Pitch 1: André Mateus: From proteome perturbation to protein function and interactions
Assistant Professor at Department of Chemistry, Umeå University
The human gut microbiome is increasingly recognized as playing a role in human health and disease. Yet, the function of a large proportion of proteins encoded by these organisms remains unknown. André’s lab takes a reductionist approach and studies isolated microbiome species using systems biology approaches based on proteomics to characterize protein function at scale. They perturb the protein network of each organism using drugs or complex carbohydrates and measure proteome-wide protein abundance and thermal stability changes. By identifying which proteins change in concert across perturbations, they map the functional units of each organism. This allows them to suggest the function of currently uncharacterized proteins based on the functional units they associate with. They can also understand how drugs act on these organisms, or which proteins are responsible for metabolizing specific carbohydrates, by looking at the specific proteins that are altered in each perturbation. They hope that this knowledge will open new opportunities to manipulate complex microbiome communities towards healthy states.
About André Mateus: Since 2022, André holds a joint position as Assistant Professor at the Department of Chemistry at Umeå University (Sweden) and Team Leader at the Laboratory for Molecular Infection Medicine Sweden (MIMS). His newly established lab aims to map protein function and interactions in the understudied species that compose the human gut microbiome, to enable the elimination of species associated with disease.
Pitch 2: Jenny Persson: A novel algorithm to predict treatment response to therapies in breast cancer
Professor at Department of Molecular Biology, Umeå University
There is an urgent need for developing new biomarker tools to accurately predict treatment response of breast cancer, especially the deadly triple-negative breast cancer. Jenny aimed to develop gene-mutation-based machine learning algorithms as biomarker classifier to predict treatment response of first-line chemotherapy with high precision.
Methods: Random Forest machine learning (ML) was applied to screen the algorithms of various combination of gene mutation profiles of primary tumors at diagnosis using the TCGA Cohort (n = 399) as a training set and validated in the MSK Cohort (n =807). Subtypes of breast cancer including triple-negative and luminal A (ER+, PR+ and HER2-) were assessed. The performance of the candidate algorithms as classifiers and predictive biomarker was further assessed using logistic regression, progression-free survival (PFS) up to 220 months follow-up and univariate/multivariate Cox proportional hazard analyses.
Results: A novel algorithm termed the 12-Gene Algorithm based on mutation profiles of KRAS, PIK3CA, MAP3K1, MAP2K4, PTEN, TP53, CDH1, GATA3, KMT2C, ARID1A, RunX1, and ESR1, was identified. The performance of this algorithm to distinguish non-progressed (responder) vs. progressed (non-responder) to treatment in the TCGA Cohort as determined using AUC was 0.96 (95% CI 0.94-0.98). It predicted progression-free survival (PFS) with hazard ratio (HR) of 21.6 (95% CI 11.3-41.5) (p<0.001) in all patients. This algorithm predicted PFS in the triple-negative subgroup with HR of 19.3 (n=42, p<0.001). The 12-Gene Algorithm was validated in MSK cohort. Similar to what was observed in the TCGA cohort, this algorithm had performance of AUC of 0.97 (95% CI 0.96-0.98) to distinguish responder vs. non-responder patients to treatment in MSK cohort, and had a HR of 18.6 to predict PFS in triple-negative subgroup (n=75, p<0.001) in MSK cohort.
Conclusions: The novel 12-Gene algorithm based on multitude gene-mutation profiles identified through ML has great potential to predict treatment response to therapies in subgroups of breast cancer patients, which may assist personalized therapies and reduce mortality.