What is the difference between mutations and cancer

Somatic variants are detected by either testing the tumor directly or liquid biopsy of a blood sample with circulating tumor cells to identify the DNA sequencing changes driving tumor growth. Understand the variants for a particular malignancy may help providers determine which therapies might be most effective. Information provided by germline and somatic biomarker testing may overlap. Depending on the cancer type, both tumor and germline testing may be used to help select treatment options.

Oncology nurses should understand when to refer patients for germline testing and how to provide patient education about the difference between germline and somatic variants. Educating patients and families about the different types of variants enables patients to have insight into the development of cancer and what it means for their treatment and recommendations for prevention and early detection of other cancers.

Create a personalised ads profile. Select personalised ads. Apply market research to generate audience insights. Measure content performance. Develop and improve products. List of Partners vendors. The difference between hereditary germline and acquired somatic gene mutations in cancer can lead to much confusion. This is especially true if you're hearing about genetic testing for a genetic predisposition to cancer at the same time you hear about genetic testing for mutations that may be treatable in a cancer already present.

Somatic mutations are those that are acquired in the process of a cancer forming, and are not present at birth. They cannot be passed down to children and are present only in the cells affected by cancer. Targeted therapies are now available for many gene mutations found in tumors that can often control the growth of the cancer at least for a period of time.

Germline mutations, in contrast, are inherited from a mother or father and increase the chance a person will develop cancer. That said, there is overlap between the two that adds further confusion. We will take a look at exactly what a gene mutation is, characteristics of hereditary and acquired mutations, and give examples you may be familiar with. Gene mutations are important in the development of cancer as it is the accumulation of mutations DNA damage that results in the formation of cancer.

Genes are segments of DNA, and these segments, in turn, are the blueprint for the production of proteins. Not all gene mutations raise the risk of developing cancer, but rather it is mutations in genes responsible for the growth of cells driver mutations that can lead to development of the disease.

Some mutations are harmful, some do not cause any changes, and some are actually beneficial. Genes can be damaged in a number of ways. The bases that make up the backbone of DNA adenine, guanine, cytosine, and thymine are the code that is interpreted. Each three base sequence is associated with a particular amino acid. Proteins, in turn, are formed by chains of amino acids. Simplistically, mutations may involve the substitution, deletion, addition, or rearrangement of base pairs.

In some cases, parts of two chromosomes may be interchanged translocation. There are two primary types of genes involved in cancer development:. Oncogenes : Protooncogenes are genes that are normally present in the body that code for the growth of cells, with most of these genes being "active" primarily during development.

When mutated, protooncogenes are converted to oncogenes , genes that code for proteins that drive the growth of cells later in life when they would ordinarily be dormant. Tumor suppressor genes : Tumor suppressor genes code for proteins that essentially have an anti-cancer effect.

When genes are damaged see below , these proteins may either repair the damage or lead to death of the damaged cell so that it can't continue to grow and become a malignant tumor. Not everyone who is exposed to carcinogens will develop cancer, and the presence of tumor suppressor genes is part of the reason why this is the case.

Examples of tumor suppressor genes include BRCA genes and the p53 gene. It is usually but not always a combination of mutations in oncogenes and tumor suppressor genes that leads to the development of cancer. Genes and chromosomes can be damaged in a number of different ways. We implemented a weighted set cover algorithm to identify 2-hit combinations of cancer causing genes with mutations using a randomly selected Training set of tumor and normal tissue samples see Methods. This result is robust to different Training and Test set partitions of the available tumor and normal tissue samples.

Although the identified combinations contain many genes previously implicated in cancer, our approach has also identified several potentially novel cancer genes. We implemented the weighted set cover algorithm described in Methods , for identifying a set of 2-hit combinations with the goal of maximizing accuracy sensitivity and specificity in differentiating between tumor and normal samples.

Using a randomly selected Training set see Methods , we identified a set of 2-hit combinations for each of the seventeen cancer types with at least two hundred matched tumor and blood-derived normal samples. Table 1 shows the sample sizes, sensitivity, and specificity for the Training and Test sets for each of the seventeen cancer types.

The number of combinations identified varies from 8—20 for the 17 cancer types Table 1. In total, combinations were identified Tables S2 — S The top three 2-hit combinations are summarized in Fig. The combinations include unique genes with genes occurring in more than one combination. Top three 2-hit combinations for 17 cancer types. See Table 1 for abbreviations for cancer types. Each line in the center of the Circos plot connects the two genes in a 2-hit combination.

