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5 results on '"Beck, AH"'

1. Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

Author

Amgad, M, Stovgaard, ES, Balslev, E, Thagaard, J, Chen, W, Dudgeon, S, Sharma, A, Kerner, JK, Denkert, C, Yuan, Y, AbdulJabbar, K, Wienert, S, Savas, P, Voorwerk, L, Beck, AH, Madabhushi, A, Hartman, J, Sebastian, MM, Horlings, HM, Hudecek, J, Ciompi, F, Moore, DA, Singh, R, Roblin, E, Balancin, ML, Mathieu, M-C, Lennerz, JK, Kirtani, P, Chen, I-C, Braybrooke, JP, Pruneri, G, Demaria, S, Adams, S, Schnitt, SJ, Lakhani, SR, Rojo, F, Comerma, L, Badve, SS, Khojasteh, M, Symmans, WF, Sotiriou, C, Gonzalez-Ericsson, P, Pogue-Geile, KL, Kim, RS, Rimm, DL, Viale, G, Hewitt, SM, Bartlett, JMS, Penault-Llorca, F, Goel, S, Lien, H-C, Loibl, S, Kos, Z, Loi, S, Hanna, MG, Michiels, S, Kok, M, Nielsen, TO, Lazar, AJ, Bago-Horvath, Z, Kooreman, LFS, van der Laak, JAWM, Saltz, J, Gallas, BD, Kurkure, U, Barnes, M, Salgado, R, Cooper, LAD, Amgad, M, Stovgaard, ES, Balslev, E, Thagaard, J, Chen, W, Dudgeon, S, Sharma, A, Kerner, JK, Denkert, C, Yuan, Y, AbdulJabbar, K, Wienert, S, Savas, P, Voorwerk, L, Beck, AH, Madabhushi, A, Hartman, J, Sebastian, MM, Horlings, HM, Hudecek, J, Ciompi, F, Moore, DA, Singh, R, Roblin, E, Balancin, ML, Mathieu, M-C, Lennerz, JK, Kirtani, P, Chen, I-C, Braybrooke, JP, Pruneri, G, Demaria, S, Adams, S, Schnitt, SJ, Lakhani, SR, Rojo, F, Comerma, L, Badve, SS, Khojasteh, M, Symmans, WF, Sotiriou, C, Gonzalez-Ericsson, P, Pogue-Geile, KL, Kim, RS, Rimm, DL, Viale, G, Hewitt, SM, Bartlett, JMS, Penault-Llorca, F, Goel, S, Lien, H-C, Loibl, S, Kos, Z, Loi, S, Hanna, MG, Michiels, S, Kok, M, Nielsen, TO, Lazar, AJ, Bago-Horvath, Z, Kooreman, LFS, van der Laak, JAWM, Saltz, J, Gallas, BD, Kurkure, U, Barnes, M, Salgado, R, and Cooper, LAD

Abstract

Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring.

