Deep sequencing reveals clonal evolution patterns and mutation events associated with relapse in B-cell lymphomas

Background

Molecular mechanisms associated with frequent relapse of diffuse large B-cell lymphoma (DLBCL) are poorly defined. It is especially unclear how primary tumor clonal heterogeneity contributes to relapse. Here, we explore unique features of B-cell lymphomas - VDJ recombination and somatic hypermutation - to address this question.

Results

We performed high-throughput sequencing of rearranged VDJ junctions in 14 pairs of matched diagnosis-relapse tumors, among which 7 pairs were further characterized by exome sequencing. We identify two distinctive modes of clonal evolution of DLBCL relapse: an early-divergent mode in which clonally related diagnosis and relapse tumors diverged early and developed in parallel; and a late-divergent mode in which relapse tumors developed directly from diagnosis tumors with minor divergence. By examining mutation patterns in the context of phylogenetic information provided by VDJ junctions, we identified mutations in epigenetic modifiers such as KMT2D as potential early driving events in lymphomagenesis and immune escape alterations as relapse-associated events.

Conclusions

Altogether, our study for the first time provides important evidence that DLBCL relapse may result from multiple, distinct tumor evolutionary mechanisms, providing rationale for therapies for each mechanism. Moreover, this study highlights the urgent need to understand the driving roles of epigenetic modifier mutations in lymphomagenesis, and immune surveillance factor genetic lesions in relapse.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0432-0) contains supplementary material, which is available to authorized users.     

Natural and chemotherapy-induced clonal evolution of tumors

Evolution and natural selection of tumoral clones in the process of transformation and the following carcinogenesis can be called natural clonal evolution. Its main driving factors are internal: genetic instability initiated by driver mutations and microenvironment, which enables selective pressure while forming the environment for cell transformation and their survival. We present our overview of contemporary research dealing with mechanisms of carcinogenesis in different localizations from precancerous pathologies to metastasis and relapse. It shows that natural clonal evolution establishes intratumoral heterogeneity and enables tumor progression. Tumors of monoclonal origin are of low-level intratumoral heterogeneity in the initial stages, and this increases with the size of the tumor. Tumors of polyclonal origin are of extremely high-level intratumoral heterogeneity in the initial stages and become more homogeneous when larger due to clonal expansion. In cases of chemotherapy-induced clonal evolution of a tumor, chemotherapy becomes the leading factor in treatment. The latest research shows that the impact of chemotherapy can radically increase the speed of clonal evolution and lead to new malignant and resistant clones that cause tumor metastasis. Another option of chemotherapy-induced clonal evolution is formation of a new dominant clone from a clone that was minor in the initial tumor and obtained free space due to elimination of sensitive clones by chemotherapy. As a result, in ~20% of cases, chemotherapy can stimulate metastasis and relapse of tumors due to clonal evolution. The conclusion of the overview formulates approaches to tumor treatment based on clonal evolution: in particular, precision therapy, prediction of metastasis stimulation in patients treated with chemotherapy, methods of genetic evaluation of chemotherapy efficiency and clonal-oriented treatment, and approaches to manipulating the clonal evolution of tumors are presented.     

SciClone: Inferring Clonal Architecture and Tracking the Spatial and Temporal Patterns of Tumor Evolution

The sensitivity of massively-parallel sequencing has confirmed that most cancers are oligoclonal, with subpopulations of neoplastic cells harboring distinct mutations. A fine resolution view of this clonal architecture provides insight into tumor heterogeneity, evolution, and treatment response, all of which may have clinical implications. Single tumor analysis already contributes to understanding these phenomena. However, cryptic subclones are frequently revealed by additional patient samples (e.g., collected at relapse or following treatment), indicating that accurately characterizing a tumor requires analyzing multiple samples from the same patient. To address this need, we present SciClone, a computational method that identifies the number and genetic composition of subclones by analyzing the variant allele frequencies of somatic mutations. We use it to detect subclones in acute myeloid leukemia and breast cancer samples that, though present at disease onset, are not evident from a single primary tumor sample. By doing so, we can track tumor evolution and identify the spatial origins of cells resisting therapy.     

