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In This Article
  Introduction
   The Promise of G...
   Challenges and F...
  Conclusion
   References
   Article Tables

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EDITORIAL
Year : 2023  |  Volume : 55  |  Issue : 4  |  Page : 213-215
 

Genomic biomarkers: Unveiling the potential for precise cancer therapy response


Department of Pharmacology, Postgraduate Institute of Medical Education and Research, Chandigarh, India

Date of Submission11-Jul-2023
Date of Decision25-Aug-2023
Date of Acceptance30-Aug-2023
Date of Web Publication11-Sep-2023

Correspondence Address:
Bikash Medhi
Department of Pharmacology, Postgraduate Institute of Medical Education and Research, Research Block B, 4th Floor, Lab No 4044, Chandigarh - 160 012
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijp.ijp_442_23

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How to cite this article:
Kaur G, Pahwa P, Prakash A, Medhi B. Genomic biomarkers: Unveiling the potential for precise cancer therapy response. Indian J Pharmacol 2023;55:213-5

How to cite this URL:
Kaur G, Pahwa P, Prakash A, Medhi B. Genomic biomarkers: Unveiling the potential for precise cancer therapy response. Indian J Pharmacol [serial online] 2023 [cited 2023 Sep 24];55:213-5. Available from: https://www.ijp-online.com/text.asp?2023/55/4/213/385498





  Introduction Top


The quest for the battle against cancer for more effective treatments has taken a major leap forward with the advent of precision medicine. Advancing next-generation technologies results in the exploration of genomic biomarkers, which have emerged as powerful tools for predicting cancer therapy response. However, the development of genomic biomarkers has ushered in a new era of personalized cancer treatment, allowing physicians to tailor therapies to individual patients based on their genetic profiles. In this editorial, we delve into the transformative potential of genomic biomarkers in cancer therapy response, highlighting their impact on patient care and the future of precision medicine.


  The Promise of Genomic Biomarkers Top


Genomic biomarkers are specific genetic characteristics that can be identified in an individual's DNA that can be germline variants or somatic variants. Previously, large studies on a total of 10,389 cases and 33 different cancer types have reported disease-causing germline variants that are associated with the occurrence of multiple cancers.[1],[2] Whereas somatic genomic alternations were most common due to genomic instability mediating cancer evolutions, also driving the adaptation of cancer niche after cancer therapy.[3],[4] By analyzing cancer tissue both genomic and somatic profiles, oncologists can identify predictive genomic biomarkers for a patient's response to specific cancer treatments. Predictive genomic biomarkers have estimated the response of a specific therapeutic intervention on a cancer subject before the initiation of treatment. Cancer subjects can be allocated as the suspected responders or the nonresponders to radiotherapy, immunotherapy, endocrine therapy, and chemotherapy.[5] These biomarkers are also capable of the identification of patients that would most likely show extreme toxicity after any therapies. Therefore, predictive genomic biomarkers can be very beneficial for adjusting the different doses of treatment and guiding alternative therapies in nonresponders. The germline variants of the TPMT or TYMS genes are notable examples of potential pharmacogenetic-based predictive biomarkers,[6] which predict toxicity and effectiveness of treatments with mercaptopurine or fluorouracil for treating gastric, colon, and bladder carcinoma, respectively. Furthermore, tyrosine kinase inhibitors have been used in subjects with non-small cell lung cancer with mutations in the effective glomerular filtration rate gene as a first-line treatment to delay resistance.[7],[8] Further, different studies have shown that UGT1 gene variations are associated with increased toxicity due to impaired irinotecan activity, which is an anticancer drug for the therapeutic management of colorectal carcinoma.[9],[10]

