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An ever-increasing number of studies report the importance of the composition of the intestinal microbiota in the treatment of cancers. The mechanisms involved are varied. The reciprocal interaction of drugs with bacteria can have an effect on the efficacy as well as the toxicity of treatments. In addition, some species or strains are able to stimulate CD8+ T cell production and the effectiveness of immune checkpoint blockers (ICBs). As part of these treatments, there is therefore a potential interest in being able to isolate and cultivate bacterial species associated with the response to ICBs in different cancers. The objective is to better understand the mechanisms involved and possibly develop complementary therapeutic and/or diagnostic solutions. However, this approach is still limited by the fact that a large number of commensal bacterial species in the intestine are difficult to cultivate, due to specific nutritive requirements, extreme oxygen sensitivity (EOS), or under-representation in the whole bacterial population. In an attempt to circumvent these limitations, we developed flow cytometry (FCM) and cell sorting in anaerobic conditions. A series of polyclonal antibodies specific to commensal species known to be beneficial to the host such as Faecalibacterium prausnitzii, Akkermansia muciniphila, Christensenella minuta, and Eubacterium hallii have been produced and used in combination with viability and Gram staining to rapidly detect, sort, and culture species of interest. This strategy has enabled a rapid collection of strains to be built up from the stools of healthy volunteers, demonstrating the usefulness of the approach. Developments are under way to apply reverse genomics strategies to identify sequences encoding immunogenic proteins in order to produce antibodies even in the absence of already cultivated strains, which is still the case for approximately 70% of the human gut microbiota species. In conclusion, our work confirms that FCM is well adapted for complex bacterial microbiota studies. When used in conjunction with appropriate staining, it gives a general overview of microbiota composition and variations in longitudinal studies, including bacterial load, which is an important piece of information. In addition to sorting and cultivating target species of interest, it can be used for a quick and easy assessment of microbiota composition, either before starting a drug treatment or for monitoring the effects of the treatment on commensal microbiota. Citation Format: Samuel Bellais, Mélanie Nehlich, Aurore Duquenoy, Maryne Ania, Jan Baijer, Ilia Belotserkovsky, Vincent Thomas. Flow cytometry for targeted culturomics of gut commensal species and rapid overview of microbiota composition [abstract]. In: Proceedings of the AACR Special Conference on the Microbiome, Viruses, and Cancer; 2020 Feb 21-24; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2020;80(8 Suppl):Abstract nr B14.

A key goal in immuno-oncology is the identification of tumor antigens recognized by CD8 T cells. Immune modulators such as PD-1 inhibitors indirectly promote T-cell attack against tumor antigens, and may be augmented by antigen-directed therapeutic immunization or adoptive cell therapy to increase clinical benefit. Such personalized therapeutics require accurate antigen identification from patient samples, which remains elusive today. Methods: We generated the largest reported dataset of human tumor transcriptomes and HLA class I peptidomes (N=111) from specimens of multiple tumor types. We used these data to train a deep learning model of HLA peptide presentation for antigen prediction. Our model architecture addressed two key challenges: (1) learning HLA-allele-specific models from tumor data where each sample expressed up to 6 unique HLA class I alleles and (2) incorporating information about all aspects of HLA presentation, including gene expression, proteasomal processing and stable binding of peptides to HLA. We evaluated the performance of the model on two independent test datasets. First, we tested the model on HLA presented peptides from five held-out tumor mass spectrometry samples. Then, to establish that accurate prediction of HLA presentation translates to prediction of antigens recognized by T cells in vivo, we compiled from three published studies a dataset of >2,000 mutations in 16 patients, with 23 mutations (i.e. neoantigens) recognized by PD1+ PBMC or TIL CD8 T cells. Recognition of a peptide by TIL or activated