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2. Translating prognostic quantification of c-MYC and BCL2 from tissue microarrays to whole slide images in diffuse large B-cell lymphoma using deep learning

Author

Thomas E. Tavolara, M. Khalid Khan Niazi, Andrew L. Feldman, David L. Jaye, Christopher Flowers, Lee A.D. Cooper, and Metin N. Gurcan

Subjects
Deep learning, Diffuse large B-cell Lymphoma, c-MYC, BCL2, Immunohistochemistry, Multiple instance learning, Pathology, RB1-214
Abstract

Abstract Background c-MYC and BCL2 positivity are important prognostic factors for diffuse large B-cell lymphoma. However, manual quantification is subject to significant intra- and inter-observer variability. We developed an automated method for quantification in whole-slide images of tissue sections where manual quantification requires evaluating large areas of tissue with possibly heterogeneous staining. We train this method using annotations of tumor positivity in smaller tissue microarray cores where expression and staining are more homogeneous and then translate this model to whole-slide images. Methods Our method applies a technique called attention-based multiple instance learning to regress the proportion of c-MYC-positive and BCL2-positive tumor cells from pathologist-scored tissue microarray cores. This technique does not require annotation of individual cell nuclei and is trained instead on core-level annotations of percent tumor positivity. We translate this model to scoring of whole-slide images by tessellating the slide into smaller core-sized tissue regions and calculating an aggregate score. Our method was trained on a public tissue microarray dataset from Stanford and applied to whole-slide images from a geographically diverse multi-center cohort produced by the Lymphoma Epidemiology of Outcomes study. Results In tissue microarrays, the automated method had Pearson correlations of 0.843 and 0.919 with pathologist scores for c-MYC and BCL2, respectively. When utilizing standard clinical thresholds, the sensitivity/specificity of our method was 0.743 / 0.963 for c-MYC and 0.938 / 0.951 for BCL2. For double-expressors, sensitivity and specificity were 0.720 and 0.974. When translated to the external WSI dataset scored by two pathologists, Pearson correlation was 0.753 & 0.883 for c-MYC and 0.749 & 0.765 for BCL2, and sensitivity/specificity was 0.857/0.991 & 0.706/0.930 for c-MYC, 0.856/0.719 & 0.855/0.690 for BCL2, and 0.890/1.00 & 0.598/0.952 for double-expressors. Survival analysis demonstrates that for progression-free survival, model-predicted TMA scores significantly stratify double-expressors and non double-expressors (p = 0.0345), whereas pathologist scores do not (p = 0.128). Conclusions We conclude that proportion of positive stains can be regressed using attention-based multiple instance learning, that these models generalize well to whole slide images, and that our models can provide non-inferior stratification of progression-free survival outcomes.

Published
2024
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10. Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Author

Pathologie, Cancer, Jahangir, Chowdhury Arif, Page, David B., Broeckx, Glenn, Gonzalez, Claudia A., Burke, Caoimbhe, Murphy, Clodagh, Reis-Filho, Jorge S., Ly, Amy, Harms, Paul W., Gupta, Rajarsi R., Vieth, Michael, Hida, Akira I., Kahila, Mohamed, Kos, Zuzana, van Diest, Paul J., Verbandt, Sara, Thagaard, Jeppe, Khiroya, Reena, Abduljabbar, Khalid, Acosta Haab, Gabriela, Acs, Balazs, Adams, Sylvia, Almeida, Jonas S., Alvarado-Cabrero, Isabel, Azmoudeh-Ardalan, Farid, Badve, Sunil, Baharun, Nurkhairul Bariyah, Bellolio, Enrique R., Bheemaraju, Vydehi, Blenman, Kim R.M., Botinelly Mendonça Fujimoto, Luciana, Burgues, Octavio, Chardas, Alexandros, Cheang, Maggie Chon U., Ciompi, Francesco, Cooper, Lee A.D., Coosemans, An, Corredor, Germán, Dantas Portela, Flavio Luis, Deman, Frederik, Demaria, Sandra, Dudgeon, Sarah N., Elghazawy, Mahmoud, Fernandez-Martín, Claudio, Fineberg, Susan, Fox, Stephen B., Giltnane, Jennifer M., Gnjatic, Sacha, Gonzalez-Ericsson, Paula I., Grigoriadis, Anita, Halama, Niels, Hanna, Matthew