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1. HyperCLOVA X Technical Report

Author

Yoo, Kang Min, Han, Jaegeun, In, Sookyo, Jeon, Heewon, Jeong, Jisu, Kang, Jaewook, Kim, Hyunwook, Kim, Kyung-Min, Kim, Munhyong, Kim, Sungju, Kwak, Donghyun, Kwak, Hanock, Kwon, Se Jung, Lee, Bado, Lee, Dongsoo, Lee, Gichang, Lee, Jooho, Park, Baeseong, Shin, Seongjin, Yu, Joonsang, Baek, Seolki, Byeon, Sumin, Cho, Eungsup, Choe, Dooseok, Han, Jeesung, Jin, Youngkyun, Jun, Hyein, Jung, Jaeseung, Kim, Chanwoong, Kim, Jinhong, Kim, Jinuk, Lee, Dokyeong, Park, Dongwook, Sohn, Jeong Min, Han, Sujung, Heo, Jiae, Hong, Sungju, Jeon, Mina, Jung, Hyunhoon, Jung, Jungeun, Jung, Wangkyo, Kim, Chungjoon, Kim, Hyeri, Kim, Jonghyun, Kim, Min Young, Lee, Soeun, Park, Joonhee, Shin, Jieun, Yang, Sojin, Yoon, Jungsoon, Lee, Hwaran, Bae, Sanghwan, Cha, Jeehwan, Gylleus, Karl, Ham, Donghoon, Hong, Mihak, Hong, Youngki, Hong, Yunki, Jang, Dahyun, Jeon, Hyojun, Jeon, Yujin, Jeong, Yeji, Ji, Myunggeun, Jin, Yeguk, Jo, Chansong, Joo, Shinyoung, Jung, Seunghwan, Kim, Adrian Jungmyung, Kim, Byoung Hoon, Kim, Hyomin, Kim, Jungwhan, Kim, Minkyoung, Kim, Minseung, Kim, Sungdong, Kim, Yonghee, Kim, Youngjun, Kim, Youngkwan, Ko, Donghyeon, Lee, Dughyun, Lee, Ha Young, Lee, Jaehong, Lee, Jieun, Lee, Jonghyun, Lee, Jongjin, Lee, Min Young, Lee, Yehbin, Min, Taehong, Min, Yuri, Moon, Kiyoon, Oh, Hyangnam, Park, Jaesun, Park, Kyuyon, Park, Younghun, Seo, Hanbae, Seo, Seunghyun, Sim, Mihyun, Son, Gyubin, Yeo, Matt, Yeom, Kyung Hoon, Yoo, Wonjoon, You, Myungin, Ahn, Doheon, Ahn, Homin, Ahn, Joohee, Ahn, Seongmin, An, Chanwoo, An, Hyeryun, An, Junho, An, Sang-Min, Byun, Boram, Byun, Eunbin, Cha, Jongho, Chang, Minji, Chang, Seunggyu, Cho, Haesong, Cho, Youngdo, Choi, Dalnim, Choi, Daseul, Choi, Hyoseok, Choi, Minseong, Choi, Sangho, Choi, Seongjae, Choi, Wooyong, Chun, Sewhan, Go, Dong Young, Ham, Chiheon, Han, Danbi, Han, Jaemin, Hong, Moonyoung, Hong, Sung Bum, Hwang, Dong-Hyun, Hwang, Seongchan, Im, Jinbae, Jang, Hyuk Jin, Jang, Jaehyung, Jang, Jaeni, Jang, Sihyeon, Jang, Sungwon, Jeon, Joonha, Jeong, Daun, Jeong, Joonhyun, Jeong, Kyeongseok, Jeong, Mini, Jin, Sol, Jo, Hanbyeol, Jo, Hanju, Jo, Minjung, Jung, Chaeyoon, Jung, Hyungsik, Jung, Jaeuk, Jung, Ju Hwan, Jung, Kwangsun, Jung, Seungjae, Ka, Soonwon, Kang, Donghan, Kang, Soyoung, Kil, Taeho, Kim, Areum, Kim, Beomyoung, Kim, Byeongwook, Kim, Daehee, Kim, Dong-Gyun, Kim, Donggook, Kim, Donghyun, Kim, Euna, Kim, Eunchul, Kim, Geewook, Kim, Gyu Ri, Kim, Hanbyul, Kim, Heesu, Kim, Isaac, Kim, Jeonghoon, Kim, Jihye, Kim, Joonghoon, Kim, Minjae, Kim, Minsub, Kim, Pil Hwan, Kim, Sammy, Kim, Seokhun, Kim, Seonghyeon, Kim, Soojin, Kim, Soong, Kim, Soyoon, Kim, Sunyoung, Kim, Taeho, Kim, Wonho, Kim, Yoonsik, Kim, You