Published
2020

2. Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer

Author

Kos, Z, Roblin, E, Kim, RS, Michiels, S, Gallas, BD, Chen, W, van de Vijver, KK, Goel, S, Adams, S, Demaria, S, Viale, G, Nielsen, TO, Badve, SS, Symmans, WF, Sotiriou, C, Rimm, DL, Hewitt, S, Denkert, C, Loibl, S, Luen, SJ, Bartlett, JMS, Savas, P, Pruneri, G, Dillon, DA, Cheang, MCU, Tutt, A, Hall, JA, Kok, M, Horlings, HM, Madabhushi, A, van der Laak, J, Ciompi, F, Laenkholm, A-V, Bellolio, E, Gruosso, T, Fox, SB, Araya, JC, Floris, G, Hudecek, J, Voorwerk, L, Beck, AH, Kerner, J, Larsimont, D, Declercq, S, Van den Eynden, G, Pusztai, L, Ehinger, A, Yang, W, AbdulJabbar, K, Yuan, Y, Singh, R, Hiley, C, al Bakir, M, Lazar, AJ, Naber, S, Wienert, S, Castillo, M, Curigliano, G, Dieci, M-V, Andre, F, Swanton, C, Reis-Filho, J, Sparano, J, Balslev, E, Chen, I-C, Stovgaard, EIS, Pogue-Geile, K, Blenman, KRM, Penault-Llorca, F, Schnitt, S, Lakhani, SR, Vincent-Salomon, A, Rojo, F, Braybrooke, JP, Hanna, MG, Soler-Monso, MT, Bethmann, D, Castaneda, CA, Willard-Gallo, K, Sharma, A, Lien, H-C, Fineberg, S, Thagaard, J, Comerma, L, Gonzalez-Ericsson, P, Brogi, E, Loi, S, Saltz, J, Klaushen, F, Cooper, L, Amgad, M, Moore, DA, Salgado, R, Kos, Z, Roblin, E, Kim, RS, Michiels, S, Gallas, BD, Chen, W, van de Vijver, KK, Goel, S, Adams, S, Demaria, S, Viale, G, Nielsen, TO, Badve, SS, Symmans, WF, Sotiriou, C, Rimm, DL, Hewitt, S, Denkert, C, Loibl, S, Luen, SJ, Bartlett, JMS, Savas, P, Pruneri, G, Dillon, DA, Cheang, MCU, Tutt, A, Hall, JA, Kok, M, Horlings, HM, Madabhushi, A, van der Laak, J, Ciompi, F, Laenkholm, A-V, Bellolio, E, Gruosso, T, Fox, SB, Araya, JC, Floris, G, Hudecek, J, Voorwerk, L, Beck, AH, Kerner, J, Larsimont, D, Declercq, S, Van den Eynden, G, Pusztai, L, Ehinger, A, Yang, W, AbdulJabbar, K, Yuan, Y, Singh, R, Hiley, C, al Bakir, M, Lazar, AJ, Naber, S, Wienert, S, Castillo, M, Curigliano, G, Dieci, M-V, Andre, F, Swanton, C, Reis-Filho, J, Sparano, J, Balslev, E, Chen, I-C, Stovgaard, EIS, Pogue-Geile, K, Blenman, KRM, Penault-Llorca, F, Schnitt, S, Lakhani, SR, Vincent-Salomon, A, Rojo, F, Braybrooke, JP, Hanna, MG, Soler-Monso, MT, Bethmann, D, Castaneda, CA, Willard-Gallo, K, Sharma, A, Lien, H-C, Fineberg, S, Thagaard, J, Comerma, L, Gonzalez-Ericsson, P, Brogi, E, Loi, S, Saltz, J, Klaushen, F, Cooper, L, Amgad, M, Moore, DA, and Salgado, R

Abstract

Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls.

Published
2020

3. Discovery of molecular subtypes in leiomyosarcoma through integrative molecular profiling.

Author

Beck, AH, Beck, AH, Lee, C-H, Witten, DM, Gleason, BC, Edris, B, Espinosa, I, Zhu, S, Li, R, Montgomery, KD, Marinelli, RJ, Tibshirani, R, Hastie, T, Jablons, DM, Rubin, BP, Fletcher, CD, West, RB, van de Rijn, M, Beck, AH, Beck, AH, Lee, C-H, Witten, DM, Gleason, BC, Edris, B, Espinosa, I, Zhu, S, Li, R, Montgomery, KD, Marinelli, RJ, Tibshirani, R, Hastie, T, Jablons, DM, Rubin, BP, Fletcher, CD, West, RB, and van de Rijn, M

Abstract

Leiomyosarcoma (LMS) is a soft tissue tumor with a significant degree of morphologic and molecular heterogeneity. We used integrative molecular profiling to discover and characterize molecular subtypes of LMS. Gene expression profiling was performed on 51 LMS samples. Unsupervised clustering showed three reproducible LMS clusters. Array comparative genomic hybridization (aCGH) was performed on 20 LMS samples and showed that the molecular subtypes defined by gene expression showed distinct genomic changes. Tumors from the 'muscle-enriched' cluster showed significantly increased copy number changes (P=0.04). A majority of the muscle-enriched cases showed loss at 16q24, which contains Fanconi anemia, complementation group A, known to have an important role in DNA repair, and loss at 1p36, which contains PRDM16, of which loss promotes muscle differentiation. Immunohistochemistry (IHC) was performed on LMS tissue microarrays (n=377) for five markers with high levels of messenger RNA in the muscle-enriched cluster (ACTG2, CASQ2, SLMAP, CFL2 and MYLK) and showed significantly correlated expression of the five proteins (all pairwise P<0.005). Expression of the five markers was associated with improved disease-specific survival in a multivariate Cox regression analysis (P<0.04). In this analysis that combined gene expression profiling, aCGH and IHC, we characterized distinct molecular LMS subtypes, provided insight into their pathogenesis, and identified prognostic biomarkers.

