Rigorous Bounds to Retarded Learning

We show that the lower bound to the critical fraction of data needed to infer (learn) the orientation of the anisotropy axis of a probability distribution, determined by Herschkowitz and Opper [Phys.Rev.Lett. 86, 2174 (2001)], is not always valid. If there is some structure in the data along the anisotropy axis, their analysis is incorrect, and learning is possible with much less data points.Comment: 1 page, 1 figure. Comment accepted for publication in Physical Review Letter

Breast Cancer Subtypes, Mouse Models, and Microarrays

Breast cancer can no longer be viewed as a single disease. Molecular profiling studies have altered the way we consider breast cancer, showing us that there are several subtypes, each with their own unique biology. There are many model systems available in which to study breast cancer, however, each of these comes with advantages and disadvantages. We chose the mouse as a model to investigate breast cancer biology because it gives us the ability to study tumor progression and response to therapy in vivo. Numerous mouse models of breast carcinomas have been developed. The extent to which any faithfully represent clinically significant human phenotypes was unknown. Analogous to our human studies, we characterized mammary tumor gene expression profiles from a large number of murine models using DNA microarrays and compared the resulting data to our human breast tumor dataset. Two major applications of across-species tumor comparisons surfaced from these studies. First, we were able to determine that mouse models contain many of the global characteristics of particular classes or subtypes of human tumors. This included basal versus luminal distinctions, a proliferation/cell cycle signature, and a fibroblast signature. Second, the mouse models were able to inform the human disease; for example, we identified an amplicon that included the K-ras gene present in both mouse and human basal tumors. The high proliferation seen in common between mouse models of Rb loss and human basal-like breast tumors hinted that there is an Rb defect in this human subtype. And finally the mouse spindloid tumors shared significant gene overlap with a new molecular subtype of breast cancer. Although no single murine model recapitulated all the expression features of a given human subtype, these shared expression features have provided us a common framework so that we can now integrate these murine mammary tumor models into our studies of human breast cancer

Retarded Learning: Rigorous Results from Statistical Mechanics

We study learning of probability distributions characterized by an unknown symmetry direction. Based on an entropic performance measure and the variational method of statistical mechanics we develop exact upper and lower bounds on the scaled critical number of examples below which learning of the direction is impossible. The asymptotic tightness of the bounds suggests an asymptotically optimal method for learning nonsmooth distributions.Comment: 8 pages, 1 figur

Molecular analysis reveals heterogeneity of mouse mammary tumors conditionally mutant for Brca1

<p>Abstract</p> <p>Background</p> <p>Development of therapies for patients with BRCA1 mutations has been hampered by lack of readily available <it>in vitro </it>and <it>in vivo </it>models. We recently showed that transplantation of transgenic mammary tumors as cell suspensions into naïve recipients generates reproducible tumors with remarkable stability of gene expression profile. We examined the expression profiles of original and serially transplanted mammary tumors from <it>Brca1 </it>deficient mice, and tumor derived cell lines to validate their use for preclinical testing and studies of tumor biology.</p> <p>Methods</p> <p>Original tumors, serially transplanted and multiple cell lines derived from <it>Brca1 </it>mammary tumors were characterized by morphology, gene and protein expression, and cell surface markers.</p> <p>Results</p> <p>Gene expression among <it>Brca1 </it>tumors showed more heterogeneity than among previously characterized tumors from MMTV-<it>PyMT </it>and -<it>Wnt1 </it>models. Gene expression data segregated <it>Brca1 </it>tumors into 3 distinct types: basal, mixed luminal, and tumors with epithelial-to-mesenchymal transition (EMT). Serial transplantation of individual tumors and multiple cell lines derived from the original tumors recapitulated the molecular characteristics of each tumor of origin. One tumor had distinct features of EMT and gave rise to cell lines that contained a distinct CD44⁺/CD24^-/lowpopulation that may correlate with human breast cancer stem cells.</p> <p>Conclusion</p> <p>Although individual tumors expanded by transplantation maintain the genomic profile of the original tumors, the heterogeneity among <it>Brca1 </it>tumors limits the extent of their use for preclinical testing. However, cell lines offer a robust material for understanding tumor biology and response to therapies driven by BRCA1 deficiency.</p

The functional loss of the retinoblastoma tumor suppressor is a common event in basal-like and Luminal B breast carcinomas

Abstract Introduction Breast cancers can be classified using whole genome expression into distinct subtypes that show differences in prognosis. One of these groups, the basal-like subtype, is poorly differentiated, highly metastatic, genomically unstable, and contains specific genetic alterations such as the loss of tumour protein 53 (TP53). The loss of the retinoblastoma tumour suppressor encoded by the RB1 locus is a well-characterised occurrence in many tumour types; however, its role in breast cancer is less clear with many reports demonstrating a loss of heterozygosity that does not correlate with a loss of RB1 protein expression. Methods We used gene expression analysis for tumour subtyping and polymorphic markers located at the RB1 locus to assess the frequency of loss of heterozygosity in 88 primary human breast carcinomas and their normal tissue genomic DNA samples. Results RB1 loss of heterozygosity was observed at an overall frequency of 39%, with a high frequency in basal-like (72%) and luminal B (62%) tumours. These tumours also concurrently showed low expression of RB1 mRNA. p16INK4a was highly expressed in basal-like tumours, presumably due to a previously reported feedback loop caused by RB1 loss. An RB1 loss of heterozygosity signature was developed and shown to be highly prognostic, and was potentially a predictive marker of response to neoadjuvant chemotherapy. Conclusions These results suggest that the functional loss of RB1 is common in basal-like tumours, which may play a key role in dictating their aggressive biology and unique therapeutic responses

