Research describing the immunological characterization of tumors and the identification of biomarkers associated with the response to immunotherapies underlying the platform ONCO-ND1 are described in some of the publications, largely joint between the groups of research by Prof. Michele Ceccarelli, Prof. Davide Bedognetti and Prof. Antonio Iavarone that follow:

• Roelands, J., W. Hendrickx, G. Zoppoli, R. Mall, M. Saad, K. Halliwill, G. Curigliano, D. Rinchai, J. Decock, LG Delogu, M. Ceccarelli, and D. Bedognetti (2020). Oncogenic states dictate the prognostic and predictive connotations of intratumoral immune response. Journal for immunotherapy of cancer 8 (1).
• Hendrickx, W., I. Simeone, S. Anjum, Y. Mokrab, F. Bertucci, P. Finetti, G. Curigliano, B. Seliger, L. Cerulo, S. Tomei, LG Delogu, C. Maccalli, E. Wang, LD Miller, FM Marincola, M. Ceccarelli , and D. Bedognetti (2017). Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis. OncoImmunology 6 (2) , e1253654.
• Zhang, J., FP Caruso, JK Sa, S. Justesen, D.-H. Nam, P. Sims, M. Ceccarelli, A. Lasorella, and A. Iavarone (2019). The combination of neoantigen quality and T lymphocyte infiltrates identifies glioblastomas with the longest survival. Communications Biology 2 (1), 135.
• Turan, T., S. Kongpachith, K. Halliwill, J. Roelands, W. Hendricks, FM Marincola, TJ Hudson, HJ Jacob, D. Bedognetti, J. Samayoa, and M. Ceccarelli. A balance score between immune stimulatory and suppressive microenvironments identifies mediators of tumor immunity and predicts pan-cancer survival. British Journal of Cancer (2020).
• Bedognetti D, Ceccarelli M, et al. Toward a comprehensive view of cancer immune responsiveness: a synopsis from the SITC workshop. Journal for Immunotherapy of Cancer. 2019 May 22; 7 (1): 131.A
• ngelova M, Mlecnik B, Vasaturo A, Bindea G, Fredriksen T, Lafontaine L, Buttard B, Morgand E, Bruni D, Jouret-Mourin A, Hubert C, Kartheuser A, Humblet Y, Ceccarelli M, Syed N, Marincola FM, Bedognetti D, Van den Eynde M, Galon J. Evolution of Metastases in Space and Time under Immune Selection. Cell 2018 Oct 18; 175 (3): 751-765.
• Bertucci F, Finetti P, Simeone I, Hendrickx W, Wang E, Marincola FM, Viens P, Mamessier E, Ceccarelli M, Birnbaum D, Bedognetti D. The immunologic constant of rejection classification refines the prognostic value of conventional prognostic signatures in breast cancer. British Journal of Cancer. 2018 Nov; 119 (11): 1383-1391.
• Thorsson V, Gibbs, … Bedognetti D, et al .. The immune landscape of cancer. Immunity, 2018
• Wang E, Bedognetti D, Marincola FM. Prediction of response to anticancer immunotherapy using gene signatures. Journal of Clinical Oncology 2013 – Bhardwaj N, … Bedognetti et al. Flt3 Ligand Significantly Augments Immune Responses to a Dendritic Cell Targeting anti-DEC-205-NY-ESO-1 Vaccine through Expansion of Dendritic Cell Subsets, Nature Cancer (in press).
• Sayaman R, Saad M, Thorsson V, Wouter Hendrickx, Roelands J, Mokrab Y, Farshidfar F, Kirchhoff T, Sweis RS, Bathe OD, Porta-Pardo E, Campbell MJ, Stretch C, Hu D, Huntsman S, Graff RE, Syed N, Radvanyi L, Shelley S, Wolf D, Marincola FM, Ceccarelli M, Galon J,…., Ziv E, Bedognetti D. Germline genetic contribution to the immune landscape of cancer. Immunity (under revision), preprint available in Biorxiv
• Bedognetti D. A fresh multi-layer molecular of the immune diversity across hematologic malignancies. Cancer Cell, September 2020.
• Ceccarelli …, Iavarone A, Verhaak R, et al. Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma, Cell, 2016
• Rozenblit M … Marincola F, Bedognetti D, Adams S. Transcriptomic profiles conducive to immune-mediated tumor rejection in human breast cancer skin metastases treated with Imiquimod. Scientific Reports, 2019
• Bedognetti D, Hendrickx W, Ceccarelli M, Miller LD, Seliger B. Curr Opin Immunol. 2016 Bedognetti D, … Rosenberg SA, Marincola FM. CXCR3 / CCR5 pathways in metastatic melanoma patients treated with adoptive therapy and interleukin-2. British Journal of Cancer 2013.
• Insaldi E., Ballestrero A, Bedognetti D, Zoppoli G. Schlafen-11 expression is associated with immune signatures and basal-like phenotype in breast cancer. Breast Canc Res and Treat, 2019.
• Galon J, Angell HK, Bedognetti D, Marincola FM The continuum of cancer immunosurveillance: prognostic, predictive, and mechanistic signatures. Immunity, 2013

In these scientific works, the proponents were able to demonstrate how by means of Next Generation Sequencing it is possible to characterize the tumor and its microenvironment as well how it is possible to extract specific biomarkers of response to immunotherapy. These articles scientific studies were accompanied by the development of developed open source software protocols by the same authors who will be integrated with other solutions developed ad hoc and optimized in ONCO-ND1 system. Some of these protocols are:

• ExomePipeline available at https://github.com/miccec/ExomePipeline, for analysis, annotation and clinical prioritization of exome sequencing data
• IPSC, available at https://github.com/Sidra-TBI-FCO/ISPC for analysis integration of mutations, gene expression and copy number and their association with intratumoral immune response measured by the ICR (Immunological Constant of Rejection).

