Quantification of the Selective Advantage of Driver Mutations Is Dependent on the Underlying Model and Stage of Tumor Evolution

2022 ◽  
Vol 82 (1) ◽  
pp. 21-24
Author(s):  
Ivana Bozic
Blood ◽  
2019 ◽  
Vol 133 (13) ◽  
pp. 1436-1445 ◽  
Author(s):  
Jyoti Nangalia ◽  
Emily Mitchell ◽  
Anthony R. Green

Abstract Interrogation of hematopoietic tissue at the clonal level has a rich history spanning over 50 years, and has provided critical insights into both normal and malignant hematopoiesis. Characterization of chromosomes identified some of the first genetic links to cancer with the discovery of chromosomal translocations in association with many hematological neoplasms. The unique accessibility of hematopoietic tissue and the ability to clonally expand hematopoietic progenitors in vitro has provided fundamental insights into the cellular hierarchy of normal hematopoiesis, as well as the functional impact of driver mutations in disease. Transplantation assays in murine models have enabled cellular assessment of the functional consequences of somatic mutations in vivo. Most recently, next-generation sequencing–based assays have shown great promise in allowing multi-“omic” characterization of single cells. Here, we review how clonal approaches have advanced our understanding of disease development, focusing on the acquisition of somatic mutations, clonal selection, driver mutation cooperation, and tumor evolution.


2015 ◽  
Author(s):  
Giulio Caravagna ◽  
Alex Graudenzi ◽  
DANIELE RAMAZZOTTI ◽  
Rebeca Sanz-Pamplona ◽  
Luca De Sano ◽  
...  

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next generation sequencing (NGS) data, and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent works on "selective advantage" relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications as it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations and progression model inference. We demonstrate PiCnIc's ability to reproduce much of the current knowledge on colorectal cancer progression, as well as to suggest novel experimentally verifiable hypotheses.


2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e13506-e13506
Author(s):  
Li Chen ◽  
Rajesh Patidar ◽  
Biswajit Das ◽  
Yvonne A Evrard ◽  
Chris Alan Karlovich ◽  
...  

e13506 Background: The National Cancer Institute has developed a repository of preclinical models [Patient-Derived Models Repository (NCI PDMR, https://pdmr.cancer.gov )] including patient derived xenografts (PDXs), organoids (PDOrgs) and in vitro tumor cultures (PDCs) from patients with solid tumor cancer histologies. A subset of these preclinical models is derived from post-mortem collections from rapid autopsies representing the end point in disease progression. Clinical annotations and genomic datasets associated with these models provide a unique opportunity to study tumor evolution, mechanistic insights into the metastatic process, and treatment resistance. Methods: To date, 43 PDXs, 21 PDCs, and 23 PDOrgs using rapid autopsy specimens from 8 primary and 35 metastatic sites of 18 patients have been developed by the Biological Testing Branch (DTP, DCTD, NCI Frederick, MD) for the PDMR. Whole exome (WES) and total transcriptome (RNASeq) data were processed to generate mutation, copy number alteration (CNA) and gene expression data. Multi-model lineage trees were reconstructed based on putative somatic variants for all the models derived from all patients. The fraction of the genome affected by CNA was compared both within and across PDX models. Results: Most of the rapid autopsy PDX models (32/43) are derived from pancreatic adenocarcinoma (PAAD) patients (13/18), with metastatic specimens originating from sites including liver, colon, omentum, and lung. Driver mutations are present in all preclinical model specimens derived from the same patient. For instance, KRAS p.G12D is present in all patient-derived model specimens derived from PAAD patient 521955. The fraction of the genome affected by CNA remains stable within a PDX model across passages (n = 24, mean = 6.39%, sd = 5.90%). However, we found that this increased when comparing PDX models derived from metastatic sites versus the primary site (n = 19, mean = 16.92%, sd = 10.46%). This indicates presence of tumor heterogeneity between metastatic and primary sites. The lineage tree for models from patient 521955 indicates that one liver metastasis has a unique seeding event compared to the other 4 metastatic sites. Unsupervised clustering analysis on gene expression data also confirms the observed tumor site relationships. Conclusions: Our data demonstrate the potential use of these preclinical models available from the NCI PDMR. These models provide a unique resource for preclinical studies in tumor evolution, metastatic spread mediators, and drug resistance.


2016 ◽  
Vol 113 (28) ◽  
pp. E4025-E4034 ◽  
Author(s):  
Giulio Caravagna ◽  
Alex Graudenzi ◽  
Daniele Ramazzotti ◽  
Rebeca Sanz-Pamplona ◽  
Luca De Sano ◽  
...  

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next-generation sequencing data and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent work on the “selective advantage” relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular, and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications because it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations, and progression model inference. We demonstrate PiCnIc’s ability to reproduce much of the current knowledge on colorectal cancer progression as well as to suggest novel experimentally verifiable hypotheses.


