Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging

In clinical diagnostics, it is of outmost importance to correctly identify the source of a metastatic tumor, especially if no apparent primary tumor is present. Tissue-based proteomics might allow correct tumor classification. As a result, we performed MALDI imaging to generate proteomic signatures for different tumors. These signatures were used to classify common cancer types. At first, a cohort comprised of tissue samples from six adenocarcinoma entities located at different organ sites (esophagus, breast, colon, liver, stomach, thyroid gland, n = 171) was classified using two algorithms for a training and test set. For the test set, Support Vector Machine and Random Forest yielded overall accuracies of 82.74 and 81.18%, respectively. Then, colon cancer liver metastasis samples (n = 19) were introduced into the classification. The liver metastasis samples could be discriminated with high accuracy from primary tumors of colon cancer and hepatocellular carcinoma. Additionally, colon cancer liver metastasis samples could be successfully classified by using colon cancer primary tumor samples for the training of the classifier. These findings demonstrate that MALDI imaging-derived proteomic classifiers can discriminate between different tumor types at different organ sites and in the same site.

Expression patterns of Bmi-1 and p16 significantly correlate with overall, disease-specific, and recurrence-free survival in oropharyngeal squamous cell carcinoma

BACKGROUND: The objective of this study was to link expression patterns of B-cell-specific Moloney murine leukemia virus integration site 1 (Bmi-1) and p16 to patient outcome (recurrence and survival) in a cohort of 252 patients with oral and oropharyngeal squamous cell cancer (OSCC). METHODS: Expression levels of Bmi-1 and p16 in samples from 252 patients with OSCC were evaluated immunohistochemically using the tissue microarray method. Staining intensity was determined by calculating an intensity reactivity score (IRS). Staining intensity and the localization of expression within tumor cells (nuclear or cytoplasmic) were correlated with overall, disease-specific, and recurrence-free survival. RESULTS: The majority of cancers were localized in the oropharynx (61.1%). In univariate analysis, patients who had OSCC and strong Bmi-1 expression (IRS >10) had worse outcomes compared with patients who had low and moderate Bmi-1 expression (P = .008; hazard ratio [HR], 1.82; 95% confidence interval [CI], 1.167-2.838); this correlation was also observed for atypical cytoplasmic Bmi-1 expression (P = .001; HR, 2.164; 95% CI, 1.389-3.371) and for negative p16 expression (P < .001; HR, 0.292; 95% CI, 0.178-0.477). The combination of both markers, as anticipated, had an even stronger correlation with overall survival (P < .001; HR, 8.485; 95% CI, 4.237-16.994). Multivariate analysis demonstrated significant results for patients with oropharyngeal cancers, but not for patients with oral cavity tumors: Tumor classification (P = .011; HR, 1.838; 95%CI, 1.146-2.947) and the combined marker expression patterns (P < .001; HR, 6.254; 95% CI, 2.869-13.635) were correlated with overall survival, disease-specific survival (tumor classification: P = .002; HR, 2.807; 95% CI, 1.477-5.334; combined markers: P = .002; HR, 5.386; 95% CI, 1.850-15.679), and the combined markers also were correlated with recurrence-free survival (P = .001; HR, 8.943; 95% CI, 2.562-31.220). CONCLUSIONS: Cytoplasmic Bmi-1 expression, an absence of p16 expression, and especially the combination of those 2 predictive markers were correlated negatively with disease-specific and recurrence-free survival in patients with oropharyngeal cancer. Therefore, the current results indicate that these may be applicable as predictive markers in combination with other factors to select patients for more aggressive treatment and follow-up. Cancer 2011;. © 2011 American Cancer Society.

MR imaging of parotid tumors: typical lesion characteristics in MR imaging improve discrimination between benign and malignant disease

The surgical approach to parotid tumors is different for benign and malignant neoplasms, but the clinical symptoms do not correlate well with histology. Difficulties in tumor classification also arise in imaging modalities, in which sonography has the lowest and MR imaging, the highest accuracy. The purpose of this study was to review our experience using conventional MR imaging of the neck in the evaluation of parotid tumors and to evaluate which MR imaging findings are best able to predict malignant histology.

