Publications
36 results found
Chen M, Copley SJ, Viola P, et al., 2023, Radiomics and artificial intelligence for precision medicine in lung cancer treatment., Semin Cancer Biol, Vol: 93, Pages: 97-113
Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.
Aboagye E, Lu H, Lou H, et al., 2023, Tumour and local lymphoid tissue interaction determines prognosis in high grade serous ovarian cancer, Cell Reports Medicine, Vol: 4, Pages: 1-24, ISSN: 2666-3791
Tertiary lymphoid structure (TLS) is associated with prognosis in copy number-driven tumours,including high grade serous ovarian cancer (HGSOC), although the function of TLS and its interactionwith copy-number alterations in HGSOC is not fully understood. In the current study, we confirmthat TLS-high HGSOC patients show significantly better progression free survival. We show thatpresence of TLS in HGSOC tumours is associated with B-cell maturation and cytotoxic tumourspecific T-cells activation and proliferation. Additionally, the copy number loss of IL15 and CXCL10may limit TLS formation in HGSOC; a list of genes that may dysregulate TLS function is also proposed.Manuscript Click here to view linked ReferencesLastly, a radiomics-based signature is developed to predict presence of TLS, which independentlypredicts PFS in both HGSOC patients and ICI-treated NSCLC patients. Overall, we reveal that TLScoordinates intratumoural B-cell and T-cell response against HGSOC tumour, while cancer genomeevolves to counteract TLS formation and function.
Chen M, Lu H, Copley SJ, et al., 2023, A novel radiogenomics biomarker for predicting treatment response and pneumotoxicity from programmed cell death protein or ligand-1 inhibition immunotherapy in NSCLC, Journal of Thoracic Oncology, Vol: 18, Pages: 718-730, ISSN: 1556-0864
INTRODUCTION: Patient selection for checkpoint inhibitor immunotherapy is currently guided by programmed death-ligand 1 (PD-L1) expression obtained from immunohistochemical staining of tumor tissue samples. This approach is susceptible to limitations resulting from the dynamic and heterogeneous nature of cancer cells and the invasiveness of the tissue sampling procedure. To address these challenges, we developed a novel computed tomography (CT) radiomic-based signature for predicting disease response in patients with NSCLC undergoing programmed cell death protein 1 (PD-1) or PD-L1 checkpoint inhibitor immunotherapy. METHODS: This retrospective study comprises a total of 194 patients with suitable CT scans out of 340. Using the radiomic features computed from segmented tumors on a discovery set of 85 contrast-enhanced chest CTs of patients diagnosed with having NSCLC and their CD274 count, RNA expression of the protein-encoding gene for PD-L1, as the response vector, we developed a composite radiomic signature, lung cancer immunotherapy-radiomics prediction vector (LCI-RPV). This was validated in two independent testing cohorts of 66 and 43 patients with NSCLC treated with PD-1 or PD-L1 inhibition immunotherapy, respectively. RESULTS: LCI-RPV predicted PD-L1 positivity in both NSCLC testing cohorts (area under the curve [AUC] = 0.70, 95% confidence interval [CI]: 0.57-0.84 and AUC = 0.70, 95% CI: 0.46-0.94). In one cohort, it also demonstrated good prediction of cases with high PD-L1 expression exceeding key treatment thresholds (>50%: AUC = 0.72, 95% CI: 0.59-0.85 and >90%: AUC = 0.66, 95% CI: 0.45-0.88), the tumor's objective response to treatment at 3 months (AUC = 0.68, 95% CI: 0.52-0.85), and pneumonitis occurrence (AUC = 0.64, 95% CI: 0.48-0.80). LCI-RPV achieved statistically significant stratification of the patients into a high- and low-risk survival group (hazard ratio = 2.26, 95% CI: 1.21-4.24, p = 0.011 a
Hu Z, Cunnea P, Zhong Z, et al., 2023, Table S2 from The Oxford Classic Links Epithelial-to-Mesenchymal Transition to Immunosuppression in Poor Prognosis Ovarian Cancers
<jats:p><p>Table S2. The QuPath classification results for the immunohistochemistry of CAPS in SOC tissue microarrays.</p></jats:p>
Hu Z, Cunnea P, Zhong Z, et al., 2023, Data from The Oxford Classic Links Epithelial-to-Mesenchymal Transition to Immunosuppression in Poor Prognosis Ovarian Cancers
<jats:p><div>AbstractPurpose:<p>Using RNA sequencing, we recently developed the 52-gene–based Oxford classifier of carcinoma of the ovary (Oxford Classic, OxC) for molecular stratification of serous ovarian cancers (SOCs) based on the molecular profiles of their cell of origin in the fallopian tube epithelium. Here, we developed a 52-gene NanoString panel for the OxC to test the robustness of the classifier.</p>Experimental Design:<p>We measured the expression of the 52 genes in an independent cohort of prospectively collected SOC samples (<i>n</i> = 150) from a homogenous cohort who were treated with maximal debulking surgery and chemotherapy. We performed data mining of published expression profiles of SOCs and validated the classifier results on tissue arrays comprising 137 SOCs.</p>Results:<p>We found evidence of profound nongenetic heterogeneity in SOCs. Approximately 20% of SOCs were classified as epithelial-to-mesenchymal transition–high (EMT-high) tumors, which were associated with poor survival. This was independent of established prognostic factors, such as tumor stage, tumor grade, and residual disease after surgery (HR, 3.3; <i>P</i> = 0.02). Mining expression data of 593 patients revealed a significant association between the EMT scores of tumors and the estimated fraction of alternatively activated macrophages (M2; <i>P</i> < 0.0001), suggesting a mechanistic link between immunosuppression and poor prognosis in EMT-high tumors.</p>Conclusions:<p>The OxC-defined EMT-high SOCs carry particularly poor prognosis independent of established clinical parameters. These tumors are associated with high frequency of immunosuppressive macrophages, suggesting a potential therapeutic target to improve clinical outcome.</p></div&
