Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.

/pdf/gene-expression-profiling-predicts-clinical-outcome-of-5bu8dmnf6h.pdf

Gene expression profiling predicts clinical outcome of breast cancer

Characteristic patterns of gene expression measured by DNA microarrays have been used to classify tumors into clinically relevant subgroups. In this study, we have refined the previously defined subtypes of breast tumors that could be distinguished by their distinct patterns of gene expression. A total of 115 malignant breast tumors were analyzed by hierarchical clustering based on patterns of expression of 534 "intrinsic" genes and shown to subdivide into one basal-like, one ERBB2-overexpressing, two luminal-like, and one normal breast tissue-like subgroup. The genes used for classification were selected based on their similar expression levels between pairs of consecutive samples taken from the same tumor separated by 15 weeks of neoadjuvant treatment. Similar cluster analyses of two published, independent data sets representing different patient cohorts from different laboratories, uncovered some of the same breast cancer subtypes. In the one data set that included information on time to development of distant metastasis, subtypes were associated with significant differences in this clinical feature. By including a group of tumors from BRCA1 carriers in the analysis, we found that this genotype predisposes to the basal tumor subtype. Our results strongly support the idea that many of these breast tumor subtypes represent biologically distinct disease entities.

https://www.pnas.org/content/pnas/100/14/8418.full.pdf

Repeated observation of breast tumor subtypes in independent gene expression data sets

https://www.nhlcyberfamily.org/tests/gene%20profiling%20diffuse.pdf

Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray

Metastasis is the principal event leading to death in individuals with cancer, yet its molecular basis is poorly understood. To explore the molecular differences between human primary tumors and metastases, we compared the gene-expression profiles of adenocarcinoma metastases of multiple tumor types to unmatched primary adenocarcinomas. We found a gene-expression signature that distinguished primary from metastatic adenocarcinomas. More notably, we found that a subset of primary tumors resembled metastatic tumors with respect to this gene-expression signature. We confirmed this finding by applying the expression signature to data on 279 primary solid tumors of diverse types. We found that solid tumors carrying the gene-expression signature were most likely to be associated with metastasis and poor clinical outcome (P < 0.03). These results suggest that the metastatic potential of human tumors is encoded in the bulk of a primary tumor, thus challenging the notion that metastases arise from rare cells within a primary tumor that have the ability to metastasize.

/pdf/a-molecular-signature-of-metastasis-in-primary-solid-tumors-40wevnxxty.pdf

A molecular signature of metastasis in primary solid tumors.

Apoptosis or programmed cell death is a key regulator of physiological growth control and regulation of tissue homeostasis. One of the most important advances in cancer research in recent years is the recognition that cell death mostly by apoptosis is crucially involved in the regulation of tumor formation and also critically determines treatment response. Killing of tumor cells by most anticancer strategies currently used in clinical oncology, for example, chemotherapy, γ-irradiation, suicide gene therapy or immunotherapy, has been linked to activation of apoptosis signal transduction pathways in cancer cells such as the intrinsic and/or extrinsic pathway. Thus, failure to undergo apoptosis may result in treatment resistance. Understanding the molecular events that regulate apoptosis in response to anticancer chemotherapy, and how cancer cells evade apoptotic death, provides novel opportunities for a more rational approach to develop molecular-targeted therapies for combating cancer.

Extrinsic versus intrinsic apoptosis pathways in anticancer chemotherapy.

Diffuse large B-cell lymphoma (DLBCL), the most common lymphoid malignancy in adults, is curable in less than 50% of patients. Prognostic models based on pre-treatment characteristics, such as the International Prognostic Index (IPI), are currently used to predict outcome in DLBCL. However, clinical outcome models identify neither the molecular basis of clinical heterogeneity, nor specific therapeutic targets. We analyzed the expression of 6,817 genes in diagnostic tumor specimens from DLBCL patients who received cyclophosphamide, adriamycin, vincristine and prednisone (CHOP)-based chemotherapy, and applied a supervised learning prediction method to identify cured versus fatal or refractory disease. The algorithm classified two categories of pa- tients with very different five-year overall survival rates (70% versus 12%). The model also ef- fectively delineated patients within specific IPI risk categories who were likely to be cured or to die of their disease. Genes implicated in DLBCL outcome included some that regulate responses to B-cell-receptor signaling, critical serine/threonine phosphorylation pathways and apoptosis. Our data indicate that supervised learning classification techniques can predict outcome in DLBCL and identify rational targets for intervention. © 2002 Nature Publishing Group http://medicine.nature.com