Pretherapeutic staging of recurrent laryngeal carcinoma: clinical findings and imaging studies compared with histopathology

OBJECTIVE: To assess the accuracy of preoperative imaging studies and clinical and endoscopic examinations for recurrent laryngeal carcinoma evaluation. STUDY DESIGN AND SETTING: A retrospective comparative study was performed at a university department on 42 recurrent laryngeal carcinomas. Surgical specimens were cut into whole-organ slices. Histologic findings were compared with the findings of the different preoperative diagnostic modalities. RESULTS: The craniocaudal tumor spread was correctly evaluated by endoscopy and imaging studies in 52% and 24%, respectively, and the contralateral tumor spread in 50% and 52%, respectively. The sensitivity, specificity, and accuracy for detection of tumor infiltration of the thyroid was 48%, 88%, and 64% and of the cricoid 47%, 80%, and 67%. The accuracy of recurrent tumor classification (crT) was 50%; most tumors were underclassified. CONCLUSION: The inadequately evaluated tumor spread and the inadequately classified recurrent tumors were underestimated and underclassified in most cases, respectively.

Tissue metabolomics of hepatocellular carcinoma: Tumor energy metabolism and the role of transcriptomic classification

Hepatocellular carcinoma (HCC) is one of the commonest causes of death from cancer. A plethora of metabolomic investigations of HCC have yielded molecules in biofluids that are both up- and down-regulated but no real consensus has emerged regarding exploitable biomarkers for early detection of HCC. We report here a different approach, a combined transcriptomics and metabolomics study of energy metabolism in HCC. A panel of 31 pairs of HCC tumors and corresponding nontumor liver tissues from the same patients was investigated by gas chromatography-mass spectrometry (GCMS)-based metabolomics. HCC was characterized by ∼2-fold depletion of glucose, glycerol 3- and 2-phosphate, malate, alanine, myo-inositol, and linoleic acid. Data are consistent with a metabolic remodeling involving a 4-fold increase in glycolysis over mitochondrial oxidative phosphorylation. A second panel of 59 HCC that had been typed by transcriptomics and classified in G1 to G6 subgroups was also subjected to GCMS tissue metabolomics. No differences in glucose, lactate, alanine, glycerol 3-phosphate, malate, myo-inositol, or stearic acid tissue concentrations were found, suggesting that the Wnt/β-catenin pathway activated by CTNNB1 mutation in subgroups G5 and G6 did not exhibit specific metabolic remodeling. However, subgroup G1 had markedly reduced tissue concentrations of 1-stearoylglycerol, 1-palmitoylglycerol, and palmitic acid, suggesting that the high serum α-fetoprotein phenotype of G1, associated with the known overexpression of lipid catabolic enzymes, could be detected through metabolomics as increased lipid catabolism. Conclusion: Tissue metabolomics yielded precise biochemical information regarding HCC tumor metabolic remodeling from mitochondrial oxidation to aerobic glycolysis and the impact of molecular subtypes on this process.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Cancer classification using the Immunoscore: a worldwide task force

Prediction of clinical outcome in cancer is usually achieved by histopathological evaluation of tissue samples obtained during surgical resection of the primary tumor. Traditional tumor staging (AJCC/UICC-TNM classification) summarizes data on tumor burden (T), presence of cancer cells in draining and regional lymph nodes (N) and evidence for metastases (M). However, it is now recognized that clinical outcome can significantly vary among patients within the same stage. The current classification provides limited prognostic information, and does not predict response to therapy. Recent literature has alluded to the importance of the host immune system in controlling tumor progression. Thus, evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy. Accumulating data, collected from large cohorts of human cancers, has demonstrated the impact of immune-classification, which has a prognostic value that may add to the significance of the AJCC/UICC TNM-classification. It is therefore imperative to begin to incorporate the 'Immunoscore' into traditional classification, thus providing an essential prognostic and potentially predictive tool. Introduction of this parameter as a biomarker to classify cancers, as part of routine diagnostic and prognostic assessment of tumors, will facilitate clinical decision-making including rational stratification of patient treatment. Equally, the inherent complexity of quantitative immunohistochemistry, in conjunction with protocol variation across laboratories, analysis of different immune cell types, inconsistent region selection criteria, and variable ways to quantify immune infiltration, all underline the urgent requirement to reach assay harmonization. In an effort to promote the Immunoscore in routine clinical settings, an international task force was initiated. This review represents a follow-up of the announcement of this initiative, and of the J Transl Med. editorial from January 2012. Immunophenotyping of tumors may provide crucial novel prognostic information. The results of this international validation may result in the implementation of the Immunoscore as a new component for the classification of cancer, designated TNM-I (TNM-Immune).

