Bulk Tumor Sequencing Research Articles

HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data.

Bulk DNA sequencing of multiple samples from the same tumor is becoming common, yet most methods to infer copy-number aberrations (CNAs) from this data analyze individual samples independently. We introduce HATCHet2, an algorithm to identify haplotype- and clone-specific CNAs simultaneously from multiple bulk samples. HATCHet2 extends the earlier HATCHet method by improving identification of focal CNAs and introducing a novel statistic, the minor haplotype B-allele frequency (mhBAF), that enables identification of mirrored-subclonal CNAs. We demonstrate HATCHet2's improved accuracy using simulations and a single-cell sequencing dataset. HATCHet2 analysis of 10 prostate cancer patients reveals previously unreported mirrored-subclonal CNAs affecting cancer genes.

Abstract 3479: Evaluation of subclonal deconvolution pipelines using reference cell-lines and patient plasma samples

Abstract Background: Intratumor heterogeneity plays a pivotal role in treatment resistance, emphasizing the need for a comprehensive understanding of tumor clonal architecture. While various methods exist for extracting this information from bulk tumor sequencing data, few studies have evaluated the entire workflow, from sample processing to subclone calls. Methods: To address this gap, we utilized cell lines with known copy number changes and somatic mutations, constructing 12 ground truth sample sets with varying cellular proportions and tumor purities. These cell lines underwent NGS pipeline using PredicineWES+ with increased sequencing depth across ~600 cancer-related genes. Subclonal analysis workflows, including FACETS, PyClone, and ECLIPSE, were implemented to to assess variant assignment and cellular fractions. The subclone analysis was extended to 91 plasma samples from cancer patients. Results: Our findings indicate that subclonal decomposition effectively segregates mutations from distinct cell lines. Notably, analyzing multiple samples with different proportions of the same subclones outperformed deconvolution from single mixture samples. In plasma samples from cancer patients, nearly one-third exhibited subclonal clinically actionable variants. Conclusions: The established subclonal deconvolution pipeline enhances biomarker analysis from assays utilizing WES with boosted sequencing depth for selected genes. Citation Format: Xiaoxi Dong, Yong Huang, Tom Zhang, Shidong Jia, Pan Du. Evaluation of subclonal deconvolution pipelines using reference cell-lines and patient plasma samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3479.

Abstract 1200: A novel algorithm for deconvolving cancer allele-specific clone copy number and copy number evolution

Abstract Both single nucleotide variants (SNVs) and somatic copy number alterations (SCNAs) accumulate in cancer cells during tumor development, fuelling clonal evolution. However, accurate inference of their coevolution from bulk DNA sequencing is challenging. We present ALPACA (ALlele-specific Phylogenetic Analysis of clone Copy number Alterations), a novel algorithm to allow the practical inference of SNV and SCNA coevolution. ALPACA is framed as an optimisation problem, that takes as input bulk tumor sample copy number mixtures and the tumor phylogenetic tree constructed from SNVs, and deconvolves the optimal, allele-specific integer copy number profiles for every distinct clone present in the tumor. To circumvent the challenge of joint SNV/SCNA inference, we postulate that in high mutation burden tumors all relevant SCNA clones are identifiable by their unique SNVs, hence ALPACA leverages phylogenetic trees constructed from bulk tumor sequencing data using SNVs. Secondly, to restrict the clone-specific copy number search space, ALPACA leverages constraints imposed by multisample sequencing, specifically in cases where clones are present across multiple samples or clones from different samples are phylogenetically related. Finally, ALPACA relies on parsimony and biologically informed constraints to further guide the deconvolution process. We demonstrate that ALPACA infers the copy number evolution of complex simulated tumors with higher accuracy than current state-of-the-art methods. We apply ALPACA to the large, multisample non-small cell lung cancer (NSCLC) cohort from the recent longitudinal, prospective TRACERx421 study, and show that ALPACA uncovers loss of heterozygosity and amplification events in minor subclones that were previously missed using standard approaches. ALPACA's assignment of SCNA driver events to branches of the phylogenetic tree reveals the evolutionary ordering of SNVs and SCNAs during NSCLC tumor evolution, such as late focal amplifications affecting the TERT gene locus occurring in subclones which expand and dominate a tumor sample. ALPACA enables an in-depth analysis of the coevolution of mutations and SCNAs in the tumor’s evolutionary history, revealing distinct patterns of copy number evolution. Notably, ALPACA identifies common patterns of copy number changes across the genome characterizing metastatic seeding clones, revealing that they harbor an increased number of SCNAs compared to clones that do not metastasize. Additionally, ALPACA uncovers subclonal enrichment for CCND1 amplification in primary tumor subclones that seed metastasis, and an overall increase of SCNA events occurring in both tumors with polyclonal seeding and extrathoracic metastases. Clone-level results obtained with ALPACA can offer new clinical insights and enable new types of analysis, e.g. copy number signature analysis including temporal order of SCNA acquisition. Citation Format: Piotr Pawlik, Kristiana K. Grigoriadis, Abigail Bunkum, Simone Zaccaria, Nicholas McGranahan, Charles Swanton. A novel algorithm for deconvolving cancer allele-specific clone copy number and copy number evolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1200.

