New clinical approaches are imperative beyond the widely adopted National Comprehensive Cancer Network (NCCN) guidelines, utilized by prominent cancer institutions. Cancer is the leading cause of death among individuals younger than 85 years within the United States. Despite significant technological advances, including the expenditure of hundreds of billions, treatment outcomes and overall survival have not notably improved for most types of advanced cancer over the last several decades. Over the past 24 years, Envita Medical Centers has pioneered a unique form of personalized treatment approach for late-stage and refractory cancer patients, introducing groundbreaking innovations in the field. Our integrated algorithm utilizes advanced genomics, transcriptomics, and highly tailored immunotherapy, resulting in remarkable outcome improvements. This study presents Envita’s innovative personalized treatment algorithms and examines the response outcomes of 199 late-stage cancer patients treated at Envita Medical Centers over a two-year period. Compared to standard of care and palliative chemotherapy, Envita’s treatment demonstrated a remarkable 35-fold improvement in overall response rates (<b>Figure 1</b>). Moreover, 88% of the patients, the majority presenting with Stage 3 or 4 cancer, experienced a 43-fold improvement in quality of life with minimal side effects, as compared to standard of care chemotherapy and palliative care. This revolutionary success is attributed to Envita’s personalized therapeutic algorithms, which incorporate customized immunotherapy. Envita’s precision care approach has also achieved a 100% better response rate compared to over 65 global chemotherapy clinical trials with more than 2700 patients. The results from this study suggest that a wider utilization of Envita’s personalized approach can significantly benefit patients with late-stage and refractory cancer.
KeywordsEnvita Medical CentersLate-stage CancerOverall Response RateQuality of LifeCirculating Tumor Cells (CTCs)Mutant Allele Frequency (MAF)Precision Care
Siegel, R.L., Giaquinto, A.N. and Jemal, A. (2024) Cancer Statistics, 2024. CA : A Cancer Journal for Clinicians , 74, 12-49. https://doi.org/10.3322/caac.21820
Benson, A. and Brown, E. (2008) Role of NCCN in Integrating Cancer Clinical Practice Guidelines into the Healthcare Debate. American Health & Drug Benefits , 1, 28-33.
Mansoori, B., Mohammadi, A., Davudian, S., Shirjang, S. and Baradaran, B. (2017) The Different Mechanisms of Cancer Drug Resistance: A Brief Review. Advanced Pharmaceutical Bulletin , 7, 339-348. https://doi.org/10.15171/apb.2017.041
Gambardella, V., Tarazona, N., Cejalvo, J.M., et al . (2020) Personalized Medicine: Recent Progress in Cancer Therapy. Cancers , 12, Article 1009. https://doi.org/10.3390/cancers12041009
Abbott, D., Ashdown, M.L., Robinson, A.P., et al . (2015) Chemotherapy for Late-Stage Cancer Patients: Meta-Analysis of Complete Response Rates. F 1000 Research , 4, Article 232. https://doi.org/10.12688/f1000research.6760.1
Wu, D., Wang, D.C., Cheng, Y., et al . (2017) Roles of Tumor Heterogeneity in the Development of Drug Resistance: A Call for Precision Therapy. Seminars in Cancer Biology , 42, 13-19. https://doi.org/10.1016/j.semcancer.2016.11.006
De Castro, D.G., Clarke, P.A., Al-Lazikani, B. and Workman, P. (2013) Personalized Cancer Medicine: Molecular Diagnostics, Predictive Biomarkers, and Drug Resistance. Clinical Pharmacology & Therapeutics , 93, 252-259. https://doi.org/10.1038/clpt.2012.237