Determination of histopathological tumor grade in neuroepithelial brain tumors by using spectral pattern analysis of in vivo spectroscopic data

OBJECT: In this study, 1H magnetic resonance (MR) spectroscopy was prospectively tested as a reliable method for presurgical grading of neuroepithelial brain tumors. METHODS: Using a database of tumor spectra obtained in patients with histologically confirmed diagnoses, 94 consecutive untreated patients were studied using single-voxel 1H spectroscopy (point-resolved spectroscopy; TE 135 msec, TE 135 msec, TR 1500 msec). A total of 90 tumor spectra obtained in patients with diagnostic 1H MR spectroscopy examinations were analyzed using commercially available software (MRUI/VARPRO) and classified using linear discriminant analysis as World Health Organization (WHO) Grade I/II, WHO Grade III, or WHO Grade IV lesions. In all cases, the classification results were matched with histopathological diagnoses that were made according to the WHO classification criteria after serial stereotactic biopsy procedures or open surgery. Histopathological studies revealed 30 Grade I/II tumors, 29 Grade III tumors, and 31 Grade IV tumors. The reliability of the histological diagnoses was validated considering a minimum postsurgical follow-up period of 12 months (range 12-37 months). Classifications based on spectroscopic data yielded 31 tumors in Grade I/II, 32 in Grade III, and 27 in Grade IV. Incorrect classifications included two Grade II tumors, one of which was identified as Grade III and one as Grade IV; two Grade III tumors identified as Grade II; two Grade III lesions identified as Grade IV; and six Grade IV tumors identified as Grade III. Furthermore, one glioblastoma (WHO Grade IV) was classified as WHO Grade I/II. This represents an overall success rate of 86%, and a 95% success rate in differentiating low-grade from high-grade tumors. CONCLUSIONS: The authors conclude that in vivo 1H MR spectroscopy is a reliable technique for grading neuroepithelial brain tumors.

Survival in surgically treated, nodal positive prostate cancer patients is predicted by histopathological characteristics of the primary tumor and its lymph node metastases

BACKGROUND: Histopathological risk factors for survival stratification of surgically treated nodal positive prostate cancer patients are poorly defined as reflected by only one category for nodal metastases. METHODS: We evaluated biochemical recurrence-free survival (RFS), disease-specific survival (DSS), and overall survival (OS) in 102 nodal positive, hormone treatment-naïve prostate cancer patients (median age: 65 years, range: 45-75 years; median follow-up 7.7 years, range: 1.0-15.9 years) who underwent radical prostatectomy and standardized extended lymphadenectomy. RESULTS: A significant stratification was possible, with the Gleason score of the primary and virtually all nodal parameters favoring patients with better differentiated primaries and metastases, lower nodal tumor burden, and without extranodal extension of metastases. In multivariate analyses, diameter of the largest metastasis (< or =10 mm vs. >10 mm) was the strongest independent predictor for RFS (P < 0.001), DSS (P < 0.001), and OS (P < 0.001) with a more than quadrupled relative risk of cancer related deaths for patients with larger metastases (Hazard ratio: 4.2, Confidence interval: 2.0-8.9; 5-year RFS/DSS/OS: 18%/57%/54%). The highest 5-year survival rates were seen in patients with micrometastases only (RFS/DSS/OS: 47%/94%/94%). CONCLUSION: The TNM classification's current allocation of only one category for nodal metastases in prostate cancers is unsatisfactory since subgroups with significantly different prognoses can be identified. The diameter of the patient's largest metastasis (< or =10 mm vs. >10 mm) should be used for substaging because of its independent prognostic value. The substage "micrometastasis only" is also useful in nodal positive prostate cancer since it designates the subgroup with the most favorable outcome.