Hu Z, Cunnea P, Zhong Z, et al., 2023, Table S2 from The Oxford Classic Links Epithelial-to-Mesenchymal Transition to Immunosuppression in Poor Prognosis Ovarian Cancers
<jats:p><p>Table S2. The QuPath classification results for the immunohistochemistry of CAPS in SOC tissue microarrays.</p></jats:p>
Hu Z, Cunnea P, Zhong Z, et al., 2023, Supplementary Information from The Oxford Classic Links Epithelial-to-Mesenchymal Transition to Immunosuppression in Poor Prognosis Ovarian Cancers
<jats:p><p>Supplementary Figures and Tables</p></jats:p>
Hu Z, Cunnea P, Zhong Z, et al., 2023, Data from The Oxford Classic Links Epithelial-to-Mesenchymal Transition to Immunosuppression in Poor Prognosis Ovarian Cancers
<jats:p><div>AbstractPurpose:<p>Using RNA sequencing, we recently developed the 52-gene–based Oxford classifier of carcinoma of the ovary (Oxford Classic, OxC) for molecular stratification of serous ovarian cancers (SOCs) based on the molecular profiles of their cell of origin in the fallopian tube epithelium. Here, we developed a 52-gene NanoString panel for the OxC to test the robustness of the classifier.</p>Experimental Design:<p>We measured the expression of the 52 genes in an independent cohort of prospectively collected SOC samples (<i>n</i> = 150) from a homogenous cohort who were treated with maximal debulking surgery and chemotherapy. We performed data mining of published expression profiles of SOCs and validated the classifier results on tissue arrays comprising 137 SOCs.</p>Results:<p>We found evidence of profound nongenetic heterogeneity in SOCs. Approximately 20% of SOCs were classified as epithelial-to-mesenchymal transition–high (EMT-high) tumors, which were associated with poor survival. This was independent of established prognostic factors, such as tumor stage, tumor grade, and residual disease after surgery (HR, 3.3; <i>P</i> = 0.02). Mining expression data of 593 patients revealed a significant association between the EMT scores of tumors and the estimated fraction of alternatively activated macrophages (M2; <i>P</i> < 0.0001), suggesting a mechanistic link between immunosuppression and poor prognosis in EMT-high tumors.</p>Conclusions:<p>The OxC-defined EMT-high SOCs carry particularly poor prognosis independent of established clinical parameters. These tumors are associated with high frequency of immunosuppressive macrophages, suggesting a potential therapeutic target to improve clinical outcome.</p></div&
Hu Z, Cunnea P, Zhong Z, et al., 2023, Supplementary Information from The Oxford Classic Links Epithelial-to-Mesenchymal Transition to Immunosuppression in Poor Prognosis Ovarian Cancers
<jats:p><p>Supplementary Figures and Tables</p></jats:p>
Talbot T, Lu H, Aboagye EO, 2023, Amplified therapeutic targets in high-grade serous ovarian carcinoma - a review of the literature with quantitative appraisal, CANCER GENE THERAPY, ISSN: 0929-1903
Palma Chaundler CS, Lu H, Fu R, et al., 2023, Kinetics and efficacy of antibody drug conjugates in 3D tumour models
<jats:title>ABSTRACT</jats:title><jats:p>Antibody-drug conjugates (ADCs) are emerging targeted agents against cancer. Current studies of ADCs are performed on monolayer cultures which do not mimic the biophysical property of a tumour. Hence,<jats:italic>in vitro</jats:italic>models that can better predict the efficacy of ADCs<jats:italic>in vivo</jats:italic>are needed. In this study, we aim to optimise 3-dimentional cancer spheroid systems, which preserve the features of the tumour structure, to test the efficacy of two ADCs (T-DM1 and T-vcMMAE). Firstly, a set of reproducible spheroid models using epithelial ovarian cancer cell lines were established. Subsequently, phenotypic changes in spheroids were characterised upon ADC treatment. The penetration dynamics of ADCs into 3D tumour structure were also studied. Our data revealed that spheroids are less sensitive to ADCs compared to monolayer cultures. Interestingly, the small molecule component of ADCs-the cytotoxic payload-showed a similar decrease in efficacy in spheroids compared to monolayer cultures. Furthermore, we also gained new insight into ADC penetration dynamics and showed that ADCs can fully penetrate a tumour-like spheroid within 24h. The results suggest that although ADCs, as large molecule biological drugs, are likely to have slower penetration dynamics than small molecule compounds such as their cytotoxic payload, they could have comparable capability to kill cancer cells in 3D structures. This may be explained by the fact that multiple cytotoxic payloads are conjugated with each single antibody, which compensates the penetration deficiency of the large molecules. In conclusion, our work confirms that the tumour 3D structure could limit the therapeutic efficacy of ADCs. Nevertheless, optimising ADC design such as adjusting drug-to-antibody ratios could help to overcome this hurdle.</jats:p>