Talib, W.H., Alsayed, A.R., Barakat, M., Abu-Taha, M.I. and Mahmod, A.I. (2021) Targeting Drug Chemo-Resistance in Cancer Using Natural Products. Biomed i cines , 9, Article 1353. https://doi.org/10.3390/biomedicines9101353
Anand, U. Dey, A., Chandel, A.K.S., et al . (2023) Cancer Chemotherapy and Beyond: Current Status, Drug Candidates, Associated Risks and Progress in Targeted Therapeutics. Genes & Diseases , 10, 1367-1401. https://doi.org/10.1016/j.gendis.2022.02.007
Patino, C.M. and Ferreira, J.C. (2018) Inclusion and Exclusion Criteria in Research Studies: Definitions and Why They Matter. Jornal Brasileiro de Pneumologia , 44, Article 84. https://doi.org/10.1590/s1806-37562018000000088
Weldring, T. and Smith, S.M.S. (2013) Article Commentary: Patient-Reported Outcomes (PROs) and Patient-Reported Outcome Measures (PROMs). Health Services Insights , 6, 61-68. https://doi.org/10.4137/HSI.S11093
Black, N. (2013) Patient Reported Outcome Measures could Help Transform Healthcare. The BMJ , 346, f167. https://doi.org/10.1136/bmj.f167
Devlin, N., Parkin, D. and Janssen, B. (2020) Methods for Analysing and Reporting EQ-5D Data. Springer, Cham. https://doi.org/10.1007/978-3-030-47622-9
Luckett, T., King, M.T., Butow, P.N., et al . (2011) Choosing between the EORTC QLQ-C30 and FACT-G for Measuring Health-Related Quality of Life in Cancer Clinical Research: Issues, Evidence and Recommendations. Annals of Oncology , 22, 2179-2190. https://doi.org/10.1093/annonc/mdq721
Taarnhøj, G.A., Kennedy, F.R., Absolom, K.L., et al . (2018) Comparison of EORTC QLQ-C30 and PRO-CTCAE TM Questionnaires on Six Symptom Items. Journal of Pain and Symptom Management , 56, 421-429. https://doi.org/10.1016/j.jpainsymman.2018.05.017
Lin, D., Shen, L., Luo, M., et al . (2021) Circulating Tumor Cells: Biology and Clinical Significance. Signal Transduction and Targeted Therapy , 6, Article No. 404. https://doi.org/10.1038/s41392-021-00817-8
Vasseur, A., Kiavue, N., Bidard, F.C., Pierga, J.Y. and Cabel, L. (2021) Clinical Utility of Circulating Tumor Cells: An Update. Molecular Oncology , 15, 1647-1666. https://doi.org/10.1002/1878-0261.12869
Bankó, P., Lee, S.Y., Nagygyörgy, V., et al . (2019) Technologies for Circulating Tumor Cell Separation from Whole Blood. Journal of Hematology & Oncology , 12, Article No. 48. https://doi.org/10.1186/s13045-019-0735-4
Goldkorn, A., Ely, B., Quinn, D.I., et al . (2014) Circulating Tumor Cell Counts Are Prognostic of Overall Survival in SWOG S0421: A Phase III Trial of Docetaxel with or without Atrasentan for Metastatic Castration-Resistant Prostate Cancer. Journal of Clinical Oncology , 32, 1136-1142. https://doi.org/10.1200/JCO.2013.51.7417
Budd, G.T., Cristofanilli, M., Ellis, M.J., et al . (2006) Circulating Tumor Cells versus Imaging—Predicting Overall Survival in Metastatic Breast Cancer. Clinical Cancer Research , 12, 6403-6409. https://doi.org/10.1158/1078-0432.CCR-05-1769
Eslami-S, Z., Cortés-Hernández, L.E. and Alix-Panabières, C. (2020) Epithelial Cell Adhesion Molecule: An Anchor to Isolate Clinically Relevant Circulating Tumor Cells. Cells , 9, Article 1836. https://doi.org/10.3390/cells9081836
