How many samples are needed to infer truly clonal mutations from heterogenous tumours?

Islands of genomic stability in the face of genetically unstable metastatic cancer

Introduction

Metastatic cancer affects millions of people worldwide annually and is the leading cause of cancer-related deaths. Most patients with metastatic disease are not eligible for surgical resection, and current therapeutic regimens have varying success rates, some with 5-year survival rates below 5%. Here we test the hypothesis that metastatic cancer can be genetically targeted by exploiting single base substitution mutations unique to individual cells that occur as part of normal aging prior to transformation. These mutations are targetable because ~10% of them form novel tumor-specific "NGG" protospacer adjacent motif (PAM) sites targetable by CRISPR-Cas9.

Methods

Whole genome sequencing was performed on five rapid autopsy cases of patient-matched primary tumor, normal and metastatic tissue from pancreatic ductal adenocarcinoma decedents. CRISPR-Cas9 PAM targets were determined by bioinformatic tumor-normal subtraction for each patient and verified in metastatic samples by high-depth capture-based sequencing.

Results

We found that 90% of PAM targets were maintained between primary carcinomas and metastases overall. We identified rules that predict PAM loss or retention, where PAMs located in heterozygous regions in the primary tumor can be lost in metastases (private LOH), but PAMs occurring in regions of loss of heterozygosity (LOH) in the primary tumor were universally conserved in metastases.

Conclusions

Regions of truncal LOH are strongly retained in the presence of genetic instability, and therefore represent genetic vulnerabilities in pancreatic adenocarcinomas. A CRISPR-based gene therapy approach targeting these regions may be a novel way to genetically target metastatic cancer.

Mutation divergence over space in tumour expansion

Mutation accumulation in tumour evolution is one major cause of intra-tumour heterogeneity (ITH), which often leads to drug resistance during treatment. Previous studies with multi-region sequencing have shown that mutation divergence among samples within the patient is common, and the importance of spatial sampling to obtain a complete picture in tumour measurements. However, quantitative comparisons of the relationship between mutation heterogeneity and tumour expansion modes, sampling distances as well as the sampling methods are still few. Here, we investigate how mutations diverge over space by varying the sampling distance and tumour expansion modes using individual-based simulations. We measure ITH by the Jaccard index between samples and quantify how ITH increases with sampling distance, the pattern of which holds in various sampling methods and sizes. We also compare the inferred mutation rates based on the distributions of variant allele frequencies under different tumour expansion modes and sampling sizes. In exponentially fast expanding tumours, a mutation rate can always be inferred for any sampling size. However, the accuracy compared with the true value decreases when the sampling size decreases, where small sampling sizes result in a high estimate of the mutation rate. In addition, such an inference becomes unreliable when the tumour expansion is slow, such as in surface growth.

cfSNV: a software tool for the sensitive detection of somatic mutations from cell-free DNA

Cell-free DNA (cfDNA) in blood, viewed as a surrogate for tumor biopsy, has many clinical applications, including diagnosing cancer, guiding cancer treatment and monitoring treatment response. All these applications depend on an indispensable, yet underdeveloped task: detecting somatic mutations from cfDNA. The task is challenging because of the low tumor fraction in cfDNA. Recently, we developed the computational method cfSNV, the first method that comprehensively considers the properties of cfDNA for the sensitive detection of mutations from cfDNA. cfSNV vastly outperformed the conventional methods that were developed primarily for calling mutations from solid tumor tissues. cfSNV can accurately detect mutations in cfDNA even with medium-coverage (e.g., ≥200×) sequencing, which makes whole-exome sequencing (WES) of cfDNA a viable option for various clinical utilities. Here, we present a user-friendly cfSNV package that exhibits fast computation and convenient user options. We also built a Docker image of it, which is designed to enable researchers and clinicians with a limited computational background to easily carry out analyses on both high-performance computing platforms and local computers. Mutation calling from a standard preprocessed WES dataset (~250× and ~70 million base pair target size) can be carried out in 3 h on a server with eight virtual CPUs and 32 GB of random access memory.

Agent-based methods facilitate integrative science in cancer

In this opinion, we highlight agent-based modeling as a key tool for exploration of cell-cell and cell-environment interactions that drive cancer progression, therapeutic resistance, and metastasis. These biological phenomena are particularly suited to be captured at the cell-scale resolution possible only within agent-based or individual-based mathematical models. These modeling approaches complement experimental work (in vitro and in vivo systems) through parameterization and data extrapolation but also feed forward to drive new experiments that test model-generated predictions.

