Cancer Prevalence Across Vertebrates

Cancer is pervasive across multicellular species, but what explains differences in cancer prevalence across species? Using 16,049 necropsy records for 292 species spanning three clades (amphibians, sauropsids and mammals) we found that neoplasia and malignancy prevalence increases with adult weight (contrary to Petos Paradox) and somatic mutation rate, but decreases with gestation time. Evolution of cancer susceptibility appears to have undergone sudden shifts followed by stabilizing selection. Outliers for neoplasia prevalence include the common porpoise (<1.3%), the Rodrigues fruit bat (<1.6%) the black-footed penguin (<0.4%), ferrets (63%) and opossums (35%). Discovering why some species have particularly high or low levels of cancer may lead to a better understanding of cancer syndromes and novel strategies for the management and prevention of cancer.

Reuniting philosophy and science to advance cancer research

Cancers rely on multiple, heterogeneous processes at different scales, pertaining to many biomedical fields. Therefore, understanding cancer is necessarily an interdisciplinary task that requires placing specialised experimental and clinical research into a broader conceptual, theoretical, and methodological framework. Without such a framework, oncology will collect piecemeal results, with scant dialogue between the different scientific communities studying cancer. We argue that one important way forward in service of a more successful dialogue is through greater integration of applied sciences (experimental and clinical) with conceptual and theoretical approaches, informed by philosophical methods. By way of illustration, we explore six central themes: (i) the role of mutations in cancer; (ii) the clonal evolution of cancer cells; (iii) the relationship between cancer and multicellularity; (iv) the tumour microenvironment; (v) the immune system; and (vi) stem cells. In each case, we examine open questions in the scientific literature through a philosophical methodology and show the benefit of such a synergy for the scientific and medical understanding of cancer.

Challenges in cybersecurity: Lessons from biological defense systems

Defending against novel, repeated, or unpredictable attacks, while avoiding attacks on the 'self', are the central problems of both mammalian immune systems and computer systems. Both systems have been studied in great detail, but with little exchange of information across the different disciplines. Here, we present a conceptual framework for structured comparisons across the fields of biological immunity and cybersecurity, by framing the context of defense, considering different (combinations of) defensive strategies, and evaluating defensive performance. Throughout this paper, we pose open questions for further exploration. We hope to spark the interdisciplinary discovery of general principles of optimal defense, which can be understood and applied in biological immunity, cybersecurity, and other defensive realms.

Abstract P1-07-02: Using clinical characteristics and molecular markers to predict the risk of subsequent ipsilateral breast events after excision of DCIS

PURPOSE To examine incremental values of estrogen receptor (ER) status, body mass index (BMI), menopausal status, and a previously reported multi-gene classifier over commonly used clinical factors (i.e. age, tumor grade, comedonecrosis, surgical margins, and treatment) in predicting risk of any ipsilateral recurrence (IR) event within five years after DCIS diagnosis.

METHODS A derivation cohort consisted of participants in the Translational Breast Cancer Research Consortium (TBCRC) 038, a retrospective multicenter cohort study in women undergoing surgical resection for DCIS between 01/01/1998 and 02/29/2016 (n=216). The validation cohort, the Repository of Archival Human Breast Tissue (RAHBT) at Washington University School of Medicine, provided cases meeting the same eligibility criteria as TBCRC038 (n=97). Participants in both cohorts had RNA-seq data and either developed IR 1-5y after initial DCIS diagnosis or were free from subsequent breast events for at least five years. The previously reported 812-gene classifier had been developed from a subset of the TBCRC038 samples using a negative-binomial regression model to identify differentially expressed genes in the primary tumor associated with subsequent recurrence events. This classifier has been shown to be highly correlated with 5-year invasive, DCIS, and all breast cancer events, and validated in the RAHBT cohort. Cox proportional hazards regression was used to estimate hazard ratios (HRs) of IR in the TBCRC038 cohort (76 with IR). The clinical score was developed using clinical predictors (aforementioned clinical factors and ER) and their regression coefficients from the model with the maximum predictive accuracy (e.g. c-index) and the minimum number of predictors; the summary score integrated the clinical score and multi-gene classifier. Predictive performance of both clinical and summary scores was validated in the RAHBT cohort (20 with IR).

RESULTS In the TBCRC cohort derivation set, we used a multivariable model based on clinical factors alone (clinical score) and found that ER status, but not BMI or menopausal status, was independently associated with a higher IR risk (HR=2.06, 95% CI 1.18-3.58). Adding the multi-gene classifier to the clinical factors-based model (summary score) in the TBCRC038 test set increased predictive accuracy (c-index 0.68 to 0.70), with the genomic classifier-adjusted HR of 14.96 (95% CI 8.64-25.91). The summary score had higher predictive performance for IR risk than clinical score alone (c-index 0.82 vs. 0.70). In the RAHBT validation samples, model performance was similarly improved using summary scores clinical factors-based model plus multigene classifier as compared to clinical scores along (c-index 0.74 vs. 0.58).

CONCLUSION Combining clinical factors and a multigene classifier provided more accurate risk estimates of IR within five years after excision of DCIS than clinical factors alone.

Figure 1. Observed and predicted recurrence-free survival in the first five years after initial DCIS diagnosis in the RAHBT validation cohort, by risk groups defined by clinical scores (left) and clinical score plus multigene classifier (right).

Citation Format: Ying Liu, Siri H. Strand, Lorraine King, Bryan Harmon, Fergus J. Couch, Kristalyn Gallagher, Mark Kilgore, Shi Wei, Angela DeMichele, Tari King, Priscilla F. McAuliffe, Jeffrey Marks, Carlo Maley, Robert West, E Shelley Hwang, Graham A. Colditz. Using clinical characteristics and molecular markers to predict the risk of subsequent ipsilateral breast events after excision of DCIS [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-07-02.

