Association of allostatic load with overall survival in epithelial ovarian cancer.

OBJECTIVE: Elevated allostatic load (AL), an integrated, cumulative marker of physiologic damage due to socioenvironmental stress, is associated with increased mortality in patients with breast, lung, and other cancers. The relationship between allostatic load and mortality in ovarian cancer patients remains unknown. We examined the relationship between allostatic load and overall survival in ovarian cancer patients. METHODS: This cross-sectional study used data from 201 patients enrolled in a prospective observational ovarian cancer cohort study at a National Cancer Institute-designated Comprehensive Cancer Center from October 2012 through June 2022. All patients underwent debulking surgery and completed a full course of standard-of-care platinum-based chemotherapy. Follow-up was completed through January 2024. Allostatic load was calculated as a summary score by assigning one point to the worst sample quartile for each of ten biomarkers measured within 45 days before the ovarian cancer diagnosis. High allostatic load was defined as having an allostatic load in the top quartile of the summary score. A Cox proportional hazard model with robust variance tested the association between allostatic load and overall survival. RESULTS: There were no associations between allostatic load and ovarian cancer clinical characteristics. After accounting for demographic, clinical, and treatment factors, high allostatic load was associated with a significant increase in mortality (hazard ratio 2.17 [95%CI, 1.13-4.15]; P = 0.02). CONCLUSION: Higher allostatic load is associated with worse survival among ovarian cancer patients. Allostatic load could help identify patients at risk for poorer outcomes who may benefit from greater socioenvironmental support during treatment.

Facts and Hopes in Using Omics to Advance Combined Immunotherapy Strategies.

The field of oncology has been transformed by immune checkpoint inhibitors (ICI) and other immune-based agents; however, many patients do not receive a durable benefit. While biomarker assessments from pivotal ICI trials have uncovered certain mechanisms of resistance, results thus far have only scraped the surface. Mechanisms of resistance are as complex as the tumor microenvironment (TME) itself, and the development of effective therapeutic strategies will only be possible by building accurate models of the tumor-immune interface. With advancement of multi-omic technologies, high-resolution characterization of the TME is now possible. In addition to sequencing of bulk tumor, single-cell transcriptomic, proteomic, and epigenomic data as well as T-cell receptor profiling can now be simultaneously measured and compared between responders and nonresponders to ICI. Spatial sequencing and imaging platforms have further expanded the dimensionality of existing technologies. Rapid advancements in computation and data sharing strategies enable development of biologically interpretable machine learning models to integrate data from high-resolution, multi-omic platforms. These models catalyze the identification of resistance mechanisms and predictors of benefit in ICI-treated patients, providing scientific foundation for novel clinical trials. Moving forward, we propose a framework by which in silico screening, functional validation, and clinical trial biomarker assessment can be used for the advancement of combined immunotherapy strategies.

Th2 Cells Are Associated with Tumor Recurrence Following Radiation.

The curative treatment of multiple solid tumors, including head and neck squamous cell carcinoma (HNSCC), utilizes radiation. The outcomes for HPV/p16-negative HNSCC are significantly worse than HPV/p16-positive tumors, with increased radiation resistance leading to worse locoregional recurrence (LRR) and ultimately death. This study analyzed the relationship between immune function and outcomes following radiation in HPV/p16-negative tumors to identify mechanisms of radiation resistance and prognostic immune biomarkers. A discovery cohort of 94 patients with HNSCC treated uniformly with surgery and adjuvant radiation and a validation cohort of 97 similarly treated patients were utilized. Tumor immune infiltrates were derived from RNAseq gene expression. The immune cell types significantly associated with outcomes in the discovery cohort were examined in the independent validation cohort. A positive association between high Th2 infiltration and LRR was identified in the discovery cohort and validated in the validation cohort. Tumor mutations in CREBBP/EP300 and CASP8 were significantly associated with Th2 infiltration. A pathway analysis of genes correlated with Th2 cells revealed the potential repression of the antitumor immune response and the activation of BRCA1-associated DNA damage repair in multiple cohorts. The Th2 infiltrates were enriched in the HPV/p16-negative HNSCC tumors and associated with LRR and mutations in CASP8, CREBBP/EP300, and pathways previously shown to impact the response to radiation.

Psoriatic arthritis subtypes are phenocopied in humanized mice.

Psoriatic arthritis (PsA) is a complex inflammatory disease that challenges diagnosis and complicates the rational selection of effective therapies. Although T cells are considered active effectors in psoriasis and PsA, the role of CD8+ T cells in pathogenesis is not well understood. We selected the humanized mouse model NSG-SGM3 transgenic strain to examine psoriasis and PsA endotypes. Injection of PBMCs and sera from patients with psoriasis and PsA generated parallel skin and joint phenotypes in the recipient mouse. The transfer of human circulating memory T cells was followed by migration and accumulation in the skin and synovia of these immunodeficient mice. Unexpectedly, immunoglobulins were required for recapitulation of the clinical phenotype of psoriasiform lesions and PsA domains (dactylitis, enthesitis, bone erosion). Human CD8+ T cells expressing T-bet, IL-32 and CXCL14 were detected by spatial transcriptomics in murine synovia and by immunofluorescence in the human PsA synovia. Importantly, depletion of human CD8+ T cells prevented skin and synovial inflammation in mice humanized with PsA peripheral blood cells. The humanized model of psoriasis and PsA represents a valid platform for accelerating the understanding of disease pathogenesis, improving the design of personalized therapies, and revealing psoriatic disease targets.

