Dorys Lopez-Ramos

Infiltration of human melanomas with cytotoxic immune cells correlates with spontaneous type I IFN activation and a favorable prognosis. Therapeutic blockade of immune-inhibitory receptors in patients with preexisting lymphocytic infiltrates prolongs survival, but new complementary strategies are needed to activate cellular antitumor immunity in immune cell-poor melanomas. Here, we show that primary melanomas in Hgf-Cdk4(R24C) mice, which imitate human immune cell-poor melanomas with a poor outcome, escape IFN-induced immune surveillance and editing. Peritumoral injections of immunostimulatory RNA initiated a cytotoxic inflammatory response in the tumor microenvironment and significantly impaired tumor growth. This critically required the coordinated induction of type I IFN responses by dendritic, myeloid, natural killer, and T cells. Importantly, antibody-mediated blockade of the IFN-induced immune-inhibitory interaction between PD-L1 and PD-1 receptors further prolonged the survival. These results highlight important interconnections between type I IFNs and immune-inhibitory receptors in melanoma pathogenesis, which serve as targets for combination immunotherapies.Using a genetically engineered mouse melanoma model, we demonstrate that targeted activation of the type I IFN system with immunostimulatory RNA in combination with blockade of immune-inhibitory receptors is a rational strategy to expose immune cell-poor tumors to cellular immune surveillance.

Abstract Evolution of tumor cell phenotypes promotes heterogeneity and therapy resistance. Here we found that induction of CD73, the enzyme that generates immunosuppressive adenosine, is linked to melanoma phenotype switching. Activating MAPK mutations and growth factors drove CD73 expression, which marked both nascent and full activation of a mesenchymal-like melanoma cell state program. Proinflammatory cytokines like TNFα cooperated with MAPK signaling through the c-Jun/AP-1 transcription factor complex to activate CD73 transcription by binding to an intronic enhancer. In a mouse model of T-cell immunotherapy, CD73 was induced in relapse melanomas, which acquired a mesenchymal-like phenotype. We also detected CD73 upregulation in melanoma patients progressing under adoptive T-cell transfer or immune checkpoint blockade, arguing for an adaptive resistance mechanism. Our work substantiates CD73 as a target to combine with current immunotherapies, but its dynamic regulation suggests limited value of CD73 pretreatment expression as a biomarker to stratify melanoma patients. Cancer Res; 77(17); 4697–709. ©2017 AACR.

Abstract Background: Traditionally, multi-focal breast cancer results in mastectomy. The Alliance Trial offers a paradigm shift in surgical options available for multi-focal breast cancer patients in the context of adjuvant chemotherapy. In the trial, patients with multi-focal disease (< 3 tumors) who underwent breast conservation surgery (BCS) were found to have similar outcomes to patients undergoing mastectomy. BCS for large volume tumors ( >30%) has been cited as having a high potential for cosmetic defect, and hence represents a typical upper limit for potential tissue removal in BCS. Here, we evaluated a patient cohort to better understand the economic impact of the Alliance trial and further categorize patients that would most benefit without suffering cosmetic impact. We employed a novel computational technology to quantify the ratio of tumor size to breast tissue volume. Methods: Using a publicly available, single site cohort (n=243, DUMC) of breast cancer patients that underwent mastectomy, we segmented the tumors using our TumorSight Viz software platform. This platform uses artificial intelligence to segment the tumor and surrounding tissues and allows for a volumetric and morphologic assessment in 3D space. We then applied relevant inclusion/exclusion criteria from the Alliance Trial to the cohort (Saha et al, 2018). In trial eligible patients, we used TumorSight Viz to create a convex hull (CH) around the multi-focal disease using dilations of 1 cm and 2 cm. The volume of the CH, corresponding to proposed surgical extirpation, and the overall breast volume (BV) were then computationally assessed in 3D. The ratio of CH to BV (CH:BV) was calculated and a cutoff of 30% (high potential for cosmetic deformity) was applied. A cost analysis was then carried out. We determined the aggregate per annum savings that could potentially be realized by transforming a subset of mastectomies to BCS by tabulating total costs of mastectomy+reconstruction vs. BCS+WBI (whole-breast irradiation), as well as adjusted for relative rates of adjuvant therapy (~80%) across the nationwide patient population. Results: We found that 19.3% of adjuvant mastectomy patients were eligible for BCS based on Alliance Trial criteria. Of those, 68% had tumor CH:BV < 30% when using a 1 cm dilation around the tumor. When using a 2 cm dilation, 56% had tumor CH:BV < 30%. Together, these results indicate that of all adjuvant mastectomy patients, an estimated 10.8-13.1% are eligible for BCS based on volumetric measures of cosmetically acceptable breast tissue removal. Our economic analysis of BCS vs. mastectomy revealed an estimated $28,500 cost savings for patients with private insurance, suggesting that both decreased costs and improved quality-of-life (QOL) can be mutually aligned. By assessing the nationwide number of patients receiving adjuvant therapy for breast cancer, alongside the percentage potentially eligible for BCS using the above cosmetic defect analysis, we estimate that BCS conversion from mastectomy offers to provide a net savings of $300-350 million annually. Conclusion: The Alliance Trial guidelines unveiled the potential option of BCS in ~20% of patients with multi-focal disease in our cohort, demonstrating considerable cost-savings. Computational tools can further differentiate individuals who may not be best candidates for BCS in this setting, ensuring high QOL and informed decision-making. Citation Format: John Pfeiffer, Matthew Biancalana, Dorys Lopez-Ramos, Bradley Feiger, Anuja Antony. Applying the Alliance Trial Guidelines in Multi-focal Breast Disease Using an Artificial Intelligence Computational Platform: Economic Analysis and Cosmetic Sensitivity [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-07-04.

