Multi-Site Clinical Evaluation of DW-MRI as a Treatment Response Metric for Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy

Purpose

To evaluate diffusion weighted MRI (DW-MR) as a response metric for assessment of neoadjuvant chemotherapy (NAC) in patients with primary breast cancer using prospective multi-center trials which provided MR scans along with clinical outcome information.

Materials and Methods

A total of 39 patients with locally advanced breast cancer accrued from three different prospective clinical trials underwent DW-MR examination prior to and at 3–7 days (Hull University), 8–11 days (University of Michigan) and 35 days (NeoCOMICE) post-treatment initiation. Thirteen patients, 12 of which participated in treatment response study, from UM underwent short interval (<1hr) MRI examinations, referred to as “test-retest” for examination of repeatability. To further evaluate stability in ADC measurements, a thermally controlled diffusion phantom was used to assess repeatability of diffusion measurements. MRI sequences included contrast-enhanced T1-weighted, when appropriate, and DW images acquired at b-values of 0 and 800 s/mm². Histogram analysis and a voxel-based analytical technique, the Parametric Response Map (PRM), were used to derive diffusion response metrics for assessment of treatment response prediction.

Results

Mean tumor apparent diffusion coefficient (ADC) values generated from patient test-retest examinations were found to be very reproducible (|ΔADC|<0.1x10^-3mm²/s). This data was used to calculate the 95% CI from the linear fit of tumor voxel ADC pairs of co-registered examinations (±0.45x10^-3mm²/s) for PRM analysis of treatment response. Receiver operating characteristic analysis identified the PRM metric to be predictive of outcome at the 8–11 (AUC = 0.964, p = 0.01) and 35 day (AUC = 0.770, p = 0.05) time points (p<.05) while whole-tumor ADC changes where significant at the later 35 day time interval (AUC = 0.825, p = 0.02).

Conclusion

This study demonstrates the feasibility of performing a prospective analysis of DW-MRI as a predictive biomarker of NAC in breast cancer patients. In addition, we provide experimental evidence supporting the use of sensitive analytical tools, such as PRM, for evaluating ADC measurements.     

Integrated Multimodal Imaging of Dynamic Bone-Tumor Alterations Associated with Metastatic Prostate Cancer

Bone metastasis occurs for men with advanced prostate cancer which promotes osseous growth and destruction driven by alterations in osteoblast and osteoclast homeostasis. Patients can experience pain, spontaneous fractures and morbidity eroding overall quality of life. The complex and dynamic cellular interactions within the bone microenvironment limit current treatment options thus prostate to bone metastases remains incurable. This study uses voxel-based analysis of diffusion-weighted MRI and CT scans to simultaneously evaluate temporal changes in normal bone homeostasis along with prostate bone metatastsis to deliver an improved understanding of the spatiotemporal local microenvironment. Dynamic tumor-stromal interactions were assessed during treatment in mouse models along with a pilot prospective clinical trial with metastatic hormone sensitive and castration resistant prostate cancer patients with bone metastases. Longitudinal changes in tumor and bone imaging metrics during delivery of therapy were quantified. Studies revealed that voxel-based parametric response maps (PRM) of DW-MRI and CT scans could be used to quantify and spatially visualize dynamic changes during prostate tumor growth and in response to treatment thereby distinguishing patients with stable disease from those with progressive disease (p<0.05). These studies suggest that PRM imaging biomarkers are useful for detection of the impact of prostate tumor-stromal responses to therapies thus demonstrating the potential of multi-modal PRM image-based biomarkers as a novel means for assessing dynamic alterations associated with metastatic prostate cancer. These results establish an integrated and clinically translatable approach which can be readily implemented for improving the clinical management of patients with metastatic bone disease.