Lawrence, R., Watters, M., Davies, C.R., Pantel, K. and Lu, Y.J. (2023) Circulating Tumour Cells for Early Detection of Clinically Relevant Cancer. Nature Reviews Clinical Oncology , 20, 487-500. https://doi.org/10.1038/s41571-023-00781-y
Punnoose, E.A., Atwal, S.K., Spoerke, J.M., et al . (2010) Molecular Biomarker Analyses Using Circulating Tumor Cells. PLOS ONE , 5, e12517. https://doi.org/10.1371/journal.pone.0012517
Jiang, M., Jin, S., Han, J., et al . (2021) Detection and Clinical Significance of Circulating Tumor Cells in Colorectal Cancer. Biomarker Research , 9, Article No. 85. https://doi.org/10.1186/s40364-021-00326-4
Yen, L.-C., Yeh, Y.-S., Chen, C.-W., et al . (2009) Detection of KRAS Oncogene in Peripheral Blood as a Predictor of the Response to Cetuximab Plus Chemotherapy in Patients with Metastatic Colorectal Cancer. Clinical Cancer Research , 15, 4508-4513. https://doi.org/10.1158/1078-0432.CCR-08-3179
Lin, C., Liu, X., Zheng, B., Ke, R. and Tzeng, C.M. (2021) Liquid Biopsy, ctDNA Diagnosis through NGS. Life , 11, Article 890. https://doi.org/10.3390/life11090890
Zhao, X., Dai, F., Mei, L., et al . (2021) The Potential Use of Dynamics Changes of ctDNA and cfDNA in the Perioperative Period to Predict the Recurrence Risk in Early NSCLC. Frontiers in Oncology , 11, Article 671963. https://doi.org/10.3389/fonc.2021.671963
Bos, M.K., Nasserinejad, K., Jansen, M.P.H.M., et al . (2021) Comparison of Variant Allele Frequency and Number of Mutant Molecules as Units of Measurement for Circulating Tumor DNA. Molecular Oncology , 15, 57-66. https://doi.org/10.1002/1878-0261.12827
Angeles, A.K., Christopoulos, P., Yuan, Z., et al . (2021) Early Identification of Disease Progression in ALK-Rearranged Lung Cancer Using Circulating Tumor DNA Analysis. NPJ Precision Oncology , 5, Article No. 100. https://doi.org/10.1038/s41698-021-00239-3
Bohers, E., Viailly, P.J. and Jardin, F. (2021) cfDNA Sequencing: Technological Approaches and Bioinformatic Issues. Pharmaceuticals , 14, Article 596. https://doi.org/10.3390/ph14060596
Pairawan, S., Hess, K.R., Janku, F., et al . (2020) Cell-Free Circulating Tumor DNA Variant Allele Frequency Associates with Survival in Metastatic Cancer. Clinical Cancer Research , 26, 1924-1931. https://doi.org/10.1158/1078-0432.CCR-19-0306
Wu, S., Liu, L., Chu, X., et al . (2022) Dynamic Change of Variant Allele Frequency Reveals Disease Status, Clonal Evolution and Survival in Pediatric Relapsed B-Cell Acute Lymphoblastic Leukaemia. Clinical and Translational Medicine , 12, e892. https://doi.org/10.1002/ctm2.892
Dentro, S.C., Leshchiner, I., Haase, K., et al . (2021) Characterizing Genetic Intra-Tumor Heterogeneity across 2,658 Human Cancer Genomes. Cell , 184, 2239-2254. E39. https://doi.org/10.1016/j.cell.2021.03.009
Noorbakhsh, J., Kim, H., Namburi, S. and Chuang, J.H. (2018) Distribution-Based Measures of Tumor Heterogeneity Are Sensitive to Mutation Calling and Lack Strong Clinical Predictive Power. Scientific Reports , 8, Article 11445. https://doi.org/10.1038/s41598-018-29154-7
Pacetti, P., Paganini, G., Orlandi, M., et al . (2015) Chemotherapy in the Last 30 Days of Life of Advanced Cancer Patients. Supportive Care in Cancer , 23, 3277-3280. https://doi.org/10.1007/s00520-015-2733-6
Prigerson, H.G., Bao, Y., Shah, M.A., et al . (2015) Chemotherapy Use, Performance Status, and Quality of Life at the End of Life. JAMA Oncology , 1, 778-784. https://doi.org/10.1001/jamaoncol.2015.2378
