Identification of a molecular signature allowing pancreatic cancer personalised treatment

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PANCREATIC CANCER : identification of a molecular signature allowing pancreatic cancer personalised treatment. This signature predicts pancreatic cancer progression and tumor sensitivity to the most commonly used chemotherapy protocol for the treatment of pancreatic cancer.

Researchers from the Cancer Research Center of Marseille (CRCM) and clinicians from the Paoli-Calmettes Institute (IPC) took part in a French consortium undertaking a clinical study promoted by IPC which describes a new classification of pancreatic cancers. This classification is based on a molecular signature – the profile of RNAs produced by cancer cells – and makes it possible to predict the disease progression and the sensitivity to the most commonly used chemotherapy protocol, and thus to adapt the treatment offered to each patient.

Despite being an overall dismal cancer, the clinical outcome of pancreatic adenocarcinoma is difficult to anticipate, with newly diagnosed patients having a potential life expectancy ranging from 3 months to more than 5 years. An accurate predictive method applicable for all patients would greatly improve individual care by identifying the most appropriate course of treatment, including surgery and the use of poly-chemo regimens. Although valuable, histological characterization is only practical in few cases (<20%) due to difficulties to access proper tumor samples, and therapeutic decisions are taken without any information from the molecular analysis of tumor material.

The team led by Juan Iovanna and Nelson Dusetti at the CRCM has focused over the last few years on the molecular characterization of pancreatic tumor heterogeneity aimed at enabling precision medicine approaches for pancreatic cancer care: that is, on adapting the treatment to the molecular characteristics of each patient’s tumor.

In collaboration with the Public Hospitals of Marseille, the BACAP program led by the Public Hospital of Toulouse, University Paris 7 and the French National CLeague against Cancer's ‘Identity Card for Tumors’ program, they used a cohort of patients included in the PaCaOmics clinical trial (NCT01692873) promoted by IPC. The global gene expression of tumors was analysed using a technology called transcriptomic profiling. This study revealed a RNA signature defining a pancreatic cancer molecular gradient (PAGM), which is highly predictive of patient overall survival, and is more accurate than histological analysis. This signature was validated in three independent series of PDAC representing a total 679 patients, including 60 advanced patients from the BACAP cohort (NCT02818829) for which tumor materiel was obtained for the diagnostic EUS-FNA biopsy. This procedure allows the PAGM signaure to be obtained using minute amounts of tumor material, thus extending the applicability of this signature to all forms of samples obtainable from Pancreatic Ductal Adeno Carcinoma.

The PAGM signature is graded, as opposed to currently used classification which are binary, which significantly improves PDAC characterization with a higher prognostic value that is highly reproducible.

Interestingly, the PAMG signature is also predictive of the response to the chemotherapeutic protocol mFOLFIRINOX, which is the most commonly used regimen for pancreatic cancer chemotherapy. Hence, this transcriptomics signature will inform clinicians about the sensitivity of each patient to this treatment a priori, and thus allow them to make an informed decision about which drug to administer to each patient. This will avoid unnecessary treatment, side effects and costs.

Reference :
Establishment of a Pancreatic Adenocarcinoma Molecular Gradient (PAMG) that predicts the clinical outcome of pancreatic cancer
Nicolle R, Blum Y, Duconseil P, Vanbrugghe C, Brandone N, Poizat F, Roques J, Bigonnet M, Gayet O, Rubis M, Elarouci N, Armenoult L, Ayadi M, de Reynies A, Giovannini M, Grandval P, Garcia S, Canivet C, Cros J, Bournet B, Buscati L, BACAP Consortium, Moutardier V, Gilabert M, Iovanna J & Dusetti N.
EBioMedicine 2020, Jul;57:102858.
doi: 10.1016/j.ebiom.2020.102858
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334821/