5728 research outputs found
Sort by
Development and implementation of efficient adaptive designs in early phase oncology trials for targeted agents
No full textRecent advancements in molecular biology and genomic research have led to the development
of a new class of cancer treatments known as targeted agents. Unlike cytotoxic therapies, which
work by directly killing cancer cells, targeted agents focus on specific molecular markers that
drive tumour growth. This distinct mechanism presents significant challenges to the traditional
early-phase clinical trial design, which was originally tailored for cytotoxic treatments. This thesis
focuses on three pivotal challenges posed by targeted therapies to the conventional design
paradigm. Key approaches have been proposed to address these challenges.
The occurrence of late-onset toxicities and rapid patient recruitment in early-phase trials
highlights the challenge of incomplete toxicity information. We conducted a methodological
review of existing designs that accommodate incomplete toxicity and evaluated the
implementation of these designs in published trials in Chapter 2. Our findings underscore the
importance of reporting sufficient information to ensure trial replicability while addressing the
issue of incomplete toxicity data. To support more robust decision-making under such conditions,
we propose the incorporation of the Dose Transition Pathways (DTP) look ahead strategy tool
with two time-to-event toxicity designs in Chapter 3. This approach aims to balance the trade-off
between accelerated dose escalation and recruitment suspension when toxicity outcomes are still
pending.
Non-monotonic dose-efficacy relationships and delayed responses further complicate the use of
traditional designs. In Chapter 4, we introduce the DTP-TITE-CFO-ET design, which accounts for
delayed toxicities, delayed efficacy, as well as non-monotonic dose-efficacy relationship. We also
extend the DTP look-ahead strategy to the joint outcomes setting with incomplete information.
This method allows for more informed and timely dose decisions, enhancing the flexibility and
applicability of advanced early phase trial designs for targeted therapies.
In Chapter 5, we propose a novel two-stage single-arm design that uses progression-free survival
(PFS) as an intermediate endpoint in the interim analysis and overall survival (OS) in the final
analysis. By employing a multi-state survival model to link PFS and OS, we improve the statistical
power of trials while providing a framework for early stopping when appropriate.
Collectively, the designs presented in this thesis offer a comprehensive approach to overcoming
the limitations of traditional early-phase clinical trials when applied to targeted therapies. These
advancements not only address the design challenges introduced by targeted agents but also
enhance decision-making through real-time data integration and advanced statistical method
Chromosome end protection by RAP1-mediated inhibition of DNA-PK.
No full textDuring classical non-homologous end joining (cNHEJ), DNA-dependent protein kinase (DNA-PK) encapsulates free DNA ends, forming a recruitment platform for downstream end-joining factors including ligase 4 (LIG4)1. DNA-PK can also bind telomeres and regulate their resection2-4, but does not initiate cNHEJ at this position. How the end-joining process is regulated in this context-specific manner is currently unclear. Here we show that the shelterin components TRF2 and RAP1 form a complex with DNA-PK that directly represses its end-joining function at telomeres. Biochemical experiments and cryo-electron microscopy reveal that when bound to TRF2, RAP1 establishes a network of interactions with KU and DNA that prevents DNA-PK from recruiting LIG4. In mouse and human cells, RAP1 is redundant with the Apollo nuclease in repressing cNHEJ at chromosome ends, demonstrating that the inhibition of DNA-PK prevents telomere fusions in parallel with overhang-dependent mechanisms. Our experiments show that the end-joining function of DNA-PK is directly and specifically repressed at telomeres, establishing a molecular mechanism for how individual linear chromosomes are maintained in mammalian cells
Deep learning models for deriving optimised measures of fat and muscle mass from MRI.
No full textFat and muscle mass are potential biomarkers of wellbeing and disease in oncology, but clinical measurement methods vary considerably. Here we evaluate the accuracy, precision and ability to track change for multiple deep learning (DL) models that quantify fat and muscle mass from abdominal MRI. Specifically, subcutaneous fat (SF), intra-abdominal fat (VF), external muscle (EM) and psoas muscle (PM) were evaluated using 15 convolutional neural network (CNN)-based and 4 transformer-based deep learning model architectures. There was negligible difference in the accuracy of human observers and all deep learning models in delineating SF or EM. Both of these tissues had excellent repeatability of their delineation. VF was measured most accurately by the human observers, then by CNN-based models, which outperformed transformer-based models. In distinction, PM delineation accuracy and repeatability was poor for all assessments. Repeatability limits of agreement determined when changes measured in individual patients were due to real change rather than test-retest variation. In summary, DL model accuracy and precision of delineating fat and muscle volumes varies between CNN-based and transformer-based models, between different tissues and in some cases with gender. These factors should be considered when investigators deploy deep learning methods to estimate biomarkers of fat and muscle mass
NXP800 Activates the Unfolded Protein Response, Altering AR and E2F Function to Impact Castration-Resistant Prostate Cancer Growth.
No full textPURPOSE: Advanced prostate cancer is invariably fatal, with the androgen receptor (AR) being a major therapeutic target. AR signaling inhibitors have improved overall survival for men with advanced prostate cancer, but treatment resistance is inevitable and includes reactivation of AR signaling. Novel therapeutic approaches targeting these mechanisms to block tumor growth is an urgent unmet clinical need. One attractive strategy is to target heat shock proteins (HSP) critical to AR functional activity. EXPERIMENTAL DESIGN: We first did transcriptome analysis on multiple castration-resistant prostate cancer (CRPC) cohorts to correlate the association between the Gene Ontology cellular response to heat gene expression signature and overall survival. Next, we analyzed the impact of targeting the heat shock factor 1 (HSF1) pathway, with an inhibitor in clinical development, namely, NXP800 (formerly CCT361814), in models of treatment-resistant prostate cancer. Finally, we confirmed our mechanistic and phenotypic findings using an NXP800-resistant model and an in vivo model of CRPC. RESULTS: We report that in multiple CRPC transcriptome cohorts, the Gene Ontology cellular response to heat gene expression signature associates with AR signaling and worse clinical outcome. We demonstrate the effects of targeting the HSF1 pathway, central to cellular stress, with an inhibitor in clinical development, namely, NXP800, in prostate cancer. Targeting the HSF1 pathway with the inhibitor NXP800 decreases HSP72 expression, activates the unfolded protein response, and inhibits AR- and E2F-mediated activity, inhibiting the growth of treatment-resistant prostate cancer models. CONCLUSIONS: Overall, NXP800 has antitumor activity against treatment-resistant prostate cancer models, including molecular subtypes with limited treatment options, supporting its consideration for prostate cancer-specific clinical development
The impact of telomeric components on a reconstituted human replisome
No full textEukaryotes face two problems in having linear chromosomes: i) the end-protection problem, where linear ends could be recognised as double strand breaks, undergoing chromosome end-end fusions and ii) the end-replication problem, defined as the loss of genetic information each round of cell division. Cells have evolved a specialised nucleoprotein structure called a telomere to counteract these problems. In humans, telomeric DNA is composed of 5-15 kilobases of TTAGGG repeats with a G-rich 3’ overhang and is bound by the multi-subunit complex shelterin that suppresses DNA damage signalling and repair mechanisms at telomeres. The enzyme telomerase can be recruited to add telomeric repeats onto the 3’ end but the main way telomeres are duplicated each cell cycle is canonical DNA replication. A failure to replicate telomeres can drive genome instability due to the loss of telomeric repeats. Despite the importance of accurate telomere replication, they are a source of replication stress. Indirect genetic evidence mainly attributes this to G-rich telomeric DNA, but the pleiotropic effects observed upon genetic deletions and the presence of other telomeric components like shelterin, complicates interpretation of this data.
This study aims to reconstitute a human replisome from purified proteins to analyse the impact of individual telomeric components on the replication process. Through this, I uncover that G-rich and C-rich telomeric DNA has minimal impact on the replication process whereas, shelterin can terminally stall the replisome similarly to other protein roadblocks. I reveal that shelterin induces a single-stranded gap specifically on the lagging strand, which is dependent upon a subcomplex of shelterin, containing protection of telomeres 1 (POT1) and its binding partner TPP1. Collectively, I uncover a dual inhibitory effect of shelterin on the replisome: i) fork stalling, and ii) defective lagging strand synthesis, providing key mechanistic insights into how shelterin interfaces with the replisome at telomeres
Muscle mass and attenuation in patients with advanced cancer: the association and change with activity and systemic anti-cancer treatment
No full textActivity and performance status are an important part of the assessment of patients with cancer, but current methods are limited as they are subjective and can only be performed at clinic review. Similarly, sarcopenia is recognised as being associated with poorer outcomes and its inclusion may improve fitness assessments but is not routinely considered in patients receiving systemic anti-cancer treatment (SACT).
