The evolution of tumour phylogenetics: principles and practice

The COMPASS complex maintains the metastatic capacity imparted by a subpopulation of cells in UPS

Intratumoral heterogeneity is common in cancer, particularly in sarcomas like undifferentiated pleomorphic sarcoma (UPS), where individual cells demonstrate a high degree of cytogenic diversity. Previous studies showed that a small subset of cells within UPS, known as the metastatic clone (MC), as responsible for metastasis. Using a CRISPR-based genomic screen in-vivo, we identified the COMPASS complex member Setd1a as a key regulator maintaining the metastatic phenotype of the MC in murine UPS. Depletion of Setd1a inhibited metastasis development in the MC. Transcriptome and chromatin sequencing revealed COMPASS complex target genes in UPS, such as Cxcl10, downregulated in the MC. Deleting Cxcl10 in non-MC cells increased their metastatic potential. Treating mice with human UPS xenografts with a COMPASS complex inhibitor suppressed metastasis without affecting tumor growth in the primary tumor. Our data identified an epigenetic program in a subpopulation of sarcoma cells that maintains metastatic potential.

A Probabilistic Approach to Estimate the Temporal Order of Pathway Mutations Accounting for Intra-Tumor Heterogeneity

The development of cancer involves the accumulation of somatic mutations in several essential biological pathways. Delineating the temporal order of pathway mutations during tumorigenesis is crucial for comprehending the biological mechanisms underlying cancer development and identifying potential targets for therapeutic intervention. Several computational and statistical methods have been introduced for estimating the order of somatic mutations based on mutation profile data from a cohort of patients. However, one major issue of current methods is that they do not take into account intra-tumor heterogeneity (ITH), which limits their ability to accurately discern the order of pathway mutations. To address this problem, we propose PATOPAI, a probabilistic approach to estimate the temporal order of mutations at the pathway level by incorporating ITH information as well as pathway and functional annotation information of mutations. PATOPAI uses a maximum likelihood approach to estimate the probability of pathway mutational events occurring in a specific sequence, wherein it focuses on the orders that are consistent with the phylogenetic structure of the tumors. Applications to whole exome sequencing data from The Cancer Genome Atlas (TCGA) illustrate our method's ability to recover the temporal order of pathway mutations in several cancer types.

Detecting branching rate heterogeneity in multifurcating trees with applications in lineage tracing data

Understanding cellular birth rate differences is crucial for predicting cancer progression and interpreting tumor-derived genetic data. Lineage tracing experiments enable detailed reconstruction of cellular genealogies, offering new opportunities to measure branching rate heterogeneity. However, the lineage tracing process can introduce complex tree features that complicate this effort. Here, we examine tree characteristics in lineage tracing-derived genealogies and find that editing window placement leads to multifurcations at a tree's root or tips. We propose several ways in which existing tree topology-based metrics can be extended to test for rate heterogeneity on trees even in the presence of lineage-tracing associated distortions. Although these methods vary in power and robustness, a test based on the J 1 statistic effectively detects branching rate heterogeneity in simulated lineage tracing data. Tests based on other common statistics ( ŝ and the Sackin index) show interior performance to J 1 . We apply our validated methods to xenograft experimental data and find widespread rate heterogeneity across multiple study systems. Our results demonstrate the potential of tree topology statistics in analyzing lineage tracing data, and highlight the challenges associated with adapting phylogenetic methods to these systems.

scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data

BACKGROUND

Accurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH.

RESULTS

We introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal .

CONCLUSIONS

Joint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.

The k-Robinson-Foulds Dissimilarity Measures for Comparison of Labeled Trees

Understanding the mutational history of tumor cells is a critical endeavor in unraveling the mechanisms that drive the onset and progression of cancer. Modeling tumor cell evolution with labeled trees motivates researchers to develop different measures to compare labeled trees. Although the Robinson-Foulds (RF) distance is widely used for comparing species trees, its applicability to labeled trees reveals certain limitations. This study introduces the k-RF dissimilarity measures, tailored to address the challenges of labeled tree comparison. The RF distance is succinctly expressed as n-RF in the space of labeled trees with n nodes. Like the RF distance, the k-RF is a pseudometric for multiset-labeled trees and becomes a metric in the space of 1-labeled trees. By setting k to a small value, the k-RF dissimilarity can capture analogous local regions in two labeled trees with different size or different labels.

CoT: a transformer-based method for inferring tumor clonal copy number substructure from scDNA-seq data

Single-cell DNA sequencing (scDNA-seq) has been an effective means to unscramble intra-tumor heterogeneity, while joint inference of tumor clones and their respective copy number profiles remains a challenging task due to the noisy nature of scDNA-seq data. We introduce a new bioinformatics method called CoT for deciphering clonal copy number substructure. The backbone of CoT is a Copy number Transformer autoencoder that leverages multi-head attention mechanism to explore correlations between different genomic regions, and thus capture global features to create latent embeddings for the cells. CoT makes it convenient to first infer cell subpopulations based on the learned embeddings, and then estimate single-cell copy numbers through joint analysis of read counts data for the cells belonging to the same cluster. This exploitation of clonal substructure information in copy number analysis helps to alleviate the effect of read counts non-uniformity, and yield robust estimations of the tumor copy numbers. Performance evaluation on synthetic and real datasets showcases that CoT outperforms the state of the arts, and is highly useful for deciphering clonal copy number substructure.

Marker selection strategies for circulating tumor DNA guided by phylogenetic inference

Motivation

Blood-based profiling of tumor DNA ("liquid biopsy") has offered great prospects for non-invasive early cancer diagnosis, treatment monitoring, and clinical guidance, but require further advances in computational methods to become a robust quantitative assay of tumor clonal evolution. We propose new methods to better characterize tumor clonal dynamics from circulating tumor DNA (ctDNA), through application to two specific questions: 1) How to apply longitudinal ctDNA data to refine phylogeny models of clonal evolution, and 2) how to quantify changes in clonal frequencies that may be indicative of treatment response or tumor progression. We pose these questions through a probabilistic framework for optimally identifying maximum likelihood markers and applying them to characterizing clonal evolution.