High Throughput Sequencing Analysis of the Immunoglobulin Heavy Chain Gene from Flow-Sorted B Cell Sub-Populations Define the Dynamics of Follicular Lymphoma Clonal Evolution

Understanding the dynamics of evolution of Follicular Lymphoma (FL) clones during disease progression is important for monitoring and targeting this tumor effectively. Genetic profiling of serial FL biopsies and examples of FL transmission following bone marrow transplant suggest that this disease may evolve by divergent evolution from a common ancestor cell. However where this ancestor cell resides and how it evolves is still unclear. The analysis of the pattern of somatic hypermutation of the immunoglobulin gene (Ig) is traditionally used for tracking the physiological clonal evolution of B cells within the germinal center and allows to discriminate those cells that have just entered the germinal center and display features of ancestor cells from those B cells that keep re-circulating across different lymphoid organs. Here we investigated the pattern of somatic hypermutation of the heavy chain of the immunoglobulin gene (IgH-VH) in 4 flow-sorted B cells subpopulations belonging to different stages of differentiation, from sequential lymph node biopsies of cases displaying diverse patterns of evolution, using the GS-FLX Titanium sequencing platform. We observed an unexpectedly high level of clonality, with hundreds of distinct tumor subclones in the different subpopulations from the same sample, the majority detected at a frequency <10⁻². By using a lineage trees analysis we observed in all our FL and t-FL cases that the oligoclonal FL population was trapped in a narrow intermediate stage of maturation that maintains the capacity to undergo SHM, but was unable to further differentiate. The presence of such a complex architecture highlights challenges currently encountered in finding a cure for this disease.     

Characterization of human IgM and IgG repertoires in individuals with chronic HIV-1 infection

Advancements in high-throughput sequencing (HTS) of antibody repertoires (Ig-Seq) have unprecedentedly improved our ability to characterize the antibody repertoires on a large scale. However, currently, only a few studies explored the influence of chronic HIV-1 infection on human antibody repertoires and many of them reached contradictory conclusions, possibly limited by inadequate sequencing depth and throughput. To better understand how HIV-1 infection would impact humoral immune system, in this study, we systematically analyzed the differences between the IgM (HIV-IgM) and IgG (HIV-IgG) heavy chain repertoires of HIV-1 infected patients, as well as between antibody repertoires of HIV-1 patients and healthy donors (HH). Notably, the public unique clones accounted for only a negligible proportion between the HIV-IgM and HIV-IgG repertoires libraries, and the diversity of unique clones in HIV-IgG remarkably reduced. In aspect of somatic mutation rates of CDR1 and CDR2, the HIV-IgG repertoire was higher than HIV-IgM. Besides, the average length of CDR3 region in HIV-IgM was significant longer than that in the HH repertoire, presumably caused by the great number of novel VDJ rearrangement patterns, especially a massive use of IGHJ6. Moreover, some of the B cell clonotypes had numerous clones, and somatic variants were detected within the clonotype lineage in HIV-IgG, indicating HIV-1 neutralizing activities. The in-depth characterization of HIV-IgG and HIV-IgM repertoires enriches our knowledge in the profound effect of HIV-1 infection on human antibody repertoires and may have practical value for the discovery of therapeutic antibodies.     

Inferring Clonal Composition from Multiple Sections of a Breast Cancer

Cancers arise from successive rounds of mutation and selection, generating clonal populations that vary in size, mutational content and drug responsiveness. Ascertaining the clonal composition of a tumor is therefore important both for prognosis and therapy. Mutation counts and frequencies resulting from next-generation sequencing (NGS) potentially reflect a tumor''s clonal composition; however, deconvolving NGS data to infer a tumor''s clonal structure presents a major challenge. We propose a generative model for NGS data derived from multiple subsections of a single tumor, and we describe an expectation-maximization procedure for estimating the clonal genotypes and relative frequencies using this model. We demonstrate, via simulation, the validity of the approach, and then use our algorithm to assess the clonal composition of a primary breast cancer and associated metastatic lymph node. After dividing the tumor into subsections, we perform exome sequencing for each subsection to assess mutational content, followed by deep sequencing to precisely count normal and variant alleles within each subsection. By quantifying the frequencies of 17 somatic variants, we demonstrate that our algorithm predicts clonal relationships that are both phylogenetically and spatially plausible. Applying this method to larger numbers of tumors should cast light on the clonal evolution of cancers in space and time.     

Intra-tumor heterogeneity of MLH1 promoter methylation revealed by deep single molecule bisulfite sequencing

A single tumor may contain cells with different somatic mutations. By characterizing this genetic heterogeneity within tumors, advances have been made in the prognosis, treatment and understanding of tumorigenesis. In contrast, the extent of epigenetic intra-tumor heterogeneity and how it influences tumor biology is under-explored. We have characterized epigenetic heterogeneity within individual tumors using next-generation sequencing. We used deep single molecule bisulfite sequencing and sample-specific DNA barcodes to determine the spectrum of MLH1 promoter methylation across an average of 1000 molecules in each of 33 individual samples in parallel, including endometrial cancer, matched blood and normal endometrium. This first glimpse, deep into each tumor, revealed unexpectedly heterogeneous patterns of methylation at the MLH1 promoter within a subset of endometrial tumors. This high-resolution analysis allowed us to measure the clonality of methylation in individual tumors and gain insight into the accumulation of aberrant promoter methylation on both alleles during tumorigenesis.     