G., Harbhajanka, Aparna, Hart, Steven N., Hartman, Johan, Hewitt, Stephen, Horlings, Hugo M., Husain, Zaheed, Irshad, Sheeba, Janssen, Emiel A.M., Kataoka, Tatsuki R., Kawaguchi, Kosuke, Khramtsov, Andrey I., Kiraz, Umay, Kirtani, Pawan, Kodach, Liudmila L., Korski, Konstanty, Akturk, Guray, Scott, Ely, Kovács, Anikó, Lænkholm, Anne Vibeke, Lang-Schwarz, Corinna, Larsimont, Denis, Lennerz, Jochen K., Lerousseau, Marvin, Li, Xiaoxian, Madabhushi, Anant, Maley, Sai K., Manur Narasimhamurthy, Vidya, Marks, Douglas K., McDonald, Elizabeth S., Mehrotra, Ravi, Michiels, Stefan, Kharidehal, Durga, Minhas, Fayyaz ul Amir Afsar, Mittal, Shachi, Moore, David A., Mushtaq, Shamim, Nighat, Hussain, Papathomas, Thomas, Penault-Llorca, Frederique, Perera, Rashindrie D., Pinard, Christopher J., Pinto-Cardenas, Juan Carlos, Pruneri, Giancarlo, Pusztai, Lajos, Rajpoot, Nasir Mahmood, Rapoport, Bernardo Leon, Rau, Tilman T., Ribeiro, Joana M., Rimm, David, Vincent-Salomon, Anne, Saltz, Joel, Sayed, Shahin, Hytopoulos, Evangelos, Mahon, Sarah, Siziopikou, Kalliopi P., Sotiriou, Christos, Stenzinger, Albrecht, Sughayer, Maher A., Sur, Daniel, Symmans, Fraser, Tanaka, Sunao, Taxter, Timothy, Tejpar, Sabine, Teuwen, Jonas, Thompson, E. Aubrey, Tramm, Trine, Tran, William T., van der Laak, Jeroen, Verghese, Gregory E., Viale, Giuseppe, Wahab, Noorul, Walter, Thomas, Waumans, Yannick, Wen, Hannah Y., Yang, Wentao, Yuan, Yinyin, Bartlett, John, Loibl, Sibylle, Denkert, Carsten, Savas, Peter, Loi, Sherene, Specht Stovgaard, Elisabeth, Salgado, Roberto, Gallagher, William M., Rahman, Arman, Pathologie, Cancer, Jahangir, Chowdhury Arif, Page, David B., Broeckx, Glenn, Gonzalez, Claudia A., Burke, Caoimbhe, Murphy, Clodagh, Reis-Filho, Jorge S., Ly, Amy, Harms, Paul W., Gupta, Rajarsi R., Vieth, Michael, Hida, Akira I., Kahila, Mohamed, Kos, Zuzana, van Diest, Paul J., Verbandt, Sara, Thagaard, Jeppe, Khiroya, Reena, Abduljabbar, Khalid, Acosta Haab, Gabriela, Acs, Balazs, Adams, Sylvia, Almeida, Jonas S., Alvarado-Cabrero, Isabel, Azmoudeh-Ardalan, Farid, Badve, Sunil, Baharun, Nurkhairul Bariyah, Bellolio, Enrique R., Bheemaraju, Vydehi, Blenman, Kim R.M., Botinelly Mendonça Fujimoto, Luciana, Burgues, Octavio, Chardas, Alexandros, Cheang, Maggie Chon U., Ciompi, Francesco, Cooper, Lee A.D., Coosemans, An, Corredor, Germán, Dantas Portela, Flavio Luis, Deman, Frederik, Demaria, Sandra, Dudgeon, Sarah N., Elghazawy, Mahmoud, Fernandez-Martín, Claudio, Fineberg, Susan, Fox, Stephen B., Giltnane, Jennifer M., Gnjatic, Sacha, Gonzalez-Ericsson, Paula I., Grigoriadis, Anita, Halama, Niels, Hanna, Matthew G., Harbhajanka, Aparna, Hart, Steven N., Hartman, Johan, Hewitt, Stephen, Horlings, Hugo M., Husain, Zaheed, Irshad, Sheeba, Janssen, Emiel A.M., Kataoka, Tatsuki R., Kawaguchi, Kosuke, Khramtsov, Andrey I., Kiraz, Umay, Kirtani, Pawan, Kodach, Liudmila L., Korski, Konstanty, Akturk, Guray, Scott, Ely, Kovács, Anikó, Lænkholm, Anne Vibeke, Lang-Schwarz, Corinna, Larsimont, Denis, Lennerz, Jochen K., Lerousseau, Marvin, Li, Xiaoxian, Madabhushi, Anant, Maley, Sai K., Manur Narasimhamurthy, Vidya, Marks, Douglas K., McDonald, Elizabeth S., Mehrotra, Ravi, Michiels, Stefan, Kharidehal, Durga, Minhas, Fayyaz ul Amir Afsar, Mittal, Shachi, Moore, David A., Mushtaq, Shamim, Nighat, Hussain, Papathomas, Thomas, Penault-Llorca, Frederique, Perera, Rashindrie D., Pinard, Christopher J., Pinto-Cardenas, Juan Carlos, Pruneri, Giancarlo, Pusztai, Lajos, Rajpoot, Nasir Mahmood, Rapoport, Bernardo Leon, Rau, Tilman T., Ribeiro, Joana M., Rimm, David, Vincent-Salomon, Anne, Saltz, Joel, Sayed, Shahin, Hytopoulos, Evangelos, Mahon, Sarah, Siziopikou, Kalliopi P., Sotiriou, Christos, Stenzinger, Albrecht, Sughayer, Maher A., Sur, Daniel, Symmans, Fraser, Tanaka, Sunao, Taxter, Timothy, Tejpar, Sabine, Teuwen, Jonas, Thompson, E. Aubrey, Tramm, Trine, Tran, William T., van der Laak, Jeroen, Verghese, Gregory E., Viale, Giuseppe, Wahab, Noorul, Walter, Thomas, Waumans, Yannick, Wen, Hannah Y., Yang, Wentao, Yuan, Yinyin, Bartlett, John, Loibl, Sibylle, Denkert, Carsten, Savas, Peter, Loi, Sherene, Specht Stovgaard, Elisabeth, Salgado, Roberto, Gallagher, William M., and Rahman, Arman

