Studying Immunotherapy Resistance in a Melanoma Autologous Humanized Mouse Xenograft

HSPCs from cord and patient blood expand ex vivo and engraft mHM and aHM cohorts

In order to conduct a comprehensive comparison, we generated a nonhumanized model (NSG), a model with a mismatched immune system (mHM), and a model with an autologous immune system (aHM; Fig. 1A) for both CUHM003 and CUHM005 patients. To generate the aHM cohorts, we isolated HSPCs from G-CSF–stimulated patient blood. To construct mHM cohorts, we isolated HSPCs from donated cord blood. The CD34⁺ HSPCs were then expanded ex vivo for ∼8 days, after which the number of patient cells had undergone an average ∼40-fold expansion (Fig. 1B; Supplementary Table S1) and the cord blood cells had increased by ∼180-fold. We also observed and expanded a separate population of adherent CD73-CD166⁺ cells, characteristic of mesenchymal stem cells (MSC; ref. 14). Because it has been demonstrated that radiation-induced damage to bone marrow encourages HSC homing and establishment in this niche (32), we injected expanded HSPCs and MSCs into the tail veins of sublethally irradiated NSG mice to create mHM and aHM cohorts. In order to generate enough mice to reproduce patient therapy, each mHM received 400,000 HSPCs, and each aHM was engrafted with 140,000 HSPCs, a population at the lower end of what is necessary for successful humanization using cord blood HSPCs (14). We hypothesized that increased HLA matching would enhance the functional efficiency of the mature immune cells produced by the HSPCs in recognizing and interacting with implanted autologous tumor tissue and decrease the required number of HSPCs in these models. A similar phenomenon has been observed in patients receiving cord blood transplants, where a reduced mismatch (1 vs. 2 HLA) requires fewer total nucleated cells (>2.5 × 10⁷/kg vs. >5.0 × 10⁷/kg) to achieve similar engraftment and clinical outcomes (33).

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Figure 1.

Generation of autologous humanized mice (aHM). A, An overview of how aHM cohorts can be generated to investigate patient therapy. G-CSF–stimulated HSPCs from melanoma patients can reconstitute their immune system in immunocompromised mice. Patient tumors can be implanted onto the flanks and shoulder of these mice, as well as onto concurrently prepared mHM and NSG controls. All mice can be treated with the same therapies administered to the patient and their tumor responses compared. B, In vitro culture and expansion of cord- and patient-derived HSPC and MSC-like cell populations. Following CD34⁺ column selection, analysis by cell cytometry identifies a small population of CD34⁺45⁺ HSPCs in both the newly procured cord (mHM003 or mHM005) and patient (aHM003 or aHM005) blood. After 5 to 8 days of expansion, the CD34⁺ HSPC population has increased markedly for all of these cultures. A population of CD34⁻, CD73⁺166⁺ adherent MSC-like cells originating from within the HSPC population can also be identified after in vitro expansion. Total cell numbers of HSPCs before and after expansion are recorded in Supplementary Table S1. C, Representative IHC showing the relative populations of human CD45⁺ cells (dark brown) within the bone marrow of NSG, mHM, and aHM CUHM003 and CUHM005 models. Magnification is 20×; scale bar = 50 μm.

After 8 weeks, we identified a population of human B cells, comprising 0.01% to 0.4% of the total white blood cells in the mouse peripheral blood (Supplementary Fig. S1). We also quantified the human blood cell populations in the mouse bone marrow, blood, and spleen by cytometry at the conclusion of the studies (Supplementary Fig. S2). On average, the bone marrow of the mHM cohorts contained healthy populations of human CD45⁺ immune cells (mHM003, 12.37%; mHM005, 27.93% of all bone marrow cells) and smaller populations of HSPCs (mHM003, 0.94%; mHM005, 3.73%). The bone marrow of the aHM cohorts contained more modest CD45⁺ cell populations (aHM003, 0.29%; aHM005, 0.04%) and HSPC (aHM003, 0.03%; aHM005, <0.01%; Supplementary Table S2), possibly reflecting the lower initial HSPC injection numbers. We also compared human CD45⁺ cells within mHM and aHM bone marrow and spleen by IHC (Fig. 1C; Supplementary Fig. S3) and observed similar patterns of human CD45⁺ cell engraftment.

