Transcriptome free energy can serve as a dynamic patient-specific biomarker in acute myeloid leukemia.

Acute myeloid leukemia (AML) is prevalent in both adult and pediatric patients. Despite advances in patient categorization, the heterogeneity of AML remains a challenge. Recent studies have explored the use of gene expression data to enhance AML diagnosis and prognosis, however, alternative approaches rooted in physics and chemistry may provide another level of insight into AML transformation. Utilizing publicly available databases, we analyze 884 human and mouse blood and bone marrow samples. We employ a personalized medicine strategy, combining state-transition theory and surprisal analysis, to assess the RNA transcriptome of individual patients. The transcriptome is transformed into physical parameters that represent each sample's steady state and the free energy change (FEC) from that steady state, which is the state with the lowest free energy.We found the transcriptome steady state was invariant across normal and AML samples. FEC, representing active molecular processes, varied significantly between samples and was used to create patient-specific barcodes to characterize the biology of the disease. We discovered that AML samples that were in a transition state had the highest FEC. This disease state may be characterized as the most unstable and hence the most therapeutically targetable since a change in free energy is a thermodynamic requirement for disease progression. We also found that distinct sets of ongoing processes may be at the root of otherwise similar clinical phenotypes, implying that our integrated analysis of transcriptome profiles may facilitate a personalized medicine approach to cure AML and restore a steady state in each patient.

Overcoming resistance to EGFR monotherapy in HNSCC by identification and inhibition of individualized cancer processes.

Therapeutic strategies for advanced head and neck squamous carcinoma (HNSCC) consist of multimodal treatment, including Epidermal Growth Factor Receptor (EGFR) inhibition, immune-checkpoint inhibition, and radio (chemo) therapy. Although over 90% of HNSCC tumors overexpress EGFR, attempts to replace cytotoxic treatments with anti-EGFR agents have failed due to alternative signaling pathways and inter-tumor heterogeneity. Methods: Using protein expression data obtained from hundreds of HNSCC tissues and cell lines we compute individualized signaling signatures using an information-theoretic approach. The approach maps each HNSCC malignancy according to the protein-protein network reorganization in every tumor. We show that each patient-specific signaling signature (PaSSS) includes several distinct altered signaling subnetworks. Based on the resolved PaSSSs we design personalized drug combinations. Results: We show that simultaneous targeting of central hub proteins from each altered subnetwork is essential to selectively enhance the response of HNSCC tumors to anti-EGFR therapy and inhibit tumor growth. Furthermore, we demonstrate that the PaSSS-based drug combinations lead to induced expression of T cell markers and IFN-γ secretion, pointing to higher efficiency of the immune response. Conclusion: The PaSSS-based approach advances our understanding of how individualized therapies should be tailored to HNSCC tumors.