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Mutational landscape influences immunotherapy outcomes among patients with non-small-cell lung cancer with human leukocyte antigen supertype B44

Nature Cancer volume 1, pages 1167–1175 (2020)Cite this article

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Abstract

Human leukocyte antigen (HLA)-B has been recognized as a major determinant of discrepancies in disease outcomes, and recent evidence indicates a role in immune checkpoint blockade (ICB) efficacy. The B44 supertype, which features an electropositive binding pocket that preferentially displays peptides with negatively charged amino acid anchors, is associated with improved survival in ICB-treated melanoma. Yet this effect was not seen in ICB-treated non-small-cell lung cancer (NSCLC). Here we show that mutations leading to glutamic acid substitutions occur more often in melanoma than NSCLC based on mutational landscape. We additionally show stratifying B44 based on the presence of somatic mutations that lead to negatively charged glutamic acid anchors identifies patients with NSCLC with an ICB benefit similar to that seen in melanoma. We anticipate these findings could improve assessment of HLA-related outcomes and prediction of ICB benefit in those with B44, representing approximately half of the world’s population.

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Fig. 1: Survival based on B44 supertype in UCLA NSCLC cohort.
Fig. 2: Overall schema.
Fig. 3: Proportion of glutamic acid and radical charged substitutions by histology.
Fig. 4: Predicted differences in half-maximal inhibitory concentrations of B44 neoepitopes.
Fig. 5: Experimental differences in half-maximal inhibitory concentrations in HLA-B*18:01 and HLA-B*40:02 predicted neoepitopes based on motif.
Fig. 6: Overall and PFS based on presence of charged motif neoepitopes.

Data availability

WES data that support the findings of this study have been deposited in the database of Genotypes and Phenotypes (dbGaP) under accession code phs002244, which can be accessed with a usage agreement for nonprofit cancer-related research. WES data from L06, L13, L14, L21 and L26 are available from dbGaP accession code phs000980. Previously published datasets that were reanalyzed here are available under accession codes phs000452, phs000980, phs00694, phs001041, phs001565 and SRP067938. The LUAD, LUSC and melanoma data were derived from the TCGA Research Network at http://cancergenome.nih.gov/. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

The code used to support the findings of this study is publicly available at https://github.com/garon-lab/hlab44/.

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Acknowledgements

We thank N. Volkes and M. Negrao as well as D. Wu, S. Tang, C. Kravit and P. Petrousis for their assistance in this project. The results here are in part based on data generated by the TCGA Research Network: https://www.cancer.gov/tcga. This work was funded by E.B.G.’s grant NIH-NCI R01 CA28403, the Cancer Center Support grant P30 CA016042 and grant NIH-NCATS UL1TR001881. A.L.C. was funded by a Specialty Training and Advanced Research Award. Z.N. was funded by USHHS Ruth L. Kirschstein Institutional National Research Service Award no. T32 CA009120. A.L. was funded by grant NIH-NCI K08 CA245249-01A1 and a LUNGevity 2019 Career Development Award. A.A.T.B. was funded by grant NIH-NCI R01 CA226079. A.R. was funded by the Parker Institute for Cancer Immunotherapy, NIH-NCI grants R35 CA197633 and P01 CA244118, and the Ressler Family Fund. S.M.G. is funded by grant NIH-NCI IU01CA196408. E.B.G. is also funded by grant CLIN-10784.

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Authors and Affiliations

  1. Department of Medicine, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, USA

    Amy L. Cummings, Jaklin Gukasyan, Henry Y. Lu, Tristan Grogan, Charlene M. Fares, Nicholas Hornstein, Jesse Zaretsky, James Carroll, Benjamin Bachrach, Wisdom O. Akingbemi, Debory Li, Zorawar Noor, Aaron Lisberg, Jonathan W. Goldman, David Elashoff, Antoni Ribas, Steven M. Dubinett & Edward B. Garon

  2. Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, USA

    Gemalene Sunga & Maura Rossetti

  3. Department of Radiology, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, USA

    Alex A. T. Bui

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  1. Amy L. Cummings
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Contributions

A.L.C., M.R. and E.B.G. contributed substantially to the conception and design of the work. A.L.C., J.G., H.Y.L., T.G., G.S., C.M.F., N.H., J.Z., J.C., B.B., W.O.A., D.L., Z.N., A.L., J.W.G., D.E., A.A.T.B., A.R., S.M.D., M.R. and E.B.G. contributed to data acquisition, analysis and interpretation. A.L.C. and E.B.G. drafted the paper. A.L.C., T.G., J.W.G., A.A.T.B., A.R., S.M.D., M.R. and E.B.G. contributed substantially to article revision.

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Extended data

Extended Data Fig. 1 NSCLC survival based on B44 supertype subset.

