A Potential Screening Strategy of Pancreatic Cancer in New Onset Diabetes Patients Using Serum CA19-9 in Combination With Age and BMI

7017 Background: There are no established methods for pancreatic cancer (PC) screening, but the National Cancer Institute and the Pancreatic Cancer Action Network (PanCAN) are investigating risk-based screening strategies in new-onset diabetes (NOD)—a group with elevated PC risk. Preliminary estimates of the cost-effectiveness of these strategies can provide insights about potential value and inform supplemental data collection. Using data from the Enriching New-Onset Diabetes for Pancreatic Cancer (ENDPAC) risk model validation study, we assessed the potential value of CT screening for PC in those determined to be at elevated PC risk, as is being done in a planned PanCAN Early Detection Initiative (EDI) trial. Methods: We created an integrated decision tree and Markov state-transition model to assess the cost-effectiveness of screening those age ≥50 and with NOD for PC using CT imaging vs. no screening. PC prevalence, sensitivity, and specificity were derived from the ENDPAC validation study. PC stage distribution in the no screening strategy and PC survival were derived from SEER. Background mortality for diabetics, screening and cancer care expenditure, and health state utilities were derived from the literature. The base case assumed 40% of screen-detected PC cases were resectable, and a threshold analysis explored the fraction required for screening to be <$100,000 per QALY gained. Life years (LYs), quality-adjusted life years (QALYs), and costs were tracked over a lifetime horizon and discounted at 3% per year. Results are presented in 2019 USD, and we took a U.S. payer perspective. Results: In the base case, screening resulted in 0.0055 more LYs, 0.0045 more QALYs, and $305 in additional expenditure for a cost per QALY gained of $68,059 (Table). Among PC cases, screening resulted in 0.67 more LYs, 0.55 more QALYs, and $22,691 in additional expenditure. In probabilistic analyses, screening resulted in a cost per QALY gained of <$50,000 and <$100,000 in 34% and 99% of simulations, respectively. In the threshold analysis, >25% of screen-detected cases needed to be resectable for the cost per QALY gained with screening to be <$100,000. Conclusions: We found that risk-based pancreatic cancer screening in NOD is likely to be cost-effective in the U.S. if even a modest fraction (>25%) of screen-detected cases are resectable. Future studies should reassess the value of this intervention once PanCAN EDI data become available. [Table: see text]

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Screening test could detect pancreatic cancer in new-onset diabetes patients

Clinical Pharmacist ◽

10.1211/cp.2016.20201778 ◽

2016 ◽

Keyword(s):

Pancreatic Cancer ◽

Screening Test ◽

Onset Diabetes ◽

New Onset ◽

Diabetes Patients

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Screening Strategy of Pancreatic Cancer in Patients with Diabetes Mellitus

Diagnostics ◽

10.3390/diagnostics10080572 ◽

2020 ◽

Vol 10 (8) ◽

pp. 572

Author(s):

Suguru Mizuno ◽

Yousuke Nakai ◽

Kazunaga Ishigaki ◽

Kei Saito ◽

Hiroki Oyama ◽

...

Keyword(s):

Diabetes Mellitus ◽

Pancreatic Cancer ◽

Epidemiological Studies ◽

Screening Strategy ◽

Diabetic Patients ◽

Reverse Causality ◽

Pilot Studies ◽

Patients With Diabetes ◽

High Risk Populations ◽

New Onset

The incidence of pancreatic cancer (PCa) is increasing worldwide and has become one of the leading causes of cancer-related death. Screening for high risk populations is fundamental to overcome this intractable malignancy. Diabetes mellitus (DM) is classically known as a risk factor for PCa. Recently the reverse causality is in the spotlight, that is to say, DM is considered to be a manifestation of PCa. Numbers of epidemiological studies clarified that new-onset DM (≤2-year duration) was predominant in PCa patients and the relative risk for PCa inversely correlated with duration of DM. Among patients with new-onset DM, elder onset, weight loss, and rapid exacerbation of glycemic control were reported to be promising risk factors and signs, and the model was developed by combining these factors. Several pilot studies disclosed the possible utility of biomarkers to discriminate PCa-associated DM from type 2 DM. However, there is no reliable biomarkers to be used in the practice. We previously reported the application of a multivariate index for PCa based on the profile of plasma free amino acids (PFAAs) among diabetic patients. We are further investigating on the PFAA profile of PCa-associated DM, and it can be useful for developing the novel biomarker in the near future.

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New‐onset Diabetes as a Harbinger of Pancreatic Cancer: is Early Diagnosis Possible?

Clinical Pancreatology for Practising Gastroenterologists and Surgeons ◽

10.1002/9781119570097.ch51 ◽

2021 ◽

pp. 409-417

Author(s):

Dana K. Andersen ◽

Suresh T. Chari ◽

Eithne Costello ◽

Tatjana Crnogorac‐Jurcevic ◽

Phil A. Hart ◽

...