This plot was generated using RCircos To test the robustness of the above results, we randomly re-partitioned the available samples into two more alternative Training and Test sets. Figure 2 shows specificity and sensitivity of the algorithm across the seventeen cancer types considered here, for three different sets of partitions. The average difference in sensitivity between any two pairs of train-test partitions is less than 4.

However, there were significant differences between the less frequently occurring combinations with only 39 common combinations, out of total combinations, across the three sets of combinations for the three training-test partitions Fig. Clearly, the samples included in the Training set affect the set of combinations identified.

Different partitions of the tumor samples will result in different sets of these rare combinations being included in the Training set, resulting in different combinations being identified.

In addition, since the approximation algorithm used here identifies a near-optimal solution, changes in the Training set can result in different near-optimal combinations being selected by the algorithm.

Sensitivity and specificity is robust across three different random training-test partitions of available samples. The average difference between any two pairs of partitionings is less than 4.

Occurrence of the 2-hit combinations identified in tumor samples, for three representative cancer types. Figure S3 shows the distribution for all seventeen cancer types. The genes comprising the 2-hit combinations identified above fall into three categories. Table 2 summarizes, from Tables S2 — S18 , the 31 genes that comprise the top three most frequently occurring 2-hit combinations for each of the cancer types studied.

Of these genes, nine are confirmed cancer genes e. The genes in the last category have not been extensively studied, and represent potentially novel cancer genes. There are two possible reasons why we have identified TUBB8P12 as a potential cancer gene while previous bioinformatics studies have not.

The first reason is that, we considered low frequency somatic mutations, identified using matched tumor and blood derived normal samples, that were not included in many of the previous studies 9 , 12 , 29 , Biopsy specimens contain a mix of tumor and normal tissue cells, tumor infiltrating lymphocytes, and stromal cells. In addition, tumor cells themselves can be genetically diverse. Therefore many somatic mutations are likely to be present at very low frequencies 30 , Studies that use masked open-access TCGA data will exclude many such low-frequency mutations.

The second reason is that, those studies that do use controlled-access TCGA data that include these low-frequency mutations, do not use matched normal tissue and blood-derived normal samples to quantify the differential mutation frequency between tumor and normal samples 9 , 10 , 11 , By comparing somatic mutation frequency in matched tumor tissue samples to mutation frequency in matched normal tissue samples, we are able to identify genes that are significantly more frequently mutated in tumor samples relative to normal samples, while excluding genes that may be highly mutated in both tumor and normal samples.

Due to practical limitations of computational resources, it is not practical to search for more than 2-hit combinations using the current version of the algorithm presented see Methods. Mathematical models predict that the likely number of hits required for carcinogenesis ranges from two to eight.

Therefore, searching for three or more hits may further improve the accuracy of our results. Distribution of overlapping combinations for three representative cancer types. Figure S5 shows the distribution for all seventeen cancer types.

Analysis of genes within each combination shows that they are not correlated. The length of the vector is equal to the number of normal samples, and the value in the i th position of that vector represents whether the i th normal sample has a protein-altering mutation as determined by the Variant Effect Predictor VEP in that location or not.

The Pearson correlation coefficient is less than 0. If the genes within a combination were correlated it would have suggested that the combination is a result of some common underlying cause, such as being a passenger mutation or due to structural chromosomal modification, and unlikely to be causative.

We also examined the chromosomal location of genes within each combination Fig. Only two of the combinations contain genes within the same chromosome, suggesting that the genes within combinations are not due to a chromosomal abnormality that may affect multiple genes within a chromosome.

Chromosomal location of gene combinations. Each connecting line represents a 2-hit combination. Blue lines represent gene combinations across different chromosomes. Red lines represent gene combinations within the same chromosome.

Circos plot was generated using RCircos Here we discuss how the multi-hit combinations identified above can be used to identify carcinogenic driver and non-carcinogenic passenger mutations within genes. We also illustrate how these combinations may be used to design a combination therapy targeting the specific genetic mutations responsible for individual instances of cancer.

The method used to identify multi-hit combinations uses a mutation frequency based approach to preferentially select driver genes instead of passenger genes, i. For each gene, the mutation frequency in normal samples is considered to be approximately representative of the background mutation frequency for the gene. However, within these genes not all mutations are carcinogenic.

The combinations found above provide a starting point for examining a smaller subset of genes more closely to identify specific carcinogenic mutations within these genes. In identifying the multi-hit combinations, we did not take into consideration the location of mutations within genes. Clearly there are locations within a gene where certain mutations are unlikely to affect the function of the gene product.

