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Pairtree is a clone tree reconstruction algorithm that uses somatic point mutations to build clone trees describing the evolutionary history of individual cancers. Using the Pairtree software package, we describe steps to preprocess somatic mutation data, cluster mutations into subclones, search for clone trees, and visualize clone trees. Pairtree builds clone trees using up to 100 samples from a single cancer with at least 30 subclonal populations. For complete details on the use and execution of this protocol, please refer to Wintersinger et al. (2022).

Subclonal reconstruction from bulk tumor DNA sequencing has become a pillar of cancer evolution studies, providing insight into the clonality and relative ordering of mutations and mutational processes. We provide an outline of the complex computational approaches used for subclonal reconstruction from single and multiple tumor samples. We identify the underlying assumptions and uncertainties in each step, and suggest best practices for analysis and quality assessment. This guide provides a pragmatic resource for the growing user community of subclonal reconstruction methods.

1 Abstract Cancers are composed of genetically distinct subpopulations of malignant cells. By sequencing DNA from cancer tissue samples, we can characterize the somatic mutations specific to each population and build clone trees describing the evolutionary ancestry of populations relative to one another. These trees reveal critical points in disease development and inform treatment. Pairtree is a new method for constructing clone trees using DNA sequencing data from one or more bulk samples of an individual cancer. It uses Bayesian inference to compute posterior distributions over the evolutionary relationships between every pair of identified subpopulations, then uses these distributions in a Markov Chain Monte Carlo algorithm to perform efficient inference of the posterior distribution over clone trees. Unlike existing methods, Pairtree can perform clone tree reconstructions using as many as 100 samples per cancer that reveal 30 or more cell subpopulations. On simulated data, Pairtree is the only method whose performance reliably improves when provided with additional bulk samples from a cancer. This suggests a shortcoming of existing methods, as more samples provide more information, and should always make clone tree reconstruction easier. On 14 B-progenitor acute lymphoblastic leukemias with up to 90 samples from each cancer, Pairtree was the only method that could reproduce or improve upon expert-derived clone tree reconstructions. By scaling to more challenging problems, Pairtree supports new biomedical research applications that can improve our understanding of the natural history of cancer, as well as better illustrate the interplay between cancer, host, and therapeutic interventions. The Pairtree method, along with an interactive visual interface for exploring the clone tree posterior, is available at https://github.com/morrislab/pairtree.

Tumors contain multiple subpopulations of genetically distinct cancer cells. Reconstructing their evolutionary history can improve our understanding of how cancers develop and respond to treatment. Subclonal reconstruction methods cluster mutations into groups that co-occur within the same subpopulations, estimate the frequency of cells belonging to each subpopulation, and infer the ancestral relationships among the subpopulations by constructing a clone tree. However, often multiple clone trees are consistent with the data and current methods do not efficiently capture this uncertainty; nor can these methods scale to clone trees with a large number of subclonal populations.Here, we formalize the notion of a partial clone tree that defines a subset of the pairwise ancestral relationships in a clone tree, thereby implicitly representing the set of all clone trees that have these defined pairwise relationships. Also, we introduce a special partial clone tree, the Maximally-Constrained Ancestral Reconstruction (MAR), which summarizes all clone trees fitting the input data equally well. Finally, we extend commonly used clone tree validity conditions to apply to partial clone trees and describe SubMARine, a polynomial-time algorithm producing the subMAR, which approximates the MAR and guarantees that its defined relationships are a subset of those present in the MAR. We also extend SubMARine to work with subclonal copy number aberrations and define equivalence constraints for this purpose. In contrast with other clone tree reconstruction methods, SubMARine runs in time and space that scales polynomially in the number of subclones.We show through extensive simulation and a large lung cancer dataset that the subMAR equals the MAR in > 99.9% of cases where only a single clone tree exists and that it is a perfect match to the MAR in most of the other cases. Notably, SubMARine runs in less than 70 seconds on a single thread with less than one Gb of memory on all datasets presented in this paper, including ones with 50 nodes in a clone tree.The freely-available open-source code implementing SubMARine can be downloaded at https://github.com/morrislab/submarine.Author summaryCancer cells accumulate mutations over time and consist of genetically distinct subpopulations. Their evolutionary history (as represented by tumor phylogenies) can be inferred from bulk cancer genome sequencing data. Current tumor phylogeny reconstruction methods have two main issues: they are slow, and they do not efficiently represent uncertainty in the reconstruction.To address these issues, we developed SubMARine, a fast algorithm that summarizes all valid phylogenies in an intuitive format. SubMARine solved all reconstruction problems in this manuscript in less than 70 seconds, orders of magnitude faster than other methods. These reconstruction problems included those with up to 50 subclones; problems that are too large for other algorithms to even attempt. SubMARine achieves these result because, unlike other algorithms, it performs its reconstruction by identifying an upper-bound on the solution set of trees. In the vast majority of cases, this upper bound is tight: when only a single solution exists, SubMARine converges to it > 99.9% of the time; when multiple solutions exist, our algorithm correctly recovers the uncertain relationships in more than 80% of cases.In addition to solving these two major challenges, we introduce some useful new concepts for and open research problems in the field of tumor phylogeny reconstruction. Specifically, we formalize the concept of a partial clone tree which provides a set of constraints on the solution set of clone trees; and provide a complete set of conditions under which a partial clone tree is valid. These conditions guarantee that all trees in the solution set satisfy the constraints implied by the partial clone tree.

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin and drivers of ITH across cancer types are poorly understood. To address this question, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples, spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions, with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types, and identify cancer type specific subclonal patterns of driver gene mutations, fusions, structural variants and copy-number alterations, as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution, and provide an unprecedented pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.

Disease recurrence causes significant mortality in B-progenitor acute lymphoblastic leukemia (B-ALL). Genomic analysis of matched diagnosis and relapse samples shows relapse often arising from minor diagnosis subclones. However, why therapy eradicates some subclones while others survive and progress to relapse remains obscure. Elucidation of mechanisms underlying these differing fates requires functional analysis of isolated subclones. Here, large-scale limiting dilution xenografting of diagnosis and relapse samples, combined with targeted sequencing, identified and isolated minor diagnosis subclones that initiate an evolutionary trajectory toward relapse [termed diagnosis Relapse Initiating clones (dRI)]. Compared with other diagnosis subclones, dRIs were drug-tolerant with distinct engraftment and metabolic properties. Transcriptionally, dRIs displayed enrichment for chromatin remodeling, mitochondrial metabolism, proteostasis programs, and an increase in stemness pathways. The isolation and characterization of dRI subclones reveals new avenues for eradicating dRI cells by targeting their distinct metabolic and transcriptional pathways before further evolution renders them fully therapy-resistant. Significance: Isolation and characterization of subclones from diagnosis samples of patients with B-ALL who relapsed showed that relapse-fated subclones had increased drug tolerance and distinct metabolic and survival transcriptional programs compared with other diagnosis subclones. This study provides strategies to identify and target clinically relevant subclones before further evolution toward relapse. See related video: https://vimeo.com/442838617 See related article by E. Waanders et al .

Cancer cells enter a reversible drug-tolerant persister (DTP) state to evade death from chemotherapy and targeted agents. It is increasingly appreciated that DTPs are important drivers of therapy failure and tumor relapse. We combined cellular barcoding and mathematical modeling in patient-derived colorectal cancer models to identify and characterize DTPs in response to chemotherapy. Barcode analysis revealed no loss of clonal complexity of tumors that entered the DTP state and recurred following treatment cessation. Our data fit a mathematical model where all cancer cells, and not a small subpopulation, possess an equipotent capacity to become DTPs. Mechanistically, we determined that DTPs display remarkable transcriptional and functional similarities to diapause, a reversible state of suspended embryonic development triggered by unfavorable environmental conditions. Our study provides insight into how cancer cells use a developmentally conserved mechanism to drive the DTP state, pointing to novel therapeutic opportunities to target DTPs.

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.

SUMMARYIntra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin and drivers of ITH across cancer types are poorly understood. To address this question, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples, spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions, with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types, and identify cancer type specific subclonal patterns of driver gene mutations, fusions, structural variants and copy-number alterations, as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution, and provide an unprecedented pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.

Tumours evolve through time and space. Computational techniques have been developed to infer their evolutionary dynamics from DNA sequencing data. A growing number of studies have used these approaches to link molecular cancer evolution to clinical progression and response to therapy. There has not yet been a systematic evaluation of methods for reconstructing tumour subclonality, in part due to the underlying mathematical and biological complexity and to difficulties in creating gold-standards. To fill this gap, we systematically elucidated the key algorithmic problems in subclonal reconstruction and developed mathematically valid quantitative metrics for evaluating them. We then created approaches to simulate realistic tumour genomes, harbouring all known mutation types and processes both clonally and subclonally. We then simulated 580 tumour genomes for reconstruction, varying tumour read-depth and benchmarking somatic variant detection and subclonal reconstruction strategies. The inference of tumour phylogenies is rapidly becoming standard practice in cancer genome analysis; this study creates a baseline for its evaluation.

We present a new method, TrackSig, to estimate the evolutionary trajectories of signatures of different somatic mutational processes from DNA sequencing data from a single, bulk tumour sample. TrackSig uses probability distributions over mutation types, called mutational signatures, to represent different mutational processes and detects the changes in the signature activity using an optimal segmentation algorithm that groups somatic mutations based on their estimated cancer cellular fraction (CCF) and their mutation type (e.g. CAG->CTG). We use two different simulation frameworks to assess both TrackSig’s reconstruction accuracy and its robustness to violations of its assumptions, as well as to compare it to a baseline approach. We find 2-4% median error in reconstructing the signature activities on simulations with varying difficulty with one to three subclones at an average depth of 30x. The size and the direction of the activity change is consistent in 83% and 95% of cases respectively. There were an average of 0.02 missed and 0.12 false positive subclones per sample. In our simulations, grouping mutations by mutation type (TrackSig), rather than by clustering CCF (baseline strategy), performs better at estimating signature activities and at identifying subclonal populations in the complex scenarios like branching, CNA gain, violation of infinite site assumption, and the inclusion of neutrally evolving mutations. TrackSig is open source software, freely available at https://github.com/morrislab/TrackSig.

We have characterised intra-tumour heterogeneity (ITH) across 2,778 whole genome sequences of tumours in the International Cancer Genome Consortium Pan-Cancer Analysis of Whole Genomes project, representing 36 distinct cancer types. We applied 6 copy number (CNA) callers and 11 subclonal reconstruction algorithms and developed approaches to integrate the results in robust, high-confidence CNA calls and subclonal architectures. The analysis reveals widespread ITH. We find at least one subclone in nearly all (96.7%) tumours with sufficient sequencing depth. Analysis using dN/dS ratios yields clear signs of positive selection in clonal and subclonal mutations and we find subclonal driver mutations in known driver genes. However, only 24% of subclones contain a driver mutation in a known driver gene, suggesting that a multitude of undiscovered late drivers exist and that tumours continue to undergo selection after tumourigenesis, at least until diagnosis. Consistent with other studies, we find that in 9% of tumours all clinically actionable mutations are subclonal, while 20% of tumours contain at least one subclonal actionable driver. These findings emphasise the relevance of ITH in treatment decision making. Distinct patterns of ITH emerge; for example, prostate, uterus and esophageal adenocarcinomas show high proportions of both subclonal single nucleotide variants (SNVs) and CNAs. Kidney chromophobe and pancreatic endocrine tumours also contain high proportions of subclonal SNVs, but few subclonal CNAs. On the other hand, hepatocellular carcinomas and head-and-neck and lung SCCs contain low proportions of subclonal SNVs and high proportions of subclonal CNAs. Mutational signature analysis reveals changes in signature activity. Exposures to UV light in melanomas and acid reflux in stomach and oesophageal cancers contribute more clonal mutations. While APOBEC and DNA damage repair response related signatures show increased activity in subclones. These findings highlight distinct evolutionary narratives between and within histologically distinct tumour types. Citation Format: Stefan Dentro, Ignaty Leshchiner, Kerstin Haase, Jeff Wintersinger, Amit Deshwar, Maxime Tarabichi, Yulia Rubanova, Kaixian Yu, Ignacio Vázquez García, Geoff Macintyre, Kortine Kleinheinz, Dimitri Livitz, Salem Malikic, Nilgun Donmez, Subhajit Sengupta, Yuan Ji, Jonas Demeulemeester, Pavana Anur, Clemency Jolly, Marek Cmero, Daniel Rosebrock, Steve Schumacher, Yu Fan, Matthew Fittall, Xiaotong Yao, Juhee Lee, Matthias Schlesner, Hongtu Zhu, David Adams, Gad Getz, Paul Boutros, Marcin Imielinski, Rameen Beroukhim, Cenk Sahinalp, Martin Peifer, Inigo Martincorena, Florian Markowetz, Ville Mustonen, Ke Yuan, Moritz Gerstung, Wenyi Wang, Paul Spellman, Quaid Morris, David Wedge, Peter Van Loo. Pervasive intra-tumour heterogeneity and subclonal selection across cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3000.

