Publications

Fusobacteria are commonly associated with human colorectal cancer (CRC), but investigations are hampered by the absence of a stably colonized murine model. Further, Fusobacterium nucleatum subspecies isolated from human CRC have not been investigated. While F. nucleatum subspecies are commonly associated with CRC, their ability to induce tumorigenesis and contributions to human CRC pathogenesis are uncertain. We sought to establish a stably colonized murine model and to understand the inflammatory potential and virulence genes of human CRC F. nucleatum, representing the 4 subspecies, animalis, nucleatum, polymorphum, and vincentii. Five human CRC-derived and two non-CRC derived F. nucleatum strains were tested for colonization, tumorigenesis, and cytokine induction in specific-pathogen-free (SPF) and/or germfree (GF) wild-type and Apc^Min/+ mice, as well as in vitro assays and whole-genome sequencing (WGS). SPF wild-type and Apc^Min/+ mice did not achieve stable colonization with F. nucleatum, whereas certain subspecies stably colonized some GF mice but without inducing colon tumorigenesis. F. nucleatum subspecies did not form in vivo biofilms or associate with the mucosa in mice. In vivo inflammation was inconsistent across subspecies, whereas F. nucleatum induced greater cytokine responses in a human colorectal cell line, HCT116. While F. nucleatum subspecies displayed genomic variability, no distinct virulence genes associated with human CRC strains were identified that could reliably distinguish these strains from non-CRC clinical isolates. We hypothesize that the lack of F. nucleatum-induced tumorigenesis in our model reflects differences in human and murine biology and/or a synergistic role for F. nucleatum in concert with other bacteria to promote carcinogenesis.

IMPORTANCE Colon cancer is a leading cause of cancer morbidity and mortality, and it is hypothesized that dysbiosis in the gut microbiota contributes to colon tumorigenesis. Fusobacterium nucleatum, a member of the oropharyngeal microbiome, is enriched in a subset of human colon tumors. However, it is unclear whether this genetically varied species directly promotes tumor formation, modulates mucosal immune responses, or merely colonizes the tumor microenvironment. Mechanistic studies to address these questions have been stymied by the lack of an animal model that does not rely on daily orogastric gavage. Using multiple murine models, in vitro assays with a human colon cancer cell line, and whole-genome sequencing analysis, we investigated the proinflammatory and tumorigenic potential of several F. nucleatum clinical isolates. The significance of this research is development of a stable colonization model of F. nucleatum that does not require daily oral gavages in which we demonstrate that a diverse library of clinical isolates do not promote tumorigenesis.

K-RAS is frequently mutated in cancers, and its overactivation can lead to oncogene-induced senescence (OIS), a barrier to cellular transformation. Feedback onto K-RAS limits its signaling to avoid senescence while achieving the appropriate level of activation that promotes proliferation and survival. Such regulation could be mediated by miRNAs, as aberrant RAS signaling and miRNA activity coexist in several cancers, with miRNAs acting both up- and downstream of K-RAS. Several miRNAs both regulate and are regulated by K-RAS, suggesting a noncoding RNA-based feedback mechanism. Functional interactions between K-RAS and the miRNA machinery have also begun to unfold. This review comprehensively surveys the state of knowledge connecting K-RAS to miRNA function and proposes a model for the regulation of K-RAS signaling by noncoding RNAs.

Gemcitabine (dFdC) is a common treatment for pancreatic cancer; however, it is thought that treatment may fail because tumor stroma prevents drug distribution to tumor cells. Gemcitabine is a pro-drug with active metabolites generated intracellularly; therefore, visualizing the distribution of parent drug as well as its metabolites is important. A multimodal imaging approach was developed using spatially coregistered mass spectrometry imaging (MSI), imaging mass cytometry (IMC), multiplex immunofluorescence microscopy (mIF), and hematoxylin and eosin (H&E) staining to assess the local distribution and metabolism of gemcitabine in tumors from a genetically engineered mouse model of pancreatic cancer (KPC) allowing for comparisons between effects in the tumor tissue and its microenvironment. Mass spectrometry imaging (MSI) enabled the visualization of the distribution of gemcitabine (100 mg/kg), its phosphorylated metabolites dFdCMP, dFdCDP and dFdCTP, and the inactive metabolite dFdU. Distribution was compared to small-molecule ATR inhibitor AZD6738 (25 mg/kg), which was codosed. Gemcitabine metabolites showed heterogeneous distribution within the tumor, which was different from the parent compound. The highest abundance of dFdCMP, dFdCDP, and dFdCTP correlated with distribution of endogenous AMP, ADP, and ATP in viable tumor cell regions, showing that gemcitabine active metabolites are reaching the tumor cell compartment, while AZD6738 was located to nonviable tumor regions. The method revealed that the generation of active, phosphorylated dFdC metabolites as well as treatment-induced DNA damage primarily correlated with sites of high proliferation in KPC PDAC tumor tissue, rather than sites of high parent drug abundance.

Background: Improving diagnosis of ductal carcinoma in situ (DCIS) before surgery is important in choosing optimal patient management strategies. However, patients may harbor occult invasive disease not detected until definitive surgery.

Purpose: To assess the performance and clinical utility of mammographic radiomic features in the prediction of occult invasive cancer among women diagnosed with DCIS on the basis of core biopsy findings.

Materials and Methods: In this Health Insurance Portability and Accountability Act–compliant retrospective study, digital magnification mammographic images were collected from women who underwent breast core-needle biopsy for calcifications that was performed at a single institution between September 2008 and April 2017 and yielded a diagnosis of DCIS. The database query was directed at asymptomatic women with calcifications without a mass, architectural distortion, asymmetric density, or palpable disease. Logistic regression with regularization was used. Differences across training and internal test set by upstaging rate, age, lesion size, and estrogen and progesterone receptor status were assessed by using the Kruskal-Wallis or χ² test.

Results: The study consisted of 700 women with DCIS (age range, 40–89 years; mean age, 59 years ± 10 [standard deviation]), including 114 with lesions (16.3%) upstaged to invasive cancer at subsequent surgery. The sample was split randomly into 400 women for the training set and 300 for the testing set (mean ages: training set, 59 years ± 10; test set, 59 years ± 10; P = .85). A total of 109 radiomic and four clinical features were extracted. The best model on the test set by using all radiomic and clinical features helped predict upstaging with an area under the receiver operating characteristic curve of 0.71 (95% CI: 0.62, 0.79). For a fixed high sensitivity (90%), the model yielded a specificity of 22%, a negative predictive value of 92%, and an odds ratio of 2.4 (95% CI: 1.8, 3.2). High specificity (90%) corresponded to a sensitivity of 37%, positive predictive value of 41%, and odds ratio of 5.0 (95% CI: 2.8, 9.0).

Conclusion: Machine learning models that use radiomic features applied to mammographic calcifications may help predict upstaging of ductal carcinoma in situ, which can refine clinical decision making and treatment planning.

A holistic understanding of tissue and organ structure and function requires the detection of molecular constituents in their original three-dimensional (3D) context. Imaging mass cytometry (IMC) enables simultaneous detection of up to 40 antigens and transcripts using metal-tagged antibodies but has so far been restricted to two-dimensional imaging. Here we report the development of 3D IMC for multiplexed 3D tissue analysis at single-cell resolution and demonstrate the utility of the technology by analysis of human breast cancer samples. The resulting 3D models reveal cellular and microenvironmental heterogeneity and cell-level tissue organization not detectable in two dimensions. 3D IMC will prove powerful in the study of phenomena occurring in 3D space such as tumor cell invasion and is expected to provide invaluable insights into cellular microenvironments and tissue architecture.