Mahler Revsine

Mahler Revsine

PhD Student

Posts by Collection

portfolio

publications

Acute leukemia classification using transcriptional profiles from low-cost nanopore mRNA sequencing.

Published in JCO Precision Oncology, 2022

Transcriptional profiling of acute leukemia samples using nanopore technology for diagnostic classification is feasible and accurate, which has the potential to improve the accuracy of cancer diagnosis in low-resource settings.

Recommended citation: Wang, J., Bhakta, N., Miller, V. A., Revsine, M., Litzow, M. R., Paietta, E., Fedoriw, Y., Roberts, K. G., Gu, Z., Mullighan, C. G., Jones, C. D., & Alexander, T. B. (2022). Acute leukemia classification using transcriptional profiles from low-cost nanopore mRNA sequencing. JCO Precision Oncology, 6, e2100326.
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Tumor biology and immune infiltration define primary liver cancer subsets linked to overall survival after immunotherapy.

Published in Cell Reports Medicine, 2023

[W]e […] profile the transcriptome and genomic alterations among 86 hepatocellular carcinoma and cholangiocarcinoma patients prior to and following immune checkpoint inhibitor treatment […] we identify stable molecular subtypes linked to overall survival and distinguished by two axes of aggressive tumor biology and microenvironmental features.

Recommended citation: Budhu, A., Pehrsson, E. C., He, A., Goyal, L., Kelley, R. K., Dang, H., Xie, C., Monge, C., Tandon, M., Ma, L., Revsine, M., Kuhlman, L., Zhang, K., Baiev, I., Lamm, R., Patel, K., Kleiner, D. E., Hewitt, S. M., Tran, B., Shetty, J., Wu, X., Zhao, Y., Shen, T., Choudhari, S., Kriga, Y., Ylaya, K., Warner, A. C., Edmonson, E. F., Forgues, M., Greten, T. F., & Wang, X. W. (2023). Tumor biology and immune infiltration define primary liver cancer subsets linked to overall survival after immunotherapy. Cell Reports Medicine, 4 (6).
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Tumor-associated macrophages trigger MAIT cell dysfunction at the HCC invasive margin.

Published in Cell, 2023

Here, we present a MAIT cell-centered profiling of hepatocellular carcinoma (HCC) using scRNA-seq, flow cytometry, and co-detection by indexing (CODEX) imaging of paired patient samples.

Recommended citation: Ruf, B., Bruhns, M., Babaei, S., Kedei, N., Ma, L., Revsine, M., Benmebarek, M., Ma, C., Heinrich, B., Subramanyam, V., Qi, J., Wabitsch, S., Green, B. L., Bauer, K. C., Myojin, Y., Greten, L. T., McCallen, J. D., Huang, P., Trehan, R., Wang, X., Nur, A., Soika, D. Q. M., Pouzolles, M., Evans, C. N., Chari, R., Kleiner, D. E., Telford, W., Dadkhah, K., Ruchinskas, A., Stovroff, M. K., Kang, J., Oza, K., Ruchirawat, M., Kroemer, A., Wang, X. W., Claassen, M., Korangy, F., & Greten, T. F. (2023). Tumor-associated macrophages trigger MAIT cell dysfunction at the HCC invasive margin. Cell, 186 (17), 3686-3705.
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Genome-wide profiling of transcription factor activity in primary liver cancer using single-cell ATAC sequencing.

Published in Cell Reports, 2023

Here, we present genome-wide profiling of transcription regulatory elements of 16 PLC patients using single-cell assay for transposase accessible chromatin sequencing.

