JungHo Kong
Overview
Explore the profile of JungHo Kong including associated specialties, affiliations and a list of published articles.
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Articles
17
Citations
279
Followers
0
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Recent Articles
1.
Kong J, Zhao X, Singhal A, Park S, Bachelder R, Shen J, et al.
Sci Adv
. 2024 Sep;
10(38):eado9746.
PMID: 39303028
While immune checkpoint inhibitors have revolutionized cancer therapy, many patients exhibit poor outcomes. Here, we show immunotherapy responses in bladder and non-small cell lung cancers are effectively predicted by factoring...
2.
Sears T, Pagadala M, Castro A, Lee K, Kong J, Tanaka K, et al.
Cancer Immunol Res
. 2024 Sep;
12(12):1780-1795.
PMID: 39255339
Immune checkpoint blockade (ICB) has revolutionized cancer treatment; however, the mechanisms determining patient response remain poorly understood. Here, we used machine learning to predict ICB response from germline and somatic...
3.
Sharma G, Sharma A, Kim I, Cha D, Kim S, Park E, et al.
Nat Immunol
. 2024 Apr;
25(5):790-801.
PMID: 38664585
Innate immune cells generate a multifaceted antitumor immune response, including the conservation of essential nutrients such as iron. These cells can be modulated by commensal bacteria; however, identifying and understanding...
4.
Lee J, Kim D, Kong J, Ha D, Kim I, Park M, et al.
Sci Adv
. 2024 Jan;
10(5):eadj0785.
PMID: 38295179
Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, only some patients respond to ICIs, and current biomarkers for ICI efficacy have limited performance. Here, we devised an interpretable machine...
5.
Sears T, Pagadala M, Castro A, Lee K, Kong J, Tanaka K, et al.
bioRxiv
. 2024 Jan;
PMID: 38293085
Immune Checkpoint Blockade (ICB) has revolutionized cancer treatment, however mechanisms determining patient response remain poorly understood. Here we used machine learning to predict ICB response from germline and somatic biomarkers...
6.
Zhao X, Singhal A, Park S, Kong J, Bachelder R, Ideker T
Cancer Discov
. 2024 Jan;
14(3):508-523.
PMID: 38236062
Significance: Zhao and colleagues use recent advances in machine learning to study the effects of tumor mutations on the response to common therapeutics that cause RS. The resulting predictive models...
7.
Kong J, Kim J, Kim D, Lee K, Lee J, Han S, et al.
Patterns (N Y)
. 2023 Jul;
4(6):100736.
PMID: 37409049
Predicting cancer recurrence is essential to improving the clinical outcomes of patients with colorectal cancer (CRC). Although tumor stage information has been used as a guideline to predict CRC recurrence,...
8.
Kim D, Kim J, Lee J, Han S, Lee K, Kong J, et al.
iScience
. 2022 Nov;
25(11):105392.
PMID: 36345336
Predicting colorectal cancer recurrence after tumor resection is crucial because it promotes the administration of proper subsequent treatment or management to improve the clinical outcomes of patients. Several clinical or...
9.
Ha D, Kong J, Kim D, Lee K, Lee J, Park M, et al.
BMB Rep
. 2022 Oct;
56(1):43-48.
PMID: 36284440
Pre-clinical models are critical in gaining mechanistic and biological insights into disease progression. Recently, patient-derived organoid models have been developed to facilitate our understanding of disease development and to improve...
10.
Kong J, Ha D, Lee J, Kim I, Park M, Im S, et al.
Nat Commun
. 2022 Jun;
13(1):3703.
PMID: 35764641
Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer patients over the past several years. However, only a minority of patients respond to ICI treatment (~30% in solid...