Platinum-containing anticancer drugs that are used in bladder cancer, also shown to be affected by higher expression of excision repair cross complement group 1, related to the worst outcome.[11] Moreover, a large number of in vitro studies have been conducted to investigate the sensitivity of 1000 human cancer cell lines to different drugs and have shortlisted a database of sensitivity of cancer cells to chemotherapy drugs (http://www.cancerrxgene.org).[12],[13] However, cancers with higher intratumoral heterogeneity, like breast cancer, need multigene analysis for deciding the optimal therapy. In this regard, the Oncotype DX Breast Recurrence Score detects 21 genes for the selection of different treatment regimens depending on the score (prognostic biomarker) for subjects with hormone receptor+ and HER2-antigen+ early-stage invasive breast cancer.[14] An assessment of this score can help determine the most appropriate treatment for the patient, potentially resulting in better health outcomes. [Table 1] lists the clinically validated genomic biomarkers that have been approved by the Food and Drug Administration (FDA).
Table 1: Clinically validated Food and Drug Administration genomic biomarker

Click here to view


However, to evaluate the risk-versus-benefit ratio of a specific anticancer medicine, genetic markers have also been useful and are not necessarily essential for its use despite their detailed information. Since it provides physicians with relevant information, this kind of test falls under supplementary diagnostics.[15] In this regard, rucaparib, an inhibitor of poly Adenosine diphosphate (ADP-ribose) polymerase used to treat recurrent ovarian cancer, is available as a complementary test. The FoundationFocus CDx test indicates that rucaparib may increase the progression-free survival rate in patients with high genomic loss of heterozygosity (LOH) and detects LOH for Breast cancer gene (BRCA).[16]

The latest advancements in the field of immunotherapy and the usage of immune checkpoint inhibitors in multiple cancer types have also been investigated. The FDA recently approved an inhibitor of T-lymphocyte-associated antigen 4 (CTLA-4) and six other programmed cell death protein pathway inhibitors (PD1/PDL1).[17],[18] In this regard, PDL1 is thought to function as a predictor of how well immunotherapies work. Even though PDL1 expression levels are lower, PDLI overexpressing tumors have a higher response rate, which may also be advantageous for other cancer types.[19]


  Challenges and Future Prospects Top


While the potential of genomic biomarkers in cancer therapy response is undeniable, challenges remain in their widespread implementation. One key challenge is the need for standardized and validated biomarker tests. Ensuring the accuracy and reliability of these tests is crucial for their clinical utility. In addition, the high cost associated with genomic profiling and biomarker testing poses a significant barrier to access for many patients. Efforts should be made to address these challenges through collaboration between researchers, clinicians, and policymakers, with the aim of making genomic biomarker testing more accessible and affordable.


  Conclusion Top


Genomic biomarkers have revolutionized the field of cancer therapy by enabling precision medicine and personalized treatment approaches. By analyzing an individual's genetic profile, oncologists can predict and evaluate the response to specific therapies, leading to improved outcomes and increased survival rates. However, further research, investment, and collaboration are needed to overcome the challenges associated with the implementation of genomic biomarkers in routine clinical practice.

As we move forward, the integration of genomic biomarkers into cancer treatment decision-making holds great promise for the future of oncology. The continued advancement of this field will bring us closer to more effective, targeted, and personalized therapies, ultimately transforming cancer care and improving the lives of patients worldwide.



 
  References Top

1.
Huang KL, Mashl RJ, Wu Y, Ritter DI, Wang J, Oh C, et al. Pathogenic germline variants in 10,389 adult cancers. Cell 2018;173:355-70.e14.  Back to cited text no. 1
    
2.
Kaur G, Bhadada SK, Santra M, Pal R, Sarma P, Sachdeva N, et al. Multilevel annotation of germline MEN1 variants of synonymous, nonsynonymous, and uncertain significance in Indian patients with sporadic primary hyperparathyroidism. J Bone Miner Res 2022;37:1860-75.  Back to cited text no. 2
    
3.
Cortés-Ciriano I, Lee JJ, Xi R, Jain D, Jung YL, Yang L, et al. Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing. Nat Genet 2020;52:331-41.  Back to cited text no. 3
    
