Tao, Yu

ABSTRACT Angioimmunoblastic T‐cell lymphoma (AITL) is an aggressive peripheral T‐cell lymphoma with poor outcomes, and the role of autologous stem cell transplantation (ASCT) as consolidation after frontline therapy remains controversial. We conducted a retrospective cohort study using the National Cancer Database, including adults diagnosed with AITL between 2004 and 2020 who received frontline systemic therapy. Patients were categorized as chemotherapy alone (Chemo) or chemotherapy followed by ASCT (Chemo + ASCT). To mitigate immortal time bias, prespecified landmark analyses were performed, with a 6‐month landmark analysis as the primary approach and a 9‐month landmark analysis as a sensitivity analysis. Multivariable Cox regression and propensity score weighting using inverse probability of treatment weighting for the average treatment effect were used to address measured confounding. Among 3996 patients receiving systemic therapy, 686 (17.2%) underwent ASCT. In the 6‐month landmark analysis, ASCT was associated with improved overall survival (HR, 0.53; 95% CI, 0.45–0.62; p < 0.001). Findings were consistent in the 9‐month landmark analysis (HR, 0.58; 95% CI, 0.49–0.69; p < 0.001) and IPTW‐adjusted model (HR, 0.51; 95% CI, 0.44–0.60; p < 0.001). In this large real‐world cohort, ASCT consolidation was consistently associated with improved survival in patients with AITL. Although treatment‐response data were unavailable and residual confounding cannot be excluded, these findings provide supportive real‐world evidence and warrant prospective studies with treatment‐timing and response data to better define patient selection.

Abstract Purpose: Standard care for high-grade gliomas (HGG) involves maximal surgical resection followed by radiation and temozolomide. Postoperative adjuvant therapy frequently causes lymphopenia, which is associated with poor prognosis. Interleukin 7 (IL7) is essential for lymphocyte development, homeostasis, and survival. NT-I7 (efineptakin alfa), a long-acting recombinant IL7, reverses lymphopenia and improves survival in murine glioma models. However, the safety, maximum tolerated dose (MTD), and impact of NT-I7 on immune cells in patients with HGG remain unknown. Patients and Methods: We conducted a phase I trial (NCT03687957) examining the MTD and effect of NT-I7 on lymphocytes in patients with newly diagnosed HGG. The primary endpoint was dose-limiting toxicity; secondary endpoints included absolute lymphocyte count (ALC) changes over time, overall response, progression-free survival, and overall survival. Exploratory endpoints included immune profiling at different time points using single-cell RNA sequencing (scRNA-seq) in a subset of patients. Results: NT-I7 was well tolerated with a MTD of 720 µg/kg. Moreover, NT-I7 significantly increased ALCs for more than 12 weeks in duration. Early elevations in CD4+, CD8+ T cells and NK cells further coincided with increased TNF and CXCL9 cytokine levels. Comprehensive immune profiling of peripheral blood T cells revealed selective clonotype expansion within CD8+, but not CD4+, T cells following NT-I7 administration. Finally, a subset of our patients with MGMT promoter–unmethylated glioblastoma, which are typically associated with a poorer prognosis, demonstrated promising clinical responses. Conclusions: NT-I7 has the potential to maintain and increase lymphocyte counts in patients with HGG and warrants further investigation, particularly in combination with immune-based therapies.

Abstract Background: Estrogen receptors are over-expressed in around 70% of breast cancer cases. The genetic changes that occur during aromatase inhibitor (AI) treatment are not well understood and may differ depending upon the patient's response phenotype. Methods: We performed whole genome sequencing (WGS) of matched blood, pre-treatment, and post-treatment biopsy samples from 22 estrogen receptor positive breast cancer patients treated with neoadjuvant aromatase inhibitors. For 5 cases, we performed the whole genome sequencing (WGS) on patients’ matched normal, two pre AI-treatment, and two post AI-treatment DNA isolates from biopsy samples. We validated all putative coding and non-coding somatic mutations using deep sequencing. By comparing the validated somatic mutations from pre- and post- AI treatment biopsy samples, we were able to determine the alterations in the tumor genomes. In every case we defined the clonal architecture of each pair of pre-treatment and post-treatment biopsy samples by comparing the variant allele frequencies from thousands of validated somatic mutations. Results: Comparisons of the two pre AI-treatment biopsy samples from the same patient indicates that the variant allele frequencies of mutations showed high concordances in all 5 cases, 0.74 to 0.95 range of correlation coefficient. Only a small percentage of somatic mutations were detected in one pre-treatment sample and not the other (4.65% overall). In comparing the somatic variations between pre-treatment and matched post-treatment biopsy samples in 22 cases, we found that patients with good clinical response to AI treatment retained known driver mutations only in their pre-treatment tumors. Conversely, those patients with poor clinical response presented new driver mutations in their post-treatment samples. Furthermore, the variant allele frequency for most mutated genes decreased in post AI treatment samples for patients with good AI treatment response; on the contrary, the variant allele frequency increased for patients with poor clinical response. Conclusions: From WGS of matched normal, pre-treatment, and post-treatment biopsy samples, we identified new driver genes mutated in patients with poor clinical response, while patients with good clinical response had lost mutated driver genes in their post-treatment biopsy samples. The genetic landscape revealed by WGS of pre-treatment and post-treatment biopsy samples reveals mutational repertoires are remodeled by AI therapy. This finding suggests deep sequencing of AI treated samples will be necessary to reveal the complete complement of mutations present in a patient's tumor. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-423. doi:1538-7445.AM2012-LB-423