Shifts in Intra-Clonal Dynamics Rather Than Novel Mutations Are the Main Engine Driving Tumor Evolution in Relapsed CLL

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 284-284
Author(s):  
Dan-Avi Landau ◽  
Petar Stojanov ◽  
Michael S Lawrence ◽  
Carrie Sougnez ◽  
Aaron McKeena ◽  
...  

Abstract Abstract 284 Tumor evolution is a complex process, and is the biologic underpinning of disease progression, resistance to therapy and relapse. Using whole-exome sequencing (WES) of sequential samples from patients with relapsed chronic lymphocytic leukemia (CLL) treated with conventional chemotherapy, we studied genetic tumor evolution of cancer relapse. We performed WES using paired-end reads on DNA from two peripheral blood-derived CLL tumor samples at least one year apart and on germline DNA for 20 patients. Here we report the analysis of tumor exomes from the first seven patients, of whom 6 had relapsed disease after chemotherapy and one untreated patient without intervening therapy between samples. All samples had a tumor purity that exceeded 90%. Sequencing coverage was >86% of target territory, with 132x depth obtained for all samples. In total, 187 coding region mutations (124 nonsynonymous, 63 synonymous) were identified (median: 21 somatic mutations/patient; range: 10–64), not including Ig gene mutations which were >80% clonal and remained clonally stable in our cohort. We measured the abundance of specific mutations in each patient tumor to assess clonality. An allelic frequency of 0.3–0.6 likely represents heterozygous mutations in most or all tumor cells (‘clonal') while a frequency of <0.3 represents mutations in a subset of tumor cells (‘subclonal'). Overall, 118 (63%) somatic mutations were clonal, and their allelic frequency remained unchanged in the relapse samples. 65 (35%) mutations were subclonal (average allelic frequency 0.13±0.075). Ten subclonal mutations, found in 3 of 7 initial samples, evolved into clonal mutations in the relapse samples, compared with only a single opposite occurrence where a clonal mutation became subclonal (p< 0.005, FDR q<0.01). The remaining 4 of 7 tumors showed only minor shifts in allelic frequencies over time, and included the individual who did not receive chemotherapy between samples. In Patient A, a subclone with three mutations appeared to expand to become the dominant clone, with a change in allelic fraction from an average of 0.17 (0.14–0.23) to an average of 0.43 (0.41–0.46) (p<0.000001). Two of three mutations were non-silent and are likely cancer drivers: NRAS (Q61R, found in 38/38 samples in COSMIC- Catalogue of Somatic Mutations in Cancer, Sanger Institute), and a cancer related gene PLK1. The third mutation is likely a passenger mutation as it was a synonymous mutation in ADAM18. In Patient B, a subclone containing a novel, recently identified driver in CLL, SF3B1, became the dominant clone that included additional mutations in cancer-related genes, CSMD1 and KIAA1199 (change in allelic fraction from an average of 0.16 (0.12–0.18) to an average of 0.37 (0.35–0.38) (p<0.001)). In another example, Patient C, a TP53 mutation increased in allelic frequency from 0.18 in the initial sample to 0.69 in the relapse sample (p<0.005). Analysis of copy number variation (CNV) by CapSeg (a novel algorithm that examines CNV from WES) revealed this change in allelic frequency to be coupled with a ploidy change in del(17p) from 0.8 to 0.5, consistent with a loss of both alleles. Only one sample demonstrated the appearance of novel mutations with relapse (Patient C), with 19 new mutations (13 non-silent, 3 Silent) of a total of 46 appearing at relapse. All however were subclonal, and thus less likely to have driven tumor relapse. A comparison of the 10 mutations that were selected by chemotherapy to all other mutations demonstrated an enrichment in mutations seen in the COSMIC database (p<0.05), which hints at a higher proportion of cancer drivers in this set. Our ongoing analyses are focused on the association of clinical features with copy number variation and changes in gene expression. In summary, our analysis of serial exomes from seven patients provided important insights into the genetic evolution of CLL under the selective pressure of chemotherapy. We demonstrate a significant change in clonal dynamics in one half of treated patients, which suggests that relapsed disease following treatment is driven by expansion of subclones under the selective pressure of chemotherapy rather than by novel mutagenesis. This observation may have clinical implications, as it suggests that pre-treatment WES may allow not only for the delineation of current genetic abnormalities, but through investigation of subclonal mutations, may also predict genetic evolution in future relapse. Disclosures: No relevant conflicts of interest to declare.

