BRAF p.V600E mutations are key biomarkers for colorectal cancer (CRC) which are associated with poor patient prognosis and response to EGFR treatment. This paper demonstrates a proof-of-concept study for a portable Lab-on-Chip (LoC) device integrated with Ion-Sensitive Field-Effect Transistor (ISFET) sensors in detecting BRAF p.V600E biomarkers with high accuracy and speed, for Point-of-Care (PoC) testing of CRC. Wild-type and mutant-type copies of the BRAF gene were successfully distinguished using chip-compatible loop-mediated isothermal amplification (LAMP) reactions. Optimisation of the LAMP assay targeting BRAF p.V600E was performed and tested using qPCR instrumentation as a gold standard and benchmark for the LoC, exhibiting improved limits of detection (LODs) at 102copies/µL in under 15 minutes. A bespoke signal processing methodology was also developed using the Convolutional Neural Network EEGNet to classify nucleic acid amplification experiments on ISFET arrays, achieving an accuracy of over 95% and designed to be easily transferable to novel DNA/RNA targets. The findings show evidence of the potential to employ ISFET sensing in PoC diagnostics for CRC, ultimately bridging the gap in accessibility in limited resource settings.

PIK3CA p.E545K mutation is a well-studied breast cancer biomarker with a clinical significance as a therapeutic target, particularly with the use of the small molecule inhibitor Alpelisib. An issue with detecting this mutation and other mutations in this exon is that 98% of its sequence homology is identical to a pseudogene, a non-functional non-coding gene found in chromosome 22. This paper aims to use ISFET enabled Lab-on-Chip (LoC) technology, coupled with a well-studied isothermal amplification method (LAMP), as a means to distinguish wild-type (WT), mutant (MT) and pseudogene (PG) copies of DNA. In an age where affordability and accessibility of diagnostic tests is of crucial importance, the use of CMOS technology offers a great potential as an alternative for regular near-patient molecular testing using liquid biopsies. Bespoke primer design is tested and optimised with synthetic DNA to achieve high specificity and low sensitivity (100 copies). The primer efficiencies were also tested on our in-house LoC system showing near identical results to those obtained from a qPCR instrument. Spiking experiments were also conducted, where mixed populations of WT and MT were tested to assess the primers' abilities to estimate the variant allele frequency (VAF) of p.E545K and to mimic clinical scenarios. The results continue to depict how LoC technology in partnership with LAMP detection can be used in a liquid biopsy setting to detect blood DNA biomarkers to assist better patient stratification.

Recent advancements in biotechnology have contributed to the concept of precision oncology through the application of machine learning algorithms. The proposed work focuses on the improvement of a novel Deep Learning model, known as Reference drug-based Deep Neural Network (RefDNN), applied to the prediction of cancer drug response. The model utilizes drug's structure similarity profiles (SSP) to describe the similarity between different reference cancer drugs and uses an SSP vector to weigh the pre-predicted drug response probability obtained by the use of Elastic Net (EN), with the weighted response to be the input of the Deep Neural Network. The prediction performance of RefDNN has been improved by adding a t- distributed stochastic neighbor embedding (t-SNE) based feature extraction estimator, through the integration of gene expression, copy number variants (CNV) and mutation data. This adaptation was used to characterise the model and customize the prediction procedure based on cell line data to provide more precise and time-efficient results. The performance of the proposed system was based on a 5-fold cross validation and was compared to the original RefDNN model, showing significant improvements in accuracy and reduction of the computational processing time.

