Back to Atlases main

Melanoma Pre-Cancer and Progression Atlas

Melanoma is an increasingly common type of cancer that often becomes metastatic when quite small, making melanoma a dangerous disease. Routine surveillance of the skin, followed by removal of lesions suspicious for cutaneous melanoma, is common in many developed countries. However, surveillance and staging are not perfect and some dangerous lesions slip through. Moreover, individuals with less access to healthcare do not benefit from regular monitoring and some times of melanoma (e.g. acral lentiginous melanoma) are underdiagnosed and undertreated, particularly in people of color. By improving our understanding of the sequence of molecular events that drive melanoma, the Melanoma Atlas will improve diagnosis, staging, and disease management.

Melanoma is noteworthy in that it can be treated both with targeted therapy (inhibitors of the RAF and MEK kinases for the ~50% cutaneous melanomas carrying BRAF mutations) and with immunotherapy (inhibitors of the PD-1 and CTLA-4 checkpoint proteins). Understanding precisely why these therapies achieve longer and deeper remission in some patients than others is not only relevant to understanding targeted and immunotherapy in general, but also to improving patient care–choosing among treatment options at the level of individual patients is not always straightforward. The importance of understanding the high responsiveness of melanoma immunotherapy is increasing since many other types of solid cancer have proven to be much more resistant to immune therapy.

Key Questions

  • What are the earliest events in the development of melanoma precursors? How might these precursors be identified diagnostically and eradicated therapeutically?
  • What are the key events in melanoma progression and why does the immune system successfully clear many pre-melanomas but fail to stop others?
  • How can we use this information to improve our ability to recognize the subset of primary melanomas that are at high risk of progression to metastatic disease?
  • What are the molecular events that allow some melanoma cells to escape therapy and survive as residual disease from which disseminated cancer can re-arise?

Principal Investigators

  • Genevieve Boland, MD PhD, Section Head of Melanoma/Sarcoma Surgery, Massachusetts General Hospital
  • Christine Lian, MD, Associate Professor of Pathology, Brigham and Women’s Hospital
  • David Liu, MD, MPH, MS, Assistant Professor of Medicine, Dana-Farber Cancer Institute
  • George Murphy, MD, Director of Dermatopathology, Brigham and Women’s Hospital
  • Sandro Santagata, MD PhD, Associate Professor of Pathology, Brigham and Women’s Hospital and Harvard Medical School
  • Dirk Schadendorf, MD, Director of the Department of Dermatology, University Hospital Essen
  • Eugene Semenov, MD MA FAAD, Instructor of Dermatology, Massachusetts General Hospital
  • Peter Sorger, PhD, Professor of Systems Biology, Harvard Medical School

Publications

SpatialCells: Automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data.

SpatialCells: Automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data.

Wan G, Maliga Z, Yan B, Vallius T, Shi Y, Khattab S, Chang C, Nirmal AJ, Yu K-H, Liu D, Lian CG, DeSimone MS, Sorger PK, Semenov YR.(2024).
Immune Profiling of Dermatologic Adverse Events from Checkpoint Blockade using Tissue Cyclic Immunofluorescence: A Pilot Study.

Immune Profiling of Dermatologic Adverse Events from Checkpoint Blockade using Tissue Cyclic Immunofluorescence: A Pilot Study.

Maliga Z, Kim DY, Bui AN, Lin JR, Dewan AK, Jadeja S, Murphy GF, Nirmal AJ, Lian CG, Sorger PK, LeBoeuf NR. (2024).
Journal of the American Academy of Dermatology, 90(2):288-298. https://doi.org/10.1016/j.jid.2024.01.024
Development and validation of time-to-event models to predict metastatic recurrence of localized cutaneous melanoma.

Development and validation of time-to-event models to predict metastatic recurrence of localized cutaneous melanoma.

Wan G, Leung BW, DeSimone MS, Nguyen N, Rajeh A, Collier MR, Rashdan H, Roster K,...Semenov YR. (2024).
Journal of the American Academy of Dermatology, 90(2):288-298. https://doi.org/10.1016/j.jaad.2023.08.105
Prediction of early-stage melanoma recurrence using clinical and histopathologic features.

Prediction of early-stage melanoma recurrence using clinical and histopathologic features.

Wan G, Nguyen N, Liu F, DeSimone MS, Leung BW, Rajeh A, Collier MR, Choi MS, Amadife M, Tang K, Zhang S, Phillipps JS, Jairath R, Alexander NA, Hua Y, Jiao M, Chen W, Ho D, Duey S, Németh IB, Marko-Varga G, Valdés JG, Liu D, Boland GM, Gusev A, Sorger PK, Yu KH, Semenov YR. (2022).
NPJ Precision Oncology. 6(1):79 https://doi.org/10.1038/s41698-022-00321-4
The spatial landscape of progression and immunoediting in primary melanoma at single cell resolution.