Jin, Kim, Yuri, Kwon, Beomseok, Kwon, Ohsung, Kwon, Yoo-Hwan, Lee, Anna, Lee, Byungwook, Lee, Changho, Lee, Daun, Lee, Dongjae, Lee, Ha-Ram, Lee, Hodong, Lee, Hwiyeong, Lee, Hyunmi, Lee, Injae, Lee, Jaeung, Lee, Jeongsang, Lee, Jisoo, Lee, Jongsoo, Lee, Joongjae, Lee, Juhan, Lee, Jung Hyun, Lee, Junghoon, Lee, Junwoo, Lee, Se Yun, Lee, Sujin, Lee, Sungjae, Lee, Sungwoo, Lee, Wonjae, Lee, Zoo Hyun, Lim, Jong Kun, Lim, Kun, Lim, Taemin, Na, Nuri, Nam, Jeongyeon, Nam, Kyeong-Min, Noh, Yeonseog, Oh, Biro, Oh, Jung-Sik, Oh, Solgil, Oh, Yeontaek, Park, Boyoun, Park, Cheonbok, Park, Dongju, Park, Hyeonjin, Park, Hyun Tae, Park, Hyunjung, Park, Jihye, Park, Jooseok, Park, Junghwan, Park, Jungsoo, Park, Miru, Park, Sang Hee, Park, Seunghyun, Park, Soyoung, Park, Taerim, Park, Wonkyeong, Ryu, Hyunjoon, Ryu, Jeonghun, Ryu, Nahyeon, Seo, Soonshin, Seo, Suk Min, Shim, Yoonjeong, Shin, Kyuyong, Shin, Wonkwang, Sim, Hyun, Sim, Woongseob, Soh, Hyejin, Son, Bokyong, Son, Hyunjun, Son, Seulah, Song, Chi-Yun, Song, Chiyoung, Song, Ka Yeon, Song, Minchul, Song, Seungmin, Wang, Jisung, Yeo, Yonggoo, Yi, Myeong Yeon, Yim, Moon Bin, Yoo, Taehwan, Yoo, Youngjoon, Yoon, Sungmin, Yoon, Young Jin, Yu, Hangyeol, Yu, Ui Seon, Zuo, Xingdong, Bae, Jeongin, Bae, Joungeun, Cho, Hyunsoo, Cho, Seonghyun, Cho, Yongjin, Choi, Taekyoon, Choi, Yera, Chung, Jiwan, Han, Zhenghui, Heo, Byeongho, Hong, Euisuk, Hwang, Taebaek, Im, Seonyeol, Jegal, Sumin, Jeon, Sumin, Jeong, Yelim, Jeong, Yonghyun, Jiang, Can, Jiang, Juyong, Jin, Jiho, Jo, Ara, Jo, Younghyun, Jung, Hoyoun, Jung, Juyoung, Kang, Seunghyeong, Kim, Dae Hee, Kim, Ginam, Kim, Hangyeol, Kim, Heeseung, Kim, Hyojin, Kim, Hyojun, Kim, Hyun-Ah, Kim, Jeehye, Kim, Jin-Hwa, Kim, Jiseon, Kim, Jonghak, Kim, Jung Yoon, Kim, Rak Yeong, Kim, Seongjin, Kim, Seoyoon, Kim, Sewon, Kim, Sooyoung, Kim, Sukyoung, Kim, Taeyong, Ko, Naeun, Koo, Bonseung, Kwak, Heeyoung, Kwon, Haena, Kwon, Youngjin, Lee, Boram, Lee, Bruce W., Lee, Dagyeong, Lee, Erin, Lee, Euijin, Lee, Ha Gyeong, Lee, Hyojin, Lee, Hyunjeong, Lee, Jeeyoon, Lee, Jeonghyun, Lee, Jongheok, Lee, Joonhyung, Lee, Junhyuk, Lee, Mingu, Lee, Nayeon, Lee, Sangkyu, Lee, Se Young, Lee, Seulgi, Lee, Seung Jin, Lee, Suhyeon, Lee, Yeonjae, Lee, Yesol, Lee, Youngbeom, Lee, Yujin, Li, Shaodong, Liu, Tianyu, Moon, Seong-Eun, Moon, Taehong, Nihlenramstroem, Max-Lasse, Oh, Wonseok, Oh, Yuri, Park, Hongbeen, Park, Hyekyung, Park, Jaeho, Park, Nohil, Park, Sangjin, Ryu, Jiwon, Ryu, Miru, Ryu, Simo, Seo, Ahreum, Seo, Hee, Seo, Kangdeok, Shin, Jamin, Shin, Seungyoun, Sin, Heetae, Wang, Jiangping, Wang, Lei, Xiang, Ning, Xiao, Longxiang, Xu, Jing, Yi, Seonyeong, Yoo, Haanju, Yoo, Haneul, Yoo, Hwanhee, Yu, Liang, Yu, Youngjae, Yuan, Weijie, Zeng, Bo, Zhou, Qian, Cho, Kyunghyun, Ha, Jung-Woo, Park, Joonsuk, Hwang, Jihyun, Kwon, Hyoung Jo, Kwon, Soonyong, Lee, Jungyeon, Lee, Seungho, Lim, Seonghyeon, Noh, Hyunkyung, Choi, Seungho, Lee, Sang-Woo, Lim, Jung Hwa, and Sung, Nako

