Search

In the era of precision oncology, identifying abnormal genetic and epigenetic alterations has transformed the way cancer is diagnosed, managed, and treated. 5-hydroxymethylcytosine (5hmC) is an emerging epigenetic modification formed through the oxidation of 5-methylcytosine (5mC) by ten-eleven translocase (TET) enzymes. DNA hydroxymethylation exhibits tissue- and cancer-specific patterns and is essential in DNA demethylation and gene regulation. Recent advancements in 5hmC detection methods and the discovery of 5hmC in cell-free DNA (cfDNA) have highlighted the potential for cell-free 5hmC as a cancer biomarker. This review explores the current and emerging techniques and applications of DNA hydroxymethylation in cancer, particularly in the context of cfDNA. [ABSTRACT FROM AUTHOR]

Introduction: SCLC has traditionally been considered to arise from toxic exposure factors, such as smoking. Recent evidence has revealed that germline mutations may also affect the development of SCLC; however, these alterations remain understudied. We sought to identify novel germline mutations in SCLC including germline copy number variations (CNVs) in our cohort of patients., Methods: We designed a custom hybrid-capture gene panel to evaluate germline alterations in 192 cancer-predisposition and frequently mutated genes in SCLC. We applied this panel to germline analysis of a treatment-naive cohort of 67 patients with SCLC at our institution. Subsequently, we annotated the variants using the American College of Medical Genetics criteria and further classified variants of uncertain significance using a set of in silico tools, including DeepMind AlphaMissense, MutationTaster, SIFT, and Polyphen2., Results: We identified American College of Medical Genetics pathogenic or likely pathogenic alterations in seven of 67 patients. Five (71%) were novel alterations ( BCORL1 , FANCC , ATR , and BBC3 ) and a novel CNV ( SLFN11 ) with two (29%) previously described mutations ( CHEK1 and BRIP1 ). We also identified 191 variants of uncertain significance in 60 of 67 patients, of which, depending on the in silico tool, 5% to 14% were predicted to be pathogenic. Patients with SCLC with the seven pathogenic alterations were observed to have a numerically longer overall survival (hazard ratio = 0.50) and progression-free survival (hazard ratio = 0.45) though not statistically significant compared with the remaining cohort., Conclusions: Our study identifies novel germline alterations, including a CNV, and provides additional evidence that germline factors could be important contributing factors to the development of SCLC., Competing Interests: Dr. Bratman is an inventor on patents related to cell-free DNA mutation and methylation analysis technologies that have been licensed to Roche and AdelaBio, respectively, and is co-founder of, has ownership in, and serves in a leadership role at AdelaBio. Dr. Liu reports receiving grants and personal fees from AstraZeneca and Takeda; grants from Boehringer Ingelheim; and personal fees from Hoffman-La Roche, Merck, Bristol-Myers Squibb, and Pfizer outside of the submitted work. Dr. Lok reports receiving grants from 10.13039/100004319Pfizer and grants, personal fees, and nonfinancial support from 10.13039/100004325AstraZeneca outside the submitted work. The remaining authors declare no conflict of interest., (© 2024 The Authors.)