Published
2024

11. Image-based multiplex immune profiling of cancer tissues:translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Author

Jahangir, Chowdhury Arif, Page, David B., Broeckx, Glenn, Gonzalez, Claudia A., Burke, Caoimbhe, Murphy, Clodagh, Reis-Filho, Jorge S., Ly, Amy, Harms, Paul W., Gupta, Rajarsi R., Vieth, Michael, Hida, Akira I., Kahila, Mohamed, Kos, Zuzana, van Diest, Paul J., Verbandt, Sara, Thagaard, Jeppe, Khiroya, Reena, Abduljabbar, Khalid, Acosta Haab, Gabriela, Acs, Balazs, Adams, Sylvia, Almeida, Jonas S., Alvarado-Cabrero, Isabel, Azmoudeh-Ardalan, Farid, Badve, Sunil, Baharun, Nurkhairul Bariyah, Bellolio, Enrique R., Bheemaraju, Vydehi, Blenman, Kim R.M., Botinelly Mendonça Fujimoto, Luciana, Burgues, Octavio, Chardas, Alexandros, Cheang, Maggie Chon U., Ciompi, Francesco, Cooper, Lee A.D., Coosemans, An, Corredor, Germán, Dantas Portela, Flavio Luis, Deman, Frederik, Demaria, Sandra, Dudgeon, Sarah N., Elghazawy, Mahmoud, Fernandez-Martín, Claudio, Fineberg, Susan, Fox, Stephen B., Giltnane, Jennifer M., Gnjatic, Sacha, Gonzalez-Ericsson, Paula I., Grigoriadis, Anita, Halama, Niels, Hanna, Matthew G., Harbhajanka, Aparna, Hart, Steven N., Hartman, Johan, Hewitt, Stephen, Horlings, Hugo M., Husain, Zaheed, Irshad, Sheeba, Janssen, Emiel A.M., Kataoka, Tatsuki R., Kawaguchi, Kosuke, Khramtsov, Andrey I., Kiraz, Umay, Kirtani, Pawan, Kodach, Liudmila L., Korski, Konstanty, Akturk, Guray, Scott, Ely, Kovács, Anikó, Lænkholm, Anne Vibeke, Lang-Schwarz, Corinna, Larsimont, Denis, Lennerz, Jochen K., Lerousseau, Marvin, Li, Xiaoxian, Madabhushi, Anant, Maley, Sai K., Manur Narasimhamurthy, Vidya, Marks, Douglas K., McDonald, Elizabeth S., Mehrotra, Ravi, Michiels, Stefan, Kharidehal, Durga, Minhas, Fayyaz ul Amir Afsar, Mittal, Shachi, Moore, David A., Mushtaq, Shamim, Nighat, Hussain, Papathomas, Thomas, Penault-Llorca, Frederique, Perera, Rashindrie D., Pinard, Christopher J., Pinto-Cardenas, Juan Carlos, Pruneri, Giancarlo, Pusztai, Lajos, Rajpoot, Nasir Mahmood, Rapoport, Bernardo Leon, Rau, Tilman T., Ribeiro, Joana M., Rimm, David, Vincent-Salomon, Anne, Saltz, Joel, Sayed, Shahin, Hytopoulos, Evangelos, Mahon, Sarah, Siziopikou, Kalliopi P., Sotiriou, Christos, Stenzinger, Albrecht, Sughayer, Maher A., Sur, Daniel, Symmans, Fraser, Tanaka, Sunao, Taxter, Timothy, Tejpar, Sabine, Teuwen, Jonas, Thompson, E. Aubrey, Tramm, Trine, Tran, William T., van der Laak, Jeroen, Verghese, Gregory E., Viale, Giuseppe, Wahab, Noorul, Walter, Thomas, Waumans, Yannick, Wen, Hannah Y., Yang, Wentao, Yuan, Yinyin, Bartlett, John, Loibl, Sibylle, Denkert, Carsten, Savas, Peter, Loi, Sherene, Specht Stovgaard, Elisabeth, Salgado, Roberto, Gallagher, William M., Rahman, Arman, Jahangir, Chowdhury Arif, Page, David B., Broeckx, Glenn, Gonzalez, Claudia A., Burke, Caoimbhe, Murphy, Clodagh, Reis-Filho, Jorge S., Ly, Amy, Harms, Paul W., Gupta, Rajarsi R., Vieth, Michael, Hida, Akira I., Kahila, Mohamed, Kos, Zuzana, van Diest, Paul J., Verbandt, Sara, Thagaard, Jeppe, Khiroya, Reena, Abduljabbar, Khalid, Acosta Haab, Gabriela, Acs, Balazs, Adams, Sylvia, Almeida, Jonas S., Alvarado-Cabrero, Isabel, Azmoudeh-Ardalan, Farid, Badve, Sunil, Baharun, Nurkhairul Bariyah, Bellolio, Enrique R., Bheemaraju, Vydehi, Blenman, Kim R.M., Botinelly Mendonça Fujimoto, Luciana, Burgues, Octavio, Chardas, Alexandros, Cheang, Maggie Chon U., Ciompi, Francesco, Cooper, Lee A.D., Coosemans, An, Corredor, Germán, Dantas Portela, Flavio Luis, Deman, Frederik, Demaria, Sandra, Dudgeon, Sarah N., Elghazawy, Mahmoud, Fernandez-Martín, Claudio, Fineberg, Susan, Fox, Stephen B., Giltnane, Jennifer M., Gnjatic, Sacha, Gonzalez-Ericsson, Paula I., Grigoriadis, Anita, Halama, Niels, Hanna, Matthew G., Harbhajanka, Aparna, Hart, Steven N., Hartman, Johan, Hewitt, Stephen, Horlings, Hugo M., Husain, Zaheed, Irshad, Sheeba, Janssen, Emiel A.M., Kataoka, Tatsuki R., Kawaguchi, Kosuke, Khramtsov, Andrey I., Kiraz, Umay, Kirtani, Pawan, Kodach, Liudmila L., Korski, Konstanty, Akturk, Guray, Scott, Ely, Kovács, Anikó, Lænkholm, Anne Vibeke, Lang-Schwarz, Corinna, Larsimont, Denis, Lennerz, Jochen K., Lerousseau, Marvin, Li, Xiaoxian, Madabhushi, Anant, Maley, Sai K., Manur Narasimhamurthy, Vidya, Marks, Douglas K., McDonald, Elizabeth S., Mehrotra, Ravi, Michiels, Stefan, Kharidehal, Durga, Minhas, Fayyaz ul Amir Afsar, Mittal, Shachi, Moore, David A., Mushtaq, Shamim, Nighat, Hussain, Papathomas, Thomas, Penault-Llorca, Frederique, Perera, Rashindrie D., Pinard, Christopher J., Pinto-Cardenas, Juan Carlos, Pruneri, Giancarlo, Pusztai, Lajos, Rajpoot, Nasir Mahmood, Rapoport, Bernardo Leon, Rau, Tilman T., Ribeiro, Joana M., Rimm, David, Vincent-Salomon, Anne, Saltz, Joel, Sayed, Shahin, Hytopoulos, Evangelos, Mahon, Sarah, Siziopikou, Kalliopi P., Sotiriou, Christos, Stenzinger, Albrecht, Sughayer, Maher A., Sur, Daniel, Symmans, Fraser, Tanaka, Sunao, Taxter, Timothy, Tejpar, Sabine, Teuwen, Jonas, Thompson, E. Aubrey, Tramm, Trine, Tran, William T., van der Laak, Jeroen, Verghese, Gregory E., Viale, Giuseppe, Wahab, Noorul, Walter, Thomas, Waumans, Yannick, Wen, Hannah Y., Yang, Wentao, Yuan, Yinyin, Bartlett, John, Loibl, Sibylle, Denkert, Carsten, Savas, Peter, Loi, Sherene, Specht Stovgaard, Elisabeth, Salgado, Roberto, Gallagher, William M., and Rahman, Arman

Abstract

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer.

Published
2024

12. Ex-situ hepatectomy, liver autotransplantation: first Brazilian series of case from a single center

Author

Andraus, W., primary, Waisberg, D.R., additional, Silva, M.S., additional, Suazo, G.O., additional, MartinoVR Santos, R.B., additional, Pinheiro, R.S., additional, Arantes, R.M., additional, Ducatti, L., additional, Nacif, L.S., additional, Song, A.T., additional, Lee, A.D., additional, Haddad, L.B., additional, Galvão, F.H., additional, and Carneiro, L.A., additional

Published
2024
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13. Results of liver transplantation with non-tumoral portal vein thrombosis: analysis of single-center

Author

Martino, R.B., primary, Waisberg, D.R., additional, Silva, A.M., additional, Silva, M.S., additional, Suazo, G.O., additional, Santos, V.R., additional, Pinheiro, R.S., additional, Arantes, R.M., additional, Ducatti, L., additional, Nacif, L.S., additional, Song, A.T., additional, Lee, A.D., additional, Haddad, L.B., additional, Galvão, F.H., additional, Andraus, W., additional, and Carneiro, L.A., additional

Published
2024
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24. Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer:a report of the international immuno-oncology biomarker working group

