Inaugural Krantz Awards Recipient
2023 Quantum Award: Targeting transcription factors to open doors to new cancer therapies
Team: Liron Bar-Peled, PhD, Michael Lawrence, PhD and Chris Ott, PhD.
Learn more about the team's project and the Krantz Awards
Research Summary
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. Endogenous proteins in human cells can also introduce mutations. One such family of proteins, called APOBEC enzymes, are cytosine deaminases whose normal function is to attack viruses that infect cells. APOBECs are usually dormant in healthy cells, but cancer cells often cause them to become inappropriately activated, inducing them to attack the cell’s own DNA, leading to mutations throughout the genome. This can speed a cancer cell’s search of sequence space to find resistance mutations that make the cell invulnerable to targeted therapies. Understanding this tactic of cancer cells, and finding a way to inhibit APOBEC enzymes, to put the brakes on cancer evolution, are major interests of our lab.
Research Projects
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 mutations 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.
APOBEC mutations and mesoscale genomic features
A major research interest of our lab is APOBEC/AID enzymes, which normally play a role in the immune system, both the innate immune system, where they act to target viruses infecting a cell or endogenous retroviruses awakened from dormancy in the genome, as well as the adaptive immune system, where they induce somatic hypermutation of the variable regions of immunoglobulins, enabling the selection of ever more optimized antibodies and T cell receptors. Mutagenesis catalyzed by APOBEC/AID enzymes represents a rare case of a cell mutating its own DNA on purpose. Because of the potential danger in allowing this to happen, normal healthy cells usually tightly repress the activity of these enzymes.
However, in cancer cells the picture is very different. Cancer genomics studies over the past decade have revealed APOBEC mutation signatures in over half of human tumors. Work from our group and others has revealed genomic details of the preferred targets of APOBEC mutagenesis, such as a strong tendency to mutate cytosines exposed in short loops at the end of genomic stem-loop structures called “hairpins”. While APOBEC mutagenesis is widespread in primary tumors, it becomes even more frequent in tumors following treatment with targeted therapies. Our work has revealed that tumors appear to leverage APOBEC mutagenesis as a strategy for discovering useful resistance mutations that allow the tumor to escape therapeutic intervention. To combat this hijacking of the cell’s natural immune mechanisms, it would be useful to develop APOBEC inhibitors that could be given as adjuvant or neoadjuvant therapy in combination with driver-targeting drugs. Our ongoing work seeks to employ our insights about APOBEC’s preferred substrates to the development of small-molecule or hairpinmimetic APOBEC inhibitors that could enhance and extend the benefit patients receive from targeted therapies.
Research Image

Cancer cells accelerate their evolution by taking advantage of APOBEC hypermutation. This figure illustrates the clinical history of an ALK-driven lung cancer patient being treated at MGH. The initial cancer clone (grey) showed no evidence of APOBEC mutagenesis (“lego plot” lacks APOBEC signature). Crizotinib treatment led to dramatic tumor shrinkage and a two-year remission. However, a resistant clone (blue) eventually emerged, and biopsy of the relapsed tumor revealed the resistance mutation ALK E1210K, as well as hundreds of other new mutations, in aggregate displaying the characteristic APOBEC mutation signature (red stars). This pattern repeated twice more, ultimately exhausting available targeted therapies. Co-treatment with an APOBEC inhibitor could shift the arms race between cancer cells and oncologists, by slowing cancer evolution and prolonging the benefit patients receive from precision medicine.
Publications
Selected Publications
Isozaki H^, Sakhtemani R, Abbasi A, Nikpour N, Stanzione M, Oh S, Langenbucher A, Monroe S, Su W, Cabanos HF, Siddiqui FM, Phan N, Jalili P, Timonina D, Bilton S, Gomez- Caraballo M, Archibald HL, Nangia V, Dionne K, Riley A, Lawlor M, Banwait MK, Cobb RG, Zou L, Dyson NJ, Ott CJ, Benes C, Getz G, Chan CS, Shaw AT, Gainor JF, Lin JJ, Sequist LV, Piotrowska Z, Yeap BY, Engelman JA, Lee JJ, Maruvka YE, Buisson R, Lawrence MS*^, Hata AN*^. Therapy induced APOBEC3A drives evolution of persistent cancer cells. Nature. 2023 Aug;620(7973):393-401.
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.
*Co-authors
^Co-corresponding authors
We're Hiring!
Data Analyst - Cancer Computational Biology
Emails:
erheinbay@mgh.harvard.edu
mslawrence@mgh.harvard.edu
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
Principal Duties:
- 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.
Required skills:
- 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
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.