Comparing the mutations within these genes for normal and tumor samples may reveal which are carcinogenic and which are not. In this example, every one of the tumor samples contains a missense mutation at R in IDH1 and no other mutations, while the normal samples do not contain any mutations at this position Fig.

Mutations at R in IDH1 have previously been implicated in cancer On the other hand, the IDH1 mutations seen in the normal samples are unlikely to be carcinogenic. Similarly, mutations at F of MUC6, which occur most frequently in both tumor and normal samples are unlikely to be carcinogenic Fig.

Excluding such non-carcinogenic mutations can reduce the number of false positives and further increase accuracy of our algorithm. In our future work we will develop an automated method to compare and contrast the individual gene loci, so that all of these mutations within genes can be identified.

To further improve accuracy of our algorithm, variants that are likely to be carcinogenic can be weighted higher than those that are unlikely to be carcinogenic. The difference in mutations between normal and tumor samples for the same 2-hit combination can be used to further refine the search algorithm. In the above examples, a missense mutation at R in IDH1 is likely to be carcinogenic, whereas mutations at F in MUC6 are unlikely to be carcinogenic. Colored bars represent known functional protein domains.

Grey bars represent regions of unknown function. Green dots represent missense mutations, black dots represent truncating mutations and purple dots represent other protein-altering mutations. Figure generated using cBioPortal Cerami et al. Some of the genes identified by our approach may not be causative passenger mutations even though they may be correlated to cancer incidence.

Functional analysis can be used to identify genes in the above set of combinations that are unlikely to be driver genes, even though they may be frequently mutated in tumors 11 , 34 , For example, the affect of specific mutations on gene expression levels can be analyzed to determine if the mutation is likely to have a functional effect. In addition we can analyze the pathways affected by the gene combinations Tables S19 — S Studies show that combinations of driver gene mutations generally affect mutually exclusive pathways Therefore, one of the genes in a multi-hit combination affecting the same pathway may include passenger mutations.

Although in most cases multiple different pathways are affected by the gene combinations, Tables S19 — S22 shows that in some cases e. Further analysis would be required to determine if the mutations within one of these genes are passenger mutations. The search algorithm can be run iteratively to incrementally refine the list of multi-hit combinations by excluding these passenger mutations. The input to our algorithm is a list of genes with mutations for each sample.

Genes with only passenger mutations can be excluded from this list to minimize the inclusion of passenger mutations in the resulting multi-hit combinations. A more rational strategy may also reduce the risk of expensive failures such as the phase III trial of imfinzi plus tremelimumab. The combination of therapies for a given patient could be designed to target specific carcinogenic combinations of gene mutations found in the patient. Therapies that target many of the genes in both these categories may be available or under development.

Several drugs that can restore TP53 function, deplete mutant TP53 or affect downstream targets are currently in pre-clinical development For patients with this combination of mutations, a combination therapy targeting both these genes may be more effective in combination, than separately. Cancer is many different diseases, although the symptoms may be similar. These different diseases are a result of different combinations of genetic defects hits.

In this study we have developed a method for identifying combinations of genes with mutations that may be responsible for different instances of cancer. Our method is fundamentally different from current approaches which identify individual genes, instead of combinations of genes, in which mutations increase the likelihood of carcinogenesis. The problem of identifying a set of multi-hit combinations that can differentiate between tumor and normal samples was mapped to the extensively studied weighted set cover WSC problem.

We adapted a WSC algorithm to the problem of identifying multi-hit combinations. The algorithm was applied to a training set of somatic mutation data from the cancer genome atlas TCGA to identify a set of 2-hit combinations for the 17 cancer types with at least matched tumor tissue and blood-derived normal samples. Accuracy of the results were robust to different random partitionings of the available data between training and test sets.

The resulting set of combinations include potential novel cancer genes, not previously implicated in cancer. We show how carcinogenic and non-carcinogenic mutations within genes could be identified, by comparing the occurrence of different mutations in tumor and normal samples.

We also illustrate how the combination of mutations responsible for an individual instance of cancer can be used to design a combination therapy targeting the specific genes responsible for that instance of cancer.

Our approach for identifying sets of multi-hit combinations consists of two steps Fig. First, we identified somatic mutations from whole exome sequencing data for tumor and normal tissues with matched blood-derived normal samples from The Cancer Genome Atlas TCGA. Somatic variants called from matched tumor tissue and blood-derived normal samples can detect low-frequency variants, which would not be detected when using tumor samples alone.

