Proteomic identification of putative biomarkers of radiotherapy resistance

BackgroundCurrently, tumour response to radiotherapy cannot be predicted meaning that those patients with tumours resistant to the therapy endure the harmful side effects associated with ionising radiation in the absence of therapeutic gain. The aim of this project was to identify protein biomarkers predictive of radiotherapy response using comparative proteomic platforms to study radioresistant cell line models. The identification of such biomarkers will enable radiotherapy to be tailored on an individual patient basis and hence increase treatment efficacy.MethodsSeven radioresistant (RR) cell line models derived from breast, head and neck (oral), and rectal cancers were investigated to identify differentially expressed proteins (DEPs) associated with radiotherapy resistance. This included the establishment of 2 RR rectal cancer cell line models and the proteomic analysis of 2 RR oral cancer cell lines and 2 RR rectal cancer cell lines. Proteomic analysis included 3 different platforms, namely antibody microarray, 2D MS and iTRAQ. Data mining of all biomarker discovery data, from all 7 novel RR cell lines was carried out using Ingenuity Pathway Analysis (IPA) which identified canonical pathways associated with the data. Protein candidates from selected canonical pathways were confirmed by western blotting and assessed clinically using immunohistochemistry.ResultsFollowing the combination of all biomarker discovery data for all 7 RR cell lines, 373 unique DEPs were successfully mapped onto the Ingenuity Knowledge Base, generating 339 canonical pathways. Of these, 13 of the most relevant pathways were selected for further interpretation. Several proteasomal subunits were identified during the biomarker discovery phase and were mapped onto the protein ubiquitination pathway by IPA. DR4, was identified in 4/7 RR cell lines and was mapped onto the death receptor signalling pathway by IPA. Radiotherapy is typically thought to induce cellular apoptosis via the intrinsic (mitochondrial) pathway, therefore the repeated identification of the DR4 protein involved in the extrinsic apoptotic pathway has potentially lead to the discovery of a novel relationship between radiotherapy and the extrinsic death receptor pathway. The differential expression of both the 26S Proteasome and DR4 were confirmed by western blotting. Clinical assessment using immunohistochemistry revealed a significant association between expression of the 26S Proteasome and radioresistance in breast cancer.DiscussionA large number of DEPs which may be associated with radiotherapy resistance in breast, oral and rectal cancers have been identified using comparative proteomic platforms. The protein ubiquitination pathway and the death receptor signalling pathway may play a significant role in radioresistance and proteins within these pathways may be putative biomarkers of radiotherapy response

Galaxy Zoo DESI: Detailed Morphology Measurements for 8.7M Galaxies in the DESI Legacy Imaging Surveys

We present detailed morphology measurements for 8.67 million galaxies in the DESI Legacy Imaging Surveys (DECaLS, MzLS, and BASS, plus DES). These are automated measurements made by deep learning models trained on Galaxy Zoo volunteer votes. Our models typically predict the fraction of volunteers selecting each answer to within 5-10\% for every answer to every GZ question. The models are trained on newly-collected votes for DESI-LS DR8 images as well as historical votes from GZ DECaLS. We also release the newly-collected votes. Extending our morphology measurements outside of the previously-released DECaLS/SDSS intersection increases our sky coverage by a factor of 4 (5,000 to 19,000 deg$^2$) and allows for full overlap with complementary surveys including ALFALFA and MaNGA.Comment: 20 pages. Accepted at MNRAS. Catalog available via https://zenodo.org/record/7786416. Pretrained models available via https://github.com/mwalmsley/zoobot. Vizier and Astro Data Lab access not yet available. With thanks to the Galaxy Zoo volunteer

Practical galaxy morphology tools from deep supervised representation learning

Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful semantic representations of galaxies that are useful for new tasks on which the models were never trained. We exploit these representations to outperform several recent approaches at practical tasks crucial for investigating large galaxy samples. The first task is identifying galaxies of similar morphology to a query galaxy. Given a single galaxy assigned a free text tag by humans (e.g. ‘#diffuse’), we can find galaxies matching that tag for most tags. The second task is identifying the most interesting anomalies to a particular researcher. Our approach is 100 per cent accurate at identifying the most interesting 100 anomalies (as judged by Galaxy Zoo 2 volunteers). The third task is adapting a model to solve a new task using only a small number of newly-labelled galaxies. Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch. We solve each task with very few new labels; either one (for the similarity search) or several hundred (for anomaly detection or fine-tuning). This challenges the longstanding view that deep supervised methods require new large labelled datasets for practical use in astronomy. To help the community benefit from our pretrained models, we release our fine-tuning code zoobot. Zoobot is accessible to researchers with no prior experience in deep learning

