Somatic evolutionary timings of driver mutations

Strength of selection in lung tumors correlates with clinical features better than tumor mutation burden

Single nucleotide substitutions are the most common type of somatic mutations in cancer genome. The goal of this study was to use publicly available somatic mutation data to quantify negative and positive selection in individual lung tumors and test how strength of directional and absolute selection is associated with clinical features. The analysis found a significant variation in strength of selection (both negative and positive) among tumors, with median selection tending to be negative even though tumors with strong positive selection also exist. Strength of selection estimated as the density of missense mutations relative to the density of silent mutations showed only a weak correlation with tumor mutation burden. In the "all histology together" analysis we found that absolute strength of selection was strongly correlated with all clinically relevant features analyzed. In histology-stratified analysis selection was strongest in small cell lung cancer. Selection in adenocarcinoma was somewhat higher compared to squamous cell carcinoma. The study suggests that somatic mutation- based quantifying of directional and absolute selection in individual tumors can be a useful biomarker of tumor aggressiveness.

KIT/PDGFRA Variant Allele Frequency as Prognostic Factor in Gastrointestinal Stromal Tumors (GISTs): Results From a Multi-Institutional Cohort Study

BACKGROUND

The patient selection for optimal adjuvant therapy in gastrointestinal stromal tumors (GISTs) is provided by nomogram based on tumor size, mitotic index, tumor location, and tumor rupture. Although mutational status is not currently used to risk assessment, tumor genotype showed a prognostic influence on natural history and tumor relapse. Innovative measures, such as KIT/PDGFRA-mutant-specific variant allele frequency (VAF) levels detection from next-generation sequencing (NGS), may act as a surrogate of tumor burden and correlate with prognosis and overall survival of patients with GIST, helping the choice for adjuvant treatment.

PATIENTS AND METHODS

This was a multicenter, hospital-based, retrospective/prospective cohort study to investigate the prognostic role of KIT or PDGFRA-VAF of GIST in patients with radically resected localized disease. In the current manuscript, we present the results from the retrospective phase of the study.

RESULTS

Two-hundred (200) patients with GIST between 2015 and 2022 afferent to 6 Italian Oncologic Centers in the EURACAN Network were included in the study. The receiver operating characteristic (ROC) curves analysis was used to classify "low" vs. "high" VAF values, further normalized on neoplastic cellularity (nVAF). When RFS between the low and high nVAF groups were compared, patients with GIST with KIT/PDGFRA nVAF > 50% showed less favorable RFS than patients in the group of nVAF ≤ 50% (2-year RFS, 72.6% vs. 93%, respectively; P = .003). The multivariable Cox regression model confirmed these results. In the homogeneous sub-population of intermediate-risk, patients with KIT-mutated GIST, the presence of nVAF >50% was statistically associated with higher disease recurrence.

CONCLUSION

In our study, we demonstrated that higher nVAF levels were independent predictors of GIST prognosis and survival in localized GIST patients with tumors harboring KIT or PDGFRA mutations. In the cohort of intermediate-risk patients, nVAF could be helpful to improve prognostication and the use of adjuvant imatinib.

Improving cellular phylogenies through the integrated use of mutation order and optimality principles

The study of tumor evolution is being revolutionalized by single-cell sequencing technologies that survey the somatic variation of cancer cells. In these endeavors, reliable inference of the evolutionary relationship of single cells is a key step. However, single-cell sequences contain many errors and missing bases, which necessitate advancing standard molecular phylogenetics approaches for applications in analyzing these datasets. We have developed a computational approach that integratively applies standard phylogenetic optimality principles and patterns of co-occurrence of sequence variations to produce more expansive and accurate cellular phylogenies from single-cell sequence datasets. We found the new approach to also perform well for CRISPR/Cas9 genome editing datasets, suggesting that it can be useful for various applications. We apply the new approach to some empirical datasets to showcase its use for reconstructing recurrent mutations and mutational reversals as well as for phylodynamics analysis to infer metastatic cell migrations between tumors.

TP53 somatic mutations in Asian breast cancer are associated with subtype-specific effects

BACKGROUND

Recent genomics studies of breast cancer in Asian cohorts have found a higher prevalence of TP53 mutations in Asian breast cancer patients relative to Caucasian patients. However, the effect of TP53 mutations on Asian breast tumours has not been comprehensively studied.

METHODS

Here, we report an analysis of 492 breast cancer samples from the Malaysian Breast Cancer cohort where we examined the impact of TP53 somatic mutations in relation to PAM50 subtypes by comparing whole exome and transcriptome data from tumours with mutant and wild-type TP53.