Yan D, Lu H, Kaur A, et al., 2023, Development and optimisation of cationic lipid nanoparticles for mRNA delivery
<jats:title>Abstract</jats:title><jats:p>Messenger RNA (mRNA) has been proposed as a therapeutic agent for various diseases, including cancer. To ensure effective transfection of cancer cells, mRNA needs to be transported with a delivery system that protects its integrity and functionality. In this regard, cationic lipid nanoparticles composed of dioleoylphosphatidylethanolamine (DOPE) and 3β-[N-(N’,N’-dimethylaminoethane)-carbamoyl] cholesterol (DC-Chol) have emerged as common vectors to deliver mRNA. In this project, we aim to use luciferase mRNA as a reporter to synthesise mRNA-loaded cationic lipid nanoparticles, and optimise their mRNA encapsulation and transfection efficiency in ovarian cancer cells. The optimisation process included: 1) adjusting the lipid formulation; 2) adjusting the input mRNA concentration before lipid nanoparticle extrusion; and 3) adjusting the extrusion methods. After optimisation, the encapsulation efficiency was optimised to 62%, thus achieving a relatively high transfection luminescence signal (9.4 times compared to baseline). The lipid nanoparticles also demonstrated stable physical characteristics and high biocompatibility (above 75% cell viability after treatment) within 24 hours. Overall, this project evaluated the synthesis of DOPE/DC-Chol cationic lipid nanoparticles, and optimised their mRNA encapsulation and transfection efficiency in ovarian cancer cell lines. The optimised lipid nanoparticles can be utilised as an ideal system for mRNA delivery, which could be further developed as a potential platform for the immunotherapy in ovarian cancer.</jats:p>
Lu H, George J, Eslam M, et al., 2023, Discriminatory changes in circulating metabolites as a predictor of hepatocellular cancer in patients with MAFLD, Liver Cancer, Vol: 12, Pages: 19-31, ISSN: 2235-1795
Introduction: The burden of metabolic (dysfunction) associated fatty liver disease (MAFLD) is rising mirrored by an increase in hepatocellular cancer (HCC). MAFLD and its sequelae are characterized by perturbations in lipid handling, inflammation, and mitochondrial damage. The profile of circulating lipid and small molecule metabolites with the development of HCC is poorly characterized in MAFLD and could be used in future studies as a biomarker for HCC. Methods: We assessed the profile of 273 lipid and small molecule metabolites by ultra-performance liquid chromatography coupled to high-resolution mass spectrometry in serum from patients with MAFLD (n = 113) and MAFLD-associated HCC (n = 144) from six different centers. Regression models were used to identify a predictive model of HCC. Results: Twenty lipid species and one metabolite, reflecting changes in mitochondrial function and sphingolipid metabolism, were associated with the presence of cancer on a background of MAFLD with high accuracy (AUC 0.789, 95% CI: 0.721–0.858), which was enhanced with the addition of cirrhosis to the model (AUC 0.855, 95% CI: 0.793–0.917). In particular, the presence of these metabolites was associated with cirrhosis in the MAFLD subgroup (p < 0.001). When considering the HCC cohort alone, the metabolic signature was an independent predictor of overall survival (HR 1.42, 95% CI: 1.09–1.83, p < 0.01). Conclusion: These exploratory findings reveal a metabolic signature in serum which is capable of accurately detecting the presence of HCC on a background of MAFLD. This unique serum signature will be taken forward for further investigation of diagnostic performance as biomarker of early stage HCC in patients with MAFLD in the future.
Inglese M, Patel N, Linton-Reid K, et al., 2022, A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer's disease., Commun Med (Lond), Vol: 2
BACKGROUND: Alzheimer's disease, the most common cause of dementia, causes a progressive and irreversible deterioration of cognition that can sometimes be difficult to diagnose, leading to suboptimal patient care. METHODS: We developed a predictive model that computes multi-regional statistical morpho-functional mesoscopic traits from T1-weighted MRI scans, with or without cognitive scores. For each patient, a biomarker called "Alzheimer's Predictive Vector" (ApV) was derived using a two-stage least absolute shrinkage and selection operator (LASSO). RESULTS: The ApV reliably discriminates between people with (ADrp) and without (nADrp) Alzheimer's related pathologies (98% and 81% accuracy between ADrp - including the early form, mild cognitive impairment - and nADrp in internal and external hold-out test sets, respectively), without any a priori assumptions or need for neuroradiology reads. The new test is superior to standard hippocampal atrophy (26% accuracy) and cerebrospinal fluid beta amyloid measure (62% accuracy). A multiparametric analysis compared DTI-MRI derived fractional anisotropy, whose readout of neuronal loss agrees with ADrp phenotype, and SNPrs2075650 is significantly altered in patients with ADrp-like phenotype. CONCLUSIONS: This new data analytic method demonstrates potential for increasing accuracy of Alzheimer diagnosis.
