@article {334, title = {NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images.}, journal = {Med Image Anal}, volume = {78}, year = {2022}, month = {2022 May}, pages = {102384}, abstract = {

Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tumor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and provides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet unsupervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates several novel and state-of-the-art deep learning techniques, such as patch-level contrastive learning, multi-level graph embeddings, a novel max-sum pooling operation, or a metric that quantifies the relevance that each microenvironment element has in the individual predictions. We validate NaroNet using synthetic data simulating multiplex-immunostained images where a patient label is artificially associated to the -adjustable- probabilistic incidence of different microenvironment elements. We then apply our model to two sets of images of human cancer tissues: 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancer patients; and 382 35-plex mass cytometry images from 215 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions of relevant clinical information while associating those predictions to the presence of specific microenvironment elements.

}, keywords = {Breast Neoplasms, Female, Humans, Prognosis, Tumor Microenvironment}, issn = {1361-8423}, doi = {10.1016/j.media.2022.102384}, author = {Jim{\'e}nez-S{\'a}nchez, Daniel and Ariz, Mikel and Chang, Hang and Matias-Guiu, Xavier and de Andrea, Carlos E and Ortiz-de-Sol{\'o}rzano, Carlos} } @article {326, title = {A new platform for ultra-high dose rate radiobiological research using the BELLA PW laser proton beamline.}, journal = {Sci Rep}, volume = {12}, year = {2022}, month = {2022 01 27}, pages = {1484}, abstract = {

Radiotherapy is the current standard of care for more than 50\% of all cancer patients. Improvements in radiotherapy (RT) technology have increased tumor targeting and normal tissue sparing. Radiations at ultra-high dose rates required for FLASH-RT effects have sparked interest in potentially providing additional differential therapeutic benefits. We present a new experimental platform that is the first one to deliver petawatt laser-driven proton pulses of 2\ MeV energy at 0.2\ Hz repetition rate by means of a compact, tunable active plasma lens beamline to biological samples. Cell monolayers grown over a 10\ mm diameter field were exposed to clinically relevant proton doses ranging from 7 to 35\ Gy at ultra-high instantaneous dose rates of 10\ Gy/s. Dose-dependent cell survival measurements of human normal and tumor cells exposed to LD protons showed significantly higher cell survival of normal-cells compared to tumor-cells for total doses of 7\ Gy and higher, which was not observed to the same extent for X-ray reference irradiations at clinical dose rates. These findings provide preliminary evidence that compact LD proton sources enable a new and promising platform for investigating the physical, chemical and biological mechanisms underlying the FLASH effect.

}, keywords = {Cell Line, Humans, Lasers, Monte Carlo Method, Neoplasms, Proton Therapy, Radiation Oncology, Radiobiology, Radiometry, Radiotherapy Dosage, Synchrotrons}, issn = {2045-2322}, doi = {10.1038/s41598-022-05181-3}, author = {Bin, Jianhui and Obst-Huebl, Lieselotte and Mao, Jian-Hua and Nakamura, Kei and Geulig, Laura D and Chang, Hang and Ji, Qing and He, Li and De Chant, Jared and Kober, Zachary and Gonsalves, Anthony J and Bulanov, Stepan and Celniker, Susan E and Schroeder, Carl B and Geddes, Cameron G R and Esarey, Eric and Simmons, Blake A and Schenkel, Thomas and Blakely, Eleanor A and Steinke, Sven and Snijders, Antoine M} } @article {309, title = {Genetic and metabolic links between the murine microbiome and memory.}, journal = {Microbiome}, volume = {8}, year = {2020}, month = {2020 04 17}, pages = {53}, abstract = {

BACKGROUND: Recent evidence has linked the gut microbiome to host behavior via the gut-brain axis [1-3]; however, the underlying mechanisms remain unexplored. Here, we determined the links between host genetics, the gut microbiome and memory using the genetically defined Collaborative Cross (CC) mouse cohort, complemented with microbiome and metabolomic analyses in conventional and germ-free (GF) mice.

