Predicting response to checkpoint inhibitors in melanoma beyond PD-L1 and mutational burden

RESEARCH ARTICLE Open Access Predicting response to checkpoint inhibitors in melanoma beyond PD-L1 and mutational burden Carl Morrison 1,2,3,4*† , Sarabjot Pabla 4† , Jeffrey M. Conroy 1,4 , Mary K. Nesline 4 , Sean T. Glenn 3,4 , Devin Dressman 4 , Antonios Papanicolau-Sengos 4 , Blake Burgher 4 , Jonathan Andreas 4 , Vincent Giamo 4 , Moachun Qin 4 , Yirong Wang 4 , Felicia L. Lenzo 4 , Angela Omilian 2 , Wiam Bshara 2 , Matthew Zibelman 5 , Pooja Ghatalia 5 , Konstantin Dragnev 6 , Keisuke Shirai 6 , Katherine G. Madden 6 , Laura J. Tafe 6,7 , Neel Shah 8 , Deepa Kasuganti 8 , Luis de la Cruz-Merino 9 , Isabel Araujo 9 , Yvonne Saenger 10 , Margaret Bogardus 10 , Miguel Villalona-Calero 11 , Zuanel Diaz 11 , Roger Day 12 , Marcia Eisenberg 13 , Steven M. Anderson 13 , Igor Puzanov 14 , Lorenzo Galluzzi 15,16,17 , Mark Gardner 4 and Marc S. Ernstoff 14 Abstract Background: Immune checkpoint inhibitors (ICIs) have changed the clinical management of melanoma. However, not all patients respond, and current biomarkers including PD-L1 and mutational burden show incomplete predictive performance. The clinical validity and utility of complex biomarkers have not been studied in melanoma. Methods: Cutaneous metastatic melanoma patients at eight institutions were evaluated for PD-L1 expression, CD8 + T-cell infiltration pattern, mutational burden, and 394 immune transcript expression. PD-L1 IHC and mutational burden were assessed for association with overall survival (OS) in 94 patients treated prior to ICI approval by the FDA (historical-controls), and in 137 patients treated with ICIs. Unsupervised analysis revealed distinct immuneclusters with separate response rates. This comprehensive immune profiling data were then integrated to generate a continuous Response Score (RS) based upon response criteria (RECIST v.1.1). RS was developed using a single institution training cohort (n= 48) and subsequently tested in a separate eight institution validation cohort (n= 29) to mimic a real-world clinical scenario. Results: PD-L1 positivity ≥1% correlated with response and OS in ICI-treated patients, but demonstrated limited predictive performance. High mutational burden was associated with response in ICI-treated patients, but not with OS. Comprehensive immune profiling using RS demonstrated higher sensitivity (72.2%) compared to PD-L1 IHC (34.25%) and tumor mutational burden (32.5%), but with similar specificity. Conclusions: In this study, the response score derived from comprehensive immune profiling in a limited melanoma cohort showed improved predictive performance as compared to PD-L1 IHC and tumor mutational burden. Keywords: Pembrolizumab, Nivolumab, Ipilimumab, Algorithmic analysis, Inflamed, Borderline, Immune Desert * Correspondence: [email protected] † Equal contributors 1 Center for Personalized Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14263, USA 2 Department of Pathology, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14263, USA Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Morrison et al. Journal for ImmunoTherapy of Cancer (2018) 6:32 https://doi.org/10.1186/s40425-018-0344-8 Protected by copyright. on March 11, 2024 at USE/Fac Medicina Biblioteca FME.http://jitc.bmj.com/J Immunother Cancer: first published as 10.1186/s40425-018-0344-8 on 9 May 2018. Downloaded from Background The introduction of immune checkpoint inhibitors (ICIs) revolutionized the clinical management of patients with metastatic melanoma [1–8]. However, only a minority of patients obtain durable clinical benefit from ICIs. [1,4,6– 8] Moreover, ICI-based immunotherapy is associated with significant immune related side effects [1,4,6–8], and it has an estimated cost of over $300,000 per quality-adjusted life-year, [9] calling for the development of robust predictive biomarkers [10]. Despite considerable efforts in this direction, the identification and validation of biomarkers that would predict the response of melanoma patients to ICIs targeting cytotoxic T lymphocyte associated protein 4 (CTLA4) or programmed cell death 1 (PDCD1, best