Delayed tumor initiation and decreased growth in aHM and in HLA A–matched HM

Approximately 10 weeks after their humanization, tumors were implanted on both flanks and a shoulder of the NSG, mHM, and aHM cohorts, using expanded tumor tissue from the initial biopsy. Tumors appeared earlier and grew faster on the NSG and mHM cohorts. At study end, NSG003 tumors were 1.4 times larger than those in aHM003 (P = 0.04; Fig. 2A). NSG005 and mHM005 tumors were 3.0-fold (P < 0.01) and 3.3-fold (P < 0.01) larger than aHM005, respectively (Fig. 2B). There was no difference in the rate of growth between the NSG and mHM tumors in CUHM003 or CUHM005. In order to further investigate immune matching, we implanted CUHM003 tumors on NSG, HM HLA-A–mismatched (mHM003b), and HM HLA-A–matched cohorts (mHM003-HLA). Tumor occurrence was again delayed and growth trended slower (but without reaching statistical significance) in mHM003-HLA versus that observed in NSG003 and mHM003b cohorts (Supplementary Fig. S4A), suggesting that immune permissiveness modulates tumor occurrence and growth.

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Figure 2.

Tumor growth dynamics are significantly affected in aHM models. A and B, Relative growth rates of untreated CUHM003 and CUHM005 tumors in the NSG, mHM, and aHM models. CUHM003 tumors implanted on aHM (n = 7) grew significantly slower than those implanted on NSG mice (n = 7; *, P = 0.04, by two-sided t test for this and all subsequent comparisons). CUHM005 tumors implanted on aHM (n = 12) grew more slowly than those implanted in either mHM (n = 11; **, P < 0.01) or NSG mice (n = 9; ***, P < 0.01). C, For CUHM003, no significant change in tumor growth was observed in response to ipilimumab or pembrolizumab treatment in either the NSG (n = 15 or 12) or mHM (n = 11 or 6) cohorts. However, among the aHM treatment resulted in a varied response (ANOVA; P = 0.06) with ipilimumab producing a 2-fold increase in tumor growth (n = 6; +, P = 0.02), whereas pembrolizumab stimulated more than a 4-fold surge in growth (n = 7; ++, P = 0.08). D, In CUHM005, there was again no difference after ipilimumab, nivolumab, or combination therapy among the NSG (n = 9, 9, 5) or mHM (n = 15, 12, 6) cohorts. In the aHM, response was again varied (AVONA, P < 0.01). Although ipilimumab (n = 12) did not stimulate significant tumor growth, nivolumab resulted in a 2-fold increase in growth (n = 12; +++, P = 0.02), and the combination of ipilimumab and nivolumab yielded greater than a 3-fold jump in tumor growth (n = 11; ++++, P < 0.01).

Tumor growth accelerates in aHM treated with checkpoint inhibitors

Prior to trial enrollment and tumor biopsy, the CUHM003 patient had been treated with ipilimumab, initially achieving a partial response before progressing, and then pembrolizumab, resulting in rapidly progressive disease. We conducted a three-arm study (control, ipilimumab, and pembrolizumab) on the NSG003, mHM003, and aHM003 tumor–bearing mice. We observed no growth differences between NSG003 and mHM003 tumors. There were 2-fold and 4-fold increases in the growth of aHM003 ipilimumab- and pembrolizumab-treated tumors compared with controls (ANOVA P = 0.06; t test P = 0.02 and P = 0.08, respectively; Fig. 2C; Supplementary Fig. S4B).

The CUHM005 patient had exhibited rapid progressive disease during combined therapy with ipilimumab and nivolumab (a PD-1 inhibitor) given prior to humanized trial enrollment and tumor biopsy. We conducted a four-arm study (control, ipilimumab, nivolumab, and combination) on NSG005, mHM005, and aHM005 mice (Fig. 2D; Supplementary Figs. S4C and S5). We observed no difference in the growth of the treated NSG005 or mMH005 groups, but a significant acceleration in the growth of the nivolumab- and combination-treated aHM005 tumors occurred (2- and 3-fold increase; ANOVA P < 0.01; t test P = 0.02 and P < 0.01, respectively).