Abstract PD2-09: Characterization of the lymphovascular invasion microenvironment reveals immune response dichotomy

Background: Metastasis is the leading cause of cancer related deaths in breast cancer patients. Lymphovascular invasion represents one of the earliest stages of metastasis wherein the cells are introduced to a very different and distinct microenvironment. Methods: We leveraged spatial techniques developed for limited specimens in archival tissue to study patient matched cross-sectional tumor samples from different stages of breast neoplasia including normal breast, ductal carcinoma in situ (DCIS), primary invasive carcinoma (IBC), lymphovascular invasion (LVI) and regional lymph node metastasis. We selected a set of 21 patients with ER+ breast cancer to generate cross-sectional samples of each of these stages, for a total of 331 samples. The areas of LVI were identified by a combination of H&E review and immunohistochemistry for podoplanin. We performed smart-3SEQ for gene expression profiling and light pass whole genome sequencing for DNA copy number alterations. Results: We profiled the spectrum of neoplasia for transcriptome-wide gene expression. Principal component analysis of all 252 DCIS, LVI, IBC, or metastasis samples using the top 500 genes with the highest variance demonstrated that clustering was roughly based on the diagnostic stage (i.e. DCIS, LVI, IBC, or metastasis). Differential gene expression profiling identified thousands of genes increased or decreased in expression across the transitional stages with the largest change in gene expression being the transition from normal breast to DCIS, dominated by gene expression down regulation. We next performed NMF clustering on 62 samples of LVI from 18 cases and identified two patterns of gene expression which define two subgroups. Gene ontology analysis revealed that one cluster was associated with increased proliferation and metabolism, whereas the second cluster was dominated by an immune response. When we analyzed the immune and proliferative LVI subgroups separately, we found that the immune profiles in the patient matched IBC and LVI samples from the LVI Immune cluster were similar, whereas the immune profiles in the patient matched IBC and LVI samples from the Proliferative cluster were significantly different. At the LVI stage, all immune cell populations estimated by CibersortX were decreased in the Proliferative LVI cluster. These changes were validated using immunofluorescence for proliferation (Ki67), T cells (CD3) and macrophages (CD68) on the same samples. Using the LVI centroids, we built a model that could predict the same clusters in the METABRIC IBC. Kaplan-Meier analysis showed a significant difference between groups, with the Proliferative-like IBC group having a worse prognosis than the Immune-like IBC group. Conclusions: We observed a dichotomy at the LVI stage with a more proliferative cluster that may escape the immune response and an immune cluster which has a microenvironment with a similar pattern to its primary IBC. The recognition of two groups of LVI, differing in immune association and proliferation, raises the possibility that the risk of metastasis could be different in these two groups, leading to different biological pathways of progression.

Citation Format: Belén Rivero-Gutiérrez, Diego Mallo, Almudena Espín-Pérez, Sujay Vennam, Chunfang Zhu, Sushama Varma, Greg Scott, Joseph Foley, E Shelley Hwang, Carlo Maley, Robert West. Characterization of the lymphovascular invasion microenvironment reveals immune response dichotomy [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD2-09.

Molecular classification and biomarkers of clinical outcome in breast ductal carcinoma in situ: Analysis of TBCRC 038 and RAHBT cohorts

Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We perform multiscale, integrated molecular profiling of DCIS with clinical outcomes by analyzing 774 DCIS samples from 542 patients with 7.3 years median follow-up from the Translational Breast Cancer Research Consortium 038 study and the Resource of Archival Breast Tissue cohorts. We identify 812 genes associated with ipsilateral recurrence within 5 years from treatment and develop a classifier that predicts DCIS or IBC recurrence in both cohorts. Pathways associated with recurrence include proliferation, immune response, and metabolism. Distinct stromal expression patterns and immune cell compositions are identified. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome.

Spatial interplay of tissue hypoxia and T-cell regulation in ductal carcinoma in situ

Hypoxia promotes aggressive tumor phenotypes and mediates the recruitment of suppressive T cells in invasive breast carcinomas. We investigated the role of hypoxia in relation to T-cell regulation in ductal carcinoma in situ (DCIS). We designed a deep learning system tailored for the tissue architecture complexity of DCIS, and compared pure DCIS cases with the synchronous DCIS and invasive components within invasive ductal carcinoma cases. Single-cell classification was applied in tandem with a new method for DCIS ductal segmentation in dual-stained CA9 and FOXP3, whole-tumor section digital pathology images. Pure DCIS typically has an intermediate level of colocalization of FOXP3+ and CA9+ cells, but in invasive carcinoma cases, the FOXP3+ (T-regulatory) cells may have relocated from the DCIS and into the invasive parts of the tumor, leading to high levels of colocalization in the invasive parts but low levels in the synchronous DCIS component. This may be due to invasive, hypoxic tumors evolving to recruit T-regulatory cells in order to evade immune predation. Our data support the notion that hypoxia promotes immune tolerance through recruitment of T-regulatory cells, and furthermore indicate a spatial pattern of relocalization of T-regulatory cells from DCIS to hypoxic tumor cells. Spatial colocalization of hypoxic and T-regulatory cells may be a key event and useful marker of DCIS progression.

Accurate Identification of Subclones in Tumor Genomes

Understanding intra-tumor heterogeneity is critical for studying tumorigenesis and designing personalized treatments. To decompose the mixed cell population in a tumor, subclones are inferred computationally based on variant allele frequency (VAF) from bulk sequencing data. In this study, we showed that sequencing depth, mean VAF, and variance of VAF of a subclone are confounded. Without considering this effect, current methods require deep-sequencing data (>300x depth) to reliably infer subclones. Here we present a novel algorithm that incorporates depth-variance and mean-variance dependencies in a clustering error model and successfully identifies subclones in tumors sequenced at depths of as low as 30x. We implemented the algorithm as a model-based adaptive grouping of subclones (MAGOS) method. Analyses of computer simulated data and empirical sequencing data showed that MAGOS outperformed existing methods on minimum sequencing depth, decomposition accuracy, and computation efficiency. The most prominent improvements were observed in analyzing tumors sequenced at depths between 30x and 200x, while the performance was comparable between MAGOS and existing methods on deeply sequenced tumors. MAGOS supports analysis of single nucleotide variants and copy number variants from a single sample or multiple samples of a tumor. We applied MAGOS to whole-exome data of late-stage liver cancers and discovered that high subclone count in a tumor was a significant risk factor of poor prognosis. Lastly, our analysis suggested that sequencing multiple samples of the same tumor at standard depth is more cost-effective and robust for subclone characterization than deep sequencing a single sample. MAGOS is available at github (https://github.com/liliulab/magos).

