3D genomic mapping reveals multifocality of human pancreatic precancers.

Pancreatic intraepithelial neoplasias (PanINs) are the most common precursors of pancreatic cancer, but their small size and inaccessibility in humans make them challenging to study1. Critically, the number, dimensions and connectivity of human PanINs remain largely unknown, precluding important insights into early cancer development. Here, we provide a microanatomical survey of human PanINs by analysing 46 large samples of grossly normal human pancreas with a machine-learning pipeline for quantitative 3D histological reconstruction at single-cell resolution. To elucidate genetic relationships between and within PanINs, we developed a workflow in which 3D modelling guides multi-region microdissection and targeted and whole-exome sequencing. From these samples, we calculated a mean burden of 13 PanINs per cm3 and extrapolated that the normal intact adult pancreas harbours hundreds of PanINs, almost all with oncogenic KRAS hotspot mutations. We found that most PanINs originate as independent clones with distinct somatic mutation profiles. Some spatially continuous PanINs were found to contain multiple KRAS mutations; computational and in situ analyses demonstrated that different KRAS mutations localize to distinct cell subpopulations within these neoplasms, indicating their polyclonal origins. The extensive multifocality and genetic heterogeneity of PanINs raises important questions about mechanisms that drive precancer initiation and confer differential progression risk in the human pancreas. This detailed 3D genomic mapping of molecular alterations in human PanINs provides an empirical foundation for early detection and rational interception of pancreatic cancer.

Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings.

Pancreatic ductal adenocarcinoma is a rare but lethal cancer. Recent evidence reveals that pancreatic intraepithelial neoplasms (PanINs), the microscopic precursor lesions in the pancreatic ducts that can give rise to invasive pancreatic cancer, are significantly larger and more prevalent than previously believed. Better understanding of the growth law dynamics of PanINs may improve our ability to understand how a miniscule fraction of these lesions makes the transition to invasive cancer. Here, using artificial intelligence (AI)-based three-dimensional (3D) tissue mapping method, we measured the volumes of >1,000 PanIN and found that lesion size is distributed according to a power law with a fitted exponent of -1.7 over > 3 orders of magnitude. Our data also suggest that PanIN growth is not very sensitive to the pancreatic microenvironment or an individual's age, family history, and lifestyle, and is rather shaped by general growth behavior. We analyze several models of PanIN growth and fit the predicted size distributions to the observed data. The best fitting models suggest that both intraductal spread of PanIN lesions and fusing of multiple lesions into large, highly branched structures drive PanIN growth patterns. This work lays the groundwork for future mathematical modeling efforts integrating PanIN incidence, morphology, genomic, and transcriptomic features to understand pancreas tumorigenesis, and demonstrates the utility of combining experimental measurement of human tissues with dynamic modeling for understanding cancer tumorigenesis.

Three-dimensional genomic mapping of human pancreatic tissue reveals striking multifocality and genetic heterogeneity in precancerous lesions.

Pancreatic intraepithelial neoplasia (PanIN) is a precursor to pancreatic cancer and represents a critical opportunity for cancer interception. However, the number, size, shape, and connectivity of PanINs in human pancreatic tissue samples are largely unknown. In this study, we quantitatively assessed human PanINs using CODA, a novel machine-learning pipeline for 3D image analysis that generates quantifiable models of large pieces of human pancreas with single-cell resolution. Using a cohort of 38 large slabs of grossly normal human pancreas from surgical resection specimens, we identified striking multifocality of PanINs, with a mean burden of 13 spatially separate PanINs per cm3 of sampled tissue. Extrapolating this burden to the entire pancreas suggested a median of approximately 1000 PanINs in an entire pancreas. In order to better understand the clonal relationships within and between PanINs, we developed a pipeline for CODA-guided multi-region genomic analysis of PanINs, including targeted and whole exome sequencing. Multi-region assessment of 37 PanINs from eight additional human pancreatic tissue slabs revealed that almost all PanINs contained hotspot mutations in the oncogene KRAS, but no gene other than KRAS was altered in more than 20% of the analyzed PanINs. PanINs contained a mean of 13 somatic mutations per region when analyzed by whole exome sequencing. The majority of analyzed PanINs originated from independent clonal events, with distinct somatic mutation profiles between PanINs in the same tissue slab. A subset of the analyzed PanINs contained multiple KRAS mutations, suggesting a polyclonal origin even in PanINs that are contiguous by rigorous 3D assessment. This study leverages a novel 3D genomic mapping approach to describe, for the first time, the spatial and genetic multifocality of human PanINs, providing important insights into the initiation and progression of pancreatic neoplasia.