Intratumor heterogeneity and clonal evolution revealed in castration-resistant prostate cancer by longitudinal genomic analysis

Intratumor heterogeneity is a key driver for local relapse and treatment failure. Thus, using multifocal prostate cancer as a model to investigate tumor inter-clonal relationships and tumor evolution could aid in our understanding of drug resistance. Previous studies discovered genomic alterations by comparing hormone-sensitive prostate cancer (HSPC) with castration-resistant prostate cancer (CRPC) in large cohorts. However, most studies did not sequentially sample tumors from the same patient. In our study, we performed whole-exome sequencing (WES) on 14 specimens from five locally relapsed patients before and after androgen-deprivation therapy. We described the landscape of genomic alterations before and after treatment and identified critical driver events that could have contributed to the evolution of CRPC. In addition to confirming known cancer genes such as TP53 and CDK12, we also identified new candidate genes that may play a role in the progression of prostate cancer, including MYO15A, CHD6 and LZTR1. At copy number alteration (CNA) level, gain of 8q24.13-8q24.3 was observed in 60% of patients and was the most commonly altered locus in both HSPC and CRPC tumors. Finally, utilizing phylogenetic reconstruction, we explored the clonal progression pattern from HSPC to CRPC in each patient. Our findings highlight the complex and heterogeneous mechanisms underlying the development of drug resistance, and underscore the potential value of monitoring tumor clonal architectures during disease progression in a clinical setting.     

Delineating the longitudinal tumor evolution using organoid models

Cancer is an evolutionary process fueled by genetic or epigenetic alterations in the genome. Understanding the evolutionary dynamics that are operative at different stages of tumor progression might inform effective strategies in early detection, diagnosis, and treatment of cancer. However, our understanding on the dynamics of tumor evolution through time is very limited since it is usually impossible to sample patient tumors repeatedly. The recent advances in in vitro 3D organoid culture technologies have opened new avenues for the development of more realistic human cancer models that mimic many in vivo biological characteristics in human tumors. Here, we review recent progresses and challenges in cancer genomic evolution studies and advantages of using tumor organoids to study cancer evolution. We propose to establish an experimental evolution model based on continuous passages of patient-derived organoids and longitudinal sampling to study clonal dynamics and evolutionary patterns over time. Development and integration of population genetic theories and computational models into time-course genomic data in tumor organoids will help to pinpoint the key cellular mechanisms underlying cancer evolutionary dynamics, thus providing novel insights on therapeutic strategies for highly dynamic and heterogeneous tumors.     

Genetic evolution of nevus of Ota reveals clonal heterogeneity acquiring BAP1 and TP53 mutations

Melanoma presents molecular alterations based on its anatomical location and exposure to environmental factors. Due to its intrinsic genetic heterogeneity, a simple snapshot of a tumor's genetic alterations does not reflect the tumor clonal complexity or specific gene–gene cooperation. Here, we studied the genetic alterations and clonal evolution of a unique patient with a Nevus of Ota that developed into a recurring uveal‐like dermal melanoma. The Nevus of Ota and ulterior lesions contained GNAQ mutations were c‐KIT positive, and tumors showed an increased RAS pathway activity during progression. Whole‐exome sequencing of these lesions revealed the acquisition of BAP1 and TP53 mutations during tumor evolution, thereby unmasking clonal heterogeneity and allowing the identification of cooperating genes within the same tumor. Our results highlight the importance of studying tumor genetic evolution to identify cooperating mechanisms and delineate effective therapies.     