Results

We first estimate a distribution over plausible clonal lineage models, using bootstrap samples over pre-treatment tissue-based sequence data. We then refine these lineage models and the clonal frequencies they imply over successive longitudinal samples. We use the resulting framework for modeling and refining tree distributions to pose a set of optimization problems to select ctDNA markers to maximize measures of utility capturing ability to solve the two questions of reducing uncertain in phylogeny models or quantifying clonal frequencies given the models. We tested our methods on synthetic data and showed them to be effective at refining distributions of tree models and clonal frequencies so as to minimize measures of tree distance relative to the ground truth. Application of the tree refinement methods to real tumor data further demonstrated their effectiveness in refining a clonal lineage model and assessing its clonal frequencies. The work shows the power of computational methods to improve marker selection, clonal lineage reconstruction, and clonal dynamics profiling for more precise and quantitative assays of tumor progression.

Availability

https://github.com/CMUSchwartzLab/Mase-phi.git.

Contact

russells@andrew.cmu.edu.

Optimizing Design of Genomics Studies for Clonal Evolution Analysis

Genomic biotechnologies have seen rapid development over the past two decades, allowing for both the inference and modification of genetic and epigenetic information at the single cell level. While these tools present enormous potential for basic research, diagnostics, and treatment, they also raise difficult issues of how to design research studies to deploy these tools most effectively. In designing a study at the population or individual level, a researcher might combine several different sequencing modalities and sampling protocols, each with different utility, costs, and other tradeoffs. The central problem this paper attempts to address is then how one might create an optimal study design for a genomic analysis, with particular focus on studies involving somatic variation, typically for applications in cancer genomics. We pose the study design problem as a stochastic constrained nonlinear optimization problem and introduce a simulation-centered optimization procedure that iteratively optimizes the objective function using surrogate modeling combined with pattern and gradient search. Finally, we demonstrate the use of our procedure on diverse test cases to derive resource and study design allocations optimized for various objectives for the study of somatic cell populations.

Evolutionary trajectories of small cell lung cancer under therapy

The evolutionary processes that underlie the marked sensitivity of small cell lung cancer (SCLC) to chemotherapy and rapid relapse are unknown1-3. Here we determined tumour phylogenies at diagnosis and throughout chemotherapy and immunotherapy by multiregion sequencing of 160 tumours from 65 patients. Treatment-naive SCLC exhibited clonal homogeneity at distinct tumour sites, whereas first-line platinum-based chemotherapy led to a burst in genomic intratumour heterogeneity and spatial clonal diversity. We observed branched evolution and a shift to ancestral clones underlying tumour relapse. Effective radio- or immunotherapy induced a re-expansion of founder clones with acquired genomic damage from first-line chemotherapy. Whereas TP53 and RB1 alterations were exclusively part of the common ancestor, MYC family amplifications were frequently not constituents of the founder clone. At relapse, emerging subclonal mutations affected key genes associated with SCLC biology, and tumours harbouring clonal CREBBP/EP300 alterations underwent genome duplications. Gene-damaging TP53 alterations and co-alterations of TP53 missense mutations with TP73, CREBBP/EP300 or FMN2 were significantly associated with shorter disease relapse following chemotherapy. In summary, we uncover key processes of the genomic evolution of SCLC under therapy, identify the common ancestor as the source of clonal diversity at relapse and show central genomic patterns associated with sensitivity and resistance to chemotherapy.

Consensus Tree Under the Ancestor-Descendant Distance is NP-Hard

Due to uncertainty in tumor phylogeny inference from sequencing data, many methods infer multiple, equally plausible phylogenies for the same cancer. To summarize the solution space T of tumor phylogenies, consensus tree methods seek a single best representative tree S under a specified pairwise tree distance function. One such distance function is the ancestor-descendant (AD) distance [Formula: see text] , which equals the size of the symmetric difference of the transitive closures of the edge sets [Formula: see text] and [Formula: see text] . Here, we show that finding a consensus tree S for tumor phylogenies T that minimizes the total AD distance [Formula: see text] is NP-hard.

Characterizing chromosomal instability-driven cancer evolution and cell fitness at a glance

Chromosomal instability (CIN), an increased rate of chromosome segregation errors during mitosis, is a hallmark of cancer cells. CIN leads to karyotype differences between cells and thus large-scale heterogeneity among individual cancer cells; therefore, it plays an important role in cancer evolution. Studying CIN and its consequences is technically challenging, but various technologies have been developed to track karyotype dynamics during tumorigenesis, trace clonal lineages and link genomic changes to cancer phenotypes at single-cell resolution. These methods provide valuable insight not only into the role of CIN in cancer progression, but also into cancer cell fitness. In this Cell Science at a Glance article and the accompanying poster, we discuss the relationship between CIN, cancer cell fitness and evolution, and highlight techniques that can be used to study the relationship between these factors. To that end, we explore methods of assessing cancer cell fitness, particularly for chromosomally unstable cancer.

Inferring Markov Chains to Describe Convergent Tumor Evolution With CIMICE

The field of tumor phylogenetics focuses on studying the differences within cancer cell populations. Many efforts are done within the scientific community to build cancer progression models trying to understand the heterogeneity of such diseases. These models are highly dependent on the kind of data used for their construction, therefore, as the experimental technologies evolve, it is of major importance to exploit their peculiarities. In this work we describe a cancer progression model based on Single Cell DNA Sequencing data. When constructing the model, we focus on tailoring the formalism on the specificity of the data. We operate by defining a minimal set of assumptions needed to reconstruct a flexible DAG structured model, capable of identifying progression beyond the limitation of the infinite site assumption. Our proposal is conservative in the sense that we aim to neither discard nor infer knowledge which is not represented in the data. We provide simulations and analytical results to show the features of our model, test it on real data, show how it can be integrated with other approaches to cope with input noise. Moreover, our framework can be exploited to produce simulated data that follows our theoretical assumptions. Finally, we provide an open source R implementation of our approach, called CIMICE, that is publicly available on BioConductor.

Sc-TUSV-ext: Single-cell clonal lineage inference from single nucleotide variants (SNV), copy number alterations (CNA) and structural variants (SV)

Clonal lineage inference ("tumor phylogenetics") has become a crucial tool for making sense of somatic evolution processes that underlie cancer development and are increasingly recognized as part of normal tissue growth and aging. The inference of clonal lineage trees from single cell sequence data offers particular promise for revealing processes of somatic evolution in unprecedented detail. However, most such tools are based on fairly restrictive models of the types of mutation events observed in somatic evolution and of the processes by which they develop. The present work seeks to enhance the power and versatility of tools for single-cell lineage reconstruction by making more comprehensive use of the range of molecular variant types by which tumors evolve. We introduce Sc-TUSV-ext, an integer linear programming (ILP) based tumor phylogeny reconstruction method that, for the first time, integrates single nucleotide variants (SNV), copy number alterations (CNA) and structural variations (SV) into clonal lineage reconstruction from single-cell DNA sequencing data. We show on synthetic data that accounting for these variant types collectively leads to improved accuracy in clonal lineage reconstruction relative to prior methods that consider only subsets of the variant types. We further demonstrate the effectiveness on real data in resolving clonal evolution in the presence of multiple variant types, providing a path towards more comprehensive insight into how various forms of somatic mutability collectively shape tissue development.