known as PD-1), has not been very successful so far. In particular, neither assessing intratumoral expression levels of CD274 (best known as PD-L1) by immunohistochemistry, nor evaluating tumor mutational burden by whole-exome sequencing appears to suffice to predict long-term clinical benefits [3–8,10–13]. Moreover, it has become clear that robust predictions may involve different biomarkers in patients receiving CTLA- versus PD-1-targeting agents [4, 14]. The results of the CHECKMATE 067 clinical trial demonstrated that better objective response rates (ORRs) and overall survival (OS) in PD-L1 negative patients for the combination of dual agent CTLA-4 and PD-1 targeting ICIs [14,15]. Based on these findings, some oncologists are currently using negative PD-L1 expression to move past monotherapy and prescribe dual ICIs, while others consider patient preference and perceived tolerability without biomarkers to aid clinical decision making, despite the risk of increased rates of adverse events (AEs) [14]. A global investigation of the mutational and immunological aspects of the disease, going beyond the standalone assessment of mutational burden and PD-L1 expression levels, is critical to the development of superior predictive biomarkers for melanoma patients treated with ICIs. The genomic landscape of melanoma, performed by The Cancer Genome Atlas (TCGA) on 333 melanoma samples delineated 4 genomic subtypes, i.e., triple wild-type (WT), BRAF mutant, NRAS, HRAS or KRAS mutant, and NF1 mutant, as well as three transcriptomic subclasses, i.e., immune, keratin and MIFT-low. [16] Although this provided several useful insights into the disease, it did not improve our ability to predict clinical responses to ICIs amongst melanoma patients. To address this gap, we performed a comprehensive analysis of the mutational and immunological landscape of 300 samples from metastatic melanoma patients at eight institutions worldwide using a CLIA/ CAP-certified NYS-approved laboratory developed test (LDT) [17]. This test evaluates PD-L1 expression levels, CD8 + T-cell tumor infiltration pattern by immunohistochemistry, mutational burden by whole-exon DNA-seq, as well as the abundance of 394 immune transcripts by RNA- seq. [17] Each of these parameters was studied individually in a dichotomous manner for their influence on ORR and OS in patients undergoing surgery prior to the approval of ipilimumab in 2011, i.e. historical controls, versus patients receiving ICI-based immunotherapy, which allowed for the differentiation between prognostic and predictive biomarkers. Finally, a training cohort of 48 ICI-treated patients was used to develop a continuous response score (RS), subsequently tested on a validation cohort of an additional 29 ICI-treated patients. Methods Patients and clinical data Eight collaborating institutions obtained approval by their respective institutional review boards (IRBs) to submit existing de-identified specimens and associated clinical data for use in this study (Additional file 1:TableS1).A total of 300 patients were included in the study, based on the following criteria: history of metastatic cutaneous melanoma with surgical resection of a primary or metastatic tumor; availability of adequate archival formalin-fixed paraffin-embedded (FFPE) tissue collected prior to treatment with ICIs; availability of sequencing data; and availability of demographic, diagnosis, follow-up and vital status data. Patients were excluded if they died within three months of initial diagnosis, or were alive at last follow-up, but had less than six months of follow-up time from initial diagnosis of metastatic disease. Based on the initial approval of ipilimumab by the US FDA (March 25th, 2011), patients with biopsies demonstrating Stage IV disease prior to the availability of ICIs (1992–2010) were included as historical controls (n= 94). Medical and electronic pharmacy records were reviewed to identify individuals receiving FDA-approved checkpoint inhibitors (n=166)for which a subset (n= 137) with a median follow-up of 16.2 months was used for survival analysis. This latter group of 137 patients is referred to as the ICI-treated group (Table 1), and for which 78 of these were evaluable and 59 not evaluable (Additional file 1: Table S8) for response to treatment by RECIST v1.1 (Additional file 1:TableS1)[18]. From this group of 78 patients a total of 77 had complete measurements and were divided into a training set (48 patients) from a single institution (RPCCC) and a validation set (29 patients) from eight different institutions and were used to develop a Response Score (RS) for prediction of response to ICI. The training cohort represented the largest population from a single institution, whereas the validation cohort represented multiple patients from various institutions. A complete review of all patient dispositions is included in the Additional file 7(Additional file 2:FigureS1). Immunohistochemical studies The expression of PD-L1 on the surface of tumor cells was assessed in all samples by means of the Dako Omnis Morrison et al. Journal for ImmunoTherapy of Cancer (2018) 6:32 Page 2 of 12 Protected by copyright. on March 11, 2024 at USE/Fac Medicina Biblioteca FME.http://jitc.bmj.com/J Immunother Cancer: first published as 10.1186/s40425-018-0344-8 on 9 May 2018. Downloaded from Table 1 Clinical characteristics All Cases a Pre-ipi approval Post-ipi approval Post-ipi approval (n= 300) (n= 94) (n= 206) (n= 206) ICI Treated ICI Not Treated Age at initial cutaneous melanoma diagnosis (Years, %) (n= 160) (n= 46) < 30 9 (03.0) 5 (05.3) 4 (01.9) 2 (01.3) 2 (04.3) 30–39 25 (08.3) 11 (11.7) 14 (06.8) 9 (05.6) 5 (10.9) 40–49 49 (16.3) 15 (16.0) 34 (16.5) 26 (16.3) 8 (17.4) 50–59 69 (23.0) 25 (26.6) 44 (21.4) 37 (23.1) 7 (15.2) 60–69 68 (22.7) 17 (18.1) 51 (24.8) 40 (25.0) 11 (23.9) 70–79 51 (17.0) 13 (13.8) 38 (18.4) 31 (19.4) 7 (15.2) ≥80 29 (09.7) 8 (08.5) 21 (10.2) 15 (09.4) 6 (13.0) Mean 59 56 60 61 58 Year of diagnosis (Range) 1974–2016 1974–2010 1989–2016 1990–2016 1989–2016 Sex Female 111 (37.0) 36 (38.3) 75 (36.4) 53 (33.1) 22 (47.8) Male 189 (63.0) 58 (61.7) 131 (63.6) 107 (66.9) 24 (52.2) Race White 293 (97.7) 94 (100.0) 199 (96.6) 154 (96.3) 45 (97.8) Other 2 (06.0) 0 (00.0) 2 (01.0) 1 (00.6) 1 (02.2) Unknown 5 (01.7) 0 (00.0) 5 (02.4) 5 (03.1) 0 (00.0) Vital status at last follow up Alive 136 (45.3) 16 (17.0) 120 (58.3) 92 (57.5) 28 (60.9) Dead 164 (54.7) 78 (83.0) 86 (41.7) 68 (42.5) 18 (39.1) Year of bx proven Stage IV disease (range) 1992–2017 1992–2011 2011–2017 2011–2017 2011–2017 Months of follow up (Median) b 16.2 16.5 16.2 16.2 15.5 < 1 13 (04.3) 0 (00.0) 13 (06.3) 10 (06.3) 3 (06.5) 3 122 (40.7) 0 (00.0) 122 (59.2) 104 (65.0) 18 (39.1) 6 74 (24.7) 3 (03.2) 71 (34.5) 46 (28.8) 25 (54.3) 10 42 (14.0) 42 (44.7) 0 (00.0) 0 (00.0) 0 (00.0) > 10 49 (16.3) 49 (52.1) 0 (00.0) 0 (00.0) 0 (00.0) Median 4.4 10 3 2.8 4.2 Years from diagnosis to bx proven Stage IV disease (Median) 1.1 1.5 1 1 0.7 Received BRAF TKI (Yes, %) 25 (08.3) 0 (00.0) 25 (12.1) 19 (11.9) 6 (13.0) Mo from specimen collection to BRAF TKI (Median) 6·5 N/A 6·5 8 6 Checkpoint inhibitor Ipilimumab 72 (45.0) N/A Pembrolizumab 68 (42.5) N/A Nivolumab 7 (04.4) N/A Ipilimumab + Nivolumab 13 (08.1) N/A Time to progression (median days) 77.5 N/A Progression free survival (median days) 129.5 N/A a All cases are metastatic and no cutaneous samples included. b For pre-ipi approval patients follow-up is the number of months from date of specimen collection (bx proven Stage IV disease) to last date of follow up or date of death, and for post-ipi approval patients represents the number of months from date of first dose of checkpoint inhibitor to last date of follow up or date of death Morrison et al. Journal for ImmunoTherapy of Cancer (2018) 6:32 Page 3 of 12 Protected by copyright. on March 11, 2024 at USE/Fac Medicina Biblioteca FME.http://jitc.bmj.com/J Immunother Cancer: first published as 10.1186/s40425-018-0344-8 on 9 May 2018. Downloaded from platform (Agilent, Santa Clara, CA) and the 28–8 pharmDx antibody. Expression levels, and were scored as per published guidelines [19]. Additional serially sectioned tissue was evaluated for lymphocyte infiltration using the anti-CD8 antibody C8/144B (Agilent, Santa Clara, CA) and assigned a qualitative score of noninfiltrated, infiltrated, or