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Purpose The association between pathological complete response (pCR) in patients receiving neoadjuvant chemotherapy (NAC) for breast cancer and Circulating Tumour Cells (CTCs) is not clear. The aim of this study was to assess whether CTC enumeration could be used to predict pathological response to NAC in breast cancer as measured by the Miller–Payne grading system. Methods Twenty-six patients were recruited, and blood samples were taken pre- and post-NAC. CTCs were isolated using the ScreenCell device and stained using a modified Giemsa stain. CTCs were enumerated by 2 pathologists and classified as single CTCs, doublets, clusters/microemboli and correlated with the pathological response as measured by the Miller–Payne grading system. χ 2 or ANOVA was performed in SPSS 24.0 statistics software for associations. Results 89% of patients had invasive ductal carcinoma (IDC) and 11% invasive lobular carcinoma (ILC). At baseline 85% of patients had CTCs present, median 7 (0–161) CTCs per 3 ml of whole blood. Post-chemotherapy, 58% had an increase in CTCs. This did not correlate with the Miller–Payne grade of response. No significant association was identified between the number of CTCs and clinical characteristics; however, we did observe a correlation between pre-treatment CTC counts and body mass index, p  < 0.05. Conclusions Patients with a complete response to NAC still had CTCs present, suggesting enumeration is not sufficient to aid surgery stratification. Additional characterisation and larger studies are needed to further characterise CTCs isolated pre- and post-chemotherapy. Long-term follow-up of these patients will determine the significance of CTCs in NAC breast cancer patients.

The liquid biopsy has the potential to improve patient care in the diagnostic and therapeutic setting in non-small cell lung cancer (NSCLC). Consented patients with epidermal growth factor receptor (EGFR) positive disease (n = 21) were stratified into two cohorts: those currently receiving EGFR tyrosine kinase inhibitor (TKI) therapy (n = 9) and newly diagnosed EGFR TKI treatment-naïve patients (n = 12). Plasma genotyping of cell-free DNA was carried out using the FDA-approved cobas® EGFR mutation test v2 and compared to next generation sequencing (NGS) cfDNA panels. Circulating tumor cell (CTC) numbers were correlated with treatment response and EGFR exon 20 p.T790M. The prognostic significance of the neutrophil to lymphocyte ratio (NLR) and lactate dehydrogenase (LDH) was also investigated. Patients in cohort 1 with an EGFR exon 20 p.T790M mutation progressed more rapidly than those with an EGFR sensitizing mutation, while patients in cohort 2 had a significantly longer progression-free survival (p = 0.04). EGFR exon 20 p.T790M was detected by liquid biopsy prior to disease progression indicated by computed tomography (CT) imaging. The cobas® EGFR mutation test detected a significantly greater number of exon 20 p.T790M mutations (p = 0.05). High NLR and derived neutrophil to lymphocyte ratio (dNLR) were associated with shorter time to progression and worse survival outcomes (p < 0.05). High LDH levels were significantly associated with shorter time to disease progression (p = 0.03). These data support the use of liquid biopsy for monitoring EGFR mutations and inflammatory markers as prognostic indicators in NSCLC.

MET is a receptor tyrosine kinase (RTK) that plays important roles in carcinogenesis. Despite being frequently overexpressed in cancer, clinical responses to targeting this receptor have been limited. Recently novel splicing mutations involving the loss of exon 14 (called METex14 skipping) have emerged as potential biomarkers to predict for responsiveness to targeted therapies with Met inhibitors in non-small cell lung cancer (NSCLC). Currently, the diverse genomic alterations responsible for METex14 skipping pose a challenge for routine clinical diagnostic testing. In this report, we examine three different methodologies to detect METex14 and assess their potential utility for use as a diagnostic assay for both the identification of METex14 and intra-tumoural distribution in NSCLC.

Acquired, activating mutations of MPL W515 are recognised driver mutations of the myeloproliferative neoplasms (MPN), namely, essential thrombocythemia and primary myelofibrosis. The most common mutation at this codon is W515L with several other mutations also described at a lower frequency. Of these less common mutations, MPL W515S has only been reported sporadically with limited information on clinicopathological associations. We describe the case of an elderly man with persistent thrombocytosis presenting with an ischemic cerebral event. Bone marrow biopsy showed evidence of prefibrotic myelofibrosis with targeted sequencing demonstrating the presence of the rare MPL W515S mutation. Thrombolytic and cytoreductive therapies resulted in a favorable outcome and follow-up. This case provides additional, necessary, and phenotypic data for the rare MPN-associated MPL W515S mutation.