OS – overall survival, PFS – progression-free survival. Survival estimated using the Kaplan-Meier method and compared with a non-parametric log-rank test. Dashed lines represent median. B44sub – B44 subset including HLA-B*18:01, B*44:02, B*44:03, B*44:05, B*50:017 (red, N = 27 patients). Other B4 supertype patients grouped with B44sub-absent (blue, N = 38 patients). Citation numbering based on manuscript references. a, Patients with at least one B44 subset allele had a median OS 7.6 months (95% CI 3.2 – 18.4) vs 18.6 months (95% CI 9.2 – 34.1), P=0.005. b, Patients with at least one B44 subset allele had a median PFS 2.1 months (95% CI 1.8 – 4.9) vs 8.8 months (95% CI 5.1 – 14.5), P=0.002.

Source data

Extended Data Fig. 2 Observed amino acid substitution profiles comparing UCLA NSCLC and melanoma cohorts.

Amino acids represented by standard single letter notation. Heatmaps represent average proportions of amino acid substitutions with wildtype amino acids on the x-axis and substituted amino acid on the y-axis. In panels a and b, black corresponds to rare substitutions while lighter colors represent enrichment. In panel c, orange corresponds to substitutions seen equally in NSCLC and melanoma, darker colors represent substitutions seen more commonly in melanoma, whiter colors those seen more commonly in NSCLC. See Methods for additional details. a, Observed NSCLC profile (N = 38 patients). b, Observed melanoma profile (N = 14 patients). c, Difference in observed NSCLC vs melanoma profile (NSCLC-MEL) supports decreased p.G>E in NSCLC relative to melanoma.

Source data

Extended Data Fig. 3 Correlation of DNA mutations, glutamic acid substitutions, and motif neoepitopes.

MEL – melanoma, NSCLC – non-small cell lung cancer, g. – genomic, A – adenine, C – cytosine, G – guanine, T – thymine. Typical scatter plots depicted with line of best fit calculated by least square method, 95% confidence interval shown in gray. a-d: Mutations that can result in radical glutamic acid substitutions (g.A>G, g.T>A, g.G>A, and g.C>A) are pictured. Each point represents a patient (NSCLC N = 38 patients, MEL N = 14 patients). Glutamic acid and mutation proportions calculated as proportion of all radical mutations. e-g: Correlation of glutamic acid substitutions to glutamic acid neoepitopes and B44 motif neoepitopes in NSCLC B44 patients (N=21). Each point represents the number of substitutions/neoepitopes for each B44 patient. All predicted neoepitopes had an IC50 ≤ 500 nM. a, Correlation of the proportion of g.A>G mutations to proportion of radical glutamic acid substitutions, Pearson correlation coefficient 0.03, P=0.85 (that is, correlation not similar between histologies). b, Correlation of the proportion of g.T>A mutations to proportion of radical glutamic acid substitutions, Pearson correlation coefficient -0.20, P=0.16. c, Correlation of the proportion of g.G>A mutations to proportion of radical glutamic acid substitutions, Pearson correlation coefficient 0.60, P<0.001. d, Correlation of the proportion of g.C>A mutations to proportion of radical glutamic acid substitutions, Pearson correlation coefficient -0.61, P<0.001. e, Correlation of the number of glutamic acid radical substitutions with the number of glutamic acid neoepitopes, Pearson correlation coefficient 0.56, P=0.009. f, Correlation of the number of neoepitopes including glutamic acid with the number of motif neoepitopes, Pearson correlation coefficient 0.89, P<0.001 (removing outlier motif neoepitopes=7, R=0.68, P=0.001). g, Correlation of the number of glutamic acid radical substitutions with the number of motif neoepitopes, Pearson correlation coefficient 0.42, P=0.06.

Source data

Extended Data Fig. 4 Predicted differences of half-maximal inhibitory concentrations of NSCLC motif neoepitopes in non-B44 HLA supertypes.

Boxplots summarize distribution of points with solid lines representing the median proportion (center), boxplot boundaries define the 25th and 75th percentiles, whisker boundaries define the minima and maxima. Black points appear next to outliers greater than 1.5 times the interquartile range. Each point represents a neoepitope prediction. Note HLA alleles with undefined B-pockets were not included. Motif neoepitopes require a radical substitution in the anchor position with a known C-terminus (see Methods). All predicted neoepitopes had an IC50 ≤ 500 nM. a, Predicted B07 neoepitope IC50 differences based on mutation to motif (present N = 32 predicted neoepitopes, absent N = 1566 predicted neoepitopes), Wilcoxon test of difference P<0.001. b, Predicted B27 neoepitope IC50 differences based on mutation to motif (present N = 5 predicted neoepitopes, absent N = 76 predicted neoepitopes), Wilcoxon test of difference P=0.019. c, Predicted B58 neoepitope IC50 differences based on mutation to motif (present N = 12 predicted neoepitopes, absent N = 310 predicted neoepitopes), Wilcoxon test of P<0.001. d, Predicted B62 neoepitope IC50 differences based on mutation to motif (present N = 34 predicted neoepitopes, absent N = 680 predicted neoepitopes), Wilcoxon test of difference P<0.001.

Source data

Extended Data Fig. 5 NSCLC overall and progression-free survival based on the presence of motif neoepitopes in all supertypes.

