Search

To chart cell composition and cell state changes that occur during the transformation of healthy colon to precancerous adenomas to colorectal cancer (CRC), we generated single-cell chromatin accessibility profiles and single-cell transcriptomes from 1,000 to 10,000 cells per sample for 48 polyps, 27 normal tissues and 6 CRCs collected from patients with or without germline APC mutations. A large fraction of polyp and CRC cells exhibit a stem-like phenotype, and we define a continuum of epigenetic and transcriptional changes occurring in these stem-like cells as they progress from homeostasis to CRC. Advanced polyps contain increasing numbers of stem-like cells, regulatory T cells and a subtype of pre-cancer-associated fibroblasts. In the cancerous state, we observe T cell exhaustion, RUNX1-regulated cancer-associated fibroblasts and increasing accessibility associated with HNF4A motifs in epithelia. DNA methylation changes in sporadic CRC are strongly anti-correlated with accessibility changes along this continuum, further identifying regulatory markers for molecular staging of polyps.

Hispanic/Latino patients have a higher incidence of gastric cancer and worse cancer-related outcomes compared with patients of other backgrounds. Whether there is a molecular basis for these disparities is unknown, as very few Hispanic/Latino patients have been included in previous studies. To determine the genomic landscape of gastric cancer in Hispanic/Latino patients, we performed whole-exome sequencing (WES) and RNA sequencing on tumor samples from 57 patients; germline analysis was conducted on 83 patients. The results were compared with data from Asian and White patients published by The Cancer Genome Atlas. Hispanic/Latino patients had a significantly larger proportion of genomically stable subtype tumors compared with Asian and White patients (65% vs. 21% vs. 20%, P < 0.001). Transcriptomic analysis identified molecular signatures that were prognostic. Of the 43 Hispanic/Latino patients with diffuse-type cancer, 7 (16%) had germline variants in CDH1. Variant carriers were significantly younger than noncarriers (41 vs. 50 years, P < 0.05). In silico algorithms predicted five variants to be deleterious. For two variants that were predicted to be benign, in vitro modeling demonstrated that these mutations conferred increased migratory capability, suggesting pathogenicity. Hispanic/Latino patients with gastric cancer possess unique genomic landscapes, including a high rate of CDH1 germline variants that may partially explain their aggressive clinical phenotypes. Individualized screening, genetic counseling, and treatment protocols based on patient ethnicity and race may be necessary.Significance:Gastric cancer in Hispanic/Latino patients has unique genomic profiles that may contribute to the aggressive clinical phenotypes seen in these patients.

Supplementary Table 3

Supplementary Table Legend

Supplementary Table 1

Supplementary Table 2

Supplementary Table 4

a. Age at the time of diagnosis of Hispanic/Latino (Hs/L) patients from this study, and of Asian and White patients analyzed by The Cancer Genome Atlas (TCGA). Horizontal lines, medians; boxes, interquartile ranges; whiskers, maximum and minimum values. P < 0.001. b. Second view of the principal component analysis of whole-exome sequencing data, as analyzed by Locating Ancestry from SEquence Reads to define patient ancestry of Asian and White patients analyzed by the TCGA and Hispanic/Latino patients from this study, as compared to reference from the Human Genome Diversity Project (HGDP).

Purpose:BRAF mutations are found in a subset of non–small cell lung cancers (NSCLC). We examined the clinical characteristics and treatment outcomes of patients with NSCLC harboring BRAF mutations.Experimental Design: Using DNA sequencing, we successfully screened 883 patients with NSCLC for BRAF mutations between July 1, 2009 and July 16, 2012. Baseline characteristics and treatment outcomes were compared between patients with and without BRAF mutations. Wild-type controls consisted of patients with NSCLC without a somatic alteration in BRAF, KRAS, EGFR, and ALK. In vitro studies assessed the biologic properties of selected non-V600E BRAF mutations identified from patients with NSCLC.Results: Of 883 tumors screened, 36 (4%) harbored BRAF mutations (V600E, 18; non-V600E, 18) and 257 were wild-type for BRAF, EGFR, KRAS, and ALK negative. Twenty-nine of 36 patients with BRAF mutations were smokers. There were no distinguishing clinical features between BRAF-mutant and wild-type patients. Patients with advanced NSCLC with BRAF mutations and wild-type tumors showed similar response rates and progression-free survival (PFS) to platinum-based combination chemotherapy and no difference in overall survival. Within the BRAF cohort, patients with V600E-mutated tumors had a shorter PFS to platinum-based chemotherapy compared with those with non-V600E mutations, although this did not reach statistical significance (4.1 vs. 8.9 months; P = 0.297). We identified five BRAF mutations not previously reported in NSCLC; two of five were associated with increased BRAF kinase activity.Conclusions:BRAF mutations occur in 4% of NSCLCs and half are non-V600E. Prospective trials are ongoing to validate BRAF as a therapeutic target in NSCLC. Clin Cancer Res; 19(16); 4532–40. ©2013 AACR.