Abstract B004: Adaptive therapy in preclinical models of hormone-refractory breast cancer

The goal of this study was to test whether adaptive therapy works in preclinical mouse models with hormone-refractory breast cancer and compare adaptive therapy with standard therapy. Specifically, we tested whether the drugs gemcitabine and/or capecitabine can control the MCF7 cancer cell line which is resistant to palbociclib and fulvestrant. We treated MCF7 resistant breast cancers growing orthotopically in the mammary fat pads of NSG immunodeficient mice with gemcitabine, capecitabine, or both drugs together. In the combined drug treatment, we switched drugs in every application (every 3 days) (“ping-pong”) or applied the drugs together (in tandem). In both single and multi-drug therapy, we applied drugs by either adjusting their doses or applying intermittently. We measured tumor size by bioluminescence imaging and caliper. We saw a reduction in the tumor burden of most mice treated with adaptive therapy along with prolonging overall survival. We found that the combination of gemcitabine and capecitabine in both intermittent and dose adjustment protocols significantly increased the survival of mice in comparison to standard therapy when we applied the drugs in a ping-pong strategy. The combination therapy in tandem was less successful compared to dose adjustment, but better than standard therapy in increasing overall survival. Moreover, we had several weeks off-treatment in most of the mice when the tumor was under the threshold, therefore lower drug doses were used. Among single drug therapies, capecitabine dose adjustment protocol was better than using both intermittent and the maximum tolerated dose in terms of increasing overall survival of mice and accumulating less dose of the drug. However, the capecitabine intermittent treatment was better compared to the capecitabine maximum tolerated dose. In contrast, there were no significant differences between adaptive protocols and standard therapy. However, in both intermittent and dose adjustment protocols we had several weeks off-treatment in most of the mice, so lower drug doses were used. In conclusion, we found that lower drug doses can be used under adaptive therapy, once the tumor is controlled. We had several weeks off- treatment for some cases and we saw a continued decline in tumor size. Overall, adaptive therapy strategies could prolong progression-free survival besides reduction in the tumor burden.

Citation Format: Sareh Seyedi, Carlo Maley. Adaptive therapy in preclinical models of hormone-refractory breast cancer [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B004.

Abstract IA014: Insights from comparative oncology

Comparative biology facilitates the exploration of the major factors that determine cancer susceptibility and resistance. We are only at the beginning of exploring the solutions that nature has found to the problem of preventing cancer, and the weaknesses that have evolved, leaving some animals exceptionally vulnerable to particular types of cancers. Some cancer defenses appear to be cell-intrinsic, such as the propensity of elephant cells to apoptose when they suffer DNA damage. Other defenses appear to depend on the environment, such as the invasion suppressive effects of fibroblasts of some species discovered by Wagner and his colleagues. I will review our early data on patterns of cancer prevalence across species and our initial results supporting Peto’s paradox but showing that contrary to our predictions, life history factors do not seem to explain patterns of cancer prevalence across species.

Citation Format: Carlo Maley. Insights from comparative oncology [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr IA014.

Abstract GS4-07: The Breast PreCancer Atlas DCIS genomic signatures define biology and correlate with clinical outcomes: An analysis of TBCRC 038 and RAHBT cohorts

Background. DCIS consists of a molecularly heterogeneous group of premalignant lesions, with variable risk of invasive progression. Understanding biomarkers for invasive progression could help individualize treatment recommendations based upon tumor biology. As part of the NCI Human Tumor Atlas Network (HTAN), we conducted comprehensive genomic analyses on two large DCIS case-control cohorts. Methods. We performed smart3-seq and low-pass whole genome sequencing on two independent, retrospective, longitudinally sampled DCIS case-control cohorts. TBCRC 038 was a multicenter cohort diagnosed with DCIS between 1998 and 2016 at one of the Translational Breast Cancer Research sites; the RAHBT (Resource of Archival Human Breast Tissue) cohort included women identified through the St. Louis Breast Tissue Repository, and the Women’s Health Repository diagnosed between 1997 and 2001. We studied the spectrum of molecular changes present and sought genomic predictors of subsequent ipsilateral breast events (iBEs: DCIS recurrence or invasive progression) in both DCIS epithelium and stroma in formalin fixed paraffin embedded tissue. We generated de novo tumor and stroma-centric subtypes for DCIS that represents fundamental transcriptomic organization. Copy number analysis was performed using low-pass DNA sequencing. Non-negative matrix factorization (NMF) was applied to the RNA expression of all coding genes to identify clusters. A negative-binomial regression model was used to identify differentially expressed genes. Results. We analyzed 677 DCIS samples from 481 patients with 7.1 years median follow-up. In TBCRC samples, we identified three clusters via NMF in TBCRC referred to as ER low, quiescent, and ER high. The ER-low cluster had significantly higher levels of ERBB2 and lower levels of ESR1 compared to quiescent and ER-high clusters. Quiescent cluster lesions were less proliferative and less metabolically active than ER high and ER low subtypes. These findings were replicated in the RAHBT cohort. Focusing on the stromal component of DCIS from laser capture microdissection in RAHBT samples, we identified four distinct DCIS-associated stromal clusters. A “normal-like” stromal cluster with ECM organization and PI3K-AKT signaling; a “collagen-rich” stromal cluster; a “desmoplastic” stromal cluster with high fibroblast and total myeloid abundance, mostly associated with macrophages and myeloid dendritic cells (mDC); and an “immune-dense” stromal cluster. Further, we compared differentially expressed genes in patients with or without subsequent iBEs within 5 years of diagnosis. Hypothesizing that the resulting 812 DE genes (DESeq2) represent multiple routes to subsequent iBEs, we leveraged NMF to identify paths to progression. In both TBCRC and RAHBT cohorts, poor outcome groups exhibited increased ER, MYC signaling, and oxidative phosphorylation, supporting that these pathways are important for DCIS recurrence and progression. Conclusion. Comprehensive genomic profiling in two independent DCIS cohorts with longitudinal outcomes shows distinct DCIS stromal expression patterns and immune cell composition. RNA expression profiles reveal underlying tumor biology that is associated with later iBEs in both cohorts. These studies provide new insight into DCIS biology and will guide the design of diagnostic strategies to prevent invasive progression.

