A transcriptomic signature to predict adjuvant gemcitabine sensitivity in pancreatic adenocarcinoma

Nicolle, Rémy; Gayet, Odile; Duconseil, Pauline; Vanbrugghe, Charles; Roques, Julie; Bigonnet, Martin; Blum, Yuna; Elarouci, Nabila; Armenoult, Lucile; Ayadi, Mira; Reyniès, Aurélien de; Puleo, Francesco; Augustin, Jérémy; Émile, Jean-François; Svrcek, Magali; Arsenijević, Tatjana; Hammel, Pascal; Giovannini, Marc; Grandval, Philippe; Dahan, Laétitia; Moutardier, Vincent; Gilabert, Marine; Laethem, Jean Luc Van; Bachet, Jean‐Baptiste; Cros, Jérôme; Iovanna, Juan; Dusetti, Nelson

doi:10.1016/j.annonc.2020.10.601

Cited by 57 publications

(67 citation statements)

References 48 publications

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“…Moreover, our results also highlight the fact that it is vital to perform a proper transcriptomic characterization to maintain the chemosensitivity traceability and correctly interpret results. In agreement with us, previous studies on gemcitabine [30], carfilzomib [24] and folfirinox [15] have shown similar findings, unraveling complex multigene signatures to predict chemosensitivity tuning the final output through a cross-model validation approach. This work suggests that inter-model transcriptomic harmonization following the chemosensitivity profile is a key factor in determining the clinical translatability of results obtained with these models.…”

Section: Discussionsupporting

confidence: 90%

Exploring the Complementarity of Pancreatic Ductal Adenocarcinoma Preclinical Models

Hoare

Fraunhoffer

El-Kaoutari

et al. 2021

Cancers

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Purpose: Compare pancreatic ductal adenocarcinoma (PDAC), preclinical models, by their transcriptome and drug response landscapes to evaluate their complementarity. Experimental Design: Three paired PDAC preclinical models—patient-derived xenografts (PDX), xenograft-derived pancreatic organoids (XDPO) and xenograft-derived primary cell cultures (XDPCC)—were derived from 20 patients and analyzed at the transcriptomic and chemosensitivity level. Transcriptomic characterization was performed using the basal-like/classical subtyping and the PDAC molecular gradient (PAMG). Chemosensitivity for gemcitabine, irinotecan, 5-fluorouracil and oxaliplatin was established and the associated biological pathways were determined using independent component analysis (ICA) on the transcriptome of each model. The selection criteria used to identify the different components was the chemosensitivity score (CSS) found for each drug in each model. Results: PDX was the most dispersed model whereas XDPO and XDPCC were mainly classical and basal-like, respectively. Chemosensitivity scoring determines that PDX and XDPO display a positive correlation for three out of four drugs tested, whereas PDX and XDPCC did not correlate. No match was observed for each tumor chemosensitivity in the different models. Finally, pathway analysis shows a significant association between PDX and XDPO for the chemosensitivity-associated pathways and PDX and XDPCC for the chemoresistance-associated pathways. Conclusions: Each PDAC preclinical model possesses a unique basal-like/classical transcriptomic phenotype that strongly influences their global chemosensitivity. Each preclinical model is imperfect but complementary, suggesting that a more representative approach of the clinical reality could be obtained by combining them. Translational Relevance: The identification of molecular signatures that underpin drug sensitivity to chemotherapy in PDAC remains clinically challenging. Importantly, the vast majority of studies using preclinical in vivo and in vitro models fail when transferred to patients in a clinical setting despite initially promising results. This study presents for the first time a comparison between three preclinical models directly derived from the same patients. We show that their applicability to preclinical studies should be considered with a complementary focus, avoiding tumor-based direct extrapolations, which might generate misleading conclusions and consequently the overlook of clinically relevant features.

