Keynote speakers

Jasmin Fisher

Cancer Digital Twins

Cancer is a complex systemic disease driven by genetic and epigenetic aberrations that impact a multitude of signalling pathways operating in different cell types. The dynamic, evolving nature of the disease leads to tumour heterogeneity and an inevitable resistance to treatment, which poses considerable challenges for the design of therapeutic strategies to combat cancer. Digital twins for cancer tumours are emerging as a transformative tool in oncology to enable a more personalised and dynamic approach to cancer treatment. In this talk, I will showcase a growing library of mechanistic, data-driven computational models, focused on the signalling pathways within tumour cells and their microenvironment in various types of cancer (namely triple-negative breast cancer, non-small cell lung cancer, melanoma and glioblastoma). These computational models are mechanistically interpretable, enabling us to understand and anticipate emergent resistance mechanisms and to design patient-specific treatment strategies that counteract the forces of clonal selection driving treatment relapse, to improve outcomes for patients with hard-to-treat cancers.

Sebastien Benzekry

Mechanistic learning to predict response and survival in immuno-oncology

This presentation will explore methodological tools integrating machine learning, mechanistic modeling and nonlinear mixed-effects modeling (NLME for pharmacometrics) to analyze data pre- and on-treatment data of advanced cancer patients treated with immune-checkpoint inhibitors(ICI). The objective is to predict primary resistance to immunotherapy. The first dataset comes from the PIONeeR RHU project. It comprises multimodal deep biomarkers derived both from tumor tissue (multiplex immunohistochemistry) and blood samples (immune-monitoring, vasculo-monitoring, hematology and biochemistry). Together, this dataset contains 439 patients and p = 433 pre-treatment biomarkers. I will focus on the of stable variable selection in high dimension with complex. We will see that classical approaches such as LASSO are not stable and will present novel methods to tackle this issue. Second, recent results from the SChISM (Size cfDNA Immunotherapies Signature Monitoring) clinical study will be presented. It investigates the utility of circulating cell-free DNA (cfDNA) fragment size profiles — collectively known as the fragmentome — as a non-invasive biomarker to predict early progression (EP) and progression-free survival (PFS) in patients with advanced carcinomas treated with ICIs. cfDNA profiles were obtained pre- and on-treatment across 128 pan-cancer patients, using a patented BIABooster-based technology that avoids DNA extraction. Pre-treatment fragmentome-derived features—such as the concentration of long fragments (≥1650 base pairs) — were statistically associated with treatment outcomes.

Noemie Leblay

Tolerogenic dendritic cells and immunosuppressive milieu cause resistance to daratumumab and IMiDs in multiple myeloma

The combination of the anti-CD38 monoclonal antibody daratumumab with immunomodulatory agents (IMiDs) has demonstrated high efficacy in multiple myeloma (MM). However, MM invariably relapses and the mechanisms mediating primary or acquired resistance to daratumumab-IMiDs therapies remain to be fully elucidated. We herein serially profiled (at baseline, cycle 3 and progression) the static and dynamic single-cells’ transcriptomes of the bone marrow (BM) immune residents (CD138-) and plasma cells (CD138+) in relapsed MM patients (n=29) treated with daratumumab-IMiDs. At baseline, an activated immune microenvironment enriched with highly cytotoxic T and NK cells was noted in responders. In contrast, non-responders exhibited a highly inflammatory microenvironment dominated by indoleamine 2,3-dioxygenase IDO1 and tolerogenic classical dendritic cells (cDC1 and cDC2). At relapse, a comparable transcriptomic profile to baseline non-responders was also observed, with increased number of exhausted CD8+ T cells, loss of NK cytotoxic markers and enriched tolerogenic classical dendritic cells. Tumor-intrinsic factors such as CD38 downregulation and increased expression of CD47 were commonly noted at progression. Plasma and BM cell-cell interactions also identified SIRPG-CD47 and MIF-CD74:CXCR4 as mediators of the immunosuppressive response seen at progression. Therefore, we herein defined the static and dynamic BM immune profiles of daratumumab-IMiDs treated MM patients and identified novel druggable drivers of resistance.

Gaoussou Sanou

IMGT/mAb-KG: the international ImMunoGeneTics information system-knowledge graph dedicated to monoclonal antibodies