Citation Format: Siri H Strand, Belén Rivero-Gutiérrez, Kathleen E Houlahan, Jose A Seoane, Lorraine M King, Tyler Risom, Lunden Simpson, Sujay Vennam, Aziz Khan, Timothy Hardman, Bryan E Harmon, Fergus J Couch, Kristalyn Gallagher, Mark Kilgore, Shi Wei, Angela DeMichele, Tari King, Priscilla F McAuliffe, Julie Nangia, Joanna Lee, Jennifer Tseng, Anna Maria Storniolo, Alastair Thompson, Gaorav Gupta, Robyn Burns, Deborah J Veis, Katherine DeSchryver, Chunfang Zhu, Magdalena Matusiak, Jason Wang, Shirley X Zhu, Jen Tappenden, Daisy Yi Ding, Dadong Zhang, Jingqin Luo, Shu Jiang, Sushama Varma, Cody Straub, Sucheta Srivastava, Christina Curtis, Rob Tibshirani, Robert Michael Angelo, Allison Hall, Kouros Owzar, Kornelia Polyak, Carlo Maley, Jeffrey R Marks, Graham A Colditz, E Shelley Hwang, Robert B West. The Breast PreCancer Atlas DCIS genomic signatures define biology and correlate with clinical outcomes: An analysis of TBCRC 038 and RAHBT cohorts [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr GS4-07.

Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma

Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that is thought to be a precursor to invasive breast cancer (IBC). To understand the changes in the tumor microenvironment (TME) accompanying transition to IBC, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) and a 37-plex antibody staining panel to interrogate 79 clinically annotated surgical resections using machine learning tools for cell segmentation, pixel-based clustering, and object morphometrics. Comparison of normal breast with patient-matched DCIS and IBC revealed coordinated transitions between four TME states that were delineated based on the location and function of myoepithelium, fibroblasts, and immune cells. Surprisingly, myoepithelial disruption was more advanced in DCIS patients that did not develop IBC, suggesting this process could be protective against recurrence. Taken together, this HTAN Breast PreCancer Atlas study offers insight into drivers of IBC relapse and emphasizes the importance of the TME in regulating these processes.

ENDLESS FORMS MOST BEAUTIFUL: A GARDEN SHOWS THAT CANCER IS A PART OF LIFE

Endless Forms Most Beautiful is a crested cactus garden that embodies both an aesthetic and medically transformative approach to cancer. The cacti in this garden have mutations in their meristem cells causing uncontrolled growths-which are, by some definitions, cancer. The garden was installed near the new Biodesign Institute C building on the Arizona State University campus in Tempe. Crested cacti, and other fasciated plants, are examples of organisms that live with cancer, but do not die from it. These plants help to widen the framework for thinking about what cancer is, how to live with it, and ultimately inspired a new center, the Arizona Cancer Evolution (ACE) Center, which investigates cancer across life.

Unmasking the immune microecology of ductal carcinoma in situ with deep learning

Despite increasing evidence supporting the clinical relevance of tumour infiltrating lymphocytes (TILs) in invasive breast cancer, TIL spatial variability within ductal carcinoma in situ (DCIS) samples and its association with progression are not well understood. To characterise tissue spatial architecture and the microenvironment of DCIS, we designed and validated a new deep learning pipeline, UNMaSk. Following automated detection of individual DCIS ducts using a new method IM-Net, we applied spatial tessellation to create virtual boundaries for each duct. To study local TIL infiltration for each duct, DRDIN was developed for mapping the distribution of TILs. In a dataset comprising grade 2-3 pure DCIS and DCIS adjacent to invasive cancer (adjacent DCIS), we found that pure DCIS cases had more TILs compared to adjacent DCIS. However, the colocalisation of TILs with DCIS ducts was significantly lower in pure DCIS compared to adjacent DCIS, which may suggest a more inflamed tissue ecology local to DCIS ducts in adjacent DCIS cases. Our study demonstrates that technological developments in deep convolutional neural networks and digital pathology can enable an automated morphological and microenvironmental analysis of DCIS, providing a new way to study differential immune ecology for individual ducts and identify new markers of progression.

Abstract PD5-08: The human tumor atlas network (HTAN) breast pre cancer atlas: A multi-omic integrative analysis of ductal carcinoma in situ (DCIS) and correlation with clinical outcomes

