Michael P Menden

University of Melbourne | MSD · Biochemistry and Pharmacology

Background Diabetic sensorimotor polyneuropathy (DSPN) is often asymptomatic and remains undiagnosed. The ability of clinical and anthropometric variables to identify individuals likely to have DSPN might be limited. Here, we aimed to integrate protein biomarkers for reliably predicting present DSPN. Methods Using the proximity extension assay, we...

Childhood neuroblastoma with MYCN-amplification is classified as high-risk and often relapses after intensive treatments. Immune checkpoint blockade therapy against the PD-1/L1 axis shows limited efficacy in neuroblastoma patients and the cancer intrinsic immune regulatory network is poorly understood. Here, we leverage genome-wide CRISPR/Cas9 scre...

The development of functional neurons is a complex orchestration of multiple signaling pathways controlling cell proliferation and differentiation. Because the balance of antioxidants is important for neuronal survival and development, we hypothesized that ferroptosis must be suppressed to gain neurons. We find that removal of antioxidants diminish...

Background: Generative artificial intelligence (AI) facilitates the development of digital twins, which enable virtual representations of real patients to explore, predict and simulate patient health trajectories, ultimately aiding treatment selection and clinical trial design, among other applications. Recent advances in forecasting utilizing gene...

Representation learning for tumor gene expression (GEx) data with deep neural networks is limited by the large gene feature space and the scarcity of available clinical and preclinical data. The translation of the learned representation between these data sources is further hindered by inherent molecular differences. To address these challenges, we...

Immune checkpoint blockade therapy aims to activate the immune system to eliminate cancer cells. However, clinical benefits are only recorded in a subset of patients. Here, we leverage genome-wide CRISPR/Cas9 screens in a Tumor-Immune co-Culture System focusing on triple-negative breast cancer (TNBC). We reveal that NEDD8 loss in cancer cells cause...

Immune checkpoint blockade therapy aims to activate the immune system to eliminate cancer cells. However, clinical benefits are only recorded in a subset of patients. Here, we leveraged genome-wide CRISPR/Cas9 screens in a Tumor-Immune co-Culture System focusing on triple-negative breast cancer. We revealed that NEDD8 loss caused a vulnerability to...

Sebaceous glands drive acne, however, their role in other inflammatory skin diseases remains unclear. To shed light on their potential contribution to disease development, we investigated the spatial transcriptome of sebaceous glands in psoriasis and atopic dermatitis patients across lesional and non-lesional human skin samples. Both atopic dermati...

13 Background: Optimal patient selection for first-line treatment targeting epithelial growth factor receptor (EGFR) in RAS-WT mCRC is based on primary tumor sidedness (PTS) with anti-EGFR being the preferred option for patients with left-sided mCRC (LC). Right-sided mCRCs (RC) are preferentially treated in combination with bevacizumab targeting va...

The epithelial-mesenchymal transition (EMT) is characterised by the loss of cell-cell adhesion and cell polarity, which is often exploited by cancer cells to adopt a motile, invasive and metastatic phenotype. Whilst EMT is often linked with cancer progression and therapy resistance, strategies for its selective targeting remain limited. In order to...

Introduction: The concept of Digital Twins (DTs) translated to drug development and clinical trials describes virtual representations of systems of various complexities, ranging from individual cells to entire humans, and enables in silico simulations and experiments. DTs increase the efficiency of drug discovery and development by digitalizing pr...

Precision medicine has revolutionised cancer treatments; however, actionable biomarkers remain scarce. To address this, we develop the Oncology Biomarker Discovery (OncoBird) framework for analysing the molecular and biomarker landscape of randomised controlled clinical trials. OncoBird identifies biomarkers based on single genes or mutually exclus...

Constitutive MALT1 activity drives survival of malignant lymphomas addicted to chronic B-cell receptor (BCR) signaling, oncogenic CARD11, or the API2-MALT1 (also BIRC3::MALT1) fusion oncoprotein. While MALT1 scaffolding induces NF-kB-dependent survival signaling, MALT1 protease function is thought to augment NF-kB activation by cleaving signaling m...

Mechanistic insight into ageing may empower prolonging the lifespan of humans; however, a complete understanding of this process is still lacking despite a plethora of ageing theories. In order to address this, we investigated the association of lifespan with eight phenotypic traits, that is, litter size, body mass, female and male sexual maturity,...

Aberrant DNA methylation accompanies genetic alterations during oncogenesis and tumour homeostasis and contributes to the transcriptional deregulation of key signalling pathways in cancer. Despite increasing efforts in DNA methylation profiling of cancer patients, there is still a lack of epigenetic biomarkers to predict treatment efficacy. To addr...

Highly specific and efficient drugs have been developed to treat noncommunicable chronic inflammatory skin diseases (ncISD). Due to their specificity, these drugs require precise diagnostics to attribute the most efficient treatment to each patient. Diagnosis is complicated by the complex pathogenesis of ncISD and their clinical and histological ov...