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs., Comment: 44 pages; updated authors list and fixed author names

Published
2024

2. Deep learning-based single-shot computational spectrometer using multilayer thin films

Author

Kim, Cheolsun, Park, Dongju, Lee, Jioh, and Lee, Heung-No

Subjects
Physics - Optics, Physics - Instrumentation and Detectors
Abstract

Computational spectrometers have mobile application potential, such as on-site detection and self-diagnosis, by offering compact size, fast operation time, high resolution, wide working range, and low-cost production. Although these spectrometers have been extensively studied, demonstrations are confined to a few examples of straightforward spectra. This study demonstrates deep learning (DL)-based single-shot computational spectrometer for narrow and broad spectra using a multilayer thin-film filter array. For measuring light intensities, the device was built by attaching the filter array, fabricated using a wafer-level stencil lithography process, to a complementary metal-oxide-semiconductor image sensor. All the intensities were extracted from a monochrome image captured with a single exposure. A DL architecture comprising a dense layer and a U-Net backbone with residual connections was employed for spectrum reconstruction. The measured intensities were fed into the DL architecture for reconstruction as spectra. We reconstructed 323 continuous spectra with an average root mean squared error of 0.0288 in a 500-850 nm wavelength range with 1-nm spacing. Our computational spectrometer achieved a compact size, fast measuring time, high resolution, and wide working range., Comment: 11 pages, 6 figures

Published
2022

3. What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers

Author

Kim, Boseop, Kim, HyoungSeok, Lee, Sang-Woo, Lee, Gichang, Kwak, Donghyun, Jeon, Dong Hyeon, Park, Sunghyun, Kim, Sungju, Kim, Seonhoon, Seo, Dongpil, Lee, Heungsub, Jeong, Minyoung, Lee, Sungjae, Kim, Minsub, Ko, Suk Hyun, Kim, Seokhun, Park, Taeyong, Kim, Jinuk, Kang, Soyoung, Ryu, Na-Hyeon, Yoo, Kang Min, Chang, Minsuk, Suh, Soobin, In, Sookyo, Park, Jinseong, Kim, Kyungduk, Kim, Hiun, Jeong, Jisu, Yeo, Yong Goo, Ham, Donghoon, Park, Dongju, Lee, Min Young, Kang, Jaewook, Kang, Inho, Ha, Jung-Woo, Park, Woomyoung, and Sung, Nako

Subjects
Computer Science - Computation and Language
Abstract

GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data. Here we address some remaining issues less reported by the GPT-3 paper, such as a non-English LM, the performances of different sized models, and the effect of recently introduced prompt optimization on in-context learning. To achieve this, we introduce HyperCLOVA, a Korean variant of 82B GPT-3 trained on a Korean-centric corpus of 560B tokens. Enhanced by our Korean-specific tokenization, HyperCLOVA with our training configuration shows state-of-the-art in-context zero-shot and few-shot learning performances on various downstream tasks in Korean. Also, we show the performance benefits of prompt-based learning and demonstrate how it can be integrated into the prompt engineering pipeline. Then we discuss the possibility of materializing the No Code AI paradigm by providing AI prototyping capabilities to non-experts of ML by introducing HyperCLOVA studio, an interactive prompt engineering interface. Lastly, we demonstrate the potential of our methods with three successful in-house applications., Comment: Accepted to EMNLP2021 as a long paper. Fixed some typos

