Subclonal Reconstruction Research Articles

Reconstructing oral cavity tumor evolution through brush biopsy

Oral potentially malignant disorders (OPMDs) with genomic alterations have a heightened risk of evolving into oral squamous cell carcinoma (OSCC). Currently, genomic data are typically obtained through invasive tissue biopsy. However, brush biopsy is a non-invasive method that has been utilized for identifying dysplastic cells in OPMD but its effectiveness in reflecting the genomic landscape of OPMDs remains uncertain. This pilot study investigates the potential of brush biopsy samples in accurately reconstructing the genomic profile and tumor evolution in a patient with both OPMD and OSCC. We analyzed single nucleotide variants (SNVs), copy number aberrations (CNAs), and subclonal architectures in paired tissue and brush biopsy samples. The results showed that brush biopsy effectively captured 90% of SNVs and had similar CNA profiles as those seen in its paired tissue biopsies in all lesions. It was specific, as normal buccal mucosa did not share these genomic alterations. Interestingly, brush biopsy revealed shared SNVs and CNAs between the distinct OPMD and OSCC lesions from the same patient, indicating a common ancestral origin. Subclonal reconstruction confirmed this shared ancestry, followed by divergent evolution of the lesions. These findings highlight the potential of brush biopsies in accurately representing the genomic profile of OPL and OSCC, proving insight into reconstructing tumor evolution.

Metapipeline-DNA: A Comprehensive Germline & Somatic Genomics Nextflow Pipeline.

DNA sequencing is becoming more affordable and faster through advances in high-throughput technologies. This rise in data availability has contributed to the development of novel algorithms to elucidate previously obscure features and led to an increased reliance on complex workflows to integrate such tools into analyses pipelines. To facilitate the analysis of DNA sequencing data, we created metapipeline-DNA, a highly configurable and extensible pipeline. It encompasses a broad range of processing including raw sequencing read alignment and recalibration, variant calling, quality control and subclonal reconstruction. Metapipeline-DNA also contains configuration options to select and tune analyses while being robust to failures. This standardizes and simplifies the ability to analyze large DNA sequencing in both clinical and research settings. Metapipeline-DNA is an open-source Nextflow pipeline under the GPLv2 license and is freely available at https://github.com/uclahs-cds/metapipeline-DNA.

Pan-cancer subclonal mutation analysis of 7,827 tumors predicts clinical outcome.

Intra-tumor heterogeneity is an important driver of tumor evolution and therapy response. Advances in precision cancer treatment will require understanding of mutation clonality and subclonal architecture. Currently the slow computational speed of subclonal reconstruction hinders large cohort studies. To overcome this bottleneck, we developed Clonal structure identification through Pairwise Penalization, or CliPP, which clusters subclonal mutations using a regularized likelihood model. CliPP reliably processed whole-genome and whole-exome sequencing data from over 12,000 tumor samples within 24 hours, thus enabling large-scale downstream association analyses between subclonal structures and clinical outcomes. Through a pan-cancer investigation of 7,827 tumors from 32 cancer types, we found that high subclonal mutational load (sML), a measure of latency time in tumor evolution, was significantly associated with better patient outcomes in 16 cancer types with low to moderate tumor mutation burden (TMB). In a cohort of prostate cancer patients participating in an immunotherapy clinical trial, high sML was indicative of favorable response to immune checkpoint blockade. This comprehensive study using CliPP underscores sML as a key feature of cancer. sML may be essential for linking mutation dynamics with immunotherapy response in the large population of non-high TMB cancers.

Crowd-sourced benchmarking of single-sample tumor subclonal reconstruction.

Subclonal reconstruction algorithms use bulk DNA sequencing data to quantify parameters of tumor evolution, allowing an assessment of how cancers initiate, progress and respond to selective pressures. We launched the ICGC-TCGA (International Cancer Genome Consortium-The Cancer Genome Atlas) DREAM Somatic Mutation Calling Tumor Heterogeneity and Evolution Challenge to benchmark existing subclonal reconstruction algorithms. This 7-year community effort used cloud computing to benchmark 31 subclonal reconstruction algorithms on 51 simulated tumors. Algorithms were scored on seven independent tasks, leading to 12,061 total runs. Algorithm choice influenced performance substantially more than tumor features but purity-adjusted read depth, copy-number state and read mappability were associated with the performance of most algorithms on most tasks. No single algorithm was a top performer for all seven tasks and existing ensemble strategies were unable to outperform the best individual methods, highlighting a key research need. All containerized methods, evaluation code and datasets are available to support further assessment of the determinants of subclonal reconstruction accuracy and development of improved methods to understand tumor evolution.