Trial Registration

ClinicalTrials.gov NCT02064283     

Impact of deformable registration methods for prediction of recurrence free survival response to neoadjuvant chemotherapy in breast cancer: Results from the ISPY 1/ACRIN 6657 trial

PurposeImage registration plays a vital role in spatially aligning multiple MRI scans for better longitudinal assessment of tumor morphological features. The objective was to evaluate the effect of registration accuracy of six established deformable registration methods(ANTs, DRAMMS, ART, NiftyReg, SSD-FFD, and NMI-FFD) on the predictive value of extracted radiomic features when modeling recurrence-free-survival(RFS) for women after neoadjuvant chemotherapy(NAC) for locally advanced breast cancer.Methods130 women had DCE-MRI scans available from the first two visits in the ISPY1/ACRIN-6657 cohort. We calculated the transformation field from each of the different deformable registration methods, and used it to compute voxel-wise parametric-response-maps(PRM) for established four kinetic features.104-radiomic features were computed from each PRM map to characterize intra-tumor heterogeneity. We evaluated performance for RFS using Cox-regression, C-statistic, and Kaplan-Meier(KM) plots.ResultsA baseline model(F1:Age, Race, and Hormone-receptor-status) had a 0.54 C-statistic, and model F2(baseline + functional-tumor-volume at early treatment visit(FTV₂)) had 0.63. The F2+ANTs had the highest C-statistic(0.72) with the smallest landmark differences(5.40±4.40mm) as compared to other models. The KM curve for model F2 gave p=0.004 for separation between women above and below the median hazard compared to the model F1(p=0.31). A models augmented with radiomic features, also achieved significant KM curve separation(p<0.001) except the F2+ART model.ConclusionIncorporating image registration in quantifying changes in tumor heterogeneity during NAC can improve prediction of RFS. Radiomic features of PRM maps derived from warping the DCE-MRI kinetic maps using ANTs registration method further improved the early prediction of RFS as compared to other methods.     

A Feasibility Study of Parametric Response Map Analysis of Diffusion-Weighted Magnetic Resonance Imaging Scans of Head and Neck Cancer Patients for Providing Early Detection of Therapeutic Efficacy

The parametric response map (PRM) was evaluated as an early surrogate biomarker for monitoring treatment-induced tissue alterations in patients with head and neck squamous cell carcinoma (HNSCC). Diffusion-weighted magnetic resonance imaging (DW-MRI) was performed on 15 patients with HNSCC at baseline and 3 weeks after treatment initiation of a nonsurgical organ preservation therapy (NSOPT) using concurrent radiation and chemotherapy. PRM was applied on serial apparent diffusion coefficient (ADC) maps that were spatially aligned using a deformable image registration algorithm to measure the tumor volume exhibiting significant changes in ADC (PRM_ADC). Pretherapy and midtherapy ADC maps, quantified from the DWIs, were analyzed by monitoring the percent change in whole-tumor mean ADC and the PRM metric. The prognostic values of percentage change in tumor volume and mean ADC and PRM_ADC as a treatment response biomarker were assessed by correlating with tumor control at 6 months. Pixel-wise differences as part of PRM_ADC analysis revealed regions where water mobility increased. Analysis of the tumor ADC histograms also showed increases in mean ADC as early as 3 weeks into therapy in patients with a favorable outcome. Nevertheless, the percentage change in mean ADC was found to not correlate with tumor control at 6 months. In contrast, significant differences in PRM_ADC and percentage change in tumor volume were observed between patients with pathologically different outcomes. Observations from this study have found that diffusion MRI, when assessed by PRM_ADC, has the potential to provide both prognostic and spatial information during NSOPT of head and neck cancer.     

Comparison of Pathologic Response Evaluation Systems after Anthracycline with/without Taxane-Based Neoadjuvant Chemotherapy among Different Subtypes of Breast Cancers

Purpose

Several methods are used to assess the pathologic response of breast cancer after neoadjuvant chemotherapy (NAC) to predict clinical outcome. However, the clinical utility of these systems for each molecular subtype of breast cancer is unclear. Therefore, we applied six pathologic response assessment systems to specific subtypes of breast cancer and compared the results.

Patients and Methods

Five hundred and eighty eight breast cancer patients treated with anthracycline with/without taxane-based NAC were retrospectively analyzed, and the ypTNM stage, residual cancer burden (RCB), residual disease in breast and nodes (RDBN), tumor response ratio, Sataloff’s classification, and Miller—Payne grading system were evaluated. The results obtained for each assessment system were analyzed in terms of patient survival.