Lipscomb, J., Gotay, C.C. and Snyder, C.F. (2007) Patient-Reported Outcomes in Cancer: A Review of Recent Research and Policy Initiatives. CA : A Cancer Journal for Clinicians , 57, 278-300. https://doi.org/10.3322/CA.57.5.278
Kenzik, K.M., Ganz, P.A., Martin, M.Y., et al . (2015) How Much Do Cancer-Related Symptoms Contribute to Health-Related Quality of Life in Lung and Colorectal Cancer Patients? A Report from the Cancer Care Outcomes Research and Surveillance (CanCORS) Consortium. Cancer , 121, 2831-2839. https://doi.org/10.1002/cncr.29415
Basch, E., Deal, A.M., Kris, M.G., et al . (2016) Symptom Monitoring with Patient-Reported Outcomes during Routine Cancer Treatment: A Randomized Controlled Trial. Journal of Clinical Oncology , 34, 557-565. https://doi.org/10.1200/JCO.2015.63.0830
Mayrbäurl, B., Wintner, L.M., Giesinger, J.M., et al . (2012) Chemotherapy Line-Associated Differences in Quality of Life in Patients with Advanced Cancer. Supportive Care in Cancer , 20, 2399-2405. https://doi.org/10.1007/s00520-011-1355-x
Wright, A.A., Zhang, B., Keating, N.L., Weeks, J.C. and Prigerson, H.G. (2014) Associations between Palliative Chemotherapy and Adult Cancer Patients’ End of Life Care and Place of Death: Prospective Cohort Study. The BMJ , 348, g1219. https://doi.org/10.1136/bmj.g1219
Akhlaghi, E., Lehto, R.H., Torabikhah, M., et al . (2020) Chemotherapy Use and Quality of Life in Cancer Patients at the End of Life: An Integrative Review. Health and Quality of Life Outcomes , 18, Article No. 332. https://doi.org/10.1186/s12955-020-01580-0
Woldie, I., Elfiki, T., Kulkarni, S., et al . (2022) Chemotherapy during the Last 30 Days of Life and the Role of Palliative Care Referral, a Single Center Experience. BMC Palliative Care , 21, Article No. 20. https://doi.org/10.1186/s12904-022-00910-x
Wintner, L.M., Giesinger, J.M., Zabernigg, A., et al . (2013) Quality of Life during Chemotherapy in Lung Cancer Patients: Results across Different Treatment Lines. British Journal of Cancer , 109, 2301-2308. https://doi.org/10.1038/bjc.2013.585
Rossi, E., Basso, U., Celadin, R., et al . (2010) M30 Neoepitope Expression in Epithelial Cancer: Quantification of Apoptosis in Circulating Tumor Cells by CellSearch Analysis. Clinical Cancer Research , 16, 5233-5243. https://doi.org/10.1158/1078-0432.CCR-10-1449
Lei, X., Mao, X., Grey, A., et al . (2020) Noninvasive Detection of Clinically Significant Prostate Cancer Using Circulating Tumor Cells. The Journal of Urology , 203, 73-82. https://doi.org/10.1097/JU.0000000000000475
Shao, X. Jin, X., Chen, Z., et al . (2022) A Comprehensive Comparison of Circulating Tumor Cells and Breast Imaging Modalities as Screening Tools for Breast Cancer in Chinese Women. Frontiers in Oncology , 12, Article 890248. https://doi.org/10.3389/fonc.2022.890248
Giuliano, M., Giordano, A., Jackson, S., et al . (2011) Circulating Tumor Cells as Prognostic and Predictive Markers in Metastatic Breast Cancer Patients Receiving First-Line Systemic Treatment. Breast Cancer Research , 13, Article No. R67. https://doi.org/10.1186/bcr2907
Jiang, L., Wang, L., Shen, C., et al . (2020) Impact of Mutational Variant Allele Frequency on Prognosis in Myelodysplastic Syndromes. American Journal of Cancer Research , 10, 4476-4487. https://e-century.us/web/journal.php?journal=ajcr
Mantovani, F., Collavin, L. and Del Sal, G. (2019) Mutant p53 as a Guardian of the Cancer Cell. Cell Death and Differentiation , 26, 199-212. https://doi.org/10.1038/s41418-018-0246-9
