Confirmation of Sentinel Lymph Node Identity by Analysis of Fine-Needle Biopsy Samples Using Inductively Coupled Plasma–Mass Spectrometry

Background: The sentinel lymph node (SLN) biopsy technique is a reliable means of determining the tumor-harboring status of regional lymph nodes in melanoma patients. When technetium 99 m-labeled antimony trisulfide colloid (99 mTc-Sb2S3) particles are used to perform preoperative lymphoscintigraphy for SLN identification, they are retained in the SLN but are absent or present in only tiny amounts in non-SLNs. The present study investigated the potential for a novel means of assessing the accuracy of surgical identification of SLNs. This involved the use of inductively coupled plasma-mass spectrometry (ICP-MS) to analyze antimony concentrations in fine-needle biopsy (FNB) samples from surgically procured lymph nodes. Methods: A total of 47 FNB samples from surgically excised lymph nodes (32 SLNs and 15 non-SLNs) were collected. The SLNs were localized by preoperative lymphoscintigraphy that used 99 mTc-Sb2S3, blue dye, and gamma probe techniques. The concentrations of antimony were measured in the FNB samples by ICP-MS. Results: The mean and median antimony concentrations (in parts per billion) were .898 and .451 in the SLNs, and .015 and .068 in the non-SLNs, the differences being highly statistically significant (P < .00005). Conclusions: Our results show that ICP-MS analysis of antimony concentrations in FNB specimens from lymph nodes can accurately confirm the identity of SLNs. Used in conjunction with techniques such as proton magnetic resonance spectroscopy for the nonsurgical evaluation of SLNs, ICP-MS analysis of antimony concentrations in FNB samples could potentially serve as a minimally invasive alternative to surgery and histopathologic evaluation to objectively classify a given node as sentinel or nonsentinel and determine its tumor-harboring status. © 2007 The Author(s)

Clinical and biological characterization of skeletal muscle tissue biopsies of surgical cancer patients

BACKGROUND: Researchers increasingly use intraoperative muscle biopsy to investigate mechanisms of skeletal muscle atrophy in patients with cancer. Muscles have been assessed for morphological, cellular, and biochemical features. The aim of this study was to conduct a state‐of‐the‐science review of this literature and, secondly, to evaluate clinical and biological variation in biopsies of rectus abdominis (RA) muscle from a cohort of patients with malignancies. METHODS: Literature was searched for reports on muscle biopsies from patients with a cancer diagnosis. Quality of reports and risk of bias were assessed. Data abstracted included patient characteristics and diagnoses, sample size, tissue collection and biobanking procedures, and results. A cohort of cancer patients (n = 190, 88% gastrointestinal malignancies), who underwent open abdominal surgery as part of their clinical care, consented to RA biopsy from the site of incision. Computed tomography (CT) scans were used to quantify total abdominal muscle and RA cross‐sectional areas and radiodensity. Biopsies were assessed for muscle fibre area (μm(2)), fibre types, myosin heavy chain isoforms, and expression of genes selected for their involvement in catabolic pathways of muscle. RESULTS: Muscle biopsy occurred in 59 studies (total N = 1585 participants). RA was biopsied intraoperatively in 40 studies (67%), followed by quadriceps (26%; percutaneous biopsy) and other muscles (7%). Cancer site and stage, % of male participants, and age were highly variable between studies. Details regarding patient medical history and biopsy procedures were frequently absent. Lack of description of the population(s) sampled and low sample size contributed to low quality and risk of bias. Weight‐losing cases were compared with weight stable cancer or healthy controls without considering a measure of muscle mass in 21 out of 44 studies. In the cohort of patients providing biopsy for this study, 78% of patients had preoperative CT scans and a high proportion (64%) met published criteria for sarcopenia. Fibre type distribution in RA was type I (46% ± 13), hybrid type I/IIA (1% ± 1), type IIA (36% ± 10), hybrid type IIA/D (15% ± 14), and type IID (2% ± 5). Sexual dimorphism was prominent in RA CT cross‐sectional area, mean fibre cross‐sectional area, and in expression of genes associated with muscle growth, apoptosis, and inflammation (P < 0.05). Medical history revealed multiple co‐morbid conditions and medications. CONCLUSIONS: Continued collaboration between researchers and cancer surgeons enables a more complete understanding of mechanisms of cancer‐associated muscle atrophy. Standardization of biobanking practices, tissue manipulation, patient characterization, and classification will enhance the consistency, reliability, and comparability of future studies

