Browsing by Author "Kim, Rim S."

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    Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer
    (Wolters Kluwer, 2024) Chen, Lujia; Wang, Ying; Cai, Chunhui; Ding, Ying; Kim, Rim S.; Lipchik, Corey; Gavin, Patrick G.; Yothers, Greg; Allegra, Carmen J.; Petrelli, Nicholas J.; Suga, Jennifer Marie; Hopkins, Judith O.; Saito, Naoyuki G.; Evans, Terry; Jujjavarapu, Srinivas; Wolmark, Norman; Lucas, Peter C.; Paik, Soonmyung; Sun, Min; Pogue-Geile, Katherine L.; Lu, Xinghua; Medicine, School of Medicine
    Purpose: A combination of fluorouracil, leucovorin, and oxaliplatin (FOLFOX) is the standard for adjuvant therapy of resected early-stage colon cancer (CC). Oxaliplatin leads to lasting and disabling neurotoxicity. Reserving the regimen for patients who benefit from oxaliplatin would maximize efficacy and minimize unnecessary adverse side effects. Methods: We trained a new machine learning model, referred to as the colon oxaliplatin signature (COLOXIS) model, for predicting response to oxaliplatin-containing regimens. We examined whether COLOXIS was predictive of oxaliplatin benefits in the CC adjuvant setting among 1,065 patients treated with 5-fluorouracil plus leucovorin (FULV; n = 421) or FULV + oxaliplatin (FOLFOX; n = 644) from NSABP C-07 and C-08 phase III trials. The COLOXIS model dichotomizes patients into COLOXIS+ (oxaliplatin responder) and COLOXIS- (nonresponder) groups. Eight-year recurrence-free survival was used to evaluate oxaliplatin benefits within each of the groups, and the predictive value of the COLOXIS model was assessed using the P value associated with the interaction term (int P) between the model prediction and the treatment effect. Results: Among 1,065 patients, 526 were predicted as COLOXIS+ and 539 as COLOXIS-. The COLOXIS+ prediction was associated with prognosis for FULV-treated patients (hazard ratio [HR], 1.52 [95% CI, 1.07 to 2.15]; P = .017). The model was predictive of oxaliplatin benefits: COLOXIS+ patients benefited from oxaliplatin (HR, 0.65 [95% CI, 0.48 to 0.89]; P = .0065; int P = .03), but COLOXIS- patients did not (COLOXIS- HR, 1.08 [95% CI, 0.77 to 1.52]; P = .65). Conclusion: The COLOXIS model is predictive of oxaliplatin benefits in the CC adjuvant setting. The results provide evidence supporting a change in CC adjuvant therapy: reserve oxaliplatin only for COLOXIS+ patients, but further investigation is warranted.
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    Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group
    (Nature Research, 2020-05-12) Amgad, Mohamed; Stovgaard, Elisabeth Specht; Balslev, Eva; Thagaard, Jeppe; Chen, Weijie; Dudgeon, Sarah; Sharma, Ashish; Kerner, Jennifer K.; Denkert, Carsten; Yuan, Yinyin; AbdulJabbar, Khalid; Wienert, Stephan; Savas, Peter; Voorwerk, Leonie; Beck, Andrew H.; Madabhushi, Anant; Hartman, Johan; Sebastian, Manu M.; Horlings, Hugo M.; Hudeček, Jan; Ciompi, Francesco; Moore, David A.; Singh, Rajendra; Roblin, Elvire; Balancin, Marcelo Luiz; Mathieu, Marie-Christine; Lennerz, Jochen K.; Kirtani, Pawan; Chen, I-Chun; Braybrooke, Jeremy P.; Pruneri, Giancarlo; Demaria, Sandra; Adams, Sylvia; Schnitt, Stuart J.; Lakhani, Sunil R.; Rojo, Federico; Comerma, Laura; Badve, Sunil S.; Khojasteh, Mehrnoush; Symmans, W. Fraser; Sotiriou, Christos; Gonzalez-Ericsson, Paula; Pogue-Geile, Katherine L.; Kim, Rim S.; Rimm, David L.; Viale, Giuseppe; Hewitt, Stephen M.; Bartlett, John M. S.; Penault-Llorca, Frédérique; Goel, Shom; Lien, Huang-Chun; Loibl, Sibylle; Kos, Zuzana; Loi, Sherene; Hanna, Matthew G.; Michiels, Stefan; Kok, Marleen; Nielsen, Torsten O.; Lazar, Alexander J.; Bago-Horvath, Zsuzsanna; Kooreman, Loes F. S.; Van der Laak, Jeroen A.W. M.; Saltz, Joel; Gallas, Brandon D.; Kurkure, Uday; Barnes, Michael; Salgado, Roberto; Cooper, Lee A. D.; International Immuno-Oncology Biomarker Working Group; Pathology and Laboratory Medicine, School of Medicine
    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.