Small-cell lung cancer (SCLC) methylome is understudied. Here, we comprehensively profile SCLC using cell-free methylated DNA immunoprecipitation followed by sequencing (cfMeDIP-seq). Cell-free DNA (cfDNA) from plasma of 74 patients with SCLC pre-treatment and from 20 non-cancer participants, genomic DNA (gDNA) from peripheral blood leukocytes from the same 74 patients, and 7 accompanying circulating tumor cell-derived xenografts (CDXs) underwent cfMeDIP-seq. Peripheral blood leukocyte methylation (PRIME) subtraction to improve tumor specificity. SCLC cfDNA methylation is distinct from non-cancer but correlates with CDX tumor methylation. PRIME and k-means consensus identified two methylome clusters with prognostic associations that related to axon guidance, neuroactive ligand-receptor interaction, pluripotency of stem cells, and differentially methylated at long noncoding RNA and other repeats features. We comprehensively profiled the SCLC methylome in a large patient cohort and identified methylome clusters with prognostic associations. Our work demonstrates the potential of liquid biopsies in examining SCLC biology encoded in the methylome., Competing Interests: S.S. reports grants from AstraZeneca, BMS, Janssen, Von Tobel foundation, Fill the Gap, and has served on all institutional advisory board for BMS, AstraZeneca, MSD. N.M. reports personal fees from Takeda Oncology, Pfizer, and Novartis, outside of the submitted work. M.-S.T. reports research grant from Bayer and AstraZeneca, outside the submitted work, and personal fees from AstraZeneca, Amgen, BMS, Daiichi-Sankyo and Lilly, outside the submitted work. D. de Carvalho received research funds from Pfizer and is co-founder and shareholder of Adela Inc. S.V.B. is inventor on patents related to cell-free DNA mutation and methylation analysis technologies that have been licensed to Roche and Adela, respectively, and is co-founder of, has ownership in, and serves in a leadership role at Adela. G.L. reports grants and personal fees from AstraZeneca and Takeda; grants from Boehringer Ingelheim; and personal fees from Hoffman La Roche, Merck, Bristol Myers Squibb, and Pfizer outside the submitted work. B.H.L. reports grants from Pfizer; and grants, personal fees, and non-financial support from AstraZeneca outside the submitted work., (© 2022 The Author(s).)

Background: Testing for tumor programmed death ligand-1 (PD-L1) expression was initially developed with histology specimens in non-small cell lung cancer (NSCLC). However, cytology specimens are widely used for primary diagnosis and biomarker studies in clinical practice. Limited clinical data exist on the predictiveness of cytology-derived PD-L1 scores for response to immune checkpoint inhibitor (ICI) therapy., Methods: We reviewed all NSCLC specimens clinically tested at the University Health Network (UHN) for PD-L1 with 22C3pharmDx, from 01/2013 to 04/2021. Treatment outcomes in patients treated with single agent ICI therapy were reviewed and compared according to cytology- and histology-derived PD-L1 scores., Results: We identified 494 and 1942 unique patients with cytology- and histology-derived tumor proportion scores, respectively, during the study period. Informative testing rates were 95 % vs 98 % for cytology and histology, respectively. Clinical data were available for 152 patients treated with single agent ICI: 61 cytology and 91 histology. Overall response rates (ORR) were similar for cytology and histology (36 % vs 34 %; p = 0.23), as well as median progression free survival (PFS) (4.9 vs 4.2 months; p = 0.99) and overall survival (23.4 vs 19.7 months; p = 0.99). The results remained similar even after adjusting for PD-L1 expression levels and line of ICI treatment (PFS HR 1.15; 95 %CI 0.78-1.70; p = 0.47)., Conclusions: Treatment outcomes to single agent ICI based on cytology-derived PD-L1 scores were comparable to histology controls. Our results support PD-L1 biomarker testing on both cytology and histology specimens., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Introduction: Real-world evidence is important in regulatory and funding decisions. Manual data extraction from electronic health records (EHRs) is time-consuming and challenging to maintain. Automated extraction using natural language processing (NLP) and artificial intelligence may facilitate this process. Whereas NLP offers a faster solution than manual methods of extraction, the validity of extracted data remains in question. The current study compared manual and automated data extraction from the EHR of patients with advanced lung cancer., Methods: Previously, we extracted EHRs from 1209 patients diagnosed with advanced lung cancer (stage IIIB or IV) between January 2015 and December 2017 at Princess Margaret Cancer Centre (Toronto, Canada) using the commercially available artificial intelligence engine, DARWEN (Pentavere, Ontario, Canada). For comparison, 100 of 333 patients that received systemic therapy were randomly selected and clinical data manually extracted by two trained abstractors using the same accepted gold standard feature definitions, including patient, disease characteristics, and treatment data. All cases were re-reviewed by an expert adjudicator. Accuracy and concordance between automated and manual methods are reported., Results: Automated extraction required considerably less time (<1 day) than manual extraction (∼225 person-hr). The collection of demographic data (age, sex, diagnosis) was highly accurate and concordant with both methods (96%-100%). Accuracy (for either extraction approach) and concordance were lower for unstructured data elements in EHR, such as performance status, date of diagnosis, and smoking status (NLP accuracy: 88%-94%; Manual accuracy: 78%-94%; concordance: 71%-82%). Concurrent medications (86%-100%) and comorbid conditions (96%-100%), were reported with high accuracy and concordance. Treatment details were also accurately captured with both methods (84%-100%) and highly concordant (83%-99%). Detection of whether biomarker testing was performed was highly accurate and concordant (96%-98%), although detection of biomarker test results was more variable (accuracy 84%-100%, concordance 84%-99%). Features with syntactic or semantic variation requiring clinical interpretation were extracted with slightly lower accuracy by both NLP and manual review. For example, metastatic sites were more accurately identified through NLP extraction (NLP: 88%-99%; manual: 71%-100%; concordance: 70%-99%) with the exception of lung and lymph node metastases (NLP: 66%-71%; manual: 87%-92%; concordance: 58%) owing to analogous terms used in radiology reports not being included in the accepted gold standard definition., Conclusions: Automated data abstraction from EHR is highly accurate and faster than manual abstraction. Key challenges include poorly structured EHR and the use of analogous terms beyond the accepted gold standard definition. The application of NLP can facilitate real-world evidence studies at a greater scale than could be achieved with manual data extraction., (© 2022 The Authors.)