aHM tumors show a distinct pattern of infiltration by immune cells

To determine the basis for this phenomenon, we analyzed the pattern of human immune cell invasion in fresh tumor samples by cytometry (Supplementary Fig. S6) and in paraffin-embedded tumors by IHC (Supplementary Fig. S7). Compared with their originating patient samples, melanoma tumors on HM contained less stroma and fewer infiltrating human immune cells. Cytometric analysis, however, indicated that small populations of human T cells, B cells, and macrophages similarly infiltrated the tumors in both mHM and aHM—a noteworthy observation considering the relatively lower bone marrow engraftment observed in the aHM models. When normalized by the percentage of human cells in the bone marrow, relative T-cell presence in aHM003 was nearly 8 times greater than in mHM003, whereas there were 2000 times more tumor-infiltrating T cells in aHM005 than in mHM005, indicating that autologous T cells have a greater capacity to interact with tumor tissue, even though this infiltrative capacity decreased upon immunotherapy treatment (Table 1). IHC revealed that the human immune cells were generally congregated at the tumor capsule, implying that their association with the tumor is especially transient, as has been described for immunotherapy-refractory melanoma when comparing pre- and postimmunotherapy tumor samples from both patients (Supplementary Fig. S7), where the relatively few T cells present lacked infiltrative capacity and accumulated mainly near the tumor capsule (15, 16).

Whole-exome sequencing identifies known mutations associated with growth regulation

In order to identify genetic alterations associated with these observations, we identified 281 nonsynonymous gene mutations in CUHM003 patient's tumor (Supplementary Table S3A). By the time of tumor progression after immunotherapy, mutations in 187 additional genes were present (Supplementary Table S3B). There were mutations in 99 genes in the CUHM005 patient tumor at diagnosis and 262 additional gene mutations upon progression (Supplementary Table S3C and S3D). Because neither of these patients initially responded to a PD-1 inhibitor, we identified mutant genes present in both tumors, which might be relevant to the observed treatment failure. At diagnosis, the tumors shared mutations in 11 genes (Supplementary Fig. S8A), and after progression, they shared an additional 64 mutations (highlighted in Supplementary Table S3A and S3D). Of these genes, known NRAS mutations, previously described in the Catalogue of Somatic Mutations in Cancer (COSMIC) and shown to be activating and oncogenic, seemed particularly noteworthy (Supplementary Fig. S8B; ref. 34). Although there is some correlation between NRAS mutations and a response to a PD-1 inhibitor (35), mutations in NRAS are also known to activate the transcription factor STAT3, a downstream component of the IFNγ-stimulated Jak–STAT pathway and may be associated with rapid tumor growth after anti–PD-1 treatment (36).

Transcriptome analysis defines a specific response to interferon stimulation in aHM tumors

We next examined RNA expression in these tumors by next-generation sequencing. When genes expressed only in the HM and corresponding patient tumors were analyzed using the NIH DAVID, the transcriptomes of both CUHM003 and CUHM005 humanized mice tumors were enriched in immunity-related genes, as previously observed (Supplementary Table S4; Supplementary Table S5A–S5D; ref. 13). No significant differences in the transcriptomic fingerprint between mHM and aHM tumors existed (differential expression analysis; adjusted P = 0.006), an observation that supports the higher relative activity of aHM immune cells, given the differential engraftment in the mHM and aHM cohorts.

To investigate the basis for the accelerated growth in the treated aHM tumors, we analyzed GSEA hallmarks, comparing the patient tumor to and between the NSG, mHM, and aHM models (Supplementary Fig. S9; Supplementary Table S6). The IFNα and IFNγ hallmarks were uniquely upregulated in untreated aHM003 tumors, indicating their expression reverts to a more similar state to that observed in the patient. We next observed that CTLA4 and/or PD-1 inhibition resulted in upregulation of IFNα and IFNγ pathways in both NSG003 and NSG005 as well as upregulation of IFNα in mHM003 (Fig. 3A). In contrast, CTLA4 and/or PD-1 inhibition led to downregulation of the IFNα and IFNγ pathways in aHM003 and aHM005 tumors (Fig. 3A; Supplementary Table S7). A comparison of the normalized enrichment score of the GSEA hallmarks between control and PD-1 inhibitor–treated tumors depicts the reduction in the IFNα hallmark after treatment in aHM003, whereas both IFNα and IFNγ are downregulated in mHM005 and aHM005 (Fig. 3B). In CUHM005 tumors after combination treatment, only the EMT and TNFα pathways rise in aHM005 compared with NSG005 and/or mHM005 (Fig. 3C), suggesting IFN signaling is specifically modulated by PD-1 inhibition.