The ONCO-ND1 system requires that a paraffin biopsy is extracted from a paraffin biopsy sample quantity of DNA and RNA sufficient for sequencing with medium coverage> 200X up the whole exome (> 20,000 genes), ultra-deep coverage on T-cell receptor regions, e in regions covering genes of interest for targeted therapies and a blood sample for the extraction of germline DNA in order to identify specific mutations in the tumor and for the identification of germline polymorphisms Immune markers identified by the platform ONCO-ND1 are:

• A Tumor Mutational Burden (associated with response to immunotherapy)
• B Neoantigen Load (associated with response to immunotherapy) – C The presence of microsatellite instability (MSI) (strong predictor of response immunotherapy)
• D Repertory of T-Cell receptors (high clonality is associated with response to immunotherapy) E
• E The HLA subtype (the presence of HLA heterozygosity is associated with high response immunotherapy, some haplotypes predict the risk of developing pneumonia)
• F Expressions of immune response and resistance immune signatures (associated with response or resistance to immunotherapy)
• G The presence of specific somatic tumor mutations associated with resistance to immunotherapy (specific mutations of the interferon pathway and other mutations such as those inherent in the EGFR / MAPK pathways are associated with resistance immunotherapy)
• H The presence of response-associated germline polymorphisms
• I The composition of the tumor microbiome (specific bacteria are associated with response immunotherapy)
• L Peripheral blood expression of miRNA signatures predictive of response ad immunotherapy

The ONCO-ORACLE platform is based on the protocols developed in the following publications scientific: – Nuzzo PV, Berchuck JE, Korthauer K, Spisak S, et al. Detection of renal cell carcinoma using plasma and urine cell-free DNA methylomes. Nature Medicine 2020 Jul; 26 (7): 1041-1043. – Lasseter K, Nassar AH, Hamieh L, Berchuck JE, Nuzzo PV, Korthauer K, Shinagare AB, Ogorek B, McKay R, Thorner AR, Lee GM, Braun DA, Bhatt RS, Freedman M, Choueiri TK, Kwiatkowski DJ. Plasma cell-free DNA variant analysis compared with methylated DNA analysis in renal cell carcinoma. Genet Med. 2020 Aug; 22 (8): 1366-1373. The cfMeDIP – seq technique from Nuzzo et al. introduced a revolutionary method for the detection of tumor-specific alterations based on the cfDNA methylation profile that potentially can significantly reduce mortality through early identification e non-invasive of tumor pathology and offer a useful tool for patient monitoring oncological. With the development of the high sensitivity cfMeDIP – seq assay it is possible to introduce it into practice clinical early screening methods. The sage in fact proved capable of identifying in plasma the presence of the methylation profile of seven tumor types both at the initial stage and metastatic (colon, pancreas, kidney, breast, lung, urothelial and leukemia cancers acute myeloid) with very high sensitivity and specificity. Furthermore, the cfMeDIP-seq assay has demonstrated a very high sensitivity of diagnosis in renal cell carcinoma, in which they are completely absent markers of screening and early diagnosis and in 35% of cases the tumor comes diagnosed in the metastatic phase. In fact, in the study by Nuzzo et al. Nature Medicine 2020 Jul; 26 (7): 1041-1043, in a large cohort of renal cell carcinoma patients, the cfMeDIP – seq demonstrated an accuracy measured by the ROC curve of 99% per detection of renal cell carcinoma, even in early stage patients. The ONCO-ORACLE platform will be based on the industrialization of the cfMeDIP – seq test, with the possibility ‘of being able to use it for all types of cancer thanks to the ease’ of finding the plasma samples to be analyzed, at the minimum quantity of cfDNA for the execution of the protocol of immunoprecipitation now optimized, at the speed of sequencing and analysis of the sequenced samples. The ONCO-ND1 system in fact provides for the isolation from the patient’s plasma of cfDNA and his own characterization of its methyloma through the cfMeDIP-seq assay. A withdrawal of blood in the classic EDTA tube, which is used for common blood chemistry tests, to obtain a 1ml of plasma from which 10 ng of cfDNA will be isolated, essential for the analysis. The isolated cfDNA undergoes an immunoprecipitation process through the use of an antibody directed against 5-methyl-cytosine. The immunoprecipitated cfmeDNA comes amplified and sent high-throughput sequencing in order to locate sequences tumor-specific methylation through specific pipelines.