peripheral T-cells implies not only tumor presentation of the peptide, but also its ability to prime T cell responses, and thus represents the most stringent test of tumor antigen prediction. Results: The model demonstrated a breakthrough in prediction accuracy. On the mass spectrometry test data, it achieved a >10-fold improvement in positive predictive value (PPV) vs standard HLA binding affinity (~50% PPV at 40% recall for the MS-based model vs ~5% PPV for binding affinity prediction). On the T-cell test dataset, the model ranked T-cell recognized neoantigens on average >4-fold higher than standard prediction (median rank ~7 for the MS-based model vs ~30 for binding affinity). When selecting candidate neoantigens for hypothetical 10-neoantigen personalized immunotherapy, the model prioritized at least 1 T-cell recognized neoantigen in the top 10 for 9/11 patients with neoantigen responses vs 3/11 for binding affinity. For a hypothetical 20-neoantigen immunotherapy, the model correctly selected the majority (16/23, 70%) of recognized neoantigens. Conclusion: We used the largest dataset of tumor transcriptomes and HLA peptidomes reported to-date to train a deep learning model of HLA epitope presentation. The new model significantly outperforms state of the art methods, and has sufficient predictive accuracy for in silico antigen selection for personalized cancer immunotherapy. Citation Format: Brendan Bulik-Sullivan, Jennifer Busby, Matthew Davis, Lauren Young, Tyler Murphy, Andrew Clark, Fujiko Duke, Michele Busby, Adnan Derti, Mojca Skoberne, Karin Jooss, Corinne E. Gustafson, Assunta De Rienzo, William G. Richards, Nhien T. Dao, Hyeong R. Kim, Jamie E. Anderson, Chang-Min Choi, Vincent De Montpreville, Se Jin Jang, Olaf Mercier, Raphael Bueno, Elie Fadel, Joshua Francis, Roman Yelensky. Antigen identification for cancer immunotherapy by deep learning on tumor HLA peptides [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5722.

CovIsoLink™ (Covalently Isopeptide crosslinking) relates to methods for enzymatic covalently coupling drugs and other compounds through transglutaminase site specific generated in the targeted proteins including, polypeptides, proteins and immunoglobulins (patent pending1). Transglutaminases (TGases) catalyze covalent cross- linking of specific glutamine residues to the primary amine of peptide-bound lysine residues or primary amines of other compounds such as polyamines2. Using an in house peptide library and the transglutaminase colorometric assay3, we identified several amino acid sequences that were recognized as glutamine donor substrates. The optimum peptide sequences were selected and we further confirmed that these sequences have improved affinity compared with the conventional small peptides Z-QG. In different experiments we engineered Fc-containing polypeptide at the C-terminal domain and showed that mTG incorporates with high efficiency several types of amine donors to the engineered antibodies. CovIsoLink™ is now used to develop new antibody drug conjugates (ADCs) since the major advantage of this method is to obtain a homogenous immunoconjugate with uniform stoichiometry. We developed and characterized different recombinant anti Her2 IgG1 Mab carrying optimized enzymatic substrates (tag) by genetic insertion in the coding sequence of MAb. We then evaluated the best linkers and conformation to incorporate different compounds through bacterial Transglutaminase (mTG) enzymatic reaction. We set up experimental conditions, production, purification, HPLC/MS analysis and control of the immunoreactivity of CovIsoLink™ Her2-ADC. Using mTG, we obtained site specific conjugation of different modified drugs with optimized linker on the anti Her2 IgG1 antibody. By HIC analysis, we validated a specific and reproducible DAR reaching DAR2 depending on drugs and experimental conditions. In vitro and In vivo characterization and dose response studies of CovIsolink-ADC specificity and reactivity are currently performed in Her2 positive models demonstrating an improved efficacy by comparison with Kadcyla (T-DM1).1) PCT/EP2014/0792278, 2) El Alaoui S et al, Int J Cancer, 1991; 3) Perez AM et al, Anal Biochem, 2009. Citation Format: Eva Sivado, Vincent Thomas, Meddy El Alaoui, Anne-Catherine Jallas, Mike R. Dyson, John McCafferty, Said El Alaoui, Sandrine Valsesia-Wittmann. CovisoLinK: New bacterial transglutaminase Q-Tag substrate for the development of site specific antibody-drug conjugates [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 752.

