Characterization of tumor immune infiltrates and immune dependency of anti-tumor activity following RASi + CDK4/6i treatment

TOX is a nuclear factor critical for thymic development of CD4+ thymocytes, natural killer and innate lymphoid cells. In post-thymic antigen-specific CD8+ T cells, TOX is highly expressed in settings of chronic antigen encounter such as cancer and chronic infection and required for the persistence of exhausted CD8+ T cells. The role of TOX in CD4+ T cells is less clear. Here, we show that TOX is critical for CD4+ type 1 helper T (TH1) cell differentiation. Gain-of-function and loss-of-function studies show that TOX induces TH1 cell-associated molecular programs that drive TH1 cell-like phenotypes and interferon-γ production. TOX expression in CD4+ T cells from individuals with cancer was associated with increased cytotoxicity, antitumor immunity and improved responses to immunotherapy, as well as pathogenic responses in autoimmune and inflammatory diseases in mice and humans. Thus, TOX has opposing functions in CD4+ versus CD8+ T cells: while TOX is associated with CD8+ T cell exhaustion and generally with poor responsiveness to immunotherapy, in CD4+ T cells TOX drives TH1 cell fate commitment and is associated with antitumor immunity and pathogenic autoimmune responses.

CD4 T cells prevent outgrowth of residual disease. A, Scheme of experimental design (KPC1 orthotopic transplant into wild-type C57Bl/6 mice). B, Percentage change in tumor volume compared with day 0, as measured by weekly ultrasound. The y-axis indicates the percentage change in tumor volume, and the x-axis indicates the time point after treatment initiation. Each dot represents an individual mouse. Statistical testing: Two-way ANOVA with multiple comparisons (within each time point, comparing treatment groups with every other treatment group) and correcting for multiple comparisons with a Tukey test. All statistically significant comparisons are shown. C, Representative hematoxylin and eosin (H&E) images of residual tissue following 42 days of indicated treatment. D, Scheme of experimental design for E–H (KPC1-ZsGreen orthotopic transplant into wild-type C57Bl/6 mice). E, Fraction of CD8+ T cells out of total CD45 cells by flow cytometry at 7 days after the initiation of indicated treatments. See F for the legend. Statistical testing for E–G: Ordinary one-way ANOVA with multiple comparisons, comparing the means of each treatment group with every other treatment group and correcting for multiple comparisons using a Sidak test. Only relevant statistically significant comparisons are shown. F, Fraction of CD4+ T cells out of total CD45 cells by flow cytometry at 7 days after the initiation of indicated treatments. Each dot represents an individual mouse. G, Fraction of tumor cells (%) positive for MHC-II by flow cytometry at 7 days after the initiation of indicated treatments. See F for the legend. Each dot represents an individual mouse. Statistical testing: Ordinary one-way ANOVA, comparing the means of every treatment group with every other treatment group and correcting for multiple comparisons with a Sidak test. All statistically significant comparisons are shown. H, Fraction of CD4 T cells (%) positive for indicated cytotoxicity and activation of Th1 markers by flow cytometry 7 days after treatment initiation. Statistical testing: Two-way ANOVA with multiple comparisons, comparing the mean of each treatment group with every other treatment group within the relevant time point and correcting for multiple comparisons with a Tukey test. Only statistically significant comparisons are shown. I, Representative IF images of residual tissue following 42 days of indicated treatments (see scheme in A) staining for pan-CK, Ki67, cleaved caspase 3, and DAPI. (A and D, Created with BioRender.com.)