CODA: quantitative 3D reconstruction of large tissues at cellular resolution.

A central challenge in biology is obtaining high-content, high-resolution information while analyzing tissue samples at volumes relevant to disease progression. We address this here with CODA, a method to reconstruct exceptionally large (up to multicentimeter cubed) tissues at subcellular resolution using serially sectioned hematoxylin and eosin-stained tissue sections. Here we demonstrate CODA's ability to reconstruct three-dimensional (3D) distinct microanatomical structures in pancreas, skin, lung and liver tissues. CODA allows creation of readily quantifiable tissue volumes amenable to biological research. As a testbed, we assess the microanatomy of the human pancreas during tumorigenesis within the branching pancreatic ductal system, labeling ten distinct structures to examine heterogeneity and structural transformation during neoplastic progression. We show that pancreatic precancerous lesions develop into distinct 3D morphological phenotypes and that pancreatic cancer tends to spread far from the bulk tumor along collagen fibers that are highly aligned to the 3D curves of ductal, lobular, vascular and neural structures. Thus, CODA establishes a means to transform broadly the structural study of human diseases through exploration of exhaustively labeled 3D microarchitecture.

Trends in dermatology eponyms.

Background: Eponyms are ubiquitous in dermatology; however, their usage trends have not been studied. Objective: To characterize the usage of eponyms in dermatology from 1880 to 2020. Methods: Candidate eponyms were collected from a textbook and an online resource. A subset of these eponyms was deemed to be dermatology-focused by a panel of experienced dermatologists. Python scripts were used to permute eponyms into multiple variations and automatically search PubMed using BioPython's Entrez library. Results: The dermatologist panel designated 373 of 529 candidate eponyms as dermatology-focused. These eponyms were permuted into 3159 variations and searched in PubMed. The highest occurring dermatology-focused eponyms (DFEs) in the year 2020 included Leishmania, Behçet syndrome, Kaposi sarcoma, Langerhans cell histiocytosis, and Mohs surgery. Increased DFE usage in the general medical literature parallels the overall increase in the use of other eponyms in the medical literature. However, in the most cited dermatology journals, DFE usage did not increase in the past decade. There were several eponyms with decreased usage. Limitations: This study is limited to the publications in PubMed; only titles and abstracts could be queried. Conclusion: DFEs are increasing in usage in the general medical literature, but the usage of eponyms in the most cited dermatology journals has plateaued.

Multispecialty Enterprise Imaging Workgroup Consensus on Interactive Multimedia Reporting Current State and Road to the Future: HIMSS-SIIM Collaborative White Paper.

Diagnostic and evidential static image, video clip, and sound multimedia are captured during routine clinical care in cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, endoscopic procedural specialties, and other medical disciplines. Providers typically describe the multimedia findings in contemporaneous electronic health record clinical notes or associate a textual interpretative report. Visual communication aids commonly used to connect, synthesize, and supplement multimedia and descriptive text outside medicine remain technically challenging to integrate into patient care. Such beneficial interactive elements may include hyperlinks between text, multimedia elements, alphanumeric and geometric annotations, tables, graphs, timelines, diagrams, anatomic maps, and hyperlinks to external educational references that patients or provider consumers may find valuable. This HIMSS-SIIM Enterprise Imaging Community workgroup white paper outlines the current and desired clinical future state of interactive multimedia reporting (IMR). The workgroup adopted a consensus definition of IMR as "interactive medical documentation that combines clinical images, videos, sound, imaging metadata, and/or image annotations with text, typographic emphases, tables, graphs, event timelines, anatomic maps, hyperlinks, and/or educational resources to optimize communication between medical professionals, and between medical professionals and their patients." This white paper also serves as a precursor for future efforts toward solving technical issues impeding routine interactive multimedia report creation and ingestion into electronic health records.

Artificial intelligence for automating the measurement of histologic image biomarkers.

