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PubMed2025

Novel approaches to clinical trial design in cancer neuroscience.

The emerging field of cancer neuroscience has revealed profound bidirectional interactions between the nervous system and cancer cells, identifying novel therapeutic vulnerabilities across diverse malignancies. This review examines the unique challenges and strategies for translating these insights into effective therapies. We propose innovative approaches to overcome these barriers through drug repurposing, enhanced biomarker development, and optimized trial designs. Repurposing neuroactive drugs with established safety profiles offers an accelerated path to clinical impact, particularly for targeting glutamatergic, adrenergic, and neurotrophic signaling pathways. Emphasizing mitigation of neurotoxicity and improved patient quality of life will be paramount moving forward. Repurposed agents that show preliminary potential for "dual use" (i.e., simultaneous toxicity mitigation and synergistic anti-tumor effects) are highlighted for special consideration. Master protocols and window-of-opportunity trials provide platforms to rapidly validate mechanisms while addressing patient-centered outcomes. By systematically addressing these foundational elements across disciplines, cancer neuroscience can translate its profound mechanistic insights into meaningful therapeutic advances for patients with treatment-resistant malignancies.

Humans↗
PubMedDate not supplied

Clinical proteomics: Applications for prostate cancer biomarker discovery and detection.

The science of proteomics comprises much more than simply generating lists of proteins that change in expression as a cause of or consequence of pathophysiology. The goal of proteomics should be to characterize the information flow through the intercellular protein circuitry that communicates with the extracellular microenvironment and then ultimately to the serum/plasma macroenvironment. Serum proteomic pattern diagnostics is a new type of proteomic concept in which patterns of ion signatures generated from high dimensional mass spectrometry data are used as diagnostic classifiers. This recent approach has exciting potential for clinical utility of diagnostic patterns because low molecular weight metabolites, peptides, and protein fragments may have higher accuracy than traditional biomarkers of cancer detection. Intriguingly, we now have discovered that this diagnostic information exists in a bound state, complexed with circulating highly abundant carrier proteins. These diagnostic fragments may one day be harvested by circulating nanoparticles, designed to absorb, enrich, and amplify the repertoire of diagnostic biomarkers generated-even at the critical, initial stages of carcinogenesis.

Biomarkers, Tumor↗
PubMed2002

Discovery of novel tumor markers of pancreatic cancer using global gene expression technology.

Despite several advances in our basic understanding and in the clinical management of pancreatic cancer, virtually all patients who will be diagnosed with pancreatic cancer will die from this disease. The high mortality of pancreatic cancer is predominantly because of diagnosis at an advanced stage of disease and a lack of effective treatments. We used the Gene Logic Inc. BioExpress platform and Affymetrix GeneChip arrays to identify genes differentially expressed in pancreatic cancer. cDNA was prepared from samples of normal pancreas (n = 11), normal gastrointestinal mucosa (n = 22), resected pancreas cancer tissues (n = 14), and pancreas cancer cell lines (n = 8), and was hybridized to the complete Affymetrix Human Genome U95 GeneChip set (arrays U95 A, B, C, D, and E) for simultaneous analysis of 60,000 cDNA fragments, with 12,000 fragments covering full-length genes and 48,000 fragments covering expressed sequence tags (ESTs). Genes expressed at levels at least fivefold greater in the pancreatic cancers ascompared to normal tissues were identified. Serial analysis of gene expression (SAGE) libraries (http://www.ncbi.nlm.nih.gov/SAGE/) of two normal pancreatic ductal cell cultures (HX and H126) were used to exclude genes expressed in the normal ducts (more than five tags per library). Differential expression of selected candidate genes was validated by immunohistochemical analysis (n = 3), by in situ hybridization (n = 1), and by reverse transcriptase-polymerase chain reaction (n = 8). One hundred eighty fragments were identified as having fivefold or greater expression levels in pancreas cancer specimens as compared to normal tissue, of which 124 corresponded to known genes and 56 to ESTs. Of these 124 fragments, 10 genes were represented by two or more fragments, resulting in 107 known genes identified as differentially expressed in pancreatic cancer. An additional 10 genes were expressed in the SAGE libraries of normal pancreatic duct epithelium, and were excluded from further analysis. A literature search indicated that 28 of the remaining 97 genes have been reported in association with pancreatic cancer, validating this approach. The remaining 69 genes have not been implicated in pancreatic cancer before, and have immediate potential as novel therapeutic targets and tumor markers of pancreatic cancer.

Biomarkers, Tumor↗
PubMed2002

The promise of biomarkers in cancer screening and detection.

Despite the recent decline in the incidence of cancer, long-term mortality rates remain unchanged. One of the most important factors in the survival of cancer is detection at an early stage. Clinical assays that detect the early events of cancer offer an opportunity to intervene and prevent cancer progression. Biomarkers are important molecular signatures of the phenotype of a cell that aid in early cancer detection and risk assessment. Although new information and technologies are clearly important for new biomarker discovery, we face major hurdles in translating new findings into clinical application. Here, we discuss examples of recent advances and limitations in cancer biomarker identification and validation, and the implications for cancer prevention.