Keyword(s):

Pancreatic Cancer ◽

Early Diagnosis ◽

Onset Diabetes ◽

New Onset

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Reply: Letter to the Editor: Prospective Assessment for Prediabetes and New-Onset Diabetes in High-Risk Individuals Undergoing Pancreatic Cancer Screening

Gastroenterology ◽

10.1053/j.gastro.2022.01.014 ◽

2022 ◽

Author(s):

Ishani Shah ◽

Mandeep S. Sawhney

Keyword(s):

Pancreatic Cancer ◽

Cancer Screening ◽

High Risk ◽

Letter To The Editor ◽

Onset Diabetes ◽

Pancreatic Cancer Screening ◽

Prospective Assessment ◽

New Onset

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Serum metabolomics differentiating pancreatic cancer from new-onset diabetes

Oncotarget ◽

10.18632/oncotarget.16249 ◽

2017 ◽

Vol 8 (17) ◽

pp. 29116-29124 ◽

Cited By ~ 15

Author(s):

Xiangyi He ◽

Jie Zhong ◽

Shuwei Wang ◽

Yufen Zhou ◽

Lei Wang ◽

...

Keyword(s):

Pancreatic Cancer ◽

Onset Diabetes ◽

Serum Metabolomics ◽

New Onset

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Prospective assessment for prediabetes and new-onset diabetes in high-risk individuals undergoing pancreatic cancer screening

Gastroenterology ◽

10.1053/j.gastro.2021.06.055 ◽

2021 ◽

Author(s):

Ishani Shah ◽

Vaibhav Wadhwa ◽

Mohammad Bilal ◽

Katharine A. Germansky ◽

Mandeep S. Sawhney ◽

...

Keyword(s):

Pancreatic Cancer ◽

Cancer Screening ◽

High Risk ◽

Onset Diabetes ◽

Pancreatic Cancer Screening ◽

Prospective Assessment ◽

New Onset

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Pancreatic cancer detection using EpiDetect signatures in plasma-derived cell free DNA in high-risk patients with new onset diabetes.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e16265 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e16265-e16265

Author(s):

Gulfem Guler ◽

Anna Bergamaschi ◽

David Haan ◽

Michael Kesling ◽

Yuhong Ning ◽

...

Keyword(s):

Pancreatic Cancer ◽

Early Stage ◽

Model Performance ◽

Training Data ◽

Diabetes Diagnosis ◽

Whole Genome ◽

Data Set ◽

Independent Validation ◽

Onset Diabetes ◽

New Onset

e16265 Background: Pancreatic cancer (PaCa) is the third leading cause of cancer death in the United States despite its low incidence rate, owing to a 5-year survival rate of 10%. It is often asymptomatic in early stage, resulting in the majority of diagnoses occurring when cancer has already metastasized to distant organs. Late diagnosis deprives patients of potentially curative treatments such as surgery and impacts survival rates. Diabetes can be an early symptom of PaCa. Indeed, 25% of PaCa patients had a preceding diabetes diagnosis. Among all people with new onset diabetes (NOD), 0.85% will be diagnosed with PaCa within 3 years, which represents 6-8 fold increased risk for PaCa compared to the general population. Surveillance of the NOD population for PaCa presents an opportunity to shift PaCa diagnosis to earlier stage by finding it sooner. Methods: Whole blood was obtained from a cohort of 117 PaCa patients as well as 800 non-cancer controls with and without NOD. Plasma was processed to isolate cfDNA and 5hmC and low pass whole genome libraries were generated and sequenced. The EpiDetect assay combines 5hmC and whole genome sequencing data and were generated using Bluestar Genomics’s technology platform. Results: To investigate whether PaCa can be detected in plasma, we interrogated plasma-derived cfDNA epigenomic and genomic signal from PaCa patients and non-cancer controls. We first trained stacked ensemble models on PaCa and non-cancer samples utilizing 5hmC, fragmentation and CNV-based biomarkers from cfDNA. These models performed stably with a median of 72.8% sensitivity and 90.1% specificity measured across 25 outer fold iterations using the training data set, which was composed of 50% early stage (Stages I & II) disease. The final binomial ensemble model was trained using all of the training data, yielding an area under the receiver operating characteristic curve (auROC) of 0.9, with 75% sensitivity and 89% specificity. This model was then tested on an independent validation data set from 33 PaCa patients (24 with diabetes, 15 of which was NOD) and 202 non-cancer control patients (76 with diabetes, 51 of which was NOD) and yielded a classification performance auROC of 0.9 with 67% sensitivity at 92% specificity. Lastly, model performance in the subset of patient cohort with NOD only had an auROC of 0.87 with 60% sensitivity at 88% specificity. Conclusions: Our results indicate that 5hmC profiles along with CNV and fragmentation patterns from cfDNA can be used to detect PaCa in plasma-derived cfDNA. Overall, model performance was stable and consistent between the training and independent validation datasets. A larger clinical study is under development to investigate the utility of the model described in this pilot study in identifying occult PaCa within the NOD population, with the aim of shifting diagnosis to early stage and potentially improving patient outcomes.

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