Immunocompetent murine model of Ewing sarcoma reveals role for TGFß inhibition to enhance immune infiltrates in Ewing tumors during radiation.

Ewing sarcoma (ES) is an aggressive cancer diagnosed in adolescents and young adults. The fusion oncoprotein (EWSR1::FLI1) that drives Ewing sarcoma is known to downregulate TGFBR2 expression (part of the TGFß receptor). Because TGFBR2 is downregulated, it was thought that TGFß likely plays an inconsequential role in Ewing biology. However, the expression of TGFß in the Ewing tumor immune microenvironment (TIME) and functional impact of TGFß in the TIME remains largely unknown given the historical lack of immunocompetent preclinical models. Here, we use single-cell RNAseq analysis of human Ewing tumors to show that immune cells, such as NK cells, are the largest source of TGFß production in human Ewing tumors. We develop a humanized (immunocompetent) mouse model of ES and demonstrate distinct TME signatures and metastatic potential in these models as compared to tumors developed in immunodeficient mice. Using this humanized model, we study the effect of TGFß inhibition on the Ewing TME during radiation therapy, a treatment that both enhances TGFß activation and is used to treat aggressive ES. Utilizing a trivalent ligand TGFß TRAP to inhibit TGFß, we demonstrate that in combination with radiation, TGFß inhibition both increases ES immune cell infiltration and decreases lung metastatic burden in vivo . The culmination of these data demonstrates the value of humanized models to address immunobiologic preclinical questions in Ewing sarcoma and suggests TGFß inhibition as a promising intervention during radiation therapy to promote metastatic tumor control.

Radiomic analysis of patient and inter-organ heterogeneity in response to immunotherapies and BRAF targeted therapy in metastatic melanoma.

Background: Variability in treatment response may be attributable to organ-level heterogeneity in tumor lesions. Radiomic analysis of medical images can elucidate non-invasive biomarkers of clinical outcome. Organ-specific radiomic comparison across immunotherapies and targeted therapies has not been previously reported. Methods: We queried UPMC Hillman Cancer Center registry for patients with metastatic melanoma (MEL) treated with immune checkpoint inhibitors (ICI) (anti-PD1/CTLA4 [ipilimumab+nivolumab; I+N] or anti-PD1 monotherapy) or BRAF targeted therapy. Best overall response was measured using RECIST v1.1. Lesions were segmented into discrete volume-of-interest with 400 radiomics features extracted. Overall and organ-specific machine-learning models were constructed to predict disease control (DC) versus progressive disease (PD) using XGBoost. Results: 291 MEL patients were identified, including 242 ICI (91 I+N, 151 PD1) and 49 BRAF. 667 metastases were analyzed, including 541 ICI (236 I+N, 305 PD1) and 126 BRAF. Across cohorts, baseline demographics included 39-47% female, 24-29% M1C, 24-46% M1D, and 61-80% with elevated LDH. Among patients experiencing DC, the organs with the greatest reduction were liver (-88%±12%, I+N; mean±S.E.M.) and lung (-72%±8%, I+N). For patients with multiple same-organ target lesions, the highest inter-lesion heterogeneity was observed in brain among patients who received ICI while no intra-organ heterogeneity was observed in BRAF. 267 patients were kept for radiomic modeling, including 221 ICI (86 I+N, 135 PD1) and 46 BRAF. Models consisting of optimized radiomic signatures classified DC/PD across I+N (AUC=0.85) and PD1 (0.71) and within individual organ sites (AUC=0.72∼0.94). Integration of clinical variables improved the models' performance. Comparison of models between treatments and across organ sites suggested mostly non-overlapping DC or PD features. Skewness, kurtosis, and informational measure of correlation (IMC) were among the radiomic features shared between overall response models. Kurtosis and IMC were also utilized by multiple organ-site models. Conclusions: Differential organ-specific response was observed across BRAF and ICI with within organ heterogeneity observed for ICI but not for BRAF. Radiomic features of organ-specific response demonstrated little overlap. Integrating clinical factors with radiomics improves the prediction of disease course outcome and prediction of tumor heterogeneity.

Autologous HER2-specific CAR T cells after lymphodepletion for advanced sarcoma: a phase 1 trial.

In this prospective, interventional phase 1 study for individuals with advanced sarcoma, we infused autologous HER2-specific chimeric antigen receptor T cells (HER2 CAR T cells) after lymphodepletion with fludarabine (Flu) ± cyclophosphamide (Cy): 1 × 108 T cells per m2 after Flu (cohort A) or Flu/Cy (cohort B) and 1 × 108 CAR+ T cells per m2 after Flu/Cy (cohort C). The primary outcome was assessment of safety of one dose of HER2 CAR T cells after lymphodepletion. Determination of antitumor responses was the secondary outcome. Thirteen individuals were treated in 14 enrollments, and seven received multiple infusions. HER2 CAR T cells expanded after 19 of 21 infusions. Nine of 12 individuals in cohorts A and B developed grade 1-2 cytokine release syndrome. Two individuals in cohort C experienced dose-limiting toxicity with grade 3-4 cytokine release syndrome. Antitumor activity was observed with clinical benefit in 50% of individuals treated. The tumor samples analyzed showed spatial heterogeneity of immune cells and clustering by sarcoma type and by treatment response. Our results affirm HER2 as a CAR T cell target and demonstrate the safety of this therapeutic approach in sarcoma. ClinicalTrials.gov registration: NCT00902044 .