Inglese M, Patel N, Linton-Reid K, et al., 2022, A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease, Communications Medicine, Vol: 2, ISSN: 2730-664X
Background:Alzheimer’s disease, the most common cause of dementia, causes a progressive and irreversible deterioration of cognition that can sometimes be difficult to diagnose, leading to suboptimal patient care.Methods:We developed a predictive model that computes multi-regional statistical morpho-functional mesoscopic traits from T1-weighted MRI scans, with or without cognitive scores. For each patient, a biomarker called “Alzheimer’s Predictive Vector” (ApV) was derived using a two-stage least absolute shrinkage and selection operator (LASSO).Results:The ApV reliably discriminates between people with (ADrp) and without (nADrp) Alzheimer’s related pathologies (98% and 81% accuracy between ADrp - including the early form, mild cognitive impairment - and nADrp in internal and external hold-out test sets, respectively), without any a priori assumptions or need for neuroradiology reads. The new test is superior to standard hippocampal atrophy (26% accuracy) and cerebrospinal fluid beta amyloid measure (62% accuracy). A multiparametric analysis compared DTI-MRI derived fractional anisotropy, whose readout of neuronal loss agrees with ADrp phenotype, and SNPrs2075650 is significantly altered in patients with ADrp-like phenotype.Conclusions:This new data analytic method demonstrates potential for increasing accuracy of Alzheimer diagnosis.
Fotopoulou C, Rockall A, Lu H, et al., 2021, Validation analysis of the novel imaging-based prognostic radiomic signature in patients undergoing primary surgery for advanced high-grade serous ovarian cancer (HGSOC), British Journal of Cancer, Vol: 126, Pages: 1047-1054, ISSN: 0007-0920
BackgroundPredictive models based on radiomics features are novel, highly promising approaches for gynaecological oncology. Here, we wish to assess the prognostic value of the newly discovered Radiomic Prognostic Vector (RPV) in an independent cohort of high-grade serous ovarian cancer (HGSOC) patients, treated within a Centre of Excellence, thus avoiding any bias in treatment quality.MethodsRPV was calculated using standardised algorithms following segmentation of routine preoperative imaging of patients (n = 323) who underwent upfront debulking surgery (01/2011-07/2018). RPV was correlated with operability, survival and adjusted for well-established prognostic factors (age, postoperative residual disease, stage), and compared to previous validation models.ResultsThe distribution of low, medium and high RPV scores was 54.2% (n = 175), 33.4% (n = 108) and 12.4% (n = 40) across the cohort, respectively. High RPV scores independently associated with significantly worse progression-free survival (PFS) (HR = 1.69; 95% CI:1.06–2.71; P = 0.038), even after adjusting for stage, age, performance status and residual disease. Moreover, lower RPV was significantly associated with total macroscopic tumour clearance (OR = 2.02; 95% CI:1.56–2.62; P = 0.00647).ConclusionsRPV was validated to independently identify those HGSOC patients who will not be operated tumour-free in an optimal setting, and those who will relapse early despite complete tumour clearance upfront. Further prospective, multicentre trials with a translational aspect are warranted for the incorporation of this radiomics approach into clinical routine.
Aboagye E, Li Y, Inglese M, et al., 2021, Consideration of metabolite efflux in radiolabelled choline kinetics, Pharmaceutics, Vol: 13, Pages: 1-18, ISSN: 1999-4923
Hypoxia is a complex microenvironmental condition known to regulate choline kinase α (CHKA) activity and choline transport through transcription factor hypoxia-inducible factor-1α (HIF-1α) and, therefore may confound uptake of choline radiotracer[18F]fluoromethyl-[1,2-2H4]-choline ([18 F]-D4-FCH). The aim of this study was to investigate how hypoxia affects choline radiotracer dynamics. Three underlying mechanisms by which hypoxiacould potentially alter the uptake of the choline radiotracer, [18 F]-D4-FCH, were investigated: 18F-D4-FCH import, CHKA phosphorylation activity, and efflux of [18 F]-D4-FCH and its phosphorylated product [18F]-D4-FCHP. Effects of hypoxia on [18 F]-D4-FCH uptake were studied in CHKA-overexpressing cell lines of prostate cancer, PC-3, and breast cancer, MDA-MB-231 cells. Mechanisms of radiotracer efflux were assessed by cell uptake and immunofluorescence in vitro, and examined in vivo (N=24). Mathematical modelling methodology was further developed to verify efflux hypothesis using [18 F]-D4-FCH dynamic PET scans from non-small cell lung cancer (NSCLC) patients (N=17). We report a novel finding involving export of phosphorylated[18F]-D4-FCH, [18 F]-D4-FCHP, via HIF-1α-responsive efflux transporters including ABCB4 when HIF-1α level is augmented. This is supported by graphical analysis of human data with a compartmental model (M2T6k+k5) that accounts for efflux. Hypoxia/HIF-1α increases the efflux of phosphorylated radiolabelled choline species, thus supporting consideration of efflux in modelling of radiotracer dynamics.
