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. 2024 Nov 29;10(48):eadp4670.
doi: 10.1126/sciadv.adp4670. Epub 2024 Nov 27.

Genomic heterogeneity and ploidy identify patients with intrinsic resistance to PD-1 blockade in metastatic melanoma

Giuseppe Tarantino  1   2   3 , Cora A Ricker  1   3 , Annette Wang  2 , William Ge  2 , Tyler J Aprati  1   3 , Amy Y Huang  1   2   3   4 , Shariq Madha  5   6 , Jiajia Chen  1   3 , Yingxiao Shi  1   2   3 , Marc Glettig  1   3 , Catherine H Feng  7 , Dennie T Frederick  3 , Samuel Freeman  3 , Marta M Holovatska  1 , Michael P Manos  1 , Lisa Zimmer  8 , Alexander Rösch  8 , Anne Zaremba  8 , Elisabeth Livingstone  8 , Jacob C Jameson  9 , Soroush Saghafian  10 , Andrew Lee  11 , Karena Zhao  11 , Luc G T Morris  11 , Brendan Reardon  1   3 , Jihye Park  1   3 , Haitham A Elmarakeby  1   3   12 , Bastian Schilling  8   13 , Anita Giobbie-Hurder  1 , Natalie I Vokes  7 , Elizabeth I Buchbinder  1 , Keith T Flaherty  14 , Rizwan Haq  1 , Catherine J Wu  1   2   3   15 , Genevieve M Boland  14 , F Stephen Hodi  1 , Eliezer M Van Allen  1   2   3 , Dirk Schadendorf  8 , David Liu  1   2   3
Affiliations

Genomic heterogeneity and ploidy identify patients with intrinsic resistance to PD-1 blockade in metastatic melanoma

Giuseppe Tarantino et al. Sci Adv. .

Abstract

The introduction of immune checkpoint blockade (ICB) has markedly improved outcomes for advanced melanoma. However, many patients develop resistance through unknown mechanisms. While combination ICB has improved response rate and progression-free survival, it substantially increases toxicity. Biomarkers to distinguish patients who would benefit from combination therapy versus aPD-1 remain elusive. We analyzed whole-exome sequencing of pretreatment tumors from four cohorts (n = 140) of ICB-naïve patients treated with aPD-1. High genomic heterogeneity and low ploidy robustly identified patients intrinsically resistant to aPD-1. To establish clinically actionable predictions, we optimized and validated a predictive model using ploidy and heterogeneity to confidently identify (90% PPV) patients with intrinsic resistance to and worse survival on aPD-1. We further observed that three of seven (43%) patients predicted to be intrinsically resistant to single-agent PD-1 ICB responded to combination ICB, suggesting that these patients may benefit disproportionately from combination ICB. These findings highlight the importance of heterogeneity and ploidy, nominating an approach toward clinical actionability.