Subclonal mutational signatures in breast cancer brain metastases.

e13108 Background: Breast cancer is the leading cause of cancer-related deaths among women in Brazil and the cancer type with highest incidence. When diagnosed at early stages, it has an overall five-year survival rate of up to 90%. However, in advanced stages, survival rate is reduced to about 24%, and 90% of women in stage IV die of metastatic complications. Brain is one of the most common metastatic sites in breast cancer patients, and even though it is a site with poor prognosis, the mutational profile of brain tumors is poorly described in the literature. Therefore, we understand that the identification of this profile may contribute to elucidate mechanisms associated with tumor progression. The aim of this study was to reconstruct the subclonal evolution of breast cancer metastases in the brain. Methods: To carry out this study, the whole exome was accessed through Next Generation Sequencing (NGS) using the Illumina platform in DNA samples extracted from buffy coat cryopreserved and paraffin material of breast tumors and paired brain metastases (n=10). The median read coverage was 68x. DNA was aligned to a Human Reference Genome using the Burrows–Wheeler Aligner, Genome Analysis Toolkit was used for germline short variants calling, and Mutect2 and MuSE were used for variant calling with somatic character (SNVs and INDELs). FACETS was used for assessing the somatic DNA copy number (Copy Number Aberrations). PyClone-VI tool was used for the subclonal reconstruction and to infer the phylogeny it was considered the mutation loci, Cancer Cell Fraction (CCF) and clustering assignments, using R Statistical Environment and Stats, Argparse, and Devtools packages. Results: We identified the mutational signature in these patients with subclonal and clonal mutations that segregated in approximately two clusters with a median between ten and forty variants related with tumor progression process. Conclusions: These findings show a set of variants with driver potential for breast cancer brain metastasis, bringing new perspectives in identifying mechanisms associated with disease progression.

Molecular and clonal evolution in vivo reveal a common pathway of distant relapse gliomas

The evolutionary trajectories of genomic alterations underlying distant recurrence in glioma remain largely unknown. To elucidate glioma evolution, we analyzed the evolutionary trajectories of matched pairs of primary tumors and relapse tumors or tumor in situ fluid (TISF) based on deep whole-genome sequencing data (ctDNA). We found that MMR gene mutations occurred in the late stage in IDH-mutant glioma during gene evolution, which activates multiple signaling pathways and significantly increases distant recurrence potential. The proneural subtype characterized by PDGFRA amplification was likely prone to hypermutation and distant recurrence following treatment. The classical and mesenchymal subtypes tended to progress locally through subclonal reconstruction, trunk genes transformation, and convergence evolution. EGFR and NOTCH signaling pathways and CDNK2A mutation play an important role in promoting tumor local progression. Glioma subtypes displayed distinct preferred evolutionary patterns. ClinicalTrials.gov, NCT05512325.

Abstract 4272: Pan-cancer analysis of intra-tumor heterogeneity in 9,116 cancers using a novel regularized likelihood model

