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Tumorigenesis begins long before the growth of a clinically detectable lesion and, indeed, even before any of the usual morphological correlates of pre-malignancy are recognizable. Field cancerization, which is the replacement of the normal cell population by a cancer-primed cell population that may show no morphological change, is now recognized to underlie the development of many types of cancer, including the common carcinomas of the lung, colon, skin, prostate and bladder. Field cancerization is the consequence of the evolution of somatic cells in the body that results in cells that carry some but not all phenotypes required for malignancy. Here, we review the evidence of field cancerization across organs and examine the biological mechanisms that drive the evolutionary process that results in field creation. We discuss the clinical implications, principally, how measurements of the cancerized field could improve cancer risk prediction in patients with pre-malignant disease.

Andrea Sottoriva, Trevor Graham and colleagues analyze tumor sequencing data and show that a substantial proportion of cancers of many different types are characterized by neutral evolution resulting in a characteristic power-law distribution of the mutant allele frequencies. This neutral framework provides a new way to interpret cancer genomic data and to discriminate between functional and non-functional intratumoral heterogeneity. Despite extraordinary efforts to profile cancer genomes, interpreting the vast amount of genomic data in the light of cancer evolution remains challenging. Here we demonstrate that neutral tumor evolution results in a power-law distribution of the mutant allele frequencies reported by next-generation sequencing of tumor bulk samples. We find that the neutral power law fits with high precision 323 of 904 cancers from 14 types and from different cohorts. In malignancies identified as evolving neutrally, all clonal selection seemingly occurred before the onset of cancer growth and not in later-arising subclones, resulting in numerous passenger mutations that are responsible for intratumoral heterogeneity. Reanalyzing cancer sequencing data within the neutral framework allowed the measurement, in each patient, of both the in vivo mutation rate and the order and timing of mutations. This result provides a new way to interpret existing cancer genomic data and to discriminate between functional and non-functional intratumoral heterogeneity.

The authors analyze the extent of intratumor heterogeneity across 12 tumor types to reveal that increased heterogeneity is a general phenomenon and has a biphasic contribution to tumor progression. Intratumor heterogeneity (ITH) drives neoplastic progression and therapeutic resistance. We used the bioinformatics tools 'expanding ploidy and allele frequency on nested subpopulations' (EXPANDS) and PyClone to detect clones that are present at a ≥10% frequency in 1,165 exome sequences from tumors in The Cancer Genome Atlas. 86% of tumors across 12 cancer types had at least two clones. ITH in the morphology of nuclei was associated with genetic ITH (Spearman's correlation coefficient, ρ = 0.24–0.41; P < 0.001). Mutation of a driver gene that typically appears in smaller clones was a survival risk factor (hazard ratio (HR) = 2.15, 95% confidence interval (CI): 1.71–2.69). The risk of mortality also increased when >2 clones coexisted in the same tumor sample (HR = 1.49, 95% CI: 1.20–1.87). In two independent data sets, copy-number alterations affecting either <25% or >75% of a tumor's genome predicted reduced risk (HR = 0.15, 95% CI: 0.08–0.29). Mortality risk also declined when >4 clones coexisted in the sample, suggesting a trade-off between the costs and benefits of genomic instability. ITH and genomic instability thus have the potential to be useful measures that can universally be applied to all cancers.

Clinical archives of patient material near-exclusively consist of formalin-fixed and paraffin-embedded (FFPE) blocks. The ability to precisely characterise mutational signatures from FFPE-derived DNA has tremendous translational potential. However, sequencing of DNA derived from FFPE material is known to be riddled with artefacts. Here we derive genome-wide mutational signatures caused by formalin fixation. We show that the FFPE-signature is highly similar to signature 30 (the signature of Base Excision Repair deficiency due to NTHL1 mutations), and chemical repair of DNA lesions leads to a signature highly similar to signature 1 (clock-like signature due to spontaneous deamination of methylcytosine). We demonstrate that using uncorrected mutational catalogues of FFPE samples leads to major mis-assignment of signature activities. To correct for this, we introduce FFPEsig, a computational algorithm to rectify the formalin-induced artefacts in the mutational catalogue. We demonstrate that FFPEsig enables accurate mutational signature analysis both in simulated and whole-genome sequenced FFPE cancer samples. FFPEsig thus provides an opportunity to unlock additional clinical potential of archival patient tissues. Many archived tumour samples are stored as formalin-fixed and paraffin-embedded (FFPE) blocks, but this treatment can impact downstream genomics analyses. Here, the authors derive the mutational signatures of formalin on the cancer genome, and present FFPEsig, an algorithm that can distinguish and correct FFPE mutational signatures in archived cancer samples.

