Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones

Sep 21, 2026

Uploaded by Rowan

Intratumoral cellular heterogeneity drives therapy resistance and limits the ability to select patient-specific drug regimens that selectively co-inhibit malignant cell populations. A machine learning framework, scTherapy, uses single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Across acute myeloid leukemia and high-grade serous ovarian carcinoma, predicted therapies show selective efficacy or synergy in 96% of cases and low toxicity to normal cells in 83%, with shared and patient-specific selections varying across tumor types.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Introduction

Intratumoral heterogeneity and therapy resistance

Single-cell analysis in cancer research

Challenges in precision medicine translation

Need for selective multi-targeting combinations

Article [https://doi.org/10.1038/s41467-024-52980-5](https://doi.org/10.1038/s41467-024-52980-5) Single-cell transcriptomes identify patienttailored therapies for selective co-inhibition of cancer clones Received: 15 August 2023 | Accepted: 27 September 2024 | | --- | | | | Check for updates | Aleksandr Ianevski 1,10, Kristen Nader1,10, KyriakiDriva2, Wojciech Senkowski 2, Daria Bulanova1,2, Lidia Moyano-Galceran 2, Tanja Ruokoranta 1,3, Heikki Kuusanmäki1,2,4, Nemo Ikonen 1, Philipp Sergeev1, Markus Vähä-Koskela1, Anil K. Giri 1,4, Anna Vähärautio 4,5,6, Mika Kontro1,3,4, Kimmo Porkka 3,6, Esa Pitkänen 1,6,7, Caroline A. Heckman 1,6, Krister Wennerberg 2 & Tero Aittokallio 1,6,8,9 Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical beneﬁts in advanced malignancies. However, systematic identiﬁcation of patient-speciﬁc treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested inpatient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic proﬁles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-speciﬁc treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations conﬁrm that 96% of the multi-targeting treatments exhibit selective efﬁcacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efﬁcacy and safety. Ina pan-cancer analysis across ﬁve cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-speciﬁc. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success. High intratumoral cellular heterogeneity and clonal evolution of cancer cell populations are major drivers of therapy resistance both in hematological malignancies and solid tumors1–5. In acute myeloid leukemia (AML), several single-cell genomic analyses have mapped the clonal evolutionary processes of disease progression and therapy resistance at the cell subpopulation level, as well as deciphered cellular hierarchy and reprogramming among the leukemic cell subpopulations involved in chemoresistance, relapse and clinical outcomes6–9. Similarly in solid tumors, intratumoral and interpatient heterogeneity are signiﬁcant medical challenges both for disease diagnosis and treatment optimization. The highly heterogeneous tumor ecosystem contains not only malignant cells but also other cell types, such as A full list of afﬁliations appears at the end of the paper. e-mail: tero.aittokallio@helsinki.ﬁ endothelial cells, stromal ﬁbroblasts, anda variety ofimmune cells that control tumor growth and invasion. Notable studies in melanoma, ovarian and colorectal cancers have demonstrated that speciﬁc characteristics of the tumor immune microenvironment (TME) can, to some extent, predict a patient’s clinical outcome10. For instance, clonal analysis and longitudinal sampling of patients with high-grade serous ovarian carcinoma (HGSC) revealed evolutionary trajectories, with distinct genomic and morphological features across patients that associate with therapy responses11. Moreover, the interaction between immune and non-immune cells within the TME can inﬂuence the effectiveness of immune responses, leading to varied treatment outcomes among patients. Single-cell analyzes in cancer research utilize a wide ar

FAQs

What problem does scTherapy address in cancer treatment selection?

It tackles the difficulty of systematically identifying patient-specific therapies that selectively co-inhibit cancer cell populations despite the combinatorial explosion of drug-dose possibilities.

How does scTherapy use single-cell data?

It leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options tailored to individual patients.

What validation results are reported for the predicted multi-targeting treatments?

Experimental validation indicates 96% of multi-targeting treatments show selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells.

Sep 21, 2026

Uploaded by Rowan