Computational validation of clonal and subclonal copy number alterations from bulk tumor sequencing using CNAqc

Copy number alterations (CNAs) are among the most important genetic events in cancer, but their detection from sequencing data is challenging because of unknown sample purity, tumor ploidy, and general intra-tumor heterogeneity. Here, we present CNAqc, an evolution-inspired method to perform the computational validation of clonal and subclonal CNAs detected from bulk DNA sequencing. CNAqc is validated using single-cell data and simulations, is applied to over 4000 TCGA and PCAWG samples, and is incorporated into the validation process for the clinically accredited bioinformatics pipeline at Genomics England. CNAqc is designed to support automated quality control procedures for tumor somatic data validation.

Stromal cell-expressed malignant gene patterns contribute to the progression of squamous cell carcinomas across different sites.

Squamous cell carcinomas (SCCs) across different anatomical locations possess common molecular features. Recent studies showed that stromal cells may contribute to tumor progression and metastasis of SCCs. Limited by current sequencing technology and analysis methods, it has been difficult to combine stroma expression profiles with a large number of clinical information. With the help of transfer learning on the cell line, single-cell, and bulk tumor sequencing data, we identified and validated 2 malignant gene patterns (V1 and V5) expressed by stromal cells of SCCs from head and neck (HNSCC), lung (LUSC), cervix (CESC), esophagus, and breast. Pattern V5 reflected a novel malignant feature that explained the mixed signals of HNSCC molecular subtypes. Higher expression of pattern V5 was related to shorter PFI with gender and cancer-type specificity. The other stromal gene pattern V1 was associated with poor PFI in patients after surgery in all the three squamous cancer types (HNSCC p = 0.0055, LUSC p = 0.0292, CESC p = 0.0451). Cancer-associated fibroblasts could induce HNSCC cancer cells to express pattern V1. Adjuvant radiotherapy may weaken the effect of high V1 on recurrence and metastasis, depending on the tumor radiosensitivity. Considering the prognostic value of stromal gene patterns and its universality, we suggest that the genetic subtype classification of SCCs may be improved to a new system that integrates both malignant and non-malignant components.

Characterization of tumor-associated reactive astrocytes in gliomas by single-cell and bulk tumor sequencing.

Astrocytes constitute approximately 30% of cells in gliomas and play important roles in synapse construction and survival. Recently, JAK/STAT pathway activation associated with a new type of astrocyte was reported. However, the implications of these tumor-associated reactive astrocytes (TARAs) in glioma are not known. We comprehensively assessed TARAs in gliomas, both in single cells and at the bulk tumor level, by analyzing five independent datasets. First, we analyzed two single-cell RNA sequencing datasets of 35,563 cells from 23 patients to estimate the infiltration level of TARAs in gliomas. Second, we collected clinical information and genomic and transcriptomic data of 1,379 diffuse astrocytoma and glioblastoma samples from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas datasets to evaluate the genomic, transcriptomic and clinical characteristics of TARA infiltration. Third, we downloaded expression profiles of recurrent glioblastoma samples from patients receiving PD-1 inhibitors to analyze the predictive value of TARAs for immune checkpoint inhibition. Single-cell RNA sequencing data showed TARAs were abundant in the glioma micro-environment (15.7% in the CGGA dataset and 9.1% in the Gene Expression Omnibus GSE141383 dataset, respectively). Bulk tumor sequencing data showed that the extent of TARA infiltration was highly associated with major clinical and molecular features of astrocytic gliomas. Patients with more TARA infiltration were more likely to have MUC16, FLG, and PICK3A mutations, chromosome 9p21.3, 10q23.3, and 13q14.2 deletions and 7p11.2 amplification. Gene Ontology analysis revealed that the high level of astrocyte infiltration was characterized by immune and oncogenic pathways, such as the inflammatory response, positive regulation of the JAK-STAT cascade, positive regulation of NIK/NF-kappa B signaling and the tumor necrosis factor biosynthetic process. Patients with greater TARA infiltration showed inferior prognosis. Meanwhile, the extent of reactive astrocyte infiltration exhibited a predictive value for recurrent glioblastoma patients undergoing anti-PD-1 immune therapy. TARA infiltration might promote glioma tumor progression and can be used as a diagnostic, predictive and prognostic marker in gliomas. Prevention of TARA infiltration might be a new therapeutic strategy for glioma.

