Department of Biochemistry

The introduction of bispecific antibodies (BsAbs) has led to significant improvements in survival for patients with relapsed and refractory B-cell lymphomas. Despite these advances, there remains a significant number of patients who experience disease progression after these novel therapies. Predicting which patients may respond to certain treatments and the durability of their responses remains challenging. Measurable residual disease (MRD) has become easier to detect and quantify through the use of genomic next-generation sequencing tools and has been studied as a possible biomarker to predict long-term outcomes and risk-stratify patients after BsAb therapy in several lymphoma subtypes. Here, we review recent data demonstrating that MRD negativity is associated with radiographic response and improved progression-free survival. Because of heterogeneity in assay choice, assessment timing, and technical parameters, further work is needed before MRD testing is ready to be incorporated into clinical practice in the context of BsAb treatment for B-cell lymphomas.

Background: Oncogenic KRAS mutations are nearly ubiquitous in pancreatic ductal adenocarcinoma (PDAC), yet therapeutic attempts to target KRAS, as well as downstream MAPK pathway effectors, have shown limited clinical success. While KRAS canonically drives MAPK signaling via RAF-MEK-ERK, it is also known to play a role in PI3K-AKT signaling. Methods: Our therapeutic study targeted the PI3K pathway with the drug Omipalisib (p110α/β/δ/γ and mTORC1/2 inhibitor) in combination with two different MAPK pathway inhibitors: Trametinib (MEK1/2 inhibitor) or SHP099-HCL (SHP099; SHP2 inhibitor). Western blot analysis demonstrated that the application of Trametinib or SHP099 alone selectively blocked ERK phosphorylation (pERK) but failed to suppress phosphorylated AKT (pAKT). Conversely, Omipalisib alone successfully inhibited pAKT but failed to suppress pERK. Therefore, we hypothesized that a combination therapeutic comprised of Omipalisib with either Trametinib or SHP099 would inhibit two prominent mitogenic pathways, MAPK and PI3K-AKT, and effectively suppress PDAC growth. Results: In vitro studies demonstrated that, in several cell lines, both Omipalisib/Trametinib and Omipalisib/SHP099 combination therapeutic strategies were more effective than treatment with each drug individually at reducing proliferation, colony formation, and cell migration compared to vehicle controls. In vivo oral administration of combined Omipalisib/Trametinib treatment was significantly more effective than Omipalisib/SHP099 in reducing implanted tumor growth, and the Omipalisib/Trametinib treatment more effectively reduced tumor progression and prolonged survival in an aggressive genetically engineered mouse model of PDAC than either Omipalisib or Trametinib alone. Conclusions: Altogether, our data support a rationale for a dual treatment strategy targeting both PI3K and MAPK pathways in pancreatic cancers.

Background: Improving prediction models to timely detect lung cancer is paramount. Our aim is to develop and validate prediction models for early detection of lung cancer in primary care, based on free-text consultation notes, that exploit the order and context among words and sentences. Methods: Data of all patients enlisted in 49 general practices between 2002 and 2021 were assessed, and we included those older than 30 years with at least one free-text note. We developed two models using a hierarchical architecture that relies on attention and bidirectional long short-term memory networks. One model used only text, while the other combined text with clinical variables. The models were trained on data excluding the five months leading up to the diagnosis, using target replication and a tuning set, and were tested on a separate dataset for discrimination, PPV, and calibration. Results: A total of 250,021 patients were enlisted, with 1507 having a lung cancer diagnosis. Included in the analysis were 183,012 patients, of which 712 had the diagnosis. From the two models, the combined model showed slightly better performance, achieving an AUROC on the test set of 0.91, an AUPRC of 0.05, and a PPV of 0.034 (0.024, 0.043), and showed good calibration. To early detect one cancer patient, 29 high-risk patients would require additional diagnostic testing. Conclusions: Our models showed excellent discrimination by leveraging the word and sentence structure. Including clinical variables in addition to text slightly improved performance. The number needed to treat holds promise for clinical practice. Investigating external validation and model suitability in clinical practice is warranted.