Supplementary Methods

a. Chromatograms confirming germline CDH1 mutations identified on whole-exome sequencing. b. Age of patients who are wild-type for CDH1 (blue) or carry germline CDH1 mutations (red). Bar denotes median.

Subgroup analysis of overall survival for the Hispanic/Latino cohort. Kaplan-Meier curves comparing: a. all patients by individual mRNA clusters, P < 0.01, b. patients with genomically stable tumors, P < 0.05, c. patients with chromosomal instable tumors, P < 0.05, d. patients with diffuse-type tumors, P < 0.05, e. patients with intestinal-type tumors, P < 0.05.

a. The Cancer Genome Atlas algorithm to categorize gastric cancer into four molecular subtypes: Epstein-Barr virus infected (EBV, red), microsatellite instability (MSI, blue), chromosomal instability (CIN, purple), and genomically stable (GS, green). SCNA = somatic copy number alterations. b. MSIsensor score. Whole-exome sequence data from each Hispanic/Latino cancer sample were analyzed with MSIsenor. Gray bar denotes total mutation burden per megabase. Yellow bar denotes calculated MSIsensor score. Samples with score {greater than or equal to} 10 were considered to be microsatellite unstable.

a. Immunohistochemistry for E-cadherin in six patients who have germline CDH1 mutations. Red long arrows denote cancer cells, short green arrows denote normal stomach glands. P: patient. For P16, normal stomach and cancer cells are shown in two separate panels. Scale bar = 50 μm. b. Amount of DNA in which the CDH1 promoter is methylated.

Supplementary Table S1 - XLS file 32K, Baseline characteristics of the patients with driver alterations in EGFR, KRAS or ALK

Immunofluorescence staining for E-cadherin in Chinese hamster ovary cells overexpressing wild-type (WT) CDH1, A286G, or G1849A variants, all of which have wild-type membranous localization. Scale bar = 10 μm.

Context.— Stanford Pathology began stepwise subspecialty implementation of whole slide imaging (WSI) in 2018 soon after the first US Food and Drug Administration approval. In 2020, during the COVID-19 pandemic, the Centers for Medicare & Medicaid Services waived the requirement for pathologists to perform diagnostic tests in Clinical Laboratory Improvement Amendments (CLIA)–licensed facilities. This encouraged rapid implementation of WSI across all surgical pathology subspecialties. Objective.— To present our experience with validation and implementation of WSI at a large academic medical center encompassing a caseload of more than 50 000 cases per year. Design.— Validation was performed independently for 3 subspecialty services with a diagnostic concordance threshold above 95%. Analysis of user experience, staffing, infrastructure, and information technology was performed after department-wide expansion. Results.— Diagnostic concordance was achieved in 96% of neuropathology cases, 100% of gynecologic pathology cases, and 98% of immunohistochemistry cases. After full implementation, 8 high-capacity scanners were operational, with whole slide images generated on greater than 2000 slides per weekday, accounting for approximately 80% of histologic slides at Stanford Medicine. Multiple modifications in workflow and information technology were needed to improve performance. Within months of full implementation, most attending pathologists and trainees had adopted WSI for primary diagnosis. Conclusions.— WSI across all surgical subspecialities is achievable at scale at an academic medical center; however, adoption required flexibility to adjust workflows and develop tailored solutions. WSI at scale supported the health and safety of medical staff while facilitating high-quality patient care and education during COVID-19 restrictions.

