Abstract Cytokines and other immune regulatory molecules are critical players in the immune response against cancer. There is growing interest in testing the potential utility of systemic immune biomarkers to track cancer progression and to use them as predictors of effective responses to cancer therapy. The central hypothesis guiding this project is that specific immune biomarkers will serve as predictors of effective vs. ineffective immunotherapy in patients with malignant diseases. The objective of this study was to establish baseline of immune markers in patients already started treatment with immunotherapy (n=10) (T), patients starting, but not yet treated (S) with immunotherapy (n=10) and subjects without diagnosed malignant disease (W) (n=10). Blood was collected and plasma was isolated and used in the biomarker (100 markers) analysis using a protein microarray method (RayBiotech). The biomarkers in the three groups were analyzed by Principal Component Analysis, heat map with clustering, and differential expression based on p value, and Significance Analysis of Microarrays (SAM). Although 15 biomarkers were significantly different between S vs. W groups, based on SAM, only seven were found differentially expressed. Similarly, although 10 biomarkers were significantly different between T vs. W groups, based on SAM, only one biomarker was found differentially expressed. Furthermore, SAM revealed that responders (n=4) vs. stable (n=5) subgroup of patients within the T group exhibited 22 differentially expressed biomarkers. Future larger studies will be needed to evaluate whether immune markers will be able to predict effective vs. ineffective responses to immunotherapy and whether they may have therapeutic potential.
LBA501 Background: I-SPY2.2 is a multicenter phase 2 platform sequential multiple assignment randomized trial (SMART) in the neoadjuvant breast cancer setting that evaluates novel experimental regimens as first in a sequence (Block A) followed by standard chemo/targeted therapies (Blocks B/C) if indicated. The goal is to achieve a pCR after novel targeted agents alone or in sequence with standard therapies, with the optimal therapy assigned based on the tumor response predictive subtype (RPS). RPS incorporates expression-based immune, DNA repair deficiency (DRD), and luminal signatures with hormone receptor (HR) and HER2 status to subset patients into 6 subtypes: S1: HR+HER2-Immune-DRD-; S2: HR-HER2-Immune-DRD-; S3: HER2-Immune+; S4: HER2-Immune-DRD+; S5: HER2+/non-Luminal; S6: HER2+/Luminal. Methods: RPS S1, S2, S3, and S4 were eligible for assignment to Dato+Durva in Block A. Patients were followed by MRI during treatment (at 3, 6, and 12 weeks after start of Blocks A and B). Predicted responders by MRI and biopsy at the end of Block A or B have the option of going to surgery early; otherwise, they proceed to next treatment Block (B +/- C). Randomization to Block B includes a taxane-based regimen specific to the RPS, and options include S1: paclitaxel; S2 and S3: paclitaxel + carboplatin + pembrolizumab; S4: paclitaxel + carboplatin vs. paclitaxel + carboplatin + pembrolizumab. Patients who did not go to surgery after Block B proceeded to Block C (AC or AC + Pembrolizumab if HR-HER2-). The primary endpoint is pCR. Efficacy is evaluated within each RPS and HR+HER2- and HR-HER2- signatures. To estimate the arm's efficacy as a stand-alone therapy, we use a Bayesian covariate-adjusted model to estimate the pCR rate and compare the posterior distribution to a subtype-specific fixed threshold. This model uses pCR data when available and MRI data when pCR is not. To estimate pCR rate in the context of a multi-decision treatment regimen, we use a Bayesian model based on if and when a pCR occurred in the trial. The posterior is compared to a subtype-specific dynamic control generated from historical I-SPY data. Results: 106 patients were randomly assigned to the Dato+Durva arm between September 2022 and August 2023. The results for Dato+Durva as a stand-alone therapy are summarized in Table. After completion of Block A, 36 patients proceeded to surgery without completing Blocks B/C. Conclusions: Dato+Durva meets threshold for graduation within the RPS S3 subtype based on estimated pCR rate of 72% and warrants further investigation in a larger randomized controlled trial. Clinical trial information: NCT01042379 . [Table: see text]
To present the characteristics of the AKT1E117K gene variant and a description of the clinical application in a patient with metastatic breast cancer.63 y/o woman with Stage IV Invasive lobular carcinoma at diagnosis was treated with Palbociclib and aromatase inhibitors (AI). At progression, tissue was sent for comprehensive genomic profiling to Foundation Medicine (FM) which revealed AKT1E17K mutation. In lieu of available clinical data within the patient's tumor type (HR+ HER2- breast cancer), extrapolated data from the Flatiron Health-FM (FH-FMI) Clinico-genomic Database (CGDB) was discussed at our Molecular Tumor Board (MTB). After multidisciplinary discussion, the consensus recommendation was to start treatment with the combination of mTOR inhibitor everolimus, and AI, exemestane. Patient tolerated treatment without major side effects. By the second clinical visit the patient's breast showed signs of improvement. PET/CT showed diminished left axillary uptake, decreased right paratracheal lymph node PET avidity, and stable bone disease consistent with a partial response. The most recent office visit in January 2021, breast exam revealed a normal-appearing skin with only faint erythema. All other skin lesions have resolved. Although, the role of AKT1 variant described here is not well defined and therapeutic significance of M-Tor inhibitors not established in metastatic breast cancers, comprehensive approach to this case unraveled new and successful therapeutic option in this patient.This demonstrates that applying available Precision Medicine tools like MTB and real world data sets from patient populations with similar clinical and genomic profiles may provide more options for treatment.
Background and Objectives: Cytokines and other immune regulatory molecules are critical for mounting an effective immune response against cancer. The gastrointestinal (GI) microbiome plays a significant role in the pathogenesis of cancer and the response to immunotherapy. The central hypothesis guiding this project was that specific immune biomarkers and microbiome profiles will serve as predictors of effective vs. ineffective immunotherapy in patients with malignant diseases. This pilot feasibility study aims to establish baseline immune markers and microbiome profiles in subjects with newly diagnosed malignant solid tumors (n = 10), healthy subjects without diagnosed malignant disease (n = 10), and in existing patients treated with immunotherapy (n = 10). Methods: Parallel blood and stool samples were collected and used in the biomarker and microbiome analysis. The biomarkers in the two groups were analyzed by Principal Component Analysis, heat map with clustering, and differential expression based on P value, and Significance Analysis of Microarrays (SAM). The microbiome analysis was performed using long read 16S rRNA encoding gene sequencing with data visualization and analysis in R. Significant differences in alpha and beta diversity were evaluated between the groups. Results: Several biomarkers that were differentially expressed were identified. Significant taxa differences at the class (Clostridia), order (Clostridiales, Lactobacillillales), family (Eubacteriaceae, Lactobacillaceae), genus and species were identified. Furthermore, a limited analysis of samples from existing patients on immunotherapy who were responders (n = 4) vs. stable non-responders (n = 5) identified differentially expressed immune biomarkers and significant bacterial taxa differences. Conclusion: This study has established the feasibility for conducting a future larger study at the local community cancer center to evaluate whether immune and microbiome markers can predict effective vs. ineffective responses to immunotherapy and whether either or both molecular and microbial markers may have therapeutic potential.
