Cancer incidence and deaths in the United States were projected for the most common cancer types for the years 2020 and 2030 based on changing demographics and the average annual percentage changes in incidence and death rates. Breast, prostate, and lung cancers will remain the top cancer diagnoses throughout this time, but thyroid cancer will replace colorectal cancer as the fourth leading cancer diagnosis by 2030, and melanoma and uterine cancer will become the fifth and sixth most common cancers, respectively. Lung cancer is projected to remain the top cancer killer throughout this time period. However, pancreas and liver cancers are projected to surpass breast, prostate, and colorectal cancers to become the second and third leading causes of cancer-related death by 2030, respectively. Advances in screening, prevention, and treatment can change cancer incidence and/or death rates, but it will require a concerted effort by the research and healthcare communities now to effect a substantial change for the future. Cancer Res; 74(11); 2913–21. ©2014 AACR.

Purpose: To broaden access to and implementation of precision medicine in the care of patients with pancreatic cancer, the Know Your Tumor (KYT) program was initiated using a turn-key precision medicine system. Patients undergo commercially available multiomic profiling to determine molecularly rationalized clinical trials and off-label therapies. Experimental Design: Tumor samples were obtained for 640 patients from 287 academic and community practices covering 44 states. College of American Pathologists/Clinical Laboratory Improvement Amendments–accredited laboratories were used for genomic, proteomic, and phosphoprotein-based molecular profiling. Results: Tumor samples were adequate for next-generation sequencing in 96% and IHC in 91% of patients. A tumor board reviewed the results for every patient and found actionable genomic alterations in 50% of patients (with 27% highly actionable) and actionable proteomic alterations (excluding chemopredictive markers) in 5%. Actionable alterations commonly found were in DNA repair genes (BRCA1/2 or ATM mutations, 8.4%) and cell-cycle genes (CCND1/2/3 or CDK4/6 alterations, 8.1%). A subset of samples was assessed for actionable phosphoprotein markers. Among patients with highly actionable biomarkers, those who received matched therapy (n = 17) had a significantly longer median progression-free survival (PFS) than those who received unmatched therapy [n = 18; PFS = 4.1 vs. 1.9 months; HR, 0.47; 95% confidence interval (CI): 0.24–0.94; Padj = 0.03]. Conclusions: A comprehensive precision medicine system can be implemented in community and academic settings, with highly actionable findings observed in over 25% of pancreatic cancers. Patients whose tumors have highly actionable alterations and receive matched therapy demonstrated significantly increased PFS. Our findings support further prospective evaluation of precision oncology in pancreatic cancer. Clin Cancer Res; 24(20); 5018–27. ©2018 AACR.

Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer—Current Efforts; Collaborative Opportunities; and Moving Forward—Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.

The relevance of KRAS mutation alleles to clinical outcome remains inconclusive in pancreatic adenocarcinoma (PDAC). We conducted a retrospective study of 803 patients with PDAC (42% with metastatic disease) at MD Anderson Cancer Center. Overall survival (OS) analysis demonstrated that KRAS mutation status and subtypes were prognostic (p < 0.001). Relative to patients with KRAS wildtype tumors (median OS 38 months), patients with KRAS^G12R had a similar OS (median 34 months), while patients with KRAS^Q61 and KRAS^G12D mutated tumors had shorter OS (median 20 months [HR: 1.9, 95% CI 1.2–3.0, p = 0.006] and 22 months [HR: 1.7, 95% CI 1.3–2.3, p < 0.001], respectively). There was enrichment of KRAS^G12D mutation in metastatic tumors (34% vs 24%, OR: 1.7, 95% CI 1.2–2.4, p = 0.001) and enrichment of KRAS^G12R in well and moderately differentiated tumors (14% vs 9%, OR: 1.7, 95% CI 1.05–2.99, p = 0.04). Similar findings were observed in the external validation cohort (PanCAN’s Know Your Tumor® dataset, n = 408).

Pancreatic ductal adenocarcinoma (PDAC) is the only leading cause of cancer death without an early detection strategy. In retrospective studies, 0.5–1% of subjects >50 years of age who newly develop biochemically-defined diabetes have been diagnosed with PDAC within 3 years of meeting new onset hyperglycemia and diabetes (NOD) criteria. The Enriching New-onset Diabetes for Pancreatic Cancer (ENDPAC) algorithm further risk stratifies NOD subjects based on age and changes in weight and diabetes parameters. We present the methodology for the Early Detection Initiative (EDI), a randomized controlled trial of algorithm-based screening in patients with NOD for early detection of PDAC. We hypothesize that study interventions (risk stratification with ENDPAC and imaging with Computerized Tomography (CT) scan) in NOD will identify earlier stage PDAC. EDI uses a modified Zelen’s design with post-randomization consent. Eligible subjects will be identified through passive surveillance of electronic medical records and eligible study participants randomized 1:1 to the Intervention or Observation arm. The sample size is 12,500 subjects. The ENDPAC score will be calculated only in those randomized to the Intervention arm, with 50% (n=3125) expected to have a high ENDPAC score. Consenting subjects in the high ENDPAC group will undergo CT imaging for PDAC detection and an estimate of potential harm. The effectiveness and efficacy evaluation will compare proportions of late stage PDAC between Intervention and Observation arm per randomization assignment or per protocol, respectively, with a planned interim analysis. The study is designed to improve the detection of sporadic PDAC when surgical intervention is possible.