Survival estimated using the Kaplan-Meier method and compared between groups with a non-parametric log-rank test. Dashed lines represent medians. Motif neoepitopes required a radical substitution in the anchor position with a known C-terminus (see Methods), survival curves depicted in blue. The survival curves of those without motif neoepitopes are depicted in red. a, Patients with at least one motif neoepitope (N = 28 patients) had a median OS of 12.6 vs 5.2 months in those without (N = 10 patients), P=0.013. b, Patients with at least one motif neoepitope (N = 28 patients) had a median PFS of 5.6 vs 6.3 months in those without (N = 10 patients), P=0.120.

Source data

Extended Data Fig. 6 NSCLC survival based on supertype-specific motif neoepitopes.

Survival estimated using the Kaplan-Meier method and compared among supertypes with a non-parametric log-rank test. Dashed lines represent medians. Motif neoepitopes required a radical substitution in the anchor position with a known C-terminus (see Methods), survival curves depicted in blue. The survival curves of those without motif neoepitopes are depicted in red. a, UCLA B27 cohort, patients with motif neoepitopes (N = 3 patients) had a median OS not reached vs 11.5 months without (N = 2 patients) (HR cannot be calculated), P=0.039. b, UCLA B27 cohort, patients with motif neoepitopes (N = 3 patients) had a median PFS of 45.3 vs 8.2 months without (N = 2 patients) (HR 0.18), P=0.062. c, UCLA B44 cohort, patients with motif neoepitopes (N = 14 patients) had a median OS of 18.2 vs 7.0 months without (N = 7 patients) (HR 0.38), P=0.038. d, UCLA B44 cohort, patients with motif neoepitopes (N = 14 patients) had a median PFS of 8.7 vs 3.5 months without (N = 7 patients) (HR 0.40), P=0.072. e, UCLA B07 cohort, patients with motif neoepitopes (N = 15 patients) had a median PFS of 10.2 vs 10.9 months without (N = 4 patients), P=0.33. f, UCLA B58 cohort, patients with motif neoepitopes (N = 3 patients) had a median PFS of 10.2 vs 10.9 months without (N = 1 patient), P=0.92. g, UCLA B62 cohort, patients with motif neoepitopes (N = 7 patients) had a median PFS of 5.5 vs 6.2 months without (N = 1 patient), P=0.89.

Source data

Extended Data Fig. 7 Survival of cohorts based on supertype and motif neoepitopes.

Survival estimated using the Kaplan-Meier method. Survival curves of B27-(motif) absent patients are depicted in red, B27-motif (present) in blue, B27/B44-motif (present) in green, B44-(motif) absent in dark purple, B44-motif (present) in orange, other in light purple. Note given the rarity of B27/B44-motif absent patients (none in UCLA and DF-melanoma cohorts), a separate category was not created and these patient(s) were grouped as B44-(motif) absent. a, UCLA NSCLC cohort overall survival. Median survival times, longest to shortest (months): B27-motif present (not reached), B27/B44-motif present (18.7), other (18.2), B44-motif present (16.2), B44-motif absent (11.6), B27-motif absent (11.5). b, UCLA NSCLC cohort progression-free survival. Median survival times, longest to shortest (months): B27/B44-motif present (49.5), B27-motif present (44.7), other (10.6), B27-motif absent (8.2), B44-motif present (4.9), B44-motif absent (2.0). c, DF-NSCLC cohort overall survival. Median survival times, longest to shortest (months): B27/B44-motif present (not reached), B27-motif present (not reached), B44-motif present (not reached), B44-motif absent (not reached), other (13.4), B27-motif absent (8.4). Note 2 B27/B44-motif absent patients grouped with B44-motif absent (this group did not exist in other cohorts). d, DF-NSCLC cohort progression-free survival. Median survival times, longest to shortest (months): B27/B44-motif present (32.8), B27-motif present (not reached), B44-motif present (14.5), B44-motif absent (4.22), B27-motif absent (4.2), other (2.8). Note 2 B27/B44-motif absent patients grouped with B44-motif absent (this group did not exist in other cohorts). Not all patients had censoring data available leading to fewer patients in each category compared to DF-NSCLC OS. e, DF-melanoma cohort overall survival. Median survival times, longest to shortest (months): B27/B44-motif present (48.6), B44-motif present (34.5), B27-motif present (26.3), B44-motif absent (23.8), other (10.3), B27-motif absent (5.3). f, DF-melanoma cohort progression-free survival. Median survival times, longest to shortest (months): B44-motif present (19.6), B27-motif present (15.0), B27/B44-motif present (11.0), B27-motif absent (3.7), other (3.5), B44-motif absent (3.3).

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Cummings, A.L., Gukasyan, J., Lu, H.Y. et al. Mutational landscape influences immunotherapy outcomes among patients with non-small-cell lung cancer with human leukocyte antigen supertype B44. Nat Cancer 1, 1167–1175 (2020). https://doi.org/10.1038/s43018-020-00140-1

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