Quartets enable statistically consistent estimation of cell lineage trees under an unbiased error and missingness model

Cancer progression and treatment can be informed by reconstructing its evolutionary history from tumor cells. Although many methods exist to estimate evolutionary trees (called phylogenies) from molecular sequences, traditional approaches assume the input data are error-free and the output tree is fully resolved. These assumptions are challenged in tumor phylogenetics because single-cell sequencing produces sparse, error-ridden data and because tumors evolve clonally. Here, we study the theoretical utility of methods based on quartets (four-leaf, unrooted phylogenetic trees) in light of these barriers. We consider a popular tumor phylogenetics model, in which mutations arise on a (highly unresolved) tree and then (unbiased) errors and missing values are introduced. Quartets are then implied by mutations present in two cells and absent from two cells. Our main result is that the most probable quartet identifies the unrooted model tree on four cells. This motivates seeking a tree such that the number of quartets shared between it and the input mutations is maximized. We prove an optimal solution to this problem is a consistent estimator of the unrooted cell lineage tree; this guarantee includes the case where the model tree is highly unresolved, with error defined as the number of false negative branches. Lastly, we outline how quartet-based methods might be employed when there are copy number aberrations and other challenges specific to tumor phylogenetics.

ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model

A tumor contains a diverse collection of somatic mutations that reflect its past evolutionary history and that range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). However, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs, complicating the inference of tumor phylogenies. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers but constrains losses of SNVs according to clusters of cells. We derive an algorithm, ConDoR, that infers phylogenies from targeted scDNA-seq data using this model. We demonstrate the advantages of ConDoR on simulated and real scDNA-seq data.

Fusang: a framework for phylogenetic tree inference via deep learning

Phylogenetic tree inference is a classic fundamental task in evolutionary biology that entails inferring the evolutionary relationship of targets based on multiple sequence alignment (MSA). Maximum likelihood (ML) and Bayesian inference (BI) methods have dominated phylogenetic tree inference for many years, but BI is too slow to handle a large number of sequences. Recently, deep learning (DL) has been successfully applied to quartet phylogenetic tree inference and tentatively extended into more sequences with the quartet puzzling algorithm. However, no DL-based tools are immediately available for practical real-world applications. In this paper, we propose Fusang (http://fusang.cibr.ac.cn), a DL-based framework that achieves comparable performance to that of ML-based tools with both simulated and real datasets. More importantly, with continuous optimization, e.g. through the use of customized training datasets for real-world scenarios, Fusang has great potential to outperform ML-based tools.

Single-cell methylation sequencing data reveal succinct metastatic migration histories and tumor progression models

Recent studies exploring the impact of methylation in tumor evolution suggest that although the methylation status of many of the CpG sites are preserved across distinct lineages, others are altered as the cancer progresses. Because changes in methylation status of a CpG site may be retained in mitosis, they could be used to infer the progression history of a tumor via single-cell lineage tree reconstruction. In this work, we introduce the first principled distance-based computational method, Sgootr, for inferring a tumor's single-cell methylation lineage tree and for jointly identifying lineage-informative CpG sites that harbor changes in methylation status that are retained along the lineage. We apply Sgootr on single-cell bisulfite-treated whole-genome sequencing data of multiregionally sampled tumor cells from nine metastatic colorectal cancer patients, as well as multiregionally sampled single-cell reduced-representation bisulfite sequencing data from a glioblastoma patient. We show that the tumor lineages constructed reveal a simple model underlying tumor progression and metastatic seeding. A comparison of Sgootr against alternative approaches shows that Sgootr can construct lineage trees with fewer migration events and with more in concordance with the sequential-progression model of tumor evolution, with a running time a fraction of that used in prior studies. Lineage-informative CpG sites identified by Sgootr are in inter-CpG island (CGI) regions, as opposed to intra-CGIs, which have been the main regions of interest in genomic methylation-related analyses.

A weighted distance-based approach for deriving consensus tumor evolutionary trees

MOTIVATION

The acquisition of somatic mutations by a tumor can be modeled by a type of evolutionary tree. However, it is impossible to observe this tree directly. Instead, numerous algorithms have been developed to infer such a tree from different types of sequencing data. But such methods can produce conflicting trees for the same patient, making it desirable to have approaches that can combine several such tumor trees into a consensus or summary tree. We introduce The Weighted m-Tumor Tree Consensus Problem (W-m-TTCP) to find a consensus tree among multiple plausible tumor evolutionary histories, each assigned a confidence weight, given a specific distance measure between tumor trees. We present an algorithm called TuELiP that is based on integer linear programming which solves the W-m-TTCP, and unlike other existing consensus methods, allows the input trees to be weighted differently.

RESULTS

On simulated data we show that TuELiP outperforms two existing methods at correctly identifying the true underlying tree used to create the simulations. We also show that the incorporation of weights can lead to more accurate tree inference. On a Triple-Negative Breast Cancer dataset, we show that including confidence weights can have important impacts on the consensus tree identified.

AVAILABILITY

An implementation of TuELiP and simulated datasets are available at https://bitbucket.org/oesperlab/consensus-ilp/src/main/.

Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees

Cancer progression is an evolutionary process shaped by both deterministic and stochastic forces. Multi-region and single-cell sequencing of tumors enable high-resolution reconstruction of the mutational history of each tumor and highlight the extensive diversity across tumors and patients. Resolving the interactions among mutations and recovering recurrent evolutionary processes may offer greater opportunities for successful therapeutic strategies. To this end, we present a novel probabilistic framework, called TreeMHN, for the joint inference of exclusivity patterns and recurrent trajectories from a cohort of intra-tumor phylogenetic trees. Through simulations, we show that TreeMHN outperforms existing alternatives that can only focus on one aspect of the task. By analyzing datasets of blood, lung, and breast cancers, we find the most likely evolutionary trajectories and mutational patterns, consistent with and enriching our current understanding of tumorigenesis. Moreover, TreeMHN facilitates the prediction of tumor evolution and provides probabilistic measures on the next mutational events given a tumor tree, a prerequisite for evolution-guided treatment strategies.

CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples

Phylogenetic trees based on copy number profiles from multiple samples of a patient are helpful to understand cancer evolution. Here, we develop a new maximum likelihood method, CNETML, to infer phylogenies from such data. CNETML is the first program to jointly infer the tree topology, node ages, and mutation rates from total copy numbers of longitudinal samples. Our extensive simulations suggest CNETML performs well on copy numbers relative to ploidy and under slight violation of model assumptions. The application of CNETML to real data generates results consistent with previous discoveries and provides novel early copy number events for further investigation.

Insights into the metastatic cascade through research autopsies

Metastasis is a complex process and the leading cause of cancer-related death globally. Recent studies have demonstrated that genomic sequencing data from paired primary and metastatic tumours can be used to trace the evolutionary origins of cells responsible for metastasis. This approach has yielded new insights into the genomic alterations that engender metastatic potential, and the mechanisms by which cancer spreads. Given that the reliability of these approaches is contingent upon how representative the samples are of primary and metastatic tumour heterogeneity, we review insights from studies that have reconstructed the evolution of metastasis within the context of their cohorts and designs. We discuss the role of research autopsies in achieving the comprehensive sampling necessary to advance the current understanding of metastasis.

Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools

Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.

Spatial cumulant models enable spatially informed treatment strategies and analysis of local interactions in cancer systems

Theoretical and applied cancer studies that use individual-based models (IBMs) have been limited by the lack of a mathematical formulation that enables rigorous analysis of these models. However, spatial cumulant models (SCMs), which have arisen from theoretical ecology, describe population dynamics generated by a specific family of IBMs, namely spatio-temporal point processes (STPPs). SCMs are spatially resolved population models formulated by a system of differential equations that approximate the dynamics of two STPP-generated summary statistics: first-order spatial cumulants (densities), and second-order spatial cumulants (spatial covariances). We exemplify how SCMs can be used in mathematical oncology by modelling theoretical cancer cell populations comprising interacting growth factor-producing and non-producing cells. To formulate model equations, we use computational tools that enable the generation of STPPs, SCMs and mean-field population models (MFPMs) from user-defined model descriptions (Cornell et al. Nat Commun 10:4716, 2019). To calculate and compare STPP, SCM and MFPM-generated summary statistics, we develop an application-agnostic computational pipeline. Our results demonstrate that SCMs can capture STPP-generated population density dynamics, even when MFPMs fail to do so. From both MFPM and SCM equations, we derive treatment-induced death rates required to achieve non-growing cell populations. When testing these treatment strategies in STPP-generated cell populations, our results demonstrate that SCM-informed strategies outperform MFPM-informed strategies in terms of inhibiting population growths. We thus demonstrate that SCMs provide a new framework in which to study cell-cell interactions, and can be used to describe and perturb STPP-generated cell population dynamics. We, therefore, argue that SCMs can be used to increase IBMs' applicability in cancer research.

Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data

Phylogenetic methods are emerging as a useful tool to understand cancer evolutionary dynamics, including tumor structure, heterogeneity, and progression. Most currently used approaches utilize either bulk whole genome sequencing or single-cell DNA sequencing and are based on calling copy number alterations and single nucleotide variants (SNVs). Single-cell RNA sequencing (scRNA-seq) is commonly applied to explore differential gene expression of cancer cells throughout tumor progression. The method exacerbates the single-cell sequencing problem of low yield per cell with uneven expression levels. This accounts for low and uneven sequencing coverage and makes SNV detection and phylogenetic analysis challenging. In this article, we demonstrate for the first time that scRNA-seq data contain sufficient evolutionary signal and can also be utilized in phylogenetic analyses. We explore and compare results of such analyses based on both expression levels and SNVs called from scRNA-seq data. Both techniques are shown to be useful for reconstructing phylogenetic relationships between cells, reflecting the clonal composition of a tumor. Both standardized expression values and SNVs appear to be equally capable of reconstructing a similar pattern of phylogenetic relationship. This pattern is stable even when phylogenetic uncertainty is taken in account. Our results open up a new direction of somatic phylogenetics based on scRNA-seq data. Further research is required to refine and improve these approaches to capture the full picture of somatic evolutionary dynamics in cancer.

Reconstructing clonal tree for phylo-phenotypic characterization of cancer using single-cell transcriptomics

Functional characterization of the cancer clones can shed light on the evolutionary mechanisms driving cancer's proliferation and relapse mechanisms. Single-cell RNA sequencing data provide grounds for understanding the functional state of cancer as a whole; however, much research remains to identify and reconstruct clonal relationships toward characterizing the changes in functions of individual clones. We present PhylEx that integrates bulk genomics data with co-occurrences of mutations from single-cell RNA sequencing data to reconstruct high-fidelity clonal trees. We evaluate PhylEx on synthetic and well-characterized high-grade serous ovarian cancer cell line datasets. PhylEx outperforms the state-of-the-art methods both when comparing capacity for clonal tree reconstruction and for identifying clones. We analyze high-grade serous ovarian cancer and breast cancer data to show that PhylEx exploits clonal expression profiles beyond what is possible with expression-based clustering methods and clear the way for accurate inference of clonal trees and robust phylo-phenotypic analysis of cancer.

Biology, vulnerabilities and clinical applications of circulating tumour cells

In recent years, exceptional technological advances have enabled the identification and interrogation of rare circulating tumour cells (CTCs) from blood samples of patients, leading to new fields of research and fostering the promise for paradigm-changing, liquid biopsy-based clinical applications. Analysis of CTCs has revealed distinct biological phenotypes, including the presence of CTC clusters and the interaction between CTCs and immune or stromal cells, impacting metastasis formation and providing new insights into cancer vulnerabilities. Here we review the progress made in understanding biological features of CTCs and provide insight into exploiting these developments to design future clinical tools for improving the diagnosis and treatment of cancer.

ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model

Tumors consist of subpopulations of cells that harbor distinct collections of somatic mutations. These mutations range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). While many approaches infer tumor phylogenies using SNVs as phylogenetic markers, CNAs that overlap SNVs may lead to erroneous phylogenetic inference. Specifically, an SNV may be lost in a cell due to a deletion of the genomic segment containing the SNV. Unfortunately, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs. For instance, recent targeted scDNA-seq technologies, such as Mission Bio Tapestri, measure SNVs with high fidelity in individual cells, but yield much less reliable measurements of CNAs. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers and partial information about CNAs in the form of clustering of cells with similar copy-number profiles. This copy-number clustering constrains where loss of SNVs can occur in the phylogeny. We develop ConDoR (Constrained Dollo Reconstruction), an algorithm to infer tumor phylogenies from targeted scDNA-seq data using the constrained k-Dollo model. We show that ConDoR outperforms existing methods on simulated data. We use ConDoR to analyze a new multi-region targeted scDNA-seq dataset of 2153 cells from a pancreatic ductal adenocarcinoma (PDAC) tumor and produce a more plausible phylogeny compared to existing methods that conforms to histological results for the tumor from a previous study. We also analyze a metastatic colorectal cancer dataset, deriving a more parsimonious phylogeny than previously published analyses and with a simpler monoclonal origin of metastasis compared to the original study.

Code availability

Software is available at https://github.com/raphael-group/constrained-Dollo.

Timed hazard networks: Incorporating temporal difference for oncogenetic analysis

Oncogenetic graphical models are crucial for understanding cancer progression by analyzing the accumulation of genetic events. These models are used to identify statistical dependencies and temporal order of genetic events, which helps design targeted therapies. However, existing algorithms do not account for temporal differences between samples in oncogenetic analysis. This paper introduces Timed Hazard Networks (TimedHN), a new statistical model that uses temporal differences to improve accuracy and reliability. TimedHN models the accumulation process as a continuous-time Markov chain and includes an efficient gradient computation algorithm for optimization. Our simulation experiments demonstrate that TimedHN outperforms current state-of-the-art graph reconstruction methods. We also compare TimedHN with existing methods on a luminal breast cancer dataset, highlighting its potential utility. The Matlab implementation and data are available at https://github.com/puar-playground/TimedHN.

Reconstructing Phylogenetic Relationship in Bladder Cancer: A Methodological Overview

Bladder cancer (BC) expresses itself as a highly heterogeneous disease both at the histological and molecular level, often occurring as synchronous or metachronous multifocal disease with high risk of recurrence and potential to metastasize. Multiple sequencing studies focusing on both non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) gave insights into the extent of both inter- and intrapatient heterogeneity, but many questions on clonal evolution in BC remain unanswered. In this review article, we provide an overview over the technical and theoretical concepts linked to reconstructing evolutionary trajectories in BC and propose a set of tools and established software for phylogenetic analysis.

HyperHMM: efficient inference of evolutionary and progressive dynamics on hypercubic transition graphs

MOTIVATION

The evolution of bacterial drug resistance and other features in biology, the progression of cancer and other diseases and a wide range of broader questions can often be viewed as the sequential stochastic acquisition of binary traits (e.g. genetic changes, symptoms or characters). Using potentially noisy or incomplete data to learn the sequences by which such traits are acquired is a problem of general interest. The problem is complicated for large numbers of traits, which may, individually or synergistically, influence the probability of further acquisitions both positively and negatively. Hypercubic inference approaches, based on hidden Markov models on a hypercubic transition network, address these complications, but previous Bayesian instances can consume substantial time for converged results, limiting their practical use.

RESULTS

Here, we introduce HyperHMM, an adapted Baum-Welch (expectation-maximization) algorithm for hypercubic inference with resampling to quantify uncertainty, and show that it allows orders-of-magnitude faster inference while making few practical sacrifices compared to previous hypercubic inference approaches. We show that HyperHMM allows any combination of traits to exert arbitrary positive or negative influence on the acquisition of other traits, relaxing a common limitation of only independent trait influences. We apply this approach to synthetic and biological datasets and discuss its more general application in learning evolutionary and progressive pathways.

AVAILABILITY AND IMPLEMENTATION

Code for inference and visualization, and data for example cases, is freely available at https://github.com/StochasticBiology/hypercube-hmm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cellular barcoding to decipher clonal dynamics in disease

Cellular barcodes are distinct DNA sequences that enable one to track specific cells across time or space. Recent advances in our ability to detect natural or synthetic cellular barcodes, paired with single-cell readouts of cell state, have markedly increased our knowledge of clonal dynamics and genealogies of the cells that compose a variety of tissues and organs. These advances hold promise to redefine our view of human disease. Here, we provide an overview of cellular barcoding approaches, discuss applications to gain new insights into disease mechanisms, and provide an outlook on future applications. We discuss unanticipated insights gained through barcoding in studies of cancer and blood cell production and describe how barcoding can be applied to a growing array of medical fields, particularly with the increasing recognition of clonal contributions in human diseases.

Somatic variation in normal tissues: friend or foe of cancer early detection?

BACKGROUND

Seemingly normal tissues progressively become populated by mutant clones over time. Most of these clones bear mutations in well-known cancer genes but only rarely do they transform into cancer. This poses questions on what triggers cancer initiation and what implications somatic variation has for cancer early detection.

DESIGN

We analysed recent mutational screens of healthy and cancer-free diseased tissues to compare somatic drivers and the causes of somatic variation across tissues. We then reviewed the mechanisms of clonal expansion and their relationships with age and diseases other than cancer. We finally discussed the relevance of somatic variation for cancer initiation and how it can help or hinder cancer detection and prevention.

RESULTS

The extent of somatic variation is highly variable across tissues and depends on intrinsic features, such as tissue architecture and turnover, as well as the exposure to endogenous and exogenous insults. Most somatic mutations driving clonal expansion are tissue-specific and inactivate tumor suppressor genes involved in chromatin modification and cell growth signaling. Some of these genes are more frequently mutated in normal tissues than cancer, indicating a context-dependent cancer promoting or protective role. Mutant clones can persist over a long time or disappear rapidly, suggesting that their fitness depends on the dynamic equilibrium with the environment. The disruption of this equilibrium is likely responsible for their transformation into malignant clones and knowing what triggers this process is key for cancer prevention and early detection. Somatic variation should be considered in liquid biopsy, where it may contribute cancer-independent mutations, and in the identification of cancer drivers, since not all mutated genes favoring clonal expansion also drive tumorigenesis.

CONCLUSIONS

Somatic variation and the factors governing homeostasis of normal tissues should be taken into account when devising strategies for cancer prevention and early detection.

Discovering significant evolutionary trajectories in cancer phylogenies

MOTIVATION

Tumors are the result of a somatic evolutionary process leading to substantial intra-tumor heterogeneity. Single-cell and multi-region sequencing enable the detailed characterization of the clonal architecture of tumors and have highlighted its extensive diversity across tumors. While several computational methods have been developed to characterize the clonal composition and the evolutionary history of tumors, the identification of significantly conserved evolutionary trajectories across tumors is still a major challenge.

RESULTS

We present a new algorithm, MAximal tumor treeS TRajectOries (MASTRO), to discover significantly conserved evolutionary trajectories in cancer. MASTRO discovers all conserved trajectories in a collection of phylogenetic trees describing the evolution of a cohort of tumors, allowing the discovery of conserved complex relations between alterations. MASTRO assesses the significance of the trajectories using a conditional statistical test that captures the coherence in the order in which alterations are observed in different tumors. We apply MASTRO to data from nonsmall-cell lung cancer bulk sequencing and to acute myeloid leukemia data from single-cell panel sequencing, and find significant evolutionary trajectories recapitulating and extending the results reported in the original studies.

AVAILABILITY AND IMPLEMENTATION

MASTRO is available at https://github.com/VandinLab/MASTRO.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Fast intratumor heterogeneity inference from single-cell sequencing data

We introduce HUNTRESS, a computational method for mutational intratumor heterogeneity inference from noisy genotype matrices derived from single-cell sequencing data, the running time of which is linear with the number of cells and quadratic with the number of mutations. We prove that, under reasonable conditions, HUNTRESS computes the true progression history of a tumor with high probability. On simulated and real tumor sequencing data, HUNTRESS is demonstrated to be faster than available alternatives with comparable or better accuracy. Additionally, the progression histories of tumors inferred by HUNTRESS on real single-cell sequencing datasets agree with the best known evolution scenarios for the associated tumors.

SCONCE2: jointly inferring single cell copy number profiles and tumor evolutionary distances

Background

Single cell whole genome tumor sequencing can yield novel insights into the evolutionary history of somatic copy number alterations. Existing single cell copy number calling methods do not explicitly model the shared evolutionary process of multiple cells, and generally analyze cells independently. Additionally, existing methods for estimating tumor cell phylogenies using copy number profiles are sensitive to profile estimation errors.

Results

We present SCONCE2, a method for jointly calling copy number alterations and estimating pairwise distances for single cell sequencing data. Using simulations, we show that SCONCE2 has higher accuracy in copy number calling and phylogeny estimation than competing methods. We apply SCONCE2 to previously published single cell sequencing data to illustrate the utility of the method.

Conclusions

SCONCE2 jointly estimates copy number profiles and a distance metric for inferring tumor phylogenies in single cell whole genome tumor sequencing across multiple cells, enabling deeper understandings of tumor evolution.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04890-w.

Accurate Identification of Subclones in Tumor Genomes

Understanding intra-tumor heterogeneity is critical for studying tumorigenesis and designing personalized treatments. To decompose the mixed cell population in a tumor, subclones are inferred computationally based on variant allele frequency (VAF) from bulk sequencing data. In this study, we showed that sequencing depth, mean VAF, and variance of VAF of a subclone are confounded. Without considering this effect, current methods require deep-sequencing data (>300x depth) to reliably infer subclones. Here we present a novel algorithm that incorporates depth-variance and mean-variance dependencies in a clustering error model and successfully identifies subclones in tumors sequenced at depths of as low as 30x. We implemented the algorithm as a model-based adaptive grouping of subclones (MAGOS) method. Analyses of computer simulated data and empirical sequencing data showed that MAGOS outperformed existing methods on minimum sequencing depth, decomposition accuracy, and computation efficiency. The most prominent improvements were observed in analyzing tumors sequenced at depths between 30x and 200x, while the performance was comparable between MAGOS and existing methods on deeply sequenced tumors. MAGOS supports analysis of single nucleotide variants and copy number variants from a single sample or multiple samples of a tumor. We applied MAGOS to whole-exome data of late-stage liver cancers and discovered that high subclone count in a tumor was a significant risk factor of poor prognosis. Lastly, our analysis suggested that sequencing multiple samples of the same tumor at standard depth is more cost-effective and robust for subclone characterization than deep sequencing a single sample. MAGOS is available at github (https://github.com/liliulab/magos).

African mitochondrial haplogroup L7: a 100,000-year-old maternal human lineage discovered through reassessment and new sequencing

Archaeological and genomic evidence suggest that modern Homo sapiens have roamed the planet for some 300–500 thousand years. In contrast, global human mitochondrial (mtDNA) diversity coalesces to one African female ancestor (“Mitochondrial Eve”) some 145 thousand years ago, owing to the ¼ gene pool size of our matrilineally inherited haploid genome. Therefore, most of human prehistory was spent in Africa where early ancestors of Southern African Khoisan and Central African rainforest hunter-gatherers (RFHGs) segregated into smaller groups. Their subdivisions followed climatic oscillations, new modes of subsistence, local adaptations, and cultural-linguistic differences, all prior to their exodus out of Africa. Seven African mtDNA haplogroups (L0–L6) traditionally captured this ancient structure—these L haplogroups have formed the backbone of the mtDNA tree for nearly two decades. Here we describe L7, an eighth haplogroup that we estimate to be ~ 100 thousand years old and which has been previously misclassified in the literature. In addition, L7 has a phylogenetic sublineage L7a*, the oldest singleton branch in the human mtDNA tree (~ 80 thousand years). We found that L7 and its sister group L5 are both low-frequency relics centered around East Africa, but in different populations (L7: Sandawe; L5: Mbuti). Although three small subclades of African foragers hint at the population origins of L5'7, the majority of subclades are divided into Afro-Asiatic and eastern Bantu groups, indicative of more recent admixture. A regular re-estimation of the entire mtDNA haplotype tree is needed to ensure correct cladistic placement of new samples in the future.