Galaxy Zoo DESI : Detailed morphology measurements for 8.7M galaxies in the DESI Legacy Imaging Surveys

We present detailed morphology measurements for 8.67 million galaxies in the DESI Legacy Imaging Surveys (DECaLS, MzLS, and BASS, plus DES). These are automated measurements made by deep learning models trained on Galaxy Zoo volunteer votes. Our models typically predict the fraction of volunteers selecting each answer to within 5–10% for every answer to every GZ question. The models are trained on newly-collected votes for DESI-LS DR8 images as well as historical votes from GZ DECaLS. We also release the newly-collected votes. Extending our morphology measurements outside of the previously-released DECaLS/SDSS intersection increases our sky coverage by a factor of 4 (5000 to 19 000 deg2) and allows for full overlap with complementary surveys including ALFALFA and MaNGA

Galaxy Zoo DESI: Detailed morphology measurements for 8.7M galaxies in the DESI Legacy Imaging Surveys

We present detailed morphology measurements for 8.67 million galaxies in the DESI Legacy Imaging Surveys (DECaLS, MzLS, and BASS, plus DES). These are automated measurements made by deep learning models trained on Galaxy Zoo volunteer votes. Our models typically predict the fraction of volunteers selecting each answer to within 5–10% for every answer to every GZ question. The models are trained on newly-collected votes for DESI-LS DR8 images as well as historical votes from GZ DECaLS. We also release the newly-collected votes. Extending our morphology measurements outside of the previously-released DECaLS/SDSS intersection increases our sky coverage by a factor of 4 (5000 to 19 000 deg2) and allows for full overlap with complementary surveys including ALFALFA and MaNGA

Proteomic identification of putative biomarkers of radiotherapy resistance

Background Currently, tumour response to radiotherapy cannot be predicted meaning that those patients with tumours resistant to the therapy endure the harmful side effects associated with ionising radiation in the absence of therapeutic gain. The aim of this project was to identify protein biomarkers predictive of radiotherapy response using comparative proteomic platforms to study radioresistant cell line models. The identification of such biomarkers will enable radiotherapy to be tailored on an individual patient basis and hence increase treatment efficacy. Methods Seven radioresistant (RR) cell line models derived from breast, head and neck (oral), and rectal cancers were investigated to identify differentially expressed proteins (DEPs) associated with radiotherapy resistance. This included the establishment of 2 RR rectal cancer cell line models and the proteomic analysis of 2 RR oral cancer cell lines and 2 RR rectal cancer cell lines. Proteomic analysis included 3 different platforms, namely antibody microarray, 2D MS and iTRAQ. Data mining of all biomarker discovery data, from all 7 novel RR cell lines was carried out using Ingenuity Pathway Analysis (IPA) which identified canonical pathways associated with the data. Protein candidates from selected canonical pathways were confirmed by western blotting and assessed clinically using immunohistochemistry. Results Following the combination of all biomarker discovery data for all 7 RR cell lines, 373 unique DEPs were successfully mapped onto the Ingenuity Knowledge Base, generating 339 canonical pathways. Of these, 13 of the most relevant pathways were selected for further interpretation. Several proteasomal subunits were identified during the biomarker discovery phase and were mapped onto the protein ubiquitination pathway by IPA. DR4, was identified in 4/7 RR cell lines and was mapped onto the death receptor signalling pathway by IPA. Radiotherapy is typically thought to induce cellular apoptosis via the intrinsic (mitochondrial) pathway, therefore the repeated identification of the DR4 protein involved in the extrinsic apoptotic pathway has potentially lead to the discovery of a novel relationship between radiotherapy and the extrinsic death receptor pathway. The differential expression of both the 26S Proteasome and DR4 were confirmed by western blotting. Clinical assessment using immunohistochemistry revealed a significant association between expression of the 26S Proteasome and radioresistance in breast cancer. Discussion A large number of DEPs which may be associated with radiotherapy resistance in breast, oral and rectal cancers have been identified using comparative proteomic platforms. The protein ubiquitination pathway and the death receptor signalling pathway may play a significant role in radioresistance and proteins within these pathways may be putative biomarkers of radiotherapy response