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Section: Discussionsupporting

confidence: 90%

Exploring the Complementarity of Pancreatic Ductal Adenocarcinoma Preclinical Models

Hoare

Fraunhoffer

El-Kaoutari

et al. 2021

Cancers

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“…Lastly, another important point to note is that genes associated with sensitivity or resistance to the drugs are not typically genes associated with survival, indicating that outcome and drug sensitivity are regulated by independent mechanisms [123] . This observation was recently confirmed in a larger cohort [ 20 , 125 ]. More recently, similar observations were published using PDAC organoids as models [ 55 , 126 ], indicating that the transcriptome may be used to discriminate sensitive and resistant patients.…”

Section: Chemogramssupporting

confidence: 57%

“…In other words, very classical or very basal-like subtypes are mainly composed by pure cells, whereas the intermediate subtype is the consequence of a mix of classical and basal-like subtypes and/or an intermediate phenotype. Clinically relevant is that stratification of PDAC cells, based on a simple RNA signature, could predict the response to mFOLFIRINOX [ 20 , 22 ], Carfilzomib [131] or Gemcitabine [125] . We assume that the intratumoral heterogeneity, in which sensitive cells coexist with resistanes ones, is responsible, at least in part, of the recurrence of the traitements.…”

Section: Increasing Models With Translational Relevancementioning

confidence: 99%

Implementing biological markers as a tool to guide clinical care of patients with pancreatic cancer

Iovanna

2021

Translational Oncology

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Highlights A major obstacle for the effective treatment of PDAC is its molecular heterogeneity. Stratification of PDAC using markers highly specific, reproducible, sensitive, easily measurable and inexpensive is necessary. At the early stages, clinician’s priority lies in rapid diagnosis, so that the patient receives surgery without delay. At advanced disease stages, priority is to determine the tumor subtype and select a suitable effective treatment.

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“…In addition, combining transcriptomic data with genomic sequencing, mutational landscape, immune infiltrate or genetic alteration can identify additional subtypes with clinical relevance (Bailey et al, 2016;Connor et al, 2017;Brunton et al, 2020;Rashid et al, 2020). More importantly than predicting patient prognosis and disease aggressiveness, recent studies find that transcriptomic data is also good at predicting chemotherapy sensitivity for PDAC (Deng et al, 2020;Nicolle et al, 2021;Nishiwada et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Several genes participating in drug uptake and metabolism have emerged as powerful predictors of drug sensitivity (Bird et al, 2017;Raffenne et al, 2019;Okamura et al, 2020). Recently, with the adventure of high throughput sequencing and bioinformatic technology, more and more gene expression signatures have been identified to evaluate drug sensitivity in PDAC (Kaissis et al, 2019;Clayton et al, 2020;Piquemal et al, 2020;Nicolle et al, 2021;Nishiwada et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

An EMT-Related Gene Signature for Predicting Response to Adjuvant Chemotherapy in Pancreatic Ductal Adenocarcinoma

Feng

Lou

et al. 2021

Front. Cell Dev. Biol.

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BackgroundFor pancreatic ductal adenocarcinoma (PDAC) patients, chemotherapy failure is the major reason for postoperative recurrence and poor outcomes. Establishment of novel biomarkers and models for predicting chemotherapeutic efficacy may provide survival benefits by tailoring treatments.MethodsUnivariate cox regression analysis was employed to identify EMT-related genes with prognostic potential for DFS. These genes were subsequently submitted to LASSO regression analysis and multivariate cox regression analysis to identify an optimal gene signature in TCGA training cohort. The predictive accuracy was assessed by Kaplan–Meier (K-M), receiver operating characteristic (ROC) and calibration curves and was validated in PACA-CA cohort and our local cohort. Pathway enrichment and function annotation analyses were conducted to illuminate the biological implication of this risk signature.ResultsLASSO and multivariate Cox regression analyses selected an 8-gene signature comprised DLX2, FGF9, IL6R, ITGB6, MYC, LGR5, S100A2, and TNFSF12. The signature had the capability to classify PDAC patients with different DFS, both in the training and validation cohorts. It provided improved DFS prediction compared with clinical indicators. This signature was associated with several cancer-related pathways. In addition, the signature could also predict the response to immune-checkpoint inhibitors (ICIs)-based immunotherapy.ConclusionWe established a novel EMT-related gene signature that was capable of predicting therapeutic response to adjuvant chemotherapy and immunotherapy. This signature might facilitate individualized treatment and appropriate management of PDAC patients.

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A transcriptomic signature to predict adjuvant gemcitabine sensitivity in pancreatic adenocarcinoma

Cited by 57 publications

References 48 publications

Exploring the Complementarity of Pancreatic Ductal Adenocarcinoma Preclinical Models

Exploring the Complementarity of Pancreatic Ductal Adenocarcinoma Preclinical Models

Implementing biological markers as a tool to guide clinical care of patients with pancreatic cancer

An EMT-Related Gene Signature for Predicting Response to Adjuvant Chemotherapy in Pancreatic Ductal Adenocarcinoma

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