Results

In triple-negative tumors, all systems were significantly associated with disease-free survival and Kaplan-Meier survival curves for disease-free survival were clearly separated by all assessment methods. For HR+/HER2- tumors, systems assessing the residual tumor (ypTNM stage, RCB, and RDBN) had prognostic significance. However, for HER2+ tumors, the association between patient survival and the pathologic response assessment results varied according to the system used, and none resulted in distinct Kaplan—Meier curves.

Conclusion

Most of the currently available pathologic assessment systems used after anthracycline with/without taxane-based NAC effectively classified triple-negative breast cancers into groups showing different prognoses. The pathologic assessment systems evaluating residual tumors only also had prognostic significance in HR+/HER2- tumors. However, new assessment methods are required to effectively evaluate the pathologic response of HR+/HER2+ and HR-/HER2+ tumors to anthracycline with/without taxane-based NAC.     

Impact of Perfusion Map Analysis on Early Survival Prediction Accuracy in Glioma Patients

Studies investigating dynamic susceptibility contrast magnetic resonance imaging-determined relative cerebral blood volume (rCBV) maps as a metric of treatment response assessment have generated conflicting results. We evaluated the potential of various analytical techniques to predict survival of patients with glioma treated with chemoradiation. rCBV maps were acquired in patients with high-grade gliomas at 0, 1, and 3 weeks into chemoradiation therapy. Various analytical techniques were applied to the same cohort of serial rCBV data for early assessment of survival. Three different methodologies were investigated: 1) percentage change of whole tumor statistics (i.e., mean, median, and percentiles), 2) physiological segmentation (low rCBV, medium rCBV, or high rCBV), and 3) a voxel-based approach, parametric response mapping (PRM). All analyses were performed using the same tumor contours, which were determined using contrast-enhanced T1-weighted and fluid attenuated inversion recovery images. The predictive potential of each response metric was assessed at 1-year and overall survival. PRM was the only analytical approach found to generate a response metric significantly predictive of patient 1-year survival. Time of acquisition and contour volume were not found to alter the sensitivity of the PRM approach for predicting overall survival. We have demonstrated the importance of the analytical approach in early response assessment using serial rCBV maps. The PRM analysis shows promise as a unified early and robust imaging biomarker of treatment response in patients diagnosed with high-grade gliomas.     

Monitoring of Tumor Response to Cisplatin Using Optical Spectroscopy

INTRODUCTION: Anatomic imaging alone is often inadequate for tuning systemic treatment for individual tumor response. Optically based techniques could potentially contribute to fast and objective response monitoring in personalized cancer therapy. In the present study, we evaluated the feasibility of dual-modality diffuse reflectance spectroscopy–autofluorescence spectroscopy (DRS-AFS) to monitor the effects of systemic treatment in a mouse model for hereditary breast cancer. METHODS: Brca1^−/−; p53^−/− mammary tumors were grown in 36 mice, half of which were treated with a single dose of cisplatin. Changes in the tumor physiology and morphology were measured for a period of 1 week using dual-modality DRS-AFS. Liver and muscle tissues were also measured to distinguish tumor-specific alterations from systemic changes. Model-based analyses were used to derive different optical parameters like the scattering and absorption coefficients, as well as sources of intrinsic fluorescence. Histopathologic analysis was performed for cross-validation with trends in optically based parameters. RESULTS: Treated tumors showed a significant decrease in Mie-scattering slope and Mie-to-total scattering fraction and an increase in both fat volume fraction and tissue oxygenation after 2 days of follow-up. Additionally, significant tumor-specific changes in the fluorescence spectra were seen. These longitudinal trends were consistent with changes observed in the histopathologic analysis, such as vital tumor content and formation of fibrosis. CONCLUSIONS: This study demonstrates that dual-modality DRS-AFS provides quantitative functional information that corresponds well with the degree of pathologic response. DRS-AFS, in conjunction with other imaging modalities, could be used to optimize systemic cancer treatment on the basis of early individual tumor response.     

