This is the ICR’s publications repository, an open access repository of full-text research articles and theses by ICR staff and students.
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Item True-T - Improving T-cell response quantification with holistic artificial intelligence based prediction in immunohistochemistry images.(ELSEVIER, 2024-12-01)The immune response associated with oncogenesis and potential oncological ther- apeutic interventions has dominated the field of cancer research over the last decade. T-cell lymphocytes in the tumor microenvironment are a crucial aspect of cancer's adaptive immunity, and the quantification of T-cells in specific can- cer types has been suggested as a potential diagnostic aid. However, this is cur- rently not part of routine diagnostics. To address this challenge, we present a new method called True-T, which employs artificial intelligence-based techniques to quantify T-cells in colorectal cancer (CRC) using immunohistochemistry (IHC) images. True-T analyses the chromogenic tissue hybridization signal of three widely recognized T-cell markers (CD3, CD4, and CD8). Our method employs a pipeline consisting of three stages: T-cell segmentation, density estimation from the segmented mask, and prediction of individual five-year survival rates. In the first stage, we utilize the U-Net method, where a pre-trained ResNet-34 is em- ployed as an encoder to extract clinically relevant T-cell features. The segmenta- tion model is trained and evaluated individually, demonstrating its generalization in detecting the CD3, CD4, and CD8 biomarkers in IHC images. In the second stage, the density of T-cells is estimated using the predicted mask, which serves as a crucial indicator for patient survival statistics in the third stage. This ap- proach was developed and tested in 1041 patients from four reference diagnostic institutions, ensuring broad applicability. The clinical effectiveness of True-T is demonstrated in stages II-IV CRC by offering valuable prognostic information that surpasses previous quantitative gold standards, opening possibilities for po- tential clinical applications. Finally, to evaluate the robustness and broader ap- plicability of our approach without additional training, we assessed the universal accuracy of the CD3 component of the True-T algorithm across 13 distinct solid tumors.Item Longitudinal Assessment of Tumor-Infiltrating Lymphocytes in Primary Breast Cancer Following Neoadjuvant Radiation Therapy.(Elsevier BV, 2024-11-01)PURPOSE: Tumor-infiltrating lymphocytes (TILs) have prognostic significance in several cancers, including breast cancer. Despite interest in combining radiation therapy with immunotherapy, little is known about the effect of radiation therapy itself on the tumor-immune microenvironment, including TILs. Here, we interrogated longitudinal dynamics of TILs and systemic lymphocytes in patient samples taken before, during, and after neoadjuvant radiation therapy (NART) from PRADA and Neo-RT breast clinical trials. METHODS AND MATERIALS: We manually scored stromal TILs (sTILs) from longitudinal tumor samples using standardized guidelines as well as deep learning-based scores at cell-level (cTIL) and cell- and tissue-level combination analyses (SuperTIL). In parallel, we interrogated absolute lymphocyte counts from routine blood tests at corresponding time points during treatment. Exploratory analyses studied the relationship between TILs and pathologic complete response (pCR) and long-term outcomes. RESULTS: Patients receiving NART experienced a significant and uniform decrease in sTILs that did not recover at the time of surgery (P < .0001). This lymphodepletive effect was also mirrored in peripheral blood. Our SuperTIL deep learning score showed good concordance with manual sTILs and importantly performed comparably to manual scores in predicting pCR from diagnostic biopsies. The analysis suggested an association between baseline sTILs and pCR, as well as sTILs at surgery and relapse, in patients receiving NART. CONCLUSIONS: This study provides novel insights into TIL dynamics in the context of NART in breast cancer and demonstrates the potential for artificial intelligence to assist routine pathology. We have identified trends that warrant further interrogation and have a bearing on future radioimmunotherapy trials.Item Biocompatibility characterisation of CMOS-based Lab-on-Chip electrochemical sensors for in vitro cancer cell culture applications.