Systems Immunology Meets Clinical Translation: Multi-Omic Approaches to Predict Therapy Response in Cancer and Autoimmune Disease

Mouse models are frequently used to test the therapeutic efficacy of anti-cancer drugs. However, the translation of murine experimental data to treatments for patients with cancer often fails due to significant differences between the species, including the differences in the immune system. Our goal is to bridge this gap and to establish an in vivo preclinical model of human tumor immunotherapy by engrafting immunodeficient mice expressing a partial human immune system with human tumor implants. Humanized NOD-scid IL2Rγ (null) (hu-NSG) mice were initially generated by transplanting NSG mice with human CD34+ hematopoietic stem and progenitor cells (HSPCs) which support human hematopoietic and immune system development. Hu-NSG mice develop functional human T cells and B cells with high levels of TCR excision circles, complex TCR repertoire diversity and antigen-specific T cell proliferative responses. Several types of patient-derived tumors (non small cell lung cancer, sarcoma, triple negative breast cancer and invasive bladder cancer) were successfully implanted into HLA mismatched hu-NSG mice. Tumor growth curves show a delay in tumor growth in hu-NSG compared to non-humanized NSG mice. In a colon cancer xenograft model, treatment with chemotherapy agent (5-FU) or with a therapeutic antibody directed against VEGF (Avastin) resulted in decreased tumor growth. In addition to PDX tumors we have also tested human cancer cell lines. Tumor growth was observed in all hu-NSG mice implanted with human ovarian tumor cell line SKOV3-Luc-D3 cells at different time points post HSPC engraftment, showing no evidence of tumor rejection. Thus, our model of humanized mice bearing tumor-derived xenografts provides opportunities to study both the safety and efficacy of current cancer therapies. Citation Format: Minan Wang, James G. Keck, Mingshan Cheng, Danying Cai, Leonard Shultz, Karolina Palucka, Jacques Banchereau, Carol Bult, Rick Huntress. Patient-derived tumor xenografts in humanized NSG mice: a model to study immune responses in cancer therapy. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-050. doi:10.1158/1538-7445.AM2015-LB-050

Next-generation androgen receptor (AR) antagonist, like enzalutamide (Enz) has revolutionized the treatment of castration-resistant prostate cancer (CRPC), the lethal form of the disease, by effectively suppressing the androgen axis. However, resistance to Enz eventually develops in nearly all patients. Recent studies indicate that increased glucocorticoid receptor (GR) expression contributes to Enz resistance. Our prior work in prostate cancer models and patient tissues suggests that Enz decreases 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2, inactivates cortisol) and increases hexose-6-phosphate dehydrogenase (H6PD, promotes cortisol production) expression in tumor, which then sustains high levels of cortisol, leading to overstimulation of GR and resistance to Enz. Collectively, the glucocorticoid axis, including GR expression level and cortisol concentration controlled by 11β-HSD2 and H6PD, plays a critical role in Enz-resistant CRPC. Studies suggest AR activation reduces GR expression in PC, and SPA counteracts glucocorticoid-induced muscle atrophy, indicating AR activation inhibits GR functions in various contexts. Patient and mouse studies show that supraphysiologic androgen (SPA) reverses Enz resistance, and ∼30% of patients respond to SPA; however, we lack a biomarker that identifies which patients will respond. A clinical study revealed that Enz treatment increases serum cortisol by suppressing11β-HSD2 expression, which is correlated with patient blood pressure. Taking all these together, we hypothesized that SPA reverses Enz through attenuation of the GR pathway by affecting GR expression and cortisol level. By using qPCR, immunoblot, high-performance liquid chromatography, and live cell imaging analysis, we observed that SPA decreases GR expression, accelerates cortisol metabolism by decreasing H6PD and increasing 11β-HSD2, and suppresses glucocorticoid-induced cell growth in Enz-resistant prostate cancer cell lines. Next, to further test our hypothesis that the glucocorticoid axis and blood pressure can serve as biomarkers for SPA therapy response. We will first confirm our cell level findings with cell-derived and patient-derived xenograft studies, then by collaborating with physician scientists, we will further evaluate the biomarker roles of the glucocorticoid axis for SPA therapy response in Enz-resistant prostate cancer by analyzing serum cortisol, expression of GR, H6PD, and 11β-HSD2 in circulating tumor cells, and blood pressure of patients who have undergone SPA treatment. This research holds the promise of not only confirming and solidifying both SPA's clinical benefits and the GR pathway's clinical significance in Enz resistance but also setting the foundation for our goal of developing the first non-invasive biomarkers for SPA therapy in patients with disease resistant to Enz. Citation Format: Jianneng Li, Zhichao Liu, Mallory Sands, Sam Adams, Jihaeng Lee, Mae Wang, TJ Walsh, Matthew Haerens, Michael Li, Leo Leon, Adriana Zablah. The glucocorticoid axis serves as biomarkers for supraphysiological androgen therapy response in enzalutamide-resistant prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 556.