RESULTS: A genome-wide association analysis (GWAS) identified 715 of 76,080 single-nucleotide polymorphisms (SNPs) that were significantly associated with short-term memory using the passive avoidance model. The identified SNPs were enriched in genes known to be involved in learning and memory functions. By 16S rRNA gene sequencing of the gut microbial community in the same CC cohort, we identified specific microorganisms that were significantly correlated with longer latencies in our retention test, including a positive correlation with Lactobacillus. Inoculation of GF mice with individual species of Lactobacillus (L. reuteri F275, L. plantarum BDGP2 or L. brevis BDGP6) resulted in significantly improved memory compared to uninoculated or E. coli DH10B inoculated controls. Untargeted metabolomics analysis revealed significantly higher levels of several metabolites, including lactate, in the stools of Lactobacillus-colonized mice, when compared to GF control mice. Moreover, we demonstrate that dietary lactate treatment alone boosted memory in conventional mice. Mechanistically, we show that both inoculation with Lactobacillus or lactate treatment significantly increased the levels of the neurotransmitter, gamma-aminobutyric acid (GABA), in the hippocampus of the mice.

CONCLUSION: Together, this study provides new evidence for a link between Lactobacillus and memory and our results open possible new avenues for treating memory impairment disorders using specific gut microbial inoculants and/or metabolites. Video Abstract.

}, issn = {2049-2618}, doi = {10.1186/s40168-020-00817-w}, author = {Mao, Jian-Hua and Kim, Young-Mo and Zhou, Yan-Xia and Hu, Dehong and Zhong, Chenhan and Hang Chang and Brislawn, Colin J and Fansler, Sarah and Langley, Sasha and Wang, Yunshan and Peisl, B Y Loulou and Celniker, Susan E and Threadgill, David W and Wilmes, Paul and Orr, Galya and Metz, Thomas O and Jansson, Janet K and Snijders, Antoine M} } @article {177, title = {Radiation acts on the microenvironment to affect breast carcinogenesis by distinct mechanisms that decrease cancer latency and affect tumor type.}, journal = {Cancer Cell}, volume = {19}, year = {2011}, month = {2011 May 17}, pages = {640-51}, abstract = {

Tissue microenvironment is an important determinant of carcinogenesis. We demonstrate that ionizing radiation, a known carcinogen, affects cancer frequency and characteristics by acting on the microenvironment. Using a mammary chimera model in which an irradiated host is transplanted with oncogenic Trp53 null epithelium, we show accelerated development of aggressive tumors whose molecular signatures were distinct from tumors arising in nonirradiated hosts. Molecular and genetic approaches show that TGFβ mediated tumor acceleration. Tumor molecular signatures implicated TGFβ, and genetically reducing TGFβ abrogated the effect on latency. Surprisingly, tumors from irradiated hosts were predominantly estrogen receptor negative. This effect was TGFβ independent and linked to mammary stem cell activity. Thus, the irradiated microenvironment affects latency and clinically relevant features of cancer through distinct and unexpected mechanisms.

}, keywords = {Animal, Animals, Breast Neoplasms, Cell Transformation, Dose-Response Relationship, Epithelial Cells, Estrogen, Female, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Inbred BALB C, Knockout, Mammary Glands, Mice, Neoplasms, Neoplastic, Radiation, Radiation Chimera, Radiation-Induced, Reaction Time, Receptors, Time Factors, Transforming Growth Factor beta1, Tumor Burden, Tumor Microenvironment, Tumor Suppressor Protein p53, Whole-Body Irradiation}, issn = {1878-3686}, doi = {10.1016/j.ccr.2011.03.011}, author = {Nguyen, David H and Oketch-Rabah, Hellen A and Illa-Bochaca, Irineu and Geyer, Felipe C and Reis-Filho, Jorge S and Jiang-Hua Mao and Ravani, Shraddha A and Zavadil, Jiri and Borowsky, Alexander D and Jerry, D Joseph and Dunphy, Karen A and Seo, Jae Hong and Haslam, Sandra and Medina, Daniel and Barcellos-Hoff, Mary Helen} }