Background Pancreatic cancer remains a fatal disease. Experimental systems are needed for personalized treatment strategies, drug testing and to further understand tumor biology. Cell cultures can serve as an excellent preclinical platform, but their generation remains challenging. Methods Tumor cells from surgically removed pancreatic ductal adenocarcinoma (PDAC) specimens were cultured under novel protocols. Cellular growth and composition were analyzed and culture conditions were continuously optimized. Characterization of cell cultures and primary tumors was performed via hematoxylin and eosin (HE) and immunofluorescence (IF) staining. Results Protocols for two- and three-dimensional PDAC primary cell cultures could successfully be established. Primary cell culture depended on dissociation techniques, growth factor supplementation and extracellular matrix components containing Matrigel being crucial for the transformation to three-dimensional PDAC organoids. The generated cultures showed to be highly resemblant to established PDAC primary cell cultures. HE and IF staining for cell culture and corresponding primary tumor characterization could successfully be performed. Conclusions The work presented herein shows novel and effective methods to successfully establish primary PDAC cell cultures in a distinct time frame. Factors contributing to cell growth and differentiation could be identified with important implications for further primary cell culture protocols. The established protocols might serve as novel tools in personalized tumor therapy.

Background In the GeparNuevo trial, the PD-L1 inhibitor durvalumab increased the rate of pathologic complete response (pCR; ypT0 ypN0) in triple-negative breast cancer if treatment started in a two-week window before neoadjuvant taxane/anthracycline chemotherapy (61 % pCR vs. 41%; p = 0.048; Loibl et al. ASCO 2018). Overall, pCR rates increased only numerically from 53 % to 44 % (p = 0.281). Herein, we aimed to evaluate the predictive value of PD-L1 immunohistochemistry in pre-therapeutic core biopsies. In addition, we identified dynamics in gene expression using repeated biopsies. Patients and Methods 174 patients were randomized to receive durvalumab or placebo with neoadjuvant chemotherapy. In the window part, 117 patients received a single dose of durvalumab (or placebo) before chemotherapy. Core biopsies were taken at three times: pre-treatment (“A”; N=174), after the window part (“B”; N=88) and after 12 weeks of nab-Paclitaxel (“C”; N=33). PD-L1 immunohistochemistry in A-biopsies (Ventana SP263 Assay) was recorded as percentage of cells with membranous staining in tumor cells and lymphocytes (TILs). We defined a tumor as PD-L1 high if ≥ 25 % of either compartment was stained. Ki-67 was stained on all available A, B and C biopsies (MIB-1, Dako, 1:100) and recorded as the percentage of tumor cells with nuclear staining. We profiled all available biopsies with targeted RNASeq using the HTG EdgeSeq platform (Oncology Biomarker panel, 2560 genes). Sequencing (IonTorrent S5) was successful in 162 A-, 79 B- and 31 C-biopsies. Results PD-L1 expression was high in 24 % of A-biopsies and was predictive for pCR in the complete cohort (OR 2.561; 1.183-5.554; p = 0.017). PD-L1 status of the TILs, but not of the tumor cells, was predictive (OR 1.313; 1.040-1.656; P= 0.022). The effect was not specific for durvalumab treatment. Higher levels of Ki-67 were predictive for pCR in B- biopsies in all patients (OR 1.399; 1.053-1.858; P =0.021) and in the placebo arm, but not in the durvalumab arm. Ki-67 levels in C-biopsies were not predictive; neither was the change in Ki-67 between pre-treatment and later time points (B vs. A or C vs. A). In a differential mRNA expression analysis (A vs. B), we found seven differentially expressed genes after one dose of durvalumab. We observed strong effects on gene expression after taxane treatment (A vs. C), but no significant difference according to treatment. These genes were associated with biological processes involved in therapy response. The pre-treatment levels of 12 of 69 markedly differentially expressed genes were associated with worse response to chemotherapy. Conclusion In A-biopsies, PD-L1 in TILs was predictive for response, and in B-biopsies, Ki-67 was predictive, but neither marker could specifically predict response to durvalumab. We observed limited effects of a single half-dose of durvalumab on global gene expression, but could identify substantial differential expression after taxane treatment. The evaluation of gene expression dynamic offers a promising approach for the identification of resistance-associated markers. The study was financially supported by AstraZeneca and Celgene Citation Format: Sinn BV, Loibl S, Karn T, Untch M, Kunze CA, Weber KE, Treue D, Wagner K, Hanusch CA, Klauschen F, Fasching PA, Huober J, Zahm D-M, Jackisch C, Thomalla J, Blohmer J-U, van Mackelenbergh M, Rhiem K, Felder B, von Minckwitz G, Burchardi N, Schneeweiss A, Denkert C. Pre-therapeutic PD-L1 expression and dynamics of Ki-67 and gene expression during neoadjuvant immune-checkpoint blockade and chemotherapy to predict response within the GeparNuevo trial [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr PD5-05.