Although glucocorticoid (GC) is widely used for treating hematopoietic malignancies including adult T-cell leukemia (ATL), the mechanism by which leukemic cells become resistant to GC in the clinical course remains unclear. Using a series of T-cell lines infected with human T lymphotropic virus type-I (HTLV-I), the causative virus of ATL, we have dissected the transformation from interleukin (IL)-2-dependent to -independent growth stage. The transformation associates the loss of thioredoxin-binding protein-2 (TBP-2), a tumor suppressor and regulator of lipid metabolism. Here we show that TBP-2 is responsible for GC-induced apoptosis in ATL cells. In the IL-2-dependent stage, dexamethasone induced TBP-2 expression and apoptosis, both of which were blocked by GC receptor (GR) antagonist RU486. Knockdown of TBP-2 consistently reduced the amount of GC-induced apoptosis. In IL-2-independent stage, however, expression of GR and TBP-2 was suppressed and GC failed to induce apoptosis. Forced expression of GR led the cells to mild sensitivity to GC, which was also accomplished by treatment with suberoylanilide hydroxamic acid, a TBP-2 inducer. A transfection experiment showed that TBP-2 expression induced apoptosis in IL-2-independent ATL cells. Thus, TBP-2 is likely to be one of the key molecules for GC-induced apoptosis and a potential target for treating the advanced stage of ATL.

Abstract Background: Metabolic reprogramming and tumor angiogenesis are two tightly linked hallmarks of cancer. Regions of high metabolic activity within a tumor can become hypoxic or nutrient starved, eliciting a cascade of pro-angiogenic signals that can lead to increased tumor perfusion and in turn greater metabolic activity. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and 18Fluorodeoxyglucose (FDG) positron emission tomography (PET) are two imaging methods commonly used in the diagnosis of cancer; the former can be also used to identify the perfusion of the tumor microenvironment (TME) and the latter provides a coarse readout of glucose metabolic activity. Here, we are presented with a unique cohort of patients who underwent DCE-MRI, 18FDG-PET and 64Cu-DOTA-Traztuzumab offering potential to perform a pilot analysis. First, we characterize the performance of our perfusion model SimBioSys- Microvasculature (SBS-MV) through a stability analysis, as well as comparison with two PET modalities. Finally, we use this model in 3D simulations of individual HER2+ breast cancer patients to forecast responses to neoadjuvant chemotherapy (NAT). Methods: SBS-MV is a modified Tofts model for pharmacokinetic modeling of DCE-MRI data, utilizing a tissue segmentation model developed in-house to inform parameter fits and ensure that the derived parameters fall within acceptable values on a tissue-by-tissue level, in addition its function for standard fitting of DCE time-course data. We first evaluated the stability of the SBS-MV model by analyzing a separate cohort of 10 patients who underwent ultrafast DCE-MRI. Temporal undersampling was performed on the original high temporal resolution data to mimic low temporal resolution from standard-of-care (SoC) DCE studies. Both datasets were processed with SBS-MV model. Next, we analyzed data from a cohort of 18 patients, each of whom underwent a pre-NAT SoC DCE-MRI study, a mid-treatment FDG PET/CT study, and a mid-treatment 64Cu-DOTA-Trastuzumab/CT study during treatment. Tissue segmentation and SBS-MV fits were performed on the pre-NAT DCE MRI. Tightly cropped boxes encompassing the tumor in the 3D SBS-MV volumes as well as the PET volumes were created. Using these identified regions, all summary statistics were calculated and cross-correlated between imaging modalities. Results: SBS-MV was stable to SoC-mimicking temporally undersampled ultrafast DCE MRI series, demonstrating relatively little variability in the output parameters (a voxel wise median deviation of approximately 0.005 min-1 ). Further, we demonstrate that SBS-MV parameters (Ktrans, ve) are correlated with FDG SUVSA and 64Cu SUVSA. In addition, the glucose concentrations used in the biophysical simulations were correlated with FDG SUVSA, indicating that downstream applications of the SBS-MV models still carry this information about perfusion. Finally, using SBS-MV, our biophysical simulations of HER2-targeted therapy accurately predict patient response. In this small cohort, our biophysical simulation platform performed well, yielding predictive accuracy of 0.83, with sensitivity and specificity of 0.83 and 0.83, respectively. These performance metrics are in line with previously published reports. Conclusion: Our MV model is capable of extracting biologically meaningful perfusion parameters from standard clinical DCE MRI time series, providing the same benefits of a comprehensive kinetic analysis, without impacting current clinical workflow. This approach could be used in both the research and clinical settings, offering actionable information on what drives individual patient therapeutic response for a more personalized care. Citation Format: John Whitman, Vikram Adhikarla, Russell Rockne, Lusine Tumyan, Joanne Mortimer, Wei Huang, Joseph Peterson, Dorys Lopez-Ramos, John Cole. Biophysical simulation using DCE-MRI to forecast response to NAT in HER2+ patients, with glucose characterization and orthogonal validation using FDG-PET [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-01-01.

