After surgical resection of pancreatic ductal adenocarcinoma (PDAC), patients are predominantly treated with adjuvant chemotherapy, commonly consisting of gemcitabine (GEM)-based regimens or the ...modified FOLFIRINOX (mFFX) regimen. While mFFX regimen has been shown to be more effective than GEM-based regimens, it is also associated with higher toxicity. Current treatment decisions are based on patient performance status rather than on the molecular characteristics of the tumor. To address this gap, the goal of this study was to develop drug-specific transcriptomic signatures for personalized chemotherapy treatment.
We used PDAC datasets from preclinical models, encompassing chemotherapy response profiles for the mFFX regimen components. From them we identified specific gene transcripts associated with chemotherapy response. Three transcriptomic artificial intelligence signatures were obtained by combining independent component analysis and the least absolute shrinkage and selection operator-random forest approach. We integrated a previously developed GEM signature with three newly developed ones. The machine learning strategy employed to enhance these signatures incorporates transcriptomic features from the tumor microenvironment, leading to the development of the ‘Pancreas-View’ tool ultimately clinically validated in a cohort of 343 patients from the PRODIGE-24/CCTG PA6 trial.
Patients who were predicted to be sensitive to the administered drugs (n = 164; 47.8%) had longer disease-free survival (DFS) than the other patients. The median DFS in the mFFX-sensitive group treated with mFFX was 50.0 months stratified hazard ratio (HR) 0.31, 95% confidence interval (CI) 0.21-0.44, P < 0.001 and 33.7 months (stratified HR 0.40, 95% CI 0.17-0.59, P < 0.001) in the GEM-sensitive group when treated with GEM. Comparatively patients with signature predictions unmatched with the treatments (n = 86; 25.1%) or those resistant to all drugs (n = 93; 27.1%) had shorter DFS (10.6 and 10.8 months, respectively).
This study presents a transcriptome-based tool that was developed using preclinical models and machine learning to accurately predict sensitivity to mFFX and GEM.
•Transcriptomic signatures were developed for key pancreatic cancer drugs to enable personalized treatment.•The Pancreas-View tool integrates four drug signatures to assist informed therapeutic decisions.•Signatures accurately identify high responder patients, indicative of improved DFS and cancer-specific survival.•Clinical validation involving a cohort of 343 patients confirms the efficacy of this signature approach.•Transcriptomic signatures that integrate predictors from preclinical models and machine learning offer a rationalized treatment strategy.
•Most patient with pancreatic cancer are treated by chemotherapy.•Treatments selection are not personalized on the tumor characteristics.•Signatures predicting chemotherapy efficiency are essential ...for personalizing treatments.•An RNA signature of gemcitabine-sensitivity is developed leveraged on the dissimilarities between 2D and 3D in vitro models.•Combining different in vitro models can help in defining clinically efficient transcriptomic signatures.
Pancreatic ductal adenocarcinoma (PDAC) patients are frequently treated by chemotherapy. Even if personalized therapy based on molecular analysis can be performed for some tumors, PDAC regimens selection is still mainly based on patients' performance status and expected efficacy. Therefore, the establishment of molecular predictors of chemotherapeutic efficacy could potentially improve prognosis by tailoring treatments. We have recently developed an RNA-based signature that predicts the efficacy of adjuvant gemcitabine using 38 PDAC primary cell cultures. While demonstrated its efficiency, a significant association with the classical/basal-like PDAC spectrum was observed. We hypothesized that this flaw was due to the basal-like biased phenotype of cellular models used in our strategy. To overcome this limitation, we generated a prospective cohort of 27 consecutive biopsied derived pancreatic organoids (BDPO) and include them in the signature identification strategy. As BDPO's do not have the same biased phenotype as primary cell cultures we expect they can compensate one with each other and cover a broader range of molecular phenotypes. We then obtained an improved signature predicting gemcitabine sensibility that was validated in a cohort of 300 resected PDAC patients that have or have not received adjuvant gemcitabine. We demonstrated a significant association between the improved signature and the overall and disease-free survival in patients predicted as sensitive and treated with adjuvant gemcitabine. We propose then that including BDPO along primary cell cultures represent a powerful strategy that helps to overcome primary cell cultures limitations producing unbiased RNA-based signatures predictive of adjuvant treatments in PDAC.