Accounting for errors in data improves divergence time estimates in single-cell cancer evolution

Single-cell sequencing provides a new way to explore the evolutionary history of cells. Compared to traditional bulk sequencing, where a population of heterogeneous cells is pooled to form a single observation, single-cell sequencing isolates and amplifies genetic material from individual cells, thereby preserving the information about the origin of the sequences. However, single-cell data is more error-prone than bulk sequencing data due to the limited genomic material available per cell. Here, we present error and mutation models for evolutionary inference of single-cell data within a mature and extensible Bayesian framework, BEAST2. Our framework enables integration with biologically informative models such as relaxed molecular clocks and population dynamic models. Our simulations show that modeling errors increase the accuracy of relative divergence times and substitution parameters. We reconstruct the phylogenetic history of a colorectal cancer patient and a healthy patient from single-cell DNA sequencing data. We find that the estimated times of terminal splitting events are shifted forward in time compared to models which ignore errors. We observed that not accounting for errors can overestimate the phylogenetic diversity in single-cell DNA sequencing data. We estimate that 30-50% of the apparent diversity can be attributed to error. Our work enables a full Bayesian approach capable of accounting for errors in the data within the integrative Bayesian software framework BEAST2.

Determining Significant Correlation Between Pairs of Extant Characters in a Small Parsimony Framework

When studying the evolutionary relationships among a set of species, the principle of parsimony states that a relationship involving the fewest number of evolutionary events is likely the correct one. Due to its simplicity, this principle was formalized in the context of computational evolutionary biology decades ago by, for example, Fitch and Sankoff. Because the parsimony framework does not require a model of evolution, unlike maximum likelihood or Bayesian approaches, it is often a good starting point when no reasonable estimate of such a model is available. In this work, we devise a method for determining if pairs of discrete characters are significantly correlated across all most parsimonious reconstructions, given a set of species on these characters, and an evolutionary tree. The first step of this method is to use Sankoff's algorithm to compute all most parsimonious assignments of ancestral states (of each character) to the internal nodes of the phylogeny. Correlation between a pair of evolutionary events (e.g., absent to present) for a pair of characters is then determined by the (co-) occurrence patterns between the sets of their respective ancestral assignments. The probability of obtaining a correlation this extreme (or more) under a null hypothesis where the events happen randomly on the evolutionary tree is then used to assess the significance of this correlation. We implement this method: parcours (PARsimonious CO-occURrenceS) and use it to identify significantly correlated evolution among vocalizations and morphological characters in the Felidae family.

HBV genome-enriched single cell sequencing revealed heterogeneity in HBV-driven hepatocellular carcinoma (HCC)

BACKGROUND

Hepatitis B virus (HBV) related hepatocellular carcinoma (HCC) is heterogeneous and frequently contains multifocal tumors, but how the multifocal tumors relate to each other in terms of HBV integration and other genomic patterns is not clear.

METHODS

To interrogate heterogeneity of HBV-HCC, we developed a HBV genome enriched single cell sequencing (HGE-scSeq) procedure and a computational method to identify HBV integration sites and infer DNA copy number variations (CNVs).

RESULTS

We performed HGE-scSeq on 269 cells from four tumor sites and two tumor thrombi of a HBV-HCC patient. HBV integrations were identified in 142 out of 269 (53%) cells sequenced, and were enriched in two HBV integration hotspots chr1:34,397,059 (CSMD2) and chr8:118,557,327 (MED30/EXT1). There were also 162 rare integration sites. HBV integration sites were enriched in DNA fragile sites and sequences around HBV integration sites were enriched for microhomologous sequences between human and HBV genomes. CNVs were inferred for each individual cell and cells were grouped into four clonal groups based on their CNVs. Cells in different clonal groups had different degrees of HBV integration heterogeneity. All of 269 cells carried chromosome 1q amplification, a recurrent feature of HCC tumors, suggesting that 1q amplification occurred before HBV integration events in this case study. Further, we performed simulation studies to demonstrate that the sequential events (HBV infecting transformed cells) could result in the observed phenotype with biologically reasonable parameters.

CONCLUSION

Our HGE-scSeq data reveals high heterogeneity of HCC tumor cells in terms of both HBV integrations and CNVs. There were two HBV integration hotspots across cells, and cells from multiple tumor sites shared some HBV integration and CNV patterns.

CONET: copy number event tree model of evolutionary tumor history for single-cell data

Copy number alterations constitute important phenomena in tumor evolution. Whole genome single-cell sequencing gives insight into copy number profiles of individual cells, but is highly noisy. Here, we propose CONET, a probabilistic model for joint inference of the evolutionary tree on copy number events and copy number calling. CONET employs an efficient, regularized MCMC procedure to search the space of possible model structures and parameters. We introduce a range of model priors and penalties for efficient regularization. CONET reveals copy number evolution in two breast cancer samples, and outperforms other methods in tree reconstruction, breakpoint identification and copy number calling.

Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution

Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth and expansion to neighboring and distal tissues. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma and tracked tumor evolution from single-transformed cells to metastatic tumors at unprecedented resolution. We found that the loss of the initial, stable alveolar-type2-like state was accompanied by a transient increase in plasticity. This was followed by the adoption of distinct transcriptional programs that enable rapid expansion and, ultimately, clonal sweep of stable subclones capable of metastasizing. Finally, tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates progression by creating novel trajectories. Our study elucidates the hierarchical nature of tumor evolution and, more broadly, enables in-depth studies of tumor progression.

Development of a Chemoresistant Risk Scoring Model for Prechemotherapy Osteosarcoma Using Single-Cell Sequencing

Background

Chemoresistance is one of the leading causes that severely limits the success of osteosarcoma treatment. Evaluating chemoresistance before chemotherapy poses a new challenge for researchers. We established an effective chemoresistance risk scoring model for prechemotherapy osteosarcoma using single-cell sequencing.

Methods

We comprehensively analyzed osteosarcoma data from the bulk mRNA sequencing dataset TARGET-OS and the single-cell RNA sequencing (scRNA-seq) dataset GSE162454. Chemoresistant tumor clusters were identified using enrichment analysis and AUCell scoring. Its differentiated trajectory was achieved with inferCNV and pseudotime analysis. Ligand-receptor interactions were annotated with iTALK. Furthermore, we established a chemoresistance risk scoring model using LASSO regression based on scRNA-seq-based markers of chemoresistant tumor clusters. The TARGET-OS dataset was used as the training group, and the bulk mRNA array dataset GSE33382 was used as the validation group. Finally, the performance was verified for its discriminatory ability and calibration.