Aims: Accumulating evidence indicates that oxidative stress is associated with inflammation, and the cellular redox status can determine the sensitivity and the final outcome in response to inflammatory stimuli. To control the redox balance, mammalian cells contain a variety of oxidoreductases belonging to the thioredoxin superfamily. The large number of these enzymes suggests a complex mechanism of redox regulation in mammals, but the precise function of each family member awaits further investigations. Results: We generated mice deficient in transmembrane thioredoxin-related protein (TMX), a transmembrane oxidoreductase in the endoplasmic reticulum (ER). When exposed to lipopolysaccharide (LPS) and d-(+)-galactosamine (GalN) to induce inflammatory liver injury, mutant mice were highly susceptible to the toxicants and developed severe liver damage. LPS-induced production of inflammatory mediators was equivalent in both wild-type and TMX−/− mice, whereas neutralization of the proinflammatory cytokine tumor necrosis factor-α suppressed the toxic effects of LPS/GalN in the mutant mice. Liver transcriptional profiles revealed enhanced activation of the p53-signaling pathway in the TMX−/− mice after LPS/GalN treatment. Furthermore, TMX deficiency also caused increased sensitivity to thioacetamide, which exerts its hepatotoxicity through the generation of reactive oxygen species. Innovation: The present study is the first to address the role of the oxidoreductase TMX in inflammatory liver injury. The phenotype of mice deficient in TMX suggests a functional link between redox regulation in the ER and susceptibility to oxidative tissue damage. Conclusion: We conclude that TMX plays a major role in host defense under the type of inflammatory conditions associated with oxidative stress. Antioxid. Redox Signal. 18, 1263–1272.

Generalizable population-based studies are unable to account for individual tumor heterogeneity that contributes to variability in a patient's response to physician-chosen therapy. Although molecular characterization of tumors has advanced precision medicine, in early-stage and locally advanced breast cancer patients, predicting a patient's response to neoadjuvant therapy (NAT) remains a gap in current clinical practice. Here, we perform a study in an independent cohort of early-stage and locally advanced breast cancer patients to forecast tumor response to NAT and assess the stability of a previously validated biophysical simulation platform.A single-blinded study was performed using a retrospective database from a single institution (9/2014-12/2020). Patients included: ≥ 18 years with breast cancer who completed NAT, with pre-treatment dynamic contrast enhanced magnetic resonance imaging. Demographics, chemotherapy, baseline (pre-treatment) MRI and pathologic data were input into the TumorScope Predict (TS) biophysical simulation platform to generate predictions. Primary outcomes included predictions of pathological complete response (pCR) versus residual disease (RD) and final volume for each tumor. For validation, post-NAT predicted pCR and tumor volumes were compared to actual pathological assessment and MRI-assessed volumes. Predicted pCR was pre-defined as residual tumor volume ≤ 0.01 cm3 (≥ 99.9% reduction).The cohort consisted of eighty patients; 36 Caucasian and 40 African American. Most tumors were high-grade (54.4% grade 3) invasive ductal carcinomas (90.0%). Receptor subtypes included hormone receptor positive (HR+)/human epidermal growth factor receptor 2 positive (HER2+, 30%), HR+/HER2- (35%), HR-/HER2+ (12.5%) and triple negative breast cancer (TNBC, 22.5%). Simulated tumor volume was significantly correlated with post-treatment radiographic MRI calculated volumes (r = 0.53, p = 1.3 × 10-7, mean absolute error of 6.57%). TS prediction of pCR compared favorably to pathological assessment (pCR: TS n = 28; Path n = 27; RD: TS n = 52; Path n = 53), for an overall accuracy of 91.2% (95% CI: 82.8% - 96.4%; Clopper-Pearson interval). Five-year risk of recurrence demonstrated similar prognostic performance between TS predictions (Hazard ratio (HR): - 1.99; 95% CI [- 3.96, - 0.02]; p = 0.043) and clinically assessed pCR (HR: - 1.76; 95% CI [- 3.75, 0.23]; p = 0.054).We demonstrated TS ability to simulate and model tumor in vivo conditions in silico and forecast volume response to NAT across breast tumor subtypes.