e12597 Background: Early-stage HER2-positive breast cancer patients who achieve pathological complete response (pCR) after neoadjuvant therapy generally experience favorable survival outcomes. However, only 40-60% of HER2-positive patients achieve pCR. This study developed a deep learning model using histopathology images to predict neoadjuvant therapy response in HER2-positive breast cancer across different regimens, aiming to guide personalized treatment choices. Methods: In this multi-centered retrospective study, we recruited 402 HER2-positive breast cancer patients from four hospitals: 320 patients from two centers were divided into the discovery cohort (n=223) and internal testing cohort (n=97), while 82 patients from the other two centers served as external validation cohorts (n=21 and n=61). We collected pre-treatment H&E-stained histopathology images, clinical information, neoadjuvant treatment regimens, and established a pCR prediction model, named Histomics-Clinic-Regimen Integrated pCR Prediction Model (HIPPM), which integrates CAMEL2 and FCNN approaches. Model performance was evaluated using area under the curve (AUC), sensitivity (SE), specificity (SP), and accuracy. Additionally, we utilized the weight matrix from the first fully connected layer to assess the relative importance of each clinical variable. Results: The initial model based solely on histopathology images demonstrated the following performance: internal testing cohort (AUC 0.71, SE 0.73, SP 0.70, accuracy 70.6%), external validation cohort 1 (AUC 0.95, SE 1.00, SP 0.94, accuracy 95.2%), and validation cohort 2 (AUC 0.61, SE 0.37, SP 0.85, accuracy 53.3%). After integrating clinical and regimen data into the HIPPM model, performance improved significantly: internal testing cohort (AUC 0.85, SE 0.73, SP 0.91, accuracy 88.2%), external validation cohort 1 (AUC 0.94, SE 1.00, SP 0.94, accuracy 95.2%), and validation cohort 2 (AUC 0.74, SE 0.82, SP 0.67, accuracy 73.3%). Model analysis revealed that T stage, HER2 expression, N stage, and targeted therapy regimen had the highest weights, while Ki67 expression, age, ER expression, and chemotherapy regimen had lower weights. Based on HIPPM, we developed a predictive tool that allows patients to upload biopsy images and clinical information pre-treatment to predict pCR probability for 12 virtual regimens and recommend the optimal drug combination. Conclusions: HIPPM effectively predicts neoadjuvant therapy response in HER2-positive breast cancer, aiding in selecting optimal targeted and chemotherapeutic regimens. This model lays the foundation for patient screening and personalized treatment strategies.