Published
2010

4. Genetically Predicted Body Mass Index and Breast Cancer Risk: Mendelian Randomization Analyses of Data from 145,000 Women of European Descent

Author

Beck, AH, Guo, Y, Andersen, SW, Shu, X-O, Michailidou, K, Bolla, MK, Wang, Q, Garcia-Closas, M, Milne, RL, Schmidt, MK, Chang-Claude, J, Dunning, A, Bojesen, SE, Ahsan, H, Aittomaki, K, Andrulis, IL, Anton-Culver, H, Arndt, V, Beckmann, MW, Beeghly-Fadiel, A, Benitez, J, Bogdanova, NV, Bonanni, B, Borresen-Dale, A-L, Brand, J, Brauch, H, Brenner, H, Bruening, T, Burwinkel, B, Casey, G, Chenevix-Trench, G, Couch, FJ, Cox, A, Cross, SS, Czene, K, Devilee, P, Doerk, T, Dumont, M, Fasching, PA, Figueroa, J, Flesch-Janys, D, Fletcher, O, Flyger, H, Fostira, F, Gammon, M, Giles, GG, Guenel, P, Haiman, CA, Hamann, U, Hooning, MJ, Hopper, JL, Jakubowska, A, Jasmine, F, Jenkins, M, John, EM, Johnson, N, Jones, ME, Kabisch, M, Kibriya, M, Knight, JA, Koppert, LB, Kosma, V-M, Kristensen, V, Le Marchand, L, Lee, E, Li, J, Lindblom, A, Luben, R, Lubinski, J, Malone, KE, Mannermaa, A, Margolin, S, Marme, F, McLean, C, Meijers-Heijboer, H, Meindl, A, Neuhausen, SL, Nevanlinna, H, Neven, P, Olson, JE, Perez, JIA, Perkins, B, Peterlongo, P, Phillips, K-A, Pylkas, K, Rudolph, A, Santella, R, Sawyer, EJ, Schmutzler, RK, Seynaeve, C, Shah, M, Shrubsole, MJ, Southey, MC, Swerdlow, AJ, Toland, AE, Tomlinson, I, Torres, D, Truong, T, Ursin, G, Van Der Luijt, RB, Verhoef, S, Whittemore, AS, Winqvist, R, Zhao, H, Zhao, S, Hall, P, Simard, J, Kraft, P, Pharoah, P, Hunter, D, Easton, DF, Zheng, W, Beck, AH, Guo, Y, Andersen, SW, Shu, X-O, Michailidou, K, Bolla, MK, Wang, Q, Garcia-Closas, M, Milne, RL, Schmidt, MK, Chang-Claude, J, Dunning, A, Bojesen, SE, Ahsan, H, Aittomaki, K, Andrulis, IL, Anton-Culver, H, Arndt, V, Beckmann, MW, Beeghly-Fadiel, A, Benitez, J, Bogdanova, NV, Bonanni, B, Borresen-Dale, A-L, Brand, J, Brauch, H, Brenner, H, Bruening, T, Burwinkel, B, Casey, G, Chenevix-Trench, G, Couch, FJ, Cox, A, Cross, SS, Czene, K, Devilee, P, Doerk, T, Dumont, M, Fasching, PA, Figueroa, J, Flesch-Janys, D, Fletcher, O, Flyger, H, Fostira, F, Gammon, M, Giles, GG, Guenel, P, Haiman, CA, Hamann, U, Hooning, MJ, Hopper, JL, Jakubowska, A, Jasmine, F, Jenkins, M, John, EM, Johnson, N, Jones, ME, Kabisch, M, Kibriya, M, Knight, JA, Koppert, LB, Kosma, V-M, Kristensen, V, Le Marchand, L, Lee, E, Li, J, Lindblom, A, Luben, R, Lubinski, J, Malone, KE, Mannermaa, A, Margolin, S, Marme, F, McLean, C, Meijers-Heijboer, H, Meindl, A, Neuhausen, SL, Nevanlinna, H, Neven, P, Olson, JE, Perez, JIA, Perkins, B, Peterlongo, P, Phillips, K-A, Pylkas, K, Rudolph, A, Santella, R, Sawyer, EJ, Schmutzler, RK, Seynaeve, C, Shah, M, Shrubsole, MJ, Southey, MC, Swerdlow, AJ, Toland, AE, Tomlinson, I, Torres, D, Truong, T, Ursin, G, Van Der Luijt, RB, Verhoef, S, Whittemore, AS, Winqvist, R, Zhao, H, Zhao, S, Hall, P, Simard, J, Kraft, P, Pharoah, P, Hunter, D, Easton, DF, and Zheng, W

