CD44 as a drug delivery target in human cancers: where are we now?

Şahin, Ibrahim Halil; Klostergaard, Jim

doi:10.1517/14728222.2015.1088834

Cited by 20 publications

(14 citation statements)

References 19 publications

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“…In addition, we unveiled multiple, putatively unreported DA-CNVRs that map to relevant candidate genes (Table 1). Among those are several well-established drug targets and others in development, including but not limited to TNFAIP8, HSPA9, SLIT3, HCN2, GRK6, ITGB8, ADK, CD44, NR1D1, and SLC38A10 [30][31][32][33][34][35][36][37][38][39][40][41][42][43] . We performed similar enrichment analyses across each set of the domain-specific DA-CNVRs, showing that a number of the functional and genomic elements were enrichments in multiple disease domains ( Supplementary Fig.…”

Section: Resultsmentioning

confidence: 99%

Rare copy number variants in over 100,000 European ancestry subjects reveal multiple disease associations

Glessner

Coe

et al. 2020

Nat Commun

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Copy number variants (CNVs) are suggested to have a widespread impact on the human genome and phenotypes. To understand the role of CNVs across human diseases, we examine the CNV genomic landscape of 100,028 unrelated individuals of European ancestry, using SNP and CGH array datasets. We observe an average CNV burden of~650 kb, identifying a total of 11,314 deletion, 5625 duplication, and 2746 homozygous deletion CNV regions (CNVRs). In all, 13.7% are unreported, 58.6% overlap with at least one gene, and 32.8% interrupt coding exons. These CNVRs are significantly more likely to overlap OMIM genes (2.94-fold), GWAS loci (1.52-fold), and non-coding RNAs (1.44-fold), compared with random distribution (P < 1 × 10 −3). We uncover CNV associations with four major disease categories, including autoimmune, cardio-metabolic, oncologic, and neurological/psychiatric diseases, and identify several drugrepurposing opportunities. Our results demonstrate robust frequency definition for large-scale rare variant association studies, identify CNVs associated with major disease categories, and illustrate the pleiotropic impact of CNVs in human disease.

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Section: Resultsmentioning

confidence: 99%

Rare copy number variants in over 100,000 European ancestry subjects reveal multiple disease associations

Glessner

Coe

et al. 2020

Nat Commun

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“…Data indicated improved efficacy of HA‐irinotecan conjugate compared to irinotecan‐alone . Although, HA‐irinotecan conjugate failed in statistically significant improvement in progression‐free survival during Phase III clinical study, CD44 remains under consideration of investigators as a valuable drug delivery target …”

Section: Chemistry Of Ha Its Modifications Its Use In Biomedical Inmentioning

confidence: 99%

Hyaluronic Acid and Its Derivatives in Coating and Delivery Systems: Applications in Tissue Engineering, Regenerative Medicine and Immunomodulation