Whole Exome Sequencing of Rapid Autopsy Tumors and Xenograft Models Reveals Possible Driver Mutations Underlying Tumor Progression

Pancreatic Ductal Adenocarcinoma (PDAC) is a highly lethal malignancy due to its propensity to invade and rapidly metastasize and remains very difficult to manage clinically. One major hindrance towards a better understanding of PDAC is the lack of molecular data sets and models representative of end stage disease. Moreover, it remains unclear how molecularly similar patient-derived xenograft (PDX) models are to the primary tumor from which they were derived. To identify potential molecular drivers in metastatic pancreatic cancer progression, we obtained matched primary tumor, metastases and normal (peripheral blood) samples under a rapid autopsy program and performed whole exome sequencing (WES) on tumor as well as normal samples. PDX models were also generated, sequenced and compared to tumors. Across the matched data sets generated for three patients, there were on average approximately 160 single-nucleotide mutations in each sample. The majority of mutations in each patient were shared among the primary and metastatic samples and, importantly, were largely retained in the xenograft models. Based on the mutation prevalence in the primary and metastatic sites, we proposed possible clonal evolution patterns marked by functional mutations affecting cancer genes such as KRAS, TP53 and SMAD4 that may play an important role in tumor initiation, progression and metastasis. These results add to our understanding of pancreatic tumor biology, and demonstrate that PDX models derived from advanced or end-stage likely closely approximate the genetics of the disease in the clinic and thus represent a biologically and clinically relevant pre-clinical platform that may enable the development of effective targeted therapies for PDAC.     

Identification of Variants in Primary and Recurrent Glioblastoma Using a Cancer-Specific Gene Panel and Whole Exome Sequencing

Glioblastoma (GBM) is an aggressive, malignant brain tumor typically resulting in death of the patient within one year following diagnosis; and those who survive beyond this point usually present with tumor recurrence within two years (5-year survival is 5%). The genetic heterogeneity of GBM has made the molecular characterization of these tumors an area of great interest and has led to identification of molecular subtypes in GBM. The availability of sequencing platforms that are both fast and economical can further the adoption of tumor sequencing in the clinical environment, potentially leading to identification of clinically actionable genetic targets. In this pilot study, comprised of triplet samples of normal blood, primary tumor, and recurrent tumor samples from three patients; we compared the ability of Illumina whole exome sequencing (ExomeSeq) and the Ion AmpliSeq Comprehensive Cancer Panel (CCP) to identify somatic variants in patient-paired primary and recurrent tumor samples. Thirteen genes were found to harbor variants, the majority of which were exclusive to the ExomeSeq data. Surprisingly, only two variants were identified by both platforms and they were located within the PTCH1 and NF1 genes. Although preliminary in nature, this work highlights major differences in variant identification in data generated from the two platforms. Additional studies with larger samples sizes are needed to further explore the differences between these technologies and to enhance our understanding of the clinical utility of panel based platforms in genomic profiling of brain tumors.     

Nucleotide Insertions and Deletions Complement Point Mutations to Massively Expand the Diversity Created by Somatic Hypermutation of Antibodies

During somatic hypermutation (SHM), deamination of cytidine by activation-induced cytidine deaminase and subsequent DNA repair generates mutations within immunoglobulin V-regions. Nucleotide insertions and deletions (indels) have recently been shown to be critical for the evolution of antibody binding. Affinity maturation of 53 antibodies using in vitro SHM in a non-B cell context was compared with mutation patterns observed for SHM in vivo. The origin and frequency of indels seen during in vitro maturation were similar to that in vivo. Indels are localized to CDRs, and secondary mutations within insertions further optimize antigen binding. Structural determination of an antibody matured in vitro and comparison with human-derived antibodies containing insertions reveal conserved patterns of antibody maturation. These findings indicate that activation-induced cytidine deaminase acting on V-region sequences is sufficient to initiate authentic formation of indels in vitro and in vivo and that point mutations, indel formation, and clonal selection form a robust tripartite system for antibody evolution.     

scDPN for High-throughput Single-cell CNV Detection to Uncover Clonal Evolution During HCC Recurrence

Single-cell genomics provides substantial resources for dissecting cellular heterogeneity and cancer evolution. Unfortunately, classical DNA amplification-based methods have low throughput and introduce coverage bias during sample preamplification. We developed a single-cell DNA library preparation method without preamplification in nanolitre scale (scDPN) to address these issues. The method achieved a throughput of up to 1800 cells per run for copy number variation (CNV) detection. Also, our approach demonstrated a lower level of amplification bias and noise than the multiple displacement amplification (MDA) method and showed high sensitivity and accuracy for cell line and tumor tissue evaluation. We used this approach to profile the tumor clones in paired primary and relapsed tumor samples of hepato-cellular carcinoma (HCC). We identified three clonal subpopulations with a multitude of aneuploid alterations across the genome. Furthermore, we observed that a minor clone of the primary tumor containing additional alterations in chro-mosomes 1q, 10q, and 14q developed into the dominant clone in the recurrent tumor, indicating clonal selection during recurrence in HCC. Overall, this approach provides a comprehensive and scalable solution to understand genome hetero-geneity and evolution.     