The spatial landscape of progression and immunoediting in primary melanoma at single cell resolution.

Nirmal, A.J., Maliga, Z., Vallius, T., Quattrochi, B., Chen, A.C., Jacobson, C.A., Pelletier, R.J., ... Lian, C.G., Murphy, G.F., Santagata, S., Sorger, P.K. (2022)
Cancer Discovery, 12(6), 1518–1541. https://doi.org/10.1158/2159-8290.CD-21-1357
Evolution of delayed resistance to immunotherapy in a melanoma responder.

Evolution of delayed resistance to immunotherapy in a melanoma responder.

Liu, D., Lin, J.-R., Robitschek, E.J., Kasumova, G.G., Heyde, A., Shi, A., Kraya, A., ... Boland, G.M. (2021).
Nature Medicine, 27, 985–992. https://doi.org/10.1038/s41591-021-01331-8

Data Explorations

Data Explorations are like museum guides and exploit the digital docents in MINERVA to guide readers through the complexities of a large image dataset via a series of narrated stories and waypoints.

Data image
Introduction to the MEL Atlas (MEL 1 Abstract)
Data image
Deep Exploration of a Primary Melanoma (MEL 1 Full Story)

Data Overviews

Data Overviews provide access to minimally processed Level 2 images with no annotation or quality control. Click any of the following thumbnail images for an interactive view of the full-resolution images.

Data image
MEL13-2-1 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL13-2-0 - H&E - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL13-1-1 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL13-1-0 - H&E - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL12-1-1 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL12-1-0 - H&E - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL11-1-1 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL11-1-0 - H&E - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL10-1-1 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL10-1-0 - H&E - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
Deep Exploration of a Primary Melanoma (MEL 1 Full Story)
Data image
Introduction to the MEL Atlas (MEL 1 Abstract)
Data image
MEL09-1-1 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL09-1-0 - H&E - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL08-1-1 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL08-1-0 - H&E - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL07-1-1 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL07-1-0 - H&E - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL06-1-1 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL06-1-0 - H&E - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL05-1-1 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL05-1-0 - H&E - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL04-1-1 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL04-1-0 - H&E - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL03-1-1 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL03-1-0 - H&E - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL02-1-1 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL02-1-0 - H&E - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL01-3-1 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL01-3-0 - H&E - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL01-2-1 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL01-2-0 - H&E - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL01-1-6 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL01-1-5 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL01-1-4 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL01-1-3 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL01-1-1 - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
MEL01-1-0 - H&E - overview - Nirmal, Maliga, Vallius, Sorger et al., 2021
Data image
Two Ways of Immune Evasion in Early Melanoma (for non-experts)
Data image
Liu et al, 2019 - PP
Data image
Liu et al, 2019 - PO
Data image
Liu et al, 2019 - PM
Data image
Liu et al, 2019 - PL
Data image
Liu et al, 2019 - PK
Data image
Liu et al, 2019 - PJ
Data image
Liu et al, 2019 - PH
Data image
Liu et al, 2019 - PG
Data image
Liu et al, 2019 - PF
Data image
Liu et al, 2019 - PE
Data image
Liu et al, 2019 - PC
Data image
Liu et al, 2019 - PB
Data image
Liu et al, 2019 - PA
Data image
Liu et al, 2019 - P9
Data image
Liu et al, 2019 - P8
Data image
Liu et al, 2019 - P7
Data image
Liu et al, 2019 - P6
Data image
Liu et al, 2019 - P5
Data image
Liu et al, 2019 - P4
Data image
Liu et al, 2019 - P2
Data image
Liu et al, 2019 - P1
Data image
Liu et al, 2019 - N3
Data image
Liu et al, 2019 - N2
Data image
Liu et al, 2019 - N1
Data image
Liu et al, 2019 - AV1
Data image
Liu et al, 2019 - AS4
Data image
Liu et al, 2019 - AS3
Data image
Liu et al, 2019 - AS2
Data image
Liu et al, 2019 - AS1
Data image
Liu et al, 2019 - AL4
Data image
Liu et al, 2019 - AL3
Data image
Liu et al, 2019 - AL2
Data image
Liu et al, 2019 - AL1

Funding

Research on precancers and primary melanoma is supported by the NCI Human Tumor Atlas Network as part of the Pre-cancer Atlases of Cutaneous & Hematologic Origin (PATCH) Center (Grant U2C-CA233262). HTAN aims to generate, publicly-accessible data on the spatial, genetic and epigenetic features of common human cancers and precancers. Research on advanced and metastatic melanomas are supported by the NCI Cancer Systems Biology Program (Grant U54-CA225088). Additional support is provided by the Ludwig Center at Harvard Medical School and the Ludwig Institute for Cancer Research.