Abstract Subpopulations of tumor cells characterized by mutation profiles may confer differential fitness and consequently influence the prognosis of cancers. Understanding subclonal architecture has the potential to provide biological insight in tumor evolution and advance cancer precision treatment. Recent subclonal reconstruction methods require heavy computational resources, prior knowledge of the number of subclones, and extensive postprocessing. These drawbacks can be addressed by using a regularized likelihood modeling approach, which is novel to the field. Therefore, we propose a model-based method, Clonal structure identification through pair-wise Penalization, or CliP, to address these drawbacks. To evaluate the performance of CliP against other methods, we generated a benchmark dataset of 4,050 simulated samples with varied tumor purity, read depth, copy number alteration rate, and true numbers of clusters. Our results suggest that CliP outperforms popular methods such as PhyloWGS in accuracy and shows similar robustness in most scenarios. We further compared CliP performance against 10 other subclonal reconstruction methods to the consensus subclonal reconstruction results on whole-genome sequencing (WGS) data from the Pan-Cancer Analysis of Whole Genomes (PCAWG, n = 1,993). Our result shows that of all 11 methods, CliP achieves the highest correlation with the consensus calls. In terms of speed, CliP can finish running a sample with 5,000 SNVs within one minute, which is ~1,000 times faster than MCMC-based algorithms. Next, we profiled the subclonal structures of 7,711 patient samples with well-annotated clinical outcomes from The Cancer Genome Atlas (TCGA) across 32 cancer types applying CliP to the whole-exome sequencing (WES) data. This is the largest and most complete pan-cancer characterization of intratumor heterogeneity (ITH) through the lens of subclonal reconstruction. We further used CliP outputs to address a commonly asked question on which sequencing platform to use for cancer evolution studies: the cost-effect WES data at higher read depth versus the more comprehensive WGS data at lower read depth. There were a total of 588 tumor samples from 21 cancer types, for which both PCAWG and TCGA have profiled using the WGS and WES platform, respectively. Using both datasets to compare results from these samples as benchmark, we observed that for most cancer types, subclonal reconstruction from WES is as informative as that from the matched WGS data. In summary, our study represents a significant methodological advancement in subclonal reconstruction and highlights the importance of measuring tumor subclone structure. Citation Format: Yujie Jiang, Kaixian Yu, Matthew D. Montierth, Shuangxi Ji, Seung Jun Shin, Shuai Guo, Shaolong Cao, Yuxin Tang, Scott Kopetz, Pavlos Msaouel, Jennifer R. Wang, Marek Kimmel, Peter Van Loo, Hongtu Zhu, Wenyi Wang. Pan-cancer analysis of intra-tumor heterogeneity in 9,116 cancers using a novel regularized likelihood model. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4272.

Abstract 4284: Enhancing subclonal reconstruction algorithm for resolving complex tumor phylogenies from multi-sample tumor DNA sequencing

Abstract Cancer is characterized by the ongoing accumulation of somatic mutations, providing selective advantages that may lead to dysregulated cellular proliferation. While the cancer genome at diagnosis has been extensively studied, many cancer types still lack strong prognostic biomarkers. The continuous acquisition and selection for driver mutations in a population of cancer cells acts as a Darwinian process, resulting in clonal expansions of progressively more aberrant and fit phenotypes. Reconstructing tumor evolution allows us to understand key events that drive cancer progression and patterns of mutation co-occurrence within clones. These evolutionary features guide our understanding of fundamental mechanisms that lead to disease lethality. Inferring tumor evolution from DNA sequencing data is becoming part of routine analysis in cancer research. As sequencing costs drop, sequencing multiple tumor samples from a patient becomes routine. These multiple samples can represent different spatial regions of a tumor, longitudinal samples from a single region or a combination of both. This provides an opportunity to study tumor evolution in much greater detail and accuracy than was previously feasible through single-sample datasets. The most widely used methods to reconstruct the subclonal evolution of a tumor utilize stochastic-search algorithms. These approaches iterate through a parameter space to select phylogenetic solutions that maximize the likelihood of observed sequencing data. They are optimized for low complexity cases, where the size and number or subclones are relatively limited. As tumor subclonal structure increases in complexity, the parameter space grows exponentially, and stochastic-search algorithms become computationally intractable. For instance, recent benchmarking studies have revealed that many methods fail to reconstruct clone trees for data with as few as ten subclones. To circumvent current computational limitations, we developed a deterministic algorithm for subclonal reconstruction that leverages fundamental principles of cancer biology to encode heuristics that reduce the solution space to biologically plausible phylogenies. When applied to samples (4-36 tumors; median 16) from 12 patients with metastatic breast cancer, our method reduced the average runtime ten-fold. We were able to delineate the evolutionary history of up to 57 distinct subclones per patient, which is infeasible with most current methods. Benchmarking using methods developed for the SMCHet DREAM challenge on real and simulated datasets further quantifies the accuracy, resolution, and scalability. We have thus presented a novel method for rapid and optimized reconstruction of tumor evolutionary histories. Citation Format: Helena Winata, Daniel Knight, Juber A. Patel, Nicholas K. Wang, Pier Selenica, Stefan E. Eng, Caroline Kostrzewa, Jaron Arbet, Yingjie Zhu, Ronglai Shen, Jorge Reis-Filho, Pedram Razafi, Paul C. Boutros. Enhancing subclonal reconstruction algorithm for resolving complex tumor phylogenies from multi-sample tumor DNA sequencing. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4284.