Recommended citation: Craig, A. J., Silveira, M. A. D., Ma, L., Revsine, M., Wang, L., Heinrich, S., Rae, Z., Ruchinskas, A., Dadkhah, K., Do, W., Behrens, S., Mehrabadi, F. R., Dominguez, D. A., Forgues, M., Budhu, A., Chaisaingmongkol, J., Hernandez, J. M., Davis, J. L., Tran, B., Marquardt, J. U., Ruchirawat, M., Kelly, M., Greten, T. F., & Wang, X. W. (2023). Genome-wide profiling of transcription factor activity in primary liver cancer using single-cell ATAC sequencing. Cell reports, 42 (11).
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Single-Cell Characterization of the Tumor Ecosystem in Liver Cancer.

Published in Liver Carcinogenesis: Methods and Protocols, 2024

Here we introduce the experimental protocol and computational methods for the single-cell study of liver cancer.

Recommended citation: Wang, L.#, Revsine, M.#, Wang, X. W., & Ma, L. (2024). Single-Cell Characterization of the Tumor Ecosystem in Liver Cancer. In Liver Carcinogenesis: Methods and Protocols (pp. 153-166). New York, NY: Springer US.
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Lineage and ecology define liver tumor evolution in response to treatment.

Published in Cell Reports Medicine, 2024

We construct a lineage and ecological score as joint dynamics of tumor cells and their microenvironments. Tumors may be classified into four main states in the lineage-ecological space, which are associated with clinical outcomes.

Recommended citation: Revsine, M., Wang, L., Forgues, M., Behrens, S., Craig, A. J., Tran, B., Kelly, M., Budhu, A., Monge, C., Xie, C., Hernandez, J. M., Greten, T. F., Ma, L., & Wang., X. W. (2024). Lineage and ecology define liver tumor evolution in response to treatment. Cell Reports Medicine, 5 (2), 101394.
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Immunosuppressive CD29+ Treg accumulation in the liver in mice on checkpoint inhibitor therapy.

Published in Gut, 2024

We found that the murine liver houses a Treg population that, unlike those found in other organs, is both highly proliferative and apoptotic at baseline. On administration of αPD-1, αPD-L1 or αCTLA4, the liver Treg population doubled regardless of the presence of an intrahepatic tumour.

Recommended citation: Green, B. L., Myojin, Y., Ma, C., Ruf, B., Ma, L., Zhang, Q., Rosato, U., Qi, J., Revsine, M., Wabitsch, S., Bauer, K., Benmebarek, M., McCallen, J., Nur, A., Wang, X., Sehra, V., Gupta, R., Claassen, M., Wang, X. W., Korangy, F., & Greten, T. F. (2024). Immunosuppressive CD29+ Treg accumulation in the liver in mice on checkpoint inhibitor therapy. Gut, 73 (3), 509-520.
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High-coverage nanopore sequencing of samples from the 1000 Genomes Project to build a comprehensive catalog of human genetic variation.

Published in Genome Research, 2024

Here, we present data from analysis of the first 100 samples [of the 1000 Genomes Project], representing all 5 superpopulations and 19 subpopulations.

Recommended citation: Gustafson, J. A., Gibson, S. B., Damaraju, N., Zalusky, M. P., Hoekzema, K., Twesigomwe, D., Yang, L., Snead, A. A., Richmond, P. A., Coster, W. D., Olson, N. D., Guarracino, A., Li, Q., Miller, A. L., Goffena, J., Anderson, Z., Storz, S. H., Ward, S. A., Sinha, M., Gonzaga-Jauregui, C., Clarke, W. E., Basile, A. O., Corvelo, A., Reeves, C., Helland, A., Musunuri, R. L., Revsine, M., Patterson, K. E., Paschal, C. R., Zakarian, C., Goodwin, S., Jensen, T. D., Robb, E., The 1000 Genomes ONT Sequencing Consortium, University of Washington Center for Rare Disease Research (UW-CRDR), Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium, McCombie, W. R., Sedlazeck, F. J., Zook, J. M., Montgomery, S. B., Garrison, E., Kolmogorov, M., Schatz, M. C., McLaughlin, R. N., Jr., , Dashnow, H., Zody, M. C., Loose, M., Jain, M., Eichler, E. E., & Miller, D. E. (2024). High-coverage nanopore sequencing of samples from the 1000 Genomes Project to build a comprehensive catalog of human genetic variation. Genome research, 34 (11), 2061-2073.
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Multiomics analysis of immune correlatives in hepatocellular carcinoma patients treated with tremelimumab plus durvalumab.

