Dr. LaBar [Candy and William Raveis Fellow] is using budding yeast and computational modeling to study the basic processes that determine how cancer cell populations evolve in response their environment. Dr. LaBar aims to understand how the number of cells in a tumor may drive the evolution of cancer cells based on their supply of new mutations. His research has the potential to shed light on specific mutations that appear in certain cancers, alterations that allow cancer cell populations to grow abnormally, and strategies that will help predict the evolution of cancer cell populations and patient prognosis.

Dr. Lalanne investigates the biophysical determinants of gene expression. Dysregulation of the expression of select oncogenes and tumor suppressors in specific tissues is sufficient to initiate tumorigenesis. Such dysregulation can arise from small-scale genetic changes that alter the binding sites of transcription factors in otherwise inactive enhancers (the short, non-coding regions of DNA to which transcription factors bind, activating gene expression). Despite substantial efforts in functional genomics, the quantitative connection between DNA sequence and expression remains largely elusive. Using a combination of single-cell transcriptomics and reporter assays, Dr. Lalanne plans to decipher the underlying sequence determinants of cell type-specific gene regulation. His goal is to formulate predictive models of which mutations in the non-coding genome can perturb the gene expression program and ultimately lead to cancer development.

In both embryonic development and disease, the same genetic mutation can lead to highly variable outcomes in different individuals. Dr. Lammers aims to shed light on the drivers of this nongenetic variability using the developing zebrafish embryo as a model system. By combining fluorescence microscopy and single-cell sequencing, he will test whether subtle differences in gene expression within individual cells can explain why some embryos with a given genetic mutation survive to adulthood, while others perish within the first 24 hours of their development. His findings will provide a quantitative foundation for understanding the genetic and molecular basis of cancer outcomes in human patients where, for instance, tumors with the same underlying mutations often exhibit dramatically different disease courses.

Dr. Lammers will train Variational Autoencoders to learn low-dimensional latent space representations of whole-embryo transcriptomes and grayscale images depicting embryonic morphology. He will then train a third neural network to translate from transcriptional latent space to morphological latent space. Together, these three networks will comprise a new computational method, morphSeq, that takes single-cell transcriptomes of mutant and wildtype embryos as input and produces predictions for corresponding embryo morphologies as its output.

Dr. Lapointe studies how the synthesis of proteins (translation) is controlled, as dysregulated translation is a ubiquitous feature of cancer. He is focused on a key question: how regulation that originates at the “tail” end of a messenger RNA (mRNA) impacts the start of translation, which occurs near the beginning of the mRNA. His goal is to reveal and analyze pathways that underlie this fundamental mechanism to control gene expression. Using an integrated approach of single-molecule fluorescence microscopy, structural, and biochemical strategies, this research will yield important insights into how translation is precisely regulated and how it is disrupted in a wide array of cancers.

The astonishing diversity of T cells makes finding ones that specifically target tumor cells but not host cells a major challenge in developing T cell immunotherapies. For this reason, the adoption of T cell immunotherapies in clinical settings has been slow despite their remarkable potential. Dr. Lashkaripour is developing a microfluidic platform capable of screening millions of T cells against millions of tumor antigens per day to identify the stimulatory pairs that drive an efficient immune response. With this research, he hopes to establish the groundwork for deciphering the rules of sequence-dependent T cell recognition of antigens. This research may guide the development of more effective and safer cancer immunotherapies. Dr. Lashkaripour received his PhD from Boston University, his MSc from the University of Tehran, and his BSc from Ferdowsi University of Mashhad.

Dr. Li [The Mark Foundation for Cancer Research Fellow] is mapping the positions of the amino acid cysteine in cancer-relevant proteins. He will perform functional screens that reveal the cysteine residues that are essential to the progression of cancer. Since the unique chemistry of cysteine makes it an attractive target for therapeutic development, this map can guide the discovery and optimization of drugs that can bind to and inhibit cancer-promoting proteins. His research has the potential to greatly accelerate the discovery of new cancer targets and their corresponding therapeutics. 

Stable levels of ions (such as sodium or potassium) are critical for human health. Imbalanced ion concentrations indicate a metabolic disorder and are related to the process of metastasis. Dr. Liu aims to develop small molecule therapies that target proteins involved in metabolic disorders. To this end, she is developing computational methods to screen billions of compounds and identify potential drug candidates. With this project she hopes to not only meet an urgent therapeutic need but also improve the computational-based drug discovery pipeline.

Dr. Liu is combining single-molecule fluorescence and force spectroscopy to study dynamic interactions between meiotic double-strand break (DSB) proteins and DNA. Meiotic recombination initiates with DSBs that are generated by the protein Spo11. Spo11 and its partner proteins ensure that DSBs occur at the right chromosome sites and at the right time. Dysregulated DSBs lead to chromosome instability, a hallmark of cancer cells. Dr. Liu’s study will elucidate the dynamics of DSB formation during meiosis, which will shed light on cancer formation and pave the way for new therapeutic alternatives.

Dr. Luo [HHMI Fellow] is focusing on the interplay between energy-producing mitochondria and the nucleus inside mammalian cells. Mitochondria contain their own small genome that encodes some proteins, but the vast majority are encoded in the cell's nucleus. The communication between mitochondria and the nucleus to produce the proteins necessary to properly function is tightly controlled, and its dysregulation has been implicated in human diseases including cancer. Dr. Luo is using ribosome profiling in parallel with CRISPR to quantitatively monitor translation (the process of protein production from RNA) on the mitochondrial surface and identify key regulators of this process. She hopes gaining an understanding of the underlying mechanism will yield fundamental insights into mitochondrial biology and its role in disease.

Patients with the same cancer diagnosis may experience very distinct disease progressions and treatment responses. These differences between patients have been associated with their degree of intra-tumor heterogeneity-the genetic, epigenetic, spatial, and environmental differences between the tumor cells. Characterizing the genetic and epigenetic states of different tumor cells is key to understanding how intra-tumor heterogeneity influences tumor progression, expansion, metastasis, and treatment response. Recent advances in single-cell RNA sequencing and spatial transcriptomics (which shows the spatial distribution of RNA molecules within a tissue sample) provide new opportunities to study intra-tumor heterogeneity in higher resolution. Dr. Ma's research aims to characterize intra-tumor heterogeneity in terms of specific genetic and epigenetic measures, and eventually develop 3D tumor models that capture this heterogeneity across multiple cancer types. Dr. Ma received her BS from Zhejiang University and her PhD in computational biology from Carnegie Mellon University.

The proposed computational methods will be based on previous methods developed in the group. Dr. Ma will develop a better method for identifying tumor clones for spatially resolved transcriptomics (SRT) data using both copy number and allele information using HMM and HMRF. She will adapt optimal transport frameworks and include biological networks as prior knowledge for integrating epigenetic data with SRT and between SRT slices to construct 3D spatial tumor multi-omics models.