Abstract 258: Whole genome analysis of hereditary leiomyomatosis and renal cell cancer identifies role of localized hypermutational events

Abstract Hereditary leiomyomatosis and renal cell cancer (HLRCC) is a rare hereditary condition affecting over 1 in 200,000 people worldwide. Individuals with HLRCC are also at risk of developing an aggressive form of renal cell carcinoma (RCC), as 15-20% of patients develop type 2 papillary RCC associated with poor prognosis. Although RCC risk and cancer penetrance of this condition has been studied, whole genome sequencing of associated tumors has yet to be examined, and the landscape of structural variants of HLRCC is largely unknown. We have therefore performed whole genome sequencing of tumor/normal pairs of HLRCC patients to study the tumor molecular architecture in this form of RCC. Through germline variant and somatic mutation calling, we have identified specific somatic mutational drivers, including those at the structural variant level for the first time in HLRCC. We have also quantified both chromothripsis and kataegis in these tumors, identifying the specific role of localized hypermutational events. Similarly, using tumour subclonal reconstruction techniques, we have inferred the clonality of HLRCC-associated RCC tumors and elucidate the relative timing of key oncogenic events and mutational processes. We also compare each of these mutational features to their equivalents in other RCC tumour subtypes. These studies shed key light on the specific mutational processes and drivers of this rare hereditary malignancy and on its lethality. Citation Format: Selina Wu, Paul C. Boutros. Whole genome analysis of hereditary leiomyomatosis and renal cell cancer identifies role of localized hypermutational events [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 258.

Abstract P5-13-01: TUMOR PROGRESSION MODEL IN BREAST CANCER WITH BRAIN METASTASIS

Abstract INTRODUCTION: Breast cancer are the main cause of related deaths cancer among women, corresponding to 25% of new cases each year. When diagnosed at early stages, it has an overall five-year survival rate of up to 90%. However, in more advanced stages, survival is reduced to about 24%, with 90% of women in stage IV dying as a result of complications related to metastases. Considering that brain metastasis is an unfavorable prognostic site, and the identification of genetic-molecular profiles in primary tumors and in metastatic sites are a subject poorly described in the literature, we understand that the identification of mutational profiles may contribute to elucidate the genetic-molecular mechanisms associated with tumor progression. AIM: The aim of this study was to identify clonal and subclonal driver mutations that lead to evolution of metastatic clones from a breast cancer progression model. MATERIAL AND METHODS: For tumor progression model, automated extraction of DNA from buffy coat and paraffin samples of breast tumors and paired brain metastases (n=9) was performed. In the present work, we used a subclonal reconstruction model based on the combination of machine learning and population genetics concepts. This proposal is based on the frequency spectrum of each somatic mutation (SNVs or indels), considering VAF (Variant Allele Frequency - ratio of mapped reads of the mutant allele) in relation to the coverage of variant locus, as known as CCF (Cancer Cell Fraction). CCF is defined as the proportion of neoplastic cells that have a certain set of mutations and then is normalized considering the sample purity and the segments with changes in number of DNA copies (Copy Number Alterations). Then, a statistical model based on finite Dirichlet mixtures with mixed distributions is applied. In this model, Beta components capture clonal expansions and population genetics concepts were applied to mutant alleles in each population considering principles of cancer evolution. Finally, confidence was computed using both parametric and non-parametric bootstraps. The functions for building the model are implemented at https://caravagnalab.github.io/mobster/, and for visualization and construction of graphs, packages in R statistical-mathematical environment were used, such as ggplot2, sads, cli, clisymbols, cowplot, crayon, ctree, dndscv, dplyr, magrittr, reshape2, and tidyr. RESULTS: With this model was possible to identify the distribution of clones according to somatic alterations in patients with breast cancer and brain metastasis. It was possible to observe common patterns, such as alterations of the SF3B1 gene as a common ancestral clone in both conditions and the frequency of the AKT1 gene in a subclonal condition. Other genes relevant to breast cancer carcinogenesis, such as PIK3CA and TP53, are found in a different clonal hierarchical pattern between the two conditions. CONCLUSION: This data suggests a model of clonal evolution capable to identify which drivers clones and subclones are involved in the metastatic process. Citation Format: Muriele B. Varuzza, Adriane F. Evangelista, Cristiano P. Souza, Márcia C. Marques. TUMOR PROGRESSION MODEL IN BREAST CANCER WITH BRAIN METASTASIS [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-13-01.