Donehower, L.A., Soussi, T., Korkut, A., et al . (2019) Integrated Analysis of TP53 Gene and Pathway Alterations in the Cancer Genome Atlas. Cell Reports , 28, 1370-1384. https://doi.org/10.1016/j.celrep.2019.07.001
Belickova, M., Vesela, J., Jonasova, A., et al . (2016) TP53 Mutation Variant Allele Frequency Is a Potential Predictor for Clinical Outcome of Patients with Lower-Risk Myelodysplastic Syndromes. Oncotarget , 7, 36266-36279. https://www.oncotarget.com/article/9200/text/ https://doi.org/10.18632/oncotarget.9200
Shah, M.V., Tran, E.N.H., Shah, S., et al . (2023) TP53 Mutation Variant Allele Frequency of ≥10% Is Associated with Poor Prognosis in Therapy-Related Myeloid Neoplasms. Blood Cancer Journal , 13, Article No. 51. https://doi.org/10.1038/s41408-023-00821-x
Nakata, J., Isohashi, K., Oka, Y., et al . (2021) Imaging Assessment of Tumor Response in the Era of Immunotherapy. Diagnostics , 11, Article 1041. https://doi.org/10.3390/diagnostics11061041
Jia, W., Gao, Q., Han, A., Zhu, H. and Yu, J. (2019) The Potential Mechanism, Recognition and Clinical Significance of Tumor Pseudoprogression after Immunotherapy. Cancer Biology and Medicine , 16, 655-670. https://doi.org/10.20892/j.issn.2095-3941.2019.0144
Zhang, Y., Zhao, J., Wang, Y., et al . (2022) Changes of Tumor Markers in Patients with Breast Cancer during Postoperative Adjuvant Chemotherapy. Disease Markers , 2022, Article ID: 7739777. https://doi.org/10.1155/2022/7739777
Kim, H.J., Lee, K.-W., Kim, Y.J., et al . (2009) Chemotherapy-Induced Transient CEA and CA19-9 Surges in Patients with Metastatic or Recurrent Gastric Cancer. Acta Oncologica , 48, 385-390. https://doi.org/10.1080/02841860802446761
Moss, E.L., Hollingworth, J. and Reynolds, T.M. (2005) The Role of CA125 in Clinical Practice. Journal of Clinical Pathology , 58, 308-312. https://doi.org/10.1136/jcp.2004.018077
Vaidyanathan, K. and Vasudevan, D.M. (2012) Organ Specific Tumor Markers: What’s New? Indian Journal of Clinical Biochemistry , 27, 110-120. https://doi.org/10.1007/s12291-011-0173-8
Maldonado, E.B., Parsons, S., Chen, E.Y., Haslam, A. and Prasad, V. (2020) Estimation of US Patients with Cancer Who May Respond to Cytotoxic Chemotherapy. Future Science OA , 6, FSO600. https://doi.org/10.2144/fsoa-2020-0024
Coventry, B.J. and Ashdown, M.L. (2012) Complete Clinical Responses to Cancer Therapy Caused by Multiple Divergent Approaches: A Repeating Theme Lost in Translation. Cancer Management and Research , 4, 137-149. https://doi.org/10.2147/CMAR.S31887
Lillie, E.O., Patay, B., Diamant, J., et al . (2011) The n-of-1 Clinical Trial: The Ultimate Strategy for Individualizing Medicine? Personalized Medicine , 8, 161-173. https://doi.org/10.2217/pme.11.7
Hu, S.X., Foster, T. and Kieffaber, A. (2005) Pharmacogenomics and Personalized Medicine: Mapping of Future Value Creation. Biotechniques , 39, S1-S6. https://doi.org/10.2144/000112048
Langreth, R. and Waldholz, M. (1999) New Era of Personalized Medicine: Targeting Drugs for Each Unique Genetic Profile . The Oncologist , 4, 426-427. https://doi.org/10.1634/theoncologist.4-5-426
Collins, F.S. (2010) Opportunities for Research and NIH. Science , 327, 36-37. https://doi.org/10.1126/science.1185055
Kiberstis, P.A. and Travis, J. (2006) Celebrating a Glass Half-Full. Science , 312, 1157. https://doi.org/10.1126/science.312.5777.1157