Health impact of US military service in a large population-based military cohort: findings of the Millennium Cohort Study, 2001-2008

<p>Abstract</p> <p>Background</p> <p>Combat-intense, lengthy, and multiple deployments in Iraq and Afghanistan have characterized the new millennium. The US military's all-volunteer force has never been better trained and technologically equipped to engage enemy combatants in multiple theaters of operations. Nonetheless, concerns over potential lasting effects of deployment on long-term health continue to mount and are yet to be elucidated. This report outlines how findings from the first 7 years of the Millennium Cohort Study have helped to address health concerns related to military service including deployments.</p> <p>Methods</p> <p>The Millennium Cohort Study was designed in the late 1990s to address veteran and public concerns for the first time using prospectively collected health and behavioral data.</p> <p>Results</p> <p>Over 150 000 active-duty, reserve, and National Guard personnel from all service branches have enrolled, and more than 70% of the first 2 enrollment panels submitted at least 1 follow-up survey. Approximately half of the Cohort has deployed in support of operations in Iraq and Afghanistan.</p> <p>Conclusion</p> <p>The Millennium Cohort Study is providing prospective data that will guide public health policymakers for years to come by exploring associations between military exposures and important health outcomes. Strategic studies aim to identify, reduce, and prevent adverse health outcomes that may be associated with military service, including those related to deployment.</p

Magnetic resonance spectroscopy in the management of melanoma

The surgical treatment of melanoma has been progressively rationalised during the last two decades. Radical excision of primary tumours and elective (prophylactic) resection of regional lymph nodes have been replaced with more selective procedures that reflect improved understanding of the metastatic potential of individual tumours. Magnetic resonance spectroscopy (MRS) is an evolving technology which has the potential to diagnose many tumours and to characterise their metastatic potential. The Institute for Magnetic Resonance Research and the Sydney Melanoma Unit have developed MRS techniques to diagnose, stage and aid in the clinical management of melanoma. It is anticipated that these techniques will ultimately be used as clinical tools to provide non-surgical diagnosis of metastatic disease in sentinel nodes, either by MRS examination of a simple outpatient fine needle biopsy specimen or by use of an entirely non-invasive in vivo MRS assessment. Experience with MRS of primary breast cancers indicates that it may also be possible to predict the metastatic potential of melanoma by spectroscopic analysis of the primary tumour and to distinguish naevi from melanomas thus better selecting patients for surger

In vivo and ex vivo proton MR spectroscopy of primary and secondary melanoma

In vivo magnetic resonance (MR) spectroscopy at 1.5T was performed on a large polypoid cutaneous melanoma, and two enlarged lymph nodes containing metastatic melanoma, from three patients. Spectra were acquired in vivo from voxels wholly within the primary tumour or secondary lymph node and were thus uncontaminated by signals from adjacent tissue. Tissue biopsies taken after resection of primary tumours and secondary lymph nodes were examined by 8.5T magnetic resonance spectroscopy (MRS) and the results compared with the in vivo spectra, and with spectra from normal skin and a benign skin lesion. There was good agreement between the dominant features of 1.5T spectra acquired in vivo and 8.5T spectra acquired from resected tissue. However, less intense resonances observed at 8.5T in malignant biopsy tissue were not consistently observed at 1.5T in vivo. In vivo spectra from primary and metastatic melanoma showed high levels of choline metabolites. An intense lactate resonance was also present in the in vivo spectrum of primary melanoma. All 8.5T spectra of biopsies from primary and secondary melanoma showed high levels of choline metabolites and lactate, and additional resonances consistent with elevated levels of taurine, alanine, lysine, and glutamate/glutamine relative to normal and benign tissue. Elevated levels of choline, lactate, taurine, and amino acids appear to be clinically useful markers for identifying the pathology of primary and metastatic melanoma

A clinically useful and biologically informative genomic classifier for papillary thyroid cancer