excluded. Non-infiltrated referred to a sparse number of CD8 + T-cells that infiltrate nests of neoplastic cells and with less than 5% of the tumor showing an infiltrating pattern. Infiltrated represents frequent CD8 + T-cells that infiltrate nests of neoplastic cells in an overlapping fashion at least focally and in more than 5% of the tumor. Excluded represents restriction of more than 95% of all CD8 + T-cells in a tumor to the periphery or interstitial stromal areas and not actively invading nest or groups of neoplastic cells. Mutational burden and RNAseq profiling DNA and RNA was co-extracted from each sample and processed for whole-exon DNAseq or RNAseq as previously described [17,20]. Mutational burden and gene expression were evaluated by targeted capture and sequencing of 409 cancer-related genes and amplicon sequencing of 394 immune transcripts, respectively, on samples that met validated quality control (QC) thresholds [17]. Somatic mutation calling was conducted using Ion Torrent Suite software’s variant caller plugin (for detailed information, please refer to Additional file 7). Mutational burden cutoff was derived from a reference population whereby the median MuB was 3.55 mutations per megabase DNA. This value was used as a baseline and a high MuB was defined as 2× this median value (7.1). Data analyses Survival analysis was performed using a log-rank test on 5-year Kaplan-Meier survival curves for immune markers including PD-L1 by IHC, pattern of CD8 expression by IHC, mutational burden by DNA-Seq and targeted transcriptome profiling of 394 immunerelated genes by RNA-Seq (for detailed information, please refer to Additional file 7). Hierarchical clustering with Pearson’s correlation dissimilarity was performed on 394 expression ranks for all samples. Overrepresentation test was performed on clusters to determine gene enrichment, mutational landscape, other primary biomarkers and clinical characteristics. A relative likelihood of response to ICI, referred to as the response score (RS) , was calculated based upon a weighted score of a generalized linear model with additional input from CD8 expression and CD8 + T-cell infiltration pattern. The RS was developed on 48 training patients and tested on a validation set of 29 patients. Results PD-L1 immunohistochemistry and mutational burden PD-L1 positivity (> 1% tumor cells with membranous PD-L1 staining) [14,15] was documented in 33% (98/ 298) of all samples compatible with immunohistochemical evaluation. Amongst ICI-treated patients, PD-L1 positivity was associated with 55.6% ORR, while only 37. 9% of patients bearing PD-L1-negative melanoma achieved an objective clinical response upon ICI-based immunotherapy (Additional file 1: Table S3). ORR for higher cut-off values for PD-L1 IHC showed similar results (Additional file 1: Table S7). A trend for improved OS was observed amongst patients bearing PD-L1- positive melanomas and undergoing treatment before the introduction of ICIs (Fig. 1a;p= 0.081; Additional file 1: Table S2). However, the impact of PD-L1 positivity on OS was much more considerable amongst patients receiving ICI-based immunotherapy (Fig. 1b;p= 0.00036; Additional file 1: Table S2), corroborating the predictive value of PD-L1 expression levels measured by IHC and stratified based on a 1% cut-off value. A high mutational burden [3,21], more than 7.1 somatic nonsynonymous mutations per million exonic bases, could be documented in 21% (63/300) of all patients. High mutational burden was associated with 33% ORR amongst ICI-treated patients, while only 21% of patients with a melanoma characterized by comparatively lower mutational burden achieved an objective clinical response following ICI-based immunotherapy (Additional file 1: Table S3). Mutational burden had no statistically significant impact on OS, neither amongst historical controls (Fig. 1c;p=0.83; Additional file 1: Table S2), nor amongst ICI-treated patients (Fig. 1d;p= 0.29; Additional file 1: Table S2). Stratifying patients into 5, rather than 2, subgroups based on mutational burden failed to convey improved predictive information (Additional file 3:FigureS2). Thus, while high