Introduction. As nonobligate precursors of invasive disease, pre-cancers provide a unique vantage point from which to study the molecular pathways and evolutionary dynamics that lead to the development of life-threatening cancers. Ductal carcinoma in situ (DCIS) is the most commonly diagnosed precursor of breast cancer, with variable propensity for invasive progression. In order to address the problems of over- and under-treatment, we performed a multimodal, integrated profile of DCIS with clinical outcomes with which to develop and validate predictors of invasive progression. Methods. We present observations on DNA, RNA, and protein expression on two independent patient cohorts of DCIS, diagnosed from 1981 to 2014, from the Translational Breast Cancer Research Consortium (TBCRC 038) and the Washington University Repository of Archival Human Breast Tissue (RAHBT). Patients initially diagnosed with DCIS, with either DCIS or invasive recurrence (cases; mean follow up 5.8 years) were matched to those without recurrence (controls; mean follow up 10.3 years), based upon age at diagnosis and year of diagnosis. Results. We present genomic and cellular changes that correlate with both disease states and patient outcomes in DCIS. DCIS can be clustered by classification systems developed for IBC. Specific immune cell types and pathways correlate with longitudinal outcome. Luminal cell adhesion and metabolism pathways are upregulated in controls and cases, respectively. Highly multiplexed ion beam imaging (MIBI) was used to validate RNA seq findings, and to provide single cell-level spatial context for molecular alterations.Conclusion. We have performed an integrated multi-omic analysis of DCIS and associated tumor micorenvironment. Our multi-scale approach employs in situ methods to generate a spatially resolved atlas of breast precancers where different modalities can be directly compared to each other, and correlated with conventional pathology findings and clinical outcome. The PreCancer Atlas represents a complex multi-modal database for DCIS study, whose design allows for future discovery and hypothesis generation.

Table 1. Breast Pre-cancer Atlas Multi-scale Characterization AssaysAssayScaleType of DataIntegration and validation with other assaysRNA-seq (Single duct, single cell, TME)Cell, duct, organ, normal tissue1. Whole transcriptome gene expression profiling per single duct (also enabling CNV and cell type prediction)2. Whole transcriptome gene expression profiling per single duct1. Prediction of CNV confirmed by DNA-seq (single duct) and FISH (single cell)2. Prediction of cell type composition (Cibersort) confirmed by multiplex IHC and multicolor flow cytometryLow-pass whole genome DNA-seqDuct and adjacent normalCNV profiling per single ductAnalysis of CNV supported by RNA-seq (single duct) and MIBI (single cell)Whole genome sequencingDuct and adjacent normalMutation status per single ductMutational analysis confirmed by RNA-seqMultiplex IHC (MIBI & Cyclic multicolor)Cell1. Cell type2. Proteomic analysisAnalysis of cell type supported by RNA-seq of ducts (Cibersort) and single cellsH&E MorphometricsCell, duct, organSpatial location of cell types, organization of ductsAnalysis of H&E images correlated with FISH data

Citation Format: Shelley Hwang, Siri H Strand, Belen Rivero, Lorraine King, Tyler Risom, Bryan Harmon, Fergus Couch, Kristalyn Gallagher, Mark Kilgore, Shi Wei, Angela DeMichele, Tari King, Priscilla McAuliffe, Julie Nangia, Ana Maria Storniolo, Alastair Thompson, Gaorav Gupta, Joanna Lee, Jennifer Tseng, Robyn Burns, ChunFang Zhu, Magda Matusiak, Shirley X Zhu, Jason Wang, Jose Seoane, Jen Tappenden, Daisy Ding, Dadong Zhang, Jingqin Luo, Sujay Vennam, Sushama Varma, Lunden Simpson, Luis Cisneros, Timmothy Hardman, Lauren Anderson, Cody Straub, Sucheta Srivastava, Deb J Veis, Christina Curtis, Rob Tibshirani, Robert Michael Angelo, Allison Hall, Kouros Owzar, Kornelia Polyak, Carlo Maley, Jeff Marks, Graham Colditz, Robert B West. The human tumor atlas network (HTAN) breast pre cancer atlas: A multi-omic integrative analysis of ductal carcinoma in situ (DCIS) and correlation with clinical outcomes [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD5-08.

Somatic selection distinguishes oncogenes and tumor suppressor genes

Motivation

Functions of cancer driver genes vary substantially across tissues and organs. Distinguishing passenger genes, oncogenes (OGs) and tumor-suppressor genes (TSGs) for each cancer type is critical for understanding tumor biology and identifying clinically actionable targets. Although many computational tools are available to predict putative cancer driver genes, resources for context-aware classifications of OGs and TSGs are limited.

Results

We show that the direction and magnitude of somatic selection of protein-coding mutations are significantly different for passenger genes, OGs and TSGs. Based on these patterns, we develop a new method (genes under selection in tumors) to discover OGs and TSGs in a cancer-type specific manner. Genes under selection in tumors shows a high accuracy (92%) when evaluated via strict cross-validations. Its application to 10 172 tumor exomes found known and novel cancer drivers with high tissue-specificities. In 11 out of 13 OGs shared among multiple cancer types, we found functional domains selectively engaged in different cancers, suggesting differences in disease mechanisms.

Availability and implementation

An R implementation of the GUST algorithm is available at https://github.com/liliulab/gust. A database with pre-computed results is available at https://liliulab.shinyapps.io/gust.

Supplementary information

Supplementary data are available at Bioinformatics online.

Kombucha: a novel model system for cooperation and conflict in a complex multi-species microbial ecosystem

Kombucha, a fermented tea beverage with an acidic and effervescent taste, is composed of a multispecies microbial ecosystem with complex interactions that are characterized by both cooperation and conflict. In kombucha, a complex community of bacteria and yeast initiates the fermentation of a starter tea (usually black or green tea with sugar), producing a biofilm that covers the liquid over several weeks. This happens through several fermentative phases that are characterized by cooperation and competition among the microbes within the kombucha solution. Yeast produce invertase as a public good that enables both yeast and bacteria to metabolize sugars. Bacteria produce a surface biofilm which may act as a public good providing protection from invaders, storage for resources, and greater access to oxygen for microbes embedded within it. The ethanol and acid produced during the fermentative process (by yeast and bacteria, respectively) may also help to protect the system from invasion by microbial competitors from the environment. Thus, kombucha can serve as a model system for addressing important questions about the evolution of cooperation and conflict in diverse multispecies systems. Further, it has the potential to be artificially selected to specialize it for particular human uses, including the development of antimicrobial ecosystems and novel materials. Finally, kombucha is easily-propagated, non-toxic, and inexpensive, making it an excellent system for scientific inquiry and citizen science.