Aberrant DNA methylation accompanies genetic alterations during oncogenesis and tumour homeostasis and contributes to the transcriptional deregulation of key signalling pathways in cancer. Despite increasing efforts in DNA methylation profiling of cancer patients, there is still a lack of epigenetic biomarkers to predict treatment efficacy. To addr...

Muscle function is an important denominator of energy balance and metabolic health. Adapting the proteome to energetic challenges, in response to diet or fasting, is facilitated by programs of proteostasis, but the adaptive role of the ubiquitin-proteasome system (UPS) in muscle remains unclear. Here, using a multi-omics approach, we uncover that t...

Muscle function is an important denominator of energy balance and metabolic health. Adapting the proteome to energetic challenges, in response to diet or fasting, is facilitated by programs of proteostasis, but the adaptive role of the ubiquitin-proteasome system (UPS) in muscle remains unclear. Here, using a multi-omics approach, we uncover that t...

Development of functional neurons is a complex orchestration of several signaling pathways controlling cell proliferation, differentiation, and homeostasis. However, details about the involved factors are not fully understood. The balance of antioxidants and vitamins is important for neuronal survival, synaptic plasticity, and early neuronal develo...

Highly effective targeted therapies are available to treat non-communicable chronic inflammatory skin diseases (ncISD). In contrast, the exact diagnosis of ncISD is complicated by their complex pathogenesis and clinical and histological overlap. Particularly, differential diagnosis of psoriasis and eczema can be challenging in special cases and mol...

The nasal epithelium is an important target for drug delivery to the nose and secondary organs such as the brain via the olfactory bulb. For both topical and brain delivery, the targeting of specific nasal regions such as the olfactory epithelium (brain) is essential, yet challenging. In this study, a numerical model was developed to predict the re...

Abundant heterogeneous immune cells infiltrate lesions in chronic inflammatory diseases and characterization of these cells is needed to distinguish disease-promoting from bystander immune cells. Here, we investigate the landscape of non-communicable inflammatory skin diseases (ncISD) by spatial transcriptomics resulting in a large repository of 62...

Background High-risk neuroblastoma (NB) with amplified MYCN oncogene is a developmental aggressive disease in children of age less than 5 years. NB patients are generally given intensive treatments in consecutive phases involving multi-agent chemotherapy, surgery, radiation therapy and immunotherapy. Over recent years, immunotherapy using checkpoin...

Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development to characterize pharmacokinetics (PK) and pharmacodynamics (PD). Pharmacokinetic/pharmacodynamic (PKPD) analysis using PM provides mechanistic insight into biological processes but is time- and labor-intensive. In contrast, ML models are much quicker trained, but...

Hospital staff are at high risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during the coronavirus disease (COVID-19) pandemic. This cross-sectional study aimed to determine the prevalence of SARS-CoV-2 infection in hospital staff at the University Hospital rechts der Isar in Munich, Germany, and identify modulating f...

Background: Parkinson's disease (PD) is the second most common neurodegenerative disorder whose prevalence is rapidly increasing worldwide. The molecular mechanisms underpinning the pathophysiology of sporadic PD remain incompletely understood. Therefore, causative therapies are still elusive. To obtain a more integrative view of disease-mediated...

Background The yearly Think Tank Meeting of the Italian Network for Tumor Biotherapy (NIBIT) Foundation, brings together in Siena, Tuscany (Italy), experts in immuno-oncology to review the learnings from current immunotherapy treatments, and to propose new pre-clinical and clinical investigations in selected research areas. Main While immunotherap...

Background Diabetes mellitus is becoming a global health issue which demands a transformation of the research and healthcare practice for better patient management. To this end, the abundance of data and advancements in technology and artificial intelligence provides opportunities for such an endeavour.AimsThis review aims to provide an overview of...

Disparities between risk, treatment outcomes and survival rates in cancer patients across the world may be attributed to socioeconomic factors. In addition, the role of ancestry is frequently discussed. In preclinical studies, high-throughput drug screens in cancer cell lines have empowered the identification of clinically relevant molecular biomar...

Parkinson's disease (PD) is the second most common neurodegenerative disorder whose prevalence is rapidly increasing worldwide. The disease mechanisms of sporadic PD are not yet completely understood. Therefore, causative therapies are still lacking. To obtain a more integrative view of disease-mediated alterations, we investigated the molecular la...

Abundant polyclonal T cells infiltrate chronic inflammatory diseases and characterization of these cells is needed to distinguish disease-driving from bystander immune cells. Here, we investigated 52,000 human cutaneous transcriptomes of non-communicable inflammatory skin diseases (ncISD) using spatial transcriptomics. Despite the expected T cell i...

Introduction: Precision medicine is the concept of treating diseases based on environmental factors, lifestyles, and molecular profiles of patients. This approach has been found to increase success rates of clinical trials and accelerate drug approvals. However, current precision medicine applications in early drug discovery use only a handful of m...

High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells’ response to a drug is typically quantified by a summary statistic from a best-fit dose-response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw r...

High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells' response to a drug is typically quantified by a summary statistic from a best-fit dose-response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw r...

