Pancreatic ductal adenocarcinoma: biological hallmarks, current status, and future perspectives of combined modality treatment approaches

Orth, Michael; Metzger, Philipp; Gerum, S.; Mayerle, Julia; Schneider, Günter; Belka, Claus; Schnurr, M.; Lauber, Kirsten

doi:10.1186/s13014-019-1345-6

Cited by 344 publications

(345 citation statements)

References 277 publications

(282 reference statements)

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“…The application of this algorithm to histopathologically unclassifiable tumors identifies two patient groups with significantly different overall survival. We therefore hypothesize that the algorithm is capable of re-identifying the dominant features of the QM and non-QM molecular subtypes in CT images and that radiomics-based phenotyping may thus offer a clinically usable classification advantageous over histopathology in the notoriously heterogenous entity of PDAC [18][19][20]. This notion is reinforced by the fact that histopathological samples are by default a significant underrepresentation of the tumor, since they are derived from a small sub-section of the tissue [21], and regions of differing molecular subtype are likely to coexist within the same tumor [22].…”

Section: Discussionmentioning

confidence: 99%

Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma

Kaissis

Ziegelmayer

Lohöfer

et al. 2020

JCM

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To bridge the translational gap between recent discoveries of distinct molecular phenotypes of pancreatic cancer and tangible improvements in patient outcome, there is an urgent need to develop strategies and tools informing and improving the clinical decision process. Radiomics and machine learning approaches can offer non-invasive whole tumor analytics for clinical imaging data-based classification. The retrospective study assessed baseline computed tomography (CT) from 207 patients with proven pancreatic ductal adenocarcinoma (PDAC). Following expert level manual annotation, Pyradiomics was used for the extraction of 1474 radiomic features. The molecular tumor subtype was defined by immunohistochemical staining for KRT81 and HNF1a as quasi-mesenchymal (QM) vs. non-quasi-mesenchymal (non-QM). A Random Forest machine learning algorithm was developed to predict the molecular subtype from the radiomic features. The algorithm was then applied to an independent cohort of histopathologically unclassifiable tumors with distinct clinical outcomes. The classification algorithm achieved a sensitivity, specificity and ROC-AUC (area under the receiver operating characteristic curve) of 0.84 ± 0.05, 0.92 ± 0.01 and 0.93 ± 0.01, respectively. The median overall survival for predicted QM and non-QM tumors was 16.1 and 20.9 months, respectively, log-rank-test p = 0.02, harzard ratio (HR) 1.59. The application of the algorithm to histopathologically unclassifiable tumors revealed two groups with significantly different survival (8.9 and 39.8 months, log-rank-test p < 0.001, HR 4.33). The machine learning-based analysis of preoperative (CT) imaging allows the prediction of molecular PDAC subtypes highly relevant for patient survival, allowing advanced pre-operative patient stratification for precision medicine applications.

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Section: Discussionmentioning

confidence: 99%

Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma

Kaissis

Ziegelmayer

Lohöfer

et al. 2020

JCM

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“…PDAC patients present for 80–90% non-resectable stage cancer or distal metastasis, so systemic chemotherapy is applied as first-line treatment [ 10 , 35 ]. This therapy includes GEM, PTX, and nucleoside analogues [ 36 ] in monotherapy or in combination with radiotherapy or by a poly-chemotherapeutic regiment [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

Cannabidiol and Oxygen-Ozone Combination Induce Cytotoxicity in Human Pancreatic Ductal Adenocarcinoma Cell Lines

Luongo¹,

Marinelli

Zeppa

et al. 2020

Cancers

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Pancreatic cancer (PC) is related to lifestyle risks, chronic inflammation, and germline mutations in BRCA1/2, ATM, MLH1, TP53, or CDKN2A. Surgical resection and adjuvant chemotherapy are the main therapeutic strategies but are less effective in patients with high-grade tumors. Oxygen-ozone (O2/O3) therapy is an emerging alternative tool for the treatment of several clinical disorders. O2/O3 therapy has been found to ameliorate mechanisms promoting chronic pain and inflammation, including hypoxia, inflammatory mediators, and infection. The advantages of using cannabinoids have been evaluated in vitro and in vivo models of several human cancers. Regarding PDAC, activation of cannabinoid receptors was found to induce pancreatic cancer cell apoptosis without affecting the normal pancreas cells. In a murine model of PDAC, a combination of cannabidiol (CBD) and gemcitabine increased survival length by nearly three times. Herein, we evaluate the anticancer effect of CBD and O2/O3, alone or in combination, on two human PDAC cell lines, PANC-1 and MiaPaCa-2, examining expression profiles of 92 pancreatic adenocarcinoma associated genes, cytotoxicity, migration properties, and cell death. Finally, we assess the combination effects with gemcitabine and paclitaxel. Summarizing, for the first time the antitumoral effect of combined therapy with CBD and oxygen-ozone therapy in PDAC is evidenced.

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“…Systemic chemotherapy is commonly employed as a first-line treatment for patients with nonresectable metastasizing PDAC [9]. Until 2011, gemcitabine was the only validated standard of care [10,11].…”

Section: Current Clinical Standard Of Carementioning

confidence: 99%

“…The dismal prognosis, with a 5-year overall survival (OS) of less than 8%, is due to infrequence of typical symptoms, resulting in late diagnosis and the high poten-tial to metastasize [4]. Only 10-20% of all PDAC patients are diagnosed with PDAC at resectable stages [5] and even where surgery is feasible, PDAC is prone to relapse and developing resistance to supportive chemotherapy, making treatment options challenging [6][7][8][9]. Another third of the patients present with locally advanced but nonmetastatic disease, and usually undergo neoadjuvant chemotherapy, with the option of radiation, followed by surgery as a possible curative treatment.…”

Section: Introductionmentioning

confidence: 99%

Precision Therapy of Pancreatic Cancer: From Bench to Bedside

Ciecielski¹,

Berninger²,

Algül³

2020

Visc Med

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Background: Pancreatic ductal adenocarcinoma (PDAC), with a mortality rate of 94% and a 5-year-survival rate of only 8%, is one of the deadliest cancer entities worldwide, and early diagnostic methods as well as effective therapies are urgently needed. Summary: This review summarizes current clinical procedure and recent developments of oncological therapy in the palliative setting of metastatic PDAC. It further gives examples of successful, as well as failed, targeted therapy approaches and finally discusses promising ongoing research into the decade-old question of the “undruggability” of KRAS. Key Messages: Bench-driven concepts change the clinical landscape from “one size fits all” towards precision medicine. With growing insight into the molecular mechanisms of pancreatic cancer the era of targeted therapy in PDAC is gaining a new momentum.

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Pancreatic ductal adenocarcinoma: biological hallmarks, current status, and future perspectives of combined modality treatment approaches

Cited by 344 publications

References 277 publications

Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma

Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma

Cannabidiol and Oxygen-Ozone Combination Induce Cytotoxicity in Human Pancreatic Ductal Adenocarcinoma Cell Lines

Precision Therapy of Pancreatic Cancer: From Bench to Bedside

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