Abstract
BACKGROUND
GB are highly aggressive tumors which systematically relapse. Our objective was to identify disease progression mechanisms and genomic drivers of GB treatment resistance.
MATERIAL ...AND METHODS
Ten paired frozen tumors from initial and recurrent surgery after RTCT were screened by CGH Array. Next, NGS of the selected genes was performed on 19 paired tumors (38 samples). Molecular alterations were correlated with patient data. TCGA was used to characterize the molecular profile of MPDZ.
RESULTS
Nineteen IDHwt GB patients with a median age of 54.5 years (37.2–72.8) were included. Using CGH array, unsupervised analysis clustered the whole samples by paired of initial and recurrent tumors. However only 44% of CGH Array alterations were shared between initial and recurrent tumors (amplifications: 55%; deletions: 30%). The new alterations detected at relapse were amplifications in 25% and deletions in 23% of tumors. Two regions corresponding to 171 genes were lost at relapse (p=0.03): 19q13.33 and 19q13.41. Using DAVID genome, 3/171 genes (related to neutrophil chemotactic factors) were identified: FPR1, FPR2, FPR3. Moreover, 24 genes were lost (including MPDZ) and 2 genes were gained in 20% of recurrent tumors. Totally, 29 genes were analyzed by NGS and 4 genes showed pathogenic mutations shared by initial and recurrent tumors: FPR2, REL, TYRP1 and MPDZ. Only MPDZ showed, at relapse, an increasing rate of mutated variants and a new mutation affecting the splicing site. These alterations were independent from classical prognostic factors (age, sexe, karnofsky performans status, MMS and MGMT status) and from patient survivals. To explore MPDZ expression, we used TCGA initial dataset and observed that a lower RNA expression of MPDZ was associated with IDHwt (p<0.001) and grade IV (p<0.001) gliomas, reinforcing the potential pejorative impact of MPDZ loss.
CONCLUSION
Our results suggest that MPDZ is frequently altered at initial diagnosis with increased alterations in recurrent IDHwt GB after RTCT, suggesting that MPDZ impairment could contribute to the resistance/relapse mechanisms. Further investigations are needed to validate these results. Our results suggest that MPDZ is frequently altered at initial diagnosis with increased alterations in recurrent IDHwt GB after RTCT, suggesting that MPDZ impairment could contribute to the resistance/relapse mechanisms. Further investigations are needed to validate these results.
Nivolumab is a standard of care in patients (pts) with metastatic clear cell renal cell carcinoma (mccRCC) after failure of prior anti-angiogenic tyrosine-kinase inhibitors (TKIs). We evaluated the ...impact of corticosteroids (CS) during nivolumab in pts with mccRCC as part of a prospective clinical trial.
We conducted an ancillary study of the GETUG-AFU 26 NIVOREN study (NCT03013335), a multicenter prospective phase II safety study of nivolumab in mccRCC after progression on anti-angiogenic TKIs. Patients receiving CS at nivolumab initiation were excluded. Overall survival (OS) and progression free survival (PFS) of pts exposed to CS (≥ 10mg of prednisone equivalents) or not during nivolumab were assessed. To overcome immortal time bias, we used two different landmark analysis methods. We first excluded pts who progressed or died before specified landmark timepoints (12 and 16 weeks). In a second method, patients treated with CS before landmark timepoints (12 and 16 and 24 weeks) were used to evaluate the effect of an early exposition to CS.
Among the 665 evaluable pts, with a median follow up of 23.9 months, 113 (17 %) were exposed to CS during nivolumab, mainly to treat immune-related adverse events of any grade (74%). Other indications included infections (15%), complications of radiotherapy and chronic obstructive pulmonary disease. Median time to the first CS treatment was 21.6 weeks. Using a landmark at 12 weeks, OS rate at 12 months were 85.6% and 73.5% in pts exposed or not to CS hazard ratio (HR), 0.57; p=0.0017. PFS rate at 12 months were 61.1% and 41.6% in pts exposed or not to CS (HR, 0.63; p=0.0065). Landmark analyses at 16 weeks showed similar results. With the second landmark method, no differences in PFS or OS were observed between groups at 12 and 16 weeks. With a landmark set at 24 weeks, OS was similar in pts exposed or not to CS (HR, 1.14; p=0.55).
