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Personalised medicine in oncology: Where are we now?

Published on 30/06/21 at 10:30am

The past decade has seen exponential growth in the field of oncology, with precision medicine at the forefront, driving positive change. Kat Jenkins takes a look at the world of precision medicine in cancer treatment, exploring the technologies and ideas that are shaping the way the healthcare industry is battling ‘the big C’

The development of personalised medicine in oncology has been momentous for millions of cancer patients and healthcare professionals alike. Focus of treatment has shifted towards a case-by-case basis, with doctors and researchers narrowing down to the molecular level, looking at DNA, family histories, and vast amounts of data, which are mined and stored faster than ever thanks to artificial intelligence (AI).

Genomics has a huge role to play in personalised medicine, particularly with the analysis of genomic information, which reveals an individual’s or population’s biomarkers. This has become a cornerstone for modern personalised medicine. For instance, analysis of germline genomic variation can be used to identify individuals who may be at increased risk of disease. An example of this is adult inherited cancer mutation testing, where healthy individuals with a strong family history of particular types of cancer can be tested for the presence of genetic variation that indicates they may be at much higher risk of developing these diseases in the future than the general population. This information can then be used to guide decisions to take – or not take – risk reducing measures, such as enhanced screening, chemoprophylaxis, or preventive surgery.

There are currently a number of different techniques for genome analysis that are used in clinical practice, as no one technology is able to effectively detect the full spectrum of genetic variations that underlie different diseases. One form that has seen recent success is circulating tumour DNA (ctDNA) testing, with researchers investigating liquid biopsies in non-small cell lung cancer (NSCLC) reporting a new breakthrough in early June.

ctDNA explained

ctDNA is released from apoptotic and necrotic tumour cells, and several sensitive techniques have been invented and adapted to quantify ctDNA genomic alterations. Looking particularly at applications of ctDNA in lung cancer, healthcare professionals can reach early diagnosis and detection, improve prognosis prediction through detecting mutations and structural alterations, identify tumour mutational burden, and track tumour evolution. Compared with surgical biopsy and radiographic imaging, the advantages of ctDNA are that it is a non-invasive procedure, allowing real-time monitoring, with relatively high sensitivity and specificity.

Mutations identified in tumour biopsy and ctDNA are highly correlated, subsequently providing an opportunity for non-invasive characterising mutational profiles of cancer. A series of techniques developed and modified for ctDNA, such as digital polymerase chain reaction and next-generation sequencing, have empowered blood examination with both high sensitivity and specificity in detecting mutations.

In the early stage of NSCLC, the proven ctDNA presence in the blood has qualified ctDNA genotyping as a valuable method for screening and early diagnosis of lung cancer. Compared with radiographic approaches and blood protein biomarkers, ctDNA is a direct measurement of the tumour based on genomic alterations and a perfect example of the benefits of precision medicine.

A recent study published in June from Angle, a liquid biopsy company, found that in addition to analysing ctDNA, circulating tumour cell (CTC) analysis can further aid targeted therapy selection. The study, based in Athens, was aiming to detect epithelial growth factor receptor (EGFR) mutations, a key target for therapy selection in ctDNA and matched CTCs from a single blood sample using a microfluidic device. Direct comparison of the CTC results with matched ctDNA results revealed significant differences in the patient’s mutational status, on which the therapy decision is based.

This in-depth, multifaceted approach to genomic analysis is just one of many examples of how researchers are striving to improve the process of therapy selection for individual cancer patients, which ultimately increases a person’s chances of survival. However, genomics is not the only way forward for personalised cancer medicine. The field of robotics is also playing an interesting and crucial role in the battle against cancer.

The future is robotic

Robotics span across mechanical engineering, electrical engineering, and computer science, with applications in surgery leading to the digitisation of radiology, pathology, and advancements in image analysis with neural networks. It has enabled 3D magnified viewpoints for surgeons alongside more precise movements, bi-manual operation, and even articulated arms.

A development most related to personalised cancer treatment is in the invention of tissue classifying surgical tools, such as the iKnife and SPIDERMASS, which analyse vaporised lipids by mass spectrometry (MS) to assist in distinguishing diseased tissue from non-diseased tissue during a surgical procedure.

SPIDERMASS was developed by Laboratoire PRISM as an in vivo, real-time instrument for diagnosis and guided surgery. It combines analysis technology with a robotic arm that enables images to be acquired from any surface by moving a laser probe above the surface. By equipping the robotic arm with a sensor, SPIDERMASS is also able to both get the topography image of the sample surface and the molecular distribution, and then plot back the molecular data, directly to the 3D topographical image without the need for image fusion.

Speaking in Science Translational Medicine, lead author of the SPIDERMASS study, Júlia Balog, said: “The technology has demonstrated that collected MS molecular profiles are specific to cell phenotypes and provide access to the molecular content in the tumour microenvironment. On the other hand, the introduction of MS directly in the surgical theatre represented a true paradigm shift by transforming MS from a lab technology into a clinical tool.”