Synthetic data generation offers a solution to the data scarcity problem in biomedicine where data are often expensive or difficult to obtain. By increasing the dataset size, more powerful and generalizable machine learning models can be trained, improving their performance in clinical decision support systems. The generation of synthetic data for cancer diagnosis has been explored in the literature, but typically in the single modality setting (e.g. whole-slide image tiles or RNA-Seq data). Given the success of text-to-image synthesis models for natural images, where one modality is used to generate a related one, we propose RNA-to-image synthesis (RNA-CDM) in a multi-cancer context. First, we trained a variational auto-encoder in order to reduce the dimensions of the patient’s gene expression profile, showing that this can accurately differentiate between different cancer types. Then, we trained a cascaded diffusion model to synthesize realistic whole-slide image tiles using the latent representation of the patient’s RNA-Seq data. We show that generated tiles preserved the cell-type distribution found in real-world data, with important cell types detectable by a state-of-the-art cell identification model in the synthetic samples. Next, we successfully used this synthetic data to pretrain a multi-cancer classification model, observing an improvement in performance after training from scratch across 5-fold cross validation. Our results demonstrate the potential utility of synthetic data for developing multi-modal machine learning models in data scarce settings, as well as the possibility of imputing missing data modalities by leveraging the information present in available data modalities.

Tumor-associated macrophages (TAMs) display heterogeneous phenotypes. Yet the exact tissue cues that shape macrophage functional diversity are incompletely understood. Here we discriminate, spatially resolve and reveal the function of five distinct macrophage niches within malignant and benign breast and colon tissue. We found that SPP1 TAMs reside in hypoxic and necrotic tumor regions, and a novel subset of FOLR2 tissue-resident macrophages (TRMs) supports the plasma cell tissue niche. We discover that IL4I1 macrophages populate niches with high cell turnover where they phagocytose dying cells. Significantly, IL4I1 TAMs abundance correlates with anti-PD1 treatment response in breast cancer. Furthermore, NLRP3 inflammasome activation in NLRP3 TAMs correlates with neutrophil infiltration in the tumors and is associated with poor outcome in breast cancer patients. This suggests the NLRP3 inflammasome as a novel cancer immunotherapy target. Our work uncovers context-dependent roles of macrophage subsets and suggests novel predictive markers and macrophage subset-specific therapy targets.

The acquisition of multi-modal biological data for the same sample, such as RNA sequencing and whole slide imaging (WSI), has increased in recent years, enabling studying human biology from multiple angles. However, despite these emerging multi-modal efforts, for the majority of studies only one modality is typically available, mostly due to financial or logistical constraints. Given these difficulties, multi-modal data imputation and multi-modal synthetic data generation are appealing as a solution for the multi-modal data scarcity problem. Currently, most studies focus on generating a single modality (e.g. WSI), without leveraging the information provided by additional data modalities (e.g. gene expression profiles). In this work, we propose an approach to generate WSI tiles by using deep generative models infused with matched gene expression profiles. First, we train a variational autoencoder (VAE) that learns a latent, lower dimensional representation of multi-tissue gene expression profiles. Then, we use this representation to infuse generative adversarial networks (GAN) that generate lung and brain cortex tissue tiles, resulting in a new model that we call RNA-GAN. Tiles generated by RNA-GAN were preferred by expert pathologists in comparison to tiles generated using traditional GANs and in addition, RNA-GAN needs fewer training epochs to generate high-quality tiles. Finally, RNA-GAN was able to generalize to gene expression profiles outside of the training set, showing imputation capabilities. A web-based quiz is available for users to play a game distinguishing real and synthetic tiles:https://rna-gan.stanford.edu/and the code for RNA-GAN is available here:https://github.com/gevaertlab/RNA-GAN.

Although 3D genome architecture is essential for long-range gene regulation, the significance of distal regulatory chromatin contacts is challenged by recent findings of low correlation between contact propensity and gene expression. To better understand the role of long-range interactions between distal regulatory elements during the early transformation from healthy colon to colorectal cancer, here we performed high resolution chromatin conformation capture for 33 samples including non-neoplastic mucosa, adenomatous polyps and adenocarcinomas, mostly from Familial Adenomatous Polyposis (FAP) patients. We identified hundreds of thousands of chromatin micro-structures, such as architectural stripes and loops, which originated from active cis-regulatory elements. Surprisingly, these structures progressively decayed throughout cancer progression, particularly at promoters. Meta-analyses revealed that this decay was independent of alterations in DNA methylation and chromatin accessibility. Interestingly, the degree of interaction loss was poorly correlated with gene expression changes. Instead, genes whose expression were disproportionately lower and higher than their relative promoter interaction in mucosa shifted their expression in polyps and adenocarcinomas to yield a more direct relationship between strength of interaction and gene expression. Our work provides the first high resolution 3D conformation maps during early cancer formation and progression, and provides novel insights into transcriptional readouts associated with fine-scale chromatin conformation alterations.