The use of CS during nivolumab in mccRCC is not associated with a detrimental effect on survival outcomes. The positive association of corticosteroid use for irAEs with outcomes was not confirmed by second landmark modalities. Immortal time bias should be carefully considered when studying time-dependent variable.
NCT03013335.
UNICANCER.
Bristol-Myers Squibb.
M. Gross-Goupil: Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: BMS; Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: MSD; Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: Roche; Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: Ipsen; Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: Pfizer; Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: Novartis. B. Laguerre: Honoraria (self), Travel / Accommodation / Expenses: BMS; Honoraria (self), Travel / Accommodation / Expenses: Pfizer; Honoraria (self): Novartis; Honoraria (self): Ipsen. P. Barthelemy: Honoraria (self), Advisory / Consultancy: BMS; Honoraria (self), Advisory / Consultancy: Pfizer; Honoraria (self), Advisory / Consultancy: Ipsen; Honoraria (self), Advisory / Consultancy: Novartis; Honoraria (self), Advisory / Consultancy: Roche; Honoraria (self), Advisory / Consultancy: MSD; Honoraria (self), Advisory / Consultancy: Janssen-Cilag; Honoraria (self), Advisory / Consultancy: Sanofi. S. Negrier: Honoraria (self): Pfizer; Honoraria (self): Bms; Honoraria (self): Novartis; Honoraria (self): IPSEN; Honoraria (self): Euspharma. L. Geoffrois: Honoraria (self), Travel / Accommodation / Expenses: MSD; Honoraria (self), Travel / Accommodation / Expenses: BMS; Honoraria (self): Ipsen; Honoraria (self): Novartis; Travel / Accommodation / Expenses: Merck. S. Ladoire: Advisory / Consultancy, Travel / Accommodation / Expenses: BMS. M. Laramas: Advisory / Consultancy, Speaker Bureau / Expert testimony: AstraZeneca; Advisory / Consultancy, Speaker Bureau / Expert testimony, Travel / Accommodation / Expenses: SANOFI; Speaker Bureau / Expert testimony, Travel / Accommodation / Expenses: Pfizer; Speaker Bureau / Expert testimony: BMS; Speaker Bureau / Expert testimony: Amgen; Speaker Bureau / Expert testimony: IPSEN; Speaker Bureau / Expert testimony: Janssen; Travel / Accommodation / Expenses: Eisai. S. Oudard: Honoraria (self), Advisory / Consultancy, Speaker Bureau / Expert testimony, Research grant / Funding (institution), Travel / Accommodation / Expenses: Bayer; Honoraria (self), Advisory / Consultancy, Speaker Bureau / Expert testimony, Research grant / Funding (institution), Travel / Accommodation / Expenses: Pfizer; Honoraria (self), Advisory / Consultancy, Speaker Bureau / Expert testimony, Research grant / Funding (institution), Travel / Accommodation / Expenses: Novartis; Honoraria (self), Advisory / Consultancy, Speaker Bureau / Expert testimony, Research grant / Funding (institution), Travel / Accommodation / Expenses: BMS; Honoraria (self), Advisory / Consultancy, Speaker Bureau / Expert testimony, Research grant / Funding (institution), Travel / Accommodation / Expenses: MSD; Honoraria (self), Advisory / Consultancy, Speaker Bureau / Expert testimony, Research grant / Funding (institution), Travel / Accommodation / Expenses: Ipsen. B. Escudier: Advisory / Consultancy, Research grant / Funding (institution), Travel / Accommodation / Expenses: BMS; Advisory / Consultancy, Research grant / Funding (institution), Travel / Accommodation / Expenses: Pfizer; Advisory / Consultancy, Research grant / Funding (institution): Novartis; Advisory / Consultancy: Roche; Advisory / Consultancy: Ipsen; Advisory / Consultancy: Eusa; Advisory / Consultancy, Research grant / Funding (institution): Avevo; Advisory / Consultancy, Research grant / Funding (institution): Pfizer. L. Albiges: Advisory / Consultancy, Compensated to institution: Pfizer; Advisory / Consultancy, Compensated to institution: Novartis; Advisory / Consultancy, Compensated to institution: BMS; Advisory / Consultancy, Compensated to institution: Ipsen; Advisory / Consultancy, Compensated to institution: Roche; Advisory / Consultancy, Compensated to institution: MSD; Advisory / Consultancy, Compensated to institution: AstraZeneca. All other authors have declared no conflicts of interest.
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