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Figure 3.

Transcriptome data identifying patterns of gene expression in tumors from humanized mice. A, When GSEA hallmark changes due to immunotherapy treatment are considered, IFNα and IFNγ responses rise in NSG and mHM models but fall in aHM, indicating that the genes in this pathway are regulated in a different manner than that occurring in the other models. B, Expression change plot for each GSEA hallmark, showing the relative change in expression after anti–PD-1 treatment for tumors in NSG, mHM, and aHM. Solid points have an adjusted FDR q < 0.05 and shaded points are <0.10, as determined by a two-sided modified Kolmogorov–Smirnov permutation test. Significant changes can be detected in the IFNα and IFNγ responses. C, Similar expression change plot for CUHM005 showing GSEA changes after combination therapy. Although the EMT and TNFα hallmarks rise in aHM, no further changes in IFN expression are noted.

Unbiased analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed both positively and negatively enriched components on the IFNγ and Jak–STAT pathways (Supplementary Fig. S10A and S10B). DAVID analyses showed enrichment in chemokine signaling pathway genes (P = 0.01) in aHM003, whereas the expression of genes participating in the PI3K–Akt and IFNγ signaling pathways was enriched in aHM005 (P < 0.05; Supplementary Table S8A and S8B). Heat maps of these gene sets emphasize the bidirectional change in expression in aHM tumors (Supplementary Fig. S10C), supporting the role of the IFNγ pathway in mediating tumor growth in response to PD-1 inhibition.

Ex vivo analysis with tumor-derived cell lines

To investigate the basis of the differential response to anti–PD-1 treatment in the aHM tumors, and given the limitation to establishing further aHM cohorts, we established cell lines from early passages of the CUHM003 and CUHM005 PDX by cell sorting. Because in vivo growth corresponded with reduced T-cell presence, we replicated the conditions caused by this phenomenon by transfecting both cell lines with an siRNA construct against the IFNγ receptor (IFNγR2; Supplementary Fig. S11), thereby desensitizing cells to the IFNγ produced by active T cells. We seeded 200,000 cells on low-adherence plates to encourage spheroid formation. Under baseline conditions, both cell lines behaved similarly: the addition of purified IFNγ had minimal effect on final spheroid size, but knockdown of IFNγR2 dramatically increased their size (CUHM003 P < 0.001, CUHM005 P = 0.006; Fig. 4A). The addition of activated T cells led to a slight increase in average spheroid size, blunting the inhibitory effect of IFNγR2 knockdown (Fig. 4B). The addition of a PD-1 inhibitor did not further affect sphere size (Fig. 4C and D; representative images in Fig. 4E), indicating that cell growth for both CUHM003 and CUHM005 is directed primarily by the presence of a functioning IFNγR2, and that IFNγ depletion (either by disappearance of autologous T cells or by absence of its receptor) increases cell growth.

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Figure 4.

In vitro response to depleted IFNγ signaling. A, CUHM003 and CUHM005 cell lines were transfected with siRNA against IFNγ receptor 2 (IFNGR2) then cultured in triplicate as spheroids in the presence of IFNγ and their sizes recorded and averaged (n ∼ 120 colonies per well; 3 wells per cell line per condition). Although the addition of IFNγ only minimally affected average spheroid size, cells transfected with an siRNA against the IFNγ receptor formed significantly larger spheroids (ANOVA P = 0.001; CUHM003, *, P < 0.001; CUHM005, **, P = 0.006 by a Dunnett comparisons test for this and subsequent comparisons). B, The addition of activated mismatched T cells had a minimal effect on spheroid size but did not alter the observed size increase after IFNγ receptor knockdown (ANOVA P = 0.005; CUHM003, ***, P < 0.001; CUHM005, ****, P = 0.01). C, Likewise, the addition of a PD-1 inhibitor (pembrolizumab for CUHM003 or nivolumab for CUHM005) did not change spheroid size and did not alter the effect of the IFNγ receptor knockdown (ANOVA P < 0.001; CUHM003, +, P < 0.001; CUHM005, ++, P = 0.001). D, The addition of both T cells and the corresponding PD-1 inhibitor also only marginally changed sphere size and did not alter the increase in spheroid size induced by IFNγ receptor siRNA knockdown (ANOVA P < 0.001; CUHM003, +++, P < 0.001; CUHM005, ++++, P < 0.001). E, Representative images showing the comparative sizes of spheroids, spheroids after the addition of IFNγ, and spheroids after transfection with an siRNA against the IFNγ receptor (top), as well as observed changes in size resulting from the addition of T cells (second panel), a PD-1 inhibitor (third panel), or of T cells plus a PD-1 inhibitor (bottom). Scale bar = 100 μm.