Artificial intelligence has been applied to histopathology for decades, but the recent increase in interest is attributable to well-publicized successes in the application of deep-learning techniques, such as convolutional neural networks, for image analysis. Recently, generative adversarial networks (GANs) have provided a method for performing image-to-image translation tasks on histopathology images, including image segmentation. In this issue of the JCI, Koyuncu et al. applied GANs to whole-slide images of p16-positive oropharyngeal squamous cell carcinoma (OPSCC) to automate the calculation of a multinucleation index (MuNI) for prognostication in p16-positive OPSCC. Multivariable analysis showed that the MuNI was prognostic for disease-free survival, overall survival, and metastasis-free survival. These results are promising, as they present a prognostic method for p16-positive OPSCC and highlight methods for using deep learning to measure image biomarkers from histopathologic samples in an inherently explainable manner.

Generative Adversarial Domain Adaptation for Nucleus Quantification in Images of Tissue Immunohistochemically Stained for Ki-67.

PURPOSE: We focus on the problem of scarcity of annotated training data for nucleus recognition in Ki-67 immunohistochemistry (IHC)-stained pancreatic neuroendocrine tumor (NET) images. We hypothesize that deep learning-based domain adaptation is helpful for nucleus recognition when image annotations are unavailable in target data sets. METHODS: We considered 2 different institutional pancreatic NET data sets: one (ie, source) containing 38 cases with 114 annotated images and the other (ie, target) containing 72 cases with 20 annotated images. The gold standards were manually annotated by 1 pathologist. We developed a novel deep learning-based domain adaptation framework to count different types of nuclei (ie, immunopositive tumor, immunonegative tumor, nontumor nuclei). We compared the proposed method with several recent fully supervised deep learning models, such as fully convolutional network-8s (FCN-8s), U-Net, fully convolutional regression network (FCRN) A, FCRNB, and fully residual convolutional network (FRCN). We also evaluated the proposed method by learning with a mixture of converted source images and real target annotations. RESULTS: Our method achieved an F1 score of 81.3% and 62.3% for nucleus detection and classification in the target data set, respectively. Our method outperformed FCN-8s (53.6% and 43.6% for nucleus detection and classification, respectively), U-Net (61.1% and 47.6%), FCRNA (63.4% and 55.8%), and FCRNB (68.2% and 60.6%) in terms of F1 score and was competitive with FRCN (81.7% and 70.7%). In addition, learning with a mixture of converted source images and only a small set of real target labels could further boost the performance. CONCLUSION: This study demonstrates that deep learning-based domain adaptation is helpful for nucleus recognition in Ki-67 IHC stained images when target data annotations are not available. It would improve the applicability of deep learning models designed for downstream supervised learning tasks on different data sets.

Clinical Application of Image Analysis in Pathology.

Quantitative biomarkers are key prognostic and predictive factors in the diagnosis and treatment of cancer. In the clinical laboratory, the majority of biomarker quantitation is still performed manually, but digital image analysis (DIA) methods have been steadily growing and account for around 25% of all quantitative immunohistochemistry (IHC) testing performed today. Quantitative DIA is primarily employed in the analysis of breast cancer IHC biomarkers, including estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2/neu; more recently clinical applications have expanded to include human epidermal growth factor receptor 2/neu in gastroesophageal adenocarcinomas and Ki-67 in both breast cancer and gastrointestinal and pancreatic neuroendocrine tumors. Evidence in the literature suggests that DIA has significant benefits over manual quantitation of IHC biomarkers, such as increased objectivity, accuracy, and reproducibility. Despite this fact, a number of barriers to the adoption of DIA in the clinical laboratory persist. These include difficulties in integrating DIA into clinical workflows, lack of standards for integrating DIA software with laboratory information systems and digital pathology systems, costs of implementing DIA, inadequate reimbursement relative to those costs, and other factors. These barriers to adoption may be overcome with international standards such as Digital Imaging and Communications in Medicine (DICOM), increased adoption of routine digital pathology workflows, the application of artificial intelligence to DIA, and the emergence of new clinical applications for DIA.

Lifting Agent Granuloma: Histologic Findings Following Use of ORISE Gel for Endoscopic Resections in the Gastrointestinal Tract

OBJECTIVES: Histologic findings after endoscopic resection using submucosal lifting agents Eleview and ORISE gel are described. METHODS: Four cases were identified based on the histologic presence of ORISE gel. Cases were selected to illustrate the histologic appearance of the lifting agent immediately after injection (day 0) and after an interval of approximately 2 months. RESULTS: Immediately after injection, the gel had an appearance similar to acellular mucin on H&E stain and showed mucicarmine positivity but was negative for periodic acid-Schiff stain and Alcian blue. At 2 months, the appearance changed drastically and was characterized by a hard, homogenous eosinophilic quality and elicited a robust foreign body-type giant cell reaction; we have proposed the name lifting agent granuloma for this histologic appearance. The aged material may be mistaken for amyloid or a pulse (legume) granuloma; however, the material was negative on Congo red stain and had a different clinical history and distribution in the tissue from those of a pulse granuloma. CONCLUSIONS: It is important to take note of the histologic appearance of these new submucosal lifting agents over a varying time interval, as outlined here, so that they are readily recognized and not mistaken for other entities.