Aboagye E, Wang N, Brickute D, et al., 2021, Novel non-congeneric derivatives of the choline kinase alpha inhibitor ICL-CCIC-0019, Pharmaceutics, Vol: 13, Pages: 1-17, ISSN: 1999-4923
Choline kinase alpha (CHKA) is a promising target for the development of cancer therapeutics. We have previously reported ICL-CCIC-0019, a potent CHKA inhibitor with high cellular activity but with some unfavorable pharmacological properties. In this work, we present an active analogue of ICL-CCIC-0019 bearing a piperazine handle (CK146) to facilitate further structural elaboration of the pharmacophore and thus improve the biological profile. Two different strategies were evaluated in this study: (1) a prodrug approach whereby selective CHKA inhibition could be achieved through modulating the activity of CK146, via the incorporation of an ε-(Ac) Lys motif, cleavable by elevated levels of histone deacetylase (HDAC) and cathepsin L (CTSL) in tumour cells; (2) a prostate-specific membrane antigen (PSMA) receptor targeted delivery strategy. Prodrug (CK145) and PSMA-targeted (CK147) derivatives were successfully synthesized and evaluated in vitro. While the exploitation of CK146 in those two strategies did not deliver the expected results, important and informative structure-activity relationships were observed and have been reported.
Lu H, Cunnea P, Nixon K, et al., 2021, Discovery of a biomarker candidate for surgical stratification in high-grade serous ovarian cancer, British Journal of Cancer, Vol: 124, Pages: 1286-1293, ISSN: 0007-0920
Background: Maximal effort cytoreductive surgery is associated with improved outcomes in advanced high-grade serous ovarian cancer (HGSOC). However, despite complete gross resection (CGR), there is a percentage of patients who will relapse and die early. The aim of this study is to identify potential candidate biomarkers to help personalise surgical radicality.Methods: 136 advanced HGSOC cases who underwent CGR were identified from three public transcriptomic datasets. Candidate prognostic biomarkers were discovered in this cohort by Cox regression analysis, and further validated by targeted RNA-sequencing in HGSOC cases from Imperial College Healthcare NHS Trust (n = 59), and a public dataset. Gene set enrichment analysis was performed to understand the biological significance of the candidate biomarker.Results: We identified ALG5 as a prognostic biomarker for early tumour progression in advanced HGSOC despite CGR (HR = 2.42, 95% CI (1.57–3.75), p < 0.0001). The prognostic value of this new candidate biomarker was additionally confirmed in two independent datasets (HR = 1.60, 95% CI (1.03–2.49), p = 0.0368; HR = 3.08, 95% CI (1.07–8.81), p = 0.0365). Mechanistically, the oxidative phosphorylation was demonstrated as a potential biological pathway of ALG5-high expression in patients with early relapse (p < 0.001).Conclusion: ALG5 has been identified as an independent prognostic biomarker for poor prognosis in advanced HGSOC patients despite CGR. This sets a promising platform for biomarker combinations and further validations towards future personalised surgical care.
Antonowicz S, Bodai Z, Wiggins T, et al., 2021, Endogenous aldehyde accumulation generates genotoxicity and exhaled biomarkers in esophageal adenocarcinoma, Nature Communications, Vol: 12, ISSN: 2041-1723
Volatile aldehydes are enriched in esophageal adenocarcinoma (EAC) patients’ breath and could improve early diagnosis, however the mechanisms of their production are unknown. Here, we show that weak aldehyde detoxification characterizes EAC, which is sufficient to cause endogenous aldehyde accumulation in vitro. Two aldehyde groups are significantly enriched in EAC biopsies and adjacent tissue: (i) short-chain alkanals, and (ii) medium-chain alkanals, including decanal. The short-chain alkanals form DNA-adducts, which demonstrates genotoxicity and confirms inadequate detoxification. Metformin, a putative aldehyde scavenger, reduces this toxicity. Tissue and breath concentrations of the medium-chain alkanal decanal are correlated, and increased decanal is linked to reduced ALDH3A2 expression, TP53 deletion, and adverse clinical features. Thus, we present a model for increased exhaled aldehydes based on endogenous accumulation from reduced detoxification, which also causes therapeutically actionable genotoxicity. These results support EAC early diagnosis trials using exhaled aldehyde analysis.