2014 ◽  
Author(s):  
Özgün Babur ◽  
Mithat Gönen ◽  
Bülent Arman Aksoy ◽  
Nikolaus Schultz ◽  
Giovanni Ciriello ◽  
...  

Recent cancer genome studies have identified numerous genomic alterations in cancer genomes. It is hypothesized that only a fraction of these genomic alterations drive the progression of cancer -- often called driver mutations. Current sample sizes for cancer studies, often in the hundreds, are sufficient to detect pivotal drivers solely based on their high frequency of alterations. In cases where the alterations for a single function are distributed among multiple genes of a common pathway, however, single gene alteration frequencies might not be statistically significant. In such cases, we expect to observe that most samples are altered in only one of those alternative genes because additional alterations would not convey an additional selective advantage to the tumor. This leads to a mutual exclusion pattern of alterations, that can be exploited to identify these groups. We developed a novel method for the identification of sets of mutually exclusive gene alterations in a signaling network. We scan the groups of genes with a common downstream effect, using a mutual exclusivity criterion that makes sure that each gene in the group significantly contributes to the mutual exclusivity pattern. We have tested the method on all available TCGA cancer genomics datasets, and detected multiple previously unreported alterations that show significant mutual exclusivity and are likely to be driver events.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sanju Sinha ◽  
Karina Barbosa ◽  
Kuoyuan Cheng ◽  
Mark D. M. Leiserson ◽  
Prashant Jain ◽  
...  

AbstractRecent studies have reported that genome editing by CRISPR–Cas9 induces a DNA damage response mediated by p53 in primary cells hampering their growth. This could lead to a selection of cells with pre-existing p53 mutations. In this study, employing an integrated computational and experimental framework, we systematically investigated the possibility of selection of additional cancer driver mutations during CRISPR-Cas9 gene editing. We first confirm the previous findings of the selection for pre-existing p53 mutations by CRISPR-Cas9. We next demonstrate that similar to p53, wildtype KRAS may also hamper the growth of Cas9-edited cells, potentially conferring a selective advantage to pre-existing KRAS-mutant cells. These selective effects are widespread, extending across cell-types and methods of CRISPR-Cas9 delivery and the strength of selection depends on the sgRNA sequence and the gene being edited. The selection for pre-existing p53 or KRAS mutations may confound CRISPR-Cas9 screens in cancer cells and more importantly, calls for monitoring patients undergoing CRISPR-Cas9-based editing for clinical therapeutics for pre-existing p53 and KRAS mutations.


2019 ◽  
Vol 116 (52) ◽  
pp. 26863-26872 ◽  
Author(s):  
Lawrence A. Loeb ◽  
Brendan F. Kohrn ◽  
Kaitlyn J. Loubet-Senear ◽  
Yasmin J. Dunn ◽  
Eun Hyun Ahn ◽  
...  

Human colorectal cancers (CRCs) contain both clonal and subclonal mutations. Clonal driver mutations are positively selected, present in most cells, and drive malignant progression. Subclonal mutations are randomly dispersed throughout the genome, providing a vast reservoir of mutant cells that can expand, repopulate the tumor, and result in the rapid emergence of resistance, as well as being a major contributor to tumor heterogeneity. Here, we apply duplex sequencing (DS) methodology to quantify subclonal mutations in CRC tumor with unprecedented depth (104) and accuracy (<10−7). We measured mutation frequencies in genes encoding replicative DNA polymerases and in genes frequently mutated in CRC, and found an unexpectedly high effective mutation rate, 7.1 × 10−7. The curve of subclonal mutation accumulation as a function of sequencing depth, using DNA obtained from 5 different tumors, is in accord with a neutral model of tumor evolution. We present a theoretical approach to model neutral evolution independent of the infinite-sites assumption (which states that a particular mutation arises only in one tumor cell at any given time). Our analysis indicates that the infinite-sites assumption is not applicable once the number of tumor cells exceeds the reciprocal of the mutation rate, a circumstance relevant to even the smallest clinically diagnosable tumor. Our methods allow accurate estimation of the total mutation burden in clinical cancers. Our results indicate that no DNA locus is wild type in every malignant cell within a tumor at the time of diagnosis (probability of all cells being wild type, 10−308).


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4101-4101
Author(s):  
Mariette Odabashian ◽  
Emanuela Carlotti ◽  
Shamzah Araf ◽  
Jessica Okosun ◽  
Francesco Forconi ◽  
...  

