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Read the Lawrence Lab 2017-2018 Annual Report
Michael S. Lawrence, PhD Assistant Professor of PathologyHarvard Medical School Assistant GeneticistMassachusetts General Hospital Cancer Center
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.
Michael S. Lawrence, PhDPrincipal Investigator
*Associate Computational Biologist based at Broad Institute
2017 Lab Ski Trip to Loon Mountain, Lincoln NH.
Tumor DNA sequencing
High-throughput DNA sequencing is a workhorse of biomedical research. There are many challenges in processing the raw DNA sequencing reads from a patient's resected tumor or biopsy material, aligning them accurately to the reference human genome, and then scanning for loci where the tumor DNA differs from the patient's bulk "normal" DNA (e.g. from a blood draw). Distinguishing true somatic mutations from sequencing or alignment artifacts can be tricky, especially for subclonal events present in only a fraction of tumor cells. We are refining a "panel of normals" (PoN) approach, which combats stochastic artifacts seen in the patient's tumor sample and not in the patient's normal sample but widespread however in many other patients' normal samples. We are continually discovering new artifact modes, making this a highly challenging and unpredictable area of research. Isolating true somatic mutations is crucial for downstream analyses of mutational signatures and driver events.
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.
Tumor Evolution and Drug Resistance
When cancer is treated with therapies that target specific driver mechanisms, the selective pressure on the cancer cells often results in the rapid emergence of drug resistance. For example, lung cancer in non-smokers is often driven by gene fusions of a kinase such as ALK. An initial biopsy may reveal that the patient's tumor is made up entirely of cells with an ALK fusion, leading the oncologist to treat the patient with an ALK inhibitor such as crizotinib. This leads to rapid improvement of the patient, with the tumor shrinking dramatically. However, after a period of treatment (sometimes as short as a few months), the cancer becomes resistant to the inhibitor and the tumor grows back. A repeat biopsy (or sampling of pleural effusions) often reveals a new point mutation in ALK, which in up to a third of patients is exactly the same mutation,L1196M, called the "gatekeeper mutation" because of its ability to block drug binding. By analyzing sequential cancer samples from the patient, we track the emergence of resistant clones and learn how cancer evolves in response to the patient's treatments.Our goal is to understand common mechanisms of drug resistance and how to thwart them.
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
Yoda S, Lin JJ, Lawrence MS, Burke BJ, Friboulet L, Langenbucher A, Dardaei L, Prutisto-Chang K, Dagogo-Jack I, Timofeevski S, Hubbeling H, Gainor JF, Ferris LA, Riley AK, Kattermann KE, Timonina D, Heist RS, Iafrate AJ, Benes CH, Lennerz JK, Mino-Kenudson M, Engelman JA, Johnson TW, Hata AN, Shaw AT. Sequential ALK Inhibitors Can Select for Lorlatinib-Resistant Compound ALK Mutations in ALK-Positive Lung Cancer. Cancer Discov. 2018 Jun;8(6):714-729.
Haradhvala NJ, Kim J, Maruvka YE, Polak P, Rosebrock D, Livitz D, Hess JM, Leshchiner I, Kamburov A, Mouw KW, Lawrence MS, Getz G. Distinct mutational signatures characterize concurrent loss of polymerase proofreading and mismatch repair. Nat Commun. 2018 May 1;9(1):1746.
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.
Lawrence MS*, Stojanov P, Polak P*, Kryukov GV, Cibulskis K, Sivachenko A, Carter SL, Stewart C, Mermel CH, Roberts SA, Kiezun A, Hammerman PS, McKenna A, Drier Y, Zou L, Ramos AH, Pugh TJ, Stransky N, Helman E, Kim J, Sougnez C, Ambrogio L, Nickerson E, Shefler E, Cortés ML, Auclair D, Saksena G, Voet D, Noble M, DiCara D, Lin P, Lichtenstein L, Heiman DI, Fennell T, Imielinski M, Hernandez B, Hodis E, Baca S, Dulak AM, Lohr J, Landau DA, Wu CJ, Melendez-Zajgla J, Hidalgo-Miranda A, Koren A, McCarroll SA, Mora J, Lee RS, Crompton B, Onofrio R, Parkin M, Winckler W, Ardlie K, Gabriel SB, Roberts CW, Biegel JA, Stegmaier K, Bass AJ, Garraway LA, Meyerson M, Golub TR, Gordenin DA, Sunyaev S, Lander ES, Getz G. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013 Jul 11;499(7457):214-8.
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