A robust prognostic signature for hormone-positive node-negative breast cancer

BACKGROUND: Systemic chemotherapy in the adjuvant setting can cure breast cancer in some patients that would otherwise recur with incurable, metastatic disease. However, since only a fraction of patients would have recurrence after surgery alone, the challenge is to stratify high-risk patients (who stand to benefit from systemic chemotherapy) from low-risk patients (who can safely be spared treatment related toxicities and costs). METHODS: We focus here on risk stratification in node-negative, ER-positive, HER2-negative breast cancer. We use a large database of publicly available microarray datasets to build a random forests classifier and develop a robust multi-gene mRNA transcription-based predictor of relapse free survival at 10 years, which we call the Random Forests Relapse Score (RFRS). Performance was assessed by internal cross-validation, multiple independent data sets, and comparison to existing algorithms using receiver-operating characteristic and Kaplan-Meier survival analysis. Internal redundancy of features was determined using k-means clustering to define optimal signatures with smaller numbers of primary genes, each with multiple alternates. RESULTS: Internal OOB cross-validation for the initial (full-gene-set) model on training data reported an ROC AUC of 0.704, which was comparable to or better than those reported previously or obtained by applying existing methods to our dataset. Three risk groups with probability cutoffs for low, intermediate, and high-risk were defined. Survival analysis determined a highly significant difference in relapse rate between these risk groups. Validation of the models against independent test datasets showed highly similar results. Smaller 17-gene and 8-gene optimized models were also developed with minimal reduction in performance. Furthermore, the signature was shown to be almost equally effective on both hormone-treated and untreated patients. CONCLUSIONS: RFRS allows flexibility in both the number and identity of genes utilized from thousands to as few as 17 or eight genes, each with multiple alternatives. The RFRS reports a probability score strongly correlated with risk of relapse. This score could therefore be used to assign systemic chemotherapy specifically to those high-risk patients most likely to benefit from further treatment

Multimodal Treatment Eliminates Cancer Stem Cells and Leads to Long-Term Survival in Primary Human Pancreatic Cancer Tissue Xenografts.

Copyright: 2013 Hermann et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.PURPOSE: In spite of intense research efforts, pancreatic ductal adenocarcinoma remains one of the most deadly malignancies in the world. We and others have previously identified a subpopulation of pancreatic cancer stem cells within the tumor as a critical therapeutic target and additionally shown that the tumor stroma represents not only a restrictive barrier for successful drug delivery, but also serves as a paracrine niche for cancer stem cells. Therefore, we embarked on a large-scale investigation on the effects of combining chemotherapy, hedgehog pathway inhibition, and mTOR inhibition in a preclinical mouse model of pancreatic cancer. EXPERIMENTAL DESIGN: Prospective and randomized testing in a set of almost 200 subcutaneous and orthotopic implanted whole-tissue primary human tumor xenografts. RESULTS: The combined targeting of highly chemoresistant cancer stem cells as well as their more differentiated progenies, together with abrogation of the tumor microenvironment by targeting the stroma and enhancing tissue penetration of the chemotherapeutic agent translated into significantly prolonged survival in preclinical models of human pancreatic cancer. Most pronounced therapeutic effects were observed in gemcitabine-resistant patient-derived tumors. Intriguingly, the proposed triple therapy approach could be further enhanced by using a PEGylated formulation of gemcitabine, which significantly increased its bioavailability and tissue penetration, resulting in a further improved overall outcome. CONCLUSIONS: This multimodal therapeutic strategy should be further explored in the clinical setting as its success may eventually improve the poor prognosis of patients with pancreatic ductal adenocarcinoma

Understanding signaling cascades in melanoma

Understanding regulatory pathways involved in melanoma development and progression has advanced significantly in recent years. It is now appreciated that melanoma is the result of complex changes in multiple signaling pathways that affect growth control, metabolism, motility and the ability to escape cell death programs. Here we review the major signaling pathways currently known to be deregulated in melanoma with an implication to its development and progression. Among these pathways are Ras, B-Raf, MEK, PTEN, phosphatidylinositol-3 kinase (PI3Ks) and Akt which are constitutively activated in a significant number of melanoma tumors, in most cases due to genomic change. Other pathways discussed in this review include the [Janus kinase/signal transducer and activator of transcription (JAK/STAT), transforming growth factor-beta pathways which are also activated in melanoma, although the underlying mechanism is not yet clear. As a paradigm for remodeled signaling pathways, melanoma also offers a unique opportunity for targeted drug development.Fil: Lopez Bergami, Pablo Roberto. Sanford-burnham Medical Research Institute; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Fitchmann, B. Sanford-burnham Medical Research Institute; Estados UnidosFil: Ronai, Ze´ev. Sanford-burnham Medical Research Institute; Estados Unido

