Finding human genetic variation in whole genome expression data with applications for “missing” heritability: The GWCoGAPS algorithm, the PatternMarkers statistic, and the ProjectoR package

Starting from a single fertilized egg, the compendium of human cells is generated via stochastic perturbations of earlier generations. Concurrently, canalization of developmental pathways limits the type and degree of variation to ensure viability; thus, it is unsurprising that deviations early in life have been linked to late manifesting diseases. Human pluripotent stem cells (hPSCs) are a highly robust and uniquely human experimental system in which to model the sources and consequences of this variability. Further, variation in hPSCs’ transcriptomes has been directly linked to both genomic background and biases in differentiation efficiency. Taking advantage of this link between genomic background and developmental phenotypes, we developed Genome-Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS), the first robust whole genome Bayesian non-negative matrix factorization (NMF), to find conserved transcriptional signatures representative of the functional effect of human genetic variation. Time course RNA-seq data obtained from three human embryonic stem cells (ESC) and three human induced pluripotent stem cells (IPSC) in three different experimental conditions was analyzed. GWCoGAPS distinguished shared developmental trajectories from unique transcriptional signatures of each of the cell lines. Further analysis of these “identity” signatures found they were predictive of lineage biases during neuronal differentiation. Additionally, lineage biases were consistent with early differences in morphogenetic phenotypes within monolayer culture, thus, linking transcriptional genomic signatures to stable quantifiable cellular features. To test whether the cell line signatures were genome specific, we next developed the projectoR algorithm to assess a given signatures robustness in independent data sets. By using the identity signatures as inputs to projectoR, we were able to identify samples from the same donor genome in datasets from multiple tissues and across technical platforms, including RNA-seq results from post-mortem brain, micro arrayed embryoid bodies, and publicly available datasets. The identification of signatures that define the functional rather than physical background of an individual’s genome has the potential to profoundly influence our view of human variation and disease

Leveraging single-cell genomics to uncover clinical and preclinical responses to cancer immunotherapy

Immune checkpoint inhibitors (ICIs) provide durable clinical responses in about 20% of cancer patients, but have been largely ineffective for non-immunogenic cancers that lack intratumoral T cells. Most tumors have somatic mutations that encode for mutant proteins that are tumor-specific and not expressed on normal cells (termed neoantigens). Cancers, such as melanoma, with the highest mutational burdens are more likely to respond to single agent ICIs. However, most cancers, including pancreatic ductal adenocarcinoma (PDAC), have lower mutational loads, resulting in fewer T cells infiltrating the tumor. Studies have previously demonstrated that an allogeneic GM-CSF-based vaccine enhances T cell infiltration into human pancreatic cancer. Recent work with Panc02 cells, which express around 60 neoantigens similar to human PDAC, showed that PancVAX, a neoantigen-targeted vaccine, when paired with immune modulators cleared tumors in Panc02-bearing mice. This data suggests that cancer vaccines targeting tumor neoantigens induce neoepitope-specific T cells, which can be further activated by ICIs, leading to tumor rejection. Currently, the impact of ICIs and neoantigen-targeted vaccines on immune cell expression states and the underlying mechanism of therapeutic response remains poorly defined. Comprehensive characterization of responding immune cells, particularly T cells, will be critical in understanding mechanisms of response and providing a rationale for combinatorial therapies. In this work, we develop innovative computational methods and analysis pipelines to analyze the tumor-immune microenvironment at single-cell resolution. We establish an algorithm to quantify differential heterogeneity in single-cell RNA-seq data, demonstrate the use of non-negative matrix factorization and transfer learning algorithms to identify previously unknown and conserved ICI responses between species, and develop a novel algorithm to physicochemically compare single-cell T cell receptor sequences. We leverage these methods in various contexts to yield new insight into the biological mechanisms underlying positive immunotherapeutic responses in diverse tumor types, including PDAC