Quantitative Imaging to Assess Tumor Response to Therapy: Common Themes of Measurement,Truth Data,and Error Sources

RATIONALE: Early detection of tumor response to therapy is a key goal. Finding measurement algorithms capable of early detection of tumor response could individualize therapy treatment as well as reduce the cost of bringing new drugs to market. On an individual basis, the urgency arises from the desire to prevent continued treatment of the patient with a high-cost and/or high-risk regimen with no demonstrated individual benefit and rapidly switch the patient to an alternative efficacious therapy for that patient. In the context of bringing new drugs to market, such algorithms could demonstrate efficacy in much smaller populations, which would allow phase 3 trials to achieve statistically significant decisions with fewer subjects in shorter trials. MATERIALS AND METHODS: This consensus-based article describes multiple, image modality-independent means to assess the relative performance of algorithms for measuring tumor change in response to therapy. In this setting, we describe specifically the example of measurement of tumor volume change from anatomic imaging as well as provide an overview of other promising generic analytic methods that can be used to assess change in heterogeneous tumors. To support assessment of the relative performance of algorithms for measuring small tumor change, data sources of truth are required. RESULTS: Very short interval clinical imaging examinations and phantom scans provide known truth for comparative evaluation of algorithms. CONCLUSIONS: For a given category of measurement methods, the algorithm that has the smallest measurement noise and least bias on average will perform best in early detection of true tumor change.     

Tumor Cell Identification in Ki-67 Images on Deep Learning

The proportion of cells staining for the nuclear antigen Ki-67 is an important predictive indicator for assessment of tumor cell proliferation and growth in routine pathological investigation. Instead of traditional scoring methods based on the experience of a trained laboratory scientist, deep learning approach can be automatically used to analyze the expression of Ki-67 as well. Deep learning based on convolutional neural networks (CNN) for image classification and single shot multibox detector (SSD) for object detection are used to investigate the expression of Ki-67 for assessment of biopsies from patients with breast cancer in this study. The results focus on estimating the probability heatmap of tumor cells using CNN with accuracy of 98% and detecting the tumor cells using SSD with accuracy of 90%. This deep learning framework will provide an objective basis for the malignant degree of breast tumors and be beneficial to the pathologists for fast and efficiently Ki-67 scoring.     

Response Prediction to Neoadjuvant Chemotherapy: Comparison between Pre-Therapeutic Gene Expression Profiles and In Vitro Chemosensitivity Assay

Although the use of (neo-)adjuvant chemotherapy in breast cancer patients has resulted in improved outcome, not all patients benefit equally. We have evaluated the utility of an in vitro chemosensitivity assay in predicting response to neoadjuvant chemotherapy. Pre-therapeutic biopsies were obtained from 30 breast cancer patients assigned to neoadjuvant epirubicin 75 mg/m2 and docetaxel 75 mg/m2 (Epi/Doc) in a prospectively randomized clinical trial. Biopsies were subjected to a standardized ATP-based Epi/Doc chemosensitivity assay, and to gene expression profiling. Patients then received 3 cycles of chemotherapy, and response was evaluated by changes in tumor diameter and Ki67 expression. The efficacy of Epi/Doc in vitro was correlated with differential changes in tumor cell proliferation in response to Epi/Doc in vivo (p = 0.0011; r = 0.73670, Spearmańs rho), but did not predict for changes in tumor size. While a pre-therapeutic gene expression signature identified tumors with a clinical response to Epi/Doc, no such signature could be found for tumors that responded to Epi/Doc in vitro, or tumors in which Epi/Doc exerted an antiproliferative effect in vivo. This is the first prospective clinical trial to demonstrate the utility of a standardized in vitro chemosensitivity assay in predicting the individual biological response to chemotherapy in breast cancer.     

Prospective assessment of the role of five tumour markers in breast cancer

Summary Retrospective analysis previously identified significant elevation of five tumour markers, carcinoembryonic antigen (CEA), ferritin, orosomucoid,C-reactive protein and erythrocyte sedimentation rate (ESR), in patients with systemic breast cancer and showed that changes in each of these markers individually correlated significantly with therapeutic response. In this study we have prospectively tested these findings. None of the five markers was significantly elevated in primary breast cancer compared to normal control or benign breast disease groups. They therefore appear to have no role either in screening or in the differential diagnosis of breast cancer. There was a significant elevation of all five markers in patients with systemic breast cancer (P <0.0001; analysis of variance) but sequential changes in CEA and ESR only correlated significantly with the UICC-assessed response. Prospective confirmation of the correlation between changes in serum CEA and ESR provides the basis for using these markers in the assessment of response to therapy in patients with systemic breast cancer.     

直肠癌MRI研究进展

MRI是目前直肠癌诊断、分期的首选影像学方法。在判断肿瘤对邻近器官、结构的浸润程度上具有明显优势,尤其是对有较高复发风险的低位肿瘤。常规MRI尤其是高分辨MRI能够清晰显示直肠相关解剖,结合扩散加权成像(Diffusion weighted imaging,DWI)通过确定肿瘤边界,直肠系膜有无受侵,淋巴结及远处转移情况,可以准确有效的进行术前诊断、分期;DWI有助于鉴别辅助治疗后失活与存活组织、筛选出辅助治疗有效的患者,在评估治疗后疗效、提示患者预后方面发挥重要作用,也为临床制定治疗方案提供依据。同时也发现准确进行淋巴结分期、鉴别复发仍然存在困难,需要在以后进一步探讨,提高评估的准确性。本文就近年来MRI在直肠癌术前评价、术后疗效评估、复发监测及表观弥散系数(Apparent diffusion coefficient,ADC)的应用做一综述。     

直肠癌MRI研究进展

MRI是目前直肠癌诊断、分期的首选影像学方法。在判断肿瘤对邻近器官、结构的浸润程度上具有明显优势,尤其是对有较高复发风险的低位肿瘤。常规MRI尤其是高分辨MRI能够清晰显示直肠相关解剖,结合扩散加权成像（Diffusion weighted imaging,DWI）通过确定肿瘤边界,直肠系膜有无受侵,淋巴结及远处转移情况,可以准确有效的进行术前诊断、分期;DWI有助于鉴别辅助治疗后失活与存活组织、筛选出辅助治疗有效的患者,在评估治疗后疗效、提示患者预后方面发挥重要作用,也为临床制定治疗方案提供依据。同时也发现准确进行淋巴结分期、鉴别复发仍然存在困难,需要在以后进一步探讨,提高评估的准确性。本文就近年来MRI在直肠癌术前评价、术后疗效评估、复发监测及表观弥散系数（Apparent diffusion coefficient,ADC）的应用做一综述。     

Flow cytometric DNA analysis using cytokeratin labeling for identification of tumor cells in carcinomas of the breast and the female genital tract.

Flow cytometric assessment of DNA-ploidy and S-phase fraction in malignant tumors is compromised by the heterogeneity of cell subpopulations derived from the malignant and surrounding connective tissue, e.g., tumor, stromal and inflammatory cells. To evaluate the effect on quality of DNA cell cycle analysis and determination of DNA ploidy, cytokeratin labeling of epithelial cells was used for tumor cell enrichment in breast, ovarian, cervical and endometrial cancer prior to DNA analysis. In a prospective study, tumor cell subpopulations of 620 malignant tumors were labeled by a FITC-conjugated cytokeratin antibody (CK 5, 6, CK18 and CK 5, 6, 8 and CK 17, respectively) prior to flow cytometric cell cycle analysis. Compared to total cell analysis, detection rate of DNA-aneuploid tumors following cytokeratin labeling was increased from 62% to 76.5% in breast cancer, from 68% to 77% in ovarian cancer, from 60% to 80% in cervical cancer and from 30% to 53% in endometrial cancer. Predominantly in DNA-diploid tumors, a significantly improved detection of S-phase fraction of the tumor cells was shown due to the elimination of contaminating nonproliferating "normal cells". S-phase fraction following tumor cell enrichment was increased by 10% (mean) following cytokeratin staining in ovarian and endometrial cancer, by 30% in breast cancer and even by 70% in cervical cancer compared to total cell analysis. Thus, diagnostic accuracy of DNA-analysis was enhanced by cytokeratin labeling of tumor cells for all tumor entities investigated.     