To dissect intracellular signaling associated with IFNγ, we interrogated STAT1 and STAT3, because these are the primary cellular mediators of extracellular IFNγ and both modulate cancer growth (37). The ratio of STAT3/STAT1 RNA-seq transcripts closely mirrored the tumor growth rates (Fig. 5A) although, much as observed during our analysis of the changes in the GSEA hallmarks (Fig. 3C), additional signals appear to have a role in driving tumor growth in the CUHM005 aHM combination tumor. The transcription of STAT1- and STAT3-dependent genes with or without PD-1 inhibitor across mouse model tumors showed that STAT1-mediated transcription dropped whereas STAT3-mediated transcription increased only in aHM tumors (Fig. 5B). Because STAT3 is abundantly expressed and thus difficult to quantify, we examined its role using tumor-derived cell lines in which a transfected siSTAT3 construct reduced its expression. In accordance with our observations using IFNγR2 knockdowns, we observed that the addition of exogenous IFNγ to these CUHM003 and CUHM005 cell lines led to a notable increase in pSTAT1 expression and a decrease in pSTAT3 expression (Fig. 5C and D; cell lines), the combination of which would inhibit cellular proliferation. We observed a similar reduction in pSTAT3 in PD-1 inhibitor–treated aHM tumors, although it was uniquely coupled to a concurrent pSTAT1 reduction (Fig. 5C and D; CUHM003-CUHM005), creating a distinct environment in which cellular proliferation increased without pSTAT3 activation. A cytokine array comparing mHM and aHM plasma from mice with PD-1 inhibitor–treated CUHM003 and CUHM005 tumors provides a possible clue to the mechanism driving this proliferation (Supplementary Fig. S12). The chemokine CXCL12, whose downregulation has been previously shown to directly activate MAPK signaling in NRAS-mutated melanoma, is markedly reduced in PD-1 inhibitor–treated aHM plasma (38). We can recapitulate such a regulatory environment in the cell lines in which the JAK–STAT pathway has been activated by IFNγ and in which STAT3 transcription has been reduced (Fig. 5D; comparing the reduction in pSTAT3 expression after IFNγ + siSTAT3 treatment in the cell lines with that observed after PD-1 inhibition in the tumors).

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Figure 5.

Ex vivo examination of Jak–STAT pathway alterations in tumor hyperprogression. A, The FPKM values for biological duplicates the STAT3 and STAT1 transcripts were used to determine the ratio of STAT3/STAT1 gene expression in the NSG, mHM, and aHM CUHM003 and CUHM005 control and treated tumors. The pattern of the STAT3/STAT1 ratio corresponds to the pattern of tumor growth observed in the mouse models. B, A compilation of the FPKM values of the genes responsive to STAT1 and STAT3 activation shows changes in STAT1 and STAT3 regulation after PD-1 inhibitor treatment in NSG, mHM, and aHM. Transcription data from both CUHM003 and CUHM005 tumors were combined for each mouse group for this analysis. C, Representative western blots of patient-derived CUHM003 and CUHM005 cell lines (left) or PDX tumor tissue (right), showing the effects of the addition of IFNγ and/or expression of an siRNA STAT3 construct on STAT1 and STAT3 protein expression and phosphorylation (in the cell lines) or the effects of PD-1 inhibitor (in the PDX). pSTAT3 expression is calculated based on total STAT3 expression. D, Densitometry averaged from triplicate western blots shows that pSTAT3 expression (as a fraction of STAT3 expression) decreases after the application of IFNγ to cell lines expressing an siRNA STAT3 construct (final 2 bars of the left chart) and in aHM tumors treated with pembrolizumab (CUHM003; middle chart) or nivolumab (CUHM005; right chart).