The role of migration in mutant dynamics in fragmented populations

Mutant dynamics in fragmented populations have been studied extensively in evolutionary biology. Yet, open questions remain, both experimentally and theoretically. Some of the fundamental properties predicted by models still need to be addressed experimentally. We contribute to this by using a combination of experiments and theory to investigate the role of migration in mutant distribution. In the case of neutral mutants, while the mean frequency of mutants is not influenced by migration, the probability distribution is. To address this empirically, we performed in vitro experiments, where mixtures of GFP-labelled ("mutant") and non-labelled ("wid-type") murine cells were grown in wells (demes), and migration was mimicked via cell transfer from well to well. In the presence of migration, we observed a change in the skewedness of the distribution of the mutant frequencies in the wells, consistent with previous and our own model predictions. In the presence of de novo mutant production, we used modelling to investigate the level at which disadvantageous mutants are predicted to exist, which has implications for the adaptive potential of the population in case of an environmental change. In panmictic populations, disadvantageous mutants can persist around a steady state, determined by the rate of mutant production and the selective disadvantage (selection-mutation balance). In a fragmented system that consists of demes connected by migration, a steady-state persistence of disadvantageous mutants is also observed, which, however, is fundamentally different from the mutation-selection balance and characterized by higher mutant levels. The increase in mutant frequencies above the selection-mutation balance can be maintained in small ( N < N c ) demes as long as the migration rate is sufficiently small. The migration rate above which the mutants approach the selection-mutation balance decays exponentially with N / N c . The observed increase in the mutant numbers is not explained by the change in the effective population size. Implications for evolutionary processes in diseases are discussed, where the pre-existence of disadvantageous drug-resistant mutant cells or pathogens drives the response of the disease to treatments.

What’s next in cancer immunotherapy? - The promise and challenges of neoantigen vaccination

The process of tumorigenesis leaves a series of indelible genetic changes in tumor cells, that when expressed, have the potential to be tumor-specific immune targets. Neoantigen vaccines that capitalize on this potential immunogenicity have shown efficacy in preclinical models and have now entered clinical trials. Here we discuss the status of personalized neoantigen vaccines and the current major challenges to this nascent field. In particular, we focus on the types of antigens that can be targeted by vaccination and on the role that preexisting immunosuppression, and in particular T-cell exhaustion, will play in the development of effective cancer vaccines.

Multiscale heterogeneity in gastric adenocarcinoma evolution is an obstacle to precision medicine

BACKGROUND

Cancer is a somatic evolutionary disease and adenocarcinomas of the stomach and gastroesophageal junction (GC) may serve as a two-dimensional model of cancer expansion, in which tumor subclones are not evenly mixed during tumor progression but rather spatially separated and diversified. We hypothesize that precision medicine efforts are compromised when clinical decisions are based on a single-sample analysis, which ignores the mechanisms of cancer evolution and resulting intratumoral heterogeneity. Using multiregional whole-exome sequencing, we investigated the effect of somatic evolution on intratumoral heterogeneity aiming to shed light on the evolutionary biology of GC.

METHODS

The study comprised a prospective discovery cohort of 9 and a validation cohort of 463 GCs. Multiregional whole-exome sequencing was performed using samples form 45 primary tumors and 3 lymph node metastases (range 3-10 tumor samples/patient) of the discovery cohort.

RESULTS

In total, the discovery cohort harbored 16,537 non-synonymous mutations. Intratumoral heterogeneity of somatic mutations and copy number variants were present in all tumors of the discovery cohort. Of the non-synonymous mutations, 53-91% were not present in each patient's sample; 399 genes harbored 2-4 different non-synonymous mutations in the same patient; 175 genes showed copy number variations, the majority being heterogeneous, including CD274 (PD-L1). Multi-sample tree-based analyses provided evidence for branched evolution being most complex in a microsatellite instable GC. The analysis of the mode of evolution showed a high degree of heterogeneity in deviation from neutrality within each tumor. We found evidence of parallel evolution and evolutionary trajectories: different mutations of SMAD4 aligned with different subclones and were found only in TP53 mutant GCs.

CONCLUSIONS

Neutral and non-neutral somatic evolution shape the mutational landscape in GC along its lateral expansions. It leads to complex spatial intratumoral heterogeneity, where lymph node metastases may stem from different areas of the primary tumor, synchronously. Our findings may have profound effects on future patient management. They illustrate the risk of mis-interpreting tumor genetics based on single-sample analysis and open new avenues for an evolutionary classification of GC, i.e., the discovery of distinct evolutionary trajectories which can be utilized for precision medicine.

Indication-specific tumor evolution and its impact on neoantigen targeting and biomarkers for individualized cancer immunotherapies

Background

Individualized neoantigen-specific immunotherapy (iNeST) requires robustly expressed clonal neoantigens for efficacy, but tumor mutational heterogeneity, loss of neoantigen expression, and variable tissue sampling present challenges. It is assumed that clonal neoantigens are preferred targets for immunotherapy, but the distributions of clonal neoantigens are not well characterized across cancer types.