Ex Vivo Treatment Response of Primary Tumors and/or Associated Metastases for Preclinical and Clinical Development of Therapeutics

The molecular analysis of established cancer cell lines has been the mainstay of cancer research for the past several decades. Cell culture provides both direct and rapid analysis of therapeutic sensitivity and resistance. However, recent evidence suggests that therapeutic response is not exclusive to the inherent molecular composition of cancer cells but rather is greatly influenced by the tumor cell microenvironment, a feature that cannot be recapitulated by traditional culturing methods. Even implementation of tumor xenografts, though providing a wealth of information on drug delivery/efficacy, cannot capture the tumor cell/microenvironment crosstalk (i.e., soluble factors) that occurs within human tumors and greatly impacts tumor response. To this extent, we have developed an ex vivo (fresh tissue sectioning) technique which allows for the direct assessment of treatment response for preclinical and clinical therapeutics development. This technique maintains tissue integrity and cellular architecture within the tumor cell/microenvironment context throughout treatment response providing a more precise means to assess drug efficacy.     

Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response

Cancer stem cells (CSCs) possess capacity to both self-renew and generate all cells within a tumor, and are thought to drive tumor recurrence. Targeting the stem cell niche to eradicate CSCs represents an important area of therapeutic development. The complex nature of many interacting elements of the stem cell niche, including both intracellular signals and microenvironmental growth factors and cytokines, creates a challenge in choosing which elements to target, alone or in combination. Stochastic stimulation techniques allow for the careful study of complex systems in biology and medicine and are ideal for the investigation of strategies aimed at CSC eradication. We present a mathematical model of the breast cancer stem cell (BCSC) niche to predict population dynamics during carcinogenesis and in response to treatment. Using data from cell line and mouse xenograft experiments, we estimate rates of interconversion between mesenchymal and epithelial states in BCSCs and find that EMT/MET transitions occur frequently. We examine bulk tumor growth dynamics in response to alterations in the rate of symmetric self-renewal of BCSCs and find that small changes in BCSC behavior can give rise to the Gompertzian growth pattern observed in breast tumors. Finally, we examine stochastic reaction kinetic simulations in which elements of the breast cancer stem cell niche are inhibited individually and in combination. We find that slowing self-renewal and disrupting the positive feedback loop between IL-6, Stat3 activation, and NF-κB signaling by simultaneous inhibition of IL-6 and HER2 is the most effective combination to eliminate both mesenchymal and epithelial populations of BCSCs. Predictions from our model and simulations show excellent agreement with experimental data showing the efficacy of combined HER2 and Il-6 blockade in reducing BCSC populations. Our findings will be directly examined in a planned clinical trial of combined HER2 and IL-6 targeted therapy in HER2-positive breast cancer.     

Potential for improvement in clinical decision-making: tumor imaging with in-111 labeled liposomes results of a phase ii-iii study

Abstract

Liposome scanning using In- 111 labeled VS102 liposomes (VesCan^R) has previously been shown to image a wide variety of common human tumors, probably related to tumor neovascular capIIIary fenestrations and binding of liposomes to tumor cells. We further tested In-III VS102 liposomes in a Phase II trial (27 patients) and a Phase III trial (38 patients). The sensitivity for detecting tumors in primary sites was 82% and in metastatic sites was 65% at the recommended lipid dose of 100 mg. There was 1 false positive scan (specificity 98%). Tumors which have been imaged include carcinomas of the breast, lung, head-neck, prostate, colon, ovary, cervix, thyroid, kidney, testes, melanoma, sarcoma and lymphoma. Sites imaged have included soft tissue, breast, mediastinum, bone, lung, lymph node, liver and pelvis. We also describe five patients in whom a In-111 liposome scan was performed in addition to standard tests, and in whom therapy plans were changed by use of liposome scan results. In two instances, no therapy would have been given without In-III liposome scan, but chemotherapy or radiotherapy were used based on liposome scan results and confirmatory tests. In one patient, surgery would have been used in the absence of In-III liposome scans, versus radiotherapy with In-III liposome scan results. In two other patients, palliative radiotherapy or chemotherapy would have been given without In-111 liposome scan. One of the patients would have required further therapy and the other needed curative surgery after liposome scan evaluation. These results suggest In-111 liposome scans may be useful to complement standard diagnostic tests in cancer patient management.     

