Shane Lofgren

CD47-blocking immunotherapies stimulate macrophage-mediated destruction of small-cell lung cancer.

Small-cell lung cancer (SCLC) is a highly aggressive subtype of lung cancer with limited treatment options. CD47 is a cell-surface molecule that promotes immune evasion by engaging signal-regulatory protein alpha (SIRPα), which serves as an inhibitory receptor on macrophages. Here, we found that CD47 is highly expressed on the surface of human SCLC cells; therefore, we investigated CD47-blocking immunotherapies as a potential approach for SCLC treatment. Disruption of the interaction of CD47 with SIRPα using anti-CD47 antibodies induced macrophage-mediated phagocytosis of human SCLC patient cells in culture. In a murine model, administration of CD47-blocking antibodies or targeted inactivation of the Cd47 gene markedly inhibited SCLC tumor growth. Furthermore, using comprehensive antibody arrays, we identified several possible therapeutic targets on the surface of SCLC cells. Antibodies to these targets, including CD56/neural cell adhesion molecule (NCAM), promoted phagocytosis in human SCLC cell lines that was enhanced when combined with CD47-blocking therapies. In light of recent clinical trials for CD47-blocking therapies in cancer treatment, these findings identify disruption of the CD47/SIRPα axis as a potential immunotherapeutic strategy for SCLC. This approach could enable personalized immunotherapeutic regimens in patients with SCLC and other cancers.

Integrated, multicohort analysis of systemic sclerosis identifies robust transcriptional signature of disease severity

Systemic sclerosis (SSc) is a rare autoimmune disease with the highest case-fatality rate of all connective tissue diseases. Current efforts to determine patient response to a given treatment using the modified Rodnan skin score (mRSS) are complicated by interclinician variability, confounding, and the time required between sequential mRSS measurements to observe meaningful change. There is an unmet critical need for an objective metric of SSc disease severity. Here, we performed an integrated, multicohort analysis of SSc transcriptome data across 7 datasets from 6 centers composed of 515 samples. Using 158 skin samples from SSc patients and healthy controls recruited at 2 centers as a discovery cohort, we identified a 415-gene expression signature specific for SSc, and validated its ability to distinguish SSc patients from healthy controls in an additional 357 skin samples from 5 independent cohorts. Next, we defined the SSc skin severity score (4S). In every SSc cohort of skin biopsy samples analyzed in our study, 4S correlated significantly with mRSS, allowing objective quantification of SSc disease severity. Using transcriptome data from the largest longitudinal trial of SSc patients to date, we showed that 4S allowed us to objectively monitor individual SSc patients over time, as (a) the change in 4S of a patient is significantly correlated with change in the mRSS, and (b) the change in 4S at 12 months of treatment could predict the change in mRSS at 24 months. Our results suggest that 4S could be used to distinguish treatment responders from nonresponders prior to mRSS change. Our results demonstrate the potential clinical utility of a novel robust molecular signature and a computational approach to SSc disease severity quantification.

Coordination of stress signals by the lysine methyltransferase SMYD2 promotes pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is a lethal form of cancer with few therapeutic options. We found that levels of the lysine methyltransferase SMYD2 (SET and MYND domain 2) are elevated in PDAC and that genetic and pharmacological inhibition of SMYD2 restricts PDAC growth. We further identified the stress response kinase MAPKAPK3 (MK3) as a new physiologic substrate of SMYD2 in PDAC cells. Inhibition of MAPKAPK3 impedes PDAC growth, identifying a potential new kinase target in PDAC. Finally, we show that inhibition of SMYD2 cooperates with standard chemotherapy to treat PDAC cells and tumors. These findings uncover a pivotal role for SMYD2 in promoting pancreatic cancer.

EMPOWERING MULTI-COHORT GENE EXPRESSION ANALYSIS TO INCREASE REPRODUCIBILITY.