RESULTS

We found that the magnitude of impact of TP53 somatic mutations appears to vary between different subtypes. TP53 somatic mutations were associated with higher HR deficiency scores as well as greater upregulation of gene expression pathways in luminal A and luminal B tumours compared to the basal-like and Her2-enriched subtypes. The only pathways that were consistently dysregulated when comparing tumours with mutant and wild-type TP53 across different subtypes were the mTORC1 signalling and glycolysis pathways.

CONCLUSION

These results suggest that therapies that target TP53 or other downstream pathways may be more effective against luminal A and B tumours in the Asian population.

A Novel Bead-Capture Nanopore Sequencing Method for Large Structural Rearrangement Detection in Cancer

Rapid, cost-effective genomic stratification of structural rearrangements in cancer is often of vital importance when determining treatment; however, existing diagnostic cytogenetic and molecular testing fails to deliver the required speed when deployed at scale. Next-generation sequencing-based methods are widely used, but these can lack sensitivity and require batching of samples to be cost-effective, with long turnaround times. Here we present a novel method for rearrangement detection from genomic DNA based on third-generation long-read sequencing that overcomes these time and cost issues. The utility of this approach for the genomic stratification of patients with acute myeloid leukemia is shown based on detection of four of the most prevalent structural rearrangements. The method not only determines the precise genomic breakpoint for each expected rearrangement but also discovers and validates novel translocations in one-third of the tested samples, 80% of which involve known oncogenes. This method may prove to be a powerful tool for the diagnosis, genomic stratification, and characterization of cancers.

Single whole genome sequencing analysis blazes the trail for precision medicine

As precision oncology evolves toward developing more targeted therapies, sequencing has moved to the forefront of treatment decision-making. Whole genome sequencing (WGS) has emerged as a technology capable of identifying candidates for rare and targeted treatments. Yet, because the tumor is constantly evolving during relapse and therapy resistance, the frequency with which WGS should be performed to identify potential new therapies for progressing patients remains unknown. A recent study in Nature Medicine by Van de Haar et al. observed a remarkably stable driver gene mutational profile among 250 biopsy pairs from 231 patients undergoing standard of care treatments during the biopsy interval. Their findings suggest that the actionable metastatic cancer genome is relatively stable over time and that a single WGS provides a complete view of the treatment opportunities available to most metastatic cancer patients.

Beyond Stamp Collecting: Evolutionary and Functional Genomics Advance Our Understanding of Cancer Biology

In this issue of Cancer Research, Emami and colleagues leveraged genetic data from over 200,000 men of European descent to implicate rare alleles that are associated with prostate cancer. However, this study went beyond a simple description of statistical associations between genetic variants and cancer risk. Polygenic risk scores were applied to large cohorts from Kaiser Permanente and the UK Biobank, demonstrating the clinical utility of genetic predictors of disease risk. Furthermore, by placing their results in an evolutionary framework and integrating genetic information with functional data, the authors of this major study were able to bridge the gap between genome-wide association studies and the biological mechanisms underlying prostate cancer risk.See related article by Emami et al., 1695.

Phylodynamics for cell biologists

Multicellular organisms are composed of cells connected by ancestry and descent from progenitor cells. The dynamics of cell birth, death, and inheritance within an organism give rise to the fundamental processes of development, differentiation, and cancer. Technical advances in molecular biology now allow us to study cellular composition, ancestry, and evolution at the resolution of individual cells within an organism or tissue. Here, we take a phylogenetic and phylodynamic approach to single-cell biology. We explain how "tree thinking" is important to the interpretation of the growing body of cell-level data and how ecological null models can benefit statistical hypothesis testing. Experimental progress in cell biology should be accompanied by theoretical developments if we are to exploit fully the dynamical information in single-cell data.

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.

Clonal evolution driven by superdriver mutations

Background

Tumors are widely recognized to progress through clonal evolution by sequentially acquiring selectively advantageous genetic alterations that significantly contribute to tumorigenesis and thus are termned drivers. Some cancer drivers, such as TP53 point mutation or EGFR copy number gain, provide exceptional fitness gains, which, in time, can be sufficient to trigger the onset of cancer with little or no contribution from additional genetic alterations. These key alterations are called superdrivers.

Results

In this study, we employ a Wright-Fisher model to study the interplay between drivers and superdrivers in tumor progression. We demonstrate that the resulting evolutionary dynamics follow global clonal expansions of superdrivers with periodic clonal expansions of drivers. We find that the waiting time to the accumulation of a set of superdrivers and drivers in the tumor cell population can be approximated by the sum of the individual waiting times.