Introduction: Pancreatic ductal adenocarcinoma (PDAC) is insidious, with only 15–20% of those diagnosed suitable for surgical resection as it is either too advanced and has invaded local structures or has already spread to distant sites. The associated tumor microenvironment provides a protective shield which limits the efficacy of chemotherapeutic agents, but also impairs the delivery of nutrients required for the PDAC cells. To compensate for this, metabolic adaptions occur to provide alternative sources of fuel. The aim of this study is to explore metabolomic differences between participants with resectable PDAC compared to healthy volunteers (HV). The objectives were to use nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to determine if resectable PDAC induces sufficient metabolic adaptations and variations which could be used to discriminate between the two groups. Methods: Plasma samples were collected from fasted individuals with resectable PDAC (n = 23, median age 68 [IQR 56–75], 69.6% male) and HV (n = 24, median age 63 [IQR 58–71], 54.2% male). Samples were analyzed using NMR and the Biocrates MxP Quant 500 kit at University Hospital Southampton. Results: NMR spectroscopy identified six independent metabolites that significantly discriminated between the PDAC and HV groups, including elevated plasma concentrations of 3-hydroxybutyrate and citrate, with decreased amounts of glutamine and histidine. MS analysis identified 84 metabolites with a significant difference between the PDAC and HV cohorts. The metabolites with a fold change (FC) > 1.5 in the PDAC population were conjugated bile acids (taurocholic acid, glycocholic acid, and glycochenodexoycholic acid). Discussion: In conclusion, using metabolomics, biochemical differences between resectable PDAC and HV were detected. These differences indicate metabolic plasticity and utilization of alternative fuel sources.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized the management of advanced non-small cell lung cancer (NSCLC). Emerging evidence suggests a potential association between elevated body mass index (BMI) and enhanced ICI efficacy, yet this relationship remains inconclusive and warrants further investigation. This study aims to evaluate the impact of BMI on treatment efficacy and survival outcomes in advanced NSCLC patients treated with first-line ICI therapy. Methods: A retrospective study was conducted at a multi-center registry to evaluate the impact of baseline BMI on overall survival (OS) and progression-free survival (PFS) in patients with stage IV NSCLC who received first-line ICI therapies. Treatment regimens included pembrolizumab or the combination of ipilimumab and nivolumab, administered either as monotherapy or in combination with chemotherapy, at the oncology department between January 2018 and December 2023. BMI was categorized according to the World Health Organization (WHO) classification, and OS and PFS were evaluated using Kaplan–Meier survival analysis and the Cox proportional hazards regression model. Results: Among 346 patients, 12.72% were underweight, 45.38% normal weight, 29.19% overweight, and 12.72% obese. Overweight and obese patients were more likely to receive pembrolizumab (p = 0.039) and less likely to undergo chemotherapy (p = 0.012). No significant differences in median overall survival (OS, log-rank: p = 0.155) or progression-free survival (PFS, log-rank: p = 0.370) were observed across BMI categories. However, differences emerged upon further analysis of PD-L1 levels (OS, log-rank: p = 0.029; PFS, log-rank: p = 0.044), additional chemotherapy (OS, log-rank: p = 0.009; PFS, log-rank: p = 0.021), type of immune checkpoint inhibitor (OS, log-rank: p < 0.001; PFS, log-rank: p < 0.001), and histologic diagnosis (OS, log-rank: p = 0.011; PFS, log-rank: p = 0.003). Conclusions: BMI was not an independent predictor of survival outcomes in advanced NSCLC treated with ICI. Incorporating BMI with other patient-specific factors into personalized immunotherapy strategies highlights the importance of tailored approaches to improve patient care and clinical outcomes.

Background/Objectives: Informed consent is a crucial part of the clinical trial enrollment process in which patients are asked to understand and provide approval for medical interventions. Consent forms can be complex and hinder patient comprehension, highlighting the need for novel tools to improve the patient enrollment experience. This feasibility study aimed to develop an immersive technology to enroll human subjects in oncology clinical trials and provide 3D avatar-based informed consent in a virtual reality (VR) environment. Methods: Clinical feasibility and the effects of head-mounted VR devices on motion sickness and educational quality were evaluated in adult oncology patients enrolled in an intravenous (IV) port placement intervention study. Participants received before- and after-questionnaires to measure their understanding of the information received in VR. A follow-up questionnaire was given four weeks post-consent to measure knowledge retention. Results: Clinical staff reported that VR technology was manageable to use. Among 16 adult participants, all reported that VR was well tolerated with no motion sickness. The mean pre-intervention knowledge score was 64.6%, with an immediate post-intervention knowledge score of 97.9%. A mean knowledge score of 93.3% four-weeks post-consent was observed among 10/16 participants who completed a follow-up questionnaire. Conclusions: These findings support that VR is well tolerated and effective at delivering information during the informed consent process for oncology clinical trials. Key limitations include the small sample size and single clinical population. Further trials are warranted to compare efficacy over traditional consenting mechanisms and include more diverse clinical populations among a wider participant pool.