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    The tale of TILs in breast cancer: A report from The International Immuno-Oncology Biomarker Working Group
    (Springer Nature, 2021-12-01) El Bairi, Khalid; Haynes, Harry R.; Blackley, Elizabeth; Fineberg, Susan; Shear, Jeffrey; Turner, Sophia; de Freitas, Juliana Ribeiro; Sur, Daniel; Amendola, Luis Claudio; Gharib, Masoumeh; Kallala, Amine; Arun, Indu; Azmoudeh-Ardalan, Farid; Fujimoto, Luciana; Sua, Luz F.; Liu, Shi-Wei; Lien, Huang-Chun; Kirtani, Pawan; Balancin, Marcelo; El Attar, Hicham; Guleria, Prerna; Yang, Wenxian; Shash, Emad; Chen, I-Chun; Bautista, Veronica; Do Prado Moura, Jose Fernando; Rapoport, Bernardo L.; Castaneda, Carlos; Spengler, Eunice; Acosta-Haab, Gabriela; Frahm, Isabel; Sanchez, Joselyn; Castillo, Miluska; Bouchmaa, Najat; Md Zin, Reena R.; Shui, Ruohong; Onyuma, Timothy; Yang, Wentao; Husain, Zaheed; Willard-Gallo, Karen; Coosemans, An; Perez, Edith A.; Provenzano, Elena; Gonzalez Ericsson, Paula; Richardet, Eduardo; Mehrotra, Ravi; Sarancone, Sandra; Ehinger, Anna; Rimm, David L.; Bartlett, John M. S.; Viale, Giuseppe; Denkert, Carsten; Hida, Akira I.; Sotiriou, Christos; Loibl, Sibylle; Hewitt, Stephen M.; Badve, Sunil; Symmans, William Fraser; Kim, Rim S.; Pruneri, Giancarlo; Goel, Shom; Francis, Prudence A.; Inurrigarro, Gloria; Yamaguchi, Rin; Garcia-Rivello, Hernan; Horlings, Hugo; Afqir, Said; Salgado, Roberto; Adams, Sylvia; Kok, Marleen; Dieci, Maria Vittoria; Michiels, Stefan; Demaria, Sandra; Loi, Sherene; International Immuno-Oncology Biomarker Working Group; Pathology and Laboratory Medicine, School of Medicine
    The advent of immune-checkpoint inhibitors (ICI) in modern oncology has significantly improved survival in several cancer settings. A subgroup of women with breast cancer (BC) has immunogenic infiltration of lymphocytes with expression of programmed death-ligand 1 (PD-L1). These patients may potentially benefit from ICI targeting the programmed death 1 (PD-1)/PD-L1 signaling axis. The use of tumor-infiltrating lymphocytes (TILs) as predictive and prognostic biomarkers has been under intense examination. Emerging data suggest that TILs are associated with response to both cytotoxic treatments and immunotherapy, particularly for patients with triple-negative BC. In this review from The International Immuno-Oncology Biomarker Working Group, we discuss (a) the biological understanding of TILs, (b) their analytical and clinical validity and efforts toward the clinical utility in BC, and (c) the current status of PD-L1 and TIL testing across different continents, including experiences from low-to-middle-income countries, incorporating also the view of a patient advocate. This information will help set the stage for future approaches to optimize the understanding and clinical utilization of TIL analysis in patients with BC.