/pdf/diffuse-large-b-cell-lymphoma-outcome-prediction-by-gene-2dnd8dxnlx.pdf

Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning

Commercial products using hardware and firmware for high-speed network flow tracking are commonplace but typically restrict users to a few predefined options. Conversely, ensemble architectures with multiple network processing units (NPUs) and specialized accelerators are flexible enough for many tasks but relatively slow at routine flow management tasks. The paper presents a system that combines a field programmable gate array (FPGA)-driven table system with an ensemble network architecture. The system is especially effective when the FPGA system tracks flows and sends only selected packets to NPUs for further processing. The principal design goal is to achieve FPGA-level speed when processing tables for flows, actions, packet modification, key search and hash extraction and yet to allow users to initialize and dynamically modify the tables in terms of flexible, high-level packetC language types and structures.

FPGA-Driven Table System to Accelerate Network Flows

High-Dose T herapy W ith A utologous B one M arrow S upport as C onsolidation o f R emission i n F ollicular L ymphoma: Long-Term C linical a nd M olecular F ollow-Up

Purpose: This study was undertaken to test the hypothesis that serum selenium concentration at presentation correlates with dose delivery, first treatment response, and overall survival in patients with aggressive B-cell nonHodgkin’s lymphoma. Patients and Methods: The patients presented between July 1986 and March 1999 and received anthracyclinebased chemotherapy, radiotherapy, or both. The total selenium content was retrospectively analyzed in 100 sera, frozen at presentation, using inductively coupled plasma mass spectrometry. Results: The serum selenium concentration ranged from 0.33 to 1.51 mol/L (mean, 0.92 mol/L; United Kingdom adult reference range, 1.07 to 1.88 mol/L). Serum selenium concentration correlated closely with performance status but with no other clinical variable. Multivariate analysis revealed that increased dose delivery, summarized by an area under the curve, correlated positively with younger age (P < .001), advanced stage (P .001), and higher serum selenium concentration (P .032). Selenium level also correlated positively with response (odds ratio, 0.62; 95% confidence interval [CI], 0.43 to 0.90; P .011) and achievement of long-term remission after first treatment (log-rank test, 4.38; P .036). On multivariate analysis, selenium concentration was positively predictive of overall survival (hazard ratio [HR], 0.76 for 0.2 mol/L increase; 95% CI, 0.60 to 0.95; P .018), whereas age indicated negative borderline significance (HR, 1.09; 95% CI, 0.99 to 1.18; P .066). Conclusion: Serum selenium concentration at presentation is a prognostic factor, predicting positively for dose delivery, treatment response, and long-term survival in aggressive non-Hodgkin’s lymphoma. Unlike most existing prognostic factors in aggressive non-Hodgkin’s lymphoma, selenium supplementation may offer a novel therapeutic strategy in this frequently curable malignancy. J Clin Oncol 21:2335-2341. © 2003 by American Society of Clinical Oncology.

Presentation S erum S elenium P redicts f or O verall S urvival, Dose D elivery, a nd F irst T reatment R esponse i n A ggressive Non-Hodgkin's L ymphoma

Andrew Norton

Papers

Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning

FPGA-Driven Table System to Accelerate Network Flows

High-Dose T herapy W ith A utologous B one M arrow S upport as C onsolidation o f R emission i n F ollicular L ymphoma: Long-Term C linical a nd M olecular F ollow-Up

Presentation S erum S elenium P redicts f or O verall S urvival, Dose D elivery, a nd F irst T reatment R esponse i n A ggressive Non-Hodgkin's L ymphoma