Abstract Introduction: Follicular lymphoma (FL) cells retain expression of a functional B cell receptor (BCR) despite the loss of one Ig allele due to the hallmark t14:18 translocation and ongoing somatic hypermutation (SHM) of the variable genes (V genes) which increases the likelihood of crippling mutations. SHM introduces N-glycosylation (N-gly) motifs within the V genes, a feature exclusively restricted to germinal centre (GC)-derived lymphomas. Oligosaccharides of the high mannose type are added to motifs and interact with calcium-dependent lectins associated with cells of the microenvironment. This activates the BCR signalling pathway and likely contributes to the survival, retention and proliferation of tumor cells in the GC. Determining at what stage of disease evolution N-gly motifs are acquired and their behaviour during progression can ascertain their importance in pathogenesis and their potential as an effective therapeutic target. To achieve this, we analysed motifs within the Ig heavy chain variable gene (IGHV) of tumor-related subclones across temporal FL samples. Method: Genomic DNA from three FL patients taken at different time points of disease progression were analysed. In total, 8 samples were selected, all carrying an IGHV3 rearranged tumor clone. IGHV DNA amplicons were sent for 2x250bp paired-end sequencing using the Miseq Illumina platform (Genewiz, NJ). Tumor-related reads with counts greater than ten were selected following analysis on IMGT/HIGH-V-QUEST. Reads were aligned and unique sequences were assigned as subclones. Additional tumor related reads sequenced on the Roche 454 Life Sciences Genome Sequencer FLX were available (Patients 4 & 5). Subclones were analysed for N-gly motifs and evolutionary pathways were generated using the IgTree program, based on intraclonal SHM profiles and homology of tumor clones to the germline IGHV sequence. Results: The earliest time point samples for Patient's 1, 3, 4 & 5 contained one N-gly site within the IGHV of the MC defined by the largest count number. These sites were conserved in >97% of unique subclones (p<0.0001) despite variations in the nucleotide sequence within the region as a result of ongoing SHM. This conservation included the most mutated subclones which had a mean SHM rate of 16.56%. Conservation was maintained across disease events. Patient 2 contained four N-gly sites located within the CDR1, FR2, CDR2, and FR3 regions. The first three sites were conserved in >97% of subclones in and across disease events, whereas the FR3 site was conserved in 95.5% of the diagnostic subclones and in ~80% of the relapsed and transformed populations. No subclones with loss of all four sites were detected for Patient 2. Patient 5 samples were taken from different anatomical sites with tumor populations acquiring distinct N-gly motifs, suggesting an early divergence in tumor evolution. Despite this, a minor population of motif positive clones are shared, suggesting a trafficking ability of subclones. Subclones with motifs made up ≥99% of the total tumor count, highlighting the motif as a feature of the tumor bulk, with motif negative clones representing a minor population. These negative clones are presumably lost during disease progression as they not shared between events. Evolutionary analysis revealed no additional sites are gained as motif positive clones expand while rare negative subclones cannot reacquire sites and do not undergo further diversification. Conclusion: We report for the first time that acquired N-gly motif sites are a clonal feature in FL disease as seen through their conservation both in the heterogeneous subclonal population and the overall tumor mass. The sites are also retained in progression-associated subclones while rare motif-negative subclones disappear. This suggests that although acquisition of additional driver mutations may dampen the tumor's microenvironment dependency, the motifs and added mannoses may retain functional significance at later stages of disease. The data indicates motifs as being a universal event of the reservoir cell pool responsible for propagating disease episodes. Targeting N-gly sites and their interacting partners may lead to the disruption of an early and vital FL-microenvironment interaction, presumably mediated through the mannose-lectin interaction, reducing relapse rates and progression of disease. Disclosures Forconi: Abbvie: Consultancy; Janssen-Cilag: Consultancy. Gribben:Abbvie: Honoraria; Roche: Honoraria; Pharmacyclics: Honoraria; Novartis: Honoraria; Cancer Research UK: Research Funding; Wellcome Trust: Research Funding; Acerta Pharma: Honoraria, Research Funding; TG Therapeutics: Honoraria; NIH: Research Funding; Kite: Honoraria; Janssen: Honoraria, Research Funding; Unum: Equity Ownership; Medical Research Council: Research Funding; Celgene: Consultancy, Honoraria, Research Funding.


2021 ◽  
Author(s):  
Pedro F Ferreira ◽  
Jack Kuipers ◽  
Niko Beerenwinkel

Cancer arises and evolves by the accumulation of somatic mutations that provide a selective advantage. The interplay of mutations and their functional consequences shape the evolutionary dynamics of tumors and contribute to different clinical outcomes. In the absence of scalable methods to jointly assay genomic and transcriptomic profiles of the same individual cell, the two data modalities are usually measured separately and need to be integrated computationally. Here, we introduce SCATrEx, a statistical model to map single-cell gene expression data onto the evolutionary history of copy number alterations of the tumor. SCATrEx jointly assigns cancer cells assayed with scRNA-seq to copy number profiles arranged in a copy number aberration tree and augments the tree with clone-specific clusters. Our simulations show that SCATrEx improves over both state-of-the-art unsupervised clustering methods and cell-to-clone assignment methods. In an application to real data, we observe that SCATrEx finds inter-clone and intra-clone gene expression heterogeneity not detectable using other integration methods. SCATrEx will allow for a better understanding of tumor evolution by jointly analysing the genomic and transcriptomic changes that drive it.


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