Personalized tumor combination therapy optimization using the single-cell transcriptome

Abstract

Background The precise characterization of individual tumors and immune microenvironments using transcriptome sequencing has provided a great opportunity for successful personalized cancer treatment. However, the cancer treatment response is often characterized by in vitro assays or bulk transcriptomes that neglect the heterogeneity of malignant tumors in vivo and the immune microenvironment, motivating the need to use single-cell transcriptomes for personalized cancer treatment. Methods Here, we present comboSC, a computational proof-of-concept study to explore the feasibility of personalized cancer combination therapy optimization using single-cell transcriptomes. ComboSC provides a workable solution to stratify individual patient samples based on quantitative evaluation of their personalized immune microenvironment with single-cell RNA sequencing and maximize the translational potential of in vitro cellular response to unify the identification of synergistic drug/small molecule combinations or small molecules that can be paired with immune checkpoint inhibitors to boost immunotherapy from a large collection of small molecules and drugs, and finally prioritize them for personalized clinical use based on bipartition graph optimization. Results We apply comboSC to publicly available 119 single-cell transcriptome data from a comprehensive set of 119 tumor samples from 15 cancer types and validate the predicted drug combination with literature evidence, mining clinical trial data, perturbation of patient-derived cell line data, and finally in-vivo samples. Conclusions Overall, comboSC provides a feasible and one-stop computational prototype and a proof-of-concept study to predict potential drug combinations for further experimental validation and clinical usage using the single-cell transcriptome, which will facilitate and accelerate personalized tumor treatment by reducing screening time from a large drug combination space and saving valuable treatment time for individual patients. A user-friendly web server of comboSC for both clinical and research users is available at www.combosc.top. The source code is also available on GitHub at https://github.com/bm2-lab/comboSC.

Potential of CDC25 phosphatases in cancer research and treatment: key to precision medicine

“…Molecular markers that are putatively ubiquitous with some cancers may not be directly related to the cell cycle, as in they do not directly act on the Cyclins, Cdks or checkpoint proteins involved, but they may affect the proliferation of cells or indicate an irregularity in cell cycle regulation as the case is for PD-L1 in various cancers (Schulz et al, 2021) even those with more complex ontology (Banchereau et al, 2021). Precision medicine studies involving machine learning algorithms were able to decipher from complex and heterogenous genomic (Tang et al, 2023) data the potential efficacy of using the PD-L1 inhibitors on various cancers based on molecular determinants of the tumours via transcriptional profiling using RNA sequencing under immune checkpoint inhibitors (Banchereau et al, 2021;Chen et al, 2021). Results from this can be used to inform and direct treatment-responsive patients to those respective therapeutic agents (Cristescu et al, 2018) or radiotherapeutic procedures (He et al, 2020;Johannet et al, 2021).…”

“…Polyphenols that are found in apple skins demonstrated, namely, APE was found to not only arrest cell cycle in G2 phase in CDC25C dependent manner but triggered ROS dependent intrinsic and extrinsic apoptosis in MDA-MB-231 (Martino et al, 2019). Once verified by in-vitro/in-vivo assays CDC25 inhibitor backbones that are not specific should be investigated, rather than discarded as there are programs using single cell transcriptomics in development that integrate patientderived data to inform on drug combinations, such as ComboSC and the work of Berlow et al (Berlow et al, 2019;Tang et al, 2023) Modelling for active site binding mediated inhibition is difficult in CDC25 due to its unique structure. CDC25 has a shallow active site region and the reactivity of the catalytic cysteine residue compound this issue of active site binding.…”

New clinical trial design in precision medicine: discovery, development and direction

“…Researchers proposed the “comboSC” AI algorithm to 119 tumor samples of 15 tumor types, demonstrating its widespread practicality and superior performance in optimizing combination therapy plans in different cancer types. 544 …”