BackgroundArtificial intelligence (AI) is rapidly fueling a fundamental transformation in the practice of pathology. However, AI’s clinical integration remains challenging, with no AI algorithms to date enjoying routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience.MethodsPerspectives were solicited using the Delphi method from 24 subject matter experts between December 2020 and February 2021 regarding the anticipated role of AI in pathology by the year 2030. The study consisted of three consecutive rounds: 1) an open-ended, free response questionnaire generating a list of survey items; 2) a Likert-scale survey scored by experts and analyzed for consensus; and 3) a repeat survey of items not reaching consensus to obtain further expert consensus.FindingsConsensus opinions were reached on 141 of 180 survey items (78.3%). Experts agreed that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030. High consensus was reached on 100 items across nine categories encompassing the impact of AI on (1) pathology key performance indicators (KPIs) and (2) the pathology workforce and specific tasks performed by (3) pathologists and (4) AP lab technicians, as well as (5) specific AI applications and their likelihood of routine use by 2030, (6) AI’s role in integrated diagnostics, (7) pathology tasks likely to be fully automated using AI, and (8) regulatory/legal and (9) ethical aspects of AI integration in pathology.InterpretationThis is the first systematic consensus study detailing the expected short/mid-term impact of AI on pathology practice. These findings provide timely and relevant information regarding future care delivery in pathology and raise key practical, ethical, and legal challenges that must be addressed prior to AI’s successful clinical implementation.FundingThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

SummaryMacrophages are the most abundant immune cell type in the tumor microenvironment (TME). Yet the spatial distribution and cell interactions that shape macrophage function are incompletely understood. Here we use single-cell RNA sequencing data and multiplex imaging to discriminate and spatially resolve macrophage niches within benign and malignant breast and colon tissue. We discover four distinct tissue-resident macrophage (TRM) layers within benign bowel, two TRM niches within benign breast, and three tumor-associated macrophage (TAM) populations within breast and colon cancer. We demonstrate that IL4I1 marks phagocytosing macrophages, SPP1 TAMs are enriched in hypoxic and necrotic tumor regions, and a novel subset of FOLR2 TRMs localizes within the plasma cell niche. Furthermore, NLRP3 TAMs that colocalize with neutrophils activate an inflammasome in the TME and in Crohn’s disease and are associated with poor outcomes in breast cancer patients. This work suggests novel macrophage therapy targets and provides a framework to study human macrophage function in clinical samples.

Hispanic/Latino patients have a higher incidence of gastric cancer and worse cancer-related outcomes compared with patients of other backgrounds. Whether there is a molecular basis for these disparities is unknown, as very few Hispanic/Latino patients have been included in previous studies. To determine the genomic landscape of gastric cancer in Hispanic/Latino patients, we performed whole-exome sequencing (WES) and RNA sequencing on tumor samples from 57 patients; germline analysis was conducted on 83 patients. The results were compared with data from Asian and White patients published by The Cancer Genome Atlas. Hispanic/Latino patients had a significantly larger proportion of genomically stable subtype tumors compared with Asian and White patients (65% vs. 21% vs. 20%, P < 0.001). Transcriptomic analysis identified molecular signatures that were prognostic. Of the 43 Hispanic/Latino patients with diffuse-type cancer, 7 (16%) had germline variants in CDH1. Variant carriers were significantly younger than noncarriers (41 vs. 50 years, P < 0.05). In silico algorithms predicted five variants to be deleterious. For two variants that were predicted to be benign, in vitro modeling demonstrated that these mutations conferred increased migratory capability, suggesting pathogenicity. Hispanic/Latino patients with gastric cancer possess unique genomic landscapes, including a high rate of CDH1 germline variants that may partially explain their aggressive clinical phenotypes. Individualized screening, genetic counseling, and treatment protocols based on patient ethnicity and race may be necessary. Significance: Gastric cancer in Hispanic/Latino patients has unique genomic profiles that may contribute to the aggressive clinical phenotypes seen in these patients.