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Figures

Fig. 1.
Fig. 1.. High genomic heterogeneity and low ploidy predict intrinsic resistance in previously ICB-naïve PD-1 treated patients.
(A) Genomic heterogeneity and ploidy in progressors (PD as best response, orange) versus responders (CR/PR as best response, green) and other (SD or MR as best response, gray) in the CTLA-4 ICB–naïve PD-1 ICB–treated subset of a large discovery cohort of patients with metastatic melanoma [Mann-Whitney Wilcoxon (MWW) P = 0.038 and P = 0.0021 for heterogeneity and ploidy, respectively]. (B) Genomic heterogeneity and ploidy comparison in progressors, other, responders of a validation CTLA-4 ICB–naïve PD-1 ICB–treated cohort drawn from two clinical trials (MWW P = 0.018 and P = 0.027 for heterogeneity and ploidy, respectively). (C) A combined discovery cohort was constructed combining the patients from three different cohorts: (14), the clinical trials CheckMate-038 and CheckMate-064. For CheckMate-064, only the patients in the arm A were selected; for these patients, the response was evaluated after 12 weeks. (D) Decision boundaries of the logistic regression model (LR) with genomic heterogeneity and ploidy as features to predict patients with intrinsic resistance (PD) using the combined discovery cohort. The orange area represents the area predicted by the model as PD, while the blue area represents the patients predicted as not PD (nPD). The observed therapy response of each patient is represented by the orange plus symbol (PD) or the blue triangle (nPD). (E) Decision boundaries for a decision tree model. (F) Structure of the decision tree with logic and split cutoff used. In each node, the top number represents the overall prediction for the node, with 1 being PD and 0 being nPD. The second number represents the probability of the patients in that group to be PD. The third number denotes the proportion of samples in that node.
Fig. 2.
Fig. 2.. WGD and its timing are associated with response to ICB.
(A) Ploidy distribution of whole-genome doubled and non–whole-genome-doubled tumors. Higher ploidy is driven by WGD events. (B) Proportion of patients with WGD event in the patients with PD versus nPD patients (Fisher’s exact P = 0.011). (C) Graphical representation on how to compute the SNV multiplicity ratio and estimate the time of WGD event. (D) Ratio of multiplicity 2:1 SNV mutations and heterogeneity scatterplot for WGD tumors. Orange dots represent patients with PD as best response (PD), and blue represents nPD. A high 2:1 SNV multiplicity ratio indicates few SNV mutations after genome doubling, consistent with a recent WGD event.
Fig. 3.
Fig. 3.. Association of heterogeneity and ploidy with survival in ICB-treated and ICB-untreated cohorts.
(A) Difference in genomic heterogeneity and ploidy between primary and metastatic ICB-untreated samples in TCGA melanoma cohort (MWW P = 0.0031 and P = 0.0062 for heterogeneity and ploidy, respectively). (B) OS survival of the TCGA samples stratified by predicted PD status using the modified decision tree model. (log-rank P = 0.0059). (C) OS survival of the TCGA samples stratified by median ploidy (log-rank P = 0.01). (D) OS survival of the TCGA samples stratified by median heterogeneity (log-rank P = 0.23). (E) Multivariate Cox regression model evaluating the effect of ploidy and heterogeneity for the OS in the TCGA cohort. (F) Multivariate Cox regression model evaluating the effect of ploidy and heterogeneity for the OS in the anti–PD-1 discovery cohort. (G) Multivariate Cox regression model evaluating the effect of ploidy and heterogeneity for the PFS in the anti–PD-1 discovery cohort.
Fig. 4.
Fig. 4.. Constructing a modified decision tree model optimizing precision and specificity for predicting intrinsic resistance to PD-1 ICB.
(A) Decision boundaries of the MDT model. (B) PFS curve stratified by patients predicted by the MDT model as PD (orange) and nPD (blue) (log-rank P < 0.0001). In the survival analysis, the samples from CheckMate-064 that received sequential treatments have been excluded (n = 13). (C) Clinical characteristics between predicted patients with PD (n = 21) and the rest of the cohort, comparing ECOG, the presence of brain metastasis, lactate dehydrogenase (LDH) level at baseline and age category. P values are from Fisher’s exact test. (D) Clinical features of the 21 patients predicted as PD. Only four patients (highlighted in red) have clinical features (brain metastasis, ocular/uveal primary type) that strongly indicate combination ICB.
Fig. 5.
Fig. 5.. Association of heterogeneity, ploidy, and predicted PD-1 ICB intrinsic resistance with ICB response in independent validation cohorts.
(A) Circos plots of copy number alterations in progressors (PD as best response, left) and responders (CR/PR as best response, right) in a PD-1 ICB–treated validation cohort (33). (B) Heterogeneity and ploidy compared in progressors versus responders in the validation PD-1 ICB cohort (MWW P = 0.008 and P = 0.23 for heterogeneity and ploidy, respectively) (33). (C) Decision boundaries for the modified decision tree model using the samples from the validation anti–PD-1 ICB cohort (33). (D) Heterogeneity and ploidy compared in responders (PFS > 6 months) versus progressors (PFS ≤ 6 months) in a combination PD-1/CTLA-4 ICB cohort (MWW P = 0.1 and P = 1 for heterogeneity and ploidy, respectively). (E) Decision boundaries for the modified decision tree model using the samples from the combination PD-1/CTLA-4 ICB cohort showing response in three of seven patients predicted to be intrinsically resistant to PD-1 ICB.
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References

    1. Hodi F. S., O’Day S. J., McDermott D. F., Weber R. W., Sosman J. A., Haanen J. B., Gonzalez R., Robert C., Schadendorf D., Hassel J. C., Akerley W., van den Eertwegh A. J. M., Lutzky J., Lorigan P., Vaubel J. M., Linette G. P., Hogg D., Ottensmeier C. H., Lebbé C., Peschel C., Quirt I., Clark J. I., Wolchok J. D., Weber J. S., Tian J., Yellin M. J., Nichol G. M., Hoos A., Urba W. J., Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med. 363, 711–723 (2010). - PMC - PubMed
    1. Robert C., Thomas L., Bondarenko I., O’Day S., Weber J., Garbe C., Lebbe C., Baurain J.-F., Testori A., Grob J.-J., Davidson N., Richards J., Maio M., Hauschild A., Miller W. H., Gascon P., Lotem M., Harmankaya K., Ibrahim R., Francis S., Chen T.-T., Humphrey R., Hoos A., Wolchok J. D., Ipilimumab plus dacarbazine for previously untreated metastatic melanoma. N. Engl. J. Med. 364, 2517–2526 (2011). - PubMed
    1. Munhoz R. R., Postow M. A., Clinical development of PD-1 in advanced melanoma. Cancer J. 24, 7–14 (2018). - PMC - PubMed
    1. Larkin J., Chiarion-Sileni V., Gonzalez R., Grob J.-J., Rutkowski P., Lao C. D., Cowey C. L., Schadendorf D., Wagstaff J., Dummer R., Ferrucci P. F., Smylie M., Hogg D., Hill A., Márquez-Rodas I., Haanen J., Guidoboni M., Maio M., Schöffski P., Carlino M. S., Lebbé C., McArthur G., Ascierto P. A., Daniels G. A., Long G. V., Bastholt L., Rizzo J. I., Balogh A., Moshyk A., Hodi F. S., Wolchok J. D., Five-year survival with combined nivolumab and ipilimumab in advanced melanoma. N. Engl. J. Med. 381, 1535–1546 (2019). - PubMed
    1. Tawbi H. A., Schadendorf D., Lipson E. J., Ascierto P. A., Matamala L., Castillo Gutiérrez E., Rutkowski P., Gogas H. J., Lao C. D., De Menezes J. J., Dalle S., Arance A., Grob J.-J., Srivastava S., Abaskharoun M., Hamilton M., Keidel S., Simonsen K. L., Sobiesk A. M., Li B., Hodi F. S., Long G. V., Relatlimab and nivolumab versus nivolumab in untreated advanced melanoma. N. Engl. J. Med. 386, 24–34 (2022). - PMC - PubMed

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