Knopf‐Marques

Pravda

Wolfová

et al. 2016

Adv Healthcare Materials

183

159

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epithelial, connective, and nerve tissues of vertebrates. It is designated as a glycosaminoglycan. [1] Glycosaminoglycans are composed of disaccharide blocks of N-acetylgalactosamine or N-acetylglucosamine, (amino sugars) and uronic sugars such as glucuronic acid, iduronic acid or galactose. This group of heteropolysaccharides comprises HA, chondroitin sulfates, dermatan sulfate, heparin and heparin sulfates whose sulfation degree is less than that of heparin. Unlike other glycosaminoglycans, hyaluronan is not sulfated and it is self-standing, i.e. without an association with a core protein. [2] It was isolated in 1934 by Meyer and Palmer from bovine vitreous humor. [3] The polymer chain of hyaluronan comprises repeating disaccharide units where the pyranose rings are connected by β-1,3 bonds. The repeating units are bonded with a β-1,4 glycosidic bond within the chain between N-acetyl-D-glucosamine and D-glucuronic acid. At physiological pH each glucuronate unit, associated with its carboxylate group, carries an anionic charge. The hundreds of negative charges are fixed to each chain. These anionic groups are balanced with mobile cations such as Na + , K + , Ca 2+ and Mg 2+ . During ionization of D-glucuronic acid with the carboxylic groups, the charges influence the organization of the chains and their interactions with their surroundings. In turn, they are affected by pH and ionic strength. The chain organization and charge directly affect the solubility in water, since hyaluronan is water-insoluble when convertedAs an Extracellular Matrix (ECM) component, Hyaluronic acid (HA) plays a multi-faceted role in cell migration, proliferation and differentiation at micro level and system level events such as tissue water homeostasis. Among its biological functions, it is known to interact with cytokines and contribute to their retention in ECM microenvironment. In addition to its biological functions, it has advantageous physical properties which result in the industrial endeavors in the synthesis and extraction of HA for variety of applications ranging from medical to cosmetic. Recently, HA and its derivatives have been the focus of active research for applications in biomedical device coatings, drug delivery systems and in the form of scaffolds or cell-laden hydrogels for tissue engineering. A specific reason for the increase in use of HA based structures is their immunomodulatory and regeneration inducing capacities. In this context, this article reviews recent literature on modulation of the implantable biomaterial microenvironment by systems based on HA and its derivatives, particularly hydrogels and microscale coatings that are able to deliver cytokines in order to reduce the adverse immune reactions and promote tissue healing.

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“…CD44, a component of CAB, is currently in clinical trials for various cancers such as lung and colorectal cancer. 10 Its role as a potential therapeutic target in breast cancer could be the subject of further investigation.…”

Section: Discussionmentioning

confidence: 99%

“…We shortlisted 5 markers and combined them with 3 clinico-pathological parameters—tumor size, tumor grade, and node status in a binary classifier that predicts risk of distant recurrence within 5 years. We used both biomarkers and clinico-pathological parameters as studies have shown that combining the 2 enables more accurate prediction of prognosis, 10-12 and therefore better clinical decision making. Here, we describe the development, pilot clinical validation, and independent prognostic ability of a novel biomarker based-risk classifier reflective of the metastatic potential of the tumor in early-stage HR+ breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor–Positive Breast Cancer

Ramkumar¹,

Buturović²,

Malpani³

et al. 2018

Biomark�Insights

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Use of proteomic strategies to identify a risk classifier that estimates probability of distant recurrence in early-stage hormone receptor (HR)-positive breast cancer is relevant to physiological cellular function and therefore to intrinsic tumor biology. We used a 298-sample retrospective training set to develop an immunohistochemistry-based novel risk classifier called CanAssist-Breast (CAB) which combines 5 prognostically relevant biomarkers and 3 clinico-pathological parameters to arrive at probability of distant recurrence within 5 years from diagnosis. Five selected biomarkers, namely, CD44, ABCC4, ABCC11, N-cadherin, and pan-cadherin, were chosen based on their role in tumor metastasis. The chosen biomarkers represent the hallmarks of cancer and are distinct from other proliferation and gene expression–based prognostic signatures. The 3 clinico-pathological parameters integrated into the machine learning–based CAB algorithm are tumor size, tumor grade, and node status. These features are used to calculate a “CAB risk score” that classifies patients into low- or high-risk groups and predicts probability of distant recurrence in 5 years. Independent clinical validation of CAB in a retrospective study comprising 196 patients indicated that distant metastasis-free survival (DMFS) was significantly different in the 2 risk groups. The difference in DMFS between the low- and high-risk categories was 19% in the validation cohort (P = .0002). In multivariate analysis, CAB risk score was the most significant independent predictor of distant recurrence with a hazard ratio of 4.3 (P = .0003). CanAssist-Breast is a precise and unique machine learning–based proteomic risk-classifier that can assist in risk stratification of patients with early-stage HR+ breast cancer.

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CD44 as a drug delivery target in human cancers: where are we now?

Cited by 20 publications

References 19 publications

Rare copy number variants in over 100,000 European ancestry subjects reveal multiple disease associations

Rare copy number variants in over 100,000 European ancestry subjects reveal multiple disease associations

Hyaluronic Acid and Its Derivatives in Coating and Delivery Systems: Applications in Tissue Engineering, Regenerative Medicine and Immunomodulation

Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor–Positive Breast Cancer

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