Chris McFarland is a Postdoctoral Fellow in the Petrov Lab (2014-now). I was a CEGH fellow from 2014-2015 and Cancer Systems Biology Fellow (2015-now). I study cancer using evolutionary modeling. In Leonid Mirny's lab at MIT, I used population genetics modeling and genomic analysis to argue that tumors accumulate many 'deleterious passenger' mutations that are harmful to cancer cells and possibly exploitable by existing and future therapies. I am now working in the Petrov Lab, along with folks in Monte Winslow's lab, to understand how various genetic events combine to induce lung cancer. Intriguingly, we find that the same combinations of genetic events sometimes progresses cells to cancer and sometimes do not. I want to quantitatively describe this variability and understand its origins.
Basic Life Science Research Associate, Biology
The damaging effect of passenger mutations on cancer progression.
Genomic instability and high mutation rates cause cancer to acquire numerous mutations and chromosomal alterations during its somatic evolution, most are termed passengers because they do not confer cancer phenotypes. Evolutionary simulations and cancer genomic studies suggest that mildly deleterious passengers accumulate and can collectively slow cancer progression. Clinical data also suggest an association between passenger load and response to therapeutics, yet no causal link between the effects of passengers and cancer progression has been established. To assess this, we introduced increasing passenger loads into human cell lines and immunocompromised mouse models. We found that passengers dramatically reduced proliferative fitness (~3% per Mb), slowed tumor growth, and reduced metastatic progression. We developed new genomic measures of damaging passenger load that can accurately predicted the fitness costs of passengers in cell lines and in human breast cancers. We conclude that genomic instability and elevated load of DNA alterations in cancer is a double-edged sword: it accelerates the accumulation of adaptive drivers, but incurs a harmful passenger load that can outweigh driver benefit. The effects of passenger alterations on cancer fitness were unrelated to enhanced immunity, as our tests were performed either in cell culture or in immunocompromised animals. Our findings refute traditional paradigms of passengers as neutral events, suggesting that passenger load reduces the fitness of cancer cells and slows or prevents progression of both primary and metastatic disease. The anti-tumor effects of chemotherapies can in part be due to induction of genomic instability and increased passenger load.
View details for DOI 10.1158/0008-5472.CAN-15-3283-T
View details for PubMedID 28536279
A quantitative and multiplexed approach to uncover the fitness landscape of tumor suppression in vivo.
Cancer growth is a multistage, stochastic evolutionary process. While cancer genome sequencing has been instrumental in identifying the genomic alterations that occur in human tumors, the consequences of these alterations on tumor growth remain largely unexplored. Conventional genetically engineered mouse models enable the study of tumor growth in vivo, but they are neither readily scalable nor sufficiently quantitative to unravel the magnitude and mode of action of many tumor-suppressor genes. Here, we present a method that integrates tumor barcoding with ultradeep barcode sequencing (Tuba-seq) to interrogate tumor-suppressor function in mouse models of human cancer. Tuba-seq uncovers genotype-dependent distributions of tumor sizes. By combining Tuba-seq with multiplexed CRISPR-Cas9-mediated genome editing, we quantified the effects of 11 tumor-suppressor pathways that are frequently altered in human lung adenocarcinoma. Tuba-seq enables the broad quantification of the function of tumor-suppressor genes with unprecedented resolution, parallelization, and precision.
View details for DOI 10.1038/nmeth.4297
View details for PubMedID 28530655
- A modified ziggurat algorithm for generating exponentially and normally distributed pseudorandom numbers JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2016; 86 (7): 1281-1294
An in vivo multiplexed small-molecule screening platform.
2016; 13 (10): 883-889
Phenotype-based small-molecule screening is a powerful method to identify molecules that regulate cellular functions. However, such screens are generally performed in vitro under conditions that do not necessarily model complex physiological conditions or disease states. Here, we use molecular cell barcoding to enable direct in vivo phenotypic screening of small-molecule libraries. The multiplexed nature of this approach allows rapid in vivo analysis of hundreds to thousands of compounds. Using this platform, we screened >700 covalent inhibitors directed toward hydrolases for their effect on pancreatic cancer metastatic seeding. We identified multiple hits and confirmed the relevant target of one compound as the lipase ABHD6. Pharmacological and genetic studies confirmed the role of this enzyme as a regulator of metastatic fitness. Our results highlight the applicability of this multiplexed screening platform for investigating complex processes in vivo.
View details for DOI 10.1038/nmeth.3992
View details for PubMedID 27617390
View details for PubMedCentralID PMC5088491