(ELSEVIER ADVANCED TECHNOLOGY, 2024-10-15)Lab-on-Chip electrochemical sensors, such as Ion-Sensitive Field-Effect Transistors (ISFETs), are being developed for use in point-of-care diagnostics, such as pH detection of tumour microenvironments, due to their integration with standard Complementary Metal Oxide Semiconductor (CMOS) technology. With this approach, the passivation of the CMOS process is used as a sensing layer to minimise post-processing, and Silicon Nitride (Si3N4) is the most common material at the microchip surface. ISFETs have the potential to be used for cell-based assays however, there is a poor understanding of the biocompatibility of microchip surfaces. Here, we quantitatively evaluated cell adhesion, morphogenesis, proliferation and mechano-responsiveness of both normal and cancer cells cultured on a Si3N4, sensor surface. We demonstrate that both normal and cancer cell adhesion decreased on Si3N4. Activation of the mechano-responsive transcription regulators, YAP/TAZ, are significantly decreased in cancer cells on Si3N4 in comparison to standard cell culture plastic, whilst proliferation marker, Ki67, expression markedly increased. Non-tumorigenic cells on chip showed less sensitivity to culture on Si3N4 than cancer cells. Treatment with extracellular matrix components increased cell adhesion in normal and cancer cell cultures, surpassing the adhesiveness of plastic alone. Moreover, poly-l-ornithine and laminin treatment restored YAP/TAZ levels in both non-tumorigenic and cancer cells to levels comparable to those observed on plastic. Thus, engineering the electrochemical sensor surface with treatments will provide a more physiologically relevant environment for future cell-based assay development on chip.Item Exploiting the Vulnerability of ARID1A Deficient Ovarian Cancers for Therapeutic Potential(Institute of Cancer Research (University Of London), 2024-09-26)The switching defective/sucrose non-fermenting (SWI/SNF) chromatin remodelling complex is important for the cellular response to replication stress. 20% of cancers harbour modifications in SWI/SNF complex subunits. ARID1A, a key component of the SWI/SNF complex, is mutated across a variety of cancers, and notably in 35-57% of ovarian clear cell carcinomas (OCCC). Clinically applicable targeted therapies for this aggressive, chemo-resistant disease remains an unmet need. G-quadruplexes (G4s) are thermodynamically stable secondary DNA structures which are a consequence of folding of guanine-rich DNA sequences. Treatment with G4 stabilising ligands, some of which have entered clinical trials, leads to DNA double strand breaks (DSBs). This represents a therapeutic vulnerability for cancers with defects in the response to G4 ligands and resulting DNA DSBs. Here, isogenic cell line models were generated using CRISPR-Cas9 gene editing and genetic complementation of ARID1A to study the potential contribution of ARID1A to genotoxic stress. We found that ARID1A deficient cells show selective sensitivity to G4 stabilising ligands, and that there is evidence of delayed repair of DNA damage when ARID1A is deficient. Mechanistically, we discovered that NHEJ factors fail to mobilise onto chromatin after treatment with stabilising ligand PDS when ARID1A is deficient. Furthermore, we showed that inhibitor of the DNA-dependent protein kinase catalytic subunit (DNA-PKcs), a protein ensuring effective NHEJ functions, when combined with PDS leads to synergistic decrease in cell viability in ARID1A deficient cells. These data provide new insights into G4 ligands-induced DNA damage and their repair in ARID1A-defective models. This knowledge could be exploited for a new therapeutic approach to treat ARID1A deficient ovarian cancer.Item The PS4-likelihood ratio calculator: flexible allocation of evidence weighting for case-control data in variant classification.(BMJ PUBLISHING GROUP, 2024-09-24)BACKGROUND: The 2015 American College of Medical Genetics/Association of Molecular Pathology (ACMG/AMP) variant classification framework specifies that case-control observations can be scored as 'strong' evidence (PS4) towards pathogenicity. METHODS: We developed the PS4-likelihood ratio calculator (PS4-LRCalc) for quantitative evidence assignment based on the observed variant frequencies in cases and controls. Binomial likelihoods are computed for two models, each defined by prespecified OR thresholds. Model 1 represents the hypothesis of association between variant and phenotype (eg, OR≥5) and model 2 represents the hypothesis of non-association (eg, OR≤1). RESULTS: PS4-LRCalc enables continuous quantitation of evidence for variant classification expressed as a likelihood ratio (LR), which can be log-converted into log LR (evidence points). Using PS4-LRCalc, observed data can be used to quantify evidence towards either pathogenicity or benignity. Variants can also be evaluated against models of different penetrance. The approach is applicable to balanced data sets generated for more common phenotypes and smaller data sets more typical in very rare disease variant evaluation. CONCLUSION: PS4-LRCalc enables flexible evidence quantitation on a continuous scale for observed case-control data. The converted LR is amenable to incorporation into the now widely used 2018 updated Bayesian ACMG/AMP framework.
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