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

A clinical solution for tracking clonal evolution of acute myeloid leukemia after allogeneic transplantation using bulk next generation sequencing

Clinical next generation sequencing (NGS) typically relies on limited gene panels run on bulk marrow or blood. Current computational tools for inferring clonal relationships is generally limited by the use of a small panel of pathogenic mutations to define clones. We developed an online software (CloneTracker) that uses 'incidentally-sequenced' single nucleotide polymorphisms (SNPs) in the regions of recurrent somatic mutations in addition to conventional mutation data from bulk NGS gene panels to provide detailed visualizations of clonal evolution during cancer treatment, alongside clinical data. Tested on 29 patients who underwent non-myeloablative transplantation for AML, CloneTracker successfully reconstructed the evolutionary dynamics of donor engraftment from bulk NGS and rendered intuitive visualizations of residual patient-derived hematopoiesis and relapsing malignant clones. The software does not require sequencing donor samples, as donor-derived clones are identifiable from post-HCT SNP data. This manuscript aims to introduce CloneTracker to the BMT community and make it available for those who would ascertain its clinical utility, e.g, in BMT trials leveraging molecular minimal residual disease (MRD) monitoring and targeted interventions to pre-empt relapse.

SVCFit: Inferring structural variant cellular fraction in tumors

Many tumors evolve through cellular mutation and selection, where subpopulations of cells (subclones) with shared ancestry compete for dominance. Introduction of next generation sequencing enables subclone identification using small somatic. However, there are several advantages to marking subclones with structural variants: they have greater functional impact, play a crucial role in late-stage tumors, and provide a more complete view of genomic instability driving tumor evolution. Here, we present SVCFit, a scalable method to estimate the cellular prevalence of somatic deletions, duplications and inversions. We demonstrate that cellular prevalence estimation can be improved by incorporating distinct read patterns for each structural variant type. Additionally, this improvement is achieved without prior knowledge of tumor purity, which is often inaccurate. Using a combination of simulated data and patient-derived metastatic samples with known mixture proportions, we show that our algorithm achieves significantly greater accuracy than state-of-the-art in estimating the structural variants cellular prevalence (p<0.05). The speed of SVCFit estimation from cost-effective bulk whole-genome sequencing (WGS) makes it well-suited for analyzing large cohorts of sequenced tumor samples, enhancing the accessibility of SV-based clonal reconstruction.

Intratumoral heterogeneity drives acquired therapy resistance in a patient with metastatic prostate cancer

Metastatic prostate cancer (PCa) is not curable due to its ability to acquire therapy resistance. Theoretically, acquired therapy resistance can be driven by changes to previously sensitive cancer cells or their environment and/or by outgrowth of a subpopulation of cancer cells with primary resistance. Direct demonstration of the latter mechanism in patients with PCa is lacking. Here we present a case report as proof-of-principle that outgrowth of a subpopulation of cancer cells lacking the genomic target and present prior to therapy initiation can drive acquired resistance to targeted therapy and threaten survival in patients with PCa.

Reconstructing tumor clonal heterogeneity and evolutionary relationships based on tumor DNA sequencing data

The heterogeneity of tumor clones drives the selection and evolution of distinct tumor cell populations, resulting in an intricate and dynamic tumor evolution process. While tumor bulk DNA sequencing helps elucidate intratumor heterogeneity, challenges such as the misidentification of mutation multiplicity due to copy number variations and uncertainties in the reconstruction process hinder the accurate inference of tumor evolution. In this study, we introduce a novel approach, REconstructing Tumor Clonal Heterogeneity and Evolutionary Relationships (RETCHER), which characterizes more realistic cancer cell fractions by accurately identifying mutation multiplicity while considering uncertainty during the reconstruction process and the credibility and reasonableness of subclone clustering. This method comprehensively and accurately infers multiple forms of tumor clonal heterogeneity and phylogenetic relationships. RETCHER outperforms existing methods on simulated data and infers clearer subclone structures and evolutionary relationships in real multisample sequencing data from five tumor types. By precisely analysing the complex clonal heterogeneity within tumors, RETCHER provides a new approach to tumor evolution research and offers scientific evidence for developing precise and personalized treatment strategies. This approach is expected to play a significant role in tumor evolution research, clinical diagnosis, and treatment. RETCHER is available for free at https://github.com/zlsys3/RETCHER.

