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They discovered that the founding clone in the primary tumor gained mutations and evolved into the relapse clone, or that a subclone of the founding clone survived initial therapy, gained additional mutations, and expanded at relapse. In all cases, chemotherapy failed to eradicate the founding clone. The researchers concluded that comparison of relapse-specific vs. These data demonstrate, the authors said, that AML relapse is associated with the addition of new mutations and clonal evolution, shaped in part by the chemotherapy that the patients receive to establish and maintain remissions.

A team working at Harvard and the Broad Institute sought to estimate the extent of clonal heterogeneity in multiple myeloma in a large-scale MM genome sequencing dataset that included untreated and previously treated patients. Jens G. Lohr, M. Commenting on the study, co-senior author Todd Golub, M.

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In the same person, there could be cancer cells with different genetic makeups. These findings indicate a need to identify the extent of genetic diversity within a tumor as we move toward precision cancer medicine and genome-based diagnostics. The Cancer Cell authors identified significantly mutated genes and copy number alterations and discovered putative tumor suppressor genes by determining homozygous deletions and loss of heterozygosity.

Mutations were often present in subclonal populations, and multiple mutations within the same pathway e. These studies demonstrate how acquisition of genetic alterations over time leads to clonal evolution. Systemic treatment with chemotherapy may affect the fitness of some subclones more than others, and thus may alter the tumor composition by promoting particular subclones. Noting that accumulating evidence suggests that intratumor heterogeneity likely is the key to understanding Glioblastoma GB treatment failure, Andrea Sottoriva, Ph.

Cancer Evolution

Their integrated genomic analysis, they said, uncovers extensive intratumor heterogeneity, with most patients displaying different GB subtypes within the same tumor. Their clonal characterization of each tumor fragment at the single-molecule level detected multiple coexisting cell lineages.

Taken together, the researchers concluded, their results reveal the genome-wide architecture of intratumor variability in GB across multiple spatial scales and patient-specific patterns of cancer evolution, with consequences for treatment design. Asked how his findings in MM and those of others with different tumors might immediately translate into practice for oncologists, Dr.

Golub said that the notion of a yes or no answer, that is, whether or not a given mutation is present in a tumor, is too simplistic, particularly in interpreting clinical trial results testing targeted agents. He added that what is needed is a quantitative approach to know in what proportion of the tumor the mutation is found. The same goes for clinical trials as you might come to different results, continued Dr. With respect to drug discovery, Dr. Beverly Mitchell, M. Kapp, M.

These include whether intra-tumor heterogeneity implies that a single targeted therapeutic, while producing short-term responses, brings us any closer to enduring disease control. Or, they ask, do we simply need a better, or a second or third, targeted therapeutic? Log in to leave a comment.

Clonal evolution in colorectal cancer

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If the tumor is clonal and diploid, the allele frequencies of all the mutations are 0. In realistic situations, because the tumor contains a small fraction of normal stromal cells, the mutation allele frequencies decrease to less than 0. Moreover, subclonal mutations are observed as mutations with low frequencies, which is not explained by the mixture of normal cells or copy number alteration. For example, in a study demonstrating whole genome deep sequencing of 21 primary breast tumors, the subclonal structures were explored by combining the information on mutant allele frequencies with allelic copy numbers.

Furthermore, the order of mutations and copy number alterations was regarded as each phase in the evolutionary history of breast cancer. Multiregion sequencing has been reported in various types of tumors, so far including brain tumors, 11 , 12 , 13 , 14 , 15 , 16 , 17 breast cancers, 18 , 19 , 20 , 21 , 22 colorectal cancers, 23 , 24 , 25 , 26 , 27 esophageal cancers, 28 , 29 , 30 head and neck cancers, 31 hepatocellular carcinomas, 32 , 33 , 34 lung cancers, 35 , 36 , 37 , 38 melanomas, 39 ovarian cancers, 40 , 41 , 42 pancreatic cancers, 43 , 44 prostate cancers 45 , 46 , 47 , 48 , 49 , 50 and urothelial carcinomas, 51 as summarized comprehensively in a previous review.

Progressor mutations showed a mutational pattern which was geographically correlated with sampling locations. Moreover, we found that, in each region, founder mutations existed as clonal mutations while progressor mutations existed as subclonal mutations. This finding suggests that, even in each region, there existed ITH which was not captured by the resolution of multiregion sequencing. Progressor mutations contained few driver mutations and parallel evolution was not confirmed, which is in contrast to the findings obtained in renal cancer. Multiregion sequencing.

B, Through multiregion mutation profiling, founder and progressor mutations were found as the common mutations in all the regions tested and only restricted regions, respectively. C, In a phylogenetic tree constructed from the multiregion mutation profile, the trunk and branches correspond to the founder and progressor mutations, respectively.

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Deep sequencing. A, A sample from a single region usually contains multiple types of cell populations with different genomes. B, Assume that the sample harbors normal cells, clone 1 with a clonal mutation, and clone 2 with the same clonal mutation and a subclonal mutation and all cell types are diploids without copy number alterations. Multiregion sequencing of colorectal cancer. A, A schema of a multiregion sampling in a primary colorectal cancer and matched metastatic liver lesion. In this case, we obtained 20 samples from the primary lesion and 1 sample from the metastatic lesion.

B, A multiregion mutation profile. The depth of red represents mutant allele frequency while the colors of sample labels were prepared so that the similarities of colors represent those of mutation patterns. C, A phylogenetic tree constructed from the multiregion mutation profile.

The time when mutations in known driver genes of colorectal cancer is acquired is indicated along the tree. As described above, genome analysis has unveiled extensive ITH in various types of tumors. However, genome analysis is not sufficient for understanding how ITH is generated. To answer this question, mathematical modeling is a powerful tool when combined with genomic analysis.

Next, we introduce some of the mathematical modeling studies that have sought to clarify the evolutionary principles underlying ITH. In a mathematical modeling study, we start from expressing a target dynamic system as a mathematical model, using mathematical expressions such as differential equations. The mathematical model contains variables representing the system status and parameters specifying the system dynamics. After constructing the mathematical model, we usually try to express the variables as a function of the parameters and initial values of the variables.

When a mathematical model is a simple differential equation, the model is analytically solvable. That is, it is possible to obtain the function by manipulating the mathematical expression. However, because most mathematical models are not analytically solvable, we use a computer simulation to numerically solve the models and analyze system dynamics. Therefore, computer simulation is currently an indispensable tool in mathematical modeling studies. In mathematical modeling of cancer evolution, it is natural to assume each cell to be an agent and thereby ITH is easily expressed as the difference of internal states of agents.

For example, in a pioneering model, agents were assumed to be cells which contained a few genes and proliferated while accumulating mutations.

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Each cell harbors n genes including d driver genes, while each cell divides and dies in a unit time with a probability p and q , respectively. In the BEP model, f can be regarded as the strength of the driver genes. Given that a cell without mutations divides according to this rule, after the normal cell acquires the first driver mutation, which accelerates cell division, the proportion of the clone originating from the cell increases in a whole cell population.

By repeating these steps, each cell gradually accumulates driver mutations as well as accompanying passenger mutations, which do not affect the cell division rate, and, finally, a tumor is formed with numerous mutations accumulated. Depending on parameter values in the course of the cancer evolution, each of the cancer cells can accumulate different combinations of mutations to generate different kinds of ITH. In this example, driver mutations gradually accumulated in the cells and a clone with 4 mutations was selected during natural selection, which finally became dominant in the tumor.

Branching evolutionary process BEP model.