Author

Thagaard, Jeppe, Broeckx, Glenn, Page, David B., Jahangir, Chowdhury Arif, Verbandt, Sara, Kos, Zuzana, Gupta, Rajarsi, Khiroya, Reena, Abduljabbar, Khalid, Acosta Haab, Gabriela, Acs, Balazs, Akturk, Guray, Almeida, Jonas S., Alvarado-Cabrero, Isabel, Amgad, Mohamed, Azmoudeh-Ardalan, Farid, Badve, Sunil, Baharun, Nurkhairul Bariyah, Balslev, Eva, Bellolio, Enrique R., Bheemaraju, Vydehi, Blenman, Kim R.M., Botinelly Mendonça Fujimoto, Luciana, Bouchmaa, Najat, Burgues, Octavio, Chardas, Alexandros, Chon U Cheang, Maggie, Ciompi, Francesco, Cooper, Lee A.D., Coosemans, An, Corredor, Germán, Dahl, Anders B., Dantas Portela, Flavio Luis, Deman, Frederik, Demaria, Sandra, Doré Hansen, Johan, Dudgeon, Sarah N., Ebstrup, Thomas, Elghazawy, Mahmoud, Fernandez-Martín, Claudio, Fox, Stephen B., Gallagher, William M., Giltnane, Jennifer M., Gnjatic, Sacha, Gonzalez-Ericsson, Paula I., Grigoriadis, Anita, Halama, Niels, Hanna, Matthew G., Harbhajanka, Aparna, Hart, Steven N., Hartman, Johan, Hauberg, Søren, Hewitt, Stephen, Hida, Akira I., Horlings, Hugo M., Husain, Zaheed, Hytopoulos, Evangelos, Irshad, Sheeba, Janssen, Emiel A.M., Kahila, Mohamed, Kataoka, Tatsuki R., Kawaguchi, Kosuke, Kharidehal, Durga, Khramtsov, Andrey I., Kiraz, Umay, Kirtani, Pawan, Kodach, Liudmila L., Korski, Konstanty, Kovács, Anikó, Laenkholm, Anne Vibeke, Lang-Schwarz, Corinna, Larsimont, Denis, Lennerz, Jochen K., Lerousseau, Marvin, Li, Xiaoxian, Ly, Amy, Madabhushi, Anant, Maley, Sai K., Manur Narasimhamurthy, Vidya, Marks, Douglas K., McDonald, Elizabeth S., Mehrotra, Ravi, Michiels, Stefan, Minhas, Fayyaz ul Amir Afsar, Mittal, Shachi, Moore, David A., Mushtaq, Shamim, Nighat, Hussain, Papathomas, Thomas, Penault-Llorca, Frederique, Perera, Rashindrie D., Pinard, Christopher J., Pinto-Cardenas, Juan Carlos, Pruneri, Giancarlo, Pusztai, Lajos, Rahman, Arman, Rajpoot, Nasir Mahmood, Rapoport, Bernardo Leon, Rau, Tilman T., Reis-Filho, Jorge S., Ribeiro, Joana M., Rimm, David, Roslind, Anne, Vincent-Salomon, Anne, Salto-Tellez, Manuel, Saltz, Joel, Sayed, Shahin, Scott, Ely, Siziopikou, Kalliopi P., Sotiriou, Christos, Stenzinger, Albrecht, Sughayer, Maher A., Sur, Daniel, Fineberg, Susan, Symmans, Fraser, Tanaka, Sunao, Taxter, Timothy, Tejpar, Sabine, Teuwen, Jonas, Thompson, E. Aubrey, Tramm, Trine, Tran, William T., van der Laak, Jeroen, van Diest, Paul J., Verghese, Gregory E., Viale, Giuseppe, Vieth, Michael, Wahab, Noorul, Walter, Thomas, Waumans, Yannick, Wen, Hannah Y., Yang, Wentao, Yuan, Yinyin, Zin, Reena Md, Adams, Sylvia, Bartlett, John, Loibl, Sibylle, Denkert, Carsten, Savas, Peter, Loi, Sherene, Salgado, Roberto, Specht Stovgaard, Elisabeth, Thagaard, Jeppe, Broeckx, Glenn, Page, David B., Jahangir, Chowdhury Arif, Verbandt, Sara, Kos, Zuzana, Gupta, Rajarsi, Khiroya, Reena, Abduljabbar, Khalid, Acosta Haab, Gabriela, Acs, Balazs, Akturk, Guray, Almeida, Jonas S., Alvarado-Cabrero, Isabel, Amgad, Mohamed, Azmoudeh-Ardalan, Farid, Badve, Sunil, Baharun, Nurkhairul Bariyah, Balslev, Eva, Bellolio, Enrique R., Bheemaraju, Vydehi, Blenman, Kim R.M., Botinelly Mendonça Fujimoto, Luciana, Bouchmaa, Najat, Burgues, Octavio, Chardas, Alexandros, Chon U Cheang, Maggie, Ciompi, Francesco, Cooper, Lee A.D., Coosemans, An, Corredor, Germán, Dahl, Anders B., Dantas Portela, Flavio Luis, Deman, Frederik, Demaria, Sandra, Doré Hansen, Johan, Dudgeon, Sarah N., Ebstrup, Thomas, Elghazawy, Mahmoud, Fernandez-Martín, Claudio, Fox, Stephen B., Gallagher, William M., Giltnane, Jennifer M., Gnjatic, Sacha, Gonzalez-Ericsson, Paula I., Grigoriadis, Anita, Halama, Niels, Hanna, Matthew G., Harbhajanka, Aparna, Hart, Steven N., Hartman, Johan, Hauberg, Søren, Hewitt, Stephen, Hida, Akira I., Horlings, Hugo M., Husain, Zaheed, Hytopoulos, Evangelos, Irshad, Sheeba, Janssen, Emiel A.M., Kahila, Mohamed, Kataoka, Tatsuki R., Kawaguchi, Kosuke, Kharidehal, Durga, Khramtsov, Andrey I., Kiraz, Umay, Kirtani, Pawan, Kodach, Liudmila L., Korski, Konstanty, Kovács, Anikó, Laenkholm, Anne Vibeke, Lang-Schwarz, Corinna, Larsimont, Denis, Lennerz, Jochen K., Lerousseau, Marvin, Li, Xiaoxian, Ly, Amy, Madabhushi, Anant, Maley, Sai K., Manur Narasimhamurthy, Vidya, Marks, Douglas K., McDonald, Elizabeth S., Mehrotra, Ravi, Michiels, Stefan, Minhas, Fayyaz ul Amir Afsar, Mittal, Shachi, Moore, David A., Mushtaq, Shamim, Nighat, Hussain, Papathomas, Thomas, Penault-Llorca, Frederique, Perera, Rashindrie D., Pinard, Christopher J., Pinto-Cardenas, Juan Carlos, Pruneri, Giancarlo, Pusztai, Lajos, Rahman, Arman, Rajpoot, Nasir Mahmood, Rapoport, Bernardo Leon, Rau, Tilman T., Reis-Filho, Jorge S., Ribeiro, Joana M., Rimm, David, Roslind, Anne, Vincent-Salomon, Anne, Salto-Tellez, Manuel, Saltz, Joel, Sayed, Shahin, Scott, Ely, Siziopikou, Kalliopi P., Sotiriou, Christos, Stenzinger, Albrecht, Sughayer, Maher A., Sur, Daniel, Fineberg, Susan, Symmans, Fraser, Tanaka, Sunao, Taxter, Timothy, Tejpar, Sabine, Teuwen, Jonas, Thompson, E. Aubrey, Tramm, Trine, Tran, William T., van der Laak, Jeroen, van Diest, Paul J., Verghese, Gregory E., Viale, Giuseppe, Vieth, Michael, Wahab, Noorul, Walter, Thomas, Waumans, Yannick, Wen, Hannah Y., Yang, Wentao, Yuan, Yinyin, Zin, Reena Md, Adams, Sylvia, Bartlett, John, Loibl, Sibylle, Denkert, Carsten, Savas, Peter, Loi, Sherene, Salgado, Roberto, and Specht Stovgaard, Elisabeth

Abstract

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer.

