Effects of Image Quantity and Image Source Variation on Machine Learning Histology Differential Diagnosis Models

Aims: Histology, the microscopic study of normal tissues, is a crucial element of most medical curricula. Learning tools focused on histology are very important to learners who seek diagnostic competency within this important diagnostic arena. Recent developments in machine learning (ML) suggest that certain ML tools may be able to benefit this histology learning platform. Here, we aim to explore how one such tool based on a convolutional neural network, can be used to build a generalizable multi-classification model capable of classifying microscopic images of human tissue samples with the ultimate goal of providing a differential diagnosis (a list of look-alikes) for each entity. Methods: We obtained three institutional training datasets and one generalizability test dataset, each containing images of histologic tissues in 38 categories. Models were trained on data from single institutions, low quantity combinations of multiple institutions, and high quantity combinations of multiple institutions. Models were tested against withheld validation data, external institutional data, and generalizability test images obtained from Google image search. Performance was measured with macro and micro accuracy, sensitivity, specificity, and f1-score. Results: In this study, we were able to show that such a model\u27s generalizability is dependent on both the training data source variety and the total number of training images used. Models which were trained on 760 images from only a single institution performed well on withheld internal data but poorly on external data (lower generalizability). Increasing data source diversity improved generalizability, even when decreasing data quantity: models trained on 684 images, but from three sources improved generalization accuracy between 4.05% and 18.59%. Maintaining this diversity and increasing the quantity of training images to 2280 further improved generalization accuracy between 16.51% and 32.79%. Conclusions: This pilot study highlights the significance of data diversity within such studies. As expected, optimal models are those that incorporate both diversity and quantity into their platforms

In Vivo RNAi Screening Identifies Regulators of Actin Dynamics as Key Determinants of Lymphoma Progression

April 1, 2010Mouse models have markedly improved our understanding of cancer development and tumor biology. However, these models have shown limited efficacy as tractable systems for unbiased genetic experimentation. Here, we report the adaptation of loss-of-function screening to mouse models of cancer. Specifically, we have been able to introduce a library of shRNAs into individual mice using transplantable Eμ-myc lymphoma cells. This approach has allowed us to screen nearly 1,000 genetic alterations in the context of a single tumor-bearing mouse. These experiments have identified a central role for regulators of actin dynamics and cell motility in lymphoma cell homeostasis in vivo. Validation experiments confirmed that these proteins represent bona fide lymphoma drug targets. Additionally, suppression of two of these targets, Rac2 and twinfilin, potentiated the action of the front-line chemotherapeutic vincristine, suggesting a critical relationship between cell motility and tumor relapse in hematopoietic malignancies.National Institutes of Health (U.S.) (RO1 CA128803-01)Massachusetts Institute of Technology. Dept. of Biology (Training Grant)Massachusetts Institute of Technology. Undergraduate Research Opportunities ProgramNational Cancer Institute (U.S.). Integrative Cancer Biology Program (Grant 1-U54-CA112967

Common Breast Cancer Susceptibility Alleles and the Risk of Breast Cancer for BRCA1 and BRCA2 Mutation Carriers: Implications for Risk Prediction

The known breast cancer (BC) susceptibility polymorphisms in FGFR2, TNRC9/TOX3, MAP3K1,LSP1 and 2q35 confer increased risks of BC for BRCA1 or BRCA2 mutation carriers. We evaluated the associations of three additional SNPs, rs4973768 in SLC4A7/NEK10, rs6504950 in STXBP4/COX11 and rs10941679 at 5p12 and reanalyzed the previous associations using additional carriers in a sample of 12,525 BRCA1 and 7,409 BRCA2 carriers. Additionally, we investigated potential interactions between SNPs and assessed the implications for risk prediction. The minor alleles of rs4973768 and rs10941679 were associated with increased BC risk for BRCA2 carriers (per-allele Hazard Ratio (HR)=1.10, 95%CI:1.03-1.18, p=0.006 and HR=1.09, 95%CI:1.01-1.19, p=0.03, respectively). Neither SNP was associated with BC risk for BRCA1 carriers and rs6504950 was not associated with BC for either BRCA1 or BRCA2 carriers. Of the nine polymorphisms investigated, seven were associated with BC for BRCA2 carriers (FGFR2, TOX3, MAP3K1, LSP1, 2q35, SLC4A7, 5p12, p-values:7×10−11-0.03), but only TOX3 and 2q35 were associated with the risk for BRCA1 carriers (p=0.0049, 0.03 respectively). All risk associated polymorphisms appear to interact multiplicatively on BC risk for mutation carriers. Based on the joint genotype distribution of the seven risk associated SNPs in BRCA2 mutation carriers, the 5% of BRCA2 carriers at highest risk (i.e. between 95th and 100th percentiles) were predicted to have a probability between 80% and 96% of developing BC by age 80, compared with 42-50% for the 5% of carriers at lowest risk. Our findings indicated that these risk differences may be sufficient to influence the clinical management of mutation carriers