Abstract 3625: Clonal architecture and evolution of treatment-resistant prostate cancer via deep whole-genome ctDNA sequencing

Abstract Background: Circulating tumor DNA (ctDNA) in blood plasma is an emerging tool for clinical cancer genotyping and longitudinal disease monitoring. However, integration of ctDNA tests into clinical management is critically impeded by the poor understanding of the distinct somatic populations comprising bulk ctDNA—including their relationship to synchronous metastatic tissue, and their temporal dynamics during standard-of-care treatment. Prior approaches relying on targeted and/or low-resolution techniques (e.g. targeted exon sequencing, low-pass (shallow) whole-genome sequencing; WGS) do not permit comprehensive dissection of clonal architecture and unbiased analysis of putative resistance mechanisms. Methods: We performed deep WGS on serial plasma ctDNA (median depth: 185×) and synchronous metastatic tissue biopsies with high tumor purity from 35 patients with metastatic castration-resistant prostate cancer. We developed a subclonal reconstruction algorithm optimized for our data enabling resolution of per-patient evolutionary histories and ctDNA clonal composition. ctDNA nucleosome footprinting was used to infer mRNA abundance in synchronously biopsied metastases and androgen receptor (AR) transcription factor activity at 3224 AR binding sites (ARBS). Results: We comprehensively assess all classes of genomic alterations and demonstrate that ctDNA harbors greater populational heterogeneity than metastatic tissue (p<0.001). The evolutionary histories of ctDNA populations indicate frequent whole-genome doubling and attenuation of C>T aging-associated mutation signature during subclonal differentiation. Although driver alterations were largely concordant between tissue and ctDNA, each individual metastasis contributed only a minor share of total ctDNA (average ctDNA contribution: 17%). By comparing serial ctDNA before and after clinical progression on potent AR pathway inhibitors, we reveal population restructuring converging solely on AR copy augmentation as the dominant genomic driver of acquired treatment-resistance. Nucleosome depletion at transcription start-sites is highly correlated with same-patient metastatic tissue mRNA abundance, indicating that ctDNA fragmentomics can recapitulate transcriptomic patterns in metastatic lesions. Most ctDNA samples exhibited strong ARBS nucleosome depletion which correlated with AR gene copy number (R=0.36, p=0.003). Finally, serial ctDNA nucleosome profiling at ARBS revealed adaptive transcriptomic resistance to AR pathway inhibitors, including lineage switch to a neuroendocrine-like (AR-low) state. Conclusions: We show that the populations comprising ctDNA are typically complex and more heterogeneous than those found in bulk WGS of a synchronous metastasis. Our work advocates for liquid biopsy as a comprehensive multi-omic discovery tool for cancers with high ctDNA fractions. Citation Format: Cameron Herberts, Matti Annala, Joonatan Sipola, Sarah W. Ng, Xinyi E. Chen, Anssi Nurminen, Olga Korhonen, Aslı D. Munzur, Kevin Beja, Elena Schönlau, Cecily Q. Bernales, Elie Ritch, Jack V. Bacon, Nathan A. Lack, Matti Nykter, Rahul Aggarwal, Eric J. Small, Martin E. Gleave, David A. Quigley, Felix Y. Feng, Kim N. Chi, Alexander W. Wyatt. Clonal architecture and evolution of treatment-resistant prostate cancer via deep whole-genome ctDNA sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3625.

