DCE-MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: pilot study findings.

PURPOSE: The purpose of this pilot study is to determine (1) if early changes in both semiquantitative and quantitative DCE-MRI parameters, observed after the first cycle of neoadjuvant chemotherapy in breast cancer patients, show significant difference between responders and nonresponders and (2) if these parameters can be used as a prognostic indicator of the eventual response. METHODS: Twenty-eight patients were examined using DCE-MRI pre-, post-one cycle, and just prior to surgery. The semiquantitative parameters included longest dimension, tumor volume, initial area under the curve, and signal enhancement ratio related parameters, while quantitative parameters included K(trans), v(e), k(ep), v(p), and τ(i) estimated using the standard Tofts-Kety, extended Tofts-Kety, and fast exchange regime models. RESULTS: Our preliminary results indicated that the signal enhancement ratio washout volume and k(ep) were significantly different between pathologic complete responders from nonresponders (P < 0.05) after a single cycle of chemotherapy. Receiver operator characteristic analysis showed that the AUC of the signal enhancement ratio washout volume was 0.75, and the AUCs of k(ep) estimated by three models were 0.78, 0.76, and 0.73, respectively. CONCLUSION: In summary, the signal enhancement ratio washout volume and k(ep) appear to predict breast cancer response after one cycle of neoadjuvant chemotherapy. This observation should be confirmed with additional prospective studies.

A Validated T Cell Radiomics Score Is Associated With Clinical Outcomes Following Multisite SBRT and Pembrolizumab.

PURPOSE: Combining immune checkpoint blockade (ICB) with stereotactic body radiation therapy (SBRT) may improve the local response to radiation and the systemic response to immunotherapy. However, no prognostic markers exist to identify patients likely to benefit from combined therapy. The degree of T cell-mediated immunity, which can be quantified with radiomics using computed tomography (CT) imaging, is predictive of immunotherapy response. Herein we investigated whether a validated T cell radiomics score (RS) is correlated with clinical outcomes after multisite SBRT and pembrolizumab (SBRT + P). METHODS AND MATERIALS: The RS was quantified for 68 patients with metastatic treatment-refractory adult solid tumors who received SBRT (30-50 Gy, 3-5 fractions) and pembrolizumab ≤7 days after SBRT. RS was calculated using 8 variables, including 5 radiomics features extracted from pretreatment CT scans. At a prespecified cutoff of the 25th percentile, we assessed the association between RS and clinical outcomes. The Kaplan-Meier method was used to estimate survival outcomes. The prognostic effect of RS was assessed via logistic regression or Cox proportional hazards models. In an exploratory analysis, RS was also analyzed as a continuous variable. RESULTS: One hundred thirty-nine tumors were analyzed. At the 25th percentile cutoff, high-RS patients were more likely to exhibit irradiated tumor responses to SBRT + P (odds ratio [OR] 10.2; 95% confidence interval [CI], 1.76-59.17; P = .012). High-RS was associated with improved TMC compared with low-RS tumors (hazard ratio [HR] 0.18; 95% CI, 0.04-0.74; P = .018). Furthermore, high-RS patients had improved PFS (HR 0.47, 95% CI, 0.26-0.85; P = .013) and OS (HR 0.39, 95% CI, 0.20-0.75; P = .005). As a continuous variable, higher RS was associated with improved PFS (HR 0.12, 95% CI, 0.03-0.51; P = .004) but did not reach statistical significance for TMC (HR 0.36, 95% CI, 0.02-7.02; P = .502) or OS (HR 0.28, 95% CI, 0.05-1.55; P = .144). CONCLUSIONS: We demonstrated the clinical validity of RS (at the 25th percentile cutoff) as a prognostic biomarker in patients treated with SBRT + P. Future validation of the prognostic value of RS in larger similarly treated patient cohorts is warranted.

Improved Survival Associated with Local Tumor Response Following Multisite Radiotherapy and Pembrolizumab: Secondary Analysis of a Phase I Trial.