Generosity in times of crisis : Australian helping behaviours during the COVID-19 pandemic

The COVID-19 pandemic has thrown the world into a health crisis that has had devastating effects on the global economy and public life in many countries. Little is known about how people have responded to two competing pressures caused by the crisis in many countries: increased community need coupled with decreased financial capacity to help others.We surveyed 1,007 Australians in August 2020 to understand how their generosity behaviours manifested and changed during the COVID-19 pandemic. By generosity we mean all forms of behaviour that people engage in with the intention of benefiting others (including people, animals, and environments).Two key findings emerged:1. Generosity behaviours were very common during the COVID-19 pandemic and manifested in diverse ways, both in Australia and around the world. The most common generosity behaviours were: (a) informal helping of friends and family, and (b) formal helping through established not-for-profits.2. Changes in people’s generosity behaviours were driven primarily by changes in their personal circumstances (e.g. financial insecurity, health threats) and government policies implemented to respond to the crisis (e.g. lockdowns, wage subsidies)

Differential proteomics in the search for biomarkers of radiotherapy resistance

The individualization of radiotherapy treatment would be beneficial for cancer patients; however, there are no predictive biomarkers of radiotherapy resistance in routine clinical use. This article describes the body of work in this field where comparative proteomics methods have been used for the discovery of putative biomarkers associated with radiotherapy resistance. A large number of differentially expressed proteins have been reported, mostly from the study of novel radiotherapy-resistant cell lines. Here, we have assessed these putative biomarkers through the discovery, confirmation and validation phases of the biomarker pipeline, and inform the reader on the current status of proteomics-based findings. Suggested avenues for future work are discussed

Proteomic (antibody microarray) exploration of the molecular mechanism of action of the specific COX-2 inhibitor DuP 697

We have previously shown that specific COX-2 inhibitors, including DuP 697, have anti-proliferative effects on mesothelioma cells and potentiate the cytotoxicity of pemetrexed. Here, we used a novel proteomic approach to explore the mechanism of action of this agent. COX-2-positive cell lines MSTO-211H (mesothelioma) and A549 (lung cancer) were exposed to DuP 697 for 72 h. Drug carrier only was added to control cells. Extracted proteins from treated and control cells were analysed using a comparative proteomic platform. Differentially expressed proteins, identified by the Panorama Xpress Profiler725 antibody microarray were submitted to Ingenuity Pathway Analysis. A total of 32 unique differentially expressed proteins were identified with a significant (>1.8-fold) difference in expression between treated and untreated cells in at least one cell line. Five molecules, BCL2L1 (Bcl-xL), BID, CHUK (IKK), FASLG and RAF1, were mapped to the Apoptosis Signaling pathway following Ingenuity Pathway Analysis. BCL2L1 (Bcl-xL) and BID were analysed using immunoblotting and differential expression was confirmed. Proteomic (antibody microarray) analysis suggests that the mechanism of action of DuP 697 may be exerted via the induction of apoptosis. The antibody microarray platform can be utilised to explore the molecular mechanism of action of novel anticancer agents

A pilot study to investigate the role of the 26S proteasome in radiotherapy resistance and loco-regional recurrence following breast conserving therapy for early breast cancer

Breast conserving therapy is a currently accepted method for managing patients with early stage breast cancer. However, approximately 7% of patients may develop loco-regional tumour recurrence within 5 years. We previously reported that expression of the 26S proteasome may be associated with radio-resistance. Here we aimed to analyse the 26S proteasome in a pilot series of early breast cancers and correlate the findings with loco-regional recurrence. Fourteen patients with early breast cancer who developed loco-regional recurrence within 4 years of completing breast conserving therapy were selected according to strict criteria and compared with those from 14 patients who were disease-free at 10 years. Decreased expression of the 26S proteasome was significantly associated with radio-resistance, manifested as the development of a loco-regional recurrence within 4 years of breast conserving therapy (p = 0.018). This small pilot study provides further suggestion that the 26S proteasome may be associated with response to radiotherapy