Published
2023

25. Data from The Digital Slide Archive: A Software Platform for Management, Integration, and Analysis of Histology for Cancer Research

Author

Lee A.D. Cooper, David Manthey, Deepak R. Chittajallu, Jonathan Beezley, Zach Mullen, Michael Nalisnik, Sanghoon Lee, Mohammed Khalilia, and David A. Gutman

Abstract

Tissue-based cancer studies can generate large amounts of histology data in the form of glass slides. These slides contain important diagnostic, prognostic, and biological information and can be digitized into expansive and high-resolution whole-slide images using slide-scanning devices. Effectively utilizing digital pathology data in cancer research requires the ability to manage, visualize, share, and perform quantitative analysis on these large amounts of image data, tasks that are often complex and difficult for investigators with the current state of commercial digital pathology software. In this article, we describe the Digital Slide Archive (DSA), an open-source web-based platform for digital pathology. DSA allows investigators to manage large collections of histologic images and integrate them with clinical and genomic metadata. The open-source model enables DSA to be extended to provide additional capabilities. Cancer Res; 77(21); e75–78. ©2017 AACR.

Published
2023

26. Data from Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers

Author

Lee A.D. Cooper, David A. Gutman, Hagar Hussein, Habiba Elfandy, Brian P. Pollack, Matt McCormick, Pooya Mobadersany, Mohamed Amgad, and Sanghoon Lee

Abstract

Whole-slide histology images contain information that is valuable for clinical and basic science investigations of cancer but extracting quantitative measurements from these images is challenging for researchers who are not image analysis specialists. In this article, we describe HistomicsML2, a software tool for learn-by-example training of machine learning classifiers for histologic patterns in whole-slide images. This tool improves training efficiency and classifier performance by guiding users to the most informative training examples for labeling and can be used to develop classifiers for prospective application or as a rapid annotation tool that is adaptable to different cancer types. HistomicsML2 runs as a containerized server application that provides web-based user interfaces for classifier training, validation, exporting inference results, and collaborative review, and that can be deployed on GPU servers or cloud platforms. We demonstrate the utility of this tool by using it to classify tumor-infiltrating lymphocytes in breast carcinoma and cutaneous melanoma.Significance:An interactive machine learning tool for analyzing digital pathology images enables cancer researchers to apply this tool to measure histologic patterns for clinical and basic science studies.

Published
2023

31. An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears

Author

Lewis, Joshua E., primary, Shebelut, Conrad W., additional, Drumheller, Bradley R., additional, Zhang, Xuebao, additional, Shanmugam, Nithya, additional, Attieh, Michel, additional, Horwath, Michael C., additional, Khanna, Anurag, additional, Smith, Geoffrey H., additional, Gutman, David A., additional, Aljudi, Ahmed, additional, Cooper, Lee A.D., additional, and Jaye, David L., additional

Published
2023
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32. A Deep Learning Approach for Histology-Based Nuclei Segmentation and Tumor Microenvironment Characterization

Author

Rong, Ruichen, primary, Sheng, Hudanyun, additional, Jin, Kevin W., additional, Wu, Fangjiang, additional, Luo, Danni, additional, Wen, Zhuoyu, additional, Tang, Chen, additional, Yang, Donghan M., additional, Jia, Liwei, additional, Amgad, Mohamed, additional, Cooper, Lee A.D., additional, Xie, Yang, additional, Zhan, Xiaowei, additional, Wang, Shidan, additional, and Xiao, Guanghua, additional

Published
2022
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33. A Deep Learning Approach for Histology-Based Nuclei Segmentation and Tumor Microenvironment Characterization

Author

Ruichen Rong, Hudanyun Sheng, Kevin W. Jin, Fangjiang Wu, Danni Luo, Zhuoyu Wen, Chen Tang, Donghan M. Yang, Liwei Jia, Mohamed Amgad, Lee A.D. Cooper, Yang Xie, Xiaowei Zhan, Shidan Wang, and Guanghua Xiao

Abstract

Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research; however, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedure and produces massive data that capture tumor histological details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists.Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nuclei segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nuclei segmentation and TME quantification within image patches; however, existing algorithms are computationally intensive and time-consuming for WSI analysis.In this study, we present Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nuclei segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing methods for WSI analysis in nuclei detection and classification accuracy, as well as computation time.

Published
2022

34. Glioma progression is shaped by genetic evolution and microenvironment interactions

Author

Frederick S. Varn, Kevin C. Johnson, Jan Martinek, Jason T. Huse, MacLean P. Nasrallah, Pieter Wesseling, Lee A.D. Cooper, Tathiane M. Malta, Taylor E. Wade, Thais S. Sabedot, Daniel Brat, Peter V. Gould, Adelheid Wöehrer, Kenneth Aldape, Azzam Ismail, Santhosh K. Sivajothi, Floris P. Barthel, Hoon Kim, Emre Kocakavuk, Nazia Ahmed, Kieron White, Indrani Datta, Hyo-Eun Moon, Steven Pollock, Christine Goldfarb, Ga-Hyun Lee, Luciano Garofano, Kevin J. Anderson, Djamel Nehar-Belaid, Jill S. Barnholtz-Sloan, Spyridon Bakas, Annette T. Byrne, Fulvio D’Angelo, Hui K. Gan, Mustafa Khasraw, Simona Migliozzi, D. Ryan Ormond, Sun Ha Paek, Erwin G. Van Meir, Annemiek M.E. Walenkamp, Colin Watts, Tobias Weiss, Michael Weller, Karolina Palucka, Lucy F. Stead, Laila M. Poisson, Houtan Noushmehr, Antonio Iavarone, Roel G.W. Verhaak, Kristin D. Alfaro, Samirkumar B. Amin, David M. Ashley, Christoph Bock, Andrew Brodbelt, Ketan R. Bulsara, Ana Valeria Castro, Jennifer M. Connelly, Joseph F. Costello, John F. de Groot, Gaetano Finocchiaro, Pim J. French, Anna Golebiewska, Ann C. Hau, Chibo Hong, Craig Horbinski, Kasthuri S. Kannan, Mathilde CM. Kouwenhoven, Anna Lasorella, Peter S. LaViolette, Keith L. Ligon, Allison K. Lowman, Shwetal Mehta, Hrvoje Miletic, Annette M. Molinaro, Ho Keung Ng, Simone P. Niclou, Johanna M. Niers, Joanna J. Phillips, Raul Rabadan, Ganesh Rao, Guido Reifenberger, Nader Sanai, Susan C. Short, Peter Sillevis Smitt, Andrew E. Sloan, Marion Smits, James M. Snyder, Hiromichi Suzuki, Ghazaleh Tabatabai, Georgette Tanner, William H. Tomaszewski, Michael Wells, Bart A. Westerman, Helen Wheeler, Jichun Xie, W.K. Alfred Yung, Gelareh Zadeh, Junfei Zhao, Roel GW. Verhaak, Pathology, CCA - Cancer biology and immunology, Neurosurgery, Neurology, Clinical Genetics, Radiology & Nuclear Medicine, and Guided Treatment in Optimal Selected Cancer Patients (GUTS)

Subjects
Adult, Evolution, Medizin, neurons, p16, Medical and Health Sciences, Article, General Biochemistry, Genetics and Molecular Biology, treatment resistance, Evolution, Molecular, Rare Diseases, glioma, genomics, Genetics, Tumor Microenvironment, 2.1 Biological and endogenous factors, Humans, spatial imaging, Aetiology, Cancer, Brain Neoplasms, Genes, p16, hypermutation, glioblastoma, Neurosciences, Molecular, Glioma, single-cell, Biological Sciences, microenvironment, Isocitrate Dehydrogenase, GLASS Consortium, macrophages, Brain Disorders, Brain Cancer, Neoplasm Recurrence, Local, Genes, Mutation, Neoplasm Recurrence, Local, Developmental Biology
Abstract