Hu Z, Cunnea P, Zhong Z, et al., 2021, The Oxford Classic links epithelial-to-mesenchymal transition to immunosuppression in poor prognosis ovarian cancers, Clinical Cancer Research, Vol: 27, Pages: 1570-1579, ISSN: 1078-0432
Purpose: Using RNA sequencing, we recently developed the 52-gene–based Oxford classifier of carcinoma of the ovary (Oxford Classic, OxC) for molecular stratification of serous ovarian cancers (SOCs) based on the molecular profiles of their cell of origin in the fallopian tube epithelium. Here, we developed a 52-gene NanoString panel for the OxC to test the robustness of the classifier.Experimental Design: We measured the expression of the 52 genes in an independent cohort of prospectively collected SOC samples (n = 150) from a homogenous cohort who were treated with maximal debulking surgery and chemotherapy. We performed data mining of published expression profiles of SOCs and validated the classifier results on tissue arrays comprising 137 SOCs.Results: We found evidence of profound nongenetic heterogeneity in SOCs. Approximately 20% of SOCs were classified as epithelial-to-mesenchymal transition–high (EMT-high) tumors, which were associated with poor survival. This was independent of established prognostic factors, such as tumor stage, tumor grade, and residual disease after surgery (HR, 3.3; P = 0.02). Mining expression data of 593 patients revealed a significant association between the EMT scores of tumors and the estimated fraction of alternatively activated macrophages (M2; P < 0.0001), suggesting a mechanistic link between immunosuppression and poor prognosis in EMT-high tumors.Conclusions: The OxC-defined EMT-high SOCs carry particularly poor prognosis independent of established clinical parameters. These tumors are associated with high frequency of immunosuppressive macrophages, suggesting a potential therapeutic target to improve clinical outcome.
Natoli M, Gallon J, Lu H, et al., 2021, Transcriptional analysis of multiple ovarian cancer cohorts reveals prognostic and immunomodulatory consequences of ERV expression, Journal for ImmunoTherapy of Cancer, Vol: 9, ISSN: 2051-1426
Background Endogenous retroviruses (ERVs) play a role in a variety of biological processes, including embryogenesis and cancer. DNA methyltransferase inhibitors (DNMTi)-induced ERV expression triggers interferon responses in ovarian cancer cells via the viral sensing machinery. Baseline expression of ERVs also occurs in cancer cells, though this process is poorly understood and previously unexplored in epithelial ovarian cancer (EOC). Here, the prognostic and immunomodulatory consequences of baseline ERV expression was assessed in EOC.Methods ERV expression was assessed using EOC transcriptional data from The Cancer Genome Atlas (TCGA) and from an independent cohort (Hammersmith Hospital, HH), as well as from untreated or DNMTi-treated EOC cell lines. Least absolute shrinkage and selection operator (LASSO) logistic regression defined an ERV expression score to predict patient prognosis. Immunohistochemistry (IHC) was conducted on the HH cohort. Combination of DNMTi treatment with γδ T cells was tested in vitro, using EOC cell lines and patient-derived tumor cells.Results ERV expression was found to define clinically relevant subsets of EOC patients. An ERV prognostic score was successfully generated in TCGA and validated in the independent cohort. In EOC patients from this cohort, a high ERV score was associated with better survival (log-rank p=0.0009) and correlated with infiltration of CD8+PD1+T cells (r=0.46, p=0.0001). In the TCGA dataset, a higher ERV score was found in BRCA1/2 mutant tumors, compared to wild type (p=0.015), while a lower ERV score was found in CCNE1 amplified tumors, compared to wild type (p=0.019). In vitro, baseline ERV expression dictates the level of ERV induction in response to DNMTi. Manipulation of an ERV expression threshold by DNMTi resulted in improved EOC cell killing by cytotoxic immune cells.Conclusions These findings uncover the potential for baseline ERV expression to robustly inform EOC patient prognosis, influence
Carvalho S, Dissanayake-Perera S, Demchenko N, et al., 2021, STUDY OF PARTICLE SIZE AND MORPHOLOGY FOR THE MULTIPLEXED DETECTION OF EPITHELIAL OVARIAN CANCER, Pages: 1609-1610
Ovarian cancer ranks globally as the 5th deadliest disease amongst women and the most lethal cancer of the reproductive system. Epithelial Ovarian Cancer (EOC) is the most prevalent and aggressive subtype of ovarian cancer. Herein we developed a lateral-flow immunoassay (LFIA) for the detection of two EOC biomarkers that have shown promise for EOC diagnosis. Monoclonal antibodies were conjugated to platinum nanoparticles (PtNPs) and gold nanoparticles (AuNPs) to provide a colorimetric readout. PtNPs showed better performance than AuNPs, being therefore employed for the development of the LFIA device, which has limits of detection (LOD) lower than the clinical cut-off limits for both biomarkers.
