Combined tumor and immune signals from genomes or transcriptomes predict outcomes of checkpoint inhibition in melanoma

Samuel S Freeman; Moshe Sade-Feldman; Jaegil Kim; Chip Stewart; Anna L K Gonye; Arvind Ravi; Monica B Arniella; Irena Gushterova; Thomas J LaSalle; Emily M Blaum; Keren Yizhak; Dennie T Frederick; Tatyana Sharova; Ignaty Leshchiner; Liudmila Elagina; Oliver G Spiro; Dimitri Livitz; Daniel Rosebrock; François Aguet; Jian Carrot-Zhang; Gavin Ha; Ziao Lin; Jonathan H Chen; Michal Barzily-Rokni; Marc R Hammond; Hans C Vitzthum von Eckstaedt; Shauna M Blackmon; Yunxin J Jiao; Stacey Gabriel; Donald P Lawrence; Lyn M Duncan; Anat O Stemmer-Rachamimov; Jennifer A Wargo; Keith T Flaherty; Ryan J Sullivan; Genevieve M Boland; Matthew Meyerson; Gad Getz; Nir Hacohen

doi:10.1016/j.xcrm.2021.100500

Combined tumor and immune signals from genomes or transcriptomes predict outcomes of checkpoint inhibition in melanoma

Cell Rep Med. 2022 Feb 15;3(2):100500. doi: 10.1016/j.xcrm.2021.100500.

Authors

Samuel S Freeman^{1

2}, Moshe Sade-Feldman^{1

3}, Jaegil Kim¹, Chip Stewart¹, Anna L K Gonye^{1

3}, Arvind Ravi^{1

4}, Monica B Arniella¹, Irena Gushterova^{1

3}, Thomas J LaSalle^{1

3}, Emily M Blaum^{1

3}, Keren Yizhak⁵, Dennie T Frederick³, Tatyana Sharova³, Ignaty Leshchiner^{1

3}, Liudmila Elagina¹, Oliver G Spiro¹, Dimitri Livitz¹, Daniel Rosebrock¹, François Aguet¹, Jian Carrot-Zhang^{1

4}, Gavin Ha⁶, Ziao Lin^{1

7}, Jonathan H Chen^{1

8}, Michal Barzily-Rokni³, Marc R Hammond³, Hans C Vitzthum von Eckstaedt³, Shauna M Blackmon³, Yunxin J Jiao^{1

9}, Stacey Gabriel¹, Donald P Lawrence¹⁰, Lyn M Duncan⁸, Anat O Stemmer-Rachamimov⁸, Jennifer A Wargo¹¹, Keith T Flaherty³, Ryan J Sullivan³, Genevieve M Boland¹², Matthew Meyerson^{1

4

13}, Gad Getz^{1

3

7

14}, Nir Hacohen^{1

3

15}

Affiliations

¹ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
² Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
³ Department of Medicine, Center for Cancer Research, Massachusetts General Hospital, Boston, MA 02114, USA.
⁴ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
⁵ Department of Cell Biology and Cancer Science, Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa 2611001, Israel.
⁶ Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle 98109, WA, USA.
⁷ Harvard University, Cambridge MA, 02138.
⁸ Department of Pathology, Massachusetts General Hospital, Boston 02114, MA, USA.
⁹ Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
¹⁰ Department of Medical Oncology, Massachusetts General Hospital, Boston, MA 02114, USA.
¹¹ Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹² Department of Surgery, Massachusetts General Hospital, Boston 02115, MA, USA.
¹³ Department of Genetics, Harvard Medical School, Boston 02115, MA, USA.
¹⁴ Department of Pathology, Harvard Medical School, Boston 02115, MA, USA.
¹⁵ Department of Medicine, Harvard Medical School, Boston 02115, MA, USA.

Abstract

Immune checkpoint blockade (CPB) improves melanoma outcomes, but many patients still do not respond. Tumor mutational burden (TMB) and tumor-infiltrating T cells are associated with response, and integrative models improve survival prediction. However, integrating immune/tumor-intrinsic features using data from a single assay (DNA/RNA) remains underexplored. Here, we analyze whole-exome and bulk RNA sequencing of tumors from new and published cohorts of 189 and 178 patients with melanoma receiving CPB, respectively. Using DNA, we calculate T cell and B cell burdens (TCB/BCB) from rearranged TCR/Ig sequences and find that patients with TMB^high and TCB^high or BCB^high have improved outcomes compared to other patients. By combining pairs of immune- and tumor-expressed genes, we identify three gene pairs associated with response and survival, which validate in independent cohorts. The top model includes lymphocyte-expressed MAP4K1 and tumor-expressed TBX3. Overall, RNA or DNA-based models combining immune and tumor measures improve predictions of melanoma CPB outcomes.

Keywords: T cell receptor; TMB; cancer genomics; cancer immunotherapy; immune checkpoint blockade; integrative model; melanoma; melanoma subtype; tumor mutational burden.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Exome Sequencing
Humans
Melanoma* / drug therapy
RNA
Sequence Analysis, RNA
Transcriptome* / genetics

Substances

RNA

Abstract

Publication types

MeSH terms

Substances

Grants and funding