Abstract P3-07-06: Evidence for tumor heterogeneity and clonal evolution during invasive progression in breast cancer

Purpose: Intratumoral heterogeneity is well recognized to be an important driver of treatment resistance and metastasis. We undertook this N of three study to measure the degree of heterogeneity in three large preinvasive lesions, all with invasive components to determine the relationship between tumor heterogeneity, spatial distribution, clonal evolution, and invasive progression.

Methods: We identified patients A, B, C with extensive DCIS measuring 7.5 cm, 6 cm, and 7 cm associated with 0.3 cm, 3.8cm, and 3.4 cm of an invasive component and 0, 7 and 1 positive lymph node, respectively. We sequenced the tumor sample for Case A from 32 unique blocks with precise geospatial localization; invasive cancer was identified in 3 of 32 blocks. Case B had 26 blocks sequenced with invasive cancer in 13 of 26 blocks. Case C had 23 blocks sequenced with invasive in 11 of 23 blocks. For germline reference, we sequenced DNA from an uninvolved tissue from each case. NGS libraries were made from FFPE derived DNA (20-40ng) for full exome sequencing. Variant calling was performed by GATK HaplotypeCaller, Platypus and Mutect. Identified somatic mutations were annotated with Oncotator and pathway enrichment analysis was performed with Bioconductor. To investigate the clonal evolution and progression history, phylogenetic trees were constructed in R and sub-clonal analysis was performed with Treeomics.

Results: The sequence data was analyzed with Platypus, MuTect and GATK HaplotypeCaller. The somatic mutation sites were concatenated into one sequence for each sample. Both neighbor-joining trees and maximum parsimony trees were built for each case. Phylogenetic analysis and sub-clonal analysis support the multi-clonal invasion model of invasive cells, in which invasive cancer can evolve from multiple clades, either early or late in the evolutionary history, independently. Dense sampling allowed reconstruction of the temporal order of mutations that accumulated in the cell lineage of the invasive cancers. Furthermore, phylogeny and sub-clone spatial analysis revealed that distant regions may be closely genetically related and showed a weak spatial sub-clone clustering pattern, which is consistent with the predictions of Big Bang model. For driver genes, we find that except for SETD2 in Case B, the majority of driver gene mutations are sub-clonal. Somatic mutations on ATP-binding cassette (ABC) transporter pathway was found in all cases.

Conclusions: Extensive sampling and sequencing of tumors yields important insights about tumor heterogeneity and tumor progression of DCIS to invasive cancer. Variable invasive propensity was identified, with foci of invasion were geospatially associated with preinvasive regions of progressively higher mutational load.

Citation Format: Ding Y, Marks J, King L, Hardman T, Hall A, Mallo D, Rodrigo A, Maley C, Hwang S. Evidence for tumor heterogeneity and clonal evolution during invasive progression in breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-07-06.

Cancer initiation and progression within the cancer microenvironment

Within the cancer microenvironment, the growth and proliferation of cancer cells in the primary site as well as in the metastatic site represent a global biological phenomenon. To understand the growth, proliferation and progression of cancer either by local expansion and/or metastasis, it is important to understand the cancer microenvironment and host response to cancer growth. Melanoma is an excellent model to study the interaction of cancer initiation and growth in relationship to its microenvironment. Social evolution with cooperative cellular groups within an organism is what gives rise to multicellularity in the first place. Cancer cells evolve to exploit their cellular environment. The foundations of multicellular cooperation break down in cancer because those cells that misbehave have an evolutionary advantage over their normally behaving neighbors. It is important to classify evolutionary and ecological aspects of cancer growth, thus, data for cancer growth and outcomes need to be collected to define these parameters so that accurate predictions of how cancer cells may proliferate and metastasize can be developed.

Carbon dating cancer: defining the chronology of metastatic progression in colorectal cancer

Background

Patients often ask oncologists how long a cancer has been present before causing symptoms or spreading to other organs. The evolutionary trajectory of cancers can be defined using phylogenetic approaches but lack of chronological references makes dating the exact onset of tumours very challenging.

Patients and methods

Here, we describe the case of a colorectal cancer (CRC) patient presenting with synchronous lung metastasis and metachronous thyroid, chest wall and urinary tract metastases over the course of 5 years. The chest wall metastasis was caused by needle tract seeding, implying a known time of onset. Using whole genome sequencing data from primary and metastatic sites we inferred the complete chronology of the cancer by exploiting the time of needle tract seeding as an in vivo 'stopwatch'. This approach allowed us to follow the progression of the disease back in time, dating each ancestral node of the phylogenetic tree in the past history of the tumour. We used a Bayesian phylogenomic approach, which accounts for possible dynamic changes in mutational rate, to reconstruct the phylogenetic tree and effectively 'carbon date' the malignant progression.

Results

The primary colon cancer emerged between 5 and 8 years before the clinical diagnosis. The primary tumour metastasized to the lung and the thyroid within a year from its onset. The thyroid lesion presented as a tumour-to-tumour deposit within a benign Hurthle adenoma. Despite rapid metastatic progression from the primary tumour, the patient showed an indolent disease course. Primary cancer and metastases were microsatellite stable and displayed low chromosomal instability. Neo-antigen analysis suggested minimal immunogenicity.

Conclusion

Our data provide the first in vivo experimental evidence documenting the timing of metastatic progression in CRC and suggest that genomic instability might be more important than the metastatic potential of the primary cancer in dictating CRC fate.