Introduction: Triple negative breast cancer (TNBC) patients exhibit varied levels of response to neoadjuvant chemotherapy (NAC), with only 27-51% of patients achieving pathological complete response (pCR). This diverse response can be attributed in-part to heterogeneity of the tumor microenvironment, affecting therapeutic delivery and efficacy. Multiparametric magnetic resonance imaging (MRI) can be used to spatially resolve intratumoral heterogeneity into distinct tumor subregions, or habitats. We investigated whether MRI-derived tumor habitats identified prior to initiation of NAC were predictive of pathological response in TNBC patients. Methods: Women with stage II/III TNBC who received a pre-treatment (baseline) breast MRI and NAC at our institution (2012-2019) were retrospectively identified. Pathological response was determined at surgery, with pCR defined as no residual tumor within the breast or lymph nodes. Both diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI data were collected prior to initiation of NAC. The apparent diffusion coefficient (a measure of cell density) was calculated for each voxel from DW-MRI. Signal enhancement ratio, percent enhancement, and wash-out slope were calculated for each voxel from DCE-MRI, providing measures of vascularity. Hierarchical clustering of voxel data was used to identify tumor habitats, with each subregion labeled in terms of “high” or “low” vascularity and cellularity based on mean parameter values for the subregion. Tumor composition was quantified as percent tumor volume comprised by each habitat. Differences between pCR and non-pCR patients were assessed using Wilcoxon rank sum test, with p<0.05 considered significant. Results: 46 women with TNBC were retrospectively identified (median age: 48, range 31-77 yrs), of which 14 (30%) achieved pCR. No significant differences between pCR and non-pCR patients were observed in baseline tumor volume or longest diameter (p>0.05). Clustering analysis yielded four tumor habitats: low-vascularity low-cellularity (LV-LC), low-vascularity high-cellularity (LV-HC), high-vascularity low-cellularity (HV-LC), and high-vascularity high-cellularity (HV-HC). Patients who achieved pCR had significantly higher fraction of the HV-HC habitat at baseline (p=0.02). No significant differences were observed for other habitats. Discussion & Conclusion: Our findings suggest multiparametric MRI can identify physiologically-distinct tumor habitats prior to NAC, which are predictive of response. A higher fraction of HV-HC habitat was associated with pCR, potentially suggestive of increased therapeutic delivery/sensitivity. Clinical translation of this approach would enable more specific characterizations of tumor heterogeneity and prediction of response, which could aid in personalizing regimens for optimal outcomes. Citation Format: Anum S. Kazerouni, Laura C. Kennedy, Shaveta Vinayak, Suzanne Dintzis, Habib Rahbar, Savannah C. Partridge. Identification of pre-treatment tumor habitats for the prediction of neoadjuvant therapy response in triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5980.

DNA sensing and immune responses in cancer therapy

BackgroundCCR5 is a chemokine receptor involved in immune regulation and tumor progression. Its role in predicting therapy response in breast cancer remains unclear.MethodsWe evaluated CCR5 protein expression in a clinical cohort of 66 breast cancer patients treated with NAC, assessing its association with pathological complete response (pCR). Prognostic relevance was validated in the Kaplan–Meier Plotter database. We further investigated CCR5’s predictive value in a cohort receiving chemo-immunotherapy (GSE173839). Immune-related transcriptional features were assessed via GSEA and deconvolution analysis. The structural impact of the V131I CCR5 variant was explored using AlphaFold modeling, molecular dynamics simulations, and AI-based protein stability prediction.ResultsHigh CCR5 expression was associated with reduced pCR rates in the NAC cohort (OR = 0.06, P = 0.012) and with poorer RFS, particularly in HER2-negative subtypes (P = 0.009). In contrast, CCR5-high tumors in the chemo-immunotherapy cohort exhibited significantly higher pCR rates (OR = 2.5, P = 0.046), suggesting a suppressed yet immune-infiltrated microenvironment potentially responsive to immune reactivation. GSEA analysis and immune cell infiltration profiling indicate a coexistence of immune activation and immunosuppression. Structural modeling of the V131I variant suggested increased conformational flexibility and reduced stability of CCR5, implying a potential sensitivity to subtle structural perturbations.ConclusionOur study supports a dual regulatory hypothesis, in which CCR5 expression may influence immune dynamics and therapeutic response, while its structural stability may serve as a potential modulatory factor. This hypothesis-generating observation suggests that CCR5 could represent a potential prognostic and predictive biomarker, particularly in NAC-refractory or immune-inflamed breast cancers.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12672-025-04189-1.

