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Wasm-iCARE: a portable and privacy-preserving web module to build, validate, and apply absolute risk models.
No full textOBJECTIVES: Absolute risk models estimate an individual's future disease risk over a specified time interval. Applications utilizing server-side risk tooling, the R-based iCARE (R-iCARE), to build, validate, and apply absolute risk models, face limitations in portability and privacy due to their need for circulating user data in remote servers for operation. We overcome this by porting iCARE to the web platform. MATERIALS AND METHODS: We refactored R-iCARE into a Python package (Py-iCARE) and then compiled it to WebAssembly (Wasm-iCARE)-a portable web module, which operates within the privacy of the user's device. RESULTS: We showcase the portability and privacy of Wasm-iCARE through 2 applications: for researchers to statistically validate risk models and to deliver them to end-users. Both applications run entirely on the client side, requiring no downloads or installations, and keep user data on-device during risk calculation. CONCLUSIONS: Wasm-iCARE fosters accessible and privacy-preserving risk tools, accelerating their validation and delivery
Efficacy of Trastuzumab Deruxtecan in HER2-Expressing Solid Tumors by Enrollment HER2 IHC Status: Post Hoc Analysis of DESTINY-PanTumor02.
No full textINTRODUCTION: DESTINY-PanTumor02 (NCT04482309) evaluated the efficacy and safety of trastuzumab deruxtecan (T-DXd) in pretreated patients with human epidermal growth factor receptor 2 (HER2)-expressing [immunohistochemistry (IHC) 3+/2+] solid tumors across seven cohorts: endometrial, cervical, ovarian, bladder, biliary tract, pancreatic, and other. Subgroup analyses by HER2 status were previously reported by central HER2 IHC testing, determined at enrollment or confirmed retrospectively. Reflecting the testing methods available in clinical practice, most patients (n = 202; 75.7%) were enrolled based on local HER2 IHC testing. Here, we report outcomes by HER2 IHC status as determined by the local or central test results used for study enrollment. METHODS: This phase 2, open-label study evaluated T-DXd (5.4 mg/kg once every 3 weeks) for HER2-expressing (IHC 3+/2+ by local or central testing) locally advanced or metastatic disease after ≥ 1 systemic treatment or without alternative treatments. The primary endpoint was investigator-assessed confirmed objective response rate (ORR). Secondary endpoints included safety, duration of response (DOR), progression-free survival (PFS), and overall survival. RESULTS: In total, 111 (41.6%) and 151 (56.6%) patients were enrolled with IHC 3+ and IHC 2+ tumors, respectively. In patients with IHC 3+ tumors, investigator-assessed confirmed ORR was 51.4% [95% confidence interval (CI) 41.7, 61.0], and median DOR was 14.2 months (95% CI 10.3, 23.6). In patients with IHC 2+ tumors, investigator-assessed ORR was 26.5% (95% CI 19.6, 34.3), and median DOR was 9.8 months (95% CI 4.5, 12.6). Safety was consistent with the known profile of T-DXd. CONCLUSION: In line with previously reported results, T-DXd demonstrated clinically meaningful benefit in patients with HER2-expressing tumors, with the greatest benefit in patients with IHC 3+ tumors. These data support the antitumor activity of T-DXd in HER2-expressing solid tumors, irrespective of whether patients are identified by local or central HER2 IHC testing
The Immune Landscape and Its Potential for Immunotherapy in Advanced Biliary Tract Cancer.
No full textBiliary tract cancers (BTC) are a highly heterogeneous group of cancers at the genomic, epigenetic and molecular levels. The vast majority of patients initially present at an advanced (unresectable) disease stage due to a lack of symptoms and an aggressive tumour biology. Chemotherapy has been the mainstay of treatment in patients with advanced BTC but the survival outcomes and prognosis remain poor. The addition of immune checkpoint inhibitors (ICI) to chemotherapy have shown only a marginal benefit over chemotherapy alone due to the complex tumour immune microenvironment of these cancers. This review appraises our current understanding of the immune landscape of advanced BTC, including emerging transcriptome-based classifications, highlighting the mechanisms of immune evasion and resistance to ICI and their therapeutic implications. It describes the shifting treatment paradigm from traditional chemotherapy to immunotherapy combinations as well as the potential biomarkers for predicting response to ICI
Neoadjuvant Radiotherapy and Endocrine Therapy for Oestrogen Receptor Positive Breast Cancers: The Neo-RT Feasibility Study.