Published
2021

5. GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation

Author

Yoo, Kang Min, Park, Dongju, Kang, Jaewook, Lee, Sang-Woo, and Park, Woomyeong

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Large-scale language models such as GPT-3 are excellent few-shot learners, allowing them to be controlled via natural text prompts. Recent studies report that prompt-based direct classification eliminates the need for fine-tuning but lacks data and inference scalability. This paper proposes a novel data augmentation technique that leverages large-scale language models to generate realistic text samples from a mixture of real samples. We also propose utilizing soft-labels predicted by the language models, effectively distilling knowledge from the large-scale language models and creating textual perturbations simultaneously. We perform data augmentation experiments on diverse classification tasks and show that our method hugely outperforms existing text augmentation methods. Ablation studies and a qualitative analysis provide more insights into our approach., Comment: Accepted to EMNLP2021 Findings; 11 pages, 7 tables, 2 figures

Published
2021

8. ISG15 mediates the function of extracellular vesicles in promoting ovarian cancer progression and metastasis

Author

Dorayappan, Kalpana Deepa Priya, primary, Wagner, Vincent, additional, Park, Dongju, additional, Newcomer, Meghan M., additional, Lightfoot, Michelle D. S., additional, Kalaiyarasan, Deepika, additional, Sakaue, Takahiko, additional, Khadraoui, Wafa, additional, Yu, Lianbo, additional, Wang, Qi‐En, additional, Maxwell, G. Larry, additional, O'Malley, David, additional, Pollock, Raphael E., additional, Cohn, David E., additional, and Selvendiran, Karuppaiyah, additional

Published
2024
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10. LSTM Encoder-Decoder with Adversarial Network for Text Generation from Keyword

Author

Park, Dongju, Ahn, Chang Wook, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Qiao, Jianyong, editor, Zhao, Xinchao, editor, Pan, Linqiang, editor, Zuo, Xingquan, editor, Zhang, Xingyi, editor, Zhang, Qingfu, editor, and Huang, Shanguo, editor

Published
2018
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11. Driving Control by Based upon the Slip Pattern of the Ball Robot

Author

Lee, Howon, Hwan, Moonjeon, Park, Dongju, Park, Seon-ho, Lee, Jangmyung, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Kubota, Naoyuki, editor, Kiguchi, Kazuo, editor, Liu, Honghai, editor, and Obo, Takenori, editor

Published
2016
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12. Abstract LB156: Identified the ISG15 mediated extracellular vesicles drives ovarian cancer progression and metastasis: extracellular vesicular ISG15 is a potential biomarker and therapeutic target

Author

Dorayappan, Kalpana Deepa Priya, primary, Wagner, Vincent, additional, Park, Dongju, additional, Newcomer, Meghan M., additional, Lightfoot, Michelle DS, additional, Kalaiyarasan, Deepika, additional, Sakaue, Takahiko, additional, Khadraoui, Wafa, additional, Cosgrove, Casey, additional, Maxwell, Larry J., additional, Wang, Qi-En Wang, additional, O’Malley, David, additional, Pollock, Raphael E., additional, Cohn, David E., additional, and Karuppaiyah, Selvendiran, additional

Published
2023
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14. Abstract 5633: Analysis of the immune microenvironment and tumor-infiltrating immune cells across different solid tumors by combined spatial transcriptomics and proteomics

Author

Femel, Julia, primary, Neil, Emily, additional, Park, Dongju, additional, Yassouri, Fabio El, additional, Appelshoffer, Anijutta, additional, Lloyd, Erica, additional, DiBuono, Michael, additional, Sauer, Henry, additional, Lafayette, Hanna, additional, Smith, Hsinyi, additional, Wang, Jinling, additional, Mangiardi, Dominic, additional, Makrigiorgos, Alex, additional, Praveen, Paurush, additional, Rüberg, Silvia, additional, Müller, Werner, additional, Wantenaar, Tanya, additional, Pinard, Robert, additional, and Bosio, Andreas, additional

Published
2023
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23. miRNA-mediated TUSC3 deficiency enhances UPR and ERAD to promote metastatic potential of NSCLC

Author

Jeon, Young-Jun, Kim, Taewan, Park, Dongju, Nuovo, Gerard J., Rhee, Siyeon, Joshi, Pooja, Lee, Bum-Kyu, Jeong, Johan, Suh, Sung-suk, Grotzke, Jeff E., Kim, Sung-Hak, Song, Jieun, Sim, Hosung, Kim, Yonghwan, Peng, Yong, Jeong, Youngtae, Garofalo, Michela, Zanesi, Nicola, Kim, Jonghwan, Liang, Guang, Nakano, Ichiro, Cresswell, Peter, Nana-Sinkam, Patrick, Cui, Ri, and Croce, Carlo M.