Prognostic implications of the seventh edition of the international union against cancer classification for patients with gastric cancer: the Western experience of patients treated in a single-center European institution

PURPOSE Validity of the seventh edition of the American Joint Committee on Cancer/International Union Against Cancer (AJCC/UICC) staging systems for gastric cancer has been evaluated in several studies, mostly in Asian patient populations. Only few data are available on the prognostic implications of the new classification system on a Western population. Therefore, we investigated its prognostic ability based on a German patient cohort. PATIENTS AND METHODS Data from a single-center cohort of 1,767 consecutive patients surgically treated for gastric cancer were classified according to the seventh edition and were compared using the previous TNM/UICC classification. Kaplan-Meier analyses were performed for all TNM stages and UICC stages in a comparative manner. Additional survival receiver operating characteristic analyses and bootstrap-based goodness-of-fit comparisons via Bayesian information criterion (BIC) were performed to assess and compare prognostic performance of the competing classification systems. RESULTS We identified the UICC pT/pN stages according to the seventh edition of the AJCC/UICC guidelines as well as resection status, age, Lauren histotype, lymph-node ratio, and tumor grade as independent prognostic factors in gastric cancer, which is consistent with data from previous Asian studies. Overall survival rates according to the new edition were significantly different for each individual's pT, pN, and UICC stage. However, BIC analysis revealed that, owing to higher complexity, the new staging system might not significantly alter predictability for overall survival compared with the old system within the analyzed cohort from a statistical point of view. CONCLUSION The seventh edition of the AJCC/UICC classification was found to be valid with distinctive prognosis for each stage. However, the AJCC/UICC classification has become more complex without improving predictability for overall survival in a Western population. Therefore, simplification with better predictability of overall survival of patients with gastric cancer should be considered when revising the seventh edition.

Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation

In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

Interactive Segmentation of MR Images from Brain Tumor Patients

Medical doctors often do not trust the result of fully automatic segmentations because they have no possibility to make corrections if necessary. On the other hand, manual corrections can introduce a user bias. In this work, we propose to integrate the possibility for quick manual corrections into a fully automatic segmentation method for brain tumor images. This allows for necessary corrections while maintaining a high objectiveness. The underlying idea is similar to the well-known Grab-Cut algorithm, but here we combine decision forest classification with conditional random field regularization for interactive segmentation of 3D medical images. The approach has been evaluated by two different users on the BraTS2012 dataset. Accuracy and robustness improved compared to a fully automatic method and our interactive approach was ranked among the top performing methods. Time for computation including manual interaction was less than 10 minutes per patient, which makes it attractive for clinical use.

The tumor border configuration of colorectal cancer as a histomorphological prognostic indicator.

Histomorphological features of colorectal cancers (CRC) represent valuable prognostic indicators for clinical decision making. The invasive margin is a central feature for prognostication shaped by the complex processes governing tumor-host interaction. Assessment of the tumor border can be performed on standard paraffin sections and shows promise for integration into the diagnostic routine of gastrointestinal pathology. In aggressive CRC, an extensive dissection of host tissue is seen with loss of a clear tumor-host interface. This pattern, termed "infiltrative tumor border configuration" has been consistently associated with poor survival outcome and early disease recurrence of CRC-patients. In addition, infiltrative tumor growth is frequently associated with presence of adverse clinicopathological features and molecular alterations related to aggressive tumor behavior including BRAFV600 mutation. In contrast, a well-demarcated "pushing" tumor border is seen frequently in CRC-cases with low risk for nodal and distant metastasis. A pushing border is a feature frequently associated with mismatch-repair deficiency and can be used to identify patients for molecular testing. Consequently, assessment of the tumor border configuration as an additional prognostic factor is recommended by the AJCC/UICC to aid the TNM-classification. To promote the assessment of the tumor border configuration in standard practice, consensus criteria on the defining features and method of assessment need to be developed further and tested for inter-observer reproducibility. The development of a standardized quantitative scoring system may lay the basis for verification of the prognostic associations of the tumor growth pattern in multivariate analyses and clinical trials. This article provides a comprehensive review of the diagnostic features, clinicopathological associations, and molecular alterations associated with the tumor border configuration in early stage and advanced CRC.