Targeting LIF-mediated paracrine interaction for pancreatic cancer therapy and monitoring.

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis largely owing to inefficient diagnosis and tenacious drug resistance. Activation of pancreatic stellate cells (PSCs) and consequent development of dense stroma are prominent features accounting for this aggressive biology1,2. The reciprocal interplay between PSCs and pancreatic cancer cells (PCCs) not only enhances tumour progression and metastasis but also sustains their own activation, facilitating a vicious cycle to exacerbate tumorigenesis and drug resistance3-7. Furthermore, PSC activation occurs very early during PDAC tumorigenesis8-10, and activated PSCs comprise a substantial fraction of the tumour mass, providing a rich source of readily detectable factors. Therefore, we hypothesized that the communication between PSCs and PCCs could be an exploitable target to develop effective strategies for PDAC therapy and diagnosis. Here, starting with a systematic proteomic investigation of secreted disease mediators and underlying molecular mechanisms, we reveal that leukaemia inhibitory factor (LIF) is a key paracrine factor from activated PSCs acting on cancer cells. Both pharmacologic LIF blockade and genetic Lifr deletion markedly slow tumour progression and augment the efficacy of chemotherapy to prolong survival of PDAC mouse models, mainly by modulating cancer cell differentiation and epithelial-mesenchymal transition status. Moreover, in both mouse models and human PDAC, aberrant production of LIF in the pancreas is restricted to pathological conditions and correlates with PDAC pathogenesis, and changes in the levels of circulating LIF correlate well with tumour response to therapy. Collectively, these findings reveal a function of LIF in PDAC tumorigenesis, and suggest its translational potential as an attractive therapeutic target and circulating marker. Our studies underscore how a better understanding of cell-cell communication within the tumour microenvironment can suggest novel strategies for cancer therapy

A multi-gene signature predicts outcome in patients with pancreatic ductal adenocarcinoma.

© 2014 Haider et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Improved usage of the repertoires of pancreatic ductal adenocarcinoma (PDAC) profiles is crucially needed to guide the development of predictive and prognostic tools that could inform the selection of treatment options

A Developmental Approach to Machine Learning?

Visual learning depends on both the algorithms and the training material. This essay considers the natural statistics of infant- and toddler-egocentric vision. These natural training sets for human visual object recognition are very different from the training data fed into machine vision systems. Rather than equal experiences with all kinds of things, toddlers experience extremely skewed distributions with many repeated occurrences of a very few things. And though highly variable when considered as a whole, individual views of things are experienced in a specific order – with slow, smooth visual changes moment-to-moment, and developmentally ordered transitions in scene content. We propose that the skewed, ordered, biased visual experiences of infants and toddlers are the training data that allow human learners to develop a way to recognize everything, both the pervasively present entities and the rarely encountered ones. The joint consideration of real-world statistics for learning by researchers of human and machine learning seems likely to bring advances in both disciplines

Feature biases in early word learning : network distinctiveness predicts age of acquisition

Do properties of a word’s features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge lengths computed using various distance measures. Feature distinctiveness was computed as a distance measure, showing how far an object in a network is from other objects based on shared and non-shared features. Feature distinctiveness predicted order of acquisition across all measures; words that were further away from other words in the network space were learned earlier. The best distance measures were based only on non-shared features (object dissimilarity) and did not include shared features (object similarity). This indicates that shared features may play less of a role in early word learning than non-shared features. In addition, the strongest effects were found for visual form and surface features. Cluster analysis further revealed that this effect is a localized effect in the object feature space, where objects’ distances from their cluster centroid were inversely correlated with their age of acquisition. Together, these results suggest a role for feature distinctiveness in early word learning