The factors driving therapy resistance in diffuse glioma remain poorly understood. To identify treatment-associated cellular and genetic changes, we analyzed RNA and/or DNA sequencing data from the temporally separated tumor pairs of 304 adult patients with isocitrate dehydrogenase (IDH)-wild-type and IDH-mutant glioma. Tumors recurred in distinct manners that were dependent on IDH mutation status and attributable to changes in histological feature composition, somatic alterations, and microenvironment interactions. Hypermutation and acquired CDKN2A deletions were associated with an increase in proliferating neoplastic cells at recurrence in both glioma subtypes, reflecting active tumor growth. IDH-wild-type tumors were more invasive at recurrence, and their neoplastic cells exhibited increased expression of neuronal signaling programs that reflected a possible role for neuronal interactions in promoting glioma progression. Mesenchymal transition was associated with the presence of a myeloid cell state defined by specific ligand-receptor interactions with neoplastic cells. Collectively, these recurrence-associated phenotypes represent potential targets to alter disease progression.

Published
2022

35. Glioma progression is shaped by genetic evolution and microenvironment interactions

Author

Varn, Frederick S., primary, Johnson, Kevin C., additional, Martinek, Jan, additional, Huse, Jason T., additional, Nasrallah, MacLean P., additional, Wesseling, Pieter, additional, Cooper, Lee A.D., additional, Malta, Tathiane M., additional, Wade, Taylor E., additional, Sabedot, Thais S., additional, Brat, Daniel, additional, Gould, Peter V., additional, Wöehrer, Adelheid, additional, Aldape, Kenneth, additional, Ismail, Azzam, additional, Sivajothi, Santhosh K., additional, Barthel, Floris P., additional, Kim, Hoon, additional, Kocakavuk, Emre, additional, Ahmed, Nazia, additional, White, Kieron, additional, Datta, Indrani, additional, Moon, Hyo-Eun, additional, Pollock, Steven, additional, Goldfarb, Christine, additional, Lee, Ga-Hyun, additional, Garofano, Luciano, additional, Anderson, Kevin J., additional, Nehar-Belaid, Djamel, additional, Barnholtz-Sloan, Jill S., additional, Bakas, Spyridon, additional, Byrne, Annette T., additional, D’Angelo, Fulvio, additional, Gan, Hui K., additional, Khasraw, Mustafa, additional, Migliozzi, Simona, additional, Ormond, D. Ryan, additional, Paek, Sun Ha, additional, Van Meir, Erwin G., additional, Walenkamp, Annemiek M.E., additional, Watts, Colin, additional, Weiss, Tobias, additional, Weller, Michael, additional, Palucka, Karolina, additional, Stead, Lucy F., additional, Poisson, Laila M., additional, Noushmehr, Houtan, additional, Iavarone, Antonio, additional, Verhaak, Roel G.W., additional, Varn, Frederick S., additional, Ryan Ormond, D., additional, Ha Paek, Sun, additional, Alfaro, Kristin D., additional, Amin, Samirkumar B., additional, Ashley, David M., additional, Bock, Christoph, additional, Brodbelt, Andrew, additional, Bulsara, Ketan R., additional, Castro, Ana Valeria, additional, Connelly, Jennifer M., additional, Costello, Joseph F., additional, de Groot, John F., additional, Finocchiaro, Gaetano, additional, French, Pim J., additional, Golebiewska, Anna, additional, Hau, Ann C., additional, Hong, Chibo, additional, Horbinski, Craig, additional, Kannan, Kasthuri S., additional, Kouwenhoven, Mathilde CM., additional, Lasorella, Anna, additional, LaViolette, Peter S., additional, Ligon, Keith L., additional, Lowman, Allison K., additional, Mehta, Shwetal, additional, Miletic, Hrvoje, additional, Molinaro, Annette M., additional, Ng, Ho Keung, additional, Niclou, Simone P., additional, Niers, Johanna M., additional, Phillips, Joanna J., additional, Rabadan, Raul, additional, Rao, Ganesh, additional, Reifenberger, Guido, additional, Sanai, Nader, additional, Short, Susan C., additional, Sillevis Smitt, Peter, additional, Sloan, Andrew E., additional, Smits, Marion, additional, Snyder, James M., additional, Suzuki, Hiromichi, additional, Tabatabai, Ghazaleh, additional, Tanner, Georgette, additional, Tomaszewski, William H., additional, Wells, Michael, additional, Westerman, Bart A., additional, Wheeler, Helen, additional, Xie, Jichun, additional, Alfred Yung, W.K., additional, Zadeh, Gelareh, additional, Zhao, Junfei, additional, and Verhaak, Roel GW., additional

Published
2022
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38. An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears

Author

Joshua E. Lewis, Conrad W. Shebelut, Bradley R. Drumheller, Xuebao Zhang, Nithya Shanmugam, Michel Attieh, Michael C. Horwath, Anurag Khanna, Geoffrey H. Smith, David A. Gutman, Ahmed Aljudi, Lee A.D. Cooper, and David L. Jaye

Subjects
Pathology and Forensic Medicine
Abstract

Pathologic diagnosis of bone marrow disorders relies in part on microscopic analysis of bone marrow aspirate (BMA) smears and manual counting of marrow nucleated cells to obtain a differential cell count (DCC). This manual process has significant limitations, including analysis of only a small subset of optimal slide areas and nucleated cells, and inter-observer variability due to differences in cell selection and classification. To address these shortcomings, we developed an automated machine learning-based pipeline for obtaining 11-component DCCs on whole-slide BMAs. This pipeline utilizes a sequential process of identifying optimal BMA regions with high proportions of marrow nucleated cells, detecting individual cells within these optimal areas, and classifying these cells into one of 11 DCC components. Convolutional neural network models were trained on 396,048 BMA region, 28,914 cell boundary, and 1,510,976 cell class images from manual annotations. The resulting automated pipeline produces 11-component DCCs that demonstrate high statistical and diagnostic concordance with manual DCCs among a heterogeneous group of testing BMA slides with varying pathologies and cellularities. Additionally, we show that automated analysis can reduce intra-slide variance in DCCs by analyzing the whole slide and marrow nucleated cells within optimal regions. Finally, pipeline outputs of region classification, cell detection, and cell classification can be visualized using whole-slide image analysis software. This study demonstrates the feasibility of a fully-automated pipeline for generating DCCs on scanned whole-slide BMA images, with the potential for improving the current standard of practice for utilizing BMA smears in the laboratory analysis of hematologic disorders.