Emerging and extensive clonal evolution in the pancreas

Pancreatic cancer is one of the most lethal malignancies, yet much remains to be learned regarding how its precursors develop. In a recent Nature publication, Braxton and Kiemen et al. found that the normal, adult pancreas harbors hundreds to thousands of pancreatic cancer precursors evolving by a variety of routes.

Assessing the merits: an opinion on the effectiveness of simulation techniques in tumor subclonal reconstruction

Summary

Neoplastic tumors originate from a single cell, and their evolution can be traced through lineages characterized by mutations, copy number alterations, and structural variants. These lineages are reconstructed and mapped onto evolutionary trees with algorithmic approaches. However, without ground truth benchmark sets, the validity of an algorithm remains uncertain, limiting potential clinical applicability. With a growing number of algorithms available, there is urgent need for standardized benchmark sets to evaluate their merits. Benchmark sets rely on in silico simulations of tumor sequence, but there are no accepted standards for simulation tools, presenting a major obstacle to progress in this field.

Availability and implementation

All analysis done in the paper was based on publicly available data from the publication of each accessed tool.

3D genomic mapping reveals multifocality of human pancreatic precancers

Pancreatic intraepithelial neoplasias (PanINs) are the most common precursors of pancreatic cancer, but their small size and inaccessibility in humans make them challenging to study1. Critically, the number, dimensions and connectivity of human PanINs remain largely unknown, precluding important insights into early cancer development. Here, we provide a microanatomical survey of human PanINs by analysing 46 large samples of grossly normal human pancreas with a machine-learning pipeline for quantitative 3D histological reconstruction at single-cell resolution. To elucidate genetic relationships between and within PanINs, we developed a workflow in which 3D modelling guides multi-region microdissection and targeted and whole-exome sequencing. From these samples, we calculated a mean burden of 13 PanINs per cm3 and extrapolated that the normal intact adult pancreas harbours hundreds of PanINs, almost all with oncogenic KRAS hotspot mutations. We found that most PanINs originate as independent clones with distinct somatic mutation profiles. Some spatially continuous PanINs were found to contain multiple KRAS mutations; computational and in situ analyses demonstrated that different KRAS mutations localize to distinct cell subpopulations within these neoplasms, indicating their polyclonal origins. The extensive multifocality and genetic heterogeneity of PanINs raises important questions about mechanisms that drive precancer initiation and confer differential progression risk in the human pancreas. This detailed 3D genomic mapping of molecular alterations in human PanINs provides an empirical foundation for early detection and rational interception of pancreatic cancer.

Evaluation of simulation methods for tumor subclonal reconstruction

Most neoplastic tumors originate from a single cell, and their evolution can be genetically traced through lineages characterized by common alterations such as small somatic mutations (SSMs), copy number alterations (CNAs), structural variants (SVs), and aneuploidies. Due to the complexity of these alterations in most tumors and the errors introduced by sequencing protocols and calling algorithms, tumor subclonal reconstruction algorithms are necessary to recapitulate the DNA sequence composition and tumor evolution in silico. With a growing number of these algorithms available, there is a pressing need for consistent and comprehensive benchmarking, which relies on realistic tumor sequencing generated by simulation tools. Here, we examine the current simulation methods, identifying their strengths and weaknesses, and provide recommendations for their improvement. Our review also explores potential new directions for research in this area. This work aims to serve as a resource for understanding and enhancing tumor genomic simulations, contributing to the advancement of the field.

Reconstructing Clonal Evolution-A Systematic Evaluation of Current Bioinformatics Approaches

The accurate reconstruction of clonal evolution, including the identification of newly developing, highly aggressive subclones, is essential for the application of precision medicine in cancer treatment. Reconstruction, aiming for correct variant clustering and clonal evolution tree reconstruction, is commonly performed by tedious manual work. While there is a plethora of tools to automatically generate reconstruction, their reliability, especially reasons for unreliability, are not systematically assessed. We developed clevRsim-an approach to simulate clonal evolution data, including single-nucleotide variants as well as (overlapping) copy number variants. From this, we generated 88 data sets and performed a systematic evaluation of the tools for the reconstruction of clonal evolution. The results indicate a major negative influence of a high number of clones on both clustering and tree reconstruction. Low coverage as well as an extreme number of time points usually leads to poor clustering results. An underlying branched independent evolution hampers correct tree reconstruction. A further major decline in performance could be observed for large deletions and duplications overlapping single-nucleotide variants. In summary, to explore the full potential of reconstructing clonal evolution, improved algorithms that can properly handle the identified limitations are greatly needed.