Cancer develops through a continuous process of somatic evolution. Whole genome sequencing provides a snapshot of the tumor genome at the point of sampling, however, the data can contain information that permits the reconstruction of a tumor's evolutionary past. Here, we apply such life history analyses on an unprecedented scale, to a set of 2,658 tumors spanning 39 cancer types. We estimated the timing of large chromosomal gains during tumor evolution, by comparing the rates of doubled to non-doubled point mutations within gained regions. Although we find that such events typically occur in the second half of clonal evolution, we also observe distinctive and early chromosomal gains in some cancer types, such as gains of chromosomes 7, 19 and 20 in glioblastoma, and isochromosome 17q in medulloblastoma. By integrating these results with the qualitative timing of individual driver mutations, we obtained an overall ranking, from early to late, of frequent somatic events per cancer type, which both identified novel patterns of tumor evolution, and incorporated additional detail into known models, such as the progression of APC-KRAS-TP53 in colorectal cancer proposed by Vogelstein and Fearon. To estimate how mutational processes acting on the tumor genome change over time, we classified mutations in each sample according to three broad time periods (early clonal, late clonal, and subclonal), and quantified the activity of mutational signatures in each period. Most mutational processes appear to remain remarkably constant, however, certain signatures show clear and consistent changes during clonal evolution. Particularly, mutational signatures associated with exposure to carcinogens, such as smoking and UV light, tend to decrease over time. In contrast, signatures associated with defective endogenous processes, such as APOBEC mutagenesis and defective double strand break repair, show an increase between early and late phases of tumor evolution. Making use of clock-like mutational signatures, we converted mutational time estimates for large events, such as whole genome duplication (WGD), and the emergence of the most recent common ancestor (MRCA), into real time estimates, which allowed us to combine our analyses into overall timelines of cancer evolution, per tumor type. For example, the typical timeline of ovarian adenocarcinoma development shows that early tumor evolution is characterized by mutations in TP53, and widespread genome instability, with WGD events taking place on average 8 years prior to diagnosis. In later stages of evolution, signatures of defective repair processes increase, and the MRCA emerges on average 1 year before diagnosis. Taken together, these data reveal the common and divergent evolutionary trajectories available to a cancer, which might be crucial in understanding specific tumor biology, and in providing new opportunities for early detection and cancer prevention. Citation Format: Clemency Jolly, Moritz Gerstung, Ignaty Leshchiner, Stefan C. Dentro, Santiago Gonzalez, Thomas J. Mitchell, Yulia Rubanova, Pavana Anur, Daniel Rosebrock, Kaixian Yu, Maxime Tarabichi, Amit Deshwar, Jeff Wintersinger, Kortine Kleinheinz, Ignacio Vásquez-García, Kerstin Haase, Subhajit Sengupta, Geoff Macintyre, Salem Malikic, Nilgun Donmez, Dimitri G. Livitz, Mark Cmero, Jonas Demeulemeester, Steve Schumacher, Yu Fan, Xiaotong Yao, Juhee Lee, Matthias Schlesner, Paul C. Boutros, David D. Bowtell, Hongtu Zhu, Gad Getz, Marcin Imielinski, Rameen Beroukhim, S Cenk Sahinalp, Yuan Ji, Martin Peifer, Florian Markowetz, Ville Mustonen, Ke Juan, Wenyi Wang, Quaid D. Morris, Paul T. Spellman, David C. Wedge, Peter Van Loo, PCAWG Evolution and Heterogeneity Working Group. The evolutionary history of 2,658 cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 218.