4.
Le Tourneau C, Kamal M, Tsimberidou AM, Bedard P, Pierron G, Callens C, et al. Treatment algorithms based on tumor molecular profiling: The essence of precision medicine trials. J Natl Cancer Inst 2016;108:djv362.  Back to cited text no. 4
    
5.
Gerhards NM, Rottenberg S. New tools for old drugs: Functional genetic screens to optimize current chemotherapy. Drug Resist Updat 2018;36:30-46.  Back to cited text no. 5
    
6.
Miteva-Marcheva NN, Ivanov HY, Dimitrov DK, Stoyanova VK. Application of pharmacogenetics in oncology. Biomark Res 2020;8:32.  Back to cited text no. 6
    
7.
Tan CS, Gilligan D, Pacey S. Treatment approaches for EGFR-inhibitor-resistant patients with non-small-cell lung cancer. Lancet Oncol 2015;16:e447-59.  Back to cited text no. 7
    
8.
Nan X, Xie C, Yu X, Liu J. EGFR TKI as first-line treatment for patients with advanced EGFR mutation-positive non-small-cell lung cancer. Oncotarget 2017;8:75712-26.  Back to cited text no. 8
    
9.
Houtsma D, Guchelaar HJ, Gelderblom H. Pharmacogenetics in oncology: A promising field. Curr Pharm Des 2010;16:155-63.  Back to cited text no. 9
    
10.
Massacesi C, Terrazzino S, Marcucci F, Rocchi MB, Lippe P, Bisonni R, et al. Uridine diphosphate glucuronosyl transferase 1A1 promoter polymorphism predicts the risk of gastrointestinal toxicity and fatigue induced by irinotecan-based chemotherapy. Cancer 2006;106:1007-16.  Back to cited text no. 10
    
11.
Bellmunt J, Paz-Ares L, Cuello M, Cecere FL, Albiol S, Guillem V, et al. Gene expression of ERCC1 as a novel prognostic marker in advanced bladder cancer patients receiving cisplatin-based chemotherapy. Ann Oncol 2007;18:522-8.  Back to cited text no. 11
    
12.
Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, et al. Genomics of drug sensitivity in cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res 2013;41:D955-61.  Back to cited text no. 12
    
13.
Kaur G, Bhadada SK, Sachdeva N, Saikia UN, Dahiya D, Seth S, et al. Establishment and characterization of long-term human primary parathyroid tumor subclones derived from Indian PHPT. 3 Biotech 2023;13:161.  Back to cited text no. 13
    
14.
Syed YY. Oncotype DX breast recurrence score(®): A review of its use in early-stage breast cancer. Mol Diagn Ther 2020;24:621-32.  Back to cited text no. 14
    
15.
Scheerens H, Malong A, Bassett K, Boyd Z, Gupta V, Harris J, et al. Current status of companion and complementary diagnostics: Strategic considerations for development and launch. Clin Transl Sci 2017;10:84-92.  Back to cited text no. 15
    
16.
Coleman RL, Oza AM, Lorusso D, Aghajanian C, Oaknin A, Dean A, et al. Rucaparib maintenance treatment for recurrent ovarian carcinoma after response to platinum therapy (ARIEL3): A randomised, double-blind, placebo-controlled, phase 3 trial. Lancet 2017;390:1949-61.  Back to cited text no. 16
    
17.
Bellmunt J, de Wit R, Vaughn DJ, Fradet Y, Lee JL, Fong L, et al. Pembrolizumab as second-line therapy for advanced urothelial carcinoma. N Engl J Med 2017;376:1015-26.  Back to cited text no. 17
    
18.
Brahmer J, Reckamp KL, Baas P, Crinò L, Eberhardt WE, Poddubskaya E, et al. Nivolumab versus docetaxel in advanced squamous-cell non-small-cell lung cancer. N Engl J Med 2015;373:123-35.  Back to cited text no. 18
    
19.
Topalian SL, Taube JM, Anders RA, Pardoll DM. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat Rev Cancer 2016;16:275-87.  Back to cited text no. 19
    



 
 
    Tables

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