Clinical management of papillary thyroid cancer depends on estimations of prognosis. Standard care, which relies on prognostication based on clinicopathologic features, is inaccurate. We applied a machine learning algorithm (HighLifeR) to 502 cases annotated by The Cancer Genome Atlas Project to derive an accurate molecular prognostic classifier. Unsupervised analysis of the 82 genes that were most closely associated with recurrence after surgery enabled the identification of three unique molecular subtypes. One subtype had a high recurrence rate, an immunosuppressed microenvironment, and enrichment of the EZH2-HOTAIR pathway. Two other unique molecular subtypes with a lower rate of recurrence were identified, including one subtype with a paucity of BRAFV600E mutations and a high rate of RAS mutations. The genomic risk classifier, in addition to tumor size and lymph node status, enabled effective prognostication that outperformed the American Thyroid Association clinical risk stratification. The genomic classifier we derived can potentially be applied preoperatively to direct clinical decision-making. Distinct biological features of molecular subtypes also have implications regarding sensitivity to radioactive iodine, EZH2 inhibitors, and immune checkpoint inhibitors

Immunohistochemical phenotyping of T cells, granulocytes, and phagocytes in the muscle of cancer patients: association with radiologically defined muscle mass and gene expression

Abstract Background Inflammation is a recognized contributor to muscle wasting. Research in injury and myopathy suggests that interactions between the skeletal muscle and immune cells confer a pro-inflammatory environment that influences muscle loss through several mechanisms; however, this has not been explored in the cancer setting. This study investigated the local immune environment of the muscle by identifying the phenotype of immune cell populations in the muscle and their relationship to muscle mass in cancer patients. Methods Intraoperative muscle biopsies were collected from cancer patients (n = 30, 91% gastrointestinal malignancies). Muscle mass was assessed histologically (muscle fiber cross-sectional area, CSA; μm2) and radiologically (lumbar skeletal muscle index, SMI; cm2/m2 by computed tomography, CT). T cells (CD4 and CD8) and granulocytes/phagocytes (CD11b, CD14, and CD15) were assessed by immunohistochemistry. Microarray analysis was conducted in the muscle of a second cancer patient cohort. Results T cells (CD3+), granulocytes/phagocytes (CD11b+), and CD3−CD4+ cells were identified. Muscle fiber CSA (μm2) was positively correlated (Spearman’s r = > 0.45; p = < 0.05) with the total number of T cells, CD4, and CD8 T cells and granulocytes/phagocytes. In addition, patients with the smallest SMI exhibited fewer CD8 T cells within their muscle. Consistent with this, further exploration with gene correlation analyses suggests that the presence of CD8 T cells is negatively associated (Pearson’s r = ≥ 0.5; p = <0.0001) with key genes within muscle catabolic pathways for signaling (ACVR2B), ubiquitin proteasome (FOXO4, TRIM63, FBXO32, MUL1, UBC, UBB, UBE2L3), and apoptosis/autophagy (CASP8, BECN1, ATG13, SIVA1). Conclusion The skeletal muscle immune environment of cancer patients is comprised of immune cell populations from the adaptive and innate immunity. Correlations of T cells, granulocyte/phagocytes, and CD3−CD4+ cells with muscle mass measurements indicate a positive relationship between immune cell numbers and muscle mass status in cancer patients. Further exploration with gene correlation analyses suggests that the presence of CD8 T cells is negatively correlated with components of muscle catabolism

Plots showing the mean and standard deviation accuracy of sex prediction on two external datasets using a predictor trained using different sample sizes from our dataset.

<p>We built predictors using different training sample sizes ranging from n = 10 (5♀, 5♂) to n = 110 (55♀, 55♂) from our full dataset. We then calculated the prediction accuracy, for each n ( = 10…110) on two external datasets (A. Dataset GSE24215 and B. Dataset GSE23697). This was repeated 50 times and the mean and standard deviation prediction accuracy for each sample size was calculated. As the training sample size increased, so did prediction accuracy on the external datasets.</p

Box-and-whiskers plot showing the mean internal cross-validation accuracy of sex prediction for different sample sizes.

<p>Sample sizes tested ranged from n = 10 (5♀, 5♂) to n = 110 (55♀, 55♂). To calculate the mean 10-fold cross validation prediction accuracy, for each n ( = 10…110), we built classification models using a randomly selected size-n subsamples of our full dataset of n = 134. This was repeated 50 times and the median prediction accuracy for each sample was calculated. As sample size increased, so did prediction accuracy.</p