2019 ◽  
Author(s):  
Brian K. Mannakee ◽  
Ryan N. Gutenkunst

AbstractDetecting somatic mutations withins tumors is key to understanding treatment resistance, patient prognosis, and tumor evolution. Mutations at low allelic frequency, those present in only a small portion of tumor cells, are particularly difficult to detect. Many algorithms have been developed to detect such mutations, but none models a key aspect of tumor biology. Namely, every tumor has its own profile of mutation types that it tends to generate. We present BATCAVE (Bayesian Analysis Tools for Context-Aware Variant Evaluation), an algorithm that first learns the individual tumor mutational profile and mutation rate then uses them in a prior for evaluating potential mutations. We also present an R implementation of the algorithm, built on the popular caller MuTect. Using simulations, we show that adding the BATCAVE algorithm to MuTect improves variant detection. It also improves the calibration of posterior probabilities, enabling more principled tradeoff between precision and recall. We also show that BATCAVE performs well on real data. Our implementation is computationally inexpensive and straightforward to incorporate into existing MuTect pipelines. More broadly, the algorithm can be added to other variant callers, and it can be extended to include additional biological features that affect mutation generation.


2021 ◽  
Author(s):  
Nicholas Navin ◽  
Jake Leighton ◽  
Min Hu ◽  
Emi Sei ◽  
Funda Meric-Bernstam

Single cell DNA sequencing (scDNA-seq) methods are powerful tools for profiling mutations in cancer cells, however most genomic regions characterized in single cells are non-informative. To overcome this issue, we developed a Multi-Patient-Targeted (MPT) scDNA-seq sequencing method. MPT involves first performing bulk exome sequencing across a cohort of cancer patients to identify somatic mutations, which are then pooled together to develop a single custom targeted panel for high-throughput scDNA-seq using a microfluidics platform. We applied MPT to profile 330 mutations across 23,500 cells from 5 TNBC patients, which showed that 3 tumors were monoclonal and 2 tumors were polyclonal. From this data, we reconstructed mutational lineages and identified early mutational and copy number events, including early TP53 mutations that occurred in all five patients. Collectively, our data suggests that MPT can overcome technical obstacles for studying tumor evolution using scDNA-seq by profiling information-rich mutation sites.


Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 625-625 ◽  
Author(s):  
Jonathan B. Reichel ◽  
Kenneth Eng ◽  
Olivier Elemento ◽  
Ethel Cesarman ◽  
Mikhail Roshal