Appendiceal mucinous neoplasms(AMN)are rare tumors of the gastrointestinal tract. They metastasize with widespread abdominal dissemination leading to pseudomyxoma peritonei(PMP), a disease with poor prognosis. The cellular features of origin, differentiation and progression in AMN and PMP remain largely unknown. We characterized the distinct cell states of AMN and PMP using single-cell RNA-sequencing in 31 samples including AMNs, PMP metastases and matched normal tissues. We identified previously undescribed cellular features and heterogeneity in AMN and PMP. We discovered developmental cell states in tumor epithelial cells that ranged from progenitor phase to goblet cell differentiation, which was accompanied by elevated mucin gene expression. Metastatic PMP cells had a distinct cell state with increased mTOR and RAS signaling pathways and a series of upregulated cancer genes. We observed heterogeneity in a single PMP tumor as well as PMP metastases from the same patient. We validated our findings with immunohistochemistry, mass spectrometry on malignant ascites from PMP patients and gene expression data from an independent set of 63 PMPs.

Familial adenomatous polyposis (FAP) patients develop hundreds of premalignant polyps that progress to colorectal cancer due to a germline mutation in the APC tumor suppressor. Polyps from FAP patients uniquely facilitate interrogation of the continuum of malignant transformation from histologically normal mucosa to benign and dysplastic polyps and eventual adenocarcinomas. As part of the Human Tumor Atlas Network (HTAN), we performed multi-omic profiling, including whole genome sequencing, on 135 samples from six FAP patients across the pre-malignant continuum spanning all physical regions of the large intestine, and serving as the most comprehensive FAP dataset available. Through a comparative analysis with a published FAP cohort (58 multi-region samples across 5 patients) and sporadic colorectal cancer cohort (n=57), each including benign and malignant samples, we evaluate the timing of driver acquisitions at each stage of malignant progression. Despite being separated by vast regions of histologically normal mucosa, independently evolving polyps show extensive mutation sharing, suggesting FAP polyps are polyclonal in origin. Finally, we leverage a simplified mechanistic model of embryonic colonic development demonstrating that the path to malignant transformation in FAP is consistent with polyclonal origins attributable to early mixing, perhaps as early as in-utero. Taken together, the HTAN FAP Atlas provides a novel window into the earliest stages of cancer formation and may illuminate barriers to malignant transformation and opportunities for earlier intervention. Citation Format: Ryan O. Schenck, Aziz Khan, Aaron Horning, Edward D. Esplin, Emma Monte, Si Wu, Casey Hanson, Nasim Bararpour, Stephanie Neves, Lihua Jiang, Kévin Contrepois, Hayan Lee, Tuhin K. Guha, Zheng Hu, Rozelle Laquindanum, Meredith A. Mills, Hassan Chaib, Roxanne Chiu, Ruiqi Jian, Jeannie Chan, Mathew Ellenberger, Winston R. Becker, Bahareh Bahmani, Basil Michael, Jeanne Shen, Samuel Lancaster, Uri Ladabaum, Anshul Kundaje, Teri A. Longacre, William J. Greenleaf, James M. Ford, Michael P. Snyder, Christina Curtis. The polyclonal path to malignant transformation in familial adenomatous polyposis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3497.

Background & Aims: Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare primary liver cancer (PLC) associated with a poor prognosis. Given the challenges in its identification and its clinical implications, biomarkers are critically needed. We aimed to investigate the diagnostic and prognostic value of the immunohistochemical expression of Nestin, a progenitor cell marker, in a large multicentric series of PLCs.Methods: We collected 461 cHCC-CCA samples from 32 different clinical centers. Control cases included 368 hepatocellular carcinomas (HCCs) and 221 intrahepatic cholangiocarcinomas (iCCAs). Nestin immunohistochemistry was performed on whole tumor sections. Diagnostic and prognostic performances of Nestin expression were determined using receiver-operating characteristic curves and Cox regression modeling.Results: Nestin was able to distinguish cHCC-CCA from HCC with AUCs of 0.85 and 0.86 on surgical and biopsy samples, respectively. Performance was lower for the distinction of cHCC-CCA from iCCA (AUCs of 0.59 and 0.60). Nestin, however, showed a high prognostic value, allowing identification of the subset of cHCC-CCA (“Nestin High”, >30% neoplastic cells with positive staining) associated with the worst clinical outcome (shorter disease-free and overall survival) after surgical resection and liver transplantation, as well as when assessment was performed on biopsies.Conclusion: We show in different clinical settings that Nestin has diagnostic value and that it is a useful biomarker to identify the subset of cHCC-CCA associated with the worst clinical outcome. Nestin immunohistochemistry may be used to refine risk stratification and improve treatment allocation for patients with this highly aggressive malignancy. Lay summary: There are different types of primary liver cancers (i.e. cancers that originate in the liver). Accurately identifying a specific subtype of primary liver cancer (and determining its associated prognosis) is important as it can have a major impact on treatment allocation. Herein, we show that a protein called Nestin could be used to refine risk stratification and improve treatment allocation for patients with combined hepatocellular carcinoma, a rare but highly aggressive subtype of primary liver cancer.

ImportanceTertiary lymphoid structures (TLSs) are associated with a favorable prognosis and improved response to cancer immunotherapy. The current approach for evaluation of TLSs is limited by interobserver variability and high complexity and cost of specialized imaging techniques.ObjectiveTo develop a machine learning model for automated and quantitative evaluation of TLSs based on routine histopathology images.Design, Setting, and ParticipantsIn this multicenter, international diagnostic/prognostic study, an interpretable machine learning model was developed and validated for automated detection, enumeration, and classification of TLSs in hematoxylin-eosin–stained images. A quantitative scoring system for TLSs was proposed, and its association with survival was investigated in patients with 1 of 6 types of gastrointestinal cancers. Data analysis was performed between June 2021 and March 2022.Main Outcomes and MeasuresThe diagnostic accuracy for classification of TLSs into 3 maturation states and the association of TLS score with survival were investigated.ResultsA total of 1924 patients with gastrointestinal cancer from 7 independent cohorts (median [IQR] age ranging from 57 [49-64] years to 68 [58-77] years; proportion by sex ranging from 214 of 409 patients who were male [52.3%] to 134 of 155 patients who were male [86.5%]). The machine learning model achieved high accuracies for detecting and classifying TLSs into 3 states (TLS1: 97.7%; 95% CI, 96.4%-99.0%; TLS2: 96.3%; 95% CI, 94.6%-98.0%; TLS3: 95.7%; 95% CI, 93.9%-97.5%). TLSs were detected in 62 of 155 esophageal cancers (40.0%) and up to 267 of 353 gastric cancers (75.6%). Across 6 cancer types, patients were stratified into 3 risk groups (higher and lower TLS score and no TLS) and survival outcomes compared between groups: higher vs lower TLS score (hazard ratio [HR]; 0.27; 95% CI, 0.18-0.41; P P P = .003).Conclusions and RelevanceIn this study, an interpretable machine learning model was developed that may allow automated and accurate detection of TLSs on routine tissue slide. This model is complementary to the cancer staging system for risk stratification in gastrointestinal cancers.

We develop a deep learning approach

Familial adenomatous polyposis (FAP) is a genetic disease causing hundreds of premalignant polyps in affected patients, leading to colorectal cancer (CRC), and is an ideal model to study early transition to CRC. We performed deep multi-omic profiling of 135 normal mucosal, benign and dysplastic polyps and adenocarcinoma samples from 6 FAP patients who consented to broad data sharing. Whole genome sequencing indicates that spatially separated polyps from the same donor harbor numerous mutations in common, but evolve independently, consistent with a model of polyclonal origin and spreading. Transcriptomic, proteomic, metabolomic and lipidomic analyses revealed a dynamic choreography of thousands of molecular and cellular events that occur during early hyperplasia, dysplasia and cancer formation. These involve processes such as cell proliferation, immune response, alterations in metabolism (including amino acids, lipids), hormones, and extracellular matrix proteins. Interestingly, activation of the arachidonic acid pathway was found to occur early in hyperplasia; this pathway is targeted by aspirin/NSAIDs, a common preventative treatment of FAP patients. Overall, our results reveal key genomic, cellular and molecular events during the earliest steps in cancer formation and potential mechanisms of pharmaceutical prophylaxis.