A major contributor to the scientific reproducibility crisis has been that the results from homogeneous, single-center studies do not generalize to heterogeneous, real world populations. Multi-cohort gene expression analysis has helped to increase reproducibility by aggregating data from diverse populations into a single analysis. To make the multi-cohort analysis process more feasible, we have assembled an analysis pipeline which implements rigorously studied meta-analysis best practices. We have compiled and made publicly available the results of our own multi-cohort gene expression analysis of 103 diseases, spanning 615 studies and 36,915 samples, through a novel and interactive web application. As a result, we have made both the process of and the results from multi-cohort gene expression analysis more approachable for non-technical users.

Gene Expression Analysis to Assess the Relevance of Rodent Models to Human Lung Injury

&NA; The relevance of animal models to human diseases is an area of intense scientific debate. The degree to which mouse models of lung injury recapitulate human lung injury has never been assessed. Integrating data from both human and animal expression studies allows for increased statistical power and identification of conserved differential gene expression across organisms and conditions. We sought comprehensive integration of gene expression data in experimental acute lung injury (ALI) in rodents compared with humans. We performed two separate gene expression multicohort analyses to determine differential gene expression in experimental animal and human lung injury. We used correlational and pathway analyses combined with external in vitro gene expression data to identify both potential drivers of underlying inflammation and therapeutic drug candidates. We identified 21 animal lung tissue datasets and three human lung injury bronchoalveolar lavage datasets. We show that the metasignatures of animal and human experimental ALI are significantly correlated despite these widely varying experimental conditions. The gene expression changes among mice and rats across diverse injury models (ozone, ventilator‐induced lung injury, LPS) are significantly correlated with human models of lung injury (Pearson r = 0.33‐0.45, P < 1E−16). Neutrophil signatures are enriched in both animal and human lung injury. Predicted therapeutic targets, peptide ligand signatures, and pathway analyses are also all highly overlapping. Gene expression changes are similar in animal and human experimental ALI, and provide several physiologic and therapeutic insights to the disease.

Leveraging heterogeneity across multiple data sets increases accuracy of cell-mixture deconvolution and reduces biological and technical biases

In silico quantification of cell proportions from mixed-cell transcriptomics data (deconvolution) requires a reference expression matrix, called basis matrix. We hypothesized that matrices created using only healthy samples from a single microarray platform would introduce biological and technical biases in deconvolution. We show presence of such biases in two existing matrices, IRIS and LM22, irrespective of the deconvolution method used. Here, we present immunoStates, a basis matrix built using 6160 samples with different disease states across 42 microarray platforms. We found that immunoStates significantly reduced biological and technical biases. We further show that cellular proportion estimates using immunoStates are consistently more correlated with measured proportions than IRIS and LM22, across all methods. Importantly, we found that different methods have virtually no effect once the basis matrix is chosen. Our results demonstrate the need and importance of incorporating biological and technical heterogeneity in a basis matrix for achieving consistently high accuracy.

Integrated molecular and clinical analysis for understanding human disease relationships

Existing knowledge of human disease relationships is incomplete. To establish a comprehensive understanding of disease, we integrated transcriptome profiles of 41,000 human samples with clinical profiles of 2 million patients, across 89 diseases. Based on transcriptome data, autoimmune diseases clustered with their specific infectious triggers, and brain disorders clustered by disease class. Clinical profiles clustered diseases according to the similarity of their initial manifestation and later complications, identifying disease relationships absent in prior co-occurrence analyses. Our integrated analysis of transcriptome and clinical profiles identified overlooked, therapeutically actionable disease relationships, such as between myositis and interstitial cystitis. Our improved understanding of disease relationships will identify disease mechanisms, offer novel therapeutic targets, and create synergistic research opportunities.We jointly examined gene-expression and electronic health record data for 104 diseases to identify unbiased clusters of molecularly and clinically related diseases. We performed gene expression meta-analysis of 41,000 samples and computed diseases’ clinical profile similarity using 2 million patient records. Based on molecular data, we observed autoimmune diseases clustering with their specific infectious triggers and brain disorders clustering by disease class. In contrast, the electronic health records based clinical profiles clustered diseases according to the similarity of their initial manifestation and later complications. Our integrated molecular and clinical analysis identified diseases with under-appreciated, therapeutically actionable relationships, such as between myositis and interstitial cystitis. This global understanding of relationships between diseases has potential to identify disease causing mechanisms and offer novel therapeutic targets.