This study proposes that activity levels and skeletal muscle parameters change significantly during SACT and may offer an additional way to assess prognosis and fitness for treatment.
The project aims to determine if the longitudinal pattern of change in muscle parameters through SACT and if reduced activity levels can feasibly be used as a trigger for clinical assessment. Alongside this, the project aims to assess the association between muscle mass and attenuation, and activity levels and determine if treatment choices impact the change in muscle mass and attenuation. As exploratory endpoints, the study aims to begin to assess if therapy interventions, including physiotherapy and dietary supplementation, impact the change in muscle parameters during treatment. It will also assess the acceptability of remote monitoring and virtual consultation to reduce hospital attendance.
The project is divided into three arms to investigate these endpoints. These have different recruitment strategies and study designs so will be discussed separately:
• Muscle parameter assessment: retrospective analysis of routine CT scans in lung cancer patients who have completed treatment.
• Activity assessment: prospective monitoring of step counts in patients with lung cancer, mesothelioma and gastrointestinal cancer currently receiving SACT.
• Remote monitoring assessment: qualitative assessment of staff opinions of virtual consultations and WATTOX participant opinions of remote step count monitorin
Interpretable Deep Learning for 3D and 4D Cell Shape Profiling
No full textA central principle in biology is the relationship between form and function. This connection, evident across all biological scales,
from molecules to whole organisms, underpins the study of cellular morphology, where deviations in shape often indicate disease
states or cellular dysfunction. Cell shape has long been recognised as a key readout for diagnosing conditions and understanding
cellular behaviours. Despite advances in microscopy, which now allow for detailed three-dimensional (3D) and even
four-dimensional (4D) imaging, most profiling efforts remain limited by static, two-dimensional (2D) data that fail to capture the full
complexity of cell structures and their dynamic responses to environmental stimuli.
This thesis aims to bridge that gap by developing interpretable deep learning techniques for 3D and 4D cell shape profiling, focusing
on improving the automation and accuracy of profiling methods while accounting for cell-to-cell heterogeneity. Through the use of
geometric deep learning and multiple instance learning (MIL), the models proposed here are capable of capturing subtle
morphological features that vary across individual cells or evolve over time, offering a more detailed view of cellular states than
traditional methods. In particular, these approaches are applied in the context of drug discovery, where accurate phenotypic
profiling can lead to more predictive models and ultimately accelerate the identification of therapeutic targets.
The contributions of this work include robust models for 3D and 4D cell shape analysis, the incorporation of interpretability into deep
learning predictions, and the application of these models to heterogeneous populations of perturbed cells. The methods developed
here offer new insights into the relationship between cell shape and biological function, demonstrating the potential for deep learning to revolutionise the field of morphological profiling and drug discovery
Pan-cancer drivers of metastasis.
No full textMetastasis remains a leading cause of cancer-related mortality, irrespective of the primary tumour origin. However, the core gene regulatory program governing distinct stages of metastasis across cancers remains poorly understood. We investigate this through single-cell transcriptome analysis encompassing over two hundred patients with metastatic and non-metastatic tumours across six cancer types. Our analysis revealed a prognostic core gene signature that provides insights into the intricate cellular dynamics and gene regulatory networks driving metastasis progression at the pan-cancer and single-cell level. Notably, the dissection of transcription factor networks active across different stages of metastasis, combined with functional perturbation, identified SP1 and KLF5 as key regulators, acting as drivers and suppressors of metastasis, respectively, at critical steps of this transition across multiple cancer types. Through in vivo and in vitro loss of function of SP1 in cancer cells, we revealed its role in driving cancer cell survival, invasive growth, and metastatic colonisation. Furthermore, tumour cells and the microenvironment increasingly engage in communication through WNT signalling as metastasis progresses, driven by SP1. Further validating these observations, a drug repurposing analysis identified distinct FDA-approved drugs with anti-metastasis properties, including inhibitors of WNT signalling across various cancers
Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer.