Multiomics technologies for comprehensive tumor microenvironment analysis in triple-negative breast cancer under neoadjuvant chemotherapy.

Triple-negative breast cancer (TNBC) is one of the most aggressive breast cancer subtypes and is characterized by abundant infiltrating immune cells within the microenvironment. As standard care, chemotherapy remains the fundamental neoadjuvant treatment in TNBC, and there is increasing evidence that supplementation with immune checkpoint inhibitors may potentiate the therapeutic efficiency of neoadjuvant chemotherapy (NAC). However, 20-60% of TNBC patients still have residual tumor burden after NAC and require additional chemotherapy; therefore, it is critical to understand the dynamic change in the tumor microenvironment (TME) during treatment to help improve the rate of complete pathological response and long-term prognosis. Traditional methods, including immunohistochemistry, bulk tumor sequencing, and flow cytometry, have been applied to elucidate the TME of breast cancer, but the low resolution and throughput may overlook key information. With the development of diverse high-throughput technologies, recent reports have provided new insights into TME alterations during NAC in four fields, including tissue imaging, cytometry, next-generation sequencing, and spatial omics. In this review, we discuss the traditional methods and the latest advances in high-throughput techniques to decipher the TME of TNBC and the prospect of translating these techniques to clinical practice.

Multimodal Dimension Reduction and Subtype Classification of Head and Neck Squamous Cell Tumors.

Traditional analysis of genomic data from bulk sequencing experiments seek to group and compare sample cohorts into biologically meaningful groups. To accomplish this task, large scale databases of patient-derived samples, like that of TCGA, have been established, giving the ability to interrogate multiple data modalities per tumor. We have developed a computational strategy employing multimodal integration paired with spectral clustering and modern dimension reduction techniques such as PHATE to provide a more robust method for cancer sub-type classification. Using this integrated approach, we have examined 514 Head and Neck Squamous Carcinoma (HNSC) tumor samples from TCGA across gene-expression, DNA-methylation, and microbiome data modalities. We show that these approaches, primarily developed for single-cell sequencing can be efficiently applied to bulk tumor sequencing data. Our multimodal analysis captures the dynamic heterogeneity, identifies new and refines subtypes of HNSC, and orders tumor samples along well-defined cellular trajectories. Collectively, these results showcase the inherent molecular complexity of tumors and offer insights into carcinogenesis and importance of targeted therapy. Computational techniques as highlighted in our study provide an organic and powerful approach to identify granular patterns in large and noisy datasets that may otherwise be overlooked.

Estimation of cancer cell fractions and clone trees from multi-region sequencing of tumors.