The Role of [18F]FDG PET/CT in Monitoring of Therapy Response in Lung Cancer.

Pancreatic cancer is the third leading cause of cancer-associated deaths in the United States. We have recently demonstrated that endoplasmic reticulum (ER) stress correlates with improved survival in human pancreatic cancer patients on gemcitabine therapy. Cancer cell stemness is a significant contributor of poor response to therapy and disease recurrence. We also observed that inducing ER stress can diminish stemness in cancer cells. Hence, to investigate the mechanism of ER stress-mediated alterations in diminishing cancer stemness, we investigated the impact of ER stress on transcriptomic alterations by performing RNAseq studies. We subjected cancer cells to treatment with thapsigargin and the harvested mRNA was utilized for RNAseq analysis. We observed significant alterations in mRNA expression levels of the stemness-associated gene set, which also showed significant enrichment in Ingenuity pathway analysis. Hence, our studies demonstrate that ER stress regulates stemness at the transcriptional level. Our ongoing and future studies will target individual genes/pathways to identify their relative contributions to ER stress-mediated regulation of stemness. Considering the role of stemness in disease recurrence, our studies will provide novel mechanistic insights that may lead to novel therapies for targeting disease recurrence in pancreatic cancer. Citation Format: Appolinaire Olou, Kamiya Mehla, Pankaj Singh. Endoplasmic reticulum stress disrupts stemness-related transcriptional regulatory network: Implication for therapy response in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1160.

PurposeThis study aimed to develop an early predictive model for neoadjuvant therapy (NAT) response in breast cancer by integrating multimodal ultrasound (conventional B-mode, shear-wave elastography, and contrast-enhanced ultrasound) and radiomics with clinical-pathological data, and to evaluate its predictive accuracy after two cycles of NAT.MethodsThis retrospective study included 239 breast cancer patients receiving neoadjuvant therapy, divided into training (n = 167) and validation (n = 72) cohorts. Multimodal ultrasound—B-mode, shear-wave elastography (SWE), and contrast-enhanced ultrasound (CEUS)—was performed at baseline and after two cycles. Tumors were segmented using a U-Net-based deep learning model with radiologist adjustment, and radiomic features were extracted via PyRadiomics. Candidate variables were screened using univariate analysis and multicollinearity checks, followed by LASSO and stepwise logistic regression to build three models: a clinical-ultrasound model, a radiomics-only model, and a combined model. Model performance for early response prediction was assessed using ROC analysis.ResultsIn the training cohort (n = 167), Model_Clinic achieved an AUC of 0.85, with HER2 positivity, maximum tumor stiffness (Emax), stiffness heterogeneity (Estd), and the CEUS “radiation sign” emerging as independent predictors (all P < 0.05). The radiomics model showed moderate performance at baseline (AUC 0.69) but improved after two cycles (AUC 0.83), and a model using radiomic feature changes achieved an AUC of 0.79. Model_Combined demonstrated the best performance with a training AUC of 0.91 (sensitivity 89.4%, specificity 82.9%). In the validation cohort (n = 72), all models showed comparable AUCs (Model_Combined ~ 0.90) without significant degradation, and Model_Combined significantly outperformed Model_Clinic and Model_RSA (DeLong P = 0.006 and 0.042, respectively).ConclusionIn our study, integrating multimodal ultrasound and radiomic features improved the early prediction of NAT response in breast cancer, and could provide valuable information to enable timely treatment adjustments and more personalized management strategies.

Manganese-based nanoadjuvants for the synergistic enhancement of immune responses in breast cancer therapy via disulfidptosis-induced ICD and cGAS-STING activation.

