Quantifying the influence of mutation detection on tumour subclonal reconstruction

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.

Basket Trials: Past, Present, and Future

Large-scale tumor molecular profiling has revealed that diverse cancer histologies are driven by common pathways with unifying biomarkers that can be exploited therapeutically. Disease-agnostic basket trials have been increasingly utilized to test biomarker-driven therapies across cancer types. These trials have led to drug approvals and improved the lives of patients while simultaneously advancing our understanding of cancer biology. This review focuses on the practicalities of implementing basket trials, with an emphasis on molecularly targeted trials. We examine the biologic subtleties of genomic biomarker and patient selection, discuss previous successes in drug development facilitated by basket trials, describe certain novel targets and drugs, and emphasize practical considerations for participant recruitment and study design. This review also highlights strategies for aiding patient access to basket trials. As basket trials become more common, steps to ensure equitable implementation of these studies will be critical for molecularly targeted drug development.

Quantifying the seed sensitivity of cancer subclonal reconstruction algorithms

Background

Intra-tumoural heterogeneity complicates cancer prognosis and impairs treatment success. One of the ways subclonal reconstruction (SRC) quantifies intra-tumoural heterogeneity is by estimating the number of subclones present in bulk DNA sequencing data. SRC algorithms are probabilistic and need to be initialized by a random seed. However, the seeds used in bioinformatics algorithms are rarely reported in the literature. Thus, the impact of the initializing seed on SRC solutions has not been studied. To address this gap, we generated a set of ten random seeds to systematically benchmark the seed sensitivity of three probabilistic SRC algorithms: PyClone-VI, DPClust, and PhyloWGS.

Results

We characterized the seed sensitivity of three algorithms across fourteen whole-genome sequences of head and neck squamous cell carcinoma and nine SRC pipelines, each composed of a single nucleotide variant caller, a copy number aberration caller and an SRC algorithm. This led to a total of 1470 subclonal reconstructions, including 1260 single-region and 210 multi-region reconstructions. The number of subclones estimated per patient vary across SRC pipelines, but all three SRC algorithms show substantial seed sensitivity: subclone estimates vary across different seeds for the same set of input using the same SRC algorithm. No seed consistently estimated the mode number of subclones across all patients for any SRC algorithm.

Conclusions

These findings highlight the variability in quantifying intra-tumoural heterogeneity introduced by the seed sensitivity of probabilistic SRC algorithms. We recommend that authors, reviewers and editors adopt guidelines to both report and randomize seed choices. It may also be valuable to consider seed-sensitivity in the benchmarking of newly developed SRC algorithms. These findings may be of interest in other areas of bioinformatics where seeded probabilistic algorithms are used and suggest consideration of formal seed reporting standards to enhance reproducibility.

In vivo growth of subclones derived from Lewis lung carcinoma is determined by the tumor microenvironment

Heterogeneity is a fundamental feature of human tumors and plays a major role in drug resistance and disease progression. In the present study, we selected single-cell-derived cell lines (SCDCLs) derived from Lewis lung carcinoma (LLC1) cells to investigate tumorigenesis and heterogeneity. SCDCLs were generated using limiting dilution. Five SCDCLs were subcutaneously injected into wild-type C57BL/6N mice; however, they displayed significant differences in tumor growth. Subclone SCC1 grew the fastest in vivo, whereas it grew slower in vitro. The growth pattern of SCC2 was the opposite to that of SCC1. Genetic differences in these two subclones showed marked differences in cell adhesion and proliferation. Pathway enrichment results indicate that signal transduction and immune system responses were the most significantly altered functional categories in SCC2 cells compared to those in SCC1 cells in vitro. The number and activation of CD3⁺ and CD8⁺ T cells and NK cells in the tumor tissue of tumor-bearing mice inoculated with SCC2 were significantly higher, whereas those of myeloid cells were significantly lower, than those in the SCC1 and LLC1 groups. Our results suggest that the in vivo growth of two subclones derived from LLC1 was determined by the tumor microenvironment rather than their intrinsic proliferative cell characteristics.