Background: Osteopontin and thrombospondin-1 are extracellular matrix proteins secreted by both stromal and tumor cells. These proteins may play a key role in the tumor microenvironment for cancer development and metastasis. This study focused on the prognostic value of circulating osteopontin and thrombospondin-1 in primary resected NSCLC patients. Methods: Between January and December of 2012, serums were collected from 171 patients prior to primary surgery and from age-matched cancer-free individuals (n = 20). At baseline, circulating osteopontin and thrombospondin-1 levels were measured by ELISA. Tissue protein expression in primary tumor samples was determined by immunohistochemical analysis. Results: High levels of serum osteopontin and thrombospondin-1 levels were found in the cohort study. The difference between patients and healthy individuals was statistically significant (p Conclusions: Baseline osteopontin and thrombospondin-1 serum levels, and even more importantly their combination, can serve as strong independent prognostic factors to predict overall survival in primary resected NSCLC patients, and therefore reflect the aggressiveness of the tumor. Citation Format: Mathieu Rouanne, Julien Adam, Aïcha Goubar, Angélique Robin, Caroline Ohana, Emilie Louvet, Jiemin Cormier, Olaf Mercier, Peter Dorfmüller, Soly Fattal, Vincent Thomas de Montpreville, Elie Fadel, Benjamin Besse, Ken André Olaussen, Christian Auclair, Jean-Charles Soria. Baseline serum levels of osteopontin and thrombospondin-1 predict shorter overall survival in primary resected non-small cell lung cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1548. doi:10.1158/1538-7445.AM2015-1548

Background CDKN2A (9p21 locus) and NF2 (22q locus) genes are inactivated in most Malignant Pleural Mesotheliomas (MPM). Few other recurrent molecular abnormalities have been described as well. A better understanding of the molecular pathways is needed to identify potential additional driver genes. We did an integrated analysis of chromosomal copy number variations from MPM specimens and cells lines. Patients and Methods In order to describe genomic profiles, we performed microarray Comparative Genomic Hybridization (CGH) (Agilent 400K). Analyses were carried out using genomic DNA samples extracted from 26 MPM primary tumours (frozen sections) and 5 MPM cell lines (Four ATCC cell lines and one cell line established in our laboratory from a pleural effusion). Subsequently the results were correlated with the baseline characteristics of the patients. Results The mean age of the patients was 60 years (30-81). The sex ratio (M/F) and the histologic epithelioid type were 1.6 and 69% respectively. Our results showed that MPM are characterized by a complex pattern of genetic changes where losses of genomic regions are more frequent than gains. Common losses (30-70%) were clustered at chromosomal regions: 1p36.13, 1p13.3, 1q21.3-1q24.2, 1p22.3-1p12 (41%) (NRAS gene), 1p36.33-1q21.2 (30%) (JUN gene) 3p22.1-3p14.2 (59%) (BAP1, RASSF1 genes), 4q13.2, 6p21.32, 6q14.1-6q27 (63%) (ROS1 gene), 7q36.1, 8p23.1, 9p24.1, 9p22.1-9p21 (52%) (CDKN2A gene), 10p11.1-10q26.3 (30%) (RET, PTEN genes), 11q25, 14q11.2, 14q32.33, 15q11.2, 15q14, 22q11.22-22q13.33 (63%) (NF2 gene). Common gains (30-50%) were located at chromosomal regions 1q44, 8p11.22, 11q11, 12p13.31, 15q11.2, 16p11, 20p13, 22q11.23, Xq22.2. The most common deletions (D) and amplifications (A) (>20%) defined as having a log2(ratio) ≤-1.0 and ≥1.0 respectively were located at chromosomal regions: 1p31.1 (A, D), 1q21, 1q24.2 (D), 6p21.32, 6q16.3, 6q27 (D), 7q22.1 (D), 8p11.22 (A), 9p21.3, 9q34.2 (D), 12p13.2 (D), 14q32.33 (D), 16q23.1, 16q22.1 (WWOX gene)(D), 20p13 (A, D), 22q11.23 (D). Higher frequency of genetic alterations was noted in cell lines than tumour specimens. Different patterns of genetic changes were observed according to gender, stage, histology, smoking history, asbestos exposure and performance status. Conclusion Our study confirmed the complexity pattern of genetic changes in MPM through high-resolution microarray CGH analysis. We found high number of chromosomal imbalances not yet well identified. Some correspond to known genes and others could be the areas of unknown genes involved in its development that we will try to identify thereafter. Moreover, identifying patient subgroups seems essential for having a more individualized therapy and personalized medicine in MPM (e.g. MOSCATO trial, ongoing in our cancer center). Citation Format: Jacques Raphael, Nicolas Dorvault, Cedric Orear, Bastien Job, Vincent Thomas de Montpreville, Francois Leroy-Ladurie, Philippe Girard, Pierre Validire, Gwenael Le Teuff, David Planchard. Chromosomal imbalances detected by microarray CGH in malignant pleural mesothelioma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1217. doi:10.1158/1538-7445.AM2013-1217