Type I interferons are evolutionally conserved cytokines, with broad antimicrobial and immunoregulatory functions. Despite well-characterised role in spontaneous cancer immunosurveillance, the function of type I IFNs in cancer immunotherapy remains incompletely understood.We utilised genetic mouse models to explore the role of the type I IFN system in CD8+ T-cell immunotherapy targeting the melanocytic lineage antigen gp100.The therapeutic efficacy of adoptively transferred T cells was found to depend on a functional type I IFN system in myeloid immune cells. Compromised type I IFN signalling in myeloid immune cells did not prevent expansion, tumor infiltration or effector function of melanoma-specific Pmel-1 CD8+ T cells. However, melanomas growing in globally (Ifnar1-/-) or conditionally (Ifnar1ΔLysM) type I IFN system-deficient mice displayed increased myeloid infiltration, hypoxia and melanoma cell dedifferentiation. Mechanistically, hypoxia was found to induce dedifferentiation and loss of the gp100 target antigen in melanoma cells and type I IFN could directly inhibit the inflammatory activation of myeloid cells. Unexpectedly, the immunotherapy induced significant reduction in tumor blood vessel density and whereas host type I IFN system was not required for the vasculosculpting, it promoted vessel permeability.Our results substantiate a complex and plastic phenotypic interconnection between melanoma and myeloid cells in the context of T-cell immunotherapy. Type I IFN signalling in myeloid cells was identified as a key regulator of the balance between antitumor immunity and disease-promoting inflammation, thus supporting the development of novel combinatorial immunotherapies targeting this immune cell compartment.

Abstract Background: Immunotherapy has emerged as an essential treatment modality for enhancing survival in triple negative breast cancer (TNBC). Despite demonstrated improvements in pathologic complete response (pCR), both toxicity and adverse events from immuno-oncology (IO) drugs remain a significant limitation. Currently, a lack of tests to differentiate patients likely to respond to IO vs. poor responders precludes a tailored approach to immunotherapy. Here we describe an imaging biomarker that allows physicians to target breast cancer patients with the highest likelihood of response to immunotherapies. Methods: We identified a rapidly assessable, non-invasive biomarker of tumor response to immunotherapy. This biomarker uses radiological imaging (DCE-MRIs) coupled with the biophysical simulation platform TumorScope® to predict a patient’s likelihood of pCR following treatment with immunotherapy and backbone chemotherapy. While this biomarker does not depend on transcriptomic data, it was designed by matching biological function (transcriptomics) to tumor microenvironmental features (as observed in biophysical simulations) derived from the SimBioSys TumorBank®. With this simulation-derived biomarker in hand, we validated our methods in a small, independent cohort. We additionally applied the SimbIOScope IO-prediction analysis to assess a large immunotherapy-naïve cohort, in order to validate if an increase in pCR rates in the presence of immunotherapy correlated with the anticipated rate of response to immunotherapy. Results: In TNBC tumors prior to neoadjuvant therapy, we found that a high immune evasive capability was associated with low nutrient utilization. Tumor immune evasion (including the PD-1/PDL-1 axis) is strongly correlated with tumor hypoxia (r = 0.45, p < 1 × 10^-6). Similarly, in HR+/HER2- tumors prior to neoadjuvant therapy, we found that immune evasion was negatively correlated with angiogenesis (r = -0.40, p = 0.006), suggesting that low tumor vascularization is associated with immune evasion capability. As these associations were identified from available transcriptomic data obtained from a single biopsy site within each patient’s tumor, they were unable to account for tumor heterogeneity. We therefore sought to identify a spatially-resolved biomarker for immune evasive potential in TNBC and HR+/HER2- tumors. We used publicly available DCE-MRIs of patients treated with the immunotherapy drug pembrolizumab and paclitaxel from the ISPY2 trial (n=63) to train a model to predict pCR in IO-treated tumors. Critically, the resulting model’s predictive power matched that obtained from transcriptomics data. SimbIOScope was then tested on IO-treated patients in a small, independent cohort and correctly predicted pCR in >91% (n=12). We further validated SimbIOScope by predicting the expected pCR rate in 292 patients from our virtual TumorBank in response to immunotherapy. Consistent with empirical increase (13.6% in TNBC, Keynote522) anticipated as seen from clinical trials, we found that SimbIOScope predicted a 14% increase in pCR rate in TNBC patients (and 9% increase in HR+ patients) with addition of immunotherapy versus chemotherapy backbone alone. Conclusions: The SimbIOScope platform offers physicians a rapid, non-invasive biomarker to differentiate patients likely to respond to immunotherapy from non-responders. This innovative technology thereby personalizes oncologic care and mitigates the potential for adverse effects by helping to optimize selection of patients best suited for immunotherapy. Citation Format: Gregory Norris, John Pfeiffer, Joseph Peterson, Nicole Liadis, Matthew Biancalana, Dorys Lopez-Ramos, Anuja K. Antony, Daniel Cook. Development of a Novel Imaging Biomarker to Ascertain Responsiveness to Immunotherapy [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-02-19.

<p>Legends to Supplemental Figures 1 to 7</p>

<p>Supplemental Table S1. DNA oligos used in this study Supplemental Table S2. Characteristics of patients with primary melanomas and cutaneous melanoma metastasis (Bonn cohorts). Supplemental Table S4. List of transcription factors binding in NT5E based on ENCODE ChIP-seq data. Supplemental Table S8. Changes of CD73 IHC score in melanoma patients from the UCLA cohort under pembrolizumab treatment (anti-PD-1). Supplemental Table S9. Characteristics and changes of CD73 expressions of melanomas from patients of the Sydney cohort treated with nivolumab or pembrolizumab (anti PD-1).</p>

<p>This file contains updated supplemental methods</p>

<p>Supplemental Figure S1. Validation of anti-CD73 antibody used for IHC and analysis of cutaneous melanoma metastases. Supplemental Figure S2. Next-generation sequencing based analysis of bisulfite conversion to assess CpG island methylation in the NT5E (CD73) promoter region. Supplemental Figure S3. c-Jun and FOSL1 protein expression in MaMel melanoma cell lines. Supplemental Figure S4. Functional role of AP-1 sites in the NT5E (CD73) genomic region for the induction of CD73 by c-Jun. Supplemental Figure S5. IHC analysis of CD73 expression by melanoma cells in mouse ACT relapse melanomas. Supplemental Figure S6. Comparison of the 'proliferative' melanoma phenotype gene set with the cell proliferation associated E2F and MYC gene sets. Supplemental Figure S7. Graphical abstract of CD73 expression in melanoma phenotype switching.</p>

<div>Abstract<p>Evolution of tumor cell phenotypes promotes heterogeneity and therapy resistance. Here we found that induction of CD73, the enzyme that generates immunosuppressive adenosine, is linked to melanoma phenotype switching. Activating MAPK mutations and growth factors drove CD73 expression, which marked both nascent and full activation of a mesenchymal-like melanoma cell state program. Proinflammatory cytokines like TNFα cooperated with MAPK signaling through the c-Jun/AP-1 transcription factor complex to activate CD73 transcription by binding to an intronic enhancer. In a mouse model of T-cell immunotherapy, CD73 was induced in relapse melanomas, which acquired a mesenchymal-like phenotype. We also detected CD73 upregulation in melanoma patients progressing under adoptive T-cell transfer or immune checkpoint blockade, arguing for an adaptive resistance mechanism. Our work substantiates CD73 as a target to combine with current immunotherapies, but its dynamic regulation suggests limited value of CD73 pretreatment expression as a biomarker to stratify melanoma patients. <i>Cancer Res; 77(17); 4697–709. ©2017 AACR</i>.</p></div>

<p>Supplemental Table S6. Hallmark gene sets up in ACT-treated HCmel3 melanomas (EDT, early during treatment) versus non-treated (NT) control HCmel3 melanomas (GSEA results).</p>

<p>Supplemental Table S6. Hallmark gene sets up in ACT-treated HCmel3 melanomas (EDT, early during treatment) versus non-treated (NT) control HCmel3 melanomas (GSEA results).</p>

<p>Supplemental Table S1. DNA oligos used in this study Supplemental Table S2. Characteristics of patients with primary melanomas and cutaneous melanoma metastasis (Bonn cohorts). Supplemental Table S4. List of transcription factors binding in NT5E based on ENCODE ChIP-seq data. Supplemental Table S8. Changes of CD73 IHC score in melanoma patients from the UCLA cohort under pembrolizumab treatment (anti-PD-1). Supplemental Table S9. Characteristics and changes of CD73 expressions of melanomas from patients of the Sydney cohort treated with nivolumab or pembrolizumab (anti PD-1).</p>

<div>Abstract<p>Evolution of tumor cell phenotypes promotes heterogeneity and therapy resistance. Here we found that induction of CD73, the enzyme that generates immunosuppressive adenosine, is linked to melanoma phenotype switching. Activating MAPK mutations and growth factors drove CD73 expression, which marked both nascent and full activation of a mesenchymal-like melanoma cell state program. Proinflammatory cytokines like TNFα cooperated with MAPK signaling through the c-Jun/AP-1 transcription factor complex to activate CD73 transcription by binding to an intronic enhancer. In a mouse model of T-cell immunotherapy, CD73 was induced in relapse melanomas, which acquired a mesenchymal-like phenotype. We also detected CD73 upregulation in melanoma patients progressing under adoptive T-cell transfer or immune checkpoint blockade, arguing for an adaptive resistance mechanism. Our work substantiates CD73 as a target to combine with current immunotherapies, but its dynamic regulation suggests limited value of CD73 pretreatment expression as a biomarker to stratify melanoma patients. <i>Cancer Res; 77(17); 4697–709. ©2017 AACR</i>.</p></div>

<p>Legends to Supplemental Figures 1 to 7</p>

<p>Supplemental Table S7. Hallmark gene sets up in ACT-treated relapse (R) HCmel3 melanomas versus early during treatment (EDT) HCmel3 melanomas (GSEA results).</p>

<p>Supplemental Table S3. Gene sets correlating with NT5E (CD73) expression in MITFhigh melanoma cell lines from the Broad melanoma cell line panel (GSEA results).</p>

<p>Supplemental Table S5. Hallmark gene sets up in amelanotic BrafV600ExCdk4R24C melanomas versus pigmented BrafV600ExCdk4R24C melanomas (GSEA results).</p>

<p>This file contains updated supplemental methods</p>

<p>Supplemental Figure S1. Validation of anti-CD73 antibody used for IHC and analysis of cutaneous melanoma metastases. Supplemental Figure S2. Next-generation sequencing based analysis of bisulfite conversion to assess CpG island methylation in the NT5E (CD73) promoter region. Supplemental Figure S3. c-Jun and FOSL1 protein expression in MaMel melanoma cell lines. Supplemental Figure S4. Functional role of AP-1 sites in the NT5E (CD73) genomic region for the induction of CD73 by c-Jun. Supplemental Figure S5. IHC analysis of CD73 expression by melanoma cells in mouse ACT relapse melanomas. Supplemental Figure S6. Comparison of the 'proliferative' melanoma phenotype gene set with the cell proliferation associated E2F and MYC gene sets. Supplemental Figure S7. Graphical abstract of CD73 expression in melanoma phenotype switching.</p>

<p>Supplemental Table S7. Hallmark gene sets up in ACT-treated relapse (R) HCmel3 melanomas versus early during treatment (EDT) HCmel3 melanomas (GSEA results).</p>

<p>Supplemental Table S3. Gene sets correlating with NT5E (CD73) expression in MITFhigh melanoma cell lines from the Broad melanoma cell line panel (GSEA results).</p>

<p>Supplemental Table S5. Hallmark gene sets up in amelanotic BrafV600ExCdk4R24C melanomas versus pigmented BrafV600ExCdk4R24C melanomas (GSEA results).</p>

<p>PDF file 309K, Results of repeated dose acute toxicity study</p>

<p>XLS file 28K, List of top 50 interferon alpha induced gene across five humane melanoma cell lines</p>

<p>PDF file 309K, Results of repeated dose acute toxicity study</p>

<p>XLS file 28K, List of top 50 interferon alpha induced gene across five humane melanoma cell lines</p>

<div>Abstract<p>Infiltration of human melanomas with cytotoxic immune cells correlates with spontaneous type I IFN activation and a favorable prognosis. Therapeutic blockade of immune-inhibitory receptors in patients with preexisting lymphocytic infiltrates prolongs survival, but new complementary strategies are needed to activate cellular antitumor immunity in immune cell–poor melanomas. Here, we show that primary melanomas in <i>Hgf-Cdk4</i><sup>R24C</sup> mice, which imitate human immune cell–poor melanomas with a poor outcome, escape IFN-induced immune surveillance and editing. Peritumoral injections of immunostimulatory RNA initiated a cytotoxic inflammatory response in the tumor microenvironment and significantly impaired tumor growth. This critically required the coordinated induction of type I IFN responses by dendritic, myeloid, natural killer, and T cells. Importantly, antibody-mediated blockade of the IFN-induced immune-inhibitory interaction between PD-L1 and PD-1 receptors further prolonged the survival. These results highlight important interconnections between type I IFNs and immune-inhibitory receptors in melanoma pathogenesis, which serve as targets for combination immunotherapies.</p><p><b>Significance:</b> Using a genetically engineered mouse melanoma model, we demonstrate that targeted activation of the type I IFN system with immunostimulatory RNA in combination with blockade of immune-inhibitory receptors is a rational strategy to expose immune cell–poor tumors to cellular immune surveillance. <i>Cancer Discov; 4(6); 674–87. ©2014 AACR</i>.</p><p>This article is highlighted in the In This Issue feature, p. 621</p></div>