Homogeneous One-Step Immunoassay Based on Switching Peptides for Detection of the Influenza Virus

In this study, a homogeneous one-step immunoassay based on switching peptides is presented for the detection of influenza viruses A and B (Inf-A and Inf-B, respectively). The one-step immunoassay represents an immunoassay method that does not involve any washing steps, only treatment of the sample. In this method, fluorescence-labeled switching peptides quantitatively dissociate from the antigen-binding site of immunoglobulin G (IgG). In particular, the one-step immunoassay based on soluble detection antibodies with switching peptides is called a homogeneous one-step immunoassay. The immunoassay developed uses switching peptides labeled with two types of fluorescence dyes (FAM and TAMRA) and detection antibodies labeled with two types of fluorescence quenchers (TQ2 for FAM and TQ3 for TAMRA). The optimal switching peptides for the detection of Inf-A and Inf-B have been selected as L1-peptide and H2-peptide. The interactions between the four kinds of switching peptides and IgG have been analyzed using computational docking simulation and SPR biosensor. The location of labeling for the fluorescence quenchers has been determined based on the distance between the fluorescence dyes of the switching peptides and the fluorescence quenchers, calculated on the basis of the efficiency of fluorescence quenching, using the Förster equation. To demonstrate the feasibility of the one-step immunoassay, binding constants (K_D) have been calculated for detection antibodies against Inf-A and Inf-B with target antigens (Inf-A and Inf-B) and switching peptides (L1- and H2-peptides), using an isotherm model. The immunoassay has been demonstrated to be feasible using antigens as well as real samples of Inf-A and Inf-B with a critical cycle number (Ct). The immunoassay has also been compared to other commercially available rapid test kits for Inf-A and Inf-B and found to be far more sensitive for detection of Inf-A and Inf-B over the entire detection range.

Clone decomposition based on mutation signatures provides novel insights into mutational processes

Intra-tumor heterogeneity is a phenomenon in which mutation profiles differ from cell to cell within the same tumor and is observed in almost all tumors. Understanding intra-tumor heterogeneity is essential from the clinical perspective. Numerous methods have been developed to predict this phenomenon based on variant allele frequency. Among the methods, CloneSig models the variant allele frequency and mutation signatures simultaneously and provides an accurate clone decomposition. However, this method has limitations in terms of clone number selection and modeling. We propose SigTracer, a novel hierarchical Bayesian approach for analyzing intra-tumor heterogeneity based on mutation signatures to tackle these issues. We show that SigTracer predicts more reasonable clone decompositions than the existing methods against artificial data that mimic cancer genomes. We applied SigTracer to whole-genome sequences of blood cancer samples. The results were consistent with past findings that single base substitutions caused by a specific signature (previously reported as SBS9) related to the activation-induced cytidine deaminase intensively lie within immunoglobulin-coding regions for chronic lymphocytic leukemia samples. Furthermore, we showed that this signature mutates regions responsible for cell-cell adhesion. Accurate assignments of mutations to signatures by SigTracer can provide novel insights into signature origins and mutational processes.

Understanding and targeting prostate cancer cell heterogeneity and plasticity

Prostate cancer (PCa) is a prevalent malignancy that occurs primarily in old males. Prostate tumors in different patients manifest significant inter-patient heterogeneity with respect to histo-morphological presentations and molecular architecture. An individual patient tumor also harbors genetically distinct clones in which PCa cells display intra-tumor heterogeneity in molecular features and phenotypic marker expression. This inherent PCa cell heterogeneity, e.g., in the expression of androgen receptor (AR), constitutes a barrier to the long-term therapeutic efficacy of AR-targeting therapies. Furthermore, tumor progression as well as therapeutic treatments induce PCa cell plasticity such that AR-positive PCa cells may turn into AR-negative cells and prostate tumors may switch lineage identity from adenocarcinomas to neuroendocrine-like tumors. This induced PCa cell plasticity similarly confers resistance to AR-targeting and other therapies. In this review, I first discuss PCa from the perspective of an abnormal organ development and deregulated cellular differentiation, and discuss the luminal progenitor cells as the likely cells of origin for PCa. I then focus on intrinsic PCa cell heterogeneity in treatment-naïve tumors with the presence of prostate cancer stem cells (PCSCs). I further elaborate on PCa cell plasticity induced by genetic alterations and therapeutic interventions, and present potential strategies to therapeutically tackle PCa cell heterogeneity and plasticity. My discussions will make it clear that, to achieve enduring clinical efficacy, both intrinsic PCa cell heterogeneity and induced PCa cell plasticity need to be targeted with novel combinatorial approaches.