Spatial Genomic Heterogeneity in Multiple Myeloma: Surrogate Markers and Its Significance for Understanding the Evolutionary Processes Leading to Relapse

IntroductionMultiple Myeloma (MM) frequently presents with genomic variation between individual cancer foci, which is also termed spatial heterogeneity (SH). However, the extent of SH differs considerably between patients with some having extensive SH while others show homogeneous mutational profiles within the bone marrow (BM). Since SH poses a significant challenge to personalized therapy, surrogate markers for its extent would be useful clinically. In this respect an understanding of the spatial evolutionary patterns taking place during MM treatment and the clonal architecture they give rise to at relapse would also be of fundamental importance. To address these important points we have analyzed paired samples from multiple BM sites collected at baseline (n=52 patients) or in a longitudinal fashion during treatment (n=30).Patients and MethodsA genomic analysis was performed on iliac crest BM and CT-guided fine needle aspirates from distinct anatomical sites from 52 newly diagnosed patients. The impact of treatment on SH was analyzed in 30 patients with a median number of 6 samples per patient (range 4-14). Plasma cells were CD138-enriched and underwent whole exome sequencing using an Illumina HiSeq 2500. Sequencing data were aligned using BWA. Mutations were identified using MuTect. Copy number aberrations were derived from Illumina HumanOmni 2.5 bead chip data using ASCAT. Subclonal reconstruction was performed using SciClone. Statistical analyses were carried out using R packages.ResultsWe investigated whether the extent of SH was associated with clinical features and found that age, an adverse prognostic factor, was positively correlated with SH (P=0.01). Patients over the age of 60 had a 3-fold higher proportion of private mutations compared to younger patients (median 15 vs. 5%). Hypothetically, this could reflect the longer evolutionary time period to MM arising at older ages. The size of the biopsied FLs was also significantly associated with the proportion of private mutations (P <0.001), with large FLs (>2.5 cm) having the highest values. Of note, a linear model including age and FL size explained 49% of the variation in SH. In contrast, neither the ISS, adverse molecular markers identified at the iliac crest, the number of FLs, nor the anatomical distance between investigated sites correlated with SH. In summary, we show that SH is increased in elderly patients and/or patients with large FLs.Next, we investigated the impact of treatment on the spatial clonal architecture. For patients who had achieved a CR, we expected relapse to be dominated by a limited number of selected clones and as a result less SH. Indeed, analyzing samples derived from multiple sites at baseline and at subsequent relapses, the predominant pattern was characterized by sweeps of advanced clones resulting in BM-wide replacement of previous clones. Of note, evidence for clonal sweeps was also seen in patients with stable disease according to IMWG criteria, which is a perplexing observation since stable serological parameters do not seem to be consistent with major sub-clonal changes.In contrast to cases with clonal sweeps, there were also patients with multiple site-specific clones at relapse. On the one hand, “mixed responses” of FLs, which are frequently seen in medical imaging after multiple relapses, could be related to this pattern of pronounced SH. On the other hand, we found private mutations affecting the same genes or pathways in patients with site-specific subclones, indicating parallel evolution. In this context, one patient showed distinct KRAS mutations which affected the exact same amino acid (Gly12Val vs. Gly12Asp), emphasizing that treatment could select for multiple clones with similar or even identical features. This observation may explain, why individual patients still respond to MAPK pathway inhibitors despite pronounced SH.ConclusionsWe provide new insights into the biology of MM progression, which has implications for diagnostic and treatment strategies. The strong association of private mutations with age and FL size may allow for the identification of patients with increased levels of SH for whom treatment approaches can be modified. The existence of clonal sweeps of aggressive clones, which replace previously dominant clones, even in poorly responding patients, highlights the need for regular molecular testing, especially if personalized treatment is intended. DisclosuresBarlogie:Millenium Pharmaceuticals: Consultancy, Research Funding; Celgene Corporation: Consultancy, Research Funding. Davies:Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Morgan:Takeda: Consultancy, Honoraria; Bristol Myers: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding.

Reconstructing tumor evolutionary histories and clone trees in polynomial-time with SubMARine.

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 partially-defined clone tree (partial clone tree for short) 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. Further, we extend SubMARine to permit noise in the estimates of the subclonal frequencies while retaining its validity conditions and guarantees. In contrast to other clone tree reconstruction methods, SubMARine runs in time and space that scale polynomially in the number of subclones. We show through extensive noise-free simulation, a large lung cancer dataset and a prostate cancer dataset that the subMAR equals the MAR in all 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. On the real-world data, SubMARine almost perfectly recovers the previously reported trees and identifies minor errors made in the expert-driven reconstructions of those trees. The freely-available open-source code implementing SubMARine can be downloaded at https://github.com/morrislab/submarine.

A practical guide to cancer subclonal reconstruction from DNA sequencing.

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.