Published
2018
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30. Comparison of Digital PCR and Quantitative PCR with Various SARS-CoV-2 Primer-Probe Sets

Author

Park, Changwoo, primary, Lee, Jina, additional, Hassan, Zohaib ul, additional, Ku, Keun Bon, additional, Kim, Seong-Jun, additional, Kim, Hong Gi, additional, Park, Edmond Changkyun, additional, Park, Gun-Soo, additional, Park, Daeui, additional, Baek, Seung-Hwa, additional, Park, Dongju, additional, Lee, Jihye, additional, Jeon, Sangeun, additional, Kim, Seungtaek, additional, Lee, Chang-Seop, additional, Yoo, Hee Min, additional, and Kim, Seil, additional

Published
2021
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32. What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers

Author

Kim, Boseop, primary, Kim, HyoungSeok, additional, Lee, Sang-Woo, additional, Lee, Gichang, additional, Kwak, Donghyun, additional, Dong Hyeon, Jeon, additional, Park, Sunghyun, additional, Kim, Sungju, additional, Kim, Seonhoon, additional, Seo, Dongpil, additional, Lee, Heungsub, additional, Jeong, Minyoung, additional, Lee, Sungjae, additional, Kim, Minsub, additional, Ko, Suk Hyun, additional, Kim, Seokhun, additional, Park, Taeyong, additional, Kim, Jinuk, additional, Kang, Soyoung, additional, Ryu, Na-Hyeon, additional, Yoo, Kang Min, additional, Chang, Minsuk, additional, Suh, Soobin, additional, In, Sookyo, additional, Park, Jinseong, additional, Kim, Kyungduk, additional, Kim, Hiun, additional, Jeong, Jisu, additional, Yeo, Yong Goo, additional, Ham, Donghoon, additional, Park, Dongju, additional, Lee, Min Young, additional, Kang, Jaewook, additional, Kang, Inho, additional, Ha, Jung-Woo, additional, Park, Woomyoung, additional, and Sung, Nako, additional

Published
2021
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34. Robust and sensitive detection of SARS-CoV-2 using PCR based methods

Author

Park, Changwoo, primary, Lee, Jina, additional, Hassan, Zohaib ul, additional, Ku, Keun Bon, additional, Kim, Seong Jun, additional, Kim, Hong Gi, additional, Park, Edmond Changkyun, additional, Park, Gun-Soo, additional, Park, Daeui, additional, Baek, Seung-Hwa, additional, Park, Dongju, additional, Lee, Jihye, additional, Jeon, Sangeun, additional, Kim, Seungtaek, additional, Lee, Chang-Seop, additional, Yoo, Hee Min, additional, and Kim, Seil, additional

Published
2020
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41. Stromal PTEN determines mammary epithelial response to radiotherapy

Author

Sizemore, Gina M., primary, Balakrishnan, Subhasree, additional, Thies, Katie A., additional, Hammer, Anisha M., additional, Sizemore, Steven T., additional, Trimboli, Anthony J., additional, Cuitiño, Maria C., additional, Steck, Sarah A., additional, Tozbikian, Gary, additional, Kladney, Raleigh D., additional, Shinde, Neelam, additional, Das, Manjusri, additional, Park, Dongju, additional, Majumder, Sarmila, additional, Krishnan, Shiva, additional, Yu, Lianbo, additional, Fernandez, Soledad A., additional, Chakravarti, Arnab, additional, Shields, Peter G., additional, White, Julia R., additional, Yee, Lisa D., additional, Rosol, Thomas J., additional, Ludwig, Thomas, additional, Park, Morag, additional, Leone, Gustavo, additional, and Ostrowski, Michael C., additional

Published
2018
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48. The role of PALB2 in BRCA1/2-mediated DNA repair and tumor suppression

Author

Park, Dongju

Subjects
Cellular Biology, Molecular Biology, DNA repair and tumor suppression, cellular biology
Abstract