Benchmarking pipelines for subclonal deconvolution of bulk tumour sequencing data

Intratumour heterogeneity provides tumours with the ability to adapt and acquire treatment resistance. The development of more effective and personalised treatments for cancers, therefore, requires accurate characterisation of the clonal architecture of tumours, enabling evolutionary dynamics to be tracked. Many methods exist for achieving this from bulk tumour sequencing data, involving identifying mutations and performing subclonal deconvolution, but there is a lack of systematic benchmarking to inform researchers on which are most accurate, and how dataset characteristics impact performance. To address this, we use the most comprehensive tumour genome simulation tool available for such purposes to create 80 bulk tumour whole exome sequencing datasets of differing depths, tumour complexities, and purities, and use these to benchmark subclonal deconvolution pipelines. We conclude that i) tumour complexity does not impact accuracy, ii) increasing either purity or purity-corrected sequencing depth improves accuracy, and iii) the optimal pipeline consists of Mutect2, FACETS and PyClone-VI. We have made our benchmarking datasets publicly available for future use.

Somatic driver mutation prevalence in 1844 prostate cancers identifies ZNRF3 loss as a predictor of metastatic relapse

Driver gene mutations that are more prevalent in metastatic, castration-resistant prostate cancer (mCRPC) than localized disease represent candidate prognostic biomarkers. We analyze 1,844 localized (1,289) or mCRPC (555) tumors and quantify the prevalence of 113 somatic driver single nucleotide variants (SNVs), copy number aberrations (CNAs), and structural variants (SVs) in each state. One-third are significantly more prevalent in mCRPC than expected while a quarter are less prevalent. Mutations in AR and its enhancer are more prevalent in mCRPC, as are those in TP53, MYC, ZNRF3 and PRKDC. ZNRF3 loss is associated with decreased ZNRF3 mRNA abundance, WNT, cell cycle & PRC1/2 activity, and genomic instability. ZNRF3 loss, RNA downregulation and hypermethylation are prognostic of metastasis and overall survival, independent of clinical and pathologic indices. These data demonstrate a strategy for identifying biomarkers of localized cancer aggression, with ZNRF3 loss as a predictor of metastasis in prostate cancer.

Aging of the progenitor cells that initiate prostate cancer

Many organs experience a loss of tissue mass and a decline in regenerative capacity during aging. In contrast, the prostate continues to grow in volume. In fact, age is the most important risk factor for prostate cancer. However, the age-related factors that influence the composition, morphology and molecular features of prostate epithelial progenitor cells, the cells-of-origin for prostate cancer, are poorly understood. Here, we review the evidence that prostate luminal progenitor cells are expanded with age. We explore the age-related changes to the microenvironment that may influence prostate epithelial cells and risk of transformation. Finally, we raise a series of questions about models of aging and regulators of prostate aging which need to be addressed. A fundamental understanding of aging in the prostate will yield critical insights into mechanisms that promote the development of age-related prostatic disease.

Technical and biological constraints on ctDNA-based genotyping

Circulating tumor DNA (ctDNA) enables real-time genomic profiling of cancer without the need for tissue biopsy. ctDNA-based technology is seeing rapid uptake in clinical practice due to the potential to inform patient management from diagnosis to advanced disease. In metastatic disease, ctDNA can identify somatic mutations, copy-number variants (CNVs), and structural rearrangements that are predictive of therapy response. However, the ctDNA fraction (ctDNA%) is unpredictable and confounds variant detection strategies, undermining confidence in liquid biopsy results. Assay design also influences which types of genomic alterations are identifiable. Here, we describe the relationships between ctDNA%, methodology, and sensitivity-specificity for major classes of genomic alterations in prostate cancer. We provide recommendations to navigate the technical complexities that constrain the detection of clinically relevant genomic alterations in ctDNA.

Inferring structural variant cancer cell fraction

We present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone's performance is comparable to single-nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy-number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity.