Publications

Background: Fusobacterium nucleatum activates oncogenic signaling pathways and induces inflammation to promote colorectal carcinogenesis.

Methods: We characterized F nucleatum and its subspecies in colorectal tumors and examined associations with tumor characteristics and colorectal cancer (CRC) specific survival. We conducted deep sequencing of nusA, nusG, and bacterial 16s rRNA genes in tumors from 1,994 CRC patients and assessed associations between F nucleatum presence and clinical characteristics, CRC-specific mortality, and somatic mutations.

Results: F nucleatum, which was present in 10.3% of tumors, was detected in a higher proportion of right-sided and advanced-stage tumors-particularly subspecies animalis. Presence of F nucleatum was associated with higher CRC-specific mortality (hazard ratio [HR], 1.97; P=0.0004). This association was restricted to non-hypermutated, microsatellite-stable tumors (HR, 2.13; P=0.0002) and those who received chemotherapy (HR = 1.92, CI: 1.07-3.45, p-value = 0.029). Only F nucleatum subspecies animalis, the main subspecies detected (65.8%), was associated with CRC-specific mortality (HR, 2.16; P=0.0016)-subspecies vincentii and nucleatum were not (HR, 1.07, P=0.86). Additional adjustment for tumor stage suggests that the effect of F nucleatum on mortality is partly driven by a stage shift. Presence of F nucleatum was associated with microsatellite instable tumors, tumors with POLE exonuclease domain mutations, ERBB3 mutations, and suggestively associated with TP53 mutations.

Conclusions: F nucleatum, and particularly subspecies animalis, was associated with a higher CRC-specific mortality and specific somatic mutated genes.

Impact: Our findings identify the F nucleatum subspecies animalis as negatively impacting CRC mortality which may occur through a stage shift and its effect on chemoresistance.

Background: Mutational signatures proved to be a useful tool for identifying patterns of mutations in genomes, often providing valuable insights about mutagenic processes or normal DNA damage. De novo extraction of signatures is commonly performed using Non-Negative Matrix Factorisation methods, however, accurate attribution of these signatures to individual samples is a distinct problem requiring uncertainty estimation, particularly in noisy scenarios or when the acting signatures have similar shapes. Whilst many packages for signature attribution exist, a few provide accuracy measures, and most are not easily reproducible nor scalable in high-performance computing environments.

Results: We present Mutational Signature Attribution (MSA), a reproducible pipeline designed to assign signatures of diferent mutation types on a single-sample basis, using Non-Negative Least Squares method with optimisation based on confgurable simulations. Parametric bootstrap is proposed as a way to measure statistical uncertainties of signature attribution. Supported mutation types include single and doublet base substitutions, indels and structural variants. Results are validated using simulations with reference COSMIC signatures, as well as randomly generated signatures.

Conclusions: MSA is a tool for optimised mutational signature attribution based on simulations, providing confdence intervals using parametric bootstrap. It comprises a set of Python scripts unifed in a single Nextfow pipeline with containerisation for cross-platform reproducibility and scalability in high-performance computing environments. The tool is publicly available from https://gitlab.com/s.senkin/MSA.

Keywords: MSA, Mutational signatures, NNLS, Parametric bootstrap, Nextfow

Cancer development is a dynamic evolutionary process characterized by marked intratumoural heterogeneity at the genetic, epigenetic and phenotypic levels. Barrett oesophagus, the pre-malignant condition to oesophageal adenocarcinoma (EAC), is an exemplary system to longitudinally study the evolution of malignancy. Evidence has emerged of Barrett oesophagus lesions pre-programmed for progression to EAC many years before clinical detection, indicating a considerable window for therapeutic intervention. In this Review, we explore the mechanisms underlying clonal expansion and contraction that establish the Barrett oesophagus clonal mosaicism over time and space and discuss intrinsic genotypic and extrinsic environmental drivers that direct the evolutionary trajectory of Barrett oesophagus towards a malignant phenotype. We propose that understanding and exploiting the evolutionary dynamics of Barrett oesophagus will identify novel therapeutic targets, improve prognostic tools and offer the opportunity for personalized surveillance programmes geared to prevent progression to EAC.

Recent technological advances have enabled spatially resolved measurements of expression profiles for hundreds to thousands of genes in fixed tissues at single-cell resolution. However, scalable computational analysis methods able to take into consideration the inherent 3D spatial organization of cell types and nonuniform cellular densities within tissues are still lacking. To address this, we developed MERINGUE, a computational framework based on spatial autocorrelation and cross-correlation analysis to identify genes with spatially heterogeneous expression patterns, infer putative cell–cell communication, and perform spatially informed cell clustering in 2D and 3D in a density-agnostic manner using spatially resolved transcriptomic data. We applied MERINGUE to a variety of spatially resolved transcriptomic data sets including multiplexed error-robust fluorescence in situ hybridization (MERFISH), spatial transcriptomics, Slide-seq, and aligned in situ hybridization (ISH) data. We anticipate that such statistical analysis of spatially resolved transcriptomic data will facilitate our understanding of the interplay between cell state and spatial organization in tissue development and disease.

Due to widespread adoption of screening mammography, there has been a significant increase in new diagnoses of ductal carcinoma in situ (DCIS). However, DCIS prognosis remains unclear. To address this gap, we developed an in vivo model, Mouse-INtraDuctal (MIND), in which patient-derived DCIS epithelial cells are injected intraductally and allowed to progress naturally in mice. Similarly to human DCIS, the cancer cells formed in situ lesions inside the mouse mammary ducts and mimicked all histologic subtypes including micropapillary, papillary, cribriform, solid, and comedo. Among 37 patient samples injected into 202 xenografts, at median duration of 9 months, 20 samples (54%) injected into 95 xenografts showed in vivo invasive progression while 17 (46%) samples injected into 107 xenografts remained noninvasive. Among the 20 samples that showed invasive progression, 9 samples injected into 54 xenografts exhibited a mixed pattern in which some xenografts showed invasive progression while others remained noninvasive. Among the clinically relevant biomarkers, only elevated progesterone receptor expression in patient DCIS and the extent of in vivo growth in xenografts predicted an invasive outcome. The Tempus XT assay was used on 16 patient DCIS FFPE sections including 8 DCIS that showed invasive progression, 5 DCIS that remained non-invasive and 3 DCIS that showed a mixed pattern in the xenografts. Analysis of the frequency of cancer related pathogenic mutations among the groups showed no significant differences (KW: P >0.05). There were also no differences in the frequency of high, moderate, or low severity mutations (KW; P >0.05). These results suggest that genetic changes in the DCIS are not the primary driver for the development of invasive disease.