Germline mutations in the cancer susceptibility genes BRCA1 or BRCA2 confer an increased risk of developing breast, ovarian and pancreatic cancer. BRCA1 and BRCA2 are key molecules in DNA damage repair, specifically repair by homologous recombination (HR). Accumulating DNA damage, due to dysfunction of the BRCA genes, leads to genome instability and increased cancer risk. In 2006, a novel protein, PALB2 (partner and localizer of BRCA2) was identified as a DNA repair pathway component (1). PALB2 co-localizes with BRCA1 and BRCA2 at DNA damage sites and is thought to act as an adaptor protein that mediates the BRCA1-BRCA2 interaction (2). Indeed, prior studies showed that BRCA1 and PALB2 directly interact through their coiled-coil domain. The C-terminal WD40 domain of PALB2 binds to BRCA2 and the BRC repeats of BRCA2 recruit a central protein for HR, the Rad51 recombinase. Recently, mutations in the PALB2 gene were reported in the tumors of breast and pancreatic cancer patients. Interestingly, some of these mutations could inhibit its direct binding of PALB2 to BRCA1 or BRCA2 thus disrupting DNA repair. Furthermore, missense mutations in the coiled-coil domain of BRCA1 (M1400V, L1407P and M1411T) that mediates the interaction with PALB2 have been reported among familial breast cancer patients, which also could increase the risk of pancreatic cancer. Therefore, we hypothesized that physical and functional interaction between BRCA1 and BRCA2 through the linker protein PALB2 is required for BRCA1/2-mediated HR and tumor suppression. To test this hypothesis, we first determined whether PALB2 is a bonafide tumor-suppressor by deleting Palb2 in the pancreas specifically in genetically engineered KrasLSL-G12D/+; p53LSL-R270H/+; Pdx1-Cre mouse model (now referred to as KPC mice) and observing these animals for pancreatic tumors in comparison with Brca1flex/flex-KPC or Brca2 flex/flex-KPC pancreatic tumor mouse models. Homozygous deletion of Palb2 in the pancreas of KPC mice accelerated pancreatic tumor development compared to KPC mice, similarly to Brca1flex/flex-KPC and Brca2 flex/flex-KPC animals. However, the histopathology of tumors was different between the groups (KPC vs Palb2 flex/flex-KPC vs Brca1flex/flex-KPC vs Brca2flex/flex-KPC). Pancreatic tumor cell lines established from Palb2 flex/flex-KPC mice showed hypersensitivity to DNA damaging agents such as interstrand cross-linkers, Mitomycin C (MMC) and Cisplatin and poly ADP ribose polymerase inhibitor (PARP inhibitor) to an extent similar to that of cells from Brca1flex/flex-KPC or Brca2flex/flex-KPC tumors, compared to cells generated from KPC tumors. In addition, Palb2 flex/flex-KPC mice in vivo treated with MMC showed clearly prolonged survival. These results suggest that PALB2 plays a key role in BRCAs mediated HR and tumor suppression. Next, we determined if the BRCA1-PALB2 interaction per se is critical for BRCA1-mediated tumor suppression by generating Brca1 L1363P point mutant knock-in mice (L1407P in human sequence) that abolishes the binding of BRCA1 to PALB2. Mouse embryonic fibroblasts (MEFs) from homozygous Brca1 L1363P mutant animals exhibit hypersensitivity to DNA damaging agents (MMC, PARP-inhibitor and ionizing radiation (IR)), and following IR mutant cells fail to recruit the Rad51 recombinase to sites of DNA damage, implying a defect of DSBs repair by HR. Accumulation of unresolved DNA damage induces hyperactivation of p53 and its downstream target p21 in Brca1L1363P/L1363P primary MEFs resulting in impaired proliferation and premature senescence. While Brca1-/- null mice are early embryonic lethal, homozygous Brca1L1363P/L1363P mice are viable. However, mutant mice exhibit growth retardation to various extents. Some mutant animals were extremely small, developed aplastic anemia and died within a month. All other Brca1L1363P/L1363P animals developed T-cell acute lymphoblastic leukemia (T-ALL) with an average latency of 3 months. Interestingly, the majority of T-ALLs (53%) from Brca1L1363P/L1363P animals acquired activating Notch1 mutations such as discovered in patients with T-ALLs. The phenotypes observed in Brca1L1363P/L1363P cells and mice recapitulate clinical phenotypes seen in Fanconi Anemia (FA) patients. Therefore, this mouse model can provide insights for developing new therapies for FA. Furthermore, this results demonstrate the importance of the BRCA1-PALB2 interaction in vivo and suggests that the interaction is essential for BRCA1-mediated tumor suppression.

Published
2017

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