Forecasting therapeutic responses by albuminuria and eGFR slope during the DAPA-CKD trial

Author(s):  
Katherine R Tuttle
2021 ◽  
Vol 22 (6) ◽  
pp. 3228
Author(s):  
Alexander C. Chacon ◽  
Alexa D. Melucci ◽  
Shuyang S. Qin ◽  
Peter A. Prieto

Metastatic melanoma remains the deadliest form of skin cancer. Immune checkpoint inhibition (ICI) immunotherapy has defined a new age in melanoma treatment, but responses remain inconsistent and some patients develop treatment resistance. The myriad of newly developed small molecular (SM) inhibitors of specific effector targets now affords a plethora of opportunities to increase therapeutic responses, even in resistant melanoma. In this review, we will discuss the multitude of SM classes currently under investigation, current and prospective clinical combinations of ICI and SM therapies, and their potential for synergism in melanoma eradication based on established mechanisms of immunotherapy resistance.


2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A17-A17
Author(s):  
Quoc Mac ◽  
James Bowen ◽  
Hathaichanok Phuengkham ◽  
Anirudh Sivakumar ◽  
Congmin Xu ◽  
...  

BackgroundDespite the curative potential of immune checkpoint blockade (ICB) therapy, only small subsets of patients achieve tumor regression while many responders relapse and acquire resistance. Monitoring treatment response and detecting the onset of resistance are critical for improving patient prognoses. Here we engineered ICB antibody-sensor conjugates known as ICB-Dx by coupling peptides sensing the activity of granzyme B (GzmB), a T cell cytotoxic protease, directly on αPD1 antibody to monitor therapeutic responses by producing a fluorescent reporter into urine. To develop biomarkers that indicate mechanisms of resistance to ICB, we generated B2m-/- and Jak1-/- tumor models and performed transcriptomic analyses to identify unique protease signatures of these resistance mechanisms. We then built a multiplexed library of αPD1-Dx capable of detecting early therapeutic response and illuminating resistance mechanisms during ICB therapy.MethodsFITC-labeled GzmB substrates were synthesized (CEM) and conjugated to αPD1 antibody. B2m-/- and Jak1-/- tumors were generated from WT MC38 cells using CRISPR/Cas9. For tumor studies, 106 cells were inoculated s.c. in B6 mice. Tumor mice were treated with αPD1 or IgG1 isotype conjugates (0.1 mg), and urine was collected at 3 hours. Tumor RNA was isolated with RNEasy kit (Qiagen) and prepared for sequencing with TruSeq mRNA kit (Illumina).ResultsTo synthesize αPD1-Dx, we coupled FITC-labeled GzmB substrates to αPD1 antibody (figure 1a). In MC38 tumors, systemic administration of αPD1-Dx lowered tumor burden relative to control treatment while producing significantly elevated urine signals that preceded tumor regression (figure 1b, c). To investigate the ability to monitor tumor resistance to ICB, we developed knockout tumors to model B2m and Jak1 mutations, which are observed in human patients. in vivo, B2m-/- and Jak1-/- MC38 tumors were resistant to αPD1 monotherapy (figure 1d). Tumor RNA sequencing revealed that gene expression was altered during αPD1 treatment only in WT tumors. Importantly, B2m-/- tumors showed very different expression profiles than Jak1-/- tumors during αPD1 treatment, indicative of unique regulation of resistance (figure 1e). We used differential expression analyses to discover unique protease signatures associated with these two resistance mechanisms. Finally, a multiplexed library of αPD1-Dx engineered to monitor both tumor and immune proteases detected early on-treatment responses and stratified B2m-/- from Jak1-/- resistance with high diagnostic validity (figure 1f).Abstract 17 Figure 1Monitoring response and resistance with ICB-Dx(a) αPD1-Dx can reinvigorate T cell response and monitor protease activities in the tumor microenvironment. (b) Growth curves of WT MC38 tumors treated with αPD1- or IgG1-Dx (ANOVA). (c) Urine signals detect treatment response to αPD1 monotherapy (ANOVA). (d) Growth curves of B2m-/- and Jak1-/- tumors treated with αPD1- or IgG1-Dx (ANOVA). (e) TSNE plot showing RNA profiles of WT, B2m-/-, Jak1-/- tumors treated with αPD1 or isotype control. (f) ROC curves of random forest classifiers built from urine signals that differentiate on-treatment response from on-treatment resistance and B2m-/- from Jak1-/- resistance.ConclusionsWe have engineered activity sensors that accurately detect therapeutic responses and stratify resistance mechanisms noninvasively from urine, thereby potentially expanding the precision of ICB therapy to benefit cancer patients.Ethics ApprovalAll animal studies were approved by Georgia Tech IACUC (A100193)


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qingqing Yin ◽  
Anni Pan ◽  
Binlong Chen ◽  
Zenghui Wang ◽  
Mingmei Tang ◽  
...  

AbstractNanoparticle internalisation is crucial for the precise delivery of drug/genes to its intracellular targets. Conventional quantification strategies can provide the overall profiling of nanoparticle biodistribution, but fail to unambiguously differentiate the intracellularly bioavailable particles from those in tumour intravascular and extracellular microenvironment. Herein, we develop a binary ratiometric nanoreporter (BiRN) that can specifically convert subtle pH variations involved in the endocytic events into digitised signal output, enabling the accurately quantifying of cellular internalisation without introducing extracellular contributions. Using BiRN technology, we find only 10.7–28.2% of accumulated nanoparticles are internalised into intracellular compartments with high heterogeneity within and between different tumour types. We demonstrate the therapeutic responses of nanomedicines are successfully predicted based on intracellular nanoparticle exposure rather than the overall accumulation in tumour mass. This nonlinear optical nanotechnology offers a valuable imaging tool to evaluate the tumour targeting of new nanomedicines and stratify patients for personalised cancer therapy.


2019 ◽  
Vol 40 (1) ◽  
pp. 114-122 ◽  
Author(s):  
Xiaolei Tang ◽  
Hui Xu ◽  
Chunju Zhou ◽  
Yun Peng ◽  
Hui Liu ◽  
...  

2015 ◽  
Vol 25 (42) ◽  
pp. 6586-6595 ◽  
Author(s):  
Youyong Yuan ◽  
Chong-Jing Zhang ◽  
Ryan T. K. Kwok ◽  
Shidang Xu ◽  
Ruoyu Zhang ◽  
...  

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