Iyare Izevbaye

Lung cancer is one of the leading causes of cancer morbidity and mortality worldwide. Majority of cases are diagnosed as late-stage disease. Non-small cell lung cancer (NSCLC) forms the major pathologic group of this disease. The high malignantly nature of NSCLC is mediated by tumor cell plasticity (TCP). Designated as a hallmark of cancer, it is the ability of cancer cells to reprogram its molecular cell state and behaviour in response to environmental stress. MicroRNAs (miRNA) play a major role in TCP of NSCLC from premalignant disease, tumor initiation, progression and invasion, immune evasion to metastasis. TCP mediated by miRNA include epithelial mesenchymal transition, cancer stem cell biology, and cross talk between cancer and its tumor microenvironment. An understanding of these mechanisms gives a more comprehensive and precise molecular profile of NSCLC behaviour and implications for diagnosis, prognosis, monitoring and treatment. Emerging technologies including liquid biopsy, tumor geospatial profiling and single cell RNA sequencing hold promise to transform the clinical management of NSCLC.

Over the last decades, a significant improvement in cancer patient outcomes has occurred due to advances in cancer cell biology, systemic immunity, tumor-immune microenvironment (TIME) and precision cancer therapy. Despite this explosion of knowledge, its usefulness in clinical practice has been limited by the ability to translate multidimensional data into clinical care. Progress in artificial intelligence (AI) opens up a new frontier, with the promise of achieving synergistic and comprehensive integration. The classification of cancer biology and immunobiology into hallmarks of cancer by Hanahan and Weinberg provides a framework for organizing this information. This systematic classification has enabled the understanding of the interplay and cross-talk between its parts. Targeted cancer therapies and immunotherapies have achieved considerable success, yet their combinatorial potential is still being uncovered. Molecular diagnostics has worked hand-in-hand with precision oncology in deploying new therapies in a cancer-informed and patient-specific way. Harnessing the full power of the advances in these three fields with the aid of AI promises a transformation of molecular diagnostics. This review conceptualizes molecular diagnostics in the context of cancer hallmarks using nonsmall cell lung cancer (NSCLC) as a template, highlighting the potential of a new diagnostic science through the integrative power of AI.

Over the last decades, significant improvement in cancer patient outcomes has occurred due to advances in cancer cell biology, systemic and tumour immune microenvironment (TIME) and precision cancer therapy. Despite this explosion of knowledge, its usefulness in clinical practice has been limited by the ability to translate multidimensional data into clinical care. Progress in Artificial Intelligence (AI) opens up a new frontier, with the promise of achieving synergistic and comprehensive integration. The classification of cancer biology into hallmarks of cancer by Hanahan and Weinberg provides a framework for organizing this information. This systematic classification has enabled the understanding of the interplay and crosstalk between its parts. Targeted cancer and immunotherapies targeting various components have achieved considerable success, yet their combinatorial potential is still being uncovered. Molecular Diagnostics has worked hand-in-hand with precision oncology in deploying new therapies in a cancer-informed and patient-specific way. Harnessing the full power of the advances in these three fields with the aid of AI promises a transformation of the field of molecular diagnostics. This review aims to conceptualize molecular diagnostics in the context of Cancer Hallmarks and TIME using Nonsmall Cell Lung Cancer (NSCLC) as a template. We aim to highlight the potential of a new diagnostic science through the integrative power of AI.

Many patients diagnosed with acute myeloid leukemia (AML) relapse within two years of the initial remission. The biology of AML relapse is incompletely understood, although cancer stem-like (CSL) cells have been hypothesized to be important. To test this hypothesis, we employed SORE6, a reporter designed to detect the transcriptional activity of the embryonic stem cell proteins Oct4 and Sox2, to identify/purify CSL cells in two FLT3-mutated AML cell lines. Both cell lines contained ~10% of SORE6⁺ cells in the steady state. Compared to SORE6⁻ cells, SORE6⁺ cells exhibited more characteristics of CSL cells, with significantly higher chemoresistance and rates of spheroid formation. SORE6⁺ cells had substantially higher expression of Myc and FLT3 proteins, which are drivers of SORE6 activity. Using a mixture of SORE6⁻/SORE6⁺ cells that were molecularly barcoded, we generated an in vitro study model for AML relapse. Specifically, after ‘in vitro remission’ induced by Ara-C, both cell lines regenerated after 13 ± 3 days. Barcode analysis revealed that most of the regenerated cells were derived from the original SORE6⁺ cells. Regenerated cells exhibited more CSL features than did the original SORE6⁺ cells, even though a proportion of them lost SORE6 activity. In bone marrow samples from a patient cohort, we found that relapsed blasts expressed significantly higher levels of Myc, a surrogate marker of SORE6 activity, compared to pre-treatment blasts. To conclude, using our in vitro model, we have provided evidence that CSL cells contribute to AML relapse.