Pixel-to-Pixel Learning With Weak Supervision for Single-Stage Nucleus Recognition in Ki67 Images.

OBJECTIVE: Nucleus recognition is a critical yet challenging step in histopathology image analysis, for example, in Ki67 immunohistochemistry stained images. Although many automated methods have been proposed, most use a multi-stage processing pipeline to categorize nuclei, leading to cumbersome, low-throughput, and error-prone assessments. To address this issue, we propose a novel deep fully convolutional network for single-stage nucleus recognition. METHODS: Instead of conducting direct pixel-wise classification, we formulate nucleus identification as a deep structured regression model. For each input image, it produces multiple proximity maps, each of which corresponds to one nucleus category and exhibits strong responses in central regions of the nuclei. In addition, by taking into consideration the nucleus distribution in histopathology images, we further introduce an auxiliary task, region of interest (ROI) extraction, to assist and boost the nucleus quantification with weak ROI annotation. The proposed network can be learned in an end-to-end, pixel-to-pixel manner for simultaneous nucleus detection and classification. RESULTS: We have evaluated this network on a pancreatic neuroendocrine tumor Ki67 image dataset, and the experiments demonstrate that our method outperforms recent state-of-the-art approaches. CONCLUSION: We present a new, pixel-to-pixel deep neural network with two sibling branches for effective nucleus recognition and observe that learning with another relevant task, ROI extraction, can further boost individual nucleus localization and classification. SIGNIFICANCE: Our method provides a clean, single-stage nucleus recognition pipeline for histopathology image analysis, especially a new perspective for Ki67 image quantification, which would potentially benefit individual object quantification in whole-slide images.

HER2 Status in Gastroesophageal Adenocarcinomas: Correlation Between Immunohistochemistry and Fluorescence In Situ Hybridization Methodologies.

Semiquantitative immunohistochemistry (IHC) is commonly used in combination with fluorescence in situ hybridization (FISH) to detect HER2 amplification in gastroesophageal adenocarcinomas. Most laboratories apply these tests in a sequential algorithm, using IHC as a frontline test and reserving FISH for IHC-equivocal cases. To gain a better understanding of the concordance of IHC and FISH results at our institution, we identified all gastroesophageal adenocarcinomas at our institution tested for HER2 (n=125). Matched IHC and FISH were available for 116 cases (94%). Cases consisted of adenocarcinoma of the distal esophagus (22%), gastroesophageal junction (24%), stomach (43%), and metastatic sites (12%). A total of 88 cases (70%) were biopsies, whereas 37 cases (30%) were resections. Overall, 15 cases (13%) were HER2 positive (IHC 3+ and/or FISH amplified). A total of 60 cases (52%) were IHC score 0; none of these were HER2 amplified by FISH. A total of 30 cases (26%) were IHC 1+; 5 (17%) of these cases were HER2 amplified by FISH. A total of 20 cases (17%) were IHC 2+; 4 (20%) of these cases were HER2 amplified by FISH. A total of 6 cases were IHC score 3+; all of these were HER2 amplified by FISH. Although there was a high overall concordance between IHC and FISH results (96%), a subset (17%) of IHC-negative cases (score 1+) were HER2 amplified as evaluated by FISH, representing 33% of all HER2 amplified cases. This suggests that the common practice of limited FISH testing to IHC 2+ cases will miss a significant number of HER2 amplified cases.

Rapid Loss of RNA Detection by In Situ Hybridization in Stored Tissue Blocks and Preservation by Cold Storage of Unstained Slides.