Perception of categories: from coding efficiency to reaction times

Reaction-times in perceptual tasks are the subject of many experimental and theoretical studies. With the neural decision making process as main focus, most of these works concern discrete (typically binary) choice tasks, implying the identification of the stimulus as an exemplar of a category. Here we address issues specific to the perception of categories (e.g. vowels, familiar faces, ...), making a clear distinction between identifying a category (an element of a discrete set) and estimating a continuous parameter (such as a direction). We exhibit a link between optimal Bayesian decoding and coding efficiency, the latter being measured by the mutual information between the discrete category set and the neural activity. We characterize the properties of the best estimator of the likelihood of the category, when this estimator takes its inputs from a large population of stimulus-specific coding cells. Adopting the diffusion-to-bound approach to model the decisional process, this allows to relate analytically the bias and variance of the diffusion process underlying decision making to macroscopic quantities that are behaviorally measurable. A major consequence is the existence of a quantitative link between reaction times and discrimination accuracy. The resulting analytical expression of mean reaction times during an identification task accounts for empirical facts, both qualitatively (e.g. more time is needed to identify a category from a stimulus at the boundary compared to a stimulus lying within a category), and quantitatively (working on published experimental data on phoneme identification tasks)

Combined Wnt/β-Catenin, Met, and CXCL12/CXCR4 Signals Characterize Basal Breast Cancer and Predict Disease Outcome

SummaryPrognosis for patients with estrogen-receptor (ER)-negative basal breast cancer is poor, and chemotherapy is currently the best therapeutic option. We have generated a compound-mutant mouse model combining the activation of β-catenin and HGF (Wnt-Met signaling), which produced rapidly growing basal mammary gland tumors. We identified the chemokine system CXCL12/CXCR4 as a crucial driver of Wnt-Met tumors, given that compound-mutant mice also deficient in the CXCR4 gene were tumor resistant. Wnt-Met activation rapidly expanded a population of cancer-propagating cells, in which the two signaling systems control different functions, self-renewal and differentiation. Molecular therapy targeting Wnt, Met, and CXCR4 in mice significantly delayed tumor development. The expression of a Wnt-Met 322 gene signature was found to be predictive of poor survival of human patients with ER-negative breast cancers. Thus, targeting CXCR4 and its upstream activators, Wnt and Met, might provide an efficient strategy for breast cancer treatment

Overexpression of miR-146a in basal-like breast cancer cells confers enhanced tumorigenic potential in association with altered p53 status

The tumor suppressor p53 is the most frequently mutated gene in human cancers, mutated in 25–30% of breast cancers. However, mutation rates differ according to breast cancer subtype, being more prevalent in aggressive estrogen receptor-negative tumors and basal-like and HER2-amplified subtypes. This heterogeneity suggests that p53 may function differently across breast cancer subtypes. We used RNAi-mediated p53 knockdown (KD) and antagomir-mediated KD of microRNAs to study how gene expression and cellular response to p53 loss differ in luminal versus basal-like breast cancer. As expected, p53 loss caused downregulation of established p53 targets (e.g. p21 and miR-34 family) and increased proliferation in both luminal and basal-like cell lines. However, some p53-dependent changes were subtype specific, including expression of miR-134, miR-146a and miR-181b. To study the cellular response to miR-146a upregulation in p53-impaired basal-like lines, antagomir KD of miR-146a was performed. KD of miR-146a caused decreased proliferation and increased apoptosis, effectively ablating the effects of p53 loss. Furthermore, we found that miR-146a upregulation decreased NF-κB expression and downregulated the NF-κB-dependent extrinsic apoptotic pathway (including tumor necrosis factor, FADD and TRADD) and antagomir-mediated miR-146a KD restored expression of these components, suggesting a plausible mechanism for miR-146a-dependent cellular responses. These findings are relevant to human basal-like tumor progression in vivo, since miR-146a is highly expressed in p53 mutant basal-like breast cancers. These findings suggest that targeting miR-146a expression may have value for altering the aggressiveness of p53 mutant basal-like tumors

Transcriptomic classification of genetically engineered mouse models of breast cancer identifies human subtype counterparts

Background: Human breast cancer is a heterogeneous disease consisting of multiple molecular subtypes. Genetically engineered mouse models are a useful resource for studying mammary cancers in vivo under genetically controlled and immune competent conditions. Identifying murine models with conserved human tumor features will facilitate etiology determinations, highlight the effects of mutations on pathway activation, and should improve preclinical drug testing. Results: Transcriptomic profiles of 27 murine models of mammary carcinoma and normal mammary tissue were determined using gene expression microarrays. Hierarchical clustering analysis identified 17 distinct murine subtypes. Cross-species analyses using three independent human breast cancer datasets identified eight murine classes that resemble specific human breast cancer subtypes. Multiple models were associated with human basal-like tumors including TgC3(1)-Tag, TgWAP-Myc and Trp53-/-. Interestingly, the TgWAPCre-Etv6 model mimicked the HER2-enriched subtype, a group of human tumors without a murine counterpart in previous comparative studies. Gene signature analysis identified hundreds of commonly expressed pathway signatures between linked mouse and human subtypes, highlighting potentially common genetic drivers of tumorigenesis. Conclusions: This study of murine models of breast carcinoma encompasses the largest comprehensive genomic dataset to date to identify human-to-mouse disease subtype counterparts. Our approach illustrates the value of comparisons between species to identify murine models that faithfully mimic the human condition and indicates that multiple genetically engineered mouse models are needed to represent the diversity of human breast cancers. The reported trans-species associations should guide model selection during preclinical study design to ensure appropriate representatives of human disease subtypes are used