No full textAIMS: To establish the safety and feasibility of delivering neoadjuvant radiotherapy and endocrine therapy for oestrogen receptor-positive breast cancers with palpable size 20mm or greater, for which radiotherapy might facilitate more conservative surgery. MATERIALS AND METHODS: A single-arm feasibility study was conducted. Patients received whole breast radiotherapy with or without radiotherapy to nodal areas. Dose/fractionation was 40Gy in 15 fractions over 3 weeks, with or without either a simultaneous integrated boost to 48Gy or sequential boost to the tumour bed. This was followed by endocrine treatment for 20 weeks, then surgery. The primary endpoint of the study was the proportion of patients successfully completing neoadjuvant radiotherapy and endocrine treatment followed by breast surgery. Response and toxicity endpoints including mastectomy rate, peri/postoperative complications, and pathological response were also evaluated. The primary analysis is descriptive. The study regimen would be considered feasible if more than 70% of patients completed treatment, while it might not be considered feasible if less than 50% did so. With a one-sided 5% significance level and 80% power, a maximum of 43 patients would be required to detect a rate of ≤50% vs ≥70%. RESULTS: 14 patients were recruited out of the planned 43. Due to slow recruitment, particularly during the COVID-19 pandemic, the decision was made to stop the trial in October 2021. One registered patient was found to be ineligible before starting treatment. 13/13 patients (100%, 90% CI: 75.3%, 100%) who received any trial treatment successfully completed all trial treatments. The lower bound of the Clopper-Pearson (exact) 90% confidence interval was 79%, indicating that the primary endpoint would have been met if the planned recruitment had been achieved. 3/13 patients underwent mastectomy. 7/13 had more conservative surgery than had been planned at baseline. 4/13 patients experienced any peri/postoperative complication. The only acute radiotherapy toxicities reported were grade 1/2 dermatitis and grade 1 fatigue. Long-term breast outcomes were clinician assessed as none/mild at all timepoints in 12/13 patients. All tumours showed evidence of some pathological response to treatment, but none had a pathological complete response. CONCLUSION: This treatment schedule is likely feasible. It is difficult to draw strong conclusions on safety/toxicity given the small numbers, but these seem in keeping with other recent reports of neoadjuvant breast radiotherapy
High-resolution cryo-EM of the human CDK-activating kinase for structure-based drug design.
No full textRational design of next-generation therapeutics can be facilitated by high-resolution structures of drug targets bound to small-molecule inhibitors. However, application of structure-based methods to macromolecules refractory to crystallization has been hampered by the often-limiting resolution and throughput of cryogenic electron microscopy (cryo-EM). Here, we use high-resolution cryo-EM to determine structures of the CDK-activating kinase, a master regulator of cell growth and division, in its free and nucleotide-bound states and in complex with 15 inhibitors at up to 1.8 Å resolution. Our structures provide detailed insight into inhibitor interactions and networks of water molecules in the active site of cyclin-dependent kinase 7 and provide insights into the mechanisms contributing to inhibitor selectivity, thereby providing the basis for rational design of next-generation therapeutics. These results establish a methodological framework for the use of high-resolution cryo-EM in structure-based drug design
Optical projection tomography implemented for accessibility and low cost (OPTImAL).