Abstract Introduction Most genomic studies of classical Hodgkin lymphoma (CHL) have been confined to cell lines due to the difficulty of isolating sparsely distributed Hodgkin and Reed-Sternberg (HRS) cells from reactive background tissue. One approach to evaluate primary cases has been to microdissect HRS cells from fresh-frozen tissue biopsies, which has been used for gene expression profiling and array comparative genomic hybridization to assess copy number alterations (Hartmann et al., Haematologica 93, 2006; Brune et al, J Exp Med 205, 2008; Steidl et al., Blood 120, 2012; Steidl et al., Blood 116, 2010; Tiacci et al., Blood 120, 2012). However, laser capture microdissection is technically challenging, does not provide a pure tumor cell population, and yields very small amounts of nucleic acids that may not be adequate for deep sequencing. Typically, generating whole-exome sequence data from fewer than 104 cells, including single cells, requires whole genome amplification (WGA), and is of a quality suitable to detect large scale copy-number alterations, but not nucleotide level information to identify point mutations. Methods We used a flow cytometric cell isolation method, which has enabled rapid isolation of thousands of viable HRS cells from primary CHL tumors (Fromm, et al., Am J Clin Pathol 126, 2006). Here we developed a new ultra-low-input DNA exome sequencing protocol which we combined with flow cytometry using CD64, CD95, CD30, CD5, CD20, CD15, CD40, and CD45, to produce what is to our knowledge the first full exome deep sequencing study of primary cases of Hodgkin lymphoma. We obtained a sequence depth of over 10X with >90% coverage of the target exome in HRS, as well as tumor-infiltrating T cells (also sorted and used as somatic control), in ten primary cases of CHL and performed mutation, copy number variation, and loss-of-heterozygosity (LOH) analysis. Results We have identified 61 recurrent mutations, and 12 genes had somatic mutations in over 30% of cases. Non-synonymous or splice site mutations were seen in genes involved in antigen presentation (B2M), chromosome integrity (BCL7A), NF-kB activation (A20/TNFAIP3) and protein ubiquitination (HECW2 and UBE2A). We also obtained high-resolution copy number variation data indicating specific regions of gains and losses and intragenic chromosomal breakpoints. The most common genetic alterations from the combined analyses were in A20, present either as mutations or LOH, and alterations in b2-microglobulin (B2M), which were found in 80% of the cases sequenced. Alterations of B2M were inactivating and bi-allelic, leading to a lack of expression of MHC class I protein complex on the cell surface. The alterations include start codon mutations, exon-one splice donor site mutations, out of frame first-exon deletions and gene loss through chromosome-level deletion (LOH). Where possible, mutations were confirmed at the RNA level and resulted in lack of B2M protein expression documented by immunohistochemistry in an expanded cohort, where a total of 20 of 27 cases (74%) lacked B2M expression. Ectopic expression of B2M in a CHL cell line induced MHC class 1 expression, indicating that this genetic alteration is singly responsible for this defect in antigen presentation. In addition, B2M inactivation was highly prevalent in nodular sclerosis CHL, but was not found in any of the five cases of mixed cellularity CHL examined, indicating that these two types of CHL may belong to two different genetic categories. Conclusions We report the first exome deep sequencing of purified HRS cells from CHL tumor specimens, and reveal consistent alterations in important biological processes. The methodology developed allows exome sequencing from very low DNA input (10 ng), which has broader applications such as using FNA specimens from multiple tumor types. The genomic landscape of CHL revealed commonalities and differences with other lymphoma subtypes, like the consistent presence of A20 alterations that can lead to NF-kB activation. Inactivating mutations in B2M explain the complete lack of MHC class I expression, which likely affect the microenvironment. Since B2M expression is normal in all the cases evaluated that were histologically classified as mixed cellularity CHL, it is possible that this genetic alteration and resulting lack of B2M protein expression is a more accurate biomarker of CHL subtype than histological distinction. Disclosures: No relevant conflicts of interest to declare.


2019 ◽  
Author(s):  
Noam Auslander ◽  
Yuri I. Wolf ◽  
Eugene V. Koonin

AbstractCancer arises through the accumulation of somatic mutations over time. Understanding the sequence of mutation occurrence during cancer progression can assist early and accurate diagnosis and improve clinical decision-making. Here we employ Long Short-Term Memory networks (LSTMs), a class of recurrent neural network, to learn the evolution of a tumor through an ordered sequence of mutations. We demonstrate the capacity of LSTMs to learn complex dynamics of the mutational time series governing tumor progression, allowing accurate prediction of the mutational burden and the occurrence of mutations in the sequence. Using the probabilities learned by the LSTM, we simulate mutational data and show that the simulation results are statistically indistinguishable from the empirical data. We identify passenger mutations that are significantly associated with established cancer drivers in the sequence and demonstrate that the genes carrying these mutations are substantially enriched in interactions with the corresponding driver genes. Breaking the network into modules consisting of driver genes and their interactors, we show that these interactions are associated with poor patient prognosis, thus likely conferring growth advantage for tumor progression. Thus, application of LSTM provides for prediction of numerous additional conditional drivers and to reveal hitherto unknown aspects of cancer evolution.SignificanceCancer is caused by the effects of somatic mutations known as drivers. Although a number of major cancer drivers have been identified, it is suspected that many more comparatively rare and conditional drivers exist, and the interactions between different cancer-associated mutations that might be relevant for tumor progression are not well understood. We applied an advanced neural network approach to learn the sequence of mutations and the mutational burden in colon and lung cancers, and to identify mutations that are associated with individual drivers. A significant ordering of driver mutations is demonstrated, and numerous, previously undetected conditional drivers are identified. These findings broaden the existing understanding of the mechanisms of tumor progression and have implications for therapeutic strategies.