Methods

We combined multiregion sequencing (MR-seq) analysis of five untreated, synchronously sampled metastatic solid tumors with re-analysis of published MR-seq data from 103 patients in order to characterize their globally clonal neoantigen content and factors that would impact neoantigen targeting.

Results

Branching evolution in colorectal cancer and renal cell carcinoma led to fewer clonal neoantigens and to clade-specific neoantigens (those shared across a subset of tumor regions but not fully clonal), with the latter not being readily distinguishable in single tumor samples. In colorectal, renal, and bladder cancer, most tumors had few globally clonal neoantigens. Prioritizing mutations with higher purity-adjusted and ploidy-adjusted variant allele frequency enriched for globally clonal neoantigens (those found in all tumor regions), whereas estimated cancer cell fraction derived from clustering-based tools, surprisingly, did not. Neoantigen quality was associated with loss of neoantigen expression in the bladder cancer case, and HLA-allele loss was observed in the renal and non-small cell lung cancer cases.

Conclusions

We show that tumor type, multilesion sampling, neoantigen expression, and HLA allele retention are important factors for iNeST targeting and patient selection, and may also be important factors to consider in the development of biomarker strategies.

Elements and evolutionary determinants of genomic divergence between paired primary and metastatic tumors

Can metastatic-primary (M-P) genomic divergence measured from next generation sequencing reveal the natural history of metastatic dissemination? This remains an open question of utmost importance in facilitating a deeper understanding of metastatic progression, and thereby, improving its prevention. Here, we utilize mathematical and computational modeling to tackle this question as well as to provide a framework that illuminates the fundamental elements and evolutionary determinants of M-P divergence. Our framework facilitates the integration of sequencing detectability of somatic variants, and hence, paves the way towards bridging the measurable between-tumor heterogeneity with analytical modeling and interpretability. We show that the number of somatic variants of the metastatic seeding cell that are experimentally undetectable in the primary tumor, can be characterized as the path of the phylogenetic tree from the last appearing variant of the seeding cell back to the most recent detectable variant. We find that the expected length of this path is principally determined by the decay in detectability of the variants along the seeding cell's lineage; and thus, exhibits a significant dependence on the underlying tumor growth dynamics. A striking implication of this fact, is that dissemination from an advanced detectable subclone of the primary tumor can lead to an abrupt drop in the expected measurable M-P divergence, thereby breaking the previously assumed monotonic relation between seeding time and M-P divergence. This is emphatically verified by our single cell-based spatial tumor growth simulation, where we find that M-P divergence exhibits a non-monotonic relationship with seeding time when the primary tumor grows under branched and linear evolution. On the other hand, a monotonic relationship holds when we condition on the dynamics of progressive diversification, or by restricting the seeding cells to always originate from undetectable subclones. Our results highlight the fact that a precise understanding of tumor growth dynamics is the sine qua non for exploiting M-P divergence to reconstruct the chronology of metastatic dissemination. The quantitative models presented here enable further careful evaluation of M-P divergence in association with crucial evolutionary and sequencing parameters.

The impact of phenotypic heterogeneity of tumour cells on treatment and relapse dynamics

Intratumour heterogeneity is increasingly recognized as a frequent problem for cancer treatment as it allows for the evolution of resistance against treatment. While cancer genotyping becomes more and more established and allows to determine the genetic heterogeneity, less is known about the phenotypic heterogeneity among cancer cells. We investigate how phenotypic differences can impact the efficiency of therapy options that select on this diversity, compared to therapy options that are independent of the phenotype. We employ the ecological concept of trait distributions and characterize the cancer cell population as a collection of subpopulations that differ in their growth rate. We show in a deterministic model that growth rate-dependent treatment types alter the trait distribution of the cell population, resulting in a delayed relapse compared to a growth rate-independent treatment. Whether the cancer cell population goes extinct or relapse occurs is determined by stochastic dynamics, which we investigate using a stochastic model. Again, we find that relapse is delayed for the growth rate-dependent treatment type, albeit an increased relapse probability, suggesting that slowly growing subpopulations are shielded from extinction. Sequential application of growth rate-dependent and growth rate-independent treatment types can largely increase treatment efficiency and delay relapse. Interestingly, even longer intervals between decisions to change the treatment type may achieve close-to-optimal efficiencies and relapse times. Monitoring patients at regular check-ups may thus provide the temporally resolved guidance to tailor treatments to the changing cancer cell trait distribution and allow clinicians to cope with this dynamic heterogeneity.

