Cancer Immunotherapy
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2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 287-287
Author(s):  
Ari M. Vanderwalde ◽  
Esprit Ma ◽  
Elaine Yu ◽  
Tania Szado ◽  
Richard Price ◽  
...  

287 Background: Recent approvals of targeted treatments (tx) have improved personalized care in aNSCLC. Biomarker testing is crucial for patients (pts) to receive optimal tx expeditiously. This study examined aNSCLC biomarker testing and tx patterns at OneOnc. Methods: Pts diagnosed with aNSCLC (stage ≥ IIIb) from 1/1/2015 to 5/31/2020, aged ≥ 18 years, and with ≥ 1 visit ≤ 90 days of advanced (Adv) diagnosis (Dx) were retrospectively evaluated using the nationwide Flatiron Health electronic health record derived de-identified database from selected OneOnc sites. Descriptive analyses were conducted to evaluate testing patterns for ALK, BRAF, EGFR, KRAS, PD-L1, and ROS-1 biomarkers and actionable mutation tx pattern. Results: Overall 3,860 aNSCLC pts were included, median age was 69 years, 47% females, 66% non-squamous, 29% squamous, 4% histology NOS, and 23% with ECOG performance status 0-1. Of the 3,152 (82%) pts tested for any biomarker, 64% received next-generation sequencing (NGS) vs. 36% received other biomarker tests only. Testing rates varied by biomarker: EGFR (74%), ALK (72%), ROS-1 (66%), PD-L1 (57%), BRAF (56%), KRAS (54%). Pts who received all 6 biomarker tests increased from 12% (2015), 23% (2016), 40% (2017), 41% (2018), 48% (2019) to 56% (2020). Among the tested pts, the median time from Adv Dx to the first test result was 20 days (d) and from specimen collection after Adv Dx to the first test result was 12 d. Pts tested and treated before test result available declined from 28% (2015) to 16% (2020). Of 1,207 pts with actionable mutations, 390 (32%) received tx before the test result: 35% chemotherapy (chemo) only, 28% chemo + cancer immunotherapy (CIT), and 15% CIT only. After the test result, 26% to 81% of pts received no or other tx not specific to actionable mutations [Table]. Conclusions: Findings from this study demonstrated an increase in aNSCLC biomarker testing at OneOnc over time, while 44% pts in 2020 did not receive testing on all 6 biomarkers. Some pts had tx prior to the test result, but this trend appeared to decline. Further studies are warranted to better understand the reasons for pts receiving tx that were not specific to their actionable mutations.[Table: see text]


Nano Letters ◽  
2021 ◽  
Author(s):  
Xiang Xiong ◽  
Jingya Zhao ◽  
Jingmei Pan ◽  
Chunping Liu ◽  
Xing Guo ◽  
...  

2021 ◽  
Author(s):  
Jonathan Nowak ◽  
Mai Chan Lau ◽  
Jennifer Borowsky ◽  
Juha Väyrynen ◽  
Koichiro Haruki ◽  
...  

Abstract Growing evidence supports the importance of quantifying tumor-immune cell interactions in the tumor microenvironment to enable precision cancer therapy. However, most existing methods rely solely upon immune cell density or nearest neighbor-type analyses and fail to fully characterize spatial heterogeneity. Herein, we describe a computational algorithm, termed Tumor-Immune Partitioning and Clustering (TIPC), that jointly measures immune cell partitioning between tumor epithelial and stromal areas and immune cell clustering versus dispersion. As proof of principle, we apply TIPC to two large colorectal cancer cohorts. TIPC identifies tumor subtypes with unique interaction signatures between tumor cells and T cells that harbor prognostic significance and are associated with distinct tumor molecular features. We extend our findings by applying TIPC to additional immune cell types identified using morphology and supervised machine learning. Spatial heterogeneity quantification and novel tumor subtype identification by TIPC may inform precision cancer immunotherapy and deepen our understanding of tumor immunobiology.


2021 ◽  
Vol 12 (9) ◽  
pp. 712-724
Author(s):  
Raymond C-F Yuen ◽  
Shiu-Ying Tsao
Keyword(s):  

2021 ◽  
Vol 157 ◽  
pp. 214-224
Author(s):  
Florentia Dimitriou ◽  
Sabrina Hogan ◽  
Alexander M. Menzies ◽  
Reinhard Dummer ◽  
Georgina V. Long

2021 ◽  
Author(s):  
Takashi MaruYama ◽  
Shuhei Kobayashi ◽  
Hiroyuki Shibata ◽  
WanJun Chen ◽  
Yuji Owada

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