2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Brian K Mannakee ◽  
Ryan N Gutenkunst

Abstract Detecting somatic mutations withins tumors is key to understanding treatment resistance, patient prognosis and tumor evolution. Mutations at low allelic frequency, those present in only a small portion of tumor cells, are particularly difficult to detect. Many algorithms have been developed to detect such mutations, but none models a key aspect of tumor biology. Namely, every tumor has its own profile of mutation types that it tends to generate. We present BATCAVE (Bayesian Analysis Tools for Context-Aware Variant Evaluation), an algorithm that first learns the individual tumor mutational profile and mutation rate then uses them in a prior for evaluating potential mutations. We also present an R implementation of the algorithm, built on the popular caller MuTect. Using simulations, we show that adding the BATCAVE algorithm to MuTect improves variant detection. It also improves the calibration of posterior probabilities, enabling more principled tradeoff between precision and recall. We also show that BATCAVE performs well on real data. Our implementation is computationally inexpensive and straightforward to incorporate into existing MuTect pipelines. More broadly, the algorithm can be added to other variant callers, and it can be extended to include additional biological features that affect mutation generation.


Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. SCI-53-SCI-53
Author(s):  
Jonathan J Keats

Multiple myeloma is a pathological definition for a series of distinct genetic entities with similar phenotypic and clinical characteristics. Multiple studies have now identified distinct subtypes of the disease, which are associated with different clinical outcomes. The genetic complexity underlying these different subtypes is very diverse. Some subtypes like those characterized by immunoglobulin translocations targeting cyclin D1 are associated with very minimal changes or no copy number changes. Conversely, subtypes defined by translocations targeting WHSC1/MMSET or the MAF and MAFB transcription factors often have highly complex and diverse copy number changes. With the substantial advances in DNA sequencing technology we now know there is a diverse array of somatic mutations in multiple myeloma tumors. Through the integration of copy number changes and somatic mutations multiple groups have now shown the existence of multiple co-existing subclones within individual tumors. Additional studies following patients through their individual disease courses have shown these subclones can ebb and flow with time through multiple rounds of therapeutic selection. This session will highlight our current understanding of how the interplay between tumor evolution and clonal heterogeneity should influence our treatment decisions, particularly when applying a personalized medicine approach. Disclosures No relevant conflicts of interest to declare.


2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e12554-e12554
Author(s):  
Anishka D'souza ◽  
Stephanie Shishedo ◽  
Darcy V. Spicer ◽  
Lisa Welter ◽  
Yuanfei Jiang ◽  
...  

e12554 Background: ERBB2 mutations in the absence of gene amplification are rare, with an incidence of 2-4%. Neratinib is a HER2/EGFR tyrosine kinase inhibitor being evaluated for use in ERBB2 mutated breast cancer. Neratinib has been found to have clinical activity on heavily pre-treated ERBB2 mutant breast cancer patients. We are evaluating the response and genomic profiles of 3 postmenopausal patients with metastatic ERBB2 mutant/non-amplified breast cancer receiving neratinib and fulvestrant NCT01953926, NCT01670877. Methods: Samples were collected at different points during treatment and CTCs were identified. Other representative cells were tracked but not classified as CTCs. CD45-CK+ cells with cytoplasmic and/or nuclear apoptosis were defined as CTC-Apoptotic. CD45- cells expressing little to no CK but otherwise meeting morphological criteria for CTCs are CTC-LowCK. CD45-CK+ cells with small nuclear size are CTC-SmallCK. Single CTCs will be analyzed using whole genome copy number variation to determine chromosomal alterations. Results: The patients had an average of 4.7 lines of therapy for metastatic disease before neratinib. Two had stable disease and one had progression. The patient who progressed had a rise in the number of CTCs from 37 to 52 cells/ml and drop in apoptotic cells from 5 to 0 cells/ml. Conclusions: The high CTC count of the patient who progressed may suggest more aggressive disease. The drop in the apoptotic cell count may correlate with a failure to respond to therapy. Samples have been sent for copy number variation profiling. The goal is to identify any genomic amplifications or deletions associated with clinical response and progression after targeted therapy. We hope to demonstrate the timeframe of tumor evolution in response to therapy and provide a framework for the use of fluid biopsies to monitor disease progression. [Table: see text]


2015 ◽  
Vol 76 (S 01) ◽  
Author(s):  
Georgios Zenonos ◽  
Peter Howard ◽  
Maureen Lyons-Weiler ◽  
Wang Eric ◽  
William LaFambroise ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document