Quantifying the influence of mutation detection on tumour subclonal reconstruction

Whole-genome sequencing can be used to estimate subclonal populations in tumours and this intra-tumoural heterogeneity is linked to clinical outcomes. Many algorithms have been developed for subclonal reconstruction, but their variabilities and consistencies are largely unknown. We evaluate sixteen pipelines for reconstructing the evolutionary histories of 293 localized prostate cancers from single samples, and eighteen pipelines for the reconstruction of 10 tumours with multi-region sampling. We show that predictions of subclonal architecture and timing of somatic mutations vary extensively across pipelines. Pipelines show consistent types of biases, with those incorporating SomaticSniper and Battenberg preferentially predicting homogenous cancer cell populations and those using MuTect tending to predict multiple populations of cancer cells. Subclonal reconstructions using multi-region sampling confirm that single-sample reconstructions systematically underestimate intra-tumoural heterogeneity, predicting on average fewer than half of the cancer cell populations identified by multi-region sequencing. Overall, these biases suggest caution in interpreting specific architectures and subclonal variants.

Abstract PO-029: CliP: A model-based method for subclonal architecture reconstruction using regularized maximum likelihood estimation

Abstract Background Subpopulations of tumor cells characterized by mutation profiles may confer differential fitness to treatment and prognosis across cancers. Understanding subclonal architecture has the potential to provide biological insight into tumor evolution and advance the precision treatment of cancers. Recent methods comprehensively integrate single nucleotide variants (SNVs) and copy number alterations (CNAs) to reconstruct subclonal architecture using whole-genome sequencing (WGS) data from bulk tumor samples. However, most methods follow a Bayesian framework and require extensive computational resources, a prior knowledge of the number of subclones, as well as ad hoc post-analysis data processing. Altogether this creates a bottleneck in processing time in large-scale studies. Objectives The primary objective of this study is to introduce a fast and accurate subclonal architecture reconstruction method, which utilizes a model-based clustering approach and addresses all the limitations above. Methods We introduce a novel model-based clustering method: Clonal structure identification through pairwise penalization (CliP). CliP assumes the number of reads observed with variant alleles follows a binomial model, which is a function of mutation cellular prevalence (CP), copy number aberrations, and tumor purity. We propose to minimize a penalized likelihood of this model with a SCAD penalty on CPs across pairs of mutations. The optimization problem is then efficiently solved via Alternating Direction Method of Multipliers (ADMM). As a subclonal reconstruction algorithm, CliP attempts to infer the population structure of heterogeneous tumors, and is the first method to utilize a regularized maximum likelihood framework in subclonal reconstruction, therefore benefiting from its computational efficiency in parameter estimation. Results Rigorous and extensive simulation results demonstrate that CliP is 100 times faster than MCMC-based algorithms without decreased performance. Unlike previous models, the CliP model is applicable to regions with or without CNAs. Furthermore, CliP generates subclonal structure without prior knowledge or post-processing. In an application to WGS data from Pan-Cancer Analysis of Whole Genomes (PCAWG), it only took 8 hours to process 2,500 tumor samples. Conclusion Since CliP executes quickly, it is ultimately suitable for 1) processing large datasets with thousands of samples and 2) participation in a group of methods to generate consensus calls. As the sizes of datasets continue to grow, CliP represents an important step towards fast and accurate subclonal reconstruction. Citation Format: Yujie Jiang, Kaixian Yu, Hongtu Zhu, Wenyi Wang. CliP: A model-based method for subclonal architecture reconstruction using regularized maximum likelihood estimation [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-029.

Subclonal reconstruction of tumors by using machine learning and population genetics.

The majority of cancer genomic data are generated from bulk samples composed of mixtures of cancer subpopulations, as well as normal cells. Subclonal reconstruction approaches based on machine learning aim to separate those subpopulations in a sample and reconstruct their evolutionary history. However, current approaches are entirely data-driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in the analysis if evolution is not accounted for, and this is exacerbated by multi-sampling of the same tumor. We present a novel approach for model-based tumor subclonal reconstruction (MOBSTER) that combines machine learning with theoretical population genetics. Using public whole-genome sequencing data from 2,606 samples from different cohorts, new data and synthetic validation, we show this method is more robust and accurate than current techniques in single sample, multi-region and longitudinal data. This approach minimizes the confounding factors of non-evolutionary methods, leading to more accurate recovery of the evolutionary history of human cancers.