PURPOSE: Multisite stereotactic body radiotherapy followed by pembrolizumab (SBRT+P) has demonstrated safety in advanced solid tumors (ASTs). However, no studies have examined the relationships between irradiated tumor response, SBRT-induced tumor gene expression, and overall survival (OS). PATIENTS AND METHODS: Patients with AST received SBRT (30-50 Gy in 3-5 fractions) to two to four metastases followed by pembrolizumab (200 mg i.v. every 3 weeks). SBRT was prescribed to a maximum tumor volume of 65 mL. Small metastases received the complete prescribed coverage (complete-Rx), while larger metastases received partial coverage (partial-Rx). Treated metastasis control (TMC) was defined as a lack of progression for an irradiated metastasis. Landmark analysis was used to assess the relationship between TMC and OS. Thirty-five biopsies were obtained from 24 patients: 19 pre-SBRT and 16 post-SBRT (11 matched) prior to pembrolizumab and were analyzed via RNA microarray. RESULTS: Sixty-eight patients (139 metastases) were enrolled with a median follow-up of 10.4 months. One-year TMC was 89.5% with no difference between complete-Rx or partial-Rx. On multivariable analysis, TMC was independently associated with a reduced risk for death (HR, 0.36; 95% confidence interval, 0.17-0.75; P = 0.006). SBRT increased expression of innate and adaptive immune genes and concomitantly decreased expression of cell cycle and DNA repair genes in the irradiated tumors. Elevated post-SBRT expression of DNASE1 correlated with increased expression of cytolytic T-cell genes and irradiated tumor response. CONCLUSIONS: In the context of SBRT+P, TMC independently correlates with OS. SBRT impacts intratumoral immune gene expression associated with TMC. Randomized trials are needed to validate these findings.

Cytosolic phospholipaseA2 inhibition with PLA-695 radiosensitizes tumors in lung cancer animal models.

Lung cancer remains the leading cause of cancer deaths in the United States and the rest of the world. The advent of molecularly directed therapies holds promise for improvement in therapeutic efficacy. Cytosolic phospholipase A2 (cPLA2) is associated with tumor progression and radioresistance in mouse tumor models. Utilizing the cPLA2 specific inhibitor PLA-695, we determined if cPLA2 inhibition radiosensitizes non small cell lung cancer (NSCLC) cells and tumors. Treatment with PLA-695 attenuated radiation induced increases of phospho-ERK and phospho-Akt in endothelial cells. NSCLC cells (LLC and A549) co-cultured with endothelial cells (bEND3 and HUVEC) and pre-treated with PLA-695 showed radiosensitization. PLA-695 in combination with irradiation (IR) significantly reduced migration and proliferation in endothelial cells (HUVEC & bEND3) and induced cell death and attenuated invasion by tumor cells (LLC &A549). In a heterotopic tumor model, the combination of PLA-695 and radiation delayed growth in both LLC and A549 tumors. LLC and A549 tumors treated with a combination of PLA-695 and radiation displayed reduced tumor vasculature. In a dorsal skin fold model of LLC tumors, inhibition of cPLA2 in combination with radiation led to enhanced destruction of tumor blood vessels. The anti-angiogenic effects of PLA-695 and its enhancement of the efficacy of radiotherapy in mouse models of NSCLC suggest that clinical trials for its capacity to improve radiotherapy outcomes are warranted.

Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy.

OBJECTIVE: To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: Quantitative dynamic contrast-enhanced MRI and diffusion-weighted MRI data were acquired on 28 patients before and after one cycle of NAC. A total of 118 semiquantitative and quantitative parameters were derived from these data and combined with 11 clinical variables. We used Bayesian logistic regression in combination with feature selection using a machine learning framework for predictive model building. RESULTS: The best predictive models using feature selection obtained an area under the curve of 0.86 and an accuracy of 0.86, with a sensitivity of 0.88 and a specificity of 0.82. DISCUSSION: With the numerous options for NAC available, development of a method to predict response early in the course of therapy is needed. Unfortunately, by the time most patients are found not to be responding, their disease may no longer be surgically resectable, and this situation could be avoided by the development of techniques to assess response earlier in the treatment regimen. The method outlined here is one possible solution to this important clinical problem. CONCLUSIONS: Predictive modeling approaches based on machine learning using readily available clinical and quantitative MRI data show promise in distinguishing breast cancer responders from non-responders after the first cycle of NAC.

High-throughput identification of putative receptors for cancer-binding peptides using biopanning and microarray analysis.

Phage-display peptide biopanning has been successfully used to identify cancer-targeting peptides in multiple models. For cancer-binding peptides, identification of the peptide receptor is necessary to demonstrate the mechanism of action and to further optimize specificity and target binding. The process of receptor identification can be slow and some peptides may turn out to bind ubiquitous proteins not suitable for further drug development. In this report, we describe a high-throughput method for screening a large number of peptides in parallel to identify peptide receptors, which we have termed "reverse biopanning." Peptides can then be selected for further development based on their receptor. To demonstrate this method, we screened a library of 39 peptides previously identified in our laboratory to bind specifically to cancers after irradiation. The reverse biopanning process identified 2 peptides, RKFLMTTRYSRV and KTAKKNVFFCSV, as candidate ligands for the protein tax interacting protein 1 (TIP-1), a protein previously identified in our laboratory to be expressed in tumors and upregulated after exposure to ionizing radiation. We used computational modeling as the initial method for rapid validation of peptide-TIP-1 binding. Pseudo-binding energies were calculated to be -360.645 kcal mol(-1), -487.239 kcal mol(-1), and -595.328 kcal mol(-1) for HVGGSSV, TTRYSRV, and NVFFCSV respectively, suggesting that the peptides would have at least similar, if not stronger, binding to TIP-1 compared to the known TIP-1 binding peptide HVGGSSV. We validated peptide binding in vitro using electrophoretic mobility shift assay, which showed strong binding of RKFLMTTRYSRV and the truncated form TTRYSRV. This method allows for the identification of many peptide receptors and subsequent selection of peptides for further drug development based on the peptide receptor.