Multi-region sequencing of solid tumors can improve our understanding of intratumor subclonal diversity and the evolutionary history of mutational events. Due to uncertainty in clonal composition and the multitude of possible ancestral relationships between clones, elucidating the most probable relationships from bulk tumor sequencing poses statistical and computational challenges. We developed a Bayesian hierarchical model called PICTograph to model uncertainty in assigning mutations to subclones, to enable posterior distributions of cancer cell fractions (CCFs) and to visualize the most probable ancestral relationships between subclones. Compared with available methods, PICTograph provided more consistent and accurate estimates of CCFs and improved tree inference over a range of simulated clonal diversity. Application of PICTograph to multi-region whole-exome sequencing of tumors from individuals with pancreatic cancer precursor lesions confirmed known early-occurring mutations and indicated substantial molecular diversity, including 6-12 distinct subclones and intra-sample mixing of subclones. Using ensemble-based visualizations, we highlight highly probable evolutionary relationships recovered in multiple models. PICTograph provides a useful approximation to evolutionary inference from cross-sectional multi-region sequencing, particularly for complex cases. https://github.com/KarchinLab/pictograph. The data underlying this article will be shared on reasonable request to the corresponding author. Supplementary data are available at Bioinformatics online.

Single-Nucleotide Variations, Insertions/Deletions and Copy Number Variations in Myelodysplastic Syndrome during Disease Progression Revealed by a Single-Cell DNA Sequencing Platform.

Simple SummaryMyelodysplastic syndrome (MDS) is a myeloid neoplasm associated with complex clonal architecture. The application of single-cell sequencing is capable of revealing the clonal dynamics of MDS during disease progression and treatment resistance. This has advantages over bulk-tumor sequencing which is limited by its resolution. In this study, we evaluated two patients with MDS for the clonal dynamics of pathogenic mutations at the single-cell level of disease progression and resistance to hypomethylating agents (HMAs). There were two key observations. First, changes in the clonal heterogeneity of the pathogenic FLT3-ITD, IDH2, EZH2, or GATA2 mutations was associated with disease progression and resistance to HMA. Secondly, disease progression and resistance to HMA was accompanied by the acquisition of copy number variations of DNMT3A, TET2, and GATA2.Myelodysplastic syndrome (MDS) is a clonal myeloid neoplasm characterized by ineffective hematopoiesis, cytopenia, dysplasia, and clonal instability, leading to leukemic transformation. Hypomethylating agents are the mainstay of treatment in higher-risk MDS. However, treatment resistance and disease transformation into acute myeloid leukemia (AML) is observed in the majority of patients and is indicative of a dismal outcome. The residual cell clones resistant to therapy or cell clones acquiring new genetic aberrations are two of the key events responsible for drug resistance. Bulk tumor sequencing often fails to detect these rare subclones that confer resistance to therapy. In this study, we employed a single-cell DNA (sc-DNA) sequencing approach to study the clonal heterogeneity and clonal evolution in two MDS patients refractory to HMA. In both patients, different single nucleotide variations (SNVs) or insertions and deletions (INDELs) were detected with bulk tumor sequencing. Rare cell clones with mutations that are undetectable by bulk tumor sequencing were detected by sc-DNA sequencing. In addition to SNVs and short INDELs, this study also revealed the presence of a clonal copy number loss of DNMT3A, TET2, and GATA2 as standalone events or in association with the small SNVs or INDELs detected during HMA resistance and disease progression.

CloneSig can jointly infer intra-tumor heterogeneity and mutational signature activity in bulk tumor sequencing data

Systematic DNA sequencing of cancer samples has highlighted the importance of two aspects of cancer genomics: intra-tumor heterogeneity (ITH) and mutational processes. These two aspects may not always be independent, as different mutational processes could be involved in different stages or regions of the tumor, but existing computational approaches to study them largely ignore this potential dependency. Here, we present CloneSig, a computational method to jointly infer ITH and mutational processes in a tumor from bulk-sequencing data. Extensive simulations show that CloneSig outperforms current methods for ITH inference and detection of mutational processes when the distribution of mutational signatures changes between clones. Applied to a large cohort of 8,951 tumors with whole-exome sequencing data from The Cancer Genome Atlas, and on a pan-cancer dataset of 2,632 whole-genome sequencing tumor samples from the Pan-Cancer Analysis of Whole Genomes initiative, CloneSig obtains results overall coherent with previous studies.