A triple combination consisting of RMC-7977, palbociclib, and a CD40 agonist maintains long-term tumor control despite the presence of residual disease. A, Scheme of experimental design (KPC1 orthotopic transplant into wild-type C57Bl/6 mice). B, Percentage change in tumor volume compared with day 0 as measured by weekly ultrasound. The y-axis indicates the percentage change in tumor volume, and the x-axis indicates the time point after treatment initiation. Each dot represents an individual mouse (n = 6 per treatment group at treatment initiation). Statistical testing: Two-way ANOVA with multiple comparisons (comparing each treatment group against every other treatment group within the same time point), with Tukey testing to correct for multiple comparisons. Only statistically significant comparisons are shown. At 28 days, n = 5 RMC-7977 + IgG mice remained, and n = 6 remained for all other treatment groups. At 35 days, n = 5 RMC-7977 + IgG mice and RMC-7977 + palbociclib + IgG mice remained, and n = 6 RMC-7977 + FGK4.5 mice and RMC-7977 + palbociclib + FGK4.5 mice remained. At 42 days, n = 4 RMC-7977 + IgG mice and RMC-7977 + palbociclib + IgG remained and n = 6 RMC-7977 + FGK4.5 mice and RMC-7977 + palbociclib + FGK4.5 mice remained. C, Representative snapshots of ultrasound imaging from RMC-7977 + palbociclib + IgG2b (top row) or RMC-7977 + palbociclib + FGK4.5 (bottom row) at indicated time points. Snapshots show the largest tumor cross-section. The red dashed line indicates tumor borders. D, Representative hematoxylin and eosin (H&E) images of residual tissue following 42 days of indicated treatment. The black dashed line surrounds residual tumor tissue. E, Scheme of experimental design (KPC1 orthotopic transplant into wild-type C57Bl/6 mice). F, Spaghetti plot showing percentage change in tumor volume compared with day 0 as measured by weekly ultrasound. The y-axis indicates the percentage change in tumor volume, and the x-axis indicates the time point after treatment initiation. Each line represents an individual mouse. G, Probability of survival (%) following treatment with indicated agents. RMC-7977 + palbociclib + IgG2b is not shown, as mice were euthanized prior to reaching humane endpoints. (A and E, Created with BioRender.com.)

Characterizing transcriptional changes in immune cells following RAS(ON) inhibitor-based combination strategies

Type 1 diabetes (T1D) is a progressive T cell-mediated autoimmune disease that results from the breakdown of tolerance mechanisms in β-cell-specific T cells. Although CD8 T cells are primarily responsible for the destruction of insulin-producing β cells, intriguingly, HLA class II allelic polymorphisms confer the greatest genetic risk for the development of T1D, suggesting a critical role of CD4 T cells in disease initiation and progression. Many aspects of autoimmune T cell differentiation remain enigmatic, including where and how autoimmune CD8 and CD4 T cells arise, which molecular programs control autoimmune T cell differentiation, and how CD8 T cells sustain β-cell destruction in the face of persistent self-antigen encounter. In this work, we summarize our current understanding of β-cell-specific CD8 and CD4 T cell differentiation and function, the role of autoimmune stem-like progenitor CD8 T cells in initiating and sustaining disease, and molecular programs and key transcription factors associated with the diabetogenic T cell response.

Abstract Introduction In autoimmunity and chronic infection, both settings of persistent (self or foreign) antigen, immune responses are sustained by stem-like CD8 T cells, which self-renew and give rise to differentiated progeny. However, if and how T stemness is epigenetically encoded, which transcription factor(s) regulate the stem-T cells, and whether the stem-T cell state is disease-specific or shared across diseases, is currently not known. Methods We used clinically relevant models of autoimmune type 1 diabetes (T1D) and chronic infection and conducted serial T cell transplantation studies in vivo, combined with single cell paired RNA- and ATAC-sequencing on antigen-specific T cells. We developed CRISPR/Cas9-mediated gene-editing approaches in primary T cells as well as CUT&RUN studies, identifying a novel hierarchy of transcription factors regulating stem T cell identity and function. Results We discovered that a small subset of stem-T cells (TSC) express lymphoid enhancer-binding factor 1 (LEF1), a member of the TCF/LEF TF family. Paired single cell transcriptomic and epigenomic analyses reveal that the LEF1+ TSC harbor a unique epigenetically encoded molecular state enriched in genes and pathways characteristic of embryonic and adult (somatic) stem cells (e.g. neural stem cells). Strikingly, we found that TSC in chronic infection harbor a LEF1+ TSC pool sharing the core stemness epigenetic and molecular program observed in autoimmune LEF1+ TSC. Loss- and gain-of-function studies in both autoimmune T1D and chronic infection confirmed the critical role of LEF1 in maintaining T cell stemness. CUT&RUN analyses provide clues as to how LEF1 instructs the epigenetically encoded program of stem-T cells. Conclusion Here we reveal novel insights into the molecular circuitries of CD8 T cell stemness and differentiation. We discover LEF1 as the master regulator defining T cell stemness and identify novel targets for therapeutic intervention. Funding Source NIH R01AI173249, JDRF SRA-2023-1410-S-B, MSKCC Basic Research Innovation Award, The Hearst Foundation Topic Categories Lymphocyte Differentiation and Peripheral Maintenance (LYM)

Samples collected for single cell RNA-sequencing studies

Combined RASi + CDK4/6i triggers favorable immune remodeling and poises tumors to respond to immunotherapy. For the scheme of experimental design, see Supplementary Fig. S5A (KPC1-zsGreen orthotopic transplant into wild-type C57Bl/6 mice). A, Fraction of CD45 cells out of total live cells by flow cytometry. Each dot represents an individual mouse. Statistical testing: Ordinary one-way ANOVA with multiple comparisons, comparing the means of each treatment group against the vehicle of the relevant time point and correcting for multiple comparisons using a Sidak test. Only statistically significant comparisons are shown. B, Fraction of indicated immune cell populations (%) out of total CD45+ immune cells at day 14 after treatment initiation. Each dot represents an individual mouse. Statistical testing: Two-way ANOVA with multiple comparisons, comparing the means of each treatment group against the vehicle and correcting for multiple comparisons using a Dunnett test. Only statistically significant comparisons are shown. C, Quantification of representative regions of IF staining for CD4 T cells (representative images shown in E) in tumors 4 hours, 3, 7, or 14 days after treatment initiation. Each dot represents an individual mouse (average of 3–5 ∼40,000 µm2 regions). Statistical testing: Ordinary one-way ANOVA with multiple comparisons, comparing the means of each treatment group against the vehicle at the relevant time point and correcting for multiple comparisons using a Bonferroni test. Only statistically significant comparisons are shown. D, Quantification of representative regions of IF staining for CD8 T cells (representative images shown in E) in tumors 4 hours, 3, 7, or 14 days after treatment initiation. Each dot represents an individual mouse (average of 3–5 ∼40,000 µm2 regions). Statistical testing: Ordinary one-way ANOVA with multiple comparisons, comparing the means of each treatment group against the vehicle of the relevant time point and correcting for multiple comparisons using a Bonferroni test. Only statistically significant comparisons are shown. E, Representative images of IF staining for T-cell markers CD3 (white), CD8 (red), and CD4 (yellow) following indicated treatments at indicated time points (D7 = 7 days after treatment initiation, D14 = 14 days after treatment initiation). F, Quantification of the number and area (µm2) of lymphoid aggregates (clusters of B cells, DCs, and other MHC-II positive cells) found in whole-slide scans of tumors. Each tick on the x-axis represents a single mouse. Each dot represents an individual lymphoid aggregate, and the size of each aggregate is reflected on the y-axis. The color of each dot indicates the treatment group, as shown in the figure legend. Arrows and corresponding letters point to lymphoid aggregates for which examples are shown in G–I. G, Example of a lymphoid aggregate from an RMC-7977 + palbociclib–treated mouse at t = 7 days after treatment initiation. H and I, Example of a lymphoid aggregate from an RMC-7977 + palbociclib–treated mouse at t = 14 days after treatment initiation. J, Highly multiplexed IF images of TLSs in tumors from mice treated with RMC-7977 + palbociclib for 7 days. Images were acquired by the Cell Dive and stained for endothelial cell (CD31), fibroblast (Podoplanin: PDPN), and activated fibroblast (α-smooth muscle actin: αSMA) markers. K, Highly multiplexed IF images of TLSs in tumors from mice treated with RMC-7977 + palbociclib for 7 days. Images were acquired by the Cell Dive and stained for B-cell (B220) and proliferation (Ki67) markers. L, Highly multiplexed IF images of TLSs in tumors from mice treated with RMC-7977 + palbociclib for 7 days. Images were acquired by the Cell Dive and stained for the indicated markers. M, Low magnification image of lymphoid aggregates shown in H and I following 14 days of RMC-7977 + palbociclib treatment.

Gating strategy for flow cytometry analysis in Fig 3A-B and Supplementary Fig S4A-N, S4Q-S.

In settings of persistent (self or foreign) antigen, such as autoimmunity and chronic infection, immune responses are sustained by stem-like T cells. Although TCF1 has emerged as a key transcription factor (TF) associated with stemness, the TCF1hi population is heterogeneous, raising the question of whether TCF1 exclusively defines the stem T cell (TSC) pool. Using preclinical models of autoimmune type 1 diabetes and chronic infection, we discover that a small subset of TCF1hi T cells express the TF LEF1. LEF1+ TCF1hi T cells define a true self-renewing TSC pool. TSC give rise to LEF1- TCF1hi progenitor T cells (TPRO), which lack stem functions and generate terminally differentiated TCF1lo T cells (TDIFF) (TSC→TPRO→TDIFF). We show that LEF1 is essential for T cell stemness. Autoimmune and exhausted LEF1+ TSC share a unique epigenetically encoded core program enriched for genes and pathways characteristic of embryonic and adult stem cells, including WNT/β-catenin and Notch signaling. Spatial positioning, niche signals, and migration regulate stem-cell fate; accordingly, targeting integrins or Notch signaling impairs T cell stemness and prevents disease. Our studies identify LEF1 and niche-derived factors as fundamental regulators of T cell stemness across chronic diseases.

Combined RAS and CDK4/6 inhibition drives tumor cells into a senescence-like state. A, Scheme of experimental design for B–D and Supplementary Fig S3A–S3I (KPC1-ZsGreen orthotopic transplant into wild-type C57Bl/6 mice). B, Representative images of pancreatic tumor tissues following detection of β-galactosidase activity using the chromogenic substrate X-Gal. C, Quantification of IF staining of pancreatic tumor tissues for β-galactosidase activity in mice 4 hours, 3, 7, or 14 days after treatment initiation. Each dot represents an individual mouse (average of 3–5 ∼120,000 µm2 regions per mouse). Where data from two separate experiments were available, mice from experiments were merged (n = 3–8 mice per treatment group). Statistical testing: Ordinary one-way ANOVA, comparing preselected pairs of columns (within treatment time point vs. vehicle only) and correcting for multiple comparisons with a Bonferroni test. Only statistically significant comparisons are shown. D, Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) showing tumor cells from each treatment group classified as “Sen-High” in blue and those classified as “Sen-Low” in yellow. “Sen-High” cells are defined as those that are positive for three senescence signatures in Supplementary Fig. S3F–S3H). Pie charts below show the fraction of total cells classified as Sen-High versus Sen-Low in each treatment group. The number of sequenced tumor cells is displayed for each treatment group. E, Scheme of experimental design (KPC1-ZsGreen-Watermelon orthotopic transplant into NSG mice) to trace the proliferative history of tumor cells following the indicated treatments. The strategy for gating DAPI−, ZsGreen+ tumor cells based on H2B-mCherry levels is shown. F, Fraction of tumor cells with highly retained (stably arrested since treatment initiation), mid, or low H2B-mCherry following the indicated treatments. Statistical testing: Two-way ANOVA, comparing the mean of each treatment group within each H2B-mCherry with every other treatment group. Multiple comparisons testing was corrected for with a Tukey test, and statistically significant results are reported here. H2B-mCherry high: vehicle versus RMC-7977 + palbociclib (P = 0.0458). H2B-mCherry mid: vehicle versus RMC-7977 + palbociclib (P < 0.0001); RMC-7977 versus RMC-7977 + palbociclib (P < 0.0001). H2B-mCherry low: vehicle versus RMC-7977 (P = 0.0128); vehicle versus RMC-7977 + palbociclib (P < 0.0001); RMC-7977 versus RMC-7977 + Palbociclib P < 0.0001). (A and E, Created with BioRender.com.)

The CDK4/6 inhibitor palbociclib increases the antitumor activity of the RAS inhibitor in mouse models of PDAC. A, Scheme of experimental design (KPC1 orthotopic transplant into wild-type C57Bl/6 mice). B, Percentage change in tumor volume compared with day 0 as measured by weekly ultrasound. The y-axis indicates the percentage change in tumor volume, and the x-axis indicates the time point after treatment initiation. Each line represents the treatment group average. Statistical analysis: A two-tailed Mann–Whitney test was performed to compare the percentage change in tumor volume between the remaining RMC-7977-treated (n = 4) and RMC-7977 + palbociclib–treated (n = 5) mice at day 28. The P value is shown. C, Scheme of experimental design (KPC GEMM model). D, Percentage change in tumor volume compared with day 0 at 14 days after treatment initiation in KPC GEMMs following treatment with indicated agents. In mice that did not survive until day 14 ultrasound measurement, day 7 measurements are shown (indicated by a cross). Statistical testing: Ordinary one-way ANOVA, comparing the mean of each treatment group with the mean of every other treatment group and correcting for multiple comparisons with the Tukey test. Only statistically significant comparisons or relevant comparisons are shown. Sample size: vehicle (n = 6), palbociclib (n = 8), RMC-7977 (n = 7), RMC-7977 + palbociclib (n = 8). E, Probability of survival (%) in KPC GEMMs following treatment with indicated agents. Statistical testing: A log-rank (Mantel–Cox) test was performed to compare survival curves, and P values are shown. Sample size: vehicle (n = 6), palbociclib (n = 8), RMC-7977 (n = 7), RMC-7977 + palbociclib (n = 8). F, Percentage mean tumor volume change from day 0 at 21 days after treatment initiation in various CDX and PDX models. The mRECIST score was determined based on percentage mean tumor volume change, where mCR > 80% regression, mPR = 30%–80% regression, mSD = 30% regression – 30% growth, and mPD > 30% growth. Statistical testing: A two-way ANOVA with multiple comparisons, comparing the mean of each treatment group within each model shown and correcting for multiple comparisons with a Tukey test. Only daraxonrasib + abemaciclib (combo) versus abemaciclib or daraxonrasib in KP-4 was statistically significant (P < 0.0001 for both comparisons). The number of mice per treatment group per model is shown in G. G, Kaplan–Meier plot of progression-free survival in various CDX and PDX models, where progression is defined by tumor volume doubling from baseline. Statistical testing: A log-rank (Mantel–Cox) test was performed to compare survival curves, and P values are shown. Models are pooled, and the number of mice per treatment group is shown in the figure. (A and C, Created with BioRender.com.)

Identification of tumor cell subsets expressing senescence gene signatures

CD4 T-cell production of IFN-γ enables long-term tumor control. A, Expression of select cytotoxic and Th1-related genes in CD4 T cells from the scRNA-seq dataset. B, Expression of select genes encoding IFN-γ sensing machinery and IFN-γ–inducible genes in Sen-High versus Sen-Low cells. C, Scheme of experimental design (KPC1 orthotopic transplant into wild-type C57Bl/6 mice). D, Probability of survival (%) in mice following treatment with indicated agents. Statistical testing: A log-rank (Mantel–Cox) test was performed to compare survival curves, and P values are shown. E, Scheme of experimental design (KPC1 orthotopic transplant into wild-type C57Bl/6 mice). F, Representative images of pancreatic tumor tissues following detection of β-galactosidase activity using the chromogenic substrate X-Gal. G, Quantification of the fraction of SA-β-gal positive area out of total area (%). Each dot represents an individual mouse (n = 3–5 per treatment group), and the value shown is the average of 3 ∼159,000 µm2 regions per mouse tumor tissue (with the exception of two mice, where tumor tissue was only large enough to quantify 1–2 regions). Statistical testing: One-way ANOVA, comparing the mean of every column with the mean of every column within that time point and correcting for multiple comparisons with a Holm–Sidak test. All comparisons are shown. (C and E, Created with BioRender.com.)