Biomarkers↗
PubMed2004

DNA-microarray analysis of brain cancer: molecular classification for therapy.

Primary brain tumours are among the most lethal of all cancers, largely as a result of their lack of responsiveness to current therapy. Numerous new therapies hold great promise for the treatment of patients with brain cancer, but the main challenge is to determine which treatment is most likely to benefit an individual patient. DNA-microarray-based technologies, which allow simultaneous analysis of expression of thousands of genes, have already begun to uncover previously unrecognized patient subsets that differ in their survival. Here, we review the progress made so far in using DNA microarrays to optimize brain cancer therapy.

Animals↗
PubMed2004

Gastric and intestinal phenotypic marker expression in gastric carcinomas and recurrence pattern after surgery-immunohistochemical analysis of 213 lesions.

Both gastric and intestinal phenotypic markers are known to be expressed in gastric carcinomas, irrespective of their histologic type. In the present study, the relation between gastric and intestinal phenotypic marker expression in gastric carcinomas and the recurrence pattern after surgery was examined. The phenotypic marker expression of the tumour was determined by examining the expression of human gastric mucin (HGM), MUC6, MUC2 and CD10 in 213 advanced gastric carcinomas in 213 patients who had undergone a curative resection (97 died from recurrent gastric carcinoma and 116 were alive without recurrence at the end of the follow-up period). Tumours were classified into gastric (G), gastric and intestinal mixed (GI), intestinal (I) or unclassified (UC) phenotypes according to the immunopositivity of HGM, MUC6, MUC2 and CD10 stainings. The incidence of HGM-positive tumours and MUC2-negative tumours was significantly higher in tumours with peritoneal recurrence than in tumours without recurrence (73.3%, 44 out of 60 cases vs 54.3%, 63 out of 116 (P=0.022); and 70.0%, 42 out of 60 vs 38.8%, 45 out of 116 (P=0.0002), respectively). The incidence of G-phenotype tumours was also significantly higher in tumours with peritoneal recurrence than in tumours without recurrence (58.3%, 35 out of 60 cases vs 28.4%, 33 out of 116 (P=0.0002)). The incidence of MUC2-negative tumours and CD10-positive tumours was significantly higher in tumours with haematogenous recurrence than in tumours without recurrence (62.5%, 20 out of 32 cases vs 38.8%, 45 out of 116 (P=0.028); and 43.8%, 14 out of 32 vs 23.3%, 27 out of 116 (P=0.039); respectively). Our present findings show that the gastric and intestinal phenotypic marker expression of the tumour, determined by immunohistochemical staining for HGM, MUC6, MUC2 and CD10, can be used to predict the pattern of gastric carcinoma recurrence after curative resection.

Aged↗
PubMed2006

Differential proteomic alterations between localised and metastatic prostate cancer.

Molecular alterations in the prostate cancer proteome mediate the functional and phenotypic transformation from clinically localised to metastatic cancer, a transition that drives patient's mortality and challenges therapeutic intervention. A first approximation of differential proteomic alterations stratified by disease stage has yielded repertoires of potential diagnostic and prognostic markers, multiplex signatures of predictive value, and yield fundamental insight into molecular commonalities in cancer progression. Deciphering these causative proteomic alterations from the molecular noise will continue to mature our understanding of tumour biology and drive new computational and integrative approaches to model a system's view that accommodates the heterogeneity of prostate cancer progression.

Biomarkers, Tumor↗
PubMed2005

An integrated data analysis approach to characterize genes highly expressed in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the major causes of cancer deaths worldwide. New diagnostic and therapeutic options are needed for more effective and early detection and treatment of this malignancy. We identified 703 genes that are highly expressed in HCC using DNA microarrays, and further characterized them in order to uncover novel tumor markers, oncogenes, and therapeutic targets for HCC. Using Gene Ontology annotations, genes with functions related to cell proliferation and cell cycle, chromatin, repair, and transcription were found to be significantly enriched in this list of highly expressed genes. We also identified a set of genes that encode secreted (e.g. GPC3, LCN2, and DKK1) or membrane-bound proteins (e.g. GPC3, IGSF1, and PSK-1), which may be attractive candidates for the diagnosis of HCC. A significant enrichment of genes highly expressed in HCC was found on chromosomes 1q, 6p, 8q, and 20q, and we also identified chromosomal clusters of genes highly expressed in HCC. The microarray analyses were validated by RT-PCR and PCR. This approach of integrating other biological information with gene expression in the analysis helps select aberrantly expressed genes in HCC that may be further studied for their diagnostic or therapeutic utility.

Biomarkers, Tumor↗
PubMed2001

Multiclass cancer diagnosis using tumor gene expression signatures.

The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a support vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.

Biomarkers, Tumor↗
PubMed2006

Gene expression in mixed type liposarcoma.