Published in Gut, 2025

We treated 28 HCC patients with durvalumab, tremelimumab and locoregional therapies. We performed a high-dimensional multiomics analysis including whole exome sequencing, single-cell RNA seq, CO-Detection by indEXing, flow cytometry and multiplex cytokine/chemokine analysis of patients’ blood and tumour samples and integrated this data to elucidate immune correlatives and response mechanisms.

Recommended citation: Myojin, Y., Babaei, S., Trehan, R., Hoffman, C., Kedei, N., Ruf, B., Benmebarek, M., Bauer, K. C., Huang, P., Ma, C., Monge, C., Xie, C., Hrones, D., Duffy, A. G., Armstrong, P., Kocheise, L., Desmond, F., Buchalter, J., Galligan, M., Cantwell, C., Ryan, R., McCann, J., Bourke, M., Nicholas, R. M., McDermott, R., Awosika, J., Cam, M., Krebs, R., Budhu, A., Revsine, M., Figg, W. D., Kleiner, D. E., Redd, B., Wood, B. J., Wang, X. W., Korangy, F., Claassen, M., & Greten, T. F. (2025). Multiomics analysis of immune correlatives in hepatocellular carcinoma patients treated with tremelimumab plus durvalumab. Gut, 74 (6), 983-995.
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Comprehensive analysis of microbial content in whole-genome sequencing samples from The Cancer Genome Atlas project.

Published in Science Translational Medicine, 2025

In recent years, a growing number of publications have reported the presence of microbial species in human tumors and mixtures of microbes that appear to be highly specific to different cancer types. […] Our results expand upon and reinforce our recent findings, which show that the presence of microbes is far smaller than had been previously reported and that many species identified in TCGA data might not be present at all.

Recommended citation: Ge, Y., Lu, J., Puiu, D., Revsine, M., & Salzberg, S. L. (2025). Comprehensive analysis of microbial content in whole-genome sequencing samples from The Cancer Genome Atlas project. Science translational medicine, 17 (814), eads6335.
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Pan-microbial serological repertoire as a biomarker of immunotherapy response in hepatocellular carcinoma.

Published in Journal for Immunotherapy of Cancer, 2025

We used phage immunoprecipitation sequencing technology to measure circulating viral and bacterial antibodies as a biomarker of ICI response. […] Our results suggest a unique microbial reactivity profile may serve as a potential biomarker of ICI response in patients with HCC.

Recommended citation: Behrens, S., Do, W. L., Wang, L., Revsine, M., Maestri, E., Jacob, A., Chang, C., Forgues, M., Sardoo, A. M., Budhu, A., Argemi, J., Sogbe, M., Sangro, B., Greten, T. F., & Wang, X. W. (2025). Pan-microbial serological repertoire as a biomarker of immunotherapy response in hepatocellular carcinoma. Journal for Immunotherapy of Cancer, 13 (10), e011742.
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Genomic Next-Token Predictors are In-Context Learners.

Published in arXiv preprint, 2025

We develop a controlled experimental framework comprising symbolic reasoning tasks instantiated in both linguistic and genomic forms, enabling direct comparison of ICL across genomic and linguistic models. Our results show that genomic models, like their linguistic counterparts, exhibit log-linear gains in pattern induction as the number of in-context demonstrations increases.

Recommended citation: Breslow, N., Mishra, A., Revsine, M., Schatz, M. C., Liu, A., & Khashabi, D. (2025). Genomic Next-Token Predictors are In-Context Learners. arXiv preprint arXiv: 2511.12797.
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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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