mutational burden was associated with a slightly higher ORR amongst ICI-treated patients, the lack of association with OS casts doubts on the predictive value of mutational burden assessed as a standalone biomarker in clinical practice. Immune signature by unsupervised transcriptomics Targeted transcriptomic analysis was performed on 394 immune transcripts including 11 housekeeping transcripts [17], covering 45 different gene functions, for 300 metastatic melanoma samples. Unsupervised hierarchical clustering identified three major groups of samples (Additional file 1: Table S2), with group 1 (n= 131; 44%) being the most common, and groups 2 (n= 81; 27%) and 3(n= 88; 29%) of comparable frequencies (Fig. 2a). An analysis of gene functions at the individual gene level across the groups allowed for an immunological delineation of groups as: “inflamed”(group 1), “borderline”(group 2), Morrison et al. Journal for ImmunoTherapy of Cancer (2018) 6:32 Page 4 of 12 Protected by copyright. on March 11, 2024 at USE/Fac Medicina Biblioteca FME.http://jitc.bmj.com/J Immunother Cancer: first published as 10.1186/s40425-018-0344-8 on 9 May 2018. Downloaded from and “immune desert”(group 3) (for detailed information, please refer to Additional file 1:TableS3).IHCforPD-L1 expression levels unveiled a statistically significant association (p= 1.63e-07) between PD-L1 positivity and the inflamed status (66/131; 50%), but PD-L1 positive tumors were still quite common amongst both the borderline (26/ 81; 32%) and immune desert (14/88; 20%) groups. Conversely, tumors with a high mutational burden were not overrepresented (p= 0·98) in the inflamed group (25/131; 19%) as compared to the borderline (22/81; 27%) and immune desert (16/88; 18%) groups. An overrepresentation test (v.test) showed that the inflamed group was enriched for tumors with a high number of CD8 + T cells, that the immune desert group was enriched for tumors with a very low number of CD8 + T cells, and that the borderline group contained a mixture of tumors with high or low CD8 + T- cell infiltration (for detailed information, please refer to Additional file 1: Tables S4 and S5). Objective responses to ICI-based immunotherapy, as assessed by RECIST v.1.1, were much more common amongst inflamed tumors (20/ 39; 51.28%), than amongst borderline (8/23; 34.78%) or immune desert (5/16; 31.25%) tumors (for detailed information, please refer to Additional file 1: Table S7). Altogether, these findings suggest that borderline tumors may be clinically closer to immune desert tumors than to inflamed tumors. In line with this notion, the inflamed status was Fig. 1 Survival analysis of melanoma patients based on PD-L1 expression levels and mutational burden. a,bOverall survival upon stratification based on PD-L1 positivity (tumor proportion score ≥1% versus < 1%) for metastatic melanoma patients treated (a) prior to the introduction of ICIs (historical controls; n= 94) and (b) ICI-treated melanoma patients (n= 137). c,dOverall survival upon stratification based on mutational burden [high (≥7.1 mut/Mb) versus non-high] for metastatic melanoma patients treated (c) prior to the introduction of ICIs (historical controls; n = 94) and (d) ICI-treated melanoma patients (n = 137). p-values are indicated Morrison et al. Journal for ImmunoTherapy of Cancer (2018) 6:32 Page 5 of 12 Protected by copyright. on March 11, 2024 at USE/Fac Medicina Biblioteca FME.http://jitc.bmj.com/J Immunother Cancer: first published as 10.1186/s40425-018-0344-8 on 9 May 2018. Downloaded from Fig. 2 (See legend on next page.) Morrison et al. Journal for ImmunoTherapy of Cancer (2018) 6:32 Page 6 of 12 Protected by copyright. on March 11, 2024 at USE/Fac Medicina Biblioteca FME.http://jitc.bmj.com/J Immunother Cancer: first published as 10.1186/s40425-018-0344-8 on 9 May 2018. Downloaded from associated with improved OS as compared with borderline or immune desert status amongst ICI-treated patients, but not amongst historical controls (Additional file 4: Figure S3). Moreover, amongst inflamed tumors receiving ICI-based immunotherapy there was a trend towards improved OS (Fig. 2b;p= 0.063; Additional file 