AIMS: Mixed type liposarcomas are rare. Here, we analysed the characteristics of an unusual case of mixed type liposarcoma, which consisted of a well-differentiated liposarcoma (WDL) and a pleomorphic liposarcoma (PL), with a special emphasis on molecular alterations. METHODS: Microscopic and immunohistochemical approaches were used to investigate this case of mixed type liposarcoma, and to identify molecular alterations in this tumour, gene expression was examined in PL, WDL, and normal adipose tissue (NA) samples using a 17,000 cDNA microarray. RESULTS: The tumour mass, 9 x 5 x 5 cm, was located in the left upper arm of a 76-year-old man. Grossly, the proximal portion of the tumour was composed of a yellowish fatty lesion, whereas the distal portion of the tumour was whitish and necrotic in nature. Histologically, the tumour was composed of two distinct components. The proximal component of the tumour was a WDL and the distal component was a PL. Immunohistochemically, S100 protein immunoreactivity highlighted lipoblasts in both tumour portions. The Ki-67 proliferation index was <1% in the WDL and 20% in the PL. MDM2 was positive in the WDL, but negative in the PL. p53 was negative in both areas. Numerous differentially expressed genes were found, which included genes coding for signal transduction, transcription, cell cycle, enzyme, structural protein, immune system and others. CONCLUSIONS: Our data demonstrate that multiple genes are differentially expressed in mixed type liposarcoma and suggest that these genes are associated with the differences in the morphological characteristics and pathogenesis of mixed type liposarcoma.

Adipose Tissue↗
PubMed2006

Binary state pattern clustering: a digital paradigm for class and biomarker discovery in gene microarray studies of cancer.

Class and biomarker discovery continue to be among the preeminent goals in gene microarray studies of cancer. We have developed a new data mining technique, which we call Binary State Pattern Clustering (BSPC) that is specifically adapted for these purposes, with cancer and other categorical datasets. BSPC is capable of uncovering statistically significant sample subclasses and associated marker genes in a completely unsupervised manner. This is accomplished through the application of a digital paradigm, where the expression level of each potential marker gene is treated as being representative of its discrete functional state. Multiple genes that divide samples into states along the same boundaries form a kind of gene-cluster that has an associated sample-cluster. BSPC is an extremely fast deterministic algorithm that scales well to large datasets. Here we describe results of its application to three publicly available oligonucleotide microarray datasets. Using an alpha-level of 0.05, clusters reproducing many of the known sample classifications were identified along with associated biomarkers. In addition, a number of simulations were conducted using shuffled versions of each of the original datasets, noise-added datasets, as well as completely artificial datasets. The robustness of BSPC was compared to that of three other publicly available clustering methods: ISIS, CTWC and SAMBA. The simulations demonstrate BSPC's substantially greater noise tolerance and confirm the accuracy of our calculations of statistical significance.

Algorithms↗
PubMed2004

BagBoosting for tumor classification with gene expression data.

MOTIVATION: Microarray experiments are expected to contribute significantly to the progress in cancer treatment by enabling a precise and early diagnosis. They create a need for class prediction tools, which can deal with a large number of highly correlated input variables, perform feature selection and provide class probability estimates that serve as a quantification of the predictive uncertainty. A very promising solution is to combine the two ensemble schemes bagging and boosting to a novel algorithm called BagBoosting. RESULTS: When bagging is used as a module in boosting, the resulting classifier consistently improves the predictive performance and the probability estimates of both bagging and boosting on real and simulated gene expression data. This quasi-guaranteed improvement can be obtained by simply making a bigger computing effort. The advantageous predictive potential is also confirmed by comparing BagBoosting to several established class prediction tools for microarray data. AVAILABILITY: Software for the modified boosting algorithms, for benchmark studies and for the simulation of microarray data are available as an R package under GNU public license at http://stat.ethz.ch/~dettling/bagboost.html.

Algorithms↗
PubMed2004

Superior feature-set ranking for small samples using bolstered error estimation.

MOTIVATION: Ranking feature sets is a key issue for classification, for instance, phenotype classification based on gene expression. Since ranking is often based on error estimation, and error estimators suffer to differing degrees of imprecision in small-sample settings, it is important to choose a computationally feasible error estimator that yields good feature-set ranking. RESULTS: This paper examines the feature-ranking performance of several kinds of error estimators: resubstitution, cross-validation, bootstrap and bolstered error estimation. It does so for three classification rules: linear discriminant analysis, three-nearest-neighbor classification and classification trees. Two measures of performance are considered. One counts the number of the truly best feature sets appearing among the best feature sets discovered by the error estimator and the other computes the mean absolute error between the top ranks of the truly best feature sets and their ranks as given by the error estimator. Our results indicate that bolstering is superior to bootstrap, and bootstrap is better than cross-validation, for discovering top-performing feature sets for classification when using small samples. A key issue is that bolstered error estimation is tens of times faster than bootstrap, and faster than cross-validation, and is therefore feasible for feature-set ranking when the number of feature sets is extremely large.

Algorithms↗