Search results
Found 10564 matches for
In late February the Department of Physics hosted a Greenlight for Girls event to encourage young women to think about studying physics and to challenge the perception that physics is for boys.
A Phase I Study of the Oral Dual-Acting Pan-PI3K/mTOR Inhibitor Bimiralisib in Patients with Advanced Solid Tumors
Background: Bimiralisib is a pan-PI3K/mTOR inhibitor demonstrating antitumor efficacy in preclinical models. The objectives of this study were to identify a maximum tolerated dose (MTD), pharmacokinetics (PK), a dosing schedule, and adverse events (AEs) in patients with advanced solid tumors. Patients and Methods: Patients received oral bimiralisib to determine the MTD of one continuous (once daily) and two intermittent schedules (A: Days 1, 2 weekly; B: Days 1, 4 weekly) until progression or unacceptable AEs occurred. Results: The MTD for the continuous schedule was 80 mg, with grade three fatigue as the dose-limiting toxicity (DLT). No MTD was reached with intermittent schedules, with only one DLT in schedule B. PK analysis suggested that 140 mg (schedule A) was within the biologically active dose range and was selected for further exploration. The most frequent treatment-emergent AEs were hyperglycemia (76.2%) in the continuous schedule, and nausea (56–62.5%) in schedules A and B. The most frequent treatment-emergent > grade three AE for all schedules combined was hyperglycemia (28.6%, continuous schedule; 12.0%, schedule A; 12.5%, schedule B). There was one partial response in a head and neck squamous cancer patient with a NOTCH1T1997M mutation. Conclusions: Bimiralisib demonstrated a manageable AE profile consistent with this compound class. Intermittent schedules had fewer > grade three AEs, while also maintaining favorable PK profiles. Intermittent schedule A is proposed for further development in biomarker-selected patient populations.
From Data to Diagnosis: Skin Cancer Image Datasets for Artificial Intelligence.
Artificial Intelligence (AI) solutions for skin cancer diagnosis continue to gain momentum, edging closer towards broad clinical use. These AI models, particularly deep learning architectures, require large digital image datasets for development. This review provides an overview of the datasets used to develop AI algorithms and highlights the importance of dataset transparency for evaluation of algorithm generalisability across varying populations and settings. Current challenges for curation of clinically valuable datasets are detailed, which include dataset shifts arising from demographic variations and differences in data collection methodologies, along with inconsistencies in labelling. These shifts can lead to differential algorithm performance, compromise of clinical utility, and the propagation of discriminatory biases when developed algorithms are implemented in mismatched populations. Limited representation of rare skin cancers and minoritised groups in existing datasets are highlighted which can further skew algorithm performance. Strategies to address these challenges are presented, which include improving transparency, representation and interoperability. Federated learning and generative methods, which may improve dataset size and diversity without compromising privacy, are also examined. Lastly, we discuss model-level techniques which may address biases entrained through the use of datasets derived from routine clinical care. As the role of AI in skin cancer diagnosis becomes more prominent, ensuring the robustness of underlying datasets is increasingly important.
A phase I open-label, dose-escalation study of NUC-3373, a targeted thymidylate synthase inhibitor, in patients with advanced cancer (NuTide:301).
PURPOSE: 5-fluorouracil (5-FU) is inefficiently converted to the active anti-cancer metabolite, fluorodeoxyuridine-monophosphate (FUDR-MP), is associated with dose-limiting toxicities and challenging administration schedules. NUC-3373 is a phosphoramidate nucleotide analog of fluorodeoxyuridine (FUDR) designed to overcome these limitations and replace fluoropyrimidines such as 5-FU. PATIENTS AND METHODS: NUC-3373 was administered as monotherapy to patients with advanced solid tumors refractory to standard therapy via intravenous infusion either on Days 1, 8, 15 and 22 (Part 1) or on Days 1 and 15 (Part 2) of 28-day cycles until disease progression or unacceptable toxicity. Primary objectives were maximum tolerated dose (MTD) and recommended Phase II dose (RP2D) and schedule of NUC-3373. Secondary objectives included pharmacokinetics (PK), and anti-tumor activity. RESULTS: Fifty-nine patients received weekly NUC-3373 in 9 cohorts in Part 1 (n = 43) and 3 alternate-weekly dosing cohorts in Part 2 (n = 16). They had received a median of 3 prior lines of treatment (range: 0-11) and 74% were exposed to prior fluoropyrimidines. Four experienced dose-limiting toxicities: two Grade (G) 3 transaminitis; one G2 headache; and one G3 transient hypotension. Commonest treatment-related G3 adverse event of raised transaminases occurred in
Intra-prostatic tumour evolution, steps in metastatic spread and histogenomic associations revealed by integration of multi-region whole-genome sequencing with histopathological features.
BACKGROUND: Extension of prostate cancer beyond the primary site by local invasion or nodal metastasis is associated with poor prognosis. Despite significant research on tumour evolution in prostate cancer metastasis, the emergence and evolution of cancer clones at this early stage of expansion and spread are poorly understood. We aimed to delineate the routes of evolution and cancer spread within the prostate and to seminal vesicles and lymph nodes, linking these to histological features that are used in diagnostic risk stratification. METHODS: We performed whole-genome sequencing on 42 prostate cancer samples from the prostate, seminal vesicles and lymph nodes of five treatment-naive patients with locally advanced disease. We spatially mapped the clonal composition of cancer across the prostate and the routes of spread of cancer cells within the prostate and to seminal vesicles and lymph nodes in each individual by analysing a total of > 19,000 copy number corrected single nucleotide variants. RESULTS: In each patient, we identified sample locations corresponding to the earliest part of the malignancy. In patient 10, we mapped the spread of cancer from the apex of the prostate to the seminal vesicles and identified specific genomic changes associated with the transformation of adenocarcinoma to amphicrine morphology during this spread. Furthermore, we show that the lymph node metastases in this patient arose from specific cancer clones found at the base of the prostate and the seminal vesicles. In patient 15, we observed increased mutational burden, altered mutational signatures and histological changes associated with whole genome duplication. In all patients in whom histological heterogeneity was observed (4/5), we found that the distinct morphologies were located on separate branches of their respective evolutionary trees. CONCLUSIONS: Our results link histological transformation with specific genomic alterations and phylogenetic branching. These findings have implications for diagnosis and risk stratification, in addition to providing a rationale for further studies to characterise the genetic changes causally linked to morphological transformation. Our study demonstrates the value of integrating multi-region sequencing with histopathological data to understand tumour evolution and identify mechanisms of prostate cancer spread.
New role of fat-free mass in cancer risk linked with genetic predisposition.
Cancer risk is associated with the widely debated measure body mass index (BMI). Fat mass and fat-free mass measurements from bioelectrical impedance may further clarify this association. The UK Biobank is a rare resource in which bioelectrical impedance and BMI data was collected on ~ 500,000 individuals. Using this dataset, a comprehensive analysis using regression, principal component and genome-wide genetic association, provided multiple levels of evidence that increasing whole body fat (WBFM) and fat-free mass (WBFFM) are both associated with increased post-menopausal breast cancer risk, and colorectal cancer risk in men. WBFM was inversely associated with prostate cancer. We also identified rs615029[T] and rs1485995[G] as associated in independent analyses with both PMBC (p = 1.56E-17 and 1.78E-11) and WBFFM (p = 2.88E-08 and 8.24E-12), highlighting splice variants of the intriguing long non-coding RNA CUPID1 (LINC01488) as a potential link between PMBC risk and fat-free mass.