OBJECTIVES: Recent commercialization of methods for in situ hybridization using Z-pair probe/branched DNA amplification has led to increasing adoption of this technology for interrogating RNA expression in formalin-fixed, paraffin-embedded (FFPE) tissues. Current practice for FFPE block storage is to maintain them at room temperature, often for many years. METHODS: To examine the effects of block storage time on FFPE tissues using a number of RNA in situ probes with the Advanced Cellular Diagnostic's RNAscope assay. RESULTS: We report marked reductions in signals after 5 years and significant reductions often after 1 year. Furthermore, storing unstained slides cut from recent cases (<1 year old) at -20°C can preserve hybridization signals significantly better than storing the blocks at room temperature and cutting the slides fresh when needed. CONCLUSIONS: We submit that the standard practice of storing FFPE tissue blocks at room temperature should be reevaluated to better preserve RNA for in situ hybridization.

PD-L1 Expression in Melanoma: A Quantitative Immunohistochemical Antibody Comparison.

Purpose: PD-L1 expression in the pretreatment tumor microenvironment enriches for response to anti-PD-1/PD-L1 therapies. The purpose of this study was to quantitatively compare the performance of five monoclonal anti-PD-L1 antibodies used in recent landmark publications.Experimental Design: PD-L1 IHC was performed on 34 formalin-fixed paraffin-embedded archival melanoma samples using the 5H1, SP142, 28-8, 22C3, and SP263 clones. The percentage of total cells (including melanocytes and immune cells) demonstrating cell surface PD-L1 staining, as well as intensity measurements/H-scores, were assessed for each melanoma specimen using a computer-assisted platform. Staining properties were compared between antibodies.Results: Strong correlations were observed between the percentage of PD-L1(+) cells across all clones studied (R2 = 0.81-0.96). When present, discordant results were attributable to geographic heterogeneity of the melanoma tissue section rather than differences in PD-L1 antibody staining characteristics. PD-L1 intensity/H-scores strongly correlated with percentage of PD-L1(+) cells (R2 > 0.78, all clones).Conclusions: The 5H1, SP142, 28-8, 22C3, and SP263 clones all demonstrated similar performance characteristics when used in a standardized IHC assay on melanoma specimens. Reported differences in PD-L1 IHC assays using these antibodies are thus most likely due to assay characteristics beyond the antibody itself. Our findings also argue against the inclusion of an intensity/H-score in chromogenic PD-L1 IHC assays. Clin Cancer Res; 23(16); 4938-44. ©2017 AACR.

Preclinical Benefit of Hypoxia-Activated Intra-arterial Therapy with Evofosfamide in Liver Cancer.

PURPOSE: To evaluate safety and characterize anticancer efficacy of hepatic hypoxia-activated intra-arterial therapy (HAIAT) with evofosfamide in a rabbit model. EXPERIMENTAL DESIGN: VX2-tumor-bearing rabbits were assigned to 4 intra-arterial therapy (IAT) groups (n = 7/group): (i) saline (control); (ii) evofosfamide (Evo); (iii) doxorubicin-lipiodol emulsion followed by embolization with 100-300 µm beads (conventional, cTACE); or (iv) cTACE and evofosfamide (cTACE + Evo). Blood samples were collected pre-IAT and 1, 2, 7, and 14 days post-IAT. A semiquantitative scoring system assessed hepatocellular damage. Tumor volumes were segmented on multidetector CT (baseline, 7/14 days post-IAT). Pathologic tumor necrosis was quantified using manual segmentation on whole-slide images. Hypoxic fraction (HF) and compartment (HC) were determined by pimonidazole staining. Tumor DNA damage, apoptosis, cell proliferation, endogenous hypoxia, and metabolism were quantified (γ-H2AX, Annexin V, caspase-3, Ki-67, HIF1α, VEGF, GAPDH, MCT4, and LDH). RESULTS: cTACE + Evo showed a similar profile of liver enzymes elevation and pathologic scores compared with cTACE. Neither hematologic nor renal toxicity were observed. Animals treated with cTACE + Evo demonstrated smaller tumor volumes, lower tumor growth rates, and higher necrotic fractions compared with cTACE. cTACE + Evo resulted in a marked reduction in the HF and HC. Correlation was observed between decreases in HF or HC and tumor necrosis. cTACE + Evo promoted antitumor effects as evidenced by increased expression of γ-H2AX, apoptotic biomarkers, and decreased cell proliferation. Increased HIF1α/VEGF expression and tumor glycolysis supported HAIAT. CONCLUSIONS: HAIAT achieved a promising step towards the locoregional targeting of tumor hypoxia. The favorable toxicity profile and enhanced anticancer effects of evofosfamide in combination with cTACE pave the way towards clinical trials in patients with liver cancer. Clin Cancer Res; 23(2); 536-48. ©2016 AACR.