Subclonal architectures are prevalent across cancer types. However, the temporal evolutionary dynamics that produce tumor subclones remain unknown. Here we measure clone dynamics in human cancers by using computational modeling of subclonal selection and theoretical population genetics applied to high-throughput sequencing data. Our method determined the detectable subclonal architecture of tumor samples and simultaneously measured the selective advantage and time of appearance of each subclone. We demonstrate the accuracy of our approach and the extent to which evolutionary dynamics are recorded in the genome. Application of our method to high-depth sequencing data from breast, gastric, blood, colon and lung cancer samples, as well as metastatic deposits, showed that detectable subclones under selection, when present, consistently emerged early during tumor growth and had a large fitness advantage (>20%). Our quantitative framework provides new insight into the evolutionary trajectories of human cancers and facilitates predictive measurements in individual tumors from widely available sequencing data. This analysis uses computational modeling of clonal selection to measure evolutionary dynamics in primary human cancers. The method employs high-throughput sequencing data and simultaneously measures the selective advantage and time of appearance of subclones.

The dominant mutational signature in colorectal cancer genomes is C > T deamination (COSMIC Signature 1) and, in a small subgroup, mismatch repair signature (COSMIC signatures 6 and 44). Mutations in common colorectal cancer driver genes are often not consistent with those signatures. Here we perform whole-genome sequencing of normal colon crypts from cancer patients, matched to a previous multi-omic tumour dataset. We analyse normal crypts that were distant vs adjacent to the cancer. In contrast to healthy individuals, normal crypts of colon cancer patients have a high incidence of pks  +  (polyketide synthases) E.coli ( Escherichia coli ) mutational and indel signatures, and this is confirmed by metagenomics. These signatures are compatible with many clonal driver mutations detected in the corresponding cancer samples, including in chromatin modifier genes, supporting their role in early tumourigenesis. These results provide evidence that pks  +  E.coli is a potential driver of carcinogenesis in the human gut. Common driver mutations in colorectal cancer (CRC) are not always consistent with frequent mutational signatures. Here, the authors analyse spatially annotated colon crypts in CRC patients and find mutational signatures of pks+ E. coli that are consistent with driver mutations, suggesting a potential role of pks+ E. coli in carcinogenesis.

Based on a consensus conference of experts in the evolution and ecology of cancer, this article proposes a framework for classifying tumours that includes four evolutionary and ecological processes: neoplastic cell diversity and changes over time in that diversity, hazards to cell survival and available resources. Neoplasms change over time through a process of cell-level evolution, driven by genetic and epigenetic alterations. However, the ecology of the microenvironment of a neoplastic cell determines which changes provide adaptive benefits. There is widespread recognition of the importance of these evolutionary and ecological processes in cancer, but to date, no system has been proposed for drawing clinically relevant distinctions between how different tumours are evolving. On the basis of a consensus conference of experts in the fields of cancer evolution and cancer ecology, we propose a framework for classifying tumours that is based on four relevant components. These are the diversity of neoplastic cells (intratumoural heterogeneity) and changes over time in that diversity, which make up an evolutionary index (Evo-index), as well as the hazards to neoplastic cell survival and the resources available to neoplastic cells, which make up an ecological index (Eco-index). We review evidence demonstrating the importance of each of these factors and describe multiple methods that can be used to measure them. Development of this classification system holds promise for enabling clinicians to personalize optimal interventions based on the evolvability of the patient's tumour. The Evo- and Eco-indices provide a common lexicon for communicating about how neoplasms change in response to interventions, with potential implications for clinical trials, personalized medicine and basic cancer research.