To chart cell composition and cell state changes that occur during the transformation of healthy colon to precancerous adenomas to colorectal cancer (CRC), we generated single-cell chromatin accessibility profiles and single-cell transcriptomes from 1,000 to 10,000 cells per sample for 48 polyps, 27 normal tissues and 6 CRCs collected from patients with or without germline APC mutations. A large fraction of polyp and CRC cells exhibit a stem-like phenotype, and we define a continuum of epigenetic and transcriptional changes occurring in these stem-like cells as they progress from homeostasis to CRC. Advanced polyps contain increasing numbers of stem-like cells, regulatory T cells and a subtype of pre-cancer-associated fibroblasts. In the cancerous state, we observe T cell exhaustion, RUNX1-regulated cancer-associated fibroblasts and increasing accessibility associated with HNF4A motifs in epithelia. DNA methylation changes in sporadic CRC are strongly anti-correlated with accessibility changes along this continuum, further identifying regulatory markers for molecular staging of polyps.

PURPOSE Large-scale analysis of real-world evidence is often limited to structured data fields that do not contain reliable information on recurrence status and disease sites. In this report, we describe a natural language processing (NLP) framework that uses data from free-text, unstructured reports to classify recurrence status and sites of recurrence for patients with breast and hepatocellular carcinomas (HCC). METHODS Using two cohorts of breast cancer and HCC cases, we validated the ability of a previously developed NLP model to distinguish between no recurrence, local recurrence, and distant recurrence, based on clinician notes, radiology reports, and pathology reports compared with manual curation. A second NLP model was trained and validated to identify sites of recurrence. We compared the ability of each NLP model to identify the presence, timing, and site of recurrence, when compared against manual chart review and International Classification of Diseases coding. RESULTS A total of 1,273 patients were included in the development and validation of the two models. The NLP model for recurrence detects distant recurrence with an area under the curve of 0.98 (95% CI, 0.96 to 0.99) and 0.95 (95% CI, 0.88 to 0.98) in breast and HCC cohorts, respectively. The mean accuracy of the NLP model for detecting any site of distant recurrence was 0.9 for breast cancer and 0.83 for HCC. The NLP model for recurrence identified a larger proportion of patients with distant recurrence in a breast cancer database (11.1%) compared with International Classification of Diseases coding (2.31%). CONCLUSION We developed two NLP models to identify distant cancer recurrence, timing of recurrence, and sites of recurrence based on unstructured electronic health record data. These models can be used to perform large-scale retrospective studies in oncology.

Pancreatic cancer is the fourth leading cause of cancer-related death among men and women in the United States. A major challenge in treatment remains patients’ advanced disease at diagnosis. The NCCN Guidelines for Pancreatic Adenocarcinoma provides recommendations for the diagnosis, evaluation, treatment, and follow-up for patients with pancreatic cancer. Although survival rates remain relatively unchanged, newer modalities of treatment, including targeted therapies, provide hope for improving patient outcomes. Sections of the manuscript have been updated to be concordant with the most recent update to the guidelines. This manuscript focuses on the available systemic therapy approaches, specifically the treatment options for locally advanced and metastatic disease.

To chart cell composition and cell state changes that occur during the transformation of healthy colon to precancerous adenomas to colorectal cancer (CRC), we generated 451,886 single-cell chromatin accessibility profiles and 208,557 single-cell transcriptomes from 48 polyps, 27 normal tissues, and 6 CRCs collected from patients with and without germline APC mutations. A large fraction of polyp and CRC cells exhibit a stem-like phenotype, and we define a continuum of epigenetic and transcriptional changes occurring in these stem-like cells as they progress from normal to CRC. Advanced polyps contain increasing numbers of stem-like cells, regulatory T-cells, and a subtype of FOX-regulated pre-cancer associated fibroblasts. In the cancerous state, we observe T-cell exhaustion, RUNX1-regulated cancer associated fibroblasts, and increasing accessibility associated with HNF4A motifs in epithelia. Methylation changes in sporadic CRC are strongly anti-correlated with accessibility changes along this continuum, further identifying regulatory markers for molecular staging of polyps.