How much loh is enough

Some of these occur only in people who are homozygous for the abnormal gene with two copies , whereas others occur in people who have only one copy of the abnormal gene heterozygous. In LOH, a gene or a whole group of neighboring genes are lost and no longer present inside the affected cell. This could happen when that part of the DNA is accidentally deleted, perhaps when the cell is undergoing normal division and replication. The gene might be completely gone, or part of it might have been moved to another location on the DNA.

In either case, the protein encoded by the gene cannot be correctly made. Instead of having two different versions of the same gene present heterozygosity , one copy of the gene is now gone. It is easy to confuse LOH with homozygosity. However, people who are homozygous for a gene have two very similar copies of the same gene, while people with LOH have only one copy.

Carcinogens can make LOH and other types of genetic errors more likely to occur. Carcinogens are substances that can damage your DNA through direct or indirect means. Some common sources of carcinogens are smoking, asbestos, and ultraviolet light from the sun. Exposure to these carcinogens increases the chance that LOH will happen.

LOH is a very common event in the process of oncogenesis , the process by which a normal cell turns into a cancerous one and begins to abnormally replicate. It is one of the types of genetic mutations that can play a role in cancer development. Cancerous cells usually show multiple types of genetic changes—LOH in one or more genes may be one of these changes to occur.

LOH can be present in both hereditary cancer syndromes and in other types of cancer. There are at least a couple of different ways that LOH can be problematic. Other times, there is a bad mutation in the remaining gene—it could either be present from birth or occur later on. In either case, not enough normal protein can be made from the necessary gene.

Some genes might be lost to LOH without causing a problem. However, LOH in specific types of genes is more of a concern. These genes, called tumor suppressor genes , are very important genes for cancer prevention and they normally work to regulate the cell cycle. When tumor suppressor genes are absent or nonfunctional due to LOH, the cell may begin to divide abnormally and become cancerous. LOH is thought to occur in many different types of cancer.

However, researchers have discovered LOH in particular genes to be very common in specific cancer types. Some examples are:.

Mutations such as LOH in other tumor suppressor genes such as p53 are thought to be present in many different individuals with many different cancer types. In general, LOH of one gene or another is thought to be relatively common in cancer of all types. Though LOH is seen in a number of different types of cancer, it may be particularly important for certain hereditary types of cancer.

Due to uneven distribution of SNPs in actual sequences, the detection probability will fluctuate with similar patterns shown in Figure 6. The results of this study have two important implications that might improve design and interpretation of future genome wide LOH screens of cancers and premalignant tissues.

First, retrospective review of previous genome-wide LOH screens indicate that technology limitations i. By using the analysis methods reported in this paper, reports of genome wide LOH could discuss the limitations of the resolution of the study in terms of what might have been missed in addition to the important loci that were discovered.

A well-designed study using carefully selected SNP sets for evaluating specific regions on several chromosomes still had more than kb distance on average between two informative SNPs [ 32 ]. However, in general, kb is still relatively large considering an average gene size is 3 to 20 kb in the human genome, and smaller regions of LOH i.

LOH has been frequently proposed as a candidate biomarker for cancer risk prediction. Our results could improve sample size calculations for design of future LOH studies. If one would like to detect the effect of an LOH event on the risk of progression to cancer, then the sample size depends on the LOH detection probability.

For example, in a study with a ratio of cases and controls, a minimum detectable relative risk of the LOH of 5, a statistical detection power 0. However, if the LOH detection probability is 0. All the results obtained in this analysis are based on the assumption that heterozygous SNPs are required for detection of LOH.

New technologies are emerging that could be used to detect chromosome copy number changes including deletion using homozygous SNPs with a reasonably high accuracy [ 33 , 34 ]. However, since LOH can result from mechanisms that do not change copy number [ 35 , 36 ], using copy number approaches can only yield a partial picture of the LOH status of a region of interest.

