Altered metabolism and machine learning for pancreatic cancer early detection

改变新陈代谢和机器学习以实现胰腺癌早期检测

• 批准号: 10705708
• 负责人: Michael H. Rosenthal
• 金额: $ 81.16万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-16 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10705708
• 关键词: Address; Architecture; Area; Atrophic; Biological Markers; Blood; Blood specimen; Cancer Center; Cancer Detection; Cancer Etiology; Cessation of life; Clinical; Clinical Data; Collaborations; Communities; Computerized Medical Record; Data; Data Collection; Data Set; Dedications; Detection; Development; Diagnosis; Diameter; Disease; Duct (organ) structure; Early Diagnosis; Early identification; Event; Excision; Exocrine pancreatic insufficiency; Feces; Fingerprint; Future; General Population; Genetic; Genetic Risk; Goals; Human; Image; Immune response; Incidence; Individual; Liver; Localized Disease; Location; Machine Learning; Malignant Neoplasms; Malignant neoplasm of pancreas; Measures; Metabolic; Metabolic dysfunction; Metabolism; Metastatic Neoplasm to the Liver; Methylation; Modeling; Muscular Atrophy; Neoplasm Metastasis; Operative Surgical Procedures; Organ; Pancreas; Pancreatectomy; Pancreatic Cyst; Pancreatic Ductal Adenocarcinoma; Pancreatic cystic neoplasia; Pancreatic duct; Patient-Focused Outcomes; Patients; Peptide Hydrolases; Performance; Peripheral; Population; Probability; Recurrence; Recurrent Malignant Neoplasm; Recurrent disease; Research; Risk; Risk Assessment; Role; Sampling; Sampling Studies; Screening for cancer; Techniques; Technology; Terminology; Testing; Time; Tissues; Translating; Tumor Tissue; United States; Work; X-Ray Computed Tomography; biobank; biomarker identification; biomarker validation; cancer biomarkers; cancer recurrence; carcinogenesis; cell free DNA; chronic pancreatitis; clinical implementation; data infrastructure; data modeling; data resource; detection test; early onset; experience; federated learning; high risk; high risk population; imaging approach; imaging study; improved; improved outcome; innovation; machine learning model; machine learning prediction; methylation pattern; mortality; mouse model; nanosensors; novel strategies; pancreas development; pancreatic ductal adenocarcinoma model; pancreatic neoplasm; patient population; patient subsets; risk prediction; risk stratification; sample collection; screening; standard of care; stool sample; structured data; surveillance imaging; tumor; unstructured data

PROJECT SUMMARY Pancreatic cancer is the 3rd leading cause of cancer death in the United States. The high mortality of pancreatic ductal adenocarcinoma (PDAC) is largely a consequence of diagnosis at an advanced stage when the tumor is no longer treatable for cure. Currently, asymptomatic screening for PDAC is not recommended for the general population, with screening pursued only for a small subset of patients with pancreatic cystic lesions or strong genetic risk for PDAC. Even when cancer is identified early, patients can have rapid recurrence after surgical resection, most often with liver metastases. To improve outcomes for patients with PDAC, a number of important advancements are urgently needed, including improved risk assessment to identify those at elevated risk for PDAC, new non-invasive biomarkers to select individuals for intensive imaging surveillance, and better strategies to identify those with localized tumors who are at risk for rapid recurrence after surgical resection. In the current proposal, we directly address these critical areas of need, focusing on: (a) machine learning models for risk assessment from electronic medical record data (Aim 1), (b) development of non-invasive biomarkers from stool and computed tomography (CT) imaging that measure metabolic alterations caused by early PDAC (Aim 2), and (c) characterization of CT imaging and circulating cell-free DNA methylation patterns to predict presence of occult metastases at the time of surgical resection (Aim 3). Furthermore, we will collect and make available clinical data, blood samples, stool samples, imaging studies, and tumor tissue from multiple patient populations critical to PDAC early detection research, including those with early-stage PDAC, chronic pancreatitis, genetic PDAC risk, pancreatic cystic lesions, and non-cancer controls (Aim 4). To accomplish the proposed work, we have assembled a highly experienced and collaborative team that is fully committed to working together and with other Pancreatic Cancer Detection Consortium units. Thus, we will leverage cutting- edge machine learning approaches, develop multiple innovative, non-invasive biomarker technologies, and collect a large array of data and clinical samples for collaborative activities within and outside the Pancreatic Cancer Detection Consortium. With a highly dedicated expert team and clear scientific plan, we expect to achieve our near-term goal of reducing pancreatic cancer mortality by finding PDAC earlier and treating it more effectively for cure.

项目概要 胰腺癌是美国癌症死亡的第三大原因。死亡率高 胰腺导管腺癌（PDAC）很大程度上是晚期诊断的结果，当 肿瘤已无法治愈。目前，不建议对 PDAC 进行无症状筛查 一般人群，仅对一小部分患有胰腺囊性病变的患者进行筛查 或 PDAC 的遗传风险较高。即使癌症被早期发现，患者也可能在术后迅速复发。 手术切除，最常见的是肝转移。为了改善 PDAC 患者的治疗结果，许多 迫切需要取得重要进展，包括改进风险评估，以确定那些处于高风险状态的人 PDAC 风险、新的非侵入性生物标志物来选择个体进行强化影像监测，以及更好的 确定手术切除后有快速复发风险的局部肿瘤患者的策略。在 在当前的提案中，我们直接解决这些关键需求领域，重点关注：（a）机器学习模型 用于电子病历数据的风险评估（目标 1），(b) 开发非侵入性生物标志物 来自粪便和计算机断层扫描 (CT) 成像，可测量早期 PDAC 引起的代谢变化 （目标 2）和（c）表征 CT 成像和循环游离 DNA 甲基化模式以预测 手术切除时存在隐匿性转移（目标 3）。此外，我们还将收集并制作 来自多名患者的可用临床数据、血液样本、粪便样本、影像学研究和肿瘤组织 对 PDAC 早期检测研究至关重要的人群，包括早期 PDAC、慢性 胰腺炎、遗传性 PDAC 风险、胰腺囊性病变和非癌症控制（目标 4）。为了实现 根据拟议的工作，我们组建了一支经验丰富且协作的团队，完全致力于 与其他胰腺癌检测联盟单位合作。因此，我们将利用削减 边缘机器学习方法，开发多种创新、非侵入性生物标记技术，以及 收集大量数据和临床样本，用于胰腺内外的协作活动 癌症检测联盟。凭借高度敬业的专家团队和清晰的科学计划，我们期望 通过更早发现 PDAC 并加强治疗，实现降低胰腺癌死亡率的近期目标 有效达到治愈目的。

项目成果

Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease.

• DOI: 10.1038/s41467-018-07466-6
• 发表时间: 2018-11-29
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Moss J;Magenheim J;Neiman D;Zemmour H;Loyfer N;Korach A;Samet Y;Maoz M;Druid H;Arner P;Fu KY;Kiss E;Spalding KL;Landesberg G;Zick A;Grinshpun A;Shapiro AMJ;Grompe M;Wittenberg AD;Glaser B;Shemer R;Kaplan T;Dor Y
• 通讯作者: Dor Y

Association Between Polycystic Ovary Syndrome and Risk of Pancreatic Cancer.

多囊卵巢综合症与胰腺癌风险之间的关联。

• DOI: 10.1001/jamaoncol.2022.4540
• 发表时间: 2022
• 期刊: JAMA oncology
• 影响因子: 28.4
• 作者: Peeri,NoahC;Landicino,MarcoV;Saldia,CAmethyst;Kurtz,RobertC;Rolston,VineetS;Du,Mengmeng
• 通讯作者: Du,Mengmeng

Liquid biopsy reveals collateral tissue damage in cancer.

• DOI: 10.1172/jci.insight.153559
• 发表时间: 2022-01-25
• 期刊: JCI insight
• 影响因子: 8
• 作者: Lubotzky A;Zemmour H;Neiman D;Gotkine M;Loyfer N;Piyanzin S;Ochana BL;Lehmann-Werman R;Cohen D;Moss J;Magenheim J;Loftus MF;Brais L;Ng K;Mostoslavsky R;Wolpin BM;Zick A;Maoz M;Grinshpun A;Kustanovich A;Makranz C;Cohen JE;Peretz T;Hubert A;Temper M;Salah A;Avniel-Polak S;Grozinsky-Glasberg S;Spalding KL;Rokach A;Kaplan T;Glaser B;Shemer R;Dor Y
• 通讯作者: Dor Y

Islet cells share promoter hypomethylation independently of expression, but exhibit cell-type-specific methylation in enhancers.

• DOI: 10.1073/pnas.1713736114
• 发表时间: 2017-12-19
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: Neiman D;Moss J;Hecht M;Magenheim J;Piyanzin S;Shapiro AMJ;de Koning EJP;Razin A;Cedar H;Shemer R;Dor Y
• 通讯作者: Dor Y

Reply to the letter to the editor 'Borderline resectable pancreatic cancer: an evolving concept' by Petrucciani et al.

回复 Petrucciani 等人给编辑的信“边缘性可切除胰腺癌：一个不断发展的概念”。

• DOI: 10.1093/annonc/mdx273
• 发表时间: 2017
• 期刊: Annals of oncology : official journal of the European Society for Medical Oncology
• 作者: Gilbert,JW;Wolpin,B;Clancy,T;Wang,J;Mamon,H;Shinagare,AB;Jagannathan,J;Rosenthal,M
• 通讯作者: Rosenthal,M