COMBINATORIAL THERAPY FOR TUMOR TREATMENT

Abstract

Cancer is a complex and multifaceted disease that continues to pose a significant challenge to global health. As the second leading cause of death worldwide. Early detection and noninvasive techniques of detecting cancer are necessary to improve treatment outcomes, save lives and improve the quality of life. Biopsies of tumors are often expensive and invasive and raise the risk of serious complications like infection, excessive bleeding, and puncture damage to nearby tissues and organs. Early detection biomarkers are often variably expressed in different patients and may even be below the detection level at an early stage. Hence PBMC that shows alteration in gene profile as a result of interaction with tumor antigens may serve as a better early detection biomarker. Also, such alterations in immune gene profile in PBMCs are more detectable in a wide variety of cancer patients despite their variability in different cancer mutants. Tumor cell biomarkers lack specificity, and tumor heterogeneity complicates accurate diagnosis and treatment. Changing biomarker expression affects treatment responses, and technical challenges impact utility. Synthetic drugs targeting tumor cells often trigger tumour cells to acquire resistance against them. Tumor progression is an outcome of tumor growth regulation in conjunction with tumor evasion by immune modulation. Therefore, understanding of immunological biomarkers is equally important. Hence, designing a prudent chemotherapeutic combination requires a detailed understanding of gene regulation altering cancer prognosis and its impact on immune regulation . Immunotherapy also has its side effects and does not provide an adequate response in all patients, and its inherent variability in patient response often makes them prohibitive. Hence, a concomitant targeting of tumour cells and modulation of immune cell function may be a particularly beneficial mechanism for cancer treatment. Machine learning tools are crucial for early cancer detection and immune modulation due to their ability to analyze complex data and identify patterns that may not be apparent through viii | P a g e traditional methods. Potential diagnostic biomarkers were predicted for breast cancer using eXplainable Artificial Intelligence (XAI) on XGBoost machine learning (ML) models trained on a binary classification dataset containing the expression data of PBMCs from 252 breast cancer patients and 194 healthy women. After effectively adding SHAP values further into the XGBoost model, ten important genes related to breast cancer development were discovered to be effective potential biomarkers. It was discovered that SVIP, BEND3, MDGA2, LEF1-AS1, PRM1, TEX14, MZB1, TMIGD2, KIT, and FKBP7 are key genes that impact model prediction. These genes may serve as early, non-invasive diagnostic and prognostic biomarkers for breast cancer patients. The impact of concomitant intervention cancer progression and immune regulation therefore necessitated identification of such biomarkers that have dual impact. Gene expression data of HNSC tumor samples and PBMCs of tumor patient datasets were analysed for the identification of differentially expressed genes. 110 DEGs were found to be common in both datasets. Further, it was identified that these 110 DEGs were involved in biological processes related to tumor regulation. Potential Immunological biomarkers were identified for HNSC cancer. The Genes that play a role in both tumour growth and immune suppression were identified by enrichment analysis followed by gene expression analysis. 10 such genes were shortlisted, Foxp3, CD274, IDO1, IL-10, SOCS1, PRKDC, AXL, CDK6, TGFB1, FADD. CD274 and IDO1 were found to have the highest degree of interaction based on their network of interactions. Synthetic drugs including many of FDA approved drugs might cause significant side effects, leading to adverse impacts on patients' quality of life. Additionally, some cancer cells may develop resistance to synthetic drugs over time, reducing treatment efficacy. Moreover, targeted therapies may only be effective in cancers with specific molecular characteristics, limiting their broad applicability. To address these limitations, ongoing research focuses on developing more targeted and personalized therapies, combining synthetic drugs with other ix | P a g e treatment modalities, and exploring alternative natural compounds with multi-target effects. Multi-target natural compounds offer the advantage of targeting multiple pathways involved in cancer progression without significant side effects. These compounds, derived from plants and other natural sources, hold promise in cancer treatment due to their diverse mechanisms of action and potential for reduced toxicity. Natural compounds that help in tumour suppression as well as functional immune modulation were identified for their dual roles. Np care and GEO databases were used for retravel of natural compounds. 102 potential anti-cancer natural compounds treatment gene expression data was analysed and key differentially regulated genes by them were identified. These 102 natural compounds were analysed for their ability to alter the expression of 110 commonly differentially expressed (identified in first objective). Salidroside was altering maximum number of 66 gene from them. Gallic acid and Shikonin were found to be the natural compounds that target CD274 and IDO1 respectively. Galic acid is extracted from leaves of bearberry, in pomegranate root bark, gallnuts, witch hazel, both in free-state and as part of the tannin molecule, whereas Shikonin is found in the extracts of dried root of the plant Lithospermum erythrorhizon. Studies have demonstrated that both Shikonin and Gallica acid exhibits anti-cancer properties. Single drug treatment can lead to the development of drug resistance, where cancer cells become less responsive to the treatment over time. Some cancers may be inherently resistant to certain drugs, restricting their effectiveness. Moreover, high doses of a single drug can cause severe side effects, impacting patients' quality of life. Additionally, single-drug therapy may not be effective due to the heterogeneity of cancer cells, allowing potential tumor recurrence. Combination therapy targets cancer cells through multiple pathways, reduces drug resistance, and enhances efficacy of treatment outcomes. Synergistic interactions can improve efficacy while minimizing side effects, advancing personalized cancer care for better patient outcomes. A combination of Salidroside, Ginsenoside Rd, Oridonin, Britanin, and Scutellarein was x | P a g e chosen such that they could alter the expression of 108 genes out of the selected 110 genes. The combination was further analyzed for regulating pathways and biological processes that were affected. Expression data analysis of HNSC cancer exhibited 1745 differentially expressed genes. Gallic acid treatment results in the downregulation of 120 genes and upregulation of 35 genes while Shikonin results in the downregulation of 660 genes and upregulation of 38 genes. Pathway analysis of these genes that were modulated by Gallic acid and Shikonin showed them to be crucially involved in pathways that were essential for cancer prognosis. Further Gallic acid and shikonin treatment impact on cancer cell line was analysed individually as well as in combination with the help of in vitro experiments. Gallic acid showed IC50 value of 46.87, 59.37, and 93.75 at 12h, 24h, and 48h treatment, respectively. Shikonin showed IC50 value of 13.86, 11.95, and 10.89 at 12h, 24h, and 48h treatment, respectively. Lowest percentage of cell viability was observed for combination of 80 µl Gallic acid and 16 µl of Shikonin. So, this combination of gallic acid and shikonin could be effective for the HNSC cancer treatment. Our studies showed a multifaceted, multi-dimensional tumor regression by altering autophagy, apoptosis, inhibiting cell proliferation, angiogenesis, metastasis and inflammatory cytokines production. Thus, the study has helped develop a unique combination of natural compounds that will markedly reduce the propensity of development of drug resistance in tumors and immune evasion by the tumors. This study is crucial to developing a combinatorial natural therapeutic cocktail with accentuated immunotherapeutic potential.