Abstract 4407: Leveraging spatial profiling combined with bulk RNA sequencing to study patient-specific immunotherapeutic mechanisms

Abstract Introduction: Mechanisms of resistance to adoptive T-cell transfer therapy (ACT) in clinical samples have been identified using bulk tumor sequencing, gene expression profiling, and immunohistochemistry. Furthermore, these datatypes are independently used to predict response to immunotherapy. However, this is often done using relative values quantified across clinical cohorts, which is not always feasible in single sample case analyses. Samples and Methods: A patient (INY3) presented with a synovial sarcoma lesion (INY3-P) and had an objective response (OR) to ACT targeting NY-ESO-1. A lung metastasis (INY3-M) progressed 10 months post-treatment, and she had a second OR to ACT plus ipilimumab. Seven months later, the primary lesion recurred (INY3-R). RNAseq was performed on snap-frozen biopsies at each timepoint. Two formalin-fixed, paraffin-embedded slides from each timepoint were assessed by either the Nanostring GeoMx Digital Spatial Profiling (DSP) 60-plex protein or 84-plex RNA panel. Regions-of-interest (ROIs) were selected for those positive for NY-ESO-1, pan-CK, CD45, or combinations of these proteins. We evaluated the correlation of DSP targets across ROIs within each sample, correlation of matched protein/RNA DSP targets across timepoints, the impact of DSP heterogeneity on bulk tumor RNAseq, and DSP patterns in ACT-resistant samples. Results: The Spearman correlation across ROIs ranged from -0.57-0.84 (median -0.14) in DSP protein and -0.59-0.75 (median -0.11) in DSP RNA expression. The variance in correlation was greatest in INY3-M (0.18 protein; 0.11 RNA), compared to INY3-P (0.08 protein; 0.13 RNA) and INY3-R (0.07 protein; 0.02 RNA), suggesting greater differences in ROIs selected in INY3-M. This was confirmed by lowest correlation between DSP RNA expression with bulk RNAseq in INY3-M (0.43-0.76 across ROIs; INY3-P, 0.31-0.61; INY3-R, 0.67-0.79). The median correlation between matched protein/RNA targets (N=31) in each ROI was 0.41 (-0.17-0.90), establishing some differences in RNA and protein expression. This was confirmed by overall lower correlation of DSP protein expression with bulk RNA expression (0.32-0.65 across ROIs; median 0.61). There were three patterns observed in ACT-resistant samples: increased CTNNB1 (INY3-M); lower expression of T cell proteins, as well as B2M, HLA-DRB, CXCL9, and PSMB10 (INY3-M, INY3-R); and increased expression of T cell surface proteins, and antigen presentation machinery (INY3-M, INY3-R). Conclusions: Bulk tumor RNAseq analysis is complicated by tumor heterogeneity, which can be overcome by DSP. Collectively, our results support complementary use of these technologies to better study individual samples, without requiring comparison to clinical cohorts, as well as address the complexities of immunotherapeutic mechanisms, identifying cell type-specific signals that may be lost using bulk RNAseq. Citation Format: Katie M. Campbell, Theodore S. Nowicki, Antoni Ribas. Leveraging spatial profiling combined with bulk RNA sequencing to study patient-specific immunotherapeutic mechanisms [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4407.

Statistical Inference for the Evolutionary History of Cancer Genomes

Recent years have seen considerable work on inference about cancer evolution from mutations identified in cancer samples. Much of the modeling work has been based on classical models of population genetics, generalized to accommodate time-varying cell population size. Reverse-time, genealogical views of such models, commonly known as coalescents, have been used to infer aspects of the past of growing populations. Another approach is to use branching processes, the simplest scenario being the classical linear birth-death process. Inference from evolutionary models of DNA often exploits summary statistics of the sequence data, a common one being the so-called Site Frequency Spectrum (SFS). In a bulk tumor sequencing experiment, we can estimate for each site at which a novel somatic point mutation has arisen, the proportion of cells that carry that mutation. These numbers are then grouped into collections of sites which have similar mutant fractions. We examine how the SFS based on birth-death processes differs from those based on the coalescent model. This may stem from the different sampling mechanisms in the two approaches. However, we also show that despite this, they are quantitatively comparable for the range of parameters typical for tumor cell populations. We also present a model of tumor evolution with selective sweeps, and demonstrate how it may help in understanding the history of a tumor as well as the influence of data pre-processing. We illustrate the theory with applications to several examples from The Cancer Genome Atlas tumors.

Patient-Derived Cancer Organoid Cultures to Predict Sensitivity to Chemotherapy and Radiation.

