Michael S. Lawrence, PhD
Assistant Professor of Pathology
Harvard Medical School
Massachusetts General Hospital Cancer Center
Center for Cancer Research
Massachusetts General Hospital Pathology Service
As seen on the cover of Cell
Mutational Strand Asymmetry
Explore the Lawrence Lab
Cancer results from alterations to DNA that lead to the activation of oncogenes or the inactivation of tumor suppressors. The Lawrence laboratory focuses on understanding the many ways this can happen, using computation as a powerful microscope to study the processes of DNA damage and repair, gene expression and genome replication, and cancer driver genes. Over our lifetimes, DNA slowly accumulates mutations due to environmental toxins and radiation, as well as from naturally occurring copying errors. The vast majority of mutations have little or no effect on a cell, but out of all possible mutations, a few may hit exactly the right place in the genome, where they can act as a “driver mutation,” pushing the cell toward aggressive growth and tumor formation. Sequencing the DNA in a tumor reveals not only its driver mutations, but also all the other “passenger mutations” that were present in the tumor-initiating cell. We seek insights about cancer from both driver and passenger mutations.
Analyzing Mutational Signatures
Cancers vary over many orders of magnitude in their total background mutation burden, ranging from very quiet tumor types such as leukemias and childhood tumors, which may have fewer than 10 somatic mutations in their exome, to carcinogen-associated tumor types such as lung cancer and melanoma, which may have over 1000. Mutations have many causes, and each mutagen can leave a telltale signature. For instance, spontaneous deamination of methylated CpG’s causes the transition mutations that dominate many tumor types. Mutagens in tobacco smoke cause G-to-T transversions. Ultraviolet radiation causes C-to-T at dipyrimidines. Agitated APOBEC enzymes cause mutations at C’s preceded by T. Loss of mismatch repair causes microsatellite instability (MSI), marked by expansion and contraction of simple sequence repeats, as well as characteristic types of single-base changes. Tumors carrying mutations in the proofreading exonuclease domain of polymerase epsilon (POLE) tend to accrue C-to-A mutations at the trinucleotide TCT. Very rare “MSI+POLE” cancers show the highest yet known somatic mutation burdens, with upwards of 10,000 coding mutations per patient. Patients affected by MSI and/or POLE mutagenesis are known to experience better clinical outcomes, probably thanks to their high neoantigen loads which attract a powerful immune response. Our most recent research has focused on a less well-studied signal in somatic mutation datasets, mutational asymmetries between the two DNA strands. These illuminate transcriptional or “T-class” mutational patterns, associated with exposure to tobacco smoke, UV radiation, and a yet-unknown agent in liver cancer, as well as replicative or “R-class” patterns, associated with MSI, APOBEC, POLE, and a yet-unknown agent in esophageal cancer.
APOBEC Mutations and Mesoscale Genomic Features
Statistical approaches for distinguishing driver mutations from passenger mutations have relied on the gold standard of recurrence across patients. Seeing exactly the same DNA base-pair mutated recurrently across patients has been taken as proof that the mutation must be under functional selection for contributing to tumor fitness. The assumption is that mutational processes, being essentially random, are unlikely to hit the exact same base-pair over and over again. Our recent discoveries about APOBEC mutagenesis have cast doubt on this assumption. We have shown that APOBEC3A has a very strong preference for mutating cytosines presented in a short loop at the end of a strongly paired DNA hairpins. Our results indicate that there are multiple routes to cancer mutational hotspots. Driver mutation hotspots in oncogenes can rise to prominence through positive selection, and are not restricted to the “favorite” sites of any particular mutagen. In contrast, special DNA sites (like hairpins) that happen to be optimal substrates for a mutagen (like APOBEC) can give rise to “passenger hotspot mutations” that owe their prevalence to substrate optimality, not to any effects on tumor fitness. These findings apply not just to APOBEC but to all mutation signatures, and remind us of the need to be careful about assuming that all recurrent mutations are causative drivers of disease.
Figure: The mutational landscape of a cancer cell across size regimes. At the smallest scale, local DNA trinucleotide sequences (lower-left foreground) correlate with the "mutational signatures" induced by various mutagens. At the largest scale (background of image), chromatin is organized into multi-megabase domains comprising Compartment B (tightly packed, gene-poor DNA lining the nuclear periphery) and Compartment A (gene-rich open DNA in the nuclear interior). Mutations induced by APOBEC enzymes (yellow points) are distributed equally across the two compartments, but most other types of mutations (blue points) are concentrated in Compartment B. Between the large and small extremes lies the "mesoscale" regime, where genomic features like hairpin-forming ability are determined. DNA exposed in a hairpin loop is vulnerable to attack by the enzyme APOBEC3A (center), giving rise to highly recurrent passenger mutations in cancer.
Langenbucher A, Bowen D, Sakhtemani R, Bournique E, Wise JF, Zou L*, Bhagwat AS*, Buisson R*, Lawrence MS*. An extended APOBEC3A mutation signature in cancer. Nat Commun. 2021 Mar 11;12(1):1602.
Jalili P, Bowen D, Langenbucher A, Park S, Aguirre K, Corcoran RB, Fleischman AG, Lawrence MS*, Zou L*, Buisson R*. Quantification of ongoing APOBEC3A activity in tumor cells by monitoring RNA editing at hotspots. Nat Commun. 2020 Jun 12;11(1):2971.
