Adaptation and selection shape clonal evolution of tumors during residual disease and recurrence.
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ABSTRACT:
SUBMITTER: Walens A
PROVIDER: S-EPMC7539014 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The potential role antigens play in growth stimulation or in clonal selection of follicular lymphomas is unknown. To study this issue, we sequenced the immunoglobulin heavy chain variable region genes expressed by a follicular lymphoma from multiple biopsy specimens and also cloned and sequenced the corresponding germ-line variable gene from this patient. Comparison to the germ-line gene revealed numerous nucleotide substitutions in all of the lymphoma variable gene sequences. Some of the substitutions may have occurred in the nonmalignant precursor B cell that gave rise to this lymphoma because they were shared among all of the variable genes, but many of the mutations accumulated as the malignant clone expanded. The mutations were distributed in such a way that strongly suggested the majority of tumor cells had been positively selected through their antigen receptor. This was especially evident for the mutations that developed late in the clonal evolution of this lymphoma. These findings indicate that antigen stimulation may be involved in the growth of follicular lymphoma tumors.
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Project description:BackgroundHigh-throughput sequencing allows the detection and quantification of frequencies of somatic single nucleotide variants (SNV) in heterogeneous tumor cell populations. In some cases, the evolutionary history and population frequency of the subclonal lineages of tumor cells present in the sample can be reconstructed from these SNV frequency measurements. But automated methods to do this reconstruction are not available and the conditions under which reconstruction is possible have not been described.ResultsWe describe the conditions under which the evolutionary history can be uniquely reconstructed from SNV frequencies from single or multiple samples from the tumor population and we introduce a new statistical model, PhyloSub, that infers the phylogeny and genotype of the major subclonal lineages represented in the population of cancer cells. It uses a Bayesian nonparametric prior over trees that groups SNVs into major subclonal lineages and automatically estimates the number of lineages and their ancestry. We sample from the joint posterior distribution over trees to identify evolutionary histories and cell population frequencies that have the highest probability of generating the observed SNV frequency data. When multiple phylogenies are consistent with a given set of SNV frequencies, PhyloSub represents the uncertainty in the tumor phylogeny using a "partial order plot". Experiments on a simulated dataset and two real datasets comprising tumor samples from acute myeloid leukemia and chronic lymphocytic leukemia patients demonstrate that PhyloSub can infer both linear (or chain) and branching lineages and its inferences are in good agreement with ground truth, where it is available.ConclusionsPhyloSub can be applied to frequencies of any "binary" somatic mutation, including SNVs as well as small insertions and deletions. The PhyloSub and partial order plot software is available from https://github.com/morrislab/phylosub/.
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Project description:BackgroundThe prognosis of patients with secondary central nervous system lymphoma (SCNSL) is poor and despite massive advances in understanding the mutational landscape of primary diffuse large B-cell lymphoma (DLBCL), the genetic comparison to SCNSL is still lacking. We therefore collected paired samples from six patients with DLBCL with available biopsies from a lymph node (LN) at primary diagnosis and the central nervous system (CNS) at recurrence.Patients and methodsA targeted, massively parallel sequencing approach was used to analyze 216 genes recurrently mutated in DLBCL. Healthy tissue from each patient was also sequenced in order to exclude germline mutations. The results of the primary biopsies were compared with those of the CNS recurrences to depict the genetic background of SCNSL and evaluate clonal evolution.ResultsSequencing was successful in five patients, all of whom had at least one discordant mutation that was not detected in one of their samples. Four patients had mutations that were found in the CNS but not in the primary LN. Discordant mutations were found in genes known to be important in lymphoma biology such as MYC, CARD11, EP300 and CCND3. Two patients had a Jaccard similarity coefficient below 0.5 indicating substantial genetic differences between the primary LN and the CNS recurrence.ConclusionsThis analysis gives an insight into the genetic landscape of SCNSL and confirms the results of our previous study on patients with systemic recurrence of DLBCL with evidence of substantial clonal diversification at relapse in some patients, which might be one of the mechanisms of treatment resistance.
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