This paper presents a method for detection of ESR1 p.E380Q, a common Breast Cancer (BC) mutation, using an ISFET (Ion-Sensitive Field-Effect Transistor) based Lab-on-Chip (LoC) platform. The LoC contains an ISFET array that can detect pH changes during DNA amplification, specifically Loop-Mediated Isothermal Amplification (LAMP). Synthetic ESR1 DNA was detected in a comparison pH-LAMP assay, carried out on the LoC platform as well as a conventional qPCR instrument. Positive detection of the allele arises due to bespoke allele-specific primers that target one base-pair difference between the wild-type and mutant alleles. The LoC and qPCR demonstrate comparable results detecting the mutant allele with mutant primers in around 25 minutes. The sensing microchip technology coupled with the molecular methods of isothermal chemistries and primer design allow this platform to be tested at a Point-of-Care setting for breast cancer patients, offering mutational tracking platform of circulating tumour DNA in liquid biopsies to assist patient stratification and allow tailored treatments.

Primers carry unique genetic information that allows them to be used as specific probes in various applications in the field of diagnostics and particularly in cancer, where the need for accurate treatment selection is crucial. Rapid and affordable detection of cancer specific targets, such as allele-specific single-nucleotide mutational changes, are of great need to improve treatment efficacy and guide clinical use. Detecting these mutations in isothermal conditions creates the opportunity to develop cost-efficient diagnostic platforms for cancer treatment. In this paper, a novel python script was constructed to develop a simple and personalised software tool that could automate the design of allele-specific primers in isothermal conditions using design parameters, such as free energies and annealing temperatures. The scripts' primers were compared to manually designed probes that were experimentally tested on a variant (ESR1 p.E380Q), commonly present in metastatic breast cancer, showcasing the applicability of the method and the potential for the script to be used as part of an automated software to design allele-specific assays for Lab-on-Chip platforms in cancer diagnostics.

A trapped charge compensation scheme for ISFET based translinear circuits is presented, as part of a system for prediction of cancer risk, based on DNA methylation. Each pixel is able to measure a DNA methylation ratio through pH-based measurements and by using in-pixel comparison to a tunable threshold, to output a result which indicates percentage of methylation used as a cancer score. The developed system was designed in a 0.35 μm CMOS technology and uses a novel trapped charge compensation scheme for ISFETs used in translinear circuits. The output scheme was able to compensate trapped charge of up to 380mV, with a ratio error below 5%, in a range of ratios between 50% and 80% which is generated from pH-based DNA methylation reactions.

We demonstrate a diagnostics platform which integrates an ISFET array and a temperature control loop for isothermal DNA detection. The controller maintains a temperature of 63°C to perform nucleic acid amplification which is detected by the on-chip sensors. The 32×32 ISFET array is first calibrated to cancel trapped charge and then measures the change in the pH of the reaction. The sensor data is sent to a microcontroller and the reaction is monitored in real-time using a MATLAB interface. Experiments confirm a change of 0.9 pH when tested for the presence of RNA associated with the Zika virus.

This paper presents a pH-based System-on-Chip DNA methylation quantification platform for real time monitoring of DNA methylation ratio in target genes. The architecture forms a novel autonomous system, capable of providing diagnostic information on the progression of a disease, notably cancer. The system is equipped with drift and trapped charge compensation schemes based on differential measurements and an auto-calibration algorithm. The simulated system in 0.35μm CMOS technology achieves a power consumption of 0.997mW, with a DNA methylation ratio output sensitivity of 0.1%. The ISFET-based detection platform occupies a total of 901μm2and allows the calculation of DNA methylation ratio in pH-monitored DNA methylation based reactions.

This paper presents the concept of a ratiometric approach for DNA methylation detection using the “Methylation Cell” for the indication of epigenetic abnormalities related to cancer. The “Methylation Cell” allows real-time detection of pH signals resulting from DNA based reactions using ISFET sensors, performing continuous computation of the ratio of DNA methylation, giving a discrete output signal when DNA methylation exceeds a certain predefined percentage. Fabricated in a typical 0.35μm CMOS process, it uses current-mode translinear circuits to perform computation in low power. Experimental results are presented, demonstrating its capabilities through an integration in a Lab-on-Chip (LoC) set-up using a microfluidic assembly.