Autotaxin Inhibition with PF-8380 Enhances the Radiosensitivity of Human and Murine Glioblastoma Cell Lines.

PURPOSE: Glioblastoma multiforme (GBM) is an aggressive primary brain tumor that is radio-resistant and recurs despite aggressive surgery, chemo, and radiotherapy. Autotaxin (ATX) is over expressed in various cancers including GBM and is implicated in tumor progression, invasion, and angiogenesis. Using the ATX specific inhibitor, PF-8380, we studied ATX as a potential target to enhance radiosensitivity in GBM. METHODS AND MATERIALS: Mouse GL261 and Human U87-MG cells were used as GBM cell models. Clonogenic survival assays and tumor transwell invasion assays were performed using PF-8380 to evaluate role of ATX in survival and invasion. Radiation dependent activation of Akt was analyzed by immunoblotting. Tumor induced angiogenesis was studied using the dorsal skin fold model in GL261. Heterotopic mouse GL261 tumors were used to evaluate the efficacy of PF-8380 as a radiosensitizer. RESULTS: Pre-treatment of GL261 and U87-MG cells with 1 µM PF-8380 followed by 4 Gy irradiation resulted in decreased clonogenic survival, decreased migration (33% in GL261; P = 0.002 and 17.9% in U87-MG; P = 0.012), decreased invasion (35.6% in GL261; P = 0.0037 and 31.8% in U87-MG; P = 0.002), and attenuated radiation-induced Akt phosphorylation. In the tumor window model, inhibition of ATX abrogated radiation induced tumor neovascularization (65%; P = 0.011). In a heterotopic mouse GL261 tumors untreated mice took 11.2 days to reach a tumor volume of 7000 mm(3), however combination of PF-8380 (10 mg/kg) with irradiation (five fractions of 2 Gy) took more than 32 days to reach a tumor volume of 7000 mm(3). CONCLUSION: Inhibition of ATX by PF-8380 led to decreased invasion and enhanced radiosensitization of GBM cells. Radiation-induced activation of Akt was abrogated by inhibition of ATX. Furthermore, inhibition of ATX led to diminished tumor vascularity and delayed tumor growth. These results suggest that inhibition of ATX may ameliorate GBM response to radiotherapy.

GSK-3ß: A Bifunctional Role in Cell Death Pathways.

Although glycogen synthase kinase-3 beta (GSK-3ß) was originally named for its ability to phosphorylate glycogen synthase and regulate glucose metabolism, this multifunctional kinase is presently known to be a key regulator of a wide range of cellular functions. GSK-3ß is involved in modulating a variety of functions including cell signaling, growth metabolism, and various transcription factors that determine the survival or death of the organism. Secondary to the role of GSK-3ß in various diseases including Alzheimer's disease, inflammation, diabetes, and cancer, small molecule inhibitors of GSK-3ß are gaining significant attention. This paper is primarily focused on addressing the bifunctional or conflicting roles of GSK-3ß in both the promotion of cell survival and of apoptosis. GSK-3ß has emerged as an important molecular target for drug development.

Early prediction of the response of breast tumors to neoadjuvant chemotherapy using quantitative MRI and machine learning.

The ability to predict early in the course of treatment the response of breast tumors to neoadjuvant chemotherapy can stratify patients based on response for patient-specific treatment strategies. Currently response to neoadjuvant chemotherapy is evaluated based on physical exam or breast imaging (mammogram, ultrasound or conventional breast MRI). There is a poor correlation among these measurements and with the actual tumor size when measured by the pathologist during definitive surgery. We tested the feasibility of using quantitative MRI as a tool for early prediction of tumor response. Between 2007 and 2010 twenty consecutive patients diagnosed with Stage II/III breast cancer and receiving neoadjuvant chemotherapy were enrolled on a prospective imaging study. Our study showed that quantitative MRI parameters along with routine clinical measures can predict responders from non-responders to neoadjuvant chemotherapy. The best predictive model had an accuracy of 0.9, a positive predictive value of 0.91 and an AUC of 0.96.