Background Next generation sequencing has yielded an unparalleled means of quickly determining the molecular make-up of patient tumors. In conjunction with emerging, effective immunotherapeutics for a number of cancers, this rapid data generation necessitates a paired high-throughput means of predicting and assessing neoantigens from tumor variants that may stimulate immune response. Results Here we offer N eo P red P ipe (Neoantigen Prediction Pipeline) as a contiguous means of predicting putative neoantigens and their corresponding recognition potentials for both single and multi-region tumor samples. NeoPredPipe is able to quickly provide summary information for researchers, and clinicians alike, on predicted neoantigen burdens while providing high-level insights into tumor heterogeneity given somatic mutation calls and, optionally, patient HLA haplotypes. Given an example dataset we show how NeoPredPipe is able to rapidly provide insights into neoantigen heterogeneity, burden, and immune stimulation potential. Conclusions Through the integration of widely adopted tools for neoantigen discovery NeoPredPipe offers a contiguous means of processing single and multi-region sequence data. NeoPredPipe is user-friendly and adaptable for high-throughput performance. NeoPredPipe is freely available at https://github.com/MathOnco/NeoPredPipe .

Most cancer genomic data are generated from bulk samples composed of mixtures of cancer subpopulations, as well as normal cells. Subclonal reconstruction methods based on machine learning aim to separate those subpopulations in a sample and infer their evolutionary history. However, current approaches are entirely data driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in the analysis if evolution is not accounted for, and this is exacerbated with multi-sampling of the same tumor. We present a novel approach for model-based tumor subclonal reconstruction, called MOBSTER, which combines machine learning with theoretical population genetics. Using public whole-genome sequencing data from 2,606 samples from different cohorts, new data and synthetic validation, we show that this method is more robust and accurate than current techniques in single-sample, multiregion and longitudinal data. This approach minimizes the confounding factors of nonevolutionary methods, thus leading to more accurate recovery of the evolutionary history of human cancers. MOBSTER is an approach for subclonal reconstruction of tumors from cancer genomics data on the basis of models that combine machine learning with evolutionary theory, thus leading to more accurate evolutionary histories of tumors.

This study provides an overview of the largest and longest-running colonoscopic surveillance program for colorectal cancer (CRC) in patients with long-standing ulcerative colitis (UC). Data were obtained from medical records, endoscopy, and histology reports. Primary end points were defined as death, colectomy, withdrawal from surveillance, or censor date (1 January 2013). A total of 1,375 UC patients were followed up for 15,234 patient-years (median, 11 years per patient). CRC was detected in 72 patients (incidence rate (IR), 4.7 per 1,000 patient-years). Time-trend analysis revealed that although there was significant decrease in incidence of colectomy performed for dysplasia (linear regression, R=-0.43; P=0.007), IR of advanced CRC and interval CRC have steadily decreased over past four decades (Pearson's correlation, -0.99; P=0.01 for both trends). The IR of early CRC has increased 2.5-fold in the current decade compared with past decade (χ(2), P=0.045); however, its 10-year survival rate was high (79.6%). The IR of dysplasia has similarly increased (χ(2), P=0.01), potentially attributable to the recent use of chromoendoscopy that was twice more effective at detecting dysplasia compared with white-light endoscopy (χ(2), P<0.001). CRCs were frequently accompanied by synchronous CRC or spatially distinct dysplasia (37.5%). Finally, the risk of CRC was not significantly different between \"indefinite\" or low-grade dysplasia (log-rank, P=0.78). Colonoscopic surveillance may have a significant role in reducing the risk of advanced and interval CRC while allowing more patients to retain their colon for longer. Given the ongoing risk of early CRC, patients with any grade of dysplasia who are managed endoscopically should be monitored closely with advanced techniques.