1:TableS2).Thesamedidnotholdtrueforborderline (Fig. 2c;p= 0.33; Additional file 1:TableS2) and immune desert (Fig. 2d;p= 0.88; Additional file 1: Table S2) tumors. Since unsupervised sample clustering closely correlated with CD8 + T-cell quantification, a qualitative assessment of CD8 + T-cell infiltration pattern was performed by IHC (for detailed information, please refer to Additional file 7). Samples were then classified into 3 patterns: noninfiltrated, infiltrated, and excluded (Fig. 3a). Infiltration (See figure on previous page.) Fig. 2 Immunological landscape of melanoma and its association with multiple variables. aUnsupervised hierarchical clustering (rows = patients, columns = genes) for 394 immune transcripts identified three major groups defined as “inflamed”(group 1), “borderline”(group 2), and “immune desert”(group 3). CD8 (assessed by RNA-seq) and PD-L1 expression levels (assessed by immunohistochemistry), response to ICIs (as per RECIST v.1.1), CD8 infiltration pattern (assessed by a trained pathologist, CM and APS), and mutational burden (assessed by whole-exon sequencing) are depicted. b-dOverall survival for melanoma patients from the (b) inflamed (n= 131), (c) borderline (n= 81), and (d) immune desert (n= 88) groups upon stratification based on treatment (historical controls versus ICI-based immunotherapy). pvalues are reported Fig. 3 CD8 + T-cell infiltration pattern and clinical benefits from immune checkpoint inhibition. aCD8 + T-cell infiltration pattern was assessed by a trained pathologist upon immunohistochemistry with a CD8-specific antibody. Representative images are depicted (scale bar = 500 μm or 1 mm). b,cOverall survival upon stratification based on infiltration pattern (non-infiltrated, infiltrated, excluded) for metastatic melanoma patients treated (b) prior to the introduction of ICIs (historical controls; n = 94) and (c) ICI-treated melanoma patients (n = 137). For all comparisons p> 0.05 Morrison et al. Journal for ImmunoTherapy of Cancer (2018) 6:32 Page 7 of 12 Protected by copyright. on March 11, 2024 at USE/Fac Medicina Biblioteca FME.http://jitc.bmj.com/J Immunother Cancer: first published as 10.1186/s40425-018-0344-8 on 9 May 2018. Downloaded from pattern correlated with immune group, with infiltrated tumors being mostly restricted to the inflamed group and non-infiltrated tumors being more common in the immune desert and borderline groups (Additional file 1: Table S2). Interestingly though, excluded tumors were evenly represented across all immune groups, representing about 10% of each. Infiltration pattern as assessed by IHC for CD8 + T-cells failed to identify patient subsets with an improved OS amongst historical controls (Fig. 3a; p>0.96 Additional file 1: Table S3). Conversely, ICI- treated patients bearing infiltrated or excluded tumors before ICI treatment exhibited a superior OS as compared to patients with non-infiltrated tumors (Fig. 3b&c; p< 0.018 for all comparisons; Additional file 1: Table S2). Relationship between tumor genomics and the immune signature Whole-exon sequencing of 409 cancer-related genes was performed with the intent of evaluating potential associations between specific mutations with immune group (inflamed, borderline, and immune desert) (Additional file 5: Figure S4; Additional file 1: Table S3). In particular, we harnessed the framework previously defined by the TCGA to examine whether immunological status and/or clinical response were associated with genetic driver subtypes: mutantBRAF,mutantRAS,mutantNF1,andtripleWT.[16] From the 300 samples analyzed, a total of 264 samples (88%) exhibited at least one genomic alteration, CDKN2A loss (51%) being the most prevalent, followed by BRAF (38%), RAS (16%) and NF1 (7.3%) mutations (Additional file 5: Figure S4). Consistent with previous reports, 46% of tumors were classified as triple WT (Additional file 5:Figure S4). Tumors bearing BRAF, RAS or NF1 mutations were slightly overrepresented (60%; v.test = 1.71; p= 0.086) in the immune desert group. The loss of CDKN2A was also significantly associated (p= 0.00046) with the immune desert status, but not with OS (p> 0.05). Apart from RAS mutations, which were slightly