The iKnife was developed by researchers at Imperial College London, trialling the device since 2014 in brain, breast, colon, and ovarian cancer surgery. In cancers involving solid tumours, removal of the cancer in surgery is generally the best hope for treatment. However, the surgeon normally takes out a margin of healthy tissue alongside the tumour. It is often impossible to tell by sight which tissue is cancerous, and one in five breast cancer patients who have surgery require a second operation to fully remove the cancer. In cases of uncertainty, the removed tissue is sent to a lab for examination while the patient remains under general anaesthetic, which is not the safest or most efficient process.

The iKnife is based on electrosurgery, a technology invented in the 1920s that is commonly used today. Electrosurgical knives use an electrical current to rapidly heat tissue, cutting through it while minimising blood loss. In doing so, they vaporise the tissue, creating smoke that is normally sucked away by extraction systems. The inventor of the iKnife, Dr Zoltan Takats, realised that this smoke would be a rich source of biological information.

To create the iKnife, he connected an electrosurgical knife to a mass spectrometer − an analytical instrument used to identify what chemicals are present in a sample. Different types of cell produce thousands of metabolites in different concentrations, so the profile of chemicals in a biological sample can reveal information about the state of that tissue. In trials, tissue identification via intraoperative rapid evaporative ionisation MS matched the post-operative histological diagnosis in 100% (all 81) of the cases studied.

AI and oncology

The field of robotics is rapidly evolving, and this evolution is only further increased by the development of AI technology. AI has the potential to drive progress in personalised medicine. Machine learning approaches have been a significant turning point for the analysis of large datasets and could help expedite novel discoveries for personalised healthcare. Currently, medical image analysis is among the most promising applications of AI technologies, with radiology and histopathology in particular having been highlighted as areas that could be transformed by AI.

An interesting example of this is the work of DeepMind, a Google-owned AI development company that has been at the forefront of AI innovation in healthcare. In particular, they have developed a breast cancer screening AI system that has been proven to outperform human mammography professionals in trials. Screening mammography is a process that aims to identify breast cancer before symptoms appear, enabling earlier therapy for more treatable disease. Despite the existence of screening programmes worldwide, interpretation of these images suffers from suboptimal rates of false positives and false negatives.

Breast cancer has the second highest cancer death rate in women and many developed countries, therefore, have large-scale mammography screening programmes. Major medical and governmental organisations recommend screening for all women starting between the ages of 40 and 50 and in the USA and UK combined, over 42 million exams are performed each year.

DeepMind used two large datasets representative of clinical practice from the US and UK to train their system, which out-performed all six professional radiologists in trials. Results showed an absolute reduction of 5.7%/1.2% (US/UK) in false positives and 9.4%/2.7% (US/UK) in false negatives, when compared to the conclusions made by individual human specialists in the original screening visits. The overall use of the software is still undecided, but DeepMind has recommended a ‘second reader’ function, wherein the software is used to double check the initial diagnosis. In trials it was found that this function alone reduced workload by 88%.

Computer-aided detection software for mammography such as this have existed since the 1990s, but have only really gained momentum in recent years due to the rapid evolution of machine learning technology.

DeepMind have also begun the development of an AI system that can help analyse and segment medical scans of head and neck cancer to a similar standard as expert clinicians. This segmentation process is an essential but time-consuming step when planning radiotherapy treatment. Radiotherapy must be completely tailored to each individual patient, with clinical staff making meticulous plans to ensure healthy tissue doesn’t get damaged by radiation. This process involves radiographers, oncologists, and/or dosimetrists manually outlining the areas of anatomy that need radiotherapy and those areas that should be avoided. The automation of this process through AI would free up time for clinicians to focus on patient care, research, and education.

As AI continues to evolve and spread into more facets of the healthcare industry, precision medicine can only evolve alongside it. AI allows for more patient-centric treatments, and can bring to the forefront more prevention, personalisation, and precision to everyday practice. Linking back to genomics, AI can rapidly process, store, and analyse vast amounts of genomic data which in turn feeds the development of epigenetics, transcriptomics, and metabolomics. All of which are areas of personalised cancer treatments.

Speaking at a TedX talk, Dr Adam Marcus, interim executive director for Winship Cancer Institute at Emory University, said: “[Precision medicine] is not some sort of fantastic new treatment. Instead, we are treating people more logically than we ever have before. This is where the promise is, this is the next revolution in cancer treatment.”

It is this sentiment, that has been shared and voiced by many in the oncology field, that encapsulates the current state of precision medicine in cancer. It is all about thinking smarter, adapting alongside technological innovations, focusing on the individual down to their DNA, and, ultimately, saving more lives.

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