<div>Abstract<p>Infiltration of human melanomas with cytotoxic immune cells correlates with spontaneous type I IFN activation and a favorable prognosis. Therapeutic blockade of immune-inhibitory receptors in patients with preexisting lymphocytic infiltrates prolongs survival, but new complementary strategies are needed to activate cellular antitumor immunity in immune cell–poor melanomas. Here, we show that primary melanomas in <i>Hgf-Cdk4</i><sup>R24C</sup> mice, which imitate human immune cell–poor melanomas with a poor outcome, escape IFN-induced immune surveillance and editing. Peritumoral injections of immunostimulatory RNA initiated a cytotoxic inflammatory response in the tumor microenvironment and significantly impaired tumor growth. This critically required the coordinated induction of type I IFN responses by dendritic, myeloid, natural killer, and T cells. Importantly, antibody-mediated blockade of the IFN-induced immune-inhibitory interaction between PD-L1 and PD-1 receptors further prolonged the survival. These results highlight important interconnections between type I IFNs and immune-inhibitory receptors in melanoma pathogenesis, which serve as targets for combination immunotherapies.</p><p><b>Significance:</b> Using a genetically engineered mouse melanoma model, we demonstrate that targeted activation of the type I IFN system with immunostimulatory RNA in combination with blockade of immune-inhibitory receptors is a rational strategy to expose immune cell–poor tumors to cellular immune surveillance. <i>Cancer Discov; 4(6); 674–87. ©2014 AACR</i>.</p><p>This article is highlighted in the In This Issue feature, p. 621</p></div>

models predict that analog signals via RasGRP1 drive positive selection of thymocytes (Prasad, 2009).Our computational models also predict that RasGRP1 activity must be controlled.RasGRP1 is an oncogene in acute myeloid leukemias (Lauchle, 2009) and T cell acute lymphoblastic leukemia (Copeland, 2002).Thus, noncancerous lymphocytes must have a mechanism to limit RasGRP1 function.Surprisingly, the regulatory mechanisms to limit proliferative RasGRP1-Ras signals are unknown.Control of proliferative signals received via extracellular receptor engagement is often achieved through the posttranslational modification (PTM) of intracellular signaling components.We have established that RasGRP1 protein levels decrease after T cell receptor stimulation and are investigating the biochemical mechanism.We are identifying PTM events that control RasGRP1 protein levels and activity in lymphocytes.We are in the process of determining the functional roles of these posttranslational modifications.In particular, how these modulate the pattern (e.g, analog), amplitude and duration of the Ras-Erk signals.These studies will increase our understanding of regulated RasGRP-1-Ras-Erk signaling and how this affects thymocyte development and leukemogenesis.