Abstract Background: The GeparNuevo trial showed a numerical increase in the pCR rate to 53% vs 44%; p=0.281 compared to placebo in TNBC with the addition of the anti-PD-L1 antibody durvalumab to a neoadjuvant anthracycline-taxane containing chemotherapy (Loibl S et al. ASCO 2018). In a predefined subgroup analysis, a significant increase of the pCR rate was observed for patients that received durvalumab for 2 weeks alone prior to the start of chemotherapy in a window phase (61% vs 41%, p interaction=0.048), while the pCR rate was not increased for the subset of patients that did start durvalumab together with chemotherapy. Here we report the main results of the translational programme for GeparNuevo with a focus on mRNA signatures predictive for pCR in pretherapeutic core biopsies. Methods: A total of 162 baseline FFPE core biopsies were evaluable for expression of 2560 genes using the HTG EdgeSeq® system that combines a modified nuclease protection assay with next generation sequencing. Data was processed as recommended by the HTG and median transformed for further analyses. For differential gene expression analyses, the data was scale-normalized (TMM normalization; EdgeR package) and linear models were fit (limma package). Prior to these analyses, genes were filtered based on minimal expression (> 4) and variability (IQR > 1). As a first step, predefined immune-genes signature (TILs signature) (Denkert et al. JCO 2016) as well as IFN-gamma signatures were evaluated for correlation with pCR in logistic regression models. Subsequently, we performed a differential gene expression analysis according to therapy response for the durvalumab-arm and the placebo arm using the pre-filtered candidate genes. Gene names are not included in this abstract to allow filing of IP, but full gene names will be presented at the SABCS meeting. Results: The predefined TIL- and IFN-gamma signatures were associated with increased pCR rates in the complete cohort (TIL-signature: OR 1.44, 95% CI 1.15-1.82, p=0.002; IFN-Gamma-signature: OR 1.63, 95% CI 1.22-2.24, p=0.002) as well as in the durvalumab arm (p=0.012 and 0.042) and the placebo arm (p=0.050 and 0.011). These signatures were general pCR predictors without specificity for durvalumab response. Additional 44 genes were significantly (p<0.05) correlated with pCR in the durvalumab arm. Of those, 21 genes were upregulated and 23 genes were downregulated in pCR patients. 14 of the 21 upregulated genes are related to tumorbiologically relevant immune cell functions. A total of 6 of the 44 genes had a positive test for interaction (interaction p<0.05) with the therapy arm (durvalumab + NACT vs. placebo + NACT), suggesting that these genes might specifically predict response to durvalumab. Additional analyses investigating the role of molecular tumor subtypes, additional immune gene signatures and other subgroup analyses will be presented at the meeting. Conclusion: Our results show that specific immune-related gene expression signatures predict response to durvalumab in primary triple negative breast cancer. The trial was financially supported by Astra Zeneca and Celgene Citation Format: Loibl S, Sinn BV, Karn T, Untch M, Treue D, Sinn H-P, Weber KE, Hanusch CA, Fasching PA, Huober J, Zahm D-M, Jackisch C, Thomalla J, Blohmer J-U, Marmé F, Klauschen F, Rhiem K, Felder B, von Minckwitz G, Burchardi N, Schneeweiss A, Denkert C. mRNA signatures predict response to durvalumab therapy in triple negative breast cancer (TNBC)– Results of the translational biomarker programme of the neoadjuvant double-blind placebo controlled GeparNuevo trial [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr PD2-07.

Background: Pancreatic adenocarcinoma (PDAC) is a molecular heterogeneous disease, but clinically relevant genetic biomarkers are still missing. There are no data from prospective studies after curatively intended surgery and adjuvant chemotherapy so far. Methods: CONKO-001, was a prospective randomized phase III study and investigated the role of adjuvant gemcitabine (Gem) as compared to observation (Obs). Formaline-fixed paraffin-embedded tissue samples of 187 patients (pts) could be collected, of which 97 could be analysed after DNA-extraction by targeted next generation sequencing (NGS), using a predefined sequencing panel including mutation hotspot regions of 36 genes (ACTN4, ACVR1B, APC, ARID1A, ARID1B, ARID2, ATM, BRAF, CDK6, CDKN2A, CNDP2, CREBBP, CTNNB1, ERBB2, FGFR1, GATA6, KDM6A, KMT2C, KMT2D, KRAS, MAP2K4, MET, MYC. PBRM1, PIK3CA, PREX2, RNF43, RPA1, SF3B1, SMAD4, SMARCA2, SMARCA4, SOX9, STK11, TGFBR2, TP53). Mutational status was correlated with survival by fitting a cox proportional hazard model. Results: Patient's characteristics were balanced between Gem (n = 49) and Obs (n = 48) group. KRAS, TP53, SMAD4 mutation was found in 73% (Gem/Obs n = 33/38), 59% (n = 28/29), 8% (n = 4/4) of patients. KRAS/SMAD4 mutation status was not associated with (treatment-related) survival. TP53 mutation was identified as a negative prognostic factor for untreated patients: hazard ratio (HR) for disease free-survival (DFS) TP53 mutant vs TP53 wildtype 2.90 (95% CI 1.55 -5.41). Furthermore, TP53 mutation was found to be a positive predictive factor for Gem: HR for DFS Gem vs Obs in TP53 wildtype patients was 0.87 (95% CI 0.46-1.66) in comparison to TP53 mutated patients with a HR of 0.22 (95% CI 0.12-0.39). Test of TP53-by-treatment-interaction was statistically significant; p = 0.002. Conclusions: To the best of our knowledge, we present the first NGS data from a prospective clinical study in PDAC. In contrast to previous data, we could not identify KRAS or SMAD4 mutation as clinically relevant factors in primarily resectable PDAC. In CONKO-001, TP53 mutated patients had an unfavorable prognosis when randomized to Obs and profited strongly from adjuvant Gem, while adjuvant treatment did not significantly prolong DFS in TP53 wildtype patients. Clinical trial identification: ISRCTN34802808. Legal entity responsible for the study: Charite Universitätsmedizin Berlin, CONKO-study group. Funding: Charite Forschungsförderung, German Cancer Consortium (DKTK), Rahel-Hirsch grant. Disclosure: M. Sinn: Honoraria, Advisory board: Baxalta. Research funding: Leo Pharma. Travel support: Amgen. All other authors have declared no conflicts of interest.

Cancer medicine relies on the paradigm that cancer is an organ‐ and tissue‐specific disease, which is the basis for classifying tumors. With the extensive genomic information now available on tumors it is possible to conduct analyses to reveal common genetic features across cancer types and to explore whether the established anatomy‐based tumor classification is actually reflected on the genetic level, which might provide important guides to new therapeutic directions. Here, we have conducted an extensive analysis of the genetic similarity of tumors from 14 major cancer entities using somatic mutation data from 4,796 cases available through The Cancer Genome Atlas (TCGA) based on all available genes as well as different cancer‐related gene sets. Our analysis provides a systematic account of the genetic similarity network for major cancer types and shows that in about 43% of the cases on average, tumors of a particular anatomic site are genetically more similar to tumors from different organs and tissues (trans‐similarity) than to tumors of the same origin (self‐similarity). The observed similarities exist not only for carcinomas from different sites but are also present among neoplasms from different tissue origin, such as melanoma, acute myeloid leukemia, and glioblastoma. The current WHO cancer classification is therefore reflected on the genetic level by only about 57% of the tumors. These results provide a rationale to reconsider organ‐ and tissue‐specificity in cancer and contribute to the discussion about whether personalized therapies targeting specific genetic alterations may be transferred to cancers from other anatomic sites with similar genetic properties.