No full textOptical projection tomography (OPT) is a three-dimensional mesoscopic imaging modality that can use absorption or fluorescence contrast, and is widely applied to fixed and live samples in the mm-cm scale. For fluorescence OPT, we present OPT implemented for accessibility and low cost, an open-source research-grade implementation of modular OPT hardware and software that has been designed to be widely accessible by using low-cost components, including light-emitting diode (LED) excitation and cooled complementary metal-oxide-semiconductor (CMOS) cameras. Both the hardware and software are modular and flexible in their implementation, enabling rapid switching between sample size scales and supporting compressive sensing to reconstruct images from undersampled sparse OPT data, e.g. to facilitate rapid imaging with low photobleaching/phototoxicity. We also explore a simple implementation of focal scanning OPT to achieve higher resolution, which entails the use of a fan-beam geometry reconstruction method to account for variation in magnification. This article is part of the Theo Murphy meeting issue 'Open, reproducible hardware for microscopy'
Development and evaluation of machine-learning methods in whole-body magnetic resonance imaging with diffusion weighted imaging for staging of patients with cancer: the MALIBO diagnostic test accuracy study
No full textBackgroundWhole-body magnetic resonance imaging is accurate, efficient and cost-effective for cancer staging. Machine learning may support radiologists reading whole-body magnetic resonance imaging.Objectives1. To develop a machine-learning algorithm to detect normal organs and cancer lesions. 2. To compare diagnostic accuracy, time and agreement of radiology reads to detect metastases using whole-body magnetic resonance imaging with concurrent machine learning (whole-body magnetic resonance imaging + machine learning) against standard whole-body magnetic resonance imaging (whole-body magnetic resonance imaging + standard deviation).Design and participantsRetrospective analysis of (1) prospective single-centre study in healthy volunteers > 18 years (n = 51) and (2) prospective multicentre STREAMLINE study patient data (n = 438).TestsIndex: whole-body magnetic resonance imaging + machine learning. Comparator: whole-body magnetic resonance imaging + standard deviation.Reference standardPreviously established expert panel consensus reference at 12 months from diagnosis.Outcome measuresPrimary: difference in per-patient specificity between whole-body magnetic resonance imaging + machine learning and whole-body magnetic resonance imaging + standard deviation. Secondary: per-patient sensitivity, per-lesion sensitivity and specificity, read time and agreement.MethodsPhase 1: classification forests, convolutional neural networks, and a multi-atlas approaches for organ segmentation. Phase 2/3: whole-body magnetic resonance imaging scans were allocated to Phase 2 (training = 226, validation = 45) and Phase 3 (testing = 193). Disease sites were manually labelled. The final algorithm was applied to 193 Phase 3 cases, generating probability heatmaps. Twenty-five radiologists (18 experienced, 7 inexperienced in whole-body magnetic resonance imaging) were randomly allocated whole-body magnetic resonance imaging + machine learning or whole-body magnetic resonance imaging + standard deviation over two or three rounds in a National Health Service setting. Read time was independently recorded.ResultsPhases 1 and 2: convolutional neural network had best Dice similarity coefficient, recall and precision measurements for healthy organ segmentation. Final algorithm used a ‘two-stage’ initial organ identification followed by lesion detection. Phase 3: evaluable scans (188/193, of which 50 had metastases from 117 colon, 71 lung cancer cases) were read between November 2019 and March 2020. For experienced readers, per-patient specificity for detection of metastases was 86.2% (whole-body magnetic resonance imaging + machine learning) and 87.7% (whole-body magnetic resonance imaging + standard deviation), (difference −1.5%, 95% confidence interval −6.4% to 3.5%; p = 0.387); per-patient sensitivity was 66.0% (whole-body magnetic resonance imaging + machine learning) and 70.0% (whole-body magnetic resonance imaging + standard deviation) (difference −4.0%, 95% confidence interval −13.5% to 5.5%; p = 0.344). For inexperienced readers (53 reads, 15 with metastases), per-patient specificity was 76.3% in both groups with sensitivities of 73.3% (whole-body magnetic resonance imaging + machine learning) and 60.0% (whole-body magnetic resonance imaging + standard deviation). Per-site specificity remained