Abstract A04: Tumor development trajectories for exploring differences between cancer subtypes

Abstract We have employed PhylogicNDT, our computational method for timing somatic events and subclonal architecture reconstruction, to explore the association of tumor development trajectory (from normal cell to malignant state) with cancer subtype and genetic landscape. Previous attempts to understand the order of somatic events that lead to the formation of cancers are mostly based on studies of premalignant samples, which are often hard or impossible to obtain due to small size or inaccessible location. Our approach allows us to use information from whole-exome and whole-genome data to computationally reconstruct the order of events preceding malignant transformation of the tissue. We show that this approach yields accurate reconstruction of premalignant events in lung, colorectal, and head and neck cancers based on previous exploration of premalignant lesions. We explored the association between the developmental trajectories, treatment outcome, and survival in several cancer types. PhylogicNDT can be used to infer the order of clonal driver events (including in precancerous stages), subclonal populations of cells and their phylogenetic relationships, and dynamics of cell populations. This methodology will enable a systematic study of tumor initiation and progression across cancer types and therapies. Citation Format: Ignaty Leshchiner, Daniel Rosebrock, Oliver Spiro, Edmund Mroz, Dimitri Livitz, James W. Rocco, Gad Getz. Tumor development trajectories for exploring differences between cancer subtypes [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Optimizing Survival and Quality of Life through Basic, Clinical, and Translational Research; 2019 Apr 29-30; Austin, TX. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_2):Abstract nr A04.

A Pipeline for Reconstructing Somatic Copy Number Alternation's Subclonal Population-Based Next-Generation Sequencing Data.

State-of-the-art next-generation sequencing (NGS)-based subclonal reconstruction methods perform poorly on somatic copy number alternations (SCNAs), due to not only it needs to simultaneously estimate the subclonal population frequency and the absolute copy number for each SCNA, but also there exist complex bias and noise in the tumor and its paired normal sequencing data. Both existing NGS-based SCNA detection methods and SCNA’s subclonal population frequency inferring tools use the read count on radio (RCR) of tumor to its paired normal as the key feature of tumor sequencing data; however, the sequencing error and bias have great impact on RCR, which leads to a large number of redundant SCNA segments that make the subsequent process of SCNA’s subclonal population frequency inferring and subclonal reconstruction time-consuming and inaccurate. We perform a mathematical analysis of the solution number of SCNA’s subclonal frequency, and we propose a computational algorithm to reduce the impact of false breakpoints based on it. We construct a new probability model that incorporates the RCR bias correction algorithm, and by stringing it with the false breakpoint filtering algorithm, we construct a whole SCNA’s subclonal population reconstruction pipeline. The experimental result shows that our pipeline outperforms the existing subclonal reconstruction programs both on simulated data and TCGA data. Source code is publicly available as a Python package at https://github.com/dustincys/msphy-SCNAClonal.

Multi-regional sequencing reveals clonal and polyclonal seeding from primary tumor to metastases in advanced gastric cancer.

Tumor metastases to lymph nodes and distant organs are associated with worse prognosis in gastric cancer. However, little is known about the genetic profiles, subclonal architecture, and evolutional processes across primary tumors and metastases. We analyzed the genetic alterations of 106 multiregional samples including primary tumors, lymph node metastases, and visceral metastases from 10 patients with advanced gastric cancer. Histologically different portions were obtained by laser-capture microdissection. We reconstructed the subclonal architectures and inferred the primary to lymph or visceral metastatic seeding patterns. The different histological portions in primary tumors had common mutations, suggesting common ancestral tumor origins transformed into distinct histological types. In almost all cases, TP53 mutations were identified as clonal mutations across primary tumors and metastases. Subclonal reconstruction and phylogenetic analysis showed primary tumors were classified into monoclonal or polyclonal tumors. All monoclonal primary tumors disseminated as metastases with the same tumor composition (100%, 26/26 samples). In contrast, polyclonal primary tumors mainly spread as metastases by way of polyclonal seeding (84%: 37/44 samples). Clonal mutations were maintained at both the primary and metastatic sites and genetic divergence of these was low. These findings shed light on the genetic basis of primary tumor dissemination and metastatic processes in advanced gastric cancer.