Elevation in Inflammatory Serum Biomarkers Predicts Response to Trastuzumab-Containing Therapy

Approximately half of all HER2/neu-overexpressing breast cancer patients do not respond to trastuzumab-containing therapy. Therefore, there remains an urgent and unmet clinical need for the development of predictive biomarkers for trastuzumab response. Recently, several lines of evidence have demonstrated that the inflammatory tumor microenvironment is a major contributor to therapy resistance in breast cancer. In order to explore the predictive value of inflammation in breast cancer patients, we measured the inflammatory biomarkers serum ferritin and C-reactive protein (CRP) in 66 patients immediately before undergoing trastuzumab-containing therapy and evaluated their progression-free and overall survival. The elevation in pre-treatment serum ferritin (>250 ng/ml) or CRP (>7.25 mg/l) was a significant predictor of reduced progression-free survival and shorter overall survival. When patients were stratified based on their serum ferritin and CRP levels, patients with elevation in both inflammatory biomarkers had a markedly poorer response to trastuzumab-containing therapy. Therefore, the elevation in inflammatory serum biomarkers may reflect a pathological state that decreases the clinical efficacy of this therapy. Anti-inflammatory drugs and life-style changes to decrease inflammation in cancer patients should be explored as possible strategies to sensitize patients to anti-cancer therapeutics.     

Expression of Estrogen-Related Gene Markers in Breast Cancer Tissue Predicts Aromatase Inhibitor Responsiveness

Aromatase inhibitors (AIs) are the most effective class of drugs in the endocrine treatment of breast cancer, with an approximate 50% treatment response rate. Our objective was to determine whether intratumoral expression levels of estrogen-related genes are predictive of AI responsiveness in postmenopausal women with breast cancer. Primary breast carcinomas were obtained from 112 women who received AI therapy after failing adjuvant tamoxifen therapy and developing recurrent breast cancer. Tumor ERα and PR protein expression were analyzed by immunohistochemistry (IHC). Messenger RNA (mRNA) levels of 5 estrogen-related genes–AKR1C3, aromatase, ERα, and 2 estradiol/ERα target genes, BRCA1 and PR–were measured by real-time PCR. Tumor protein and mRNA levels were compared with breast cancer progression rates to determine predictive accuracy. Responsiveness to AI therapy–defined as the combined complete response, partial response, and stable disease rates for at least 6 months–was 51%; rates were 56% in ERα-IHC-positive and 14% in ERα-IHC-negative tumors. Levels of ERα, PR, or BRCA1 mRNA were independently predictive for responsiveness to AI. In cross-validated analyses, a combined measurement of tumor ERα and PR mRNA levels yielded a more superior specificity (36%) and identical sensitivity (96%) to the current clinical practice (ERα/PR-IHC). In patients with ERα/PR-IHC-negative tumors, analysis of mRNA expression revealed either non-significant trends or statistically significant positive predictive values for AI responsiveness. In conclusion, expression levels of estrogen-related mRNAs are predictive for AI responsiveness in postmenopausal women with breast cancer, and mRNA expression analysis may improve patient selection.     

Impact of motion induced artifacts on automatic registration of lung tumors in Tomotherapy

PurposeTomotherapy MV-CT acquisitions of lung tumors lead to artifacts due to breathing-related motion. This could preclude the reliability of tumor based positioning. We investigate the effect of these artifacts on automatic registration and determine conditions under which correct positioning can be achieved.Materials and methodsMV-CT and 4D-CT scans of a dynamic thorax phantom were acquired with various motion amplitudes, directions, and periods. For each acquisition, the average kV-CT image was reconstructed from the 4D-CT data and rigidly registered with the corresponding MV-CT scan in a region of interest. Different kV–MV registration strategies have been assessed.ResultsAll tested registration methods led to acceptable registration errors (within 1.3 ± 1.2 mm) for motion periods of 3 and 6 s, regardless of the motion amplitude, direction, and phase difference. However, a motion period of 5 s, equal to half the Tomotherapy gantry period, induced asymmetric artifacts within MV-CT and significantly degraded the registration accuracy.ConclusionsAs long as the breathing period differs from 5 s, positioning based on averaged images of the tumor provides information about its daily baseline shift, and might therefore contribute to reducing margins, regardless of the registration method.