associated with OS amongst historical controls (p= 0.02) but not ICI-treated patients (p= 0.28), no other statistically significantassociationsbetweengenetic drivers of the disease and OS could be documented (data not shown). Predicting response to checkpoint blockade beyond PD- L1 levels and mutational burden Transcriptomic data, mutational burden, and CD8 + T-cell infiltration pattern were combined to derive an algorithmic response score (RS) from a training set of 48 melanoma patients treated with ICI-based immunotherapy, and a validation cohort of 29 patients (Additional file 2:FigureS1and Additional file 7for more details). ORR of the combined training and test patients was 41.02% (8/78 CR, 24/78 PR, 16/78 SD, 29/78 PD) with the majority (68/78; 87.17%) treated with single agent ICI-based immunotherapy. Reference-normalized expression levels of 54 immune transcripts [17] co-expressed with PD-L1 were selected from a subset of 308 genes overexpressed in inflamed tumors (Wilcoxon rank sum test p< 0.05) and combined with mutational burden to derive a linear boundary of response versus non-response via machine learning (AUC > 0·95; Additional file 6: Figure S5). These values were then weighted based on infiltration pattern (as assessed by a trained pathologist) to obtain a score ranging from 0 to 100. RSs from the training and test cohorts were then combined to build a relative likelihood of response analysis upon linear regression fit (Fig. 4e; Additional file 1:Table S6). Patients experiencing a CR showed significantly higher RSs as compared to individual with SD (p= 0.0088) or PD (p= 0.0057). Similarly, patients with PR had a significantly higher RS than patients experiencing disease progression (p= 0.0088; Fig. 4f). The distribution of objective response rates from the training set versus response score groups was evaluated for predicting response resulting in threshold RS value of 50 (Additional file 1: Table S9). With this threshold on a combined dataset, ICI-treated patients with aRS≥50 had a significantly improved OS and ORR of 82. 9% as compared to 23.8% for patients with RS < 50 (Fig. 4g; p= 0.0012). Similar analyses on PD-L1 levels (Fig. 4a-b) and mutational burden (Fig. 4c-d) were performed for comparative purposes, showing no statistically significant impact (p> 0.05). These analyses demonstrated that the RS had a broad dynamic range for predicting response that spanned from 30% to 100%. In comparison, the assessment of PD-L1 levels by IHC and the evaluation of mutational burden by whole-exon sequencing had dynamic ranges spanning from 40 to 100% and 36–45%, respectively (Additional file 1: Table S7). Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy was evaluated for all biomarkers, including RS, PD-L1 IHC, and MuB for training, test, and combined datasets (Table 2). For the combined datasets RS demonstrated higher sensitivity (72.2%) compared to PD-L1 IHC (34.25%) and tumor mutational burden (32.5%), but with similar specificity (Table 2). Accuracy, defined as the ratio of correct to total predictions, was likewise higher for RS (80. 52%) than for PD-L1 IHC (60.53%) and tumor mutational burden (55.13%). From a clinical standpoint, RS provides increased sensitivity and PPV while preserving specificity and NPV when compared to PD-L1 IHC and MuB, which is reflected in the improved accuracy. Similar results were observed for training and test sets (Table 2). Discussion Our data suggest that predicting the likelihood that melanoma patients will obtain durable clinical benefits from ICI-based immunotherapy by assessing PD-L1 expression Morrison et al. Journal for ImmunoTherapy of Cancer (2018) 6:32 Page 8 of 12 Protected by copyright. on March 11, 2024 at USE/Fac Medicina Biblioteca FME.http://jitc.bmj.com/J Immunother Cancer: first published as 10.1186/s40425-018-0344-8 on 9 May 2018. Downloaded from Fig. 4 (See legend on next page.) Morrison et al. Journal for ImmunoTherapy of Cancer (2018) 6:32 Page 9 of 12 Protected by copyright. on March 11, 2024 at USE/Fac Medicina Biblioteca FME.http://jitc.bmj.com/J Immunother Cancer: first published as 10.1186/s40425-018-0344-8 on 9 May 2018. Downloaded from