Protein phosphatase 2A (PP2A) dephosphorylates majority of Ser/Thr phosphorylated proteins. Consequently, PP2A is an antagonist of multiple oncogenic pathways and PP2A reactivation may provide an alternative route to target these pathways. Importantly, because PP2A reactivation would result in simultaneous dephosphorylation of both collateral and downstream effectors of kinase pathways, it might circumvent commonly encountered kinase inhibitor resistance mechanisms. To systematically study the role of PP2A in cancer therapy response we used RNAi targeting against PP2A inhibitor proteins CIP2A, PME-1 and SET (PP2A reactivation), and PP2A structural subunits (PP2A inhibition), together with high throughput drug sensitivity screen covering 300 clinical cancer drugs and investigational compounds. Changes in phosphorylation status of PP2A targets in response to RNAi perturbation, was studied by LC-MS/MS-based label-free quantitative phosphoproteomics analysis. Importantly, we show that cancer cell drug sensitivity across 300 compounds correlates with PP2A activity profile so that PP2A inhibition made cells on average resistant to therapies, whereas their sensitization by PP2A inhibitor protein siRNAs correlated with changes in phosphoprotein regulation. In particular, cancer cell response to kinase inhibitors followed very closely PP2A activity regulation and PP2A reactivation resulted in increased average sensitivity to 105 kinase inhibitors. Furthermore, we show that PP2A reactivation results in convergent phosphorylation patterns with targeting of kinase pathways by RAS inhibition. We also identify novel PP2A regulated phosphorylation sites in target proteins of these kinases. As a validation study, we demonstrate that PP2A inhibitor protein PME-1 mediates resistance of glioma cells to various types of survival pathway kinase inhibitors, as well as to temozolomide. Co-treatment with PME-1 siRNA and kinase inhibitors eradicates several GBM cell lines, and glioma stem cells, in vitro, and this combination strategy shows pronounced efficacy also in in vivo models. The PME-1-elicited resistance mechanism is mediated by reactivation of specific PP2A complexes and involves regulation of PP2A target HDAC4. Together our data combines 4500 drug response profiles, and more than 5000 quantitative phosphopeptide idenitifications to draw first systemic map of relevance of PP2A biology in cancer therapy responses. In particular the data reveals an unprecedented importance of PP2A activity for kinase inhibitor responses. Based on these data we propose that in order to efficiently inhibit phosphorylation-dependent signaling in cancer cells, and thus provide better therapeutic index for the kinase inhibitors, they should be combined with emerging small molecule PP2A reactivating compounds. Citation Format: Otto Kauko, Susumu Imanishi, Amanpreet Kaur, Daniel Laajala, Evgeny Kulesskiy, Mikael Jumppanen, Garry Corthals, Tero Aittokallio, Krister Wennerberg, Jukka Westermarck. Protein phosphatase 2A activity is a major determinant of therapy response in cancer cells. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5328. doi:10.1158/1538-7445.AM2015-5328

Molecular targeted tyrosine kinase inhibitors (TKIs) have produced unprecedented treatment response in cancer therapy for patients harboring specific oncogenic mutations. While the TKIs are mostly well tolerated, they were reported to increase serum levels of creatine kinase (CK) and cause muscle metabolism‐related toxicity. CK is an essential enzyme involved in cellular energy metabolism and muscle function. Elevated serum CK levels can arise from both physiological and pathological factors, as well as triggered by specific drug classes. The incidence of serum CK elevation induced by a few approved TKIs (brigatinib, binimetinib, cobimetinib‐vemurafenib combination [Food and Drug Administration, United States]; aumolertinib, and sunvozertinib [only approved by National Medical Products Administration, China]) were over 35%. CK elevation‐related symptoms include myopathy, myositis, inclusion body myositis (IBM), cardiotoxicity, rhabdomyolysis, rash, and acneiform dermatitis. High‐level or severe symptomatic CK elevation may necessitate dose reduction and indirectly dampen TKI efficacy. This review presents an updated summary about the prevalence rate and recent research about mechanisms leading to TKI‐induced serum CK elevation in cancer patients. The utility of monitoring serum CK levels for predicting TKI‐induced adverse effects and their management will also be discussed.

Cellular stress responses in cancer and cancer therapy.