Single-cell genotyping demonstrates complex clonal diversity in acute myeloid leukemia

Clonal evolution in cancer-the selection for and emergence of increasingly malignant clones during progression and therapy, resulting in cancer metastasis and relapse-has been highlighted as an important phenomenon in the biology of leukemia and other cancers. Tracking mutant alleles to determine clonality from diagnosis to relapse or from primary site to metastases in a sensitive and quantitative manner is most often performed using next-generation sequencing. Such methods determine clonal frequencies by extrapolation of allele frequencies in sequencing data of DNA from the metagenome of bulk tumor samples using a set of assumptions. The computational framework that is usually used assumes specific patterns in the order of acquisition of unique mutational events and heterozygosity of mutations in single cells. However, these assumptions are not accurate for all mutant loci in acute myeloid leukemia (AML) samples. To assess whether current models of clonal diversity within individual AML samples are appropriate for common mutations, we developed protocols to directly genotype AML single cells. Single-cell analysis demonstrates that mutations of FLT3 and NPM1 occur in both homozygous and heterozygous states, distributed among at least nine distinct clonal populations in all samples analyzed. There appears to be convergent evolution and differential evolutionary trajectories for cells containing mutations at different loci. This work suggests an underlying tumor heterogeneity beyond what is currently understood in AML, which may be important in the development of therapeutic approaches to eliminate leukemic cell burden and control clonal evolution-induced relapse.

Alteration of the epigenome during carcinogenesis of intestinal-type gastric cancer

34

Background: Next-generation sequencing (NGS) is opening a new era for understanding how the human genome is altered in the process of cancer development. We sought to establish an epigenome map based on the DNA methylome during gastric carcinogenesis, paving the way to a better understanding of the origins of gastric cancer using NGS-based epigenome analysis.

Methods: We purified DNA in laser-capture microdissected (LCM) cells of normal mucosa, intestinal metaplasia (IM), and gastric cancer from frozen samples of one patient with intestinal type gastric tumor. Two NGS-based epigenome analyses were performed on each DNA: ‘MBD-seq’ (methylated DNA binding domain sequencing) and ‘RRBS’ (reduced representation bisulfite sequencing).

Results: MBD-seq and RRBS generated ~25.7 million and ~15.1 million reads respectively, per lane on average. The sequence data matched with 79% (21,514/27,191) and 55% (14,991/27,191) of promoter sequences from UCSC genome browser, respectively. We then identified about one thousand differentially methylated promoters (DMPs), which were altered in IM and in gastric cancer or in gastric cancer compared to that of gastric mucosa. In particular, hypomethylation was detected in 30 promoters by MBD-seq and 173 by RRBS, indicating that RRBS is highly informative for identifying hypomethylated DMPs associated with gene activation during carcinogenesis.

Conclusions: We identified several novel hyper- or hypomethylated biomarkers for IM or gastric cancer using NGS-based epigenome analysis. Our reference epigenome map may provide a foundation for future studies exploring the prognostic or preventive biomarker for early stage of gastric cancer.

Abstract 101: The mechanism of clonal expansion determines the tempo and mode of neoplastic progression in Barrett's esophagus

Barrett's Esophagus is a pre-malignant condition with a low rate of progression to esophageal adenocarcinoma. Endoscopic surveillance is recommended for early detection of cancer, which allows the study neoplastic progression in vivo over space and time. Since genetic clonal diversity predicts neoplastic progression (Maley Nat. Gen. 38, 468-473 (2006)), we developed an agent-based model to explore the effect of different parameters, such as mutation rate, selective effect of mutations and clonal expansion probability, on the spatio-temporal dynamics of genetic diversity.

A typical Barrett's segment contains ∼90,000 crypts and we assume that clonal expansion occurs by crypt bifurcation, since the rate of crypt bifurcation is high in other diseases marked by chronic inflammation (Cheng The Anat. Record 216, 44-48 (1986)). The Barrett's segment is a cylinder that we model as a two-dimensional 300 by 300 hexagonal grid, wrapped around along one dimension. Crypts (agents) divide, die or mutate according to basal division, death and mutation rates as well as the mutation states of loci conferring reproductive and survival advantages. If a crypt needs to divide, and all of its six neighbors are alive, a daughter crypt displaces one of the neighbors at random with probability r, the “mechanism of clonal expansion probability”. We use a Gillespie algorithm for rapid simulation of the evolutionary dynamics of the constant-size crypt population, consistent with the stability of the Barrett's segment length.

We ran 1080 simulations, varying selective mutation rate from 1e-5 to 1e-8, neutral mutation rate from 1e-4 to 1e-7, selective effect of mutations conferring reproductive or survival advantage from 0.001 to 2, and mechanism of clonal expansion probability r from 0 to 1. Our results show that when r is high, clones expand rapidly, selective sweeps occur often and a single dominant clone bearing all reproductive advantage mutations is likely to emerge. When r is low, clones expand slowly, clonal interference occurs often and multiple independent competing clones emerge. This model predicts that genetic diversity increases over time, since a clone only expanded when it acquired mutations conferring quicker crypt division rate, which is coupled to higher stem cell division and mutation rates, thereby generating higher diversity within the expanding clone compared to its surroundings.

Our results suggest that designing experiments to measure the rate and mechanism of crypt bifurcation will help distinguish between two neoplastic progression modes: clonal evolution with successive selective sweeps versus multiple independent interfering clones. We also show that sampling few cells from many biopsies estimates the underlying genetic diversity better than sampling many cells from few biopsies.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 101.

Abstract B48: Cross sectional analysis of copy loss in Barrett’s Esophagus

B48

Introduction

Barrett’s Esophagus (BE) is a pre-malignant neoplasm that increases the risk of developing esophageal adenocarcinoma (EA) (Paulson et. al. Cancer Cell 2004;6(1):11-6). To identify groups of BE patients at high risk of cancer progression, we sought to identify common chromosomal aberrations across the full risk spectrum of the condition. We implemented a meta-analysis of three studies from the Seattle Barrett’s Esophagus Project. The goal of this analysis was to combine SNP and array-CGH datasets of chromosomal loss from BE and EA samples to pinpoint regions of common loss across patients.

Methods

The three datasets included Illumina 33k SNP arrays on whole biopsies (34 patients) and surgical resections specimens (8 patients), an Illumina 317K SNP array on 12 flow purified biopsies (1 patient) and a 4,500 spot bacterial artificial chromosome (BAC) hybridization array on 157 flow purified samples (72 patients). When there were multiple samples from a patient, we included the union of all detected lesions across those samples but only counted a lesion once per patient for the purposes of analysis. All SNP arrays were run on both BE and normal (gastric or lymphocyte) samples from the same patients for comparison. All BAC arrays were run on BE samples and compared against a common reference sample.

