Xiaoran Lai

Successful cancer immunotherapy requires not only tumor cell killing but also the activation of innate and adaptive immune responses. The inhibitor of apoptosis (IAP) proteins cIAP1, cIAP2 and XIAP act as key suppressors of RIPK1-driven immunogenic cell death, thereby enabling tumor immune evasion and limiting the effectiveness of immune checkpoint blockade (ICB). ASTX660 (tolinapant) is a non-peptidomimetic antagonist with a balanced activity against all clinically relevant IAPs, designed to lower the TNF cytotoxicity threshold and sensitize tumors to T cell-mediated killing. Here, we present results from the phase I ASTEROID trial (NCT05082259), evaluating ASTX660 in combination with pembrolizumab in patients with advanced solid tumors. Primary endpoints were recommended phase 2 dose (RP2D) and safety, while secondary endpoints were anti-tumor activity and pharmacokinetics. The combination was well tolerated and demonstrated early evidence of efficacy. RP2D was ASTX660 150 mg (7-days-on/14-days-off) with pembrolizumab. Overall response rate was 33%, including responses in ER⁺ breast cancers typically resistant to ICB. Integrated translational analyses reveal that response is associated with a TNF/IFN-enriched inflammatory tumor microenvironment, increased tumor antigenicity and enhanced TCR clonal diversity, whereas resistance correlates with a CCR4/6/7/8-driven TAM/Treg-dominant niche. Together, these results provide clinical proof-of-concept that dual IAP and PD-1 blockade sensitize tumors, while identifying candidate biomarkers to guide patient stratification in future studies.

Background: Culture of Neisseria gonorrhoeae is essential for surveillance of complete antimicrobial susceptibility profiles. In 2014, the culture success rate of N. gonorrhoeae from samples taken at the clinic for sexually transmitted infections (STI clinic), Oslo University Hospital, Norway, was only 20%. The present study aimed to improve gonococcal culture rates using bedside inoculation of patient samples on gonococcal agar plates and incubation at the STI clinic. Methods: This prospective quality improvement study was conducted by the STI clinic and the Department of Microbiology at Oslo University Hospital from May 2016 - October 2017. When culture of N. gonorrhoeae was clinically indicated, we introduced a parallel ‘bedside culture’ at the STI clinic and compared results with the standard culture at the microbiology department. Samples were taken from urethra, anorectum, pharynx and cervix. Culture rates were compared across symptomatic and asymptomatic anatomical sites. Results: From 596 gonococcal-positive PCR samples, bedside culture had a significantly higher success rate of 57% compared to 41% with standard culture (p < 0.05). Overall, culture rate from symptomatic sites was 91% v. 45% from asymptomatic sites. The culture rates from different anatomical sites were as follows: urethra 93%, anorectum 64%, pharynx 28% and cervix 70%. Bedside culture significantly (p < 0.05) improved the culture rates for symptomatic urethral and asymptomatic pharyngeal samples. Conclusions: Where feasible, bedside inoculation on gonococcal agar plates and incubation of samples from patients with gonorrhoea is recommended. This will improve the culture diagnostics and provide additional gonococcal isolates for antimicrobial resistance surveillance.

The usefulness of mechanistic models to disentangle complex multiscale cancer processes, such as treatment response, has been widely acknowledged. However, a major barrier for multiscale models to predict treatment outcomes in individual patients lies in their initialization and parametrization, which needs to reflect individual cancer characteristics accurately. In this study, we use multitype measurements acquired routinely on a single breast tumor, including histopathology, MRI, and molecular profiling, to personalize parts of a complex multiscale model of breast cancer treated with chemotherapeutic and antiangiogenic agents. The model accounts for drug pharmacokinetics and pharmacodynamics. We developed an open-source computer program that simulates cross-sections of tumors under 12-week therapy regimens and used it to individually reproduce and elucidate treatment outcomes of 4 patients. Two of the tumors did not respond to therapy, and model simulations were used to suggest alternative regimens with improved outcomes dependent on the tumor's individual characteristics. It was determined that more frequent and lower doses of chemotherapy reduce tumor burden in a low proliferative tumor while lower doses of antiangiogenic agents improve drug penetration in a poorly perfused tumor. Furthermore, using this model, we were able to correctly predict the outcome in another patient after 12 weeks of treatment. In summary, our model bridges multitype clinical data to shed light on individual treatment outcomes.Significance:Mathematical modeling is used to validate possible mechanisms of tumor growth, resistance, and treatment outcome.

Supplementary text to the main article detailing mathematical model, model initialisation and parametrisation, computational details and overview of clinical data .

The usefulness of mechanistic models to disentangle complex multiscale cancer processes, such as treatment response, has been widely acknowledged. However, a major barrier for multiscale models to predict treatment outcomes in individual patients lies in their initialization and parametrization, which needs to reflect individual cancer characteristics accurately. In this study, we use multitype measurements acquired routinely on a single breast tumor, including histopathology, MRI, and molecular profiling, to personalize parts of a complex multiscale model of breast cancer treated with chemotherapeutic and antiangiogenic agents. The model accounts for drug pharmacokinetics and pharmacodynamics. We developed an open-source computer program that simulates cross-sections of tumors under 12-week therapy regimens and used it to individually reproduce and elucidate treatment outcomes of 4 patients. Two of the tumors did not respond to therapy, and model simulations were used to suggest alternative regimens with improved outcomes dependent on the tumor's individual characteristics. It was determined that more frequent and lower doses of chemotherapy reduce tumor burden in a low proliferative tumor while lower doses of antiangiogenic agents improve drug penetration in a poorly perfused tumor. Furthermore, using this model, we were able to correctly predict the outcome in another patient after 12 weeks of treatment. In summary, our model bridges multitype clinical data to shed light on individual treatment outcomes.Significance:Mathematical modeling is used to validate possible mechanisms of tumor growth, resistance, and treatment outcome.

Supplementary text to the main article detailing mathematical model, model initialisation and parametrisation, computational details and overview of clinical data .

Purpose. To investigate the changes in the tear cytokine profile of patients with meibomian gland dysfunction (MGD) treated with eyelid warming and to correlate these changes with clinical parameters for dry eye disease (DED). Methods. Seventy patients with MGD were included and treated with the warming of eyelids. Of these, 61 still used the treatment three months after baseline, while 48 completed the whole treatment period of six months. The concentrations of 39 cytokines in the tear fluid were measured at baseline and after three and six months of treatment. All participants were examined with tests for DED, including tear film break-up time (TBUT), ocular surface staining (OSS), and the self-reporting Ocular Surface Disease Index (OSDI). Changes in cytokine concentrations were assessed from baseline to three months, from three to six months, and from baseline to six months. Correlation analyses were performed between changes in the cytokine concentrations and changes in TBUT, OSS, and OSDI during the same time intervals. Results. No significant changes were found in the concentrations of the 39 cytokines during any of the three treatment intervals. However, several correlations were detected between changes in the level of cytokines and OSS from baseline to three months of treatment. Decreasing concentrations of granulocyte chemotactic protein 2 (GCP-2/CXCL6, mean effect 2.36, ), interleukin 10 (IL-10, mean effect 1.04, ), and IL-16 (mean effect 1.36, ) were associated with decreasing OSS. Decreasing concentrations of granulocyte macrophage colony-stimulating factor (GM-CSF, mean effect −2.98, ), IL-8 (IL-8/CXCL8, mean effect −1.35, ), and macrophage migration inhibitory factor (MIF, mean effect −2.44, ) were related to increasing OSS. Conclusions. Warming of eyelids did not change the concentration of cytokines in the tear fluid of patients with MGD significantly. However, alterations in the level of several cytokines were associated with changes in the OSS. This finding indicates a close connection between tear cytokines and OSS in MGD patients treated with eyelid warming.

Mathematical modeling and simulation is a promising approach to personalized cancer medicine. Yet, the complexity, heterogeneity and multi‐scale nature of cancer pose significant computational challenges. Coupling discrete cell‐based models with continuous models using hybrid cellular automata (CA) is a powerful approach for mimicking biological complexity and describing the dynamical exchange of information across different scales. However, when clinically relevant cancer portions are taken into account, such models become computationally very expensive. While efficient parallelization techniques for continuous models exist, their coupling with discrete models, particularly CA, necessitates more elaborate solutions. Building upon FEniCS, a popular and powerful scientific computing platform for solving partial differential equations, we developed parallel algorithms to link stochastic CA with differential equations (https://bitbucket.org/HTasken/cansim). The algorithms minimize the communication between processes that share CA neighborhood values while also allowing for reproducibility during stochastic updates. We demonstrated the potential of our solution on a complex hybrid cellular automaton model of breast cancer treated with combination chemotherapy. On a single‐core processor, we obtained nearly linear scaling with an increasing problem size, whereas weak parallel scaling showed moderate growth in solving time relative to increase in problem size. Finally, we applied the algorithm to a problem that is 500 times larger than previous work, allowing us to run personalized therapy simulations based on heterogeneous cell density and tumor perfusion conditions estimated from magnetic resonance imaging data on an unprecedented scale.

The usefulness of mechanistic models to disentangle complex multiscale cancer processes, such as treatment response, has been widely acknowledged. However, a major barrier for multiscale models to predict treatment outcomes in individual patients lies in their initialization and parametrization, which needs to reflect individual cancer characteristics accurately. In this study, we use multitype measurements acquired routinely on a single breast tumor, including histopathology, MRI, and molecular profiling, to personalize parts of a complex multiscale model of breast cancer treated with chemotherapeutic and antiangiogenic agents. The model accounts for drug pharmacokinetics and pharmacodynamics. We developed an open-source computer program that simulates cross-sections of tumors under 12-week therapy regimens and used it to individually reproduce and elucidate treatment outcomes of 4 patients. Two of the tumors did not respond to therapy, and model simulations were used to suggest alternative regimens with improved outcomes dependent on the tumor's individual characteristics. It was determined that more frequent and lower doses of chemotherapy reduce tumor burden in a low proliferative tumor while lower doses of antiangiogenic agents improve drug penetration in a poorly perfused tumor. Furthermore, using this model, we were able to correctly predict the outcome in another patient after 12 weeks of treatment. In summary, our model bridges multitype clinical data to shed light on individual treatment outcomes. Significance: Mathematical modeling is used to validate possible mechanisms of tumor growth, resistance, and treatment outcome.