RELAY phase 3 randomized study: a step closer to the clinic for ctDNA in non-small cell lung cancer treatment monitoring?

Purpose The risk assessment classification schemes for gastrointestinal stromal tumors (GIST) include tumor site, size, mitotic count and variably tumor rupture. Heterogeneity in high-risk GIST poses limitations for current classification schemes. This study aims to demonstrate the clinical utility of risk stratification by gene expression profiling (GEP) using Nanostring technology. Methods Fifty-six GIST cases were analyzed using a 231 gene expression panel. GEP results were correlated with clinical and pathological data. The prognostic performance was assessed in 34 patients with available survival data using ROC curves, Kaplan–Meier survival curves and compared with traditional risk assessment schemes. Volcano plot analysis identified seven genes with significantly higher expression (FDR < .0.05) in high-risk than in non-high-risk tumors, namely TYMS, CDC2, TOP2A, CCNA2, E2F1, PCNA, and BIRC5. Together, these transcripts exhibited significantly higher expression in high-risk tumors than in intermediate (P < 0.01), low (P < 0.001), and very low (P = 0.01) risk tumors. Receiver-operating characteristic curve analysis demonstrated area under the curve (AUC) to be 0.858 for the separation of high-risk and non-high-risk tumors. Kaplan–Meier survival analysis demonstrated improved risk stratification (log-rank test P < 0.001) compared to the current risk assessment classification (P = 0.231). Conclusion In addition to current clinical and histology-based risk classification for patients with GIST, gene expression may offer complementary prognostic information.

Background: Targeted therapy of patients with non-small cell lung cancer (NSCLC) who harbour sensitising mutations by tyrosine kinase inhibitors (TKIs) has been found more effective than traditional chemotherapies. However, target genes status (eg, epidermal growth factor receptor (EGFR) TKIs sensitising and resistant mutations) need to be tested for choosing appropriate TKIs. This study is to investigate the performance of a liquid biopsy-based targeted capture sequencing assay on the molecular analysis of NSCLC. Methods: Plasma samples from patients with NSCLC who showed resistance to the first/second-generation EGFR TKIs treatment were collected. The AVENIO ctDNA Expanded Kit is a 77 pan-cancer genes detection assay that was used for detecting EGFR TKIs resistance-associated gene mutations. Through comparison of the EGFR gene testing results from the Cobas EGFR Mutation Test v2, and UltraSEEK Lung Panel, the effectiveness of the targeted capture sequencing assay was verified. Results: A total of 24 plasma cell-free DNA (cfDNA) samples were tested by the targeted capture sequencing assay. 33.3% (8/24) cfDNA samples were positive for EGFR exon 20 p.T790M which leads to EGFR dependent TKIs resistance. 8.3% (2/24) and 4.2% (1/24) samples were positive for mesenchymal-epithelial transition gene amplification and B‐Raf proto‐oncogene, serine/threonine kinase exon 15 p.V600E mutations which lead to EGFR independent TKIs resistance. The median value of the p.T790M variant allele fraction and variant copy numbers was 2% and 36.10 copies/mL plasma, respectively. The next-generation sequencing test showed higher than 90% concordance with either MassArray or qPCR-based methods for detecting either EGFR TKIs sensitising or resistance mutations. Conclusion: The targeted capture sequencing test can support comprehensive molecular analysis needed for TKIs treatment, which is promising to be clinically applied for the improved precision treatment of NSCLC.

PurposeThe risk assessment classification schemes for gastrointestinal stromal tumors (GIST) includes tumor site, size, mitotic count and variably tumour rupture. Heterogeneity in high risk GIST poses limitations for current classification schemes. Objective This study aims to demonstrate the clinical utility of risk stratification by gene expression profiling (GEP) using Nanostring technology. MEthodFifty-six GIST cases were analyzed using a 231 gene expression panel. GEP results were correlated with clinical and pathological data. The prognostic performance was assessed in 34 patients with available survival data using ROC curves, Kaplan Meier Survival curves and compared with traditional risk assessment schemes. Results Volcano plot analysis identified seven genes with significantly higher expression (FDR < .05) in high risk than in non-high risk tumors, namely, TYMS, CDC2, TOP2A, CCNA2, E2F1, PCNA, and BIRC5. Together, these transcripts exhibited significantly higher expression in high risk tumors than in intermediate ( P < .001), low ( P < .001), and very low ( P = 0.01) risk tumors. Receiver operating characteristic curve analysis demonstrated area under the curve (AUC) to be 0.861 for the separation of high risk and non-high risk tumors. Kaplan-Meier survival analysis demonstrated improved risk stratification (log-rank test P < .001) compared to the current risk assessment classification ( P = 0.18). Conclusion In addition to current clinical and histology-based risk classification for patients with GIST, gene expression may offer complementary prognostic information.