No full textThe intertumor and intratumor heterogeneity of triple-negative breast cancers, which is reflected in diverse drug responses, interplays with tumor evolution. In this study, we developed a preclinical experimental and analytical framework using patient-derived tumor xenografts (PDTX) from patients with treatment-naïve triple-negative breast cancers to test their predictive value in personalized cancer treatment approaches. Patients and their matched PDTXs exhibited concordant drug responses to neoadjuvant therapy using two trial designs and dosing schedules. This platform enabled analysis of nongenetic mechanisms involved in relapse dynamics. Treatment resulted in permanent phenotypic changes, with functional and therapeutic consequences. High-throughput drug screening methods in ex vivo PDTX cells revealed patient-specific drug response changes dependent on first-line therapy. This was validated in vivo, as exemplified by a change in olaparib sensitivity in tumors previously treated with clinically relevant cycles of standard-of-care chemotherapy. In summary, PDTXs provide a robust tool to test patient drug responses and therapeutic regimens and to model evolutionary trajectories. However, high intermodel variability and permanent nongenomic transcriptional changes constrain their use for personalized cancer therapy. This work highlights important considerations associated with preclinical drug response modeling and potential uses of the platform to identify efficacious and preferential sequential therapeutic regimens. Significance: Patient-derived tumor xenografts from treatment-naïve breast cancer samples can predict patient drug responses and model treatment-induced phenotypic and functional evolution, making them valuable preclinical tools
Bladder Tumor-Focused Adaptive Radiation Therapy: Clinical Outcomes of a Phase I Dose Escalation Study.
No full textPURPOSE: We determine the maximum tolerated tumor-focused dose (MTD) for the radical treatment of muscle invasive bladder cancer enabled by image guided adaptive radiation therapy and long-term clinical outcomes. METHODS AND MATERIALS: Fifty-nine patients with T2 to T4aN0M0 unifocal urothelial muscle invasive bladder cancer suitable for daily radical radiation therapy were recruited prospectively to an ethics-approved protocol (NCT01124682). The uninvolved bladder (PTVbladder) was planned to 52 Gy in 32 fractions. The bladder tumor (PTVtumor) was planned to an assigned dose level of 68, 70, 72, or 74 Gy. If organ at risk dose constraints were violated, then PTVtumor was planned to 64 Gy. Dose level allocation was determined by concurrent toxicity assessment of all previous patients recruited. Acute toxicity was evaluated using Common Terminology Criteria for Adverse Events v3.0; late toxicity was evaluated using Radiation Therapy Oncology Group criteria. The MTD was predefined as the highest dose level with an estimated probability of ≤ 15% ≥ G3 late toxicity and an observed rate of <50% acute G3 and <10% acute G4 toxicity. RESULTS: Twenty-six patients were assigned to 68 Gy, of whom 6 were planned to 64 Gy; 29 patients were assigned to 70 Gy of whom 1 was planned to 68 Gy, 2 patients were assigned and planned to 72 Gy; no patients were assigned to 74 Gy. Three patients did not complete the treatment as planned, of whom only 1 patient stopped treatment because dose-limiting toxicity occurred. The MTD was 70 Gy. Acute genito-urinary and gastro-intestinal G3 acute toxicity was seen in 19% and 7% of patients, respectively. No acute G4 genito-urinary or gastro-intestinal toxicity was seen. Late toxicity (any) G3 and G4 was seen in 14% and 2% of patients, respectively. The 5-year overall survival was 58% (95% CI, 44%-71%). The bladder preservation rate was 89% (95% CI, 88%-96%) with 6 patients not retaining native bladder function. CONCLUSIONS: Bladder tumor-focused dose escalation to 70 Gy using image guided adaptive radiation therapy is feasible with acceptable toxicity. This dose level has been evaluated in a phase II randomized control trial (RAIDER NCT02447549)