Second, we use a weighted set cover algorithm to identify multi-hit combinations that can differentiate between tumor and normal samples with high sensitivity and specificity. The problem of identifying a set of multi-hit combinations is computationally intractable; however, there exist algorithms for finding a near-optimal approximate solution. We used a variant of one such algorithm to identify a set of multi-hit combinations for each cancer type, using a randomly selected subset of the available tumor and normal tissue samples the Training set.

The accuracy sensitivity and specificity of the resulting multi-hit combinations was evaluated using the remaining tumor and normal tissue samples the Test set. Another category of mutations involves alterations of larger amounts of DNA, often at the level of the chromosome. These are called translocations and involve the breakage and movement of chromosome fragments.

Often, breaks in two different chromosomes allow for the formation of two 'new' chromosomes, with new combinations of genes. While it might appear that this would not cause much trouble, since all the genes are still present, the process can lead to deregulated cell growth in a number of ways- 1. The genes may not be transcribed and translated appropriately in their new location. The movement of a gene can lead to an increase or a decrease in its level of transcription.

The breakage and rejoining may also occur within a gene as shown in green above , leading to its inactivation. For some cancers, particular translocations are very common and may even be used in diagnosing the disease. Translocations are common in leukemias and lymphomas and have been less commonly identified in cancers of solid tissues. The exchange leads to the formation of a shortened form of chromosome 22 called the Philadelphia chromosome after the location of its discovery.

This translocation leads to the formation of an oncogene from the abl proto-oncogene. Other cancers that are often or always associated with particular translocations include Burkitt's lymphoma , B-cell lymphomas and several types of leukemia. In this very unusual process, the normal DNA replication process is seriously flawed. The result is that instead of making a single copy of a region of a chromosome, many copies are produced.

This leads to the production of many copies of the genes that are located on that region of the chromosome. Sometimes, so many copies of the amplified region are produced that they can actually form their own small pseudo-chromosomes called double-minute chromosomes.

The genes on each of the copies can be transcribed and translated, leading to an overproduction of the mRNA and protein corresponding to the amplified genes as shown below. The squiggly lines represent mRNA being produced via the transcription of each copy of the gene. While this process is not seen in normal cells, it occurs quite often in cancer cells. If an oncogene is included in the amplified region, then the resulting overexpression of that gene can lead to deregulated cell growth.

Gene amplification also contributes to one of the biggest problems in cancer treatment: drug resistance. Drug resistant tumors can continue to grow and spread even in the presence of chemotherapy drugs. A gene commonly involved is called MDR for m ultiple d rug r esistance. The protein product of this gene acts as a pump located in the membrane of cells. It is capable of selectively ejecting molecules from the cell, including chemotherapy drugs.

This removal renders the drugs ineffective. This is discussed in more detail in the section on Drug Resistance. The amplification of different genes can render other chemotherapy drugs ineffective. In these alterations, segments of DNA are released from a chromosome and then re-inserted in the opposite orientation. As in the previous examples, this rearrangement can lead to abnormal gene expression, either by activating an oncogene or de-activating a tumor suppressor gene.

Through replication errors, a gene or group of genes may be copied more than one time within a chromosome. This is different from gene amplification in that the genes are not replicated outside the chromosome and they are only copied one extra time, not hundreds or thousands of times. Genes may also be lost due to failure of the replication process or other genetic damage. Aneuploidy is the genetic change that involves the loss or gain of entire chromosomes. Due to problems in the cell division process, the replicated chromosomes may not separate into the daughter cells accurately.

This can result in cells that have too many chromosomes or too few chromosomes. An example of a fairly common aneuploid condition that is unrelated to cancer is Down syndrome, in which there is an extra copy of chromosome 21 in all of the cells of the affected individual.

In the animation below, copies of two chromosomes are made but when the cell divides the chromosomes are not distributed evenly to the two cells that are formed daughter cells. The result is that one of the cells has too many chromosomes and one does not have enough.

Cancer cells are very often aneuploid. Humans normally have 46 chromosomes in their cells, but cancer cells often have many more, sometimes greater than The presence of the extra chromosomes makes the cells unstable and severely disrupts the controls on cell division.

There is currently an ongoing debate as to whether or not all cancers are aneuploid. Regardless of whether that is the case, it is clear that aneuploidy is a common feature of cancer cells. In addition to actual alterations in DNA sequence, gene expression can be altered by changes to the DNA and chromatin that do not change the sequence.

Since these changes do not alter the sequence of the DNA in the genes, they are termed epi genetic changes.