ABSTRACT
Patients diagnosed with triple negative breast cancer (TNBC) have an increased risk of rapid metastasis compared to other subtypes. Predicting long-term survival post-chemotherapy in patients with TNBC is difficult, yet enhanced infiltration of tumor infiltrating lymphocytes (TILs) has been associated with therapeutic response and reduced risk of metastatic relapse. Immune biomarkers that predict the immune state of a tumor and risk of metastatic relapse pre- or mid-neoadjuvant chemotherapy are urgently needed to allow earlier implementation of alternate therapies that may reduce TNBC patient mortality. Utilizing a neoadjuvant chemotherapy trial where TNBC patients had sequential biopsies taken, we demonstrate that measurement of T-cell subsets and effector function, specifically CD45RO expression, throughout chemotherapy predicts risk of metastatic relapse. Furthermore, we identified the tumor inherent interferon regulatory factor IRF9 as a marker of active intratumoral type I and II interferon (IFN) signaling and reduced risk of distant relapse. Functional implications of tumor intrinsic IFN signaling were demonstrated using an immunocompetent mouse model of TNBC, where enhanced type I IFN signaling increased anti-tumor immunity and metastasis-free survival post-chemotherapy. Using two independent adjuvant cohorts we were able to validate loss of IRF9 as a poor prognostic biomarker pre-chemotherapy. Thus, IRF9 expression may offer early insight into TNBC patient prognosis and tumor heat, allowing for identification of patients that are unlikely to respond to chemotherapy alone and could benefit from further immune-based therapeutic intervention.
ABSTRACT
BACKGROUND: Diagnostic accuracy of lymphoma, a heterogeneous cancer, is essential for patient management. Several ancillary tests including immunophenotyping, and sometimes cytogenetics and PCR are required to aid histological diagnosis. In this proof of principle study, gene expression microarray was evaluated as a single platform test in the differential diagnosis of common lymphoma subtypes and reactive lymphadenopathy (RL) in lymph node biopsies. METHODS: 116 lymph node biopsies diagnosed as RL, classical Hodgkin lymphoma (cHL), diffuse large B cell lymphoma (DLBCL) or follicular lymphoma (FL) were assayed by mRNA microarray. Three supervised classification strategies (global multi-class, local binary-class and global binary-class classifications) using diagonal linear discriminant analysis was performed on training sets of array data and the classification error rates calculated by leave one out cross-validation. The independent error rate was then evaluated by testing the identified gene classifiers on an independent (test) set of array data. RESULTS: The binary classifications provided prediction accuracies, between a subtype of interest and the remaining samples, of 88.5%, 82.8%, 82.8% and 80.0% for FL, cHL, DLBCL, and RL respectively. Identified gene classifiers include LIM domain only-2 (LMO2), Chemokine (C-C motif) ligand 22 (CCL22) and Cyclin-dependent kinase inhibitor-3 (CDK3) specifically for FL, cHL and DLBCL subtypes respectively. CONCLUSIONS: This study highlights the ability of gene expression profiling to distinguish lymphoma from reactive conditions and classify the major subtypes of lymphoma in a diagnostic setting. A cost-effective single platform "mini-chip" assay could, in principle, be developed to aid the quick diagnosis of lymph node biopsies with the potential to incorporate other pathological entities into such an assay.