Abstract

BACKGROUND: Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or environmental factors. METHODS: We applied Mendelian randomization to evaluate the association between BMI and risk of breast cancer occurrence using data from two large breast cancer consortia. We created a weighted BMI genetic score comprising 84 BMI-associated genetic variants to predicted BMI. We evaluated genetically predicted BMI in association with breast cancer risk using individual-level data from the Breast Cancer Association Consortium (BCAC) (cases = 46,325, controls = 42,482). We further evaluated the association between genetically predicted BMI and breast cancer risk using summary statistics from 16,003 cases and 41,335 controls from the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Project. Because most studies measured BMI after cancer diagnosis, we could not conduct a parallel analysis to adequately evaluate the association of measured BMI with breast cancer risk prospectively. RESULTS: In the BCAC data, genetically predicted BMI was found to be inversely associated with breast cancer risk (odds ratio [OR] = 0.65 per 5 kg/m2 increase, 95% confidence interval [CI]: 0.56-0.75, p = 3.32 × 10-10). The associations were similar for both premenopausal (OR = 0.44, 95% CI:0.31-0.62, p = 9.91 × 10-8) and postmenopausal breast cancer (OR = 0.57, 95% CI: 0.46-0.71, p = 1.88 × 10-8). This association was replicated in the data from the DRIVE consortium (OR = 0.72, 95% CI: 0.60-0.84, p = 1.64 × 10-7). Single marker analyses identified 17 of the 84 BMI-associated single nucleotide polymorphisms (SNPs) in association with breast cancer risk at p < 0.05; for 16 of them, the allele associated with elevated BMI was associated wit

Published
2016

5. Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk

Author

Lindstroem, S, Thompson, DJ, Paterson, AD, Li, J, Gierach, GL, Scott, C, Stone, J, Douglas, JA, dos-Santos-Silva, I, Fernandez-Navarro, P, Verghase, J, Smith, P, Brown, J, Luben, R, Wareham, NJ, Loos, RJF, Heit, JA, Pankratz, VS, Norman, A, Goode, EL, Cunningham, JM, Deandrade, M, Vierkant, RA, Czene, K, Fasching, PA, Baglietto, L, Southey, MC, Giles, GG, Shah, KP, Chan, H-P, Helvie, MA, Beck, AH, Knoblauch, NW, Hazra, A, Hunter, DJ, Kraft, P, Pollan, M, Figueroa, JD, Couch, FJ, Hopper, JL, Hall, P, Easton, DF, Boyd, NF, Vachon, CM, Tamimi, RM, Lindstroem, S, Thompson, DJ, Paterson, AD, Li, J, Gierach, GL, Scott, C, Stone, J, Douglas, JA, dos-Santos-Silva, I, Fernandez-Navarro, P, Verghase, J, Smith, P, Brown, J, Luben, R, Wareham, NJ, Loos, RJF, Heit, JA, Pankratz, VS, Norman, A, Goode, EL, Cunningham, JM, Deandrade, M, Vierkant, RA, Czene, K, Fasching, PA, Baglietto, L, Southey, MC, Giles, GG, Shah, KP, Chan, H-P, Helvie, MA, Beck, AH, Knoblauch, NW, Hazra, A, Hunter, DJ, Kraft, P, Pollan, M, Figueroa, JD, Couch, FJ, Hopper, JL, Hall, P, Easton, DF, Boyd, NF, Vachon, CM, and Tamimi, RM

Abstract

Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P<5 × 10(-8)) loci for dense area (AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1, TMEM184B and SGSM3/MKL1), non-dense area (8p11.23) and percent density (PRDM6, 8p11.23 and TMEM184B). Four of these regions are known breast cancer susceptibility loci, and four additional regions were found to be associated with breast cancer (P<0.05) in a large meta-analysis. These results provide further evidence of a shared genetic basis between mammographic density and breast cancer and illustrate the power of studying intermediate quantitative phenotypes to identify putative disease-susceptibility loci.

Published
2014

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