetic Terms Tumor Suppressor Gene Home. found methylation inhibitors known as azacitidine and decitabine. These compounds can actually help prevent cancer growth by inducing re-expression of previously silenced genes, arresting the cell cycle of the tumor cell and forcing it into apoptosis.

There are further clinical trials under current investigation regarding treatments for hypermethylation as well as alternate tumor suppression therapies that include prevention of tissue hyperplasia, tumor development, or metastatic spread of tumors.

Gene therapy is used to reinstate the function of a mutated or deleted gene type. When tumor suppressor genes are altered in a way that results in less or no expression , several severe problems can arise for the host. This is why tumor suppressor genes have commonly been studied and used for gene therapy.

The two main approaches used currently to introduce genetic material into cells are viral and non-viral delivery methods. The viral method of transferring genetic material harnesses the power of viruses. The two most commonly used vectors are adenoviral vectors and adeno-associated vectors.

In vitro genetic manipulation of these types of vectors is easy and in vivo application is relatively safe compared to other vectors. This makes them safer for insertion. Then, the desired genetic material is inserted and ligated to the vector. The non-viral method of transferring genetic material is used less often than the viral method.

The viral and non-viral gene therapies mentioned above are commonly used but each has some limitations which must be considered. As the cost of DNA sequencing continues to diminish, more cancers can be sequenced.

This allows for the discovery of novel tumor suppressors and can give insight on how to treat and cure different cancers in the future.

Other examples of tumor suppressors include pVHL , APC , CD95 , ST5 , YPEL3 , ST7 , and ST14 , p16 , BRCA2. Contents move to sidebar hide. Article Talk. Read Edit View history.

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