Background Hospital staff are at high risk of infection during the coronavirus disease (COVID-19) pandemic. We analysed the exposure characteristics, efficacy of protective measures, and transmission dynamics in this hospital-wide prospective seroprevalence study. Methods and Findings Overall, 4554 individuals were tested for anti-severe acute res...

High-throughput drug screens in cancer cell lines test compounds at low concentrations, thereby enabling the identification of drug-sensitivity biomarkers, while resistance biomarkers remain underexplored. Dissecting meaningful drug responses at high concentrations is challenging due to cytotoxicity, i.e., off-target effects, thus limiting resistan...

In silico models to predict which tumors will respond to a given drug are necessary for Precision Oncology. However, predictive models are only available for a handful of cases (each case being a given drug acting on tumors of a specific cancer type). A way to generate predictive models for the remaining cases is with suitable machine learning algo...

Drug combinations can expand therapeutic options and address cancer’s resistance. However, the combinatorial space is enormous precluding its systematic exploration. Therefore, synergy prediction strategies are essential. We here present an approach to prioritise drug combinations in high-throughput screens and to stratify synergistic responses. At...

Drug high-throughput screenings across large molecular-characterised cancer cell line panels enable the discovery of biomarkers, and thereby, cancer precision medicine. The ability to experimentally generate drug response data has accelerated. However, this data is typically quantified by a summary statistic from a best-fit dose response curve, whi...

Two drugs, even with the same target, rarely have the same potency across all cancer patients - so how do we objectively select the right patients to treat with each drug? An international effort led by Michael P. Menden and Dennis Wang developed a machine learning approach called SEABED to identify groups of individuals from a population who respo...

Here we describe a proteomic data resource for the NCI-60 cell lines generated by pressure cycling technology and SWATH mass spectrometry. We developed the DIA-expert software to curate and visualize the SWATH data, leading to reproducible detection of over 3,100 SwissProt proteotypic proteins and systematic quantification of pathway activities. St...

Synergistic drug combinations are commonly sought to overcome monotherapy resistance in cancer treatment. To identify such combinations, high-throughput cancer cell line combination screens are performed; and synergy is quantified using competing models based on fundamentally different assumptions. Here, we compare the behaviour of four synergy mod...

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consis...

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consis...

We describe the rapid and reproducible acquisition of quantitative proteome maps for the NCI-60 cancer cell lines and their use to reveal cancer biology and drug response determinants. Proteome datasets for the 60 cell lines were acquired in duplicate within 30 working days using pressure cycling technology and SWATH mass spectrometry. We consisten...

The effectiveness of a particular drug has predominantly been analysed in isolation and there lacks data-driven approaches to consider the full response pattern between multiple drugs to study biomarkers at the same time. To reveal subpopulations where the pharmacological response between compounds agree and diverge, we applied a novel population s...

Patients with seemingly the same tumour can respond very differently to treatment. There are strong, well-established effects of somatic mutations on drug efficacy, but there is at-most anecdotal evidence of a germline component to drug response. Here, we report a systematic survey of how inherited germline variants affect drug susceptibility in ca...

This abstract has been withheld from publication due to its inclusion in the AACR Annual Meeting 2018 Official Press Program. It will be posted online following its presentation. Citation Format: Jonathan R. Dry, Michael P. Menden, Krishna Bulusu, Justin Guinney, Julio Saez-Rodriguez. A large cancer pharmacogenomics combination screen powering crow...

The effectiveness of most cancer targeted therapies is short lived since tumors evolve and develop resistance. Combinations of drugs offer the potential to overcome resistance, however the number of possible combinations is vast necessitating data-driven approaches to find optimal treatments tailored to a patient’s tumor. AstraZeneca carried out 11...

Selecting the right drug combination for the right patient is a complex problem. Some success has been seen from preclinical screening and computational approaches combining molecular, drug and tumor properties to predict combination benefit within the tumor cell. However the context specificity and heterogeneity of drug resistance is often overloo...

Motivation: Large pharmacogenomic screenings integrate heterogeneous cancer genomic data sets as well as anti-cancer drug responses on thousand human cancer cell lines. Mining this data to identify new therapies for cancer sub-populations would benefit from common data structures, modular computational biology tools and user-friendly interfaces....

We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes...

Motivation Large pharmacogenomic screenings integrate heterogeneous cancer genomic data sets as well as anti-cancer drug responses on thousand human cancer cell lines. Mining this data to identify new therapies for cancer sub-populations would benefit from common data structures, modular computational biology tools and user-friendly interfaces. Re...

Systematic studies of cancer genomes are providing unprecedented insights into the molecular nature of human cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here we report how cancer-driving alterations identified in 11,289 tumors from 29 tissues (integrating mutations, copy-number...

Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy numbe...

Large cancer cell line collections broadly capture the genomic diversity of human cancers and provide valuable insight into anti-cancer drug response. Here we show substantial agreement and biological consilience between drug sensitivity measurements and their associated genomic predictors from two publicly available large-scale pharmacogenomics re...