Impact of miR-21, miR-126 and miR-221 as prognostic factors of clear cell renal cell carcinoma with tumor thrombus of the inferior vena cava

Clear cell renal cell carcinoma (ccRCC) characterized by a tumor thrombus (TT) extending into the inferior vena cava (IVC) generally indicates poor prognosis. Nevertheless, the risk for tumor recurrence after nephrectomy and thrombectomy varies. An applicable and accurate prediction system to select ccRCC patients with TT of the IVC (ccRCC/TT) at high risk after nephrectomy is urgently needed, but has not been established up to now. To our knowledge, a possible role of microRNAs (miRs) for the development of ccRCC/TT or their impact as prognostic markers in ccRCC/TT has not been explored yet. Therefore, we analyzed the expression of the previously described onco-miRs miR-200c, miR-210, miR-126, miR-221, let-7b, miR-21, miR-143 and miR-141 in a study collective of 74 ccRCC patients. Using the expression profiles of these eight miRs we developed classification systems that accurately differentiate ccRCC from non-cancerous renal tissue and ccRCC/TT from tumors without TT. In the subgroup of 37 ccRCC/TT cases we found that miR-21, miR-126, and miR-221 predicted cancer related death (CRD) accurately and independently from other clinico-pathological features. Furthermore, a combined risk score based on the expression of miR-21, miR-126 and miR-221 was developed and showed high sensitivity and specificity to predict cancer specific survival (CSS) in ccRCC/TT. Using the combined risk score we were able to classify ccRCC/TT patients correctly into high and low risk cases. The risk stratification by the combined risk score (CRS) will benefit from further cohort validation and might have potential for clinical application as a molecular prediction system to identify high- risk ccRCC/TT patients.

Impact of histologic subtype on cancer-specific survival in patients with renal cell carcinoma and tumor thrombus

BACKGROUND Although different prognostic factors for patients with renal cell carcinoma (RCC) and vena cava tumor thrombus (TT) have been studied, the prognostic value of histologic subtype in these patients remains unclear. OBJECTIVE We analyzed the impact of histologic subtype on cancer-specific survival (CSS). DESIGN, SETTINGS, AND PARTICIPANTS We retrospectively analyzed the records of 1774 patients with RCC and TT who underwent radical nephrectomy and tumor thrombectomy from 1971 to 2012 at 22 US and European centers. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Multivariable ordered logistic and Cox regression models were used to quantify the impact of tumor histology on CSS. RESULTS AND LIMITATIONS Overall 5-yr CSS was 53.4% (confidence interval [CI], 50.5-56.2) in the entire group. TT level (according to the Mayo classification of macroscopic venous invasion in RCC) was I in 38.5% of patients, II in 30.6%, III in 17.3%, and IV in 13.5%. Histologic subtypes were clear cell renal cell carcinoma (cRCC) in 89.9% of patients, papillary renal cell carcinoma (pRCC) in 8.5%, and chromophobe RCC in 1.6%. In univariable analysis, pRCC was associated with a significantly worse CSS (p<0.001) compared with cRCC. In multivariable analysis, the presence of pRCC was independently associated with CSS (hazard ratio: 1.62; CI, 1.01-2.61; p<0.05). Higher TT level, positive lymph node status, distant metastasis, and fat invasion were also independently associated with CSS. CONCLUSIONS In our multi-institutional series, we found that patients with pRCC and vena cava TT who underwent radical nephrectomy and tumor thrombectomy had significantly worse cancer-specific outcomes when compared with patients with other histologic subtypes of RCC. We confirmed that higher TT level and fat invasion were independently associated with reduced CSS.