CD40 agonist prolongs responses to RMC-7977 + palbociclib

Abstract Introduction Type 1 diabetes (T1D) is a T cell—mediated autoimmune disease driven by β cell-specific CD8 T cells. How autoreactive T cells arise and sustain disease remains unclear. Using the non-obese diabetic mouse model of T1D, we previously identified a stem-like CD8 T cell population in the pancreatic lymph node (pLN) which initiates and sustains β cell destruction: stem T cells (TSC) self-renew and continuously give rise to differentiated progeny (TDIFF) that migrate to the pancreas and kill β cells. Spatial positioning of somatic stem cells is critical for their maintenance, and that niche restricted signals (i.e., WNT and NOTCH) regulate the balance between self-renewal and differentiation. However, if and how T cell stemness is associated with distinct intranodal positioning in pLN, and whether interference in migration disrupts differentiation, is unknown. Methods We conducted (i) paired single-cell RNA- and ATAC-sequencing, (ii) adoptive T cell transfer studies, (iii) high-resolution imaging, (iv) CRISPR/Cas9 mediated gene editing of autoimmune T cells in pLN to identify the molecular and functional characteristics of TSC and TDIFF. Results We discovered unique transcription factors and epigenetic programs governing autoimmune T cell stemness and differentiation. TSC and TDIFF express distinct chemokine receptors and integrins, suggesting that T cell stemness and differentiation are driven by intranodal positioning. Strikingly, WNT and NOTCH signaling were enriched in TSC, driving the expression of critical stem genes, thereby connecting T cell stemness to somatic stem cell biology. Pharmacological blockade and CRISPR/Cas9-mediated deletion of integrins and cell-cell interactions prevented autoimmune T cell differentiation and disease. Conclusion Our studies identify novel transcriptional regulators and niche-dependent signals that determine autoimmune T cell stemness and differentiation, opening novel therapeutic avenues for the prevention and treatment of T1D and other autoimmune diseases. Funding Source NIH F31DK141119, NIH R01AI173249, Juvenile Diabetes Research Foundation JDRF SRA-2023-1410-S-B, MSKCC Basic Research Innovation Award (BRIA), Hearst Foundation Topic Categories Lymphocyte Differentiation and Peripheral Maintenance (LYM)

Gene signatures used for single cell RNAseq analysis

Palbociclib does not increase RASi-driven cell death or affect RASi-driven phospho ERK inhibition

Abstract Introduction Immunotherapies demonstrate the potential power of CD8 T cells to eliminate cancer cells; however, these strategies only work in a subset of patients and tumor types. To design predictably effective immunotherapies, we must elucidate the mechanisms controlling tumor-specific T cell (TST) activation, dysfunction, and therapeutic response. T cell receptor (TCR) signal strength (determined by the affinity of TCR for peptide-bound major histocompatibility complex (pMHC)) is known to regulate T cell differentiation. However, its impact on anti-tumor immunity is less clear. Methods We developed a novel, inducible autochthonous cancer model in which we can systematically vary TCR signal strength, spanning the range observed for human tumor-infiltrating T cells: we cloned a Sleeping Beauty (SB) transposon/transposase-based vector encoding the oncogene MYC followed by an inverted inducible tumor model antigen (OVA). By single amino acid substitutions to the native OVA sequence, we generated altered peptide ligands that are recognized by OVA-specific CD8 T cells with varying TCR signal strength. SB-vectors, along with a vector silencing the tumor suppressor P53, were delivered via hydrodynamic tail vein injection into mice with inducible Cre expression, a highly-efficient method to induce liver tumors. Employing this model, we can track CD8 TST over months from tumor initiation to endpoints. Results We characterized CD8 TST in progressing tumors, and their response to immunotherapy. Tumors expressing high affinity epitopes induced TST expansion and TST ultimately acquired an exhausted phenotype with time. Tumors with low affinity epitopes lacked TST expansion. Strikingly, however, these TST could be activated through immunization, mediating robust anti-tumor immune responses. Conclusion We investigated how TCR signal strength determines T cell activation, differentiation, and anti-tumor activity, revealing interesting insights into how low-affinity TST can be therapeutically utilized in solid tumors. Funding Source NIH NCI R01CA269733 Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)