Interplay between BRCA1 and RHAMM regulates epithelial apicobasal polarization and may influence risk of breast

Differentiated mammary epithelium shows apicobasal polarity, and loss of tissue organization is an early hallmark of breast carcinogenesis. In BRCA1 mutation carriers, accumulation of stem and progenitor cells in normal breast tissue and increased risk of developing tumors of basal-like type suggest that BRCA1 regulates stem/progenitor cell proliferation and differentiation. However, the function of BRCA1 in this process and its link to carcinogenesis remain unknown. Here we depict a molecular mechanism involving BRCA1 and RHAMM that regulates apicobasal polarity and, when perturbed, may increase risk of breast cancer. Starting from complementary genetic analyses across families and populations, we identified common genetic variation at the low-penetrance susceptibility HMMR locus (encoding for RHAMM) that modifies breast cancer risk among BRCA1, but probably not BRCA2, mutation carriers: n = 7,584, weighted hazard ratio (wHR) = 1.09 (95% CI 1.02–1.16), ptrend = 0.017; and n = 3,965, wHR = 1.04 (95% CI 0.94–1.16), ptrend = 0.43; respectively. Subsequently, studies of MCF10A apicobasal polarization revealed a central role for BRCA1 and RHAMM, together with AURKA and TPX2, in essential reorganization of microtubules. Mechanistically, reorganization is facilitated by BRCA1 and impaired by AURKA, which is regulated by negative feedback involving RHAMM and TPX2. Taken together, our data provide fundamental insight into apicobasal polarization through BRCA1 function, which may explain the expanded cell subsets and characteristic tumor type accompanying BRCA1 mutation, while also linking this process to sporadic breast cancer through perturbation of HMMR/RHAMM.This work was funded by the Spanish Ministries of Health, and Science ane Innovation (CB07/02/2005; FIS 08/1120, 08/1359, 08/1635, and 09/02483; RTICCC RD06/0020/1060 and RD06/0020/0028; Transversal Action Against Cancer; the Spanish Biomedical Research Centre Networks for Epidemiology and Public Health, and Rare Diseases; and the ‘‘Ramón y Cajal’’ Young Investigator Program), the Spanish National Society of Medical Oncology (2010), the SpanishAssociation Against Cancer (AECC 2010), the AGAUR Catalan Government Agency (2009SGR1489 and 2009SGR293; and the Beatriu Pinós Postdoctoral Program), the Ramón Areces Foundation (XV), the ‘‘Roses Contra el Caàncer’’ Foundation, the Michael Cuccione Foundation for Childhood Cancer Research, Cancer Research–UK (C490/A10119, C1287/A8874, C1287/A10118, C5047/A8385, and C8197/A10123), the National Institute for Health Research (UK), the Association for International Cancer Research (AICR-07-0454), the Ligue National Contre le Cancer (France), the Association ‘‘Le cancer du sein, parlons en!’’, the Dutch Cancer Society (NKI 1998–1854, 2004–3088, and 2007–3756), the Fondazione Italiana per la Ricerca sul Cancro (‘‘Hereditary Tumors’’), the Associazione Italiana per la Ricerca sul Cancro (4017), the Italian Ministero della Salute (RFPS-2006-3-340203 and ‘‘Progetto Tumori Femminili’’), the Italian Ministero dell’Universita’ e Ricerca(RBLAO3-BETH), the Fondazione IRCCS Istituto Nazionale Tumori (INT ‘‘561000’’), the Fondazione Cassa di Risparmio di Pisa (Istituto Toscano Tumori), the National Breast Cancer Foundation (Australia), the Australian National Health and Medical Research Council (145684, 288704, and 454508), the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania, and South Australia, the Cancer Foundation of Western Australia, the German Cancer Aid (107054), the Center for Molecular Medicine Cologne (TV93), the National Cancer Institute (USA; CA128978 and CA122340), National Institutes of Health (RFA-CA-06-503, BCFR U01 CA69398, CA69417, CA69446, CA69467, CA69631, and CA69638), the Research Triangle Institute Informatics Support Center (RFP N02PC45022-4/n6), the Specialized Program of Research Excellence (SPORE P50 CA83638 and CA113916), the Department of Defense Breast Cancer Research Program (05/0612), the Eileen Stein Jacoby Fund, the Breast Cancer Research Foundation, the Marianne and Robert MacDonald Foundation, the Komen Foundation, the Helsinki University Central Hospital Research Fund, the Academy of Finland (110663), the Finnish Cancer Society, the Sigrid Juselius Foundation, and the EU FP7 (223175,HEALTH-F2-2009-223175). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip