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.

项目摘要 胰腺癌是美国癌症死亡的第三大原因。高死亡率 胰腺导管腺癌（PDAC）在很大程度上是在晚期诊断的结果， 这个肿瘤已经无法治愈了目前，不建议对以下患者进行PDAC的无症状筛查： 一般人群，仅对一小部分胰腺囊性病变患者进行筛选 或有很强的遗传风险。即使癌症在早期被发现， 手术切除，最常伴有肝转移。为了改善PDAC患者的结局， 迫切需要取得重大进展，包括改进风险评估，以确定那些在高风险地区的人。 PDAC的风险，新的非侵入性生物标志物，以选择个人进行密集的成像监测， 策略，以确定那些与局部肿瘤谁是在手术切除后迅速复发的风险。在 在目前的提案中，我们直接解决这些关键领域的需求，重点是：（a）机器学习模型 用于从电子病历数据进行风险评估（目标1），（B）开发非侵入性生物标志物 从粪便和计算机断层扫描（CT）成像测量早期PDAC引起的代谢改变 (Aim（c）表征CT成像和循环无细胞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，我们将收集和制作 来自多名患者的可用临床数据、血液样本、粪便样本、成像研究和肿瘤组织 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对照（目标4）。完成 建议的工作，我们已经组建了一个经验丰富的团队和协作，完全致力于 与其他胰腺癌检测联盟单位一起工作。因此，我们将利用削减- 边缘机器学习方法，开发多种创新的非侵入性生物标志物技术， 收集大量数据和临床样本，用于胰腺内外的合作活动 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并进行更多治疗来实现我们降低胰腺癌死亡率的近期目标 有效治愈。

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