Published
2022

40. An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears

Author

Lewis, Joshua E., primary, Shebelut, Conrad W., additional, Drumheller, Bradley R., additional, Zhang, Xuebao, additional, Shanmugam, Nithya, additional, Attieh, Michel, additional, Horwath, Michael C., additional, Khanna, Anurag, additional, Smith, Geoffrey H., additional, Gutman, David A., additional, Aljudi, Ahmed, additional, Cooper, Lee A.D., additional, and Jaye, David L., additional

Published
2022
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41. Systematic discovery of mutation-directed neo-protein-protein interactions in cancer

Author

Mo, Xiulei, primary, Niu, Qiankun, additional, Ivanov, Andrey A., additional, Tsang, Yiu Huen, additional, Tang, Cong, additional, Shu, Changfa, additional, Li, Qianjin, additional, Qian, Kun, additional, Wahafu, Alafate, additional, Doyle, Sean P., additional, Cicka, Danielle, additional, Yang, Xuan, additional, Fan, Dacheng, additional, Reyna, Matthew A., additional, Cooper, Lee A.D., additional, Moreno, Carlos S., additional, Zhou, Wei, additional, Owonikoko, Taofeek K., additional, Lonial, Sagar, additional, Khuri, Fadlo R., additional, Du, Yuhong, additional, Ramalingam, Suresh S., additional, Mills, Gordon B., additional, and Fu, Haian, additional

Published
2022
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42. A panoptic segmentation approach for tumor-infiltrating lymphocyte assessment: development of the MuTILs model and PanopTILs dataset

Author

Mohamed Amgad, Roberto Salgado, and Lee A.D. Cooper

Abstract

Tumor-Infiltrating Lymphocytes (TILs) have strong prognostic and predictive value in breast cancer, but their visual assessment is subjective. To improve reproducibility, the International Immuno-oncology Working Group recently released recommendations for the computational assessment of TILs that build on visual scoring guidelines. However, existing resources do not adequately address these recommendations due to the lack of annotation datasets that enable joint, panoptic segmentation of tissue regions and cells. Moreover, existing deep-learning architectures focus entirely on either tissue segmentation or object detection, which complicates the process of TILs assessment by necessitating the use of multiple models with inconsistent predictions. We introducePanopTILs, a region and cell-level annotation dataset containing 814,886 nuclei from 151 patients, openly accessible at:sites.google.com/view/panoptils. PanopTILs enabled us to developMuTILs, a convolutional neural network architecture optimized for assessing TILs in accordance with clinical recommendations. MuTILs is a concept bottleneck model designed to be interpretable and to encourage sensible predictions at multiple resolutions. Using a rigorous internal-external cross-validation procedure, MuTILs achieves an AUROC of 0.93 for lymphocyte detection and a DICE coefficient of 0.81 for tumor-associated stroma segmentation. Our computational score closely matched visual scores (Spearman R=0.58, p

Published
2022

44. Systematic discovery of mutation-directed neo-protein-protein interactions in cancer

Author

Mo, Xiulei, primary, Niu, Qiankun, additional, Ivanov, Andrey A., additional, Tsang, Yiu Huen, additional, Tang, Cong, additional, Shu, Changfa, additional, Wahafu, Alafate, additional, Doyle, Sean P., additional, Cicka, Danielle, additional, Yang, Xuan, additional, Fan, Dacheng, additional, Reyna, Matthew A., additional, Cooper, Lee A.D., additional, Moreno, Carlos S., additional, Zhou, Wei, additional, Owonikoko, Taofeek, additional, Lonial, Sagar, additional, Khuri, Fadlo R., additional, Du, Yuhong, additional, Ramalingam, Suresh S., additional, Mills, Gordon B., additional, and Fu, Haian, additional

Published
2021
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46. Characteristics of adult patients with chronic intestinal failure due to short bowel syndrome: An international multicenter survey

Author

Pironi, L, Steiger, E., Joly, F., Jeppesen, P.B., Wanten, G.J.A., Sasdelli, A.S., Chambrier, C., Aimasso, U., Mundi, M.S., Szczepanek, K., Jukes, A., Theilla, M., Kunecki, M., Daniels, J., Serlie, M., Poullenot, F., Cooper, S.C., Rasmussen, H.H., Compher, C., Seguy, D., Crivelli, A., Santarpia, L., Guglielmi, F.W., Kozjek, N.R., Schneider, S.M., Ellegard, L., Thibault, R., Matras, P., Matysiak, K., Gossum, A. van, Forbes, A., Wyer, N., Taus, M., Virgili, N.M., O'Callaghan, M., Chapman, B., Osland, E., Cuerda, C., Udvarhelyi, G., Jones, L., Lee, A.D. Won, Masconale, L., Orlandoni, P., Spaggiari, C., Díez, M.B., Doitchinova-Simeonova, M., Serralde-Zúñiga, A.E., Olveira, G., Krznaric, Z., Czako, L., Kekstas, G., Sanz-Paris, A., Jáuregui, M.E.P., Murillo, A.Z., Schafer, E., Arends, J., Suárez-Llanos, J.P., Youssef, N.N., Brillanti, G., Nardi, E., Lal, S., Pironi, L, Steiger, E., Joly, F., Jeppesen, P.B., Wanten, G.J.A., Sasdelli, A.S., Chambrier, C., Aimasso, U., Mundi, M.S., Szczepanek, K., Jukes, A., Theilla, M., Kunecki, M., Daniels, J., Serlie, M., Poullenot, F., Cooper, S.C., Rasmussen, H.H., Compher, C., Seguy, D., Crivelli, A., Santarpia, L., Guglielmi, F.W., Kozjek, N.R., Schneider, S.M., Ellegard, L., Thibault, R., Matras, P., Matysiak, K., Gossum, A. van, Forbes, A., Wyer, N., Taus, M., Virgili, N.M., O'Callaghan, M., Chapman, B., Osland, E., Cuerda, C., Udvarhelyi, G., Jones, L., Lee, A.D. Won, Masconale, L., Orlandoni, P., Spaggiari, C., Díez, M.B., Doitchinova-Simeonova, M., Serralde-Zúñiga, A.E., Olveira, G., Krznaric, Z., Czako, L., Kekstas, G., Sanz-Paris, A., Jáuregui, M.E.P., Murillo, A.Z., Schafer, E., Arends, J., Suárez-Llanos, J.P., Youssef, N.N., Brillanti, G., Nardi, E., and Lal, S.

Abstract

Item does not contain fulltext, BACKGROUND AND AIMS: The case-mix of patients with intestinal failure due to short bowel syndrome (SBS-IF) can differ among centres and may also be affected by the timeframe of data collection. Therefore, the ESPEN international multicenter cross-sectional survey was analyzed to compare the characteristics of SBS-IF cohorts collected within the same timeframe in different countries. METHODS: The study included 1880 adult SBS-IF patients collected in 2015 by 65 centres from 22 countries. The demographic, nutritional, SBS type (end jejunostomy, SBS-J; jejuno-colic anastomosis, SBS-JC; jejunoileal anastomosis with an intact colon and ileocecal valve, SBS-JIC), underlying disease and intravenous supplementation (IVS) characteristics were analyzed. IVS was classified as fluid and electrolyte alone (FE) or parenteral nutrition admixture (PN). The mean daily IVS volume, calculated on a weekly basis, was categorized as <1, 1-2, 2-3 and >3 L/day. RESULTS: In the entire group: 60.7% were females and SBS-J comprised 60% of cases, while mesenteric ischaemia (MI) and Crohn' disease (CD) were the main underlying diseases. IVS dependency was longer than 3 years in around 50% of cases; IVS was infused ≥5 days/week in 75% and FE in 10% of cases. Within the SBS-IF cohort: CD was twice and thrice more frequent in SBS-J than SBS-JC and SBS-JIC, respectively, while MI was more frequent in SBS-JC and SBS-JIC. Within countries: SBS-J represented 75% or more of patients in UK and Denmark and 50-60% in the other countries, except Poland where SBS-JC prevailed. CD was the main underlying disease in UK, USA, Denmark and The Netherlands, while MI prevailed in France, Italy and Poland. CONCLUSIONS: SBS-IF type is primarily determined by the underlying disease, with significant variation between countries. These novel data will be useful for planning and managing both clinical activity and research studies on SBS.

Published
2021

49. Systematic discovery of mutation-directed neo-protein-protein interactions in cancer

Author

Xiulei Mo, Qiankun Niu, Andrey A. Ivanov, Yiu Huen Tsang, Cong Tang, Changfa Shu, Qianjin Li, Kun Qian, Alafate Wahafu, Sean P. Doyle, Danielle Cicka, Xuan Yang, Dacheng Fan, Matthew A. Reyna, Lee A.D. Cooper, Carlos S. Moreno, Wei Zhou, Taofeek K. Owonikoko, Sagar Lonial, Fadlo R. Khuri, Yuhong Du, Suresh S. Ramalingam, Gordon B. Mills, and Haian Fu

Subjects
Proto-Oncogene Proteins B-raf, Kelch-Like ECH-Associated Protein 1, Carcinogenesis, NF-E2-Related Factor 2, Neoplasms, Mutation, Humans, Article, General Biochemistry, Genetics and Molecular Biology
Abstract

Comprehensive sequencing of patient tumors reveals genomic mutations across tumor types that enable tumorigenesis and progression. A subset of oncogenic driver mutations results in neomorphic activity where the mutant protein mediates functions not engaged by the parental molecule. Here, we identify prevalent variant-enabled neomorph-protein-protein interactions (neoPPI) with a quantitative High Throughput differential Screening (qHT-dS) platform. Coupling of highly sensitive BRET biosensors with miniaturized co-expression in an ultra-HTS format allows large-scale monitoring of interactions of wild-type and mutant variant counterparts with a library of cancer-associated proteins in live cells. Screening of 17,792 interactions with 2,172,864 data points revealed a landscape of gain-of-interactions encompassing both oncogenic and tumor suppressor mutations. For example, the recurrent BRAF V600E lesion mediates KEAP1 neoPPI, rewiring a BRAF(V600E)/KEAP1 signaling axis and creating collateral vulnerability to NQO1 substrates, offering a combination therapeutic strategy. Thus, cancer genomic alterations can create neo-interactions, informing variant-directed therapeutic approaches for precision medicine.

Published
2022

50. An expanded universe of cancer targets

Author

Hahn, William C., primary, Bader, Joel S., additional, Braun, Theodore P., additional, Califano, Andrea, additional, Clemons, Paul A., additional, Druker, Brian J., additional, Ewald, Andrew J., additional, Fu, Haian, additional, Jagu, Subhashini, additional, Kemp, Christopher J., additional, Kim, William, additional, Kuo, Calvin J., additional, McManus, Michael T., additional, B. Mills, Gordon, additional, Mo, Xiulei, additional, Sahni, Nidhi, additional, Schreiber, Stuart L., additional, Talamas, Jessica A., additional, Tamayo, Pablo, additional, Tyner, Jeffrey W., additional, Wagner, Bridget K., additional, Weiss, William A., additional, Gerhard, Daniela S., additional, Dancik, Vlado, additional, Gill, Shubhroz, additional, Hua, Bruce, additional, Sharifnia, Tanaz, additional, Viswanathan, Vasanthi, additional, Zou, Yilong, additional, Dela Cruz, Filemon, additional, Kung, Andrew, additional, Stockwell, Brent, additional, Boehm, Jesse, additional, Dempster, Josh, additional, Manguso, Robert, additional, Vazquez, Francisca, additional, Cooper, Lee A.D., additional, Du, Yuhong, additional, Ivanov, Andrey, additional, Lonial, Sagar, additional, Moreno, Carlos S., additional, Niu, Qiankun, additional, Owonikoko, Taofeek, additional, Ramalingam, Suresh, additional, Reyna, Matthew, additional, Zhou, Wei, additional, Grandori, Carla, additional, Shmulevich, Ilya, additional, Swisher, Elizabeth, additional, Cai, Jitong, additional, Chan, Issac S., additional, Dunworth, Matthew, additional, Ge, Yuchen, additional, Georgess, Dan, additional, Grasset, Eloïse M., additional, Henriet, Elodie, additional, Knútsdóttir, Hildur, additional, Lerner, Michael G., additional, Padmanaban, Veena, additional, Perrone, Matthew C., additional, Suhail, Yasir, additional, Tsehay, Yohannes, additional, Warrier, Manisha, additional, Morrow, Quin, additional, Nechiporuk, Tamilla, additional, Long, Nicola, additional, Saultz, Jennifer, additional, Kaempf, Andy, additional, Minnier, Jessica, additional, Tognon, Cristina E., additional, Kurtz, Stephen E., additional, Agarwal, Anupriya, additional, Brown, Jordana, additional, Watanabe-Smith, Kevin, additional, Vu, Tania Q., additional, Jacob, Thomas, additional, Yan, Yunqi, additional, Robinson, Bridget, additional, Lind, Evan F., additional, Kosaka, Yoko, additional, Demir, Emek, additional, Estabrook, Joseph, additional, Grzadkowski, Michael, additional, Nikolova, Olga, additional, Chen, Ken, additional, Deneen, Ben, additional, Liang, Han, additional, Bassik, Michael C., additional, Bhattacharya, Asmita, additional, Brennan, Kevin, additional, Curtis, Christina, additional, Gevaert, Olivier, additional, Ji, Hanlee P., additional, Karlsson, Kasper A.J., additional, Karagyozova, Kremena, additional, Lo, Yuan-Hung, additional, Liu, Katherine, additional, Nakano, Michitaka, additional, Sathe, Anuja, additional, Smith, Amber R., additional, Spees, Kaitlyn, additional, Wong, Wing Hing, additional, Yuki, Kanako, additional, Hangauer, Matt, additional, Kaufman, Dan S., additional, Balmain, Allan, additional, Bollam, Saumya R., additional, Chen, Wei-Ching, additional, Fan, QiWen, additional, Kersten, Kelly, additional, Krummel, Matthew, additional, Li, Yun Rose, additional, Menard, Marie, additional, Nasholm, Nicole, additional, Schmidt, Christin, additional, Serwas, Nina K., additional, Yoda, Hiroyuki, additional, Ashworth, Alan, additional, Bandyopadhyay, Sourav, additional, Bivona, Trevor, additional, Eades, Gabriel, additional, Oberlin, Stefan, additional, Tay, Neil, additional, Wang, Yuhao, additional, and Weissman, Jonathan, additional

Published
2021
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