Combining the analyses presented in this study and copy number could lead to a high level of reliability and a higher resolution in LOH detection for neoplastic progression research and biomarker development. Our methods can easily be extended to other ethnic group data. The simulation for chromosome segment deletion Figure 4 was done using the genotype data from the HapMap CEU population data. In the simulation process, for each of the Chromosomes 1, 3, 9, 13, 17, and 18 results of Chromosome 13 and 18 are unpublished data , a random segment was removed from the chromosome mimicking the region of LOH on a chromosome , and the number of SNPs in the region was examined based on the genotype data of the individuals.

The process was repeated 20, times for each segment size on a chromosome. The segment sizes of loss used in the simulation are: 5, 10, 20, 30, 50, , , , , 1,, 2,, 3,, 4,, and 5, kb. Based on these data, three methods negative binomial model fitting, Monte Carlo simulation, and bootstrap were used to investigate the relationship between the size of chromosome loss and probability of LOH detection. For negative binomial model fitting, which was found to fit the data best among the various theoretical distributions we evaluated, the discrete frequency distribution patterns of HET SNPs for each segment size listed above were fitted to a negative binomial model.

Specifically, for the data of each segment size of loss, the HET SNP counts in each sample along a chromosome were used to estimate the parameters of negative binomial distribution with maximum likelihood method.

The random numbers of HET SNPs were then generated based on the fitted negative binomial distribution parameters for each size of segment loss.

This was repeated 10, times for each segment size and the detection probabilities were calculated based on the process for each segment Figure 4 , magenta lines. For the Monte Carlo simulation Figure 4 , blue and black lines , for each size of deletion listed above, the number of SNPs for each segment was counted, and the number of HET SNPs and detection probabilities were determined based on the empirical distribution pattern shown in Figure 1.

For the bootstrap method, the observed detection probability Figure 4 red line was obtained by directly counting the HET SNPs in each segment based on the real genotyping data in the bootstrap sampling process.

The results in Figure 5 were obtained by the probability model described in the text. To examine the spatial pattern of LOH detection probability along a chromosome Figure 6 , we chose the kb window size along Chromosomes 1, 3, 9, and 17, and within each window samples were randomly taken with various loss sizes to calculate the probabilities of LOH detection within each window along the chromosome.

Similar patterns were found on other chromosomes unpublished data. All analyses and simulations were carried out with Matlab version 7. XL analyzed the data. Abstract Single nucleotide polymorphisms SNPs have been increasingly utilized to investigate somatic genetic abnormalities in premalignancy and cancer.

Introduction Single nucleotide polymorphisms SNPs are common DNA sequence variations and have been widely investigated for their roles in disease causation [ 1 ] or association [ 2 , 3 ], heterogeneous responses to drug therapies [ 4 — 6 ], genetic linkage analysis [ 7 , 8 ], and evolutionary biology [ 9 , 10 ].

Download: PPT. Figure 1. Figure 2. Figure 3. Figure 4. Figure 5. Figure 6. Because we had already demonstrated that S. For this analysis, we included both the canonical S. However, it is possible that, for some GEMINI variants, RNAi reagents would be unable to suppress expression sufficiently to reduce cell viability, or that sufficient allelic specificity might not be achieved.

We first sought to determine the level of PRIM1 knockdown required to substantially decrease cell proliferation. We then asked whether allele-specific shRNAs targeting the PRIM1 rs locus could decrease growth in cells representing the fully matched genotype. No putative allele-specific shRNAs were found to significantly decrease cell growth in hemizygous cells of the targeted genotype relative to heterozygous cells Supplementary Fig.

RNAi-mediated knockdown of some essential genes may be more effective at inducing cell death than others, based on differential expression thresholds required for cell survival 25 , 26 , In comparison, only 3. Small molecule—based approaches can overcome such delivery issues, but substantial obstacles exist to developing allele-specific small molecules that target GEMINI vulnerabilities. However, no clear allele-specific small molecules exist for any of the protein-altering GEMINI variants identified in our analysis.

Therefore, we focused on in silico analyses to identify and prioritize these GEMINI vulnerabilities missense, insertion, and deletion variants for potential allele-specific drug development Supplementary Data 5.