Cancer treatment is limited by inaccurate predictors of patient-specific therapeutic response. Therefore, some patients are exposed to unnecessary side effects and delays in starting effective therapy. A clinical tool that predicts treatment sensitivity for individual patients is needed. Patient-derived cancer organoids were derived across multiple histologies. The histologic characteristics, mutation profile, clonal structure, and response to chemotherapy and radiation were assessed using bright-field and optical metabolic imaging on spheroid and single-cell levels, respectively. We demonstrate that patient-derived cancer organoids represent the cancers from which they were derived, including key histologic and molecular features. These cultures were generated from numerous cancers, various biopsy sample types, and in different clinical settings. Next-generation sequencing reveals the presence of subclonal populations within the organoid cultures. These cultures allow for the detection of clonal heterogeneity with a greater sensitivity than bulk tumor sequencing. Optical metabolic imaging of these organoids provides cell-level quantification of treatment response and tumor heterogeneity allowing for resolution of therapeutic differences between patient samples. Using this technology, we prospectively predict treatment response for a patient with metastatic colorectal cancer. These studies add to the literature demonstrating feasibility to grow clinical patient-derived organotypic cultures for treatment effectiveness testing. Together, these culture methods and response assessment techniques hold great promise to predict treatment sensitivity for patients with cancer undergoing chemotherapy and/or radiation.

ClonEvol: clonal ordering and visualization in cancer sequencing

BackgroundReconstruction of clonal evolution is critical for understanding tumor progression and implementing personalized therapies. This is often done by clustering somatic variants based on their cellular prevalence estimated via bulk tumor sequencing of multiple samples. The clusters, consisting of the clonal marker variants, are then ordered based on their estimated cellular prevalence to reconstruct clonal evolution trees, a process referred to as ‘clonal ordering’. However, cellular prevalence estimate is confounded by statistical variability and errors in sequencing/data analysis, and therefore inhibits accurate reconstruction of the clonal evolution. This problem is further complicated by intra- and inter-tumor heterogeneity. Furthermore, the field lacks a comprehensive visualization tool to facilitate the interpretation of complex clonal relationships. To address these challenges we developed ClonEvol, a unified software tool for clonal ordering, visualization, and interpretation. Materials and methodsClonEvol uses a bootstrap resampling technique to estimate the cellular fraction of the clones and probabilistically models the clonal ordering constraints to account for statistical variability. The bootstrapping allows identification of the sample founding- and sub-clones, thus enabling interpretation of clonal seeding. ClonEvol automates the generation of multiple widely used visualizations for reconstructing and interpreting clonal evolution. ResultsClonEvol outperformed three of the state of the art tools (LICHeE, Canopy and PhyloWGS) for clonal evolution inference, showing more robust error tolerance and producing more accurate trees in a simulation. Building upon multiple recent publications that utilized ClonEvol to study metastasis and drug resistance in solid cancers, here we show that ClonEvol rediscovered relapsed subclones in two published acute myeloid leukemia patients. Furthermore, we demonstrated that through noninvasive monitoring ClonEvol recapitulated the emerging subclones throughout metastatic progression observed in the tumors of a published breast cancer patient. ConclusionsClonEvol has broad applicability for longitudinal monitoring of clonal populations in tumor biopsies, or noninvasively, to guide precision medicine. AvailabilityClonEvol is written in R and is available athttps://github.com/ChrisMaherLab/ClonEvol.

Tumor phylogeny inference using tree-constrained importance sampling.

MotivationA tumor arises from an evolutionary process that can be modeled as a phylogenetic tree. However, reconstructing this tree is challenging as most cancer sequencing uses bulk tumor tissue containing heterogeneous mixtures of cells.ResultsWe introduce Probabilistic Algorithm for Somatic Tree Inference (PASTRI), a new algorithm for bulk-tumor sequencing data that clusters somatic mutations into clones and infers a phylogenetic tree that describes the evolutionary history of the tumor. PASTRI uses an importance sampling algorithm that combines a probabilistic model of DNA sequencing data with a enumeration algorithm based on the combinatorial constraints defined by the underlying phylogenetic tree. As a result, tree inference is fast, accurate and robust to noise. We demonstrate on simulated data that PASTRI outperforms other cancer phylogeny algorithms in terms of runtime and accuracy. On real data from a chronic lymphocytic leukemia (CLL) patient, we show that a simple linear phylogeny better explains the data the complex branching phylogeny that was previously reported. PASTRI provides a robust approach for phylogenetic tree inference from mixed samples.Availability and ImplementationSoftware is available at compbio.cs.brown.edu/software.Supplementary information Supplementary data are available at Bioinformatics online.