Mutational Profiling Can Establish Clonal or Independent Origin in Synchronous Bilateral Breast and Other Tumors

Background

Synchronous tumors can be independent primary tumors or a primary-metastatic (clonal) pair, which may have clinical implications. Mutational profiling of tumor DNA is increasingly common in the clinic. We investigated whether mutational profiling can distinguish independent from clonal tumors in breast and other cancers, using a carefully defined test based on the Clonal Likelihood Score (CLS = 100 x # shared high confidence (HC) mutations/ # total HC mutations).

Methods

Statistical properties of a formal test using the CLS were investigated. A high CLS is evidence in favor of clonality; the test is implemented as a one-sided binomial test of proportions. Test parameters were empirically determined using 16,422 independent breast tumor pairs and 15 primary-metastatic tumor pairs from 10 cancer types using The Cancer Genome Atlas.

Results

We validated performance of the test with its established parameters, using five published data sets comprising 15,758 known independent tumor pairs (maximum CLS = 4.1%, minimum p-value = 0.48) and 283 known tumor clonal pairs (minimum CLS 13%, maximum p-value <0.01), across renal cell, testicular, and colorectal cancer. The CLS test correctly classified all validation samples but one, which it appears may have been incorrectly classified in the published data. As proof-of-concept we then applied the CLS test to two new cases of invasive synchronous bilateral breast cancer at our institution, each with one hormone receptor positive (ER+/PR+/HER2-) lobular and one triple negative ductal carcinoma. High confidence mutations were identified by exome sequencing and results were validated using deep targeted sequencing. The first tumor pair had CLS of 81% (p-value < 10–15), supporting clonality. In the second pair, no common mutations of 184 variants were validated (p-value >0.99), supporting independence. A plausible molecular mechanism for the shift from hormone receptor positive to triple negative was identified in the clonal pair.

Conclusion

We have developed the statistical properties of a carefully defined Clonal Likelihood Score test from mutational profiling of tumor DNA. Under identified conditions, the test appears to reliably distinguish between synchronous tumors of clonal and of independent origin in several cancer types. This approach may have scientific and clinical utility.     

Mechanism of relapse in pediatric acute lymphoblastic leukemia

Relapse following initial chemotherapy remains a barrier to survival in approximately 20% of children suffering from acute lymphoblastic leukemia (ALL). Recently, to investigate the mechanism of relapse, we analysed clonal populations in 27 pairs of matched diagnosis and relapse ALL samples using PCR-based detection of multiple antigen receptor gene rearrangements. These clonal markers revealed the emergence of apparently new populations at relapse in 13 patients. In those cases where the new ‘relapse clone’ could be detected in the diagnosis population, there was a close correlation between length of first remission and quantity of the relapse clone in the diagnosis sample. A shorter length of time to first relapse correlated with a higher quantity of the relapsing clone at diagnosis. This observation, together with demonstrated differential chemosensitivity between sub-clones at diagnosis, indicates that relapse in ALL patients may commonly involve selection of a minor intrinsically resistant sub-clone that is undetectable by routine PCR-based methods. From a clinical perspective, relapse prediction may be improved with strategies to detect minor potentially resistant sub-clones early during treatment, hence allowing intensification of therapy. Together with the availability of relevant in vivo experimental models and powerful technology for detailed analysis of patient specimens, this new information will help shape future experimentation towards targeted therapy for high-risk ALL.     

Towards accurate characterization of clonal heterogeneity based on structural variation

Background

Recent advances in deep digital sequencing have unveiled an unprecedented degree of clonal heterogeneity within a single tumor DNA sample. Resolving such heterogeneity depends on accurate estimation of fractions of alleles that harbor somatic mutations. Unlike substitutions or small indels, structural variants such as deletions, duplications, inversions and translocations involve segments of DNAs and are potentially more accurate for allele fraction estimations. However, no systematic method exists that can support such analysis.

Results

In this paper, we present a novel maximum-likelihood method that estimates allele fractions of structural variants integratively from various forms of alignment signals. We develop a tool, BreakDown, to estimate the allele fractions of most structural variants including medium size (from 1 kilobase to 1 megabase) deletions and duplications, and balanced inversions and translocations.

Conclusions

Evaluation based on both simulated and real data indicates that our method systematically enables structural variants for clonal heterogeneity analysis and can greatly enhance the characterization of genomically instable tumors.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-299) contains supplementary material, which is available to authorized users.