Application and Development of Computational Approaches to Optimize Treatment of Malignancies using Routine Clinical Data

The use of personalised medicine in oncology is gaining recognition as a way of enabling individualised tailored-treatment based on patient’s genetic signatures and clinical characteristics. The characterisation of drug exposure and the way it affects to the dynamics of the underlying disease under study (either through tumour size changes or biomarker dynamics), is key to support individualised disease monitoring and therapeutic strategies. The field of pharmacometrics is a potentially useful discipline which focuses on obtaining quantitative mathematical and statistical models of the different physiological processes from drug administration to measurement of drug exposure, disease dynamics (tumour size, biomarker response), and ultimately clinical outcome. In this thesis we will present examples of the use of different ways of characterising disease dynamics and pharmacometric tools to facilitate personalised medicine. These examples include applications to enable individualised medicine in routine clinical practice and to support model-based drug development

Application and Development of Computational Approaches to Optimize Treatment of Malignancies using Routine Clinical Data

The use of personalised medicine in oncology is gaining recognition as a way of enabling individualised tailored-treatment based on patient’s genetic signatures and clinical characteristics. The characterisation of drug exposure and the way it affects to the dynamics of the underlying disease under study (either through tumour size changes or biomarker dynamics), is key to support individualised disease monitoring and therapeutic strategies. The field of pharmacometrics is a potentially useful discipline which focuses on obtaining quantitative mathematical and statistical models of the different physiological processes from drug administration to measurement of drug exposure, disease dynamics (tumour size, biomarker response), and ultimately clinical outcome. In this thesis we will present examples of the use of different ways of characterising disease dynamics and pharmacometric tools to facilitate personalised medicine. These examples include applications to enable individualised medicine in routine clinical practice and to support model-based drug development

Assessing the impact of the addition of dendritic cell vaccination to neoadjuvant chemotherapy in breast cancer patients: A model-based characterization approach

Aims: Immunotherapy is a rising alternative to traditional treatment in breast cancer (BC) patients in order to transform cold into hot immune enriched tumours and improve responses and outcome. A computational modelling approach was applied to quantify modulation effects of immunotherapy and chemotherapy response on tumour shrinkage and progression-free survival (PFS) in naïve BC patients. Methods: Eighty-three Her2-negative BC patients were recruited for neoadjuvant chemotherapy with or without immunotherapy based on dendritic cell vaccination. Sequential tumour size measurements were modelled using nonlinear mixed effects modelling and linked to PFS. Data from another set of patients (n = 111) were used to validate the model. Results: Tumour size profiles over time were linked to biomarker dynamics and PFS. The immunotherapy effect was related to tumour shrinkage (P < .05), with the shrinkage 17% (95% confidence interval: 2-23%) being higher in vaccinated patients, confirmed by the finding that pathological complete response rates in the breast were higher in the vaccinated compared to the control group (25.6% vs 13.6%; P = .04). The whole tumour shrinkage time profile was the major prognostic factor associated to PFS (P < .05), and therefore, immunotherapy influences indirectly on PFS, showing a trend in decreasing the probability of progression with increased vaccine effects. Tumour subtype was also associated with PFS (P < .05), showing that luminal A BC patients have better prognosis. Conclusions: Dendritic cell-based immunotherapy is effective in decreasing tumour size. The semi-mechanistic validated model presented allows the quantification of the immunotherapy treatment effects on tumour shrinkage and establishes the relationship between the dynamics of tumour size and PFS

Final results regarding the addition of dendritic cell vaccines to neoadjuvant chemotherapy in early HER2-negative breast cancer patients: clinical and translational analysis

Background:Primary breast cancer (BC) has shown a higher immune infiltration than the metastatic disease, justifying the optimal scenario for immunotherapy. Recently, neoadjuvant chemotherapy (NAC) combined with immune checkpoint inhibitors has demonstrated a gain in pathological complete responses (tpCR) in patients with BC. The aim of our study is to evaluate the safety, feasibility, and efficacy of the addition of dendritic cell vaccines (DCV) to NAC in HER2-negative BC patients. Methods:Thirty-nine patients with early BC received DCV together with NAC conforming the vaccinated group (VG) and compared with 44 patients as the control group (CG). All patients received anthracyclines and taxanes-based NAC (ddECx4→Dx4) followed by surgery ± radiotherapy ± hormonotherapy. Results:The tpCR rate was 28.9% in the VG and 9.09% in the CG (p = 0.03). Pathological CR in the triple negative (TN) BC were 50.0% versus 30.7% (p = 0.25), 16.6% versus 0% in luminal B (p = 0.15), and none among luminal A patients in VG versus CG, respectively. Impact of DCV was significantly higher in the programmed cell death ligand 1 (PD-L1) negative population (p < 0.001). PD-L1 expression was increased in patients with residual disease in the VG as compared with the CG (p < 0.01). No grade ⩾3 vaccine-related adverse events occurred. With a median follow-up of 8 years, no changes were seen in event-free survival or overall survival. Phenotypic changes post DCV in peripheral blood were observed in myeloid-derived suppressor cells (MDSC), NK, and T cells. Increase in blood cell proliferation and interferon (IFN)-γ production was detected in 69% and 74% in the VG, respectively. Humoral response was also found. Clonality changes in TCR-β repertoire were detected in 67% of the patients with a drop in diversity index after treatment. Conclusion:The combination of DCV plus NAC is safe and increases tpCR, with a significant benefit among PD-L1-negative tumors. DCV modify tumor milieu and perform cellular and humoral responses in peripheral blood with no impact in outcome. Trial registration:ClinicalTrials.gov number: NCT01431196. EudraCT 2009-017402-36

Modification of breast cancer milieu with chemotherapy plus dendritic cell vaccine: an approach to select best therapeutic strategies

Background: The addition of dendritic cell vaccines (DCV) to NAC could induce immune responses in those patients with residual disease (RD) by transforming the tumor microenvironment. Methods: Core diagnostic biopsies and surgical specimens from 80 patients (38 in the vaccinated group plus NAC (VG) and 42 in the control group (CG, treated only with NAC) were selected. We quantify TILs (CD8, CD4 and CD45RO) using immunohistochemistry and the automated cellular imaging system (ACIS III) in paired samples. Results: A CD8 rise in TNBC samples was observed after NAC plus DCV, changing from 4.48% in the biopsy to 6.70% in the surgical specimen, not reaching statistically significant differences (p = 0.11). This enrichment was seen in up to 67% of TNBC patients in the experimental arm as compared with the CG (20%). An association between CD8 TILs before NAC (4% cut-off point) and pathological complete response in the VG was found in the univariate and multivariate analysis (OR = 1.41, IC95% 1.05-1.90; p = 0.02, and OR = 2.0, IC95% 1.05-3.9; p = 0.03, respectively). Conclusion: Our findings suggest that patients with TNBC could benefit from the stimulation of the antitumor immune system by using DCV together with NAC