The individual cells within a cancer cell population are not all equal. The heterogeneity among them can strongly affect disease progression and treatment success. Recent diagnostic advances allow measuring how the characteristics of this heterogeneity change over time. To match these advances, we developed deterministic and stochastic trait-based models that capture important characteristics of the intratumour heterogeneity and allow to evaluate different treatment types that either do or do not interact with this heterogeneity. We focus on growth rate as the decisive characteristic of the intratumour heterogeneity. We find that by shifting the trait distribution of the cancer cell population, the growth rate-dependent treatment delays an eventual relapse compared to the growth rate-independent treatment. As a downside, however, we observe a refuge effect where slower-growing subpopulations are less affected by the growth rate-dependent treatment, which may decrease the likelihood of successful therapy. We find that navigating along this trade-off may be achieved by sequentially combining both treatment types, which agrees qualitatively with current clinical practice. Interestingly, even rather large intervals between treatment changes allow for close-to-optimal treatment results, which again hints towards a practical applicability.

A practical guide to cancer subclonal reconstruction from DNA sequencing

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

A review of the use of next generation sequencing methodologies to identify biomarkers of resistance to CDK4/6 inhibitors in ER+/HER2- breast cancer

The development of cyclin-dependent kinases (CDK) 4 and 6 inhibitors represented a substantial breakthrough in the treatment of estrogen receptor positive (ER+), human epidermal growth factor receptor 2 (HER2) negative metastatic breast cancer. These drugs showed a significant clinical benefit in pivotal clinical trials. However, resistance eventually occurs, leading to disease progression. Next Generation Sequencing methodologies have been employed to investigate predictive biomarkers of response or resistance to CDK4/6 inhibitors. Whole exome and targeted sequencing of solid and liquid biopsies have revealed several possible genomic alterations associated with resistance. Notably, genomic alterations identified by DNA-sequencing did not fully recapitulate the entire landscape of resistance to CDK4/6 inhibitors. Gene expression analysis, such as RNA-Seq methodologies, have provided insights into transcriptional profiles and may need further application. Herein, we report the main findings derived from the use of NGS analysis in the context of resistance to CDK4/6 inhibitors in ER + breast cancer.

RobustClone: a robust PCA method for tumor clone and evolution inference from single-cell sequencing data

MOTIVATION

Single-cell sequencing (SCS) data provide unprecedented insights into intratumoral heterogeneity. With SCS, we can better characterize clonal genotypes and reconstruct phylogenetic relationships of tumor cells/clones. However, SCS data are often error-prone, making their computational analysis challenging.

RESULTS

To infer the clonal evolution in tumor from the error-prone SCS data, we developed an efficient computational framework, termed RobustClone. It recovers the true genotypes of subclones based on the extended robust principal component analysis, a low-rank matrix decomposition method, and reconstructs the subclonal evolutionary tree. RobustClone is a model-free method, which can be applied to both single-cell single nucleotide variation (scSNV) and single-cell copy-number variation (scCNV) data. It is efficient and scalable to large-scale datasets. We conducted a set of systematic evaluations on simulated datasets and demonstrated that RobustClone outperforms state-of-the-art methods in large-scale data both in accuracy and efficiency. We further validated RobustClone on two scSNV and two scCNV datasets and demonstrated that RobustClone could recover genotype matrix and infer the subclonal evolution tree accurately under various scenarios. In particular, RobustClone revealed the spatial progression patterns of subclonal evolution on the large-scale 10X Genomics scCNV breast cancer dataset.

AVAILABILITY AND IMPLEMENTATION

RobustClone software is available at https://github.com/ucasdp/RobustClone.

CONTACT

lwan@amss.ac.cn or maliang@ioz.ac.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The fidelity of cancer cells in PDX models: Characteristics, mechanism and clinical significance

Patient-derived xenograft (PDX) models are widely used as preclinical cancer models and are considered better than cell culture models in recapitulating the histological features, molecular characteristics and intratumoral heterogeneity (ITH) of human tumors. While the PDX model is commonly accepted for use in drug discovery and other translational studies, a growing body of evidence has suggested its limitations. Recently, the fidelity of cancer cells within a PDX has been questioned, which may impede the future application of these models. In this review, we will focus the variable phenotypes of xenograft tumors and the genomic instability and molecular inconsistency of PDX tumors after serial transplantation. Next, we will discuss the underlying mechanism of ITH and its clinical relevance. Stochastic selection bias in the sampling process and/or deterministic clonal dynamics due to murine selective pressure may have detrimental effects on the results of personalized medicine and drug screening studies. In addition, we aim to identify a possible solution for the issue of fidelity in current PDX models and to discuss emerging next-generation preclinical models.