Conclusions

Our results suggest that superdriver dynamics dominate over driver dynamics in tumorigenesis. Furthermore, our model allows studying the interplay between superdriver and driver mutations both empirically and theoretically.

Molecular Biology and Evolution of Cancer: From Discovery to Action

Cancer progression is an evolutionary process. During this process, evolving cancer cell populations encounter restrictive ecological niches within the body, such as the primary tumor, circulatory system, and diverse metastatic sites. Efforts to prevent or delay cancer evolution-and progression-require a deep understanding of the underlying molecular evolutionary processes. Herein we discuss a suite of concepts and tools from evolutionary and ecological theory that can inform cancer biology in new and meaningful ways. We also highlight current challenges to applying these concepts, and propose ways in which incorporating these concepts could identify new therapeutic modes and vulnerabilities in cancer.

Integrating context of tumor biology and vaccine design to shape multidimensional immunotherapies

Advances in cancer therapy have offered great promise but only modest clinical benefits as monotherapies to date. Patients usually respond well to therapies targeted at specific mutations, but only for a short time. Conversely, immunotherapies help fewer patients, but increase survival. Combination therapies, which could offer the best of both worlds, are currently limited by substantial toxicity. While recent advances in genomics and proteomics have yielded an unprecedented depth of enabling datasets, it has also shifted the focus toward  in silico predictions. Designing the next wave of multidimensional immunotherapies will require leveraging this knowledge while providing a renewed emphasis on tumor biology and vaccine design. This includes careful selection of tumor clinical stage in the context of pre-existing tumor microenvironments, target antigen and technology platform selections to maximize their effect, and treatment staging. Here, we review strategies on how to approach an increasingly complex landscape of immunotherapeutic agents for use in combination therapies.

Reversion of tumor hepatocytes to normal hepatocytes during liver tumor regression in an oncogene-expressing transgenic zebrafish model

Tumors are frequently dependent on primary oncogenes to maintain their malignant properties (known as 'oncogene addiction'). We have previously established several inducible hepatocellular carcinoma (HCC) models in zebrafish by transgenic expression of an oncogene. These tumor models are strongly oncogene addicted, as the induced and histologically proven liver tumors regress after suppression of oncogene expression by removal of a chemical inducer. However, the question of whether the liver tumor cells are eliminated or revert to normal cells remains unanswered. In the present study, we generated a novel Cre/loxP transgenic zebrafish line, Tg(fabp10: loxP-EGFP-stop-loxP-DsRed; TRE: CreERT2) (abbreviated to CreER), in order to trace tumor cell lineage during tumor regression after crossing with the xmrk (activated EGFR homolog) oncogene transgenic line, Tg(fabp10: rtTA; TRE: xmrk; krt4: EGFP) We found that, during HCC regression, restored normal liver contained both reverted tumor hepatocytes (RFP+) and newly differentiated hepatocytes (GFP+). RNA sequencing (RNA-seq) analyses of the RFP+ and GFP+ hepatocyte populations after tumor regression confirmed the conversion of tumor cells to normal hepatocytes, as most of the genes and pathways that were deregulated in the tumor stages were found to have normal regulation in the tumor-reverted hepatocytes. Thus, our lineage-tracing studies demonstrated the potential for transformed tumor cells to revert to normal cells after suppression of expression of a primary oncogene. This observation may provide a basis for the development of a therapeutic approach targeting addicted oncogenes or oncogenic pathways.

The Mutational Landscape of Pancreatic and Liver Cancers, as Represented by Circulating Tumor DNA

The mutational landscapes of pancreatic and liver cancers share many common genetic alterations which drive cancer progression. However, these mutations do not occur in all cases of these diseases, and this tumoral heterogeneity impedes diagnosis, prognosis, and therapeutic development. One minimally invasive method for the evaluation of tumor mutations is the analysis of circulating tumor DNA (ctDNA), released through apoptosis, necrosis, and active secretion by tumor cells into various body fluids. By observing mutations in those genes which promote transformation by controlling the cell cycle and oncogenic signaling pathways, a representation of the mutational profile of the tumor is revealed. The analysis of ctDNA is a promising technique for investigating these two gastrointestinal cancers, as many studies have reported on the accuracy of ctDNA assessment for diagnosis and prognosis using a variety of techniques.