<h3>Background</h3> Although immunotherapy has become standard-of-care for many cancers, the number of benefiting patients remains relatively small. Current biomarkers of response (e.g., PD-L1) have proven only modestly useful for distinguishing IO-responders vs. non-responders.¹ Here, we present a proof-of-concept approach for the rapid, non-invasive assessment of immunotherapy response prediction using biomarker imaging signatures. <h3>Methods</h3> We used publicly available datasets (ISPY1 and ISPY2 non-IO-treated patients)^2,3 to calculate the expression levels of gene signatures across breast cancer subtypes. We analyzed these imaging datasets using TumorScope, a biophysical simulation engine, to identify biomarkers (features) present within IO-responsive tumors. We then correlated this response to biological processes and hallmark cancer gene signatures. TumorScope uses patient standard-of-care data, including high spatiotemporal resolution medical images (e.g., DCE-MRIs) to predict a patient‘s probability of achieving a pathological complete response (pCR).⁴ Next, we calculated the spatial distribution of these features across tumors. To specifically identify the IO-response signatures, we calculated the feature thresholds associated with pCR in response to IO using the ISPY2 IO-treated patient dataset (linear regression of the training set, n=63). This spatial biomarker-based approach, SimbIOScope, showed comparable predictive power as the traditional biopsy-based transcriptomic approach. We further validated this approach in an independent cohort from a single center (n=12), and applied our method to a large IO-naïve population (n=292) to assess the model’s predictive capability. <h3>Results</h3> Spatially-resolved biomarkers of immune evasion in triple negative breast cancer (TNBC) and HR+/HER2- tumors identified individuals with resistance to IO therapy, and had similar predictive power to transcriptomic analysis. We found that baseline (prior to therapy) high levels of immune evasion signatures in IO non-responders are associated with hypoxia (r=0.45, p&lt;1x10–6) and autophagy in TNBC patients, and with low angiogenesis (r=-0.40, p=0.006) in HR+/HER2- patients. When tested on an independent patient cohort (n=12), SimbIOScope correctly predicted pCR in over 91% of cases. In a larger reference cohort (n=292), SimbIOscope predictions of pCR were consistent with the empirical increase observed in clinical trials. Our analysis predicted a 14% increase in pCR for TNBC tumors over baseline with the addition of the checkpoint inhibitor pembrolizumab, as compared to the 13.6% pCR increase observed in clinical trials (Keynote 522).⁵ <h3>Conclusions</h3> SimbIOScope imaging biomarkers and analyses efficiently identify the cohort of patients likely to respond to immunotherapy. Its imaging analytic capabilities position SimbIOScope as a critical tool when planning cancer treatment options, and expands IO-response prediction beyond traditional biomarkers. <h3>References</h3> A Marra GVGC. Recent advances in triple negative breast cancer: the immunotherapy era. <i>BMC Med</i>. 2019;<b>17</b>(1):1–9. doi:10.1186/s12916-019-1326-5 Esserman LJ, Berry DA, DeMichele A, <i>et al</i>. Pathologic Complete Response Predicts Recurrence-Free Survival More Effectively by Cancer Subset: Results From the I-SPY 1 TRIAL—CALGB 150007/150012, ACRIN 6657. <i>J Clin Oncol</i>. 2012;<b>30</b>(26):3242. doi:10.1200/JCO.2011.39.2779 Wang H, Yee D. I-SPY 2: a Neoadjuvant Adaptive Clinical Trial Designed to Improve Outcomes in High-Risk Breast Cancer. <i>Curr Breast Cancer Rep</i>. 2019;<b>11</b>(4):303. doi:10.1007/S12609-019-00334-2 Spring LM, Fell G, Arfe A, <i>et al</i>. Pathologic Complete Response after Neoadjuvant Chemotherapy and Impact on Breast Cancer Recurrence and Survival: A Comprehensive Meta-analysis. <i>Clin Cancer Res</i>. 2020;<b>26</b>(12):2838–2848. doi:10.1158/1078-0432.CCR-19-3492 Schmid P, Cortes J, Pusztai L, <i>et al</i>. Pembrolizumab for Early Triple-Negative Breast Cancer. https://doi.org/101056/NEJMoa1910549. 2020;<b>382</b>(9):810–821. doi:10.1056/NEJMOA1910549

<h3>Background</h3> Immunotherapy use is increasing across cancer types, however, how the nutrient microenvironment affects immune cell activity is not fully understood.¹ We addressed this challenge using a novel framework combining nutrient distributions within a tumor, single cell RNA-seq data, and genome-scale metabolic modeling² to understand how immune cell cytotoxic potential varies within tumor microenvironments. <h3>Methods</h3> We constructed 3D nutrient composition atlases of over 1200 breast cancer tumors using the SimBioSys TumorScope software. We then constructed a genome-scale metabolic model of tumor infiltrating T-cells based on single cell RNA-sequencing data from over 5000 single T-cells from breast cancer patients. We simulated this T-cell metabolic model across the range of nutrient compositions present in our tumor atlases to understand how nutrient availability affects the cytotoxic potential of T-cells. <h3>Results</h3> Our results demonstrated that the local nutrient composition has a dramatic impact on T cell functionality, with fundamental cellular behaviors being significantly impaired by a reduction in key nutrients such as glucose and oxygen. Additionally, the degree of impairment varies between the various types of T cells. For example, proliferative T cells are relatively insensitive to hypoxia, but very sensitive to reduced glucose, which may be related to the increase in IO response that we observe in tumors with a greater degree of hypoxia. <h3>Conclusions</h3> Overall, we found that the nutrient composition of the tumor microenvironment has a strong influence on T-cell activity, especially in hypoxic tumor regions. <h3>References</h3> Makowski L, Chaib M, Rathmell JC. Immunometabolism: From basic mechanisms to translation. <i>Immunol Rev</i>. 2020;<b>295</b>(1):5–14. doi:10.1111/imr.12858 Harcombe WR, Riehl WJ, Dukovski I, <i>et al</i>. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. <i>Cell Rep</i>. 2014;<b>7</b>(4):1104–1115. doi:10.1016/J.CELREP.2014.03.070