Is oral squamous cell carcinoma unique in terms of intra- and inter-tumoral heterogeneity?

Tumoral heterogeneity has become one of the important issues in cancer research and carries outmost weightage in designing personalized cancer medicine. Oral squamous cell carcinoma (OSCC) is present in unique microenvironment of oral cavity attributed to site-specific diversified tissue compositions, microbial flora, diversified carcinogenic attacks, potentially malignant disorders, epithelial turnover rate, saliva, and closeness to external environment. Such microenvironment has potential to modulate the pathogenesis of many oral pathologies including OSCC. We believe that such factors and microenvironment are unique for OSCC and cannot be found in other carcinomas of the body. Methodologies such as multiregional sequencing, single-cell genomics, genomic landscape of bulk tumor sequencing, and so on, will help in understanding OSCC uniqueness in terms of heterogeneity. If explored, it will enable us to know the attributes of heterogeneity and their magnitude in OSCC, which will benefit in designing predictable biomarker and effective therapeutic strategy.

Distinct mutation accumulation rates among tissues determine the variation in cancer risk.

Cancer is believed to be a result of accumulated mutations. However, this concept has not been fully confirmed owing to the impossibility of tracking down the ancestral somatic cell. We sought to verify the concept by exploring the correlation between cancer risk and mutation accumulation among different tissues. We hypothesized that the detected mutations through bulk tumor sequencing are commonly shared in majority, if not all, of tumor cells and are therefore largely a reflection of the mutations accumulated in the ancestral cell that gives rise to tumor. We collected a comprehensive list of mutation frequencies revealed by bulk tumor sequencing, and investigated its correlation with cancer risk to mirror the correlation between mutation accumulation and cancer risk. This revealed an approximate 1:1 relationship between mutation frequency and cancer risk in 41 different cancer types based on the sequencing data of 5,542 patients. The correlation strongly suggests that variation in cancer risk among tissues is mainly attributable to distinct mutation accumulation rates. Moreover, the correlation establishes a baseline to evaluate the effect of non-mutagenic carcinogens on cancer risk. Finally, our mathematic modeling provides a reasonable explanation to reinforce that cancer risk is predominantly determined by the first rate-limiting mutation.

Abstract B2-59: PhyloSpan: Using multi-mutation reads to resolve subclonal architectures from heterogeneous tumor samples

Abstract We have developed a new method that uses high-throughput reads that span multiple somatic point mutations to reconstruct multiple, genetically diverse subclonal populations from one or more heterogeneous tumor samples. Tumors often contain multiple, genetically diverse subclonal populations, as predicted by the clonal theory of cancer. These subclonal populations develop through successive waves of expansion and selection and have differing abilities to metastasize and resist treatment. Identifying these sub-populations and their evolutionary relationships can help identify driver mutations associated with cancer development and progression. Subclonal reconstruction algorithms attempt to infer the prevalence and genotype of multiple, genetically-related subclonal populations using the variant allele frequency (VAF) of somatic variants. To date, these algorithms exclusively use data on individual somatic mutations. This restriction greatly reduces their ability to fully resolve phylogenic ambiguities. In some cases, it is possible to determine the mutation status of >1 mutation in a single cell, for example, when single reads cover multiple single nucleotide variants (SNVs). This type of information can add considerable power to the phylogenetic reconstruction of the tumor subclonal population. We have developed the PhyloSpan algorithm which attempts to infer the states of multiple SNVs in single cells, and then exploits that information in subclonal reconstruction. Our algorithm starts with phasing somatic SNVs by looking for reads / read-pairs that cover both a somatic mutation and germline heterozygous single nucleotide polymorphism (SNP). These germline SNPs are often available through profiling of normal tissue. PhyloSpan then identifies SNVs that are on the same chromosome and close enough to be covered by a single read or paired reads. These pairs of mutations provide more phylogenetic certainty than can be found by looking at mutations independently. For example, if those SNVs are found in the same evolutionary branch, then we expect to see some reads containing both mutations. If however, the SNVs are an separate branches then no reads should show both SNVs. PhyloSpan integrates this phylogenetic information, along with information about the VAF of each somatic SNV in order to perform subclonal reconstruction. Incorporating these various types of information, especially given the substantial uncertainty in phasing and NGS read content, requires a rigorous statistical approach and so we have developed a Bayesian non-parametric tree-based clustering algorithm, based on our existing PhyloWGS method. This algorithm not only infers the number of subclonal populations and their genotype but also provides a measure of uncertainty about this inference, enabling users to determine which parts of the subclonal reconstruction are certain and which parts remain ambiguous. While the number of SNVs a short-read length distance away from another SNV is small, a handful of such pairs are all that is needed to eliminate a substantial amount of ambiguity in subclonal reconstruction. Furthermore, long (>10k) read technologies, such as PacBio, can be used to supplement short read sequence. Our approach generalizes to permit the integration of single-cell sequencing with bulk tumor sequencing. Furthermore, we can also use our framework to identify a small number of SNVs for which low throughput assays would be most useful to resolve subclonal reconstruction ambiguity. We will present results applying our algorithm to whole genome sequencing data showing the added value of considering multiple SNVs compared to independent SNVs. Citation Format: Amit G. Deshwar, Levi Boyles, Jeff Wintersinger, Paul C. Boutros, Yee Whye Teh, Quaid Morris, Quaid Morris. PhyloSpan: Using multi-mutation reads to resolve subclonal architectures from heterogeneous tumor samples. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-59.