Aboagye E, Sharma R, Inglese M, et al., 2020, Monitoring response to transarterial chemoembolization in hepatocellular carcinoma using 18F-Fluorothymidine Positron Emission Tomography, The Journal of Nuclear Medicine, Vol: 61, Pages: 1743-1748, ISSN: 0161-5505
Accurate disease monitoring is essential following transarterial chemoembolization (TACE) in hepatocellular carcinoma (HCC) due to potential for profound adverse event and large variation in survival outcome. Post-treatment changes on conventional imaging can confound determination of residual/recurrent disease, magnifying the clinical challenge. Based on increased expression of thymidylate synthase (TYMS), thymidine kinase-1 (TK-1) and SLC29A1 (Equilibrative nucleoside transporter 1, ENT1) in HCC compared with liver tissue, we conducted a proof of concept study evaluating the efficacy of 18F-fluorothymidine (18F-FLT)-PET to assess response to TACE. As previous PET studies in HCC have been hampered by high background liver signal, we investigated if a temporal-intensity voxel-clustering (“Kinetic Spatial Filtering”) (KSF) improved lesion detection. Methods: A tissue microarray (TMA) was built from 36 HCC samples and matched surrounding cirrhotic tissue and was stained for thymidine kinase-1 (TK-1). A prospective study was conducted; eighteen patients with a diagnosis of HCC by American Association for the Study of Liver Diseases criteria (AALSD) who were eligible to treatment with TACE were enrolled. Patients underwent baseline conventional imaging and dynamic 18F-FLT-PET/KSF followed by TACE. Repeat imaging was performed 6-8 weeks post TACE. PET parameters were compared with modified-Response Evaluation in Solid Tumours (mRECIST) enhancement-based criteria. Results: Cancer Genome Atlas analysis revealed increased RNA expression of TYMS, TK-1 and SLC29A1 in HCC. TK-1 protein expression was significantly higher in HCC (p<0.05). The sensitivity of 18F-FLT-PET for baseline HCC detection was 73% (SUVmax of 9.7 ± 3.0; tumour to liver ratio of 1.2 ± 0.3). Application of KSF did not improve lesion detection. Lesion response following TACE by mRECIST criteria was 58% (14 patients with 24 lesions). A 30% reduction in mean 18F-FLT-PET uptake was o
Raglan O, Assi N, Nautiyal J, et al., 2020, Proteomic analysis of malignant and benign endometrium according to obesity and insulin-resistance status using Reverse Phase Protein Array, Publisher: ELSEVIER SCIENCE INC, Pages: 57-72, ISSN: 1931-5244
- Author Web Link
- Cite
- Citations: 6
Avesani G, Arshad M, Lu H, et al., 2020, Radiological assessment of Peritoneal Cancer Index on preoperative CT in ovarian cancer is related to surgical outcome and survival, La Radiologia Medica, Vol: 125, Pages: 770-776, ISSN: 0033-8362
PurposeTo evaluate whether Peritoneal Cancer Index (PCI) assessed on preoperative CT (CT-PCI) can be used as non-invasive preoperative tool to predict surgical outcome, disease-free survival (DFS) and overall survival (OS).Materials and methodsThis is a retrospective, observational cohort study performed in a single institution. We considered all patients with diagnosis of ovarian cancer and preoperative CT, who had undergone upfront cytoreductive surgery between 2008 and 2010 and had post-operative clinical follow-up to December 2015. Two radiologists reviewed CT scans and assessed CT-PCI using Sugarbaker’s diagram. We assessed the discriminatory capacity of the CT-PCI score on the surgical outcome by ROC curve analysis. DFS and OS were assessed by Kaplan–Meier nonparametric curves and by multivariable Cox-regression analysis.ResultsA total of 297 patients were included in the present analysis. CT-PCI was positively correlated with post-operative residual disease [odds ratio (OR) 1.04, 95% CI 1.01–1.07, p = 0.003]. ROC curve analysis returned AUC = 0.64 for the prediction of total macroscopic tumour clearance. In multivariable analysis, patients with no peritoneal disease seen on CT had a significantly longer DFS [Hazard ratio (HR) 2.28, p = 0.007]. Radiological serosal small bowel involvement was an independent predictor for shorter OS (HR 3.01, p = 0.002).ConclusionRadiological PCI assessed on preoperative CT is associated with the probability of residual disease after cytoreductive surgery; however, it has low performance as a triage test to reliably identify patients who are likely to have complete cytoreductive surgery. CT-PCI is positively correlated with both DFS and OS and may be used as an independent prognostic factor, for example in patients with high FIGO stages.