high within all sites; above 95% (experienced) or 90% (inexperienced). Per-site sensitivity was highly variable due to low number of lesions in each site. Reading time lowered under machine learning by 6.2% (95% confidence interval −22.8% to 10.0%). Read time was primarily influenced by read round with round 2 read times reduced by 32% (95% confidence interval 20.8% to 42.8%) overall with subsequent regression analysis showing a significant effect (p = 0.0281) by using machine learning in round 2 estimated as 286 seconds (or 11%) quicker. Interobserver variance for experienced readers suggests moderate agreement, Cohen’s κ = 0.64, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + machine learning) and Cohen’s κ = 0.66, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + standard deviation).LimitationsPatient whole-body magnetic resonance imaging data were heterogeneous with relatively few metastatic lesions in a wide variety of locations, making training and testing difficult and hampering evaluation of sensitivity.ConclusionsThere was no difference in diagnostic accuracy for whole-body magnetic resonance imaging radiology reads with or without machine-learning support, although radiology read time may be slightly shortened using whole-body magnetic resonance imaging + machine learning.Future workFailure-case analysis to improve model training, automate lesion segmentation and transfer of machine-learning techniques to other tumour types and imaging modalities.Study registrationThis study is registered as ISRCTN23068310.FundingThis award was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation (EME) programme (NIHR award ref: 13/122/01) and is published in full in Efficacy and Mechanism Evaluation; Vol. 11, No. 15. See the NIHR Funding and Awards website for further award information
Elucidating acquired PARP inhibitor resistance in advanced prostate cancer.
No full textPARP inhibition (PARPi) has anti-tumor activity against castration-resistant prostate cancer (CRPC) with homologous recombination repair (HRR) defects. However, mechanisms underlying PARPi resistance are not fully understood. While acquired mutations restoring BRCA genes are well documented, their clinical relevance, frequency, and mechanism of generation remain unclear. Moreover, how resistance emerges in BRCA2 homozygously deleted (HomDel) CRPC is unknown. Evaluating samples from patients with metastatic CRPC treated in the TOPARP-B trial, we identify reversion mutations in most BRCA2/PALB2-mutated tumors (79%) by end of treatment. Among reversions mediated by frameshift deletions, 60% are flanked by DNA microhomologies, implicating POLQ-mediated repair. The number of reversions and time of their detection associate with radiological progression-free survival and overall survival (p < 0.01). For BRCA2 HomDels, selection for rare subclones without BRCA2-HomDel is observed following PARPi, confirmed by single circulating-tumor-cell genomics, biopsy fluorescence in situ hybridization (FISH), and RNAish. These data support the need for restored HRR function in PARPi resistance
Clinical practice guidelines for the diagnosis and surveillance of BAP1-tumour predisposition syndrome
No full textRegulation of PARP1/2 and the tankyrases: emerging parallels.
No full textADP-ribosylation is a prominent and versatile post-translational modification, which regulates a diverse set of cellular processes. Poly-ADP-ribose (PAR) is synthesised by the poly-ADP-ribosyltransferases PARP1, PARP2, tankyrase (TNKS), and tankyrase 2 (TNKS2), all of which are linked to human disease. PARP1/2 inhibitors have entered the clinic to target cancers with deficiencies in DNA damage repair. Conversely, tankyrase inhibitors have continued to face obstacles on their way to clinical use, largely owing to our limited knowledge of their molecular impacts on tankyrase and effector pathways, and linked concerns around their tolerability. Whilst detailed structure-function studies have revealed a comprehensive picture of PARP1/2 regulation, our mechanistic understanding of the tankyrases lags behind, and thereby our appreciation of the molecular consequences of tankyrase inhibition. Despite large differences in their architecture and cellular contexts, recent structure-function work has revealed striking parallels in the regulatory principles that govern these enzymes. This includes low basal activity, activation by intra- or inter-molecular assembly, negative feedback regulation by auto-PARylation, and allosteric communication. Here we compare these poly-ADP-ribosyltransferases and point towards emerging parallels and open questions, whose pursuit will inform future drug development efforts