Advanced diagnostics are enabling cancer treatments to become increasingly tailored to the individual through developments in immunotherapies and targeted therapies. However, long turnaround times and high costs of molecular testing hinder the widespread implementation of targeted cancer treatments. Meanwhile, gold-standard histopathological assessment carried out by a trained pathologist is widely regarded as routine and mandatory in most cancers. Recently, methods have been developed to mine hidden information from histopathological slides using deep learning applied to scanned and digitized slides; deep learning comprises a collection of computational methods which learn patterns in data in order to make predictions. Such methods have been reported to be successful in a variety of cancers for predicting the presence of biomarkers such as driver mutations, tumour mutational burden, and microsatellite instability. This information could prove valuable to pathologists and oncologists in clinical decision making for cancer treatment and triage for in-depth sequencing. In addition to identifying molecular features, deep learning has been applied to predict prognosis and treatment response in certain cancers. Despite reported successes, many challenges remain before the clinical implementation of such diagnostic strategies in the clinical setting is possible. This review aims to outline recent developments in the field of deep learning for predicting molecular genetics from histopathological slides, as well as to highlight limitations and pitfalls of working with histopathology slides in deep learning.

While molecular diagnostics are a relatively recent development in the field of oncology, the utility of this limb of pathology is undeniable. Prototypic companion diagnostics treatments are widely described in the fields of oncology and pathology; for example, KRAS mutation testing prior to anti-epidermal growth factor receptor (EGFR) monoclonal antibody therapy (cetuximab, panitumumab) for metastatic colorectal cancer (mCRC), or EGFR mutation testing in stage IIIB and stage IV non-small-cell lung cancer to establish suitability for gefitinib and erlotinib therapy. It is important that assays are selected and optimised based on clinical rather than purely analytical properties. To this end, calculation of positive and negative predictive value in the correct clinical context may eliminate misconceptions regarding the clinical applicability of an assay. A further point that can be inferred from the above discussion is that testing of populations with low mutation prevalence rates may increase the likelihood of false positive results; thus, judicious use of molecular testing is necessary to reduce this avoidable form of error.

The liquid biopsy has the potential to improve patient care in the diagnostic and therapeutic setting in non-small cell lung cancer (NSCLC). Consented patients with epidermal growth factor receptor (EGFR) positive disease (n = 21) were stratified into two cohorts: those currently receiving EGFR tyrosine kinase inhibitor (TKI) therapy (n = 9) and newly diagnosed EGFR TKI treatment-naïve patients (n = 12). Plasma genotyping of cell-free DNA was carried out using the FDA-approved cobas[sup.®] EGFR mutation test v2 and compared to next generation sequencing (NGS) cfDNA panels. Circulating tumor cell (CTC) numbers were correlated with treatment response and EGFR exon 20 p.T790M. The prognostic significance of the neutrophil to lymphocyte ratio (NLR) and lactate dehydrogenase (LDH) was also investigated. Patients in cohort 1 with an EGFR exon 20 p.T790M mutation progressed more rapidly than those with an EGFR sensitizing mutation, while patients in cohort 2 had a significantly longer progression-free survival (p = 0.04). EGFR exon 20 p.T790M was detected by liquid biopsy prior to disease progression indicated by computed tomography (CT) imaging. The cobas[sup.®] EGFR mutation test detected a significantly greater number of exon 20 p.T790M mutations (p = 0.05). High NLR and derived neutrophil to lymphocyte ratio (dNLR) were associated with shorter time to progression and worse survival outcomes (p < 0.05). High LDH levels were significantly associated with shorter time to disease progression (p = 0.03). These data support the use of liquid biopsy for monitoring EGFR mutations and inflammatory markers as prognostic indicators in NSCLC.

The Human Genome Project and rapid advances in high-throughput molecular technologies are providing an unprecedented opportunity to advance the understanding of the common polygenic diet-related diseases, including obesity, the metabolic syndrome, type 2 diabetes mellitus, CVD and some cancers. In particular, transcriptomic approaches that allow multiple simultaneous gene-expression profiles facilitate the characterisation of metabolic perturbations that underlie diet-related pathologies. The present paper will focus on ‘transcriptomic signatures’ to characterise and understand the molecular mechanisms that accurately reflect ‘metabolic health’.

The Human Genome Project and rapid advances in high-throughput molecular technologies are providing an unprecedented opportunity to advance the understanding of the common polygenic diet-related diseases, including obesity, the metabolic syndrome, type 2 diabetes mellitus, CVD and some cancers. In particular, transcriptomic approaches that allow multiple simultaneous gene-expression profiles facilitate the characterisation of metabolic perturbations that underlie diet-related pathologies. The present paper will focus on ‘transcriptomic signatures’ to characterise and understand the molecular mechanisms that accurately reflect ‘metabolic health’.