Mutplot: An easy-to-use online tool for plotting complex mutation data with flexibility

With the development of technology, an enormous amount of sequencing data is being generated rapidly. However, transforming this data into patient care is a critical challenge. There are two difficulties: how to integrate functional information into mutation interpretation and how to make the integration easy to apply. One solution is to visualize amino acid changes with protein structure and function in web app platform. There are multiple existing tools for plotting mutations, but the majority of them requires programming skills that are not common background for clinicians or researchers. Furthermore, the recurrent mutations are the focus and the recurrence cutoff varies. Yet, none of the current software offers customer-defined cutoff. Thus, we developed this user-friendly web-based tool, Mutplot (https://bioinformaticstools.shinyapps.io/lollipop/). Mutplot retrieves up-to-date domain information from the protein resource UniProt (https://www.uniprot.org/), integrates the submitted mutation information and produces lollipop diagrams with annotations and highlighted candidates. It offers flexible output options. For data that follows security standards, the app can also be hosted in web servers inside a firewall or computers without internet with Uniprot database stored on them. Altogether, Mutplot is an excellent tool for visualizing protein mutations, especially for clinicians or researchers without any bioinformatics background.

Neutral Theory in Cancer Cell Population Genetics

Kimura's neutral theory provides the whole theoretical basis of the behavior of mutations in a Wright-Fisher population. We here discuss how it can be applied to a cancer cell population, in which there is an increasing interest in genetic variation within a tumor. We explain a couple of fundamental differences between cancer cell populations and asexual organismal populations. Once these differences are taken into account, a number of powerful theoretical tools developed for a Wright-Fisher population could be readily contribute to our deeper understanding of the evolutionary dynamics of cancer cell population.

Environmental Carcinogenesis and Transgenerational Transmission of Carcinogenic Risk: From Genetics to Epigenetics

The dominant pathogenic model, somatic mutation theory (SMT), considers carcinogenesis as a ‘genetic accident’ due to the accumulation of ‘stochastic’ DNA mutations. This model was proposed and accepted by the scientific community when cancer mainly affected the elderly, but it does not explain the epidemiological observation of the continuous increase in cancer incidence among children and young adults. Somatic mutation theory has been proposed for a revision based on the emerging experimental evidence, as it does not fully address some issues that have proven to be crucial for carcinogenesis, namely: the inflammatory context of cancer; the key role played by the stroma, microenvironment, endothelial cells, activated macrophages, and surrounding tissues; and the distorted developmental course followed by the neoplastic tissue. Furthermore, SMT is often not able to consider either the existence of specific mutations resulting in a well-defined cancer type, or a clear relationship between mutations and tumor progression. Moreover, it does not explain the mechanism of action of the non-mutagenic and environmental carcinogens. In the last decade, cancer research has highlighted the prominent role of an altered regulation of gene expression, suggesting that cancer should be considered as a result of a polyclonal epigenetic disruption of stem/progenitor cells, mediated by tumour-inducing genes. The maternal and fetal exposure to a wide range of chemicals and environmental contaminants is raising the attention of the scientific community. Indeed, the most powerful procarcinogenic mechanisms of endocrine disruptors and other pollutants is linked to their potential to interfere epigenetically with the embryo-fetal programming of tissues and organs, altering the regulation of the genes involved in the cell cycle, cell proliferation, apoptosis, and other key signaling pathways. The embryo-fetal exposure to environmental, stressful, and proinflammatory triggers (first hit), seems to act as a ‘disease primer’, making fetal cells and tissues more susceptible to the subsequent environmental exposures (second hit), triggering the carcinogenic pathways. Furthermore, even at the molecular level, in carcinogenesis, ‘epigenetics precedes genetics’ as global DNA hypomethylation, and the hypermethylation of tumor suppressor genes are common both in cancerous and in precancerous cells, and generally precede mutations. These epigenetic models may better explain the increase of cancer and chronic/degenerative diseases in the last decades and could be useful to adopt appropriate primary prevention measures, essentially based on the reduction of maternal-fetal and child exposure to several procarcinogenic agents and factors dispersed in the environment and in the food-chains, as recently suggested by the World Health Organization.

Neutral Theory and the Somatic Evolution of Cancer

Kimura's neutral theory argued that positive selection was not responsible for an appreciable fraction of molecular substitutions. Correspondingly, quantitative analysis reveals that the vast majority of substitutions in cancer genomes are not detectably under selection. Insights from the somatic evolution of cancer reveal that beneficial substitutions in cancer constitute a small but important fraction of the molecular variants. The molecular evolution of cancer community will benefit by incorporating the neutral theory of molecular evolution into their understanding and analysis of cancer evolution-and accepting the use of tractable, predictive models, even when there is some evidence that they are not perfect.