Results

Using bulk RNA data, we found that osteogenic expression was upregulated in chemoresistant osteosarcoma as compared to chemosensitive osteosarcoma. Then, we transferred the bulk RNA findings to scRNA-seq and noticed osteosarcoma tumor clusters C14 and C25 showing osteogenic cancer stem cell expression patterns, which fit chemoresistant characteristics. C14 and C25 possessed bridge roles in interactions with other clusters. On the one hand, they received various growth factor stimulators and could potentially transform into a proliferative state. On the other hand, they promote local tumor angiogenesis, bone remodeling and immunosuppression. Next, we identified a ten-gene signature from the C14 and C25 markers and constructed a chemoresistant risk scoring model using LASSO regression model. Finally, we found that chemoresistant osteosarcoma had higher chemoresistance risk score and that the model showed good discriminatory ability and calibration in both the training and validation groups (AUCtrain = 0.82; AUCvalid = 0.84). Compared with that of the classic bulk RNA-based model, it showed more robust performance in validation environment (AUCvalid-scRNA = 0.84; AUCvalid-bulk DEGs = 0.54).

Conclusions

Our work provides insights into understanding chemoresistant osteosarcoma tumor cells and using single-cell sequencing to establish a chemoresistance risk scoring model. The model showed good discriminatory ability and calibration and provided us with a feasible way to evaluate chemoresistance in prechemotherapy osteosarcoma.

Computational analysis of cancer genome sequencing data

Distilling biologically meaningful information from cancer genome sequencing data requires comprehensive identification of somatic alterations using rigorous computational methods. As the amount and complexity of sequencing data have increased, so has the number of tools for analysing them. Here, we describe the main steps involved in the bioinformatic analysis of cancer genomes, review key algorithmic developments and highlight popular tools and emerging technologies. These tools include those that identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes. We also discuss issues in experimental design, the strengths and limitations of sequencing modalities and methodological challenges for the future.

Cancer's second genome: Microbial cancer diagnostics and redefining clonal evolution as a multispecies process: Humans and their tumors are not aseptic, and the multispecies nature of cancer modulates clinical care and clonal evolution: Humans and their tumors are not aseptic, and the multispecies nature of cancer modulates clinical care and clonal evolution

The presence and role of microbes in human cancers has come full circle in the last century. Tumors are no longer considered aseptic, but implications for cancer biology and oncology remain underappreciated. Opportunities to identify and build translational diagnostics, prognostics, and therapeutics that exploit cancer's second genome-the metagenome-are manifold, but require careful consideration of microbial experimental idiosyncrasies that are distinct from host-centric methods. Furthermore, the discoveries of intracellular and intra-metastatic cancer bacteria necessitate fundamental changes in describing clonal evolution and selection, reflecting bidirectional interactions with non-human residents. Reconsidering cancer clonality as a multispecies process similarly holds key implications for understanding metastasis and prognosing therapeutic resistance while providing rational guidance for the next generation of bacterial cancer therapies. Guided by these new findings and challenges, this Review describes opportunities to exploit cancer's metagenome in oncology and proposes an evolutionary framework as a first step towards modeling multispecies cancer clonality. Also see the video abstract here: https://youtu.be/-WDtIRJYZSs.

BiTSC 2: Bayesian inference of tumor clonal tree by joint analysis of single-cell SNV and CNA data

The rapid development of single-cell DNA sequencing (scDNA-seq) technology has greatly enhanced the resolution of tumor cell profiling, providing an unprecedented perspective in characterizing intra-tumoral heterogeneity and understanding tumor progression and metastasis. However, prominent algorithms for constructing tumor phylogeny based on scDNA-seq data usually only take single nucleotide variations (SNVs) as markers, failing to consider the effect caused by copy number alterations (CNAs). Here, we propose BiTSC$^2$, Bayesian inference of Tumor clonal Tree by joint analysis of Single-Cell SNV and CNA data. BiTSC$^2$ takes raw reads from scDNA-seq as input, accounts for the overlapping of CNA and SNV, models allelic dropout rate, sequencing errors and missing rate, as well as assigns single cells into subclones. By applying Markov Chain Monte Carlo sampling, BiTSC$^2$ can simultaneously estimate the subclonal scCNA and scSNV genotype matrices, subclonal assignments and tumor subclonal evolutionary tree. In comparison with existing methods on synthetic and real tumor data, BiTSC$^2$ shows high accuracy in genotype recovery, subclonal assignment and tree reconstruction. BiTSC$^2$ also performs robustly in dealing with scDNA-seq data with low sequencing depth and variant missing rate. BiTSC$^2$ software is available at https://github.com/ucasdp/BiTSC2.

Molecular Characteristics and Prognostic Role of MFAP2 in Stomach Adenocarcinoma

Molecular characteristics and prognostic role of MFAP2 were by no means stated. The MFAP2 expression and prognostic prices in this study, with Cox analysis, was employed to develop a predictive fee for MFAP2. To know about coexpression and practical networks associated with MFAP2, LinkedOmics and GEPIA2 have been used. MFAP2 expression has been increased and verified in many unbiased coalitions in TCGA-STAD tumor tissues. In addition, in each TCGA and various cohorts, increased MFAP2 was linked with lower survival. Evaluation by Cox revealed the unbiased danger to average survival, disease-specific survival, and progression-free survival of STAD used to be due to the elevated expression of MFAP2. Active community assessed the MFAP2, through which more than a few cancer-associated kinases and E2F household pathways are regulated, which shows that MFAP2 affects RNA transportation, oocyte meiosis, spliceosome, and ribosome biogenesis. MFAP2 can predict and is linked to the prediction of STAD independently. The closure of the MFAP2 link to the macrophage marker genes is, in particular, the achievable core of immune response.

Inferring ongoing cancer evolution from single tumour biopsies using synthetic supervised learning

Variant allele frequencies (VAF) encode ongoing evolution and subclonal selection in growing tumours. However, existing methods that utilize VAF information for cancer evolutionary inference are compressive, slow, or incorrectly specify the underlying cancer evolutionary dynamics. Here, we provide a proof-of-principle synthetic supervised learning method, TumE, that integrates simulated models of cancer evolution with Bayesian neural networks, to infer ongoing selection in bulk-sequenced single tumour biopsies. Analyses in synthetic and patient tumours show that TumE significantly improves both accuracy and inference time per sample when detecting positive selection, deconvoluting selected subclonal populations, and estimating subclone frequency. Importantly, we show how transfer learning can leverage stored knowledge within TumE models for related evolutionary inference tasks-substantially reducing data and computational time for further model development and providing a library of recyclable deep learning models for the cancer evolution community. This extensible framework provides a foundation and future directions for harnessing progressive computational methods for the benefit of cancer genomics and, in turn, the cancer patient.