This tool uses publicly available structural and chemical information to generate structure- and ligand-based druggability scores.

While these scores do not necessarily reflect potential for allele-specific small molecule inhibition based on the GEMINI variant of interest, they may nonetheless allow prioritization of targets based on general druggability. Furthermore, GEMINI variants reside in proteins in the top 90th percentile of ligand-based druggability as assessed by the physiochemical properties of small molecules tested against the protein or its homologs Fig. To assess which variants may reside in protein regions amenable to small molecule binding, we performed a p-blast of the protein-altering variants against protein sequences for molecular structures found in the Protein Data Bank 32 rcsb.

This analysis identified variants characterized in a homologous structure. We then visually scored 81 missense and indel variants in X-ray crystal structures for their proximity to solvent-exposed pockets or known small-molecule binding sites using a scale of 0 to 4 Methods.

We also assessed protein-altering GEMINI variants to prioritize those that may be most amenable for allele-specific small-molecule inhibition. For this analysis, we scored variants that altered the number or sign of residue charges.

For example, a variant that changes the charge of a residue from neutral to negative or that adds an additional negative charge through an inserted residue would qualify as a charge-altering variant.

Of the protein-altering variants, induced a change in residue charge Fig. We further hypothesized that variants introducing a cysteine residue could provide additional allele selectivity by enabling the potential development of a covalent inhibitor. We then integrated each of these analyses to characterize the potential druggability landscape of these protein-altering GEMINI vulnerabilities. Every variant was given a score from 0 to 7 based on the number of analyses in which it scored among the top candidates.

One variant, TGS1 rs , earned a score of 6, including in the visual scoring and cysteine categories. Nine additional variants earned a score of 5 Supplementary Data 5. These may be among the highest-priority candidates for further exploration.

Leveraging synthetic lethal interactions in cancer cells represents a promising avenue to targeting genomic differences between tumor and normal tissue. Synthetic lethality between genes occurs when singly inactivating one gene or the other maintains viability, but inactivating both genes simultaneously causes lethality Over the past 20 years, many efforts have been directed toward discovering synthetic lethal interactions with genetic driver alterations of oncogenes and tumor suppressor genes 34 , However, the number of genetically activated oncogenes and inactivated tumor suppressor genes in any given tumor is limited and, in many cancer types, is vastly outnumbered by genetically altered non-driver genes e.

While individual GEMINI genes have been described previously 3 , our work integrated genome-wide assessments of gene essentiality, genetic variation, and LOH to generate the first systematic analysis of this target class. In addition to GEMINI vulnerabilities, deletion of paralogs can result in dependency on the remaining paralog; loss or gain of function of a non-driver pathway can lead to dependencies on alternative non-driver pathways; 36 and hemizygous loss of essential genes can result in dependency on the remaining copy CYCLOPS 25 , In comparison, fewer paralog dependencies have been described 27 , 37 , 38 , 39 , Larger numbers of paralog vulnerabilities have been predicted 41 , but it is unclear whether these predictions represent viable candidates The GEMINI genes that we identified also exceed the known driver genes 42 , many of which are proposed therapeutic targets.

Thus, the possibility of allele-specific targeting presented by GEMINI genes may widen prospective therapeutic windows. Base pair substitutions that replace the targeted allele with an alternative are likely to occur in one in every 10 8 —10 9 cells, given observed mutation rates per cell division Additional alterations affecting nearby nucleic or amino acids could interfere with genetic e.

Biomarkers for detection of patients who may benefit from a GEMINI approach are relatively straightforward: one would select patients who are heterozygous for the targeted allele and for whom the tumor is found to have lost the alternative allele. One consideration is tumor heterogeneity; if the LOH event is present in only part of the tumor, resistance would be expected to arise quickly. However, in prior analyses 43 , 45 , 46 , 47 , a majority of somatic copy-number alterations, including LOH events, appeared to be clonal, although the fraction of clonal events can be lower in some loci in some tissues One approach to minimize clonal variation in LOH is to prioritize GEMINI genes that lie on chromosomes or chromosome arms that are characteristically lost early in oncogenesis e.

While we show that cells heterozygous for PRIM1 rs exhibit no substantial proliferation defects upon ablation of the targeted allele Fig. Thus, potential allele-specific gene editing approaches to leverage GEMINI vulnerabilities in the clinic would rely on a highly cancer cell—specific delivery system to avoid knockout of the targeted allele in normal tissue. While much work remains to achieve the necessary targeting specificity, advances in nanoparticle delivery systems present the possibility of targeting Cas9 DNA, mRNA, or protein in a tumor-specific manner 49 , 50 , 51 , Additionally, S.

Such reversible genetic inhibitors have seen recent success in other disease indications e. Peptide nucleic acids, or PNAs, which can suppress both transcription and translation of target genes, represent another potential allele-specific genetic approach 64 , 65 , 66 , Unlike Cas9, the Cas13 enzyme from L. The use of genetic targeting approaches would dramatically increase the number of potentially targetable GEMINI vulnerabilities by including silent as well as protein-altering variants.

However, unlike the promising therapies for genetic disorders mentioned above, a genetic inhibitor of a GEMINI vulnerability would need to be delivered to all cells in a tumor. Thus, like Cas9-based modalities, the use of Cas13 and other reversible genetic approaches to exploit GEMINI vulnerabilities would require the development of novel delivery systems. Allele-specific therapeutics in clinical use include rationally designed drugs e.

However, GEMINI vulnerabilities present an additional challenge for allele-specific inhibitor development because most variants in cell-essential genes do not reside in or near an active site Fig.

This challenge may be addressed through alternative small-molecule approaches, such as proteolysis-targeting chimera PROTAC -mediated degradation SNPs for which one allele is a cysteine could be prioritized for this approach because of the possibility of engineering a covalent inhibitor While we have rigorously validated PRIM1 and EXOSC8 as genetic dependencies in cancer, further work is necessary to explore potential therapeutic modalities for targeting them Supplementary Discussion.

The design of a tractable therapeutic that targets any single GEMINI gene in an allele-specific manner is a substantial challenge.

However, the sheer number of potential candidates suggests that some of these GEMINI vulnerabilities may represent viable targets. A list of , potentially targetable variants was downloaded from the Exome Aggregation Consortium ExAC database exac. All variant classes were included in the analysis of potential target SNPs for reversible genetic therapeutic approaches.

Autosomal regions for which the absolute copy number of one allele was zero were considered to have undergone LOH. These calls were transformed into per-gene calls for all subsequent analyses. FDR-corrected p-values from the original publications were used for both CRISPR screens; FDR q-values for the KBM7 gene trap scores were calculated using a binomial model representing equal probability of gene trap inserting in a sense versus anti-sense orientation and correction for multiple hypotheses using Benjamini and Hochberg.

This initial candidate list contained genes, with scoring as essential in all three screens. These candidate essential genes were then filtered using CCLE gene copy-number and RNA expression data to determine if loss-of-function genetic alterations were observed in human cell lines. This analysis reduced the list to candidate essential genes. This rescue yielded 17 genes, bringing the total number of candidate essential genes to All lines were genotyped for the SNP of interest using Sanger sequencing.

Lines were not assessed for contamination with mycoplasma. No commonly misidentified cell lines defined by the International Cell Line Authentication Committee have been used in these studies. Cloned plasmids were amplified using a endotoxin-free maxi-prep kit Qiagen. Cells were plated in opaque well plates Corning at , , or cells per well on the indicated day post—lentiviral infection.

Cell number was inferred by ATP-dependent luminescence using CellTiter-Glo reagent Promega and normalized to the relative luminescence on the day of plating. Clones were expanded and validated by PCR to harbor the Cellular pellets were collected from Cas9-stable cells 4 or 18 days post-infection with lentiGuide-Puro virus encoding the indicated sgRNA.

Non-altered alleles as well as those containing in-frame or frameshift indels were determined manually using the CRISPR variant output file. PCR primer sequences were as follows:. Proteins were electrophoresed on polyacrylamide gradient gels Life Technologies and detected by chemiluminescence Bio-rad.

The names, clone IDs, and target sequences used in our studies are as follows:. Briefly, the pLKO. The shRNA sequences were as follows:. Primers used in our studies are as follows:. To determine number of patients in the US that could benefit from a therapeutic approach targeting each GEMINI vulnerability, we used the following formula:. Rate of heterozygosity estimated using 2pq from Hardy-Weinberg equation 86 , A score of 0 signifies no dependency and a score of 1 signifies a strong dependency as estimated by scaling the effect to a panel of known pan-essential genes.

The canSAR protein annotation tool cansar. To determine which variants were present in PDB, DNA sequences 30mer encapsulating insertion, deletion, and missense variants were translated in all 6 frames using the Bio. Seq Python module. Output was blasted using the Bio. We manually curated these structures to verify the presence of the variant within the PDB file and eliminated structures for which correspondence between the PDB protein sequence, ExAC amino acid prediction, and UCSC Genome Browser amino acid sequence was inconclusive.

This curation yielded protein-altering variants in proteins with homologous molecular structures. Visual scoring was performed on 81 protein-altering variants that lie in X-ray crystal structures.

Further information on research design is available in the Nature Research Reporting Summary linked to this article. The source data underlying Fig. Vogelstein, B.

These thresholds are dynamically tuned as the program runs. The GC. Collect method is called. If the parameterless GC. Collect method is called or another overload is passed GC. MaxGeneration as an argument, the LOH is collected along with the rest of the managed heap.

This occurs when the garbage collector receives a high memory notification from the OS. If the garbage collector thinks that doing a generation 2 GC will be productive, it triggers one. The CLR makes the guarantee that the memory for every new object it gives out is cleared. This means the allocation cost of a large object is completely dominated by memory clearing unless it triggers a GC.

If it takes 2 cycles to clear one byte, it takes , cycles to clear the smallest large object. Clearing the memory of a 16MB object on a 2GHz machine takes approximately 16ms. That's a rather large cost. Because the LOH and generation 2 are collected together, if either one's threshold is exceeded, a generation 2 collection is triggered.

If a generation 2 collection is triggered because of the LOH, generation 2 won't necessarily be much smaller after the GC. If there's not much data on generation 2, this has minimal impact. But if generation 2 is large, it can cause performance problems if many generation 2 GCs are triggered. If many large objects are allocated on a very temporary basis and you have a large SOH, you could be spending too much time doing GCs. In addition, the allocation cost can really add up if you keep allocating and letting go of really large objects.

Very large objects on the LOH are usually arrays it's very rare to have an instance object that's really large. If the elements of an array are reference-rich, it incurs a cost that is not present if the elements are not reference-rich.

If the element doesn't contain any references, the garbage collector doesn't need to go through the array at all. For example, if you use an array to store nodes in a binary tree, one way to implement it is to refer to a node's right and left node by the actual nodes:. An alternative approach is to store the index of the right and the left nodes:. Instead of referring the left node's data as left. And the garbage collector doesn't need to look at any references for the left and right node.

Out of the three factors, the first two are usually more significant than the third. Because of this, we recommend that you allocate a pool of large objects that you reuse instead of allocating temporary ones. Before you collect performance data for a specific area, you should already have done the following:.

Exhausted other areas that you know of without finding anything that could explain the performance problem you saw.

See the blog Understand the problem before you try to find a solution for more information on the fundamentals of memory and the CPU. These performance counters are usually a good first step in investigating performance issues although we recommend that you use ETW events. You configure Performance Monitor by adding the counters that you want, as Figure 4 shows.

The ones that are relevant for the LOH are:. Displays the number of times generation 2 GCs have occurred since the process started. The counter is incremented at the end of a generation 2 collection also called a full garbage collection.

This counter displays the last observed value. Displays the current size, in bytes, including free space, of the LOH. This counter is updated at the end of a garbage collection, not at each allocation.