Background: Tumor heterogeneity is associated with poor prognosis and drug resistance, leading to therapeutic failure. Here, we aim to utilize tumor evolution analysis to decode the intra- and inter-tumoral heterogeneity of high-grade serous ovarian cancer (HGSOC), unraveling the correlation between tumor heterogeneity and prognosis as well as chemotherapy response through single-cell and spatial transcriptomic analysis. Methods: We collected and curated 28 HGSOC patients single-cell transcriptomic data from five datasets. Then, we developed a novel text mining-based machine learning approach to deconstruct the evolutionary patterns of tumor cell functions. This allowed us to identify key tumor-related genes within different evolutionary branches, elucidate the microenvironmental cell compositions that various functional tumor cells depend on, and analyze the intra- and inter-heterogeneity of tumors and their microenvironments in relation to prognosis and chemotherapy response in HGSOC patients. We further validated our findings in two spatial and seven bulk transcriptomic datasets, totally 1,030 patients. Results: By employing transcriptomic clusters as proxies for functional clonality, we identified a significant increase in tumor cell state heterogeneity, which was strongly correlated with patient prognosis and treatment response. Furthermore, increased intra- and inter-tumoral functional clonality was associated with the characteristics of cancer-associated fibroblast (CAF). We also found that the spatial proximity between CXCL12-positive CAF and tumor cells, mediated through the CXCL12/CXCR4 interaction, is highly positively correlated with poor prognosis and chemotherapy resistance in HGSOC. Finally, we constructed a panel of 24 genes through statistical modeling, that are highly correlated with CXCL12-positive fibroblasts and can predict both prognosis and chemotherapy response in HGSOC. Conclusions: Our study offers insights into the collective behavior of tumor cell communities in HGSOC, as well as potential drivers of tumor evolution in response to therapy. Functional analyses and experiments revealed a strong association between CXCL12-positive fibroblasts and tumor progression as well as treatment outcomes. Our findings provide an important theoretical basis for clinical HGSOC treatment.

The authors have declared no competing interest.

This study was supported by the co-sponsored by the Henan Province and Ministry of Health of Medical Science and Technology Program (SBGJ202302028 for Tingjie Wang), This research was supported by the Dalian Science and Technology Innovation Fund (2022JJ12SN049 for Jun Yang), the Fundamental Research Funds for the Central Universities.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

The datasets analyzed in this study are available from the gene expression omnibus (GEO) repository under the accession numbers in supplementary tables.