Low Intratumoral Mast Cells Are Associated With a Higher Risk of Prostate Cancer Recurrence.

BACKGROUND: Mast cells are of interest in prostate cancer because they possess both pro- and anti-tumorigenic properties and are present in the tumor microenvironment. We studied the association of mast cell count and densities with prostate cancer recurrence using tissue microarrays (TMAs) for 462 men who recurred (cases) and 462 controls that were matched to the cases nested in a cohort of radical prostatectomy patients. METHODS: Dual-immunostaining for mast cell tryptase and epithelial cytokeratin-8 and whole slide image analysis were used to assess total mast cell number, mast cell density (mast cell number/tissue area), and mast cell number per epithelial or stromal area in TMA spots containing tumor (up to 4 per man). We used conditional logistic regression to estimate the odds ratio (OR) and 95% confidence interval of recurrence for the mean, minimum, and maximum mast cell parameters in tumor tissue among each man's TMA spots. RESULTS: After taking into account matching factors of age, race, Gleason sum, and pathologic stage, higher minimum mast cell density in the tumor (comparing highest to lowest quartiles: OR = 0.58, 95% CI 0.40-0.86; P-trend = 0.004) was associated with a lower risk of recurrence. Patterns for mast cell number and ratio of mast cell number to epithelial or stromal area were similar to those for mast cell density. CONCLUSIONS: Our results suggest that intratumoral mast cells may be protective against prostate cancer recurrence and could potentially serve as a prognostic biomarker after prostatectomy. Prostate 77: 412-424, 2017. © 2016 Wiley Periodicals, Inc.

A map of mobile DNA insertions in the NCI-60 human cancer cell panel.

BACKGROUND: The National Cancer Institute-60 (NCI-60) cell lines are among the most widely used models of human cancer. They provide a platform to integrate DNA sequence information, epigenetic data, RNA and protein expression, and pharmacologic susceptibilities in studies of cancer cell biology. Genome-wide studies of the complete panel have included exome sequencing, karyotyping, and copy number analyses but have not targeted repetitive sequences. Interspersed repeats derived from mobile DNAs are a significant source of heritable genetic variation, and insertions of active elements can occur somatically in malignancy. METHOD: We used Transposon Insertion Profiling by microarray (TIP-chip) to map Long INterspersed Element-1 (LINE-1, L1) and Alu Short INterspersed Element (SINE) insertions in cancer genes in NCI-60 cells. We focused this discovery effort on annotated Cancer Gene Index loci. RESULTS: We catalogued a total of 749 and 2,100 loci corresponding to candidate LINE-1 and Alu insertion sites, respectively. As expected, these numbers encompass previously known insertions, polymorphisms shared in unrelated tumor cell lines, as well as unique, potentially tumor-specific insertions. We also conducted association analyses relating individual insertions to a variety of cellular phenotypes. CONCLUSIONS: These data provide a resource for investigators with interests in specific cancer gene loci or mobile element insertion effects more broadly. Our data underscore that significant genetic variation in cancer genomes is owed to LINE-1 and Alu retrotransposons. Our findings also indicate that as large numbers of cancer genomes become available, it will be possible to associate individual transposable element insertion variants with molecular and phenotypic features of these malignancies.

Metastatic progression is associated with dynamic changes in the local microenvironment.

Most cancer-associated deaths result from metastasis. However, it remains unknown whether the size, microenvironment or other features of a metastatic lesion dictate its behaviour or determine the efficacy of chemotherapy in the adjuvant (micrometastatic) setting. Here we delineate the natural history of metastasis in an autochthonous model of pancreatic ductal adenocarcinoma (PDAC), using lineage tracing to examine the evolution of disseminated cancer cells and their associated microenvironment. With increasing size, lesions shift from mesenchymal to epithelial histology, become hypovascular and accumulate a desmoplastic stroma, ultimately recapitulating the primary tumours from which they arose. Moreover, treatment with gemcitabine and nab-paclitaxel significantly reduces the overall number of metastases by inducing cell death in lesions of all sizes, challenging the paradigm that PDAC stroma imposes a critical barrier to drug delivery. These results illuminate the cellular dynamics of metastatic progression and suggest that adjuvant chemotherapy affords a survival benefit by directly targeting micrometastases.