BackgroundLung cancer is the leading cause of cancer death worldwide. With the growing number of targeted therapies and the introduction of immuno-oncology (IO), personalized medicine has become standard of care in patients with metastatic disease. The development of predictive and prognostic biomarkers is of great importance. Mutational signatures harbor potential clinical value as predictors of therapy response in cancer. Here we set out to investigate particular mutational processes by assessing mutational signatures and associations with clinical features, tumor mutational burden (TMB) and targetable mutations.MethodsIn this retrospective study, we studied tumor DNA from patients with non-small cell lung cancer (NSCLC) irrespective of stage. The samples were sequenced using a 2 megabase (Mb) gene panel. On each sample TMB was determined and defined as the total number of single nucleotide mutations per Mb (mut/Mb) including non-synonymous mutations. Mutational signature profiling was performed on tumor samples in which at least 30 somatic single base substitutions (SBS) were detected.ResultsIn total 195 samples were sequenced. Median total TMB was 10.3 mut/Mb (range 0–109.3). Mutational signatures were evaluated in 76 tumor samples (39%; median TMB 15.2 mut/Mb). SBS signature 4 (SBS4), associated with tobacco smoking, was prominently present in 25 of 76 samples (33%). SBS2 and/or SBS13, both associated with activity of the AID/APOBEC family of cytidine deaminases, were observed in 11 of 76 samples (14%). SBS4 was significantly more present in early stages (I and II) versus advanced stages (III and IV; P = .005).ConclusionIn a large proportion of NSCLC patients tissue panel sequencing with a 2 Mb panel can be used to determine the mutational signatures. In general, mutational signature SBS4 was more often found in early versus advanced stages of NSCLC. Further studies are needed to determine the clinical utility of mutational signature analyses.

Background: T-cell immune checkpoint inhibitor (ICI) therapies, such as anti-PD1 and anti-CTLA4, have revolutionized cancer treatment, offering durable responses for select patients. However, 75-85% of patients experience only transient responses or outright resistance. Enhancing the curative potential of ICIs has been a major focus for both academia and the biopharma industry over the past decade. Despite significant investments in drug discovery programs and clinical trials to develop new agents and novel combinations with ICI, most efforts have not succeeded in improving long-term response rates. Challenges: Our current approaches lack a precise understanding of which specific resistance mechanism of action (MOA) is operative in which specific subset of patients. Moreover, the application of precision based approaches to immune therapy is significantly limited by a static view of tumor-immune interactions or is overly reliant on preclinical models that fail to recapitulate the dynamic complexity of the human immune system. Hence, there remains a critical gap in understanding (1) how to identify patients, as early as possible, who will not benefit from their current therapies, (2) which druggable mechanisms underpin resistance in those patients and (3) how to match the right patients to the right therapeutics targeting specific resistance mechanisms. Insights: The immune system operates as a complex adaptive network, exhibiting non-linear responses to perturbations like ICI therapy. Notably, mechanisms driving resistance often emerge only after treatment initiation. This underscores the need for a dynamic precision medicine approach that accounts for the functional changes occurring in real-time during therapy. Approach: Our "Dynamic Precision™" approach leverages longitudinal clinical and molecular data from real-world cancer patients undergoing ICI treatment. By analyzing changes over time rather than static snapshots, we identify emergent signatures predictive of treatment resistance. This functional precision strategy uncovers dynamically regulated nodes within the immune system, offering new avenues for therapeutic intervention. Furthermore, these insights are linked with blood-based patient selection biomarkers, providing a minimally invasive method for patient stratification and monitoring. Conclusion: This dynamic approach to functional precision medicine bridges the gap between genomic insights and real-time adaptive responses in cancer therapy. By shifting from a static to a dynamic framework, we reveal novel, actionable targets for enhancing immunotherapy efficacy. We will present proof-of-concept data illustrating how this strategy refines precision treatment paradigms, ultimately contributing to the development of next-generation cancer therapies. Citation Format: Ronan O'Hagan, Lynda Chin. A dynamic approach to functional precision medicine: Developing next-generation immunotherapy to overcome treatment resistance [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr A043.