Introduction During their development, tumour cells accumulate somatic mutations, structural variants and copy number alterations (CNAs). Driver events facilitate clonal expansions and lead to intra-tumour heterogeneity (ITH). While ITH is an important therapeutic challenge, its degree among different cancer types is largely unknown. Material and methods The pan-cancer analysis of whole genomes (PCAWG) enabled us to characterise ITH in an unprecedented set of 2778 tumour samples representing 36 histologically distinct cancer types. We applied six CNA callers and eleven subclonal reconstruction algorithms to integrate their solutions into robust consensus copy number profiles and subclonal reconstructions. Results and discussions Our analysis revealed pervasive ITH in all examined cancer types. We found at least one subclone in 96.7% of the 1801 samples for which we had statistical power to detect subclones. In addition, we find that the average proportions of subclonal point mutations, indels, SVs and CNAs are highly variable across cancer types. These observations suggest distinct evolutionary narratives of each histological cancer type. Analysis of dN/dS ratios shows clear signs of positive selection within both clonal and subclonal mutations. We also identified subclonal mutations in driver genes that are recurrently hit and we found a significant enrichment of subclonal mutations in genes responsible for chromatin regulation. More than 5% of tumours contain driver mutations in genes for which specific treatment is available only in subclones, indicating the importance of assessing the clonality of targeted mutations for clinical decisions. Mutational signatures in the analysed samples show changes in activity over the course of tumour development. Characteristic carcinogen signatures, e.g. UV light exposure in melanomas, make less contributions to subclonal than clonal mutations, while APOBEC-induced mutagenesis has increased activity during the subclonal phase. Conclusion The absence of a detectable driver mutation in a majority of subclones suggests that late tumour development is frequently driven by CNAs or genomic rearrangements, or that a significant number of late drivers have yet to be identified. We found that selection is widespread and likely the rule rather than the exception and we identified differential activity of mutational signatures, reflecting successive waves of subclonal expansion.

The majority of newly diagnosed prostate cancers are slow growing, with a long natural life history. Yet a subset can metastasize with lethal consequences. We reconstructed the phylogenies of 293 localized prostate tumors linked to clinical outcome data. Multiple subclones were detected in 59% of patients, and specific subclonal architectures associate with adverse clinicopathological features. Early tumor development is characterized by point mutations and deletions followed by later subclonal amplifications and changes in trinucleotide mutational signatures. Specific genes are selectively mutated prior to or following subclonal diversification, including MTOR, NKX3-1, and RB1. Patients with low-risk monoclonal tumors rarely relapse after primary therapy (7%), while those with high-risk polyclonal tumors frequently do (61%). The presence of multiple subclones in an index biopsy may be necessary, but not sufficient, for relapse of localized prostate cancer, suggesting that evolution-aware biomarkers should be studied in prospective studies of low-risk tumors suitable for active surveillance.

Motivation: Kablammo is a web-based application that produces interactive, vector-based visualizations of sequence alignments generated by BLAST. These visualizations can illustrate many features, including shared protein domains, chromosome structural modifications and genome misassembly. Availability and implementation: Kablammo can be used at http://kablammo.wasmuthlab.org. For a local installation, the source code and instructions are available under the MIT license at http://github.com/jwintersinger/kablammo. Contact: jeff@wintersinger.org

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