Illumina’s BeadStudio software was used to call genotypes and produce signal intensity data in log2(Rsub/Rref) format that represents the difference in copy number of BE versus normal samples, where we assume normal samples have no aberrations. We then processed the SNP data to call regions of copy number loss using GLAD (Hupe et. al. Bioinformatics 2004;20(18):3413-22) setting logR ratio thresholds of -0.2 for single and -1.5 for double copy loss. BAC data was processed by a wavelet method (Hsu et. al. Biostatistics 2005;6(2):211-26) to call copy loss, copy gain or no aberration for every BAC. Regions of copy number loss, for the combination of both SNP and BAC datasets, were analyzed using STAC (Diskin et. al. Genome Res. 2006;16(9):1149-58) to identify statistically significant areas of loss across samples. The STAC analysis was performed at 0.5Mb resolution using 500 permutations.

Results

The combined STAC analysis identified 78 regions that were significant at the 95% confidence level, after multiple testing correction, including some previously known losses at chr. 3: 59-61MB (FHIT, FRA3B), chr.16: 77-77.5Mb (WWOX, FRA16D), chr. 9p: 21-32Mb (p16/CDKN2A/INK4a), and some newly discovered losses at chr. X: 31.5-32Mb (DMD), chr. 22: 22.5-23Mb (SMARCB1, DERL3, SLC2A11, MIF, GSTT1, GSTT2, DDT, CABIN1, SUSD2, GGT5) and chr. 18: 57-57.5Mb (CDH20).

Conclusions

Combining copy number data across studies in STAC increases sample size that may increase power to detect statistically significant regions of copy number loss across samples. We are currently working to extend the same analysis to loss of heterozygosity and copy gain in the SNP array data.

Citation Information: Cancer Prev Res 2008;1(7 Suppl):B48.

Abstract CN12-01: Clonal evolution over time in Barrett's Esophagus

CN12-01

Our current theory of neoplastic progression specifies that clones arise through genetic or epigenetic instability, and if they have a reproductive or survival advantage over other cells in the neoplasm, within their microenvironment, they will expand in the neoplasm in a selective sweep. Thus, we believe neoplastic progression is characterized by a series of selective sweeps. However, virtually all data on that process to date is cross-sectional, with observations of genetic diversity and large clonal expansions within neoplasms at a single time point. I will describe data that, for the first time, tracks the evolutionary dynamics of clones in a solid neoplasm over time. We have analyzed the frequency of large clonal expansions, the rate of those expansions and changes in clonal diversity over time in a cohort of 174 patients with Barrett’s esophagus. Barrett’s esophagus is a premalignant condition that predisposes to the evolution of esophageal adenocarcinoma. Unlike most other premalignant neoplasms, the Barrett’s tissue is not resected upon detection due to significant mortality and morbidity associated with esophagectomies. Instead, serial endoscopic surveillance for the early detection of cancer is the recommended management of the condition. This allows us to study neoplastic progression over both space and time in unprecedented detail. In the Seattle Barrett’s Esophagus Project, we also gather data on host and environmental factors that may impact progression, including use of non-steroidal anti-inflammatory drugs, which have been associated with a dramatic reduction of risk for progression to cancer. I will relate the factors associated with clonal expansions and genetic diversity to the parameters that determine the rate of evolution: mutation rate, population size, stem cell generation time and the fitness effects of mutations.

Citation Information: Cancer Prev Res 2008;1(7 Suppl):CN12-01.

Increasing genomic instability during premalignant neoplastic progression revealed through high resolution array-CGH

Chromosomal instability is regarded as an underlying mechanism of neoplastic progression, integral to the clonal selection and evolution that leads to cancer. We evaluated chromosomal instability in premalignant Barrett's esophagus tissue using high resolution Affymetrix mapping 100K SNP arrays as patients progressed through three molecular stages of disease-CDKN2A(LOH) only, CDKN2A(LOH)/TP53(LOH), and CDKN2A(LOH)/TP53(LOH) with aneuploidy. Within individuals over time, we observed increases in both numbers and sizes of regions of LOH or copy number change. In the earliest CDKN2A(LOH) only samples, we detected few regions with both copy change and LOH, whereas copy loss and LOH were highly correlated in more advanced samples. These data indicate that genomic instability increases in severity and changes character during neoplastic progression. In addition, distinct patterns of clonal evolution could be discerned within a segment of Barrett's esophagus. Overall, this study illustrates that pre-malignant disease can be associated with extensive instability and clonal dynamics that evolve from an initial stage characterized by small recombination-based alterations to one with larger copy change events likely associated with mitotic instability.

Workshop on imaging science development for cancer prevention and preemption

The concept of intraepithelial neoplasm (IEN) as a near-obligate precursor of cancers has generated opportunities to examine drug or device intervention strategies that may reverse or retard the sometimes lengthy process of carcinogenesis. Chemopreventive agents with high therapeutic indices, well-monitored for efficacy and safety, are greatly needed, as is development of less invasive or minimally disruptive visualization and assessment methods to safely screen nominally healthy but at-risk patients, often for extended periods of time and at repeated intervals. Imaging devices, alone or in combination with anticancer drugs, may also provide novel interventions to treat or prevent precancer.

Mutation rates as adaptations

In order to better understand life, it is helpful to look beyond the envelop of life as we know it. A simple model of coevolution was implemented with the addition of a gene for the mutation rate of the individual. This allowed the mutation rate itself to evolve in a lineage. The model shows that when the individuals interact in a sort of zero-sum game, the lineages maintain relatively high mutation rates. However, when individuals engage in interactions that have greater consequences for one individual in the interaction than the other, lineages tend to evolve relatively low mutation rates. This model suggests that one possible cause for differential mutation rates across genes may be the coevolutionary pressure of the various forms of interactions with other genes.