Raglan O, Assi N, Nautiyal J, et al., 2019, Proteomic analysis of malignant and benign endometrium according to obesity and insulin resistance status using Reverse Phase Protein Array, Translational Research: the journal of laboratory and clinical medicine, ISSN: 0022-2143
Obesity and hyperinsulinemia are known risk factors for endometrial cancer, yet thebiological pathways underlying this relationship are incompletely understood. Thisstudy investigated protein expression in endometrial cancer and benign tissue andits correlation with obesity and insulin resistance.One hundred and seven women undergoing hysterectomy for endometrial canceror benign conditions provided a fasting blood sample and endometrial tissue. Weperformed proteomic expression according to body mass index, insulin resistance,and serum marker levels. We used linear regression and independentttest for statis-tical analysis. Proteomic data from 560 endometrial cancer cases from The CancerGenome Atlas (TCGA) databank were used to assess reproducibility of results.One hundred and twenty seven proteins were significantly differentially expressedbetween 66 cancer and 26 benign patients. Protein expression involved in cellcycle progression, impacting cytoskeletal dynamics (PAK1) and cell survival (Rab25), were most significantly altered. Obese women with cancer had increasedPRAS40_pT246; a downstream marker of increased PI3K-AKT signaling. Obesewomen without cancer had increased mitogenic and antiapoptotic signaling byway of upregulation of Mcl-1, DUSP4, and Insulin Receptor-b.This exploratory study identified a number of candidate proteins specific to endo-metrioid endometrial cancer and benign endometrial tissues. Obesity and insulinresistance in women with benign endometrium leads to specific upregulation ofproteins involved in insulin and driver oncogenic signaling pathways such as thePI3K-AKT-mTOR and growth factor signaling pathways which are mitogenic andalso disruptive to metabolism. (Translational Research 2020; 000:1 16)
Birtley JR, Alomary M, Zanini E, et al., 2019, Inactivating mutations and X-ray crystal structure of the tumor suppressor OPCML reveal cancer-associated functions, Nature Communications, Vol: 10, ISSN: 2041-1723
OPCML, a tumor suppressor gene, is frequently silenced epigenetically in ovarian and other cancers. Here we report, by analysis of databases of tumor sequences, the observation of OPCML somatic missense mutations from various tumor types and the impact of these mutations on OPCML function, by solving the X-ray crystal structure of this glycoprotein to 2.65 Å resolution. OPCML consists of an extended arrangement of three immunoglobulin-like domains and homodimerizes via a network of contacts between membrane-distal domains. We report the generation of a panel of OPCML variants with representative clinical mutations and demonstrate clear phenotypic effects in vitro and in vivo including changes to anchorage-independent growth, interaction with activated cognate receptor tyrosine kinases, cellular migration, invasion in vitro and tumor growth in vivo. Our results suggest that clinically occurring somatic missense mutations in OPCML have the potential to contribute to tumorigenesis in a variety of cancers.
Lu H, Arshad M, Thornton A, et al., 2019, A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic and molecular-phenotypes of epithelial ovarian cancer, Nature Communications, Vol: 10, ISSN: 2041-1723
The five-year survival rate of epithelial ovarian cancer (EOC) is approximately 35–40% despite maximal treatment efforts, highlighting a need for stratification biomarkers for personalized treatment. Here we extract 657 quantitative mathematical descriptors from the preoperative CT images of 364 EOC patients at their initial presentation. Using machine learning, we derive a non-invasive summary-statistic of the primary ovarian tumor based on 4 descriptors, which we name “Radiomic Prognostic Vector” (RPV). RPV reliably identifies the 5% of patients with median overall survival less than 2 years, significantly improves established prognostic methods, and is validated in two independent, multi-center cohorts. Furthermore, genetic, transcriptomic and proteomic analysis from two independent datasets elucidate that stromal phenotype and DNA damage response pathways are activated in RPV-stratified tumors. RPV and its associated analysis platform could be exploited to guide personalized therapy of EOC and is potentially transferrable to other cancer types.
Arshad MA, Thornton A, Lu H, et al., 2019, Discovery of pre-therapy 2-deoxy-2-F-18-fluoro-D-glucose positron emission tomography-based radiomics classifiers of survival outcome in non-small-cell lung cancer patients, European Journal of Nuclear Medicine and Molecular Imaging, Vol: 46, Pages: 455-466, ISSN: 0340-6997
PurposeThe aim of this multi-center study was to discover and validate radiomics classifiers as image-derived biomarkers for risk stratification of non-small-cell lung cancer (NSCLC).Patients and methodsPre-therapy PET scans from a total of 358 Stage I–III NSCLC patients scheduled for radiotherapy/chemo-radiotherapy acquired between October 2008 and December 2013 were included in this seven-institution study. A semi-automatic threshold method was used to segment the primary tumors. Radiomics predictive classifiers were derived from a training set of 133 scans using TexLAB v2. Least absolute shrinkage and selection operator (LASSO) regression analysis was used for data dimension reduction and radiomics feature vector (FV) discovery. Multivariable analysis was performed to establish the relationship between FV, stage and overall survival (OS). Performance of the optimal FV was tested in an independent validation set of 204 patients, and a further independent set of 21 (TESTI) patients.ResultsOf 358 patients, 249 died within the follow-up period [median 22 (range 0–85) months]. From each primary tumor, 665 three-dimensional radiomics features from each of seven gray levels were extracted. The most predictive feature vector discovered (FVX) was independent of known prognostic factors, such as stage and tumor volume, and of interest to multi-center studies, invariant to the type of PET/CT manufacturer. Using the median cut-off, FVX predicted a 14-month survival difference in the validation cohort (N = 204, p = 0.00465; HR = 1.61, 95% CI 1.16–2.24). In the TESTI cohort, a smaller cohort that presented with unusually poor survival of stage I cancers, FVX correctly indicated a lack of survival difference (N = 21, p = 0.501). In contrast to the radiomics classifier, clinically routine PET variables including SUVmax, SUVmean and SUVpeak lacked any prognostic information.ConclusionPET-based radiomics classifiers deriv
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.