Buisson R, Langenbucher A, Bowen D, Kwan EE, Benes CH, Zou L*, Lawrence MS*. Passenger hotspot mutations in cancer driven by APOBEC3A and mesoscale features. Science. 2019 Jun 28; 364(6447):eaaw2872.
Buisson R, Lawrence MS, Benes C, Zou L. APOBEC3A and APOBEC3B activities render cancer cells susceptible to ATR inhibition. Cancer Res. 2017 Jul 11.
Haradhvala NJ, Polak P, Stojanov P, Covington KR, Shinbrot E, Hess JM, Rheinbay E, Kim J, Maruvka YE, Braunstein LZ, Kamburov A, Hanawalt PC, Wheeler DA, Koren A, Lawrence MS, Getz G. Mutational Strand Asymmetries in Cancer Genomes Reveal Mechanisms of DNA Damage and Repair. Cell. 2016 Jan 28;164(3):538-49.
Lawrence MS, Stojanov P, Mermel CH, Robinson JT, Garraway LA, Golub TR, Meyerson M, Gabriel SB, Lander ES, Getz G. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature. 2014 Jan 23;505(7484):495-501.
We're Hiring! Data Analyst Position
Data Analyst - Cancer Computational Biology
Unique opportunity to join an interdisciplinary team bridging the Harvard Medical School, the Massachusetts General Hospital, and the Broad Institute of Harvard and MIT. The Rheinbay Lab and Lawrence Lab at the Massachusetts General Hospital Cancer Center seek well-qualified candidates to join a team of computational biologists working at the forefront of cancer research and treatment. We use computation as a powerful microscope to study both the fundamental biology of cancer initiation and progression, as well the diagnosis and treatment of cancer patients in the hospital setting.
Current research interests:
- Cancer driver genes: tumors grow because of specific driver mutations that deactivate tumor suppressors or activate oncogenes. We are working to complete our understanding of the full catalog of cancer's "box of tricks".
- Resistance to targeted therapies: single drugs targeting specific driver mutations can be effective for a while, but the cancer invariably discovers a work-around. We are actively investigating mechanisms of drug resistance and how to combat it.
- Single-cell sequencing: New approaches allow us to dissect a tumor down to single cells and investigate the RNA expression or DNA mutations in each cell. Understanding intratumoral heterogeneity is shedding new light on cancer progression and patient outcomes.
- Liquid biopsies: novel state-of-the-art technologies are starting to allow us to monitor the progression of cancer (both before, during, and after treatment) through a simple blood draw. We are actively working to overcome analytical challenges inherent in the study of circulating tumor cells (CTCs) and cell-free circulating tumor DNA (ctDNA).
- Mutational processes: our genomes accumulate mutations from environmental agents such as ultraviolet radiation and tobacco smoke, as well as from intrinsic processes like errors during DNA replication. Studying these mutational background patterns can tell us what repair pathways are broken in a specific tumor, perhaps pointing the way to an effective genotoxic therapy. We are working to develop novel DNA sequencing technologies for studying mutagenesis in model systems
- Genomics of sex chromosomes in cancer: understand the genetic underpinnings of different incidence and outcome between men and women patients
- Work with Cancer Center researchers to understand experimental procedures and the kinds of data produced (e.g. DNA sequencing, RNA sequencing, epigenetic readouts, clinical outcome annotations). Meet and discuss with clinical and experimental colleagues to identify analytical challenges and goals.
- Apply existing and novel algorithms to cancer data sets, analyze data quality, critically review and analyze results, communicate results to biologists, computational biologists, software engineers and clinicians.
- Explore novel data visualization tools, with emphasis on integrating diverse data types and extracting clinically relevant insights.
- Contribute to scientific writing and creation of data figures to be included in research publications reporting novel discoveries made in the lab and clinic.
- B.A/B.S. in one of Computational Biology, Bioinformatics, Biology, Computer Science, Mathematics, Physics, or a related quantitative discipline.
- Independent, self-motivated drive to push research forward.
- Excellent programming skills (using any of Matlab, R, Java, Python, Perl, C, etc.)
- Nimble approach to programming and data analysis, with an emphasis on simple, intuitive, reasoning: quickly open unfamiliar datasets, generate simple visualizations to project the data onto our brains as usefully as possible, to stimulate hypothesis generation and the next steps of the analysis.
- Comfort using Word, Excel, Powerpoint or Google Suite tools to communicate results between team members.
- Ability to work together with multi-disciplinary teams comprising physicians, biologists, statisticians, and software engineers.
- Strong organizational and record-keeping skills
- Fluency in spoken and written English
- Experience in "machine learning" welcomed
We're Hiring! Postdoctoral Position
Postdoctoral Position at the Lawrence Laboratory
Unique opportunity to join an interdisciplinary team bridging the Harvard Medical School, the Massachusetts General Hospital, and the Broad Institute of Harvard and MIT. The Lawrence Lab at the Massachusetts General Hospital Cancer Center seeks well-?qualified candidates to join a team of computational biologists working at the forefront of cancer research and treatment.
For more information, please see this flyer.
Michael S. Lawrence, PhDPrincipal Investigator
- Soroush Hajizadeh, MSc
- Elise Hansen, MSc
- Taïsha Joseph
- Emily Lachtara
- Hanjun Lee
- Maoxuan Lin, PhD
- Ramin Sakhtemani, PhD
- Jan F. Sayilgan, PhD
- Jillian Wise, PhD
- Ben Wittner, PhD
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