Jayant Khandare

Circulating tumor cells (CTCs) are shed from the primary tumor into the bloodstream and represent dynamic molecular biomarkers for monitoring the progression of cancer. While profiling tumor tissues with over expression of cell surface markers, such as PD-L1 or HER2, is standard in guiding therapy, tissue samples are often inaccessible and inadequate, especially post-surgery or in cases of recurrence. Emerging clinical evidence indicates that CTC counts and biomarker surface expression can predict prognosis and therapeutic resistance more accurately than imaging or tissue-based approaches. Recent advancements in the CTC detection methods, based on physical properties or surface markers (e.g., EpCAM), coupled with next-generation sequencing (NGS) have enabled the isolation of these rare cells and their molecular characterization. Consequently, CTCs provide a real-time alternative, enabling repeated, longitudinal assessment of tumor phenotype and therapeutic response. This review emphasizes the translational potential of surface protein biomarkers on CTCs for profiling, namely PD-L1, HER2, and EGFR, as a clinically actionable approach to stratify patients, guide immunotherapy decisions, and monitor minimal residual disease (MRD), especially when longitudinal tissue biopsies are not feasible.

Background: With extensive research and development in the past decade, the affordability of Poly (ADP-ribose) polymerase (PARP) inhibitor therapy has drastically improved. Homologous recombination deficiency (HRD), a key biomarker, has been identified as an important guiding factor for PARP inhibitor therapeutic decisions in breast and ovarian cancer. However, identification of patients who will respond to Poly (ADP-ribose) polymerase (PARP) inhibitor therapy is challenging due to the lack of a unifying morphological phenotype. Current HRD testing via next-generation sequencing (NGS) is tissue-dependent, has high failure rates, misses relevant HRD genes, and involves longer turn-around times. Methods: To overcome these limitations, we developed a multimodal AI model, TRINITY, combining imaging, image-based transcriptome data, and clinico-molecular data, to examine whole-slide images (WSIs) obtained from hematoxylin and eosin (H&E)-stained samples to non-invasively predict HRD status. Results: The TRINITY model, tested on 316 TCGA breast and OV samples, presented a sensitivity of 0.77 and 0.91, NPV of 0.94 and 0.86, PPV of 0.63 and 0.58, specificity of 0.89 and 0.47, and AUC-ROC of 0.91 and 0.72, respectively. The model also yielded a similar outcome in a blind study of 74 samples, with a sensitivity of 81.2, NPV of 0.85, PPV of 0.77, specificity of 0.81, and high AUC-ROC value of 0.89, showing its promising preliminary evidence of predicting HRD status on external cohorts. Conclusions: These findings demonstrate TRINITY’s potential as a rapid, cost-effective, and tissue-sparing alternative to conventional NGS testing. While promising, further validation is needed to establish its generalizability across broader cancer types.

3D polymeric microfilms chemically modified with targeting ligands capture circulating tumor cells from cancer patients' blood samples with high specificity and provide anchor points for cell adhesion and growth.

Capturing circulating tumor cells (CTCs) from the peripheral blood of cancer patients, where they are disseminated among billions of other blood cells, is one of the most daunting challenge. We report OncoDiscover®, a multicomponent nano-system consisting of iron oxide (Fe₃O₄) nanoparticles (NPs), polyamidoamine generation 4 dendrimers (PAMAM-G4-NH₂), graphene oxide (GO) sheets and an anti-epithelial cell adhesion molecule (anti-EpCAM) antibody (Fe-GSH-PAMAM-GO-EpCAM) for the selective and precise capture of CTCs. We further evaluated this system for therapeutically important oncotargets, exemplifying overexpression of the programmed death ligand 1 (PD-L1) as a functional assay on CTCs in cancer patients. We retrospectively evaluated 134 cancer patients for the presence of CTCs and 113 (84%) showed the presence of CTCs. About 62 (55%) of the CTC +ve patients showed CTCs with PD-L1 expression. The personalized cancer detection demonstrated by the OncoDiscover® CTC analysis tool is highly relevant for cancer monitoring and treatment outcomes.

Designing chemical composites using advanced nanomaterials results in micro/nanorobots with exceptional autonomous navigation ability and bio-chemical functionality.

A surface modified cotton-based biomedical matrix has been designed for the controlledin vitro3D tumor expansion of captured cancer cells to investigate anti-cancer drug efficacy and multi-drug resistance (MDR) mechanisms.

Correction for ‘Designing 3D-nanosubstrates mimicking biological cell growth: pitfalls of using 2D substrates in the evaluation of anticancer efficiency’ by Ashwini Patil et al., Nanoscale, 2021, 13, 17473–17485, DOI: 10.1039/d1nr03816h.

Substrate nanoarchitecture can mimic the tissue microenvironment and promote a three-dimensional cellular growth pattern superior to conventional 2D support systems.