Abstract 4865: PhyloSpan: using multi-mutation reads to resolve subclonal architectures from heterogeneous tumor samples

Abstract We have developed a new method that uses high-throughput reads that span multiple somatic point mutations to reconstruct multiple, genetically diverse subclonal populations from one or more heterogeneous tumor samples. Subclonal reconstruction algorithms attempt to infer the prevalence and genotype of multiple, genetically-related subclonal populations using the variant allele frequency (VAF) of somatic variants. To date, these algorithms exclusively use data on individual somatic mutations. This restriction greatly reduces their ability to fully resolve phylogenic ambiguities. In some cases, it is possible to determine the mutation status of >1 mutation in a single cell, for example, when single reads cover multiple single nucleotide variants (SNVs). This type of information can add considerable power to the phylogenetic reconstruction of the tumor subclonal population. We have developed the PhyloSpan algorithm which attempts to infer the states of multiple SNVs in single cells, and then exploits that information in subclonal reconstruction. Our algorithm starts with phasing somatic SNVs by looking for reads / read-pairs that cover both a somatic mutation and germline heterozygous single nucleotide polymorphism (SNP). These germline SNPs are often available through profiling of normal tissue. PhyloSpan then identifies SNVs that are on the same chromosome and close enough to be covered by a single read or paired reads. These pairs of mutations provide more phylogenetic certainty than can be found by looking at mutations independently. For example, if those SNVs are found in the same evolutionary branch, then we expect to see some reads containing both mutations. If however, the SNVs are an separate branches then no reads should show both SNVs. PhyloSpan integrates this phylogenetic information, along with information about the VAF of each somatic SNV in order to perform subclonal reconstruction. Incorporating these various types of information requires a rigorous statistical approach, and so we have developed a Bayesian non-parametric tree-based clustering algorithm. This algorithm not only infers the number of subclonal populations and their genotype but also provides a measure of uncertainty about this inference, enabling users to determine which parts of the subclonal reconstruction are certain and which parts remain ambiguous. While the number of SNVs a short-read length distance away from another SNV is small, a handful of such pairs are all that is needed to eliminate a substantial amount of ambiguity in subclonal reconstruction. Furthermore, long read technologies, such as PacBio, can be used to supplement short reads. Our approach generalizes to permit the integration of single-cell sequencing with bulk tumor sequencing. We will present results applying our algorithm to whole genome sequencing data showing the added value of considering multiple SNVs compared to independent SNVs. Citation Format: Amit G. Deshwar, Levi Boyles, Jeff Wintersinger, Paul C. Boutros, Yee Whye Teh, Quaid Morris. PhyloSpan: using multi-mutation reads to resolve subclonal architectures from heterogeneous tumor samples. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4865. doi:10.1158/1538-7445.AM2015-4865