Introduction: In addition to the higher prevalence of EGFR mutations found among lung cancer cases in East Asian patients, it is unclear whether there are differences in treatment outcomes by ethnicity-that is, East Asian versus non-East Asian., Methods: Patients diagnosed with EGFR-mutant lung cancer between January 2004 and October 2014 at a single center were reviewed. Data captured included demographics, tumor and treatment information, and survival. Survival of patients of East Asian and non-East Asian ancestry was compared, including in the subgroup that received EGFR tyrosine kinase inhibitor (TKI) for advanced disease and in those with early-stage disease that underwent surgical resection., Results: A total of 348 patients with EGFR-mutant NSCLC were identified. There was a higher proportion of nonsmokers among those of East Asian ethnicity. No significant difference in survival was seen between patients of East Asian and non-East Asian ethnicity, median 6.7 years (95% confidence interval [CI]: 5.4-not applicable) and 5.4 years (95% CI: 4.1-7.2), respectively ( p  = 0.09). Among 196 patients that received treatment with EGFR TKI, the median survival from TKI initiation was also similar for those of East Asian and non-East Asian ethnicity, 3.0 years (95% CI: 2.1-3.5) and 2.7 years (95% CI: 2.2-3.5), respectively. Among the early-stage patients that underwent surgical resection (n = 163), those of East Asian ethnicity had similar median recurrence-free survival from surgery compared with non-East Asian patients, 5.3 years (95% CI: 3.5-not applicable) and 5.1 years (95% CI: 3.3-7.2), respectively., Conclusions: In a cohort of patients with EGFR-mutant lung cancer with access to uniform standards of care, East Asian ethnicity was not associated with improved survival after treatment with EGFR TKI or surgical resection., (© 2021 The Authors.)

Background: Tobacco exposure contributes to over 80 % of lung cancer cases. Smoking is associated with programmed death-ligand 1 (PD-L1) tumor expression and better outcomes from anti-programmed cell death protein 1 (anti-PD-1) therapy in patients with advanced non-small cell lung cancer (NSCLC). PD-L1 tumor expression is now routinely used to predict benefit from anti-PD-1 therapy in patients with advanced NSCLC. In this study, we explored the impact of smoking status on patient outcomes with anti-PD-1 therapy in addition to PD-L1 tumor expression., Methods: A prospective real-world cohort of 268 patients with advanced NSCLC treated with anti-PD-1 monotherapy at the Princess Margaret Cancer Centre (PMCC) was used for this analysis. Logistic regression was performed to test factors associated with treatment response (RECIST v1.1), including PD-L1 tumour proportion score (TPS) and smoking status., Results: Overall response rates (ORR) to immunotherapy were significantly higher in current and former smokers than never smokers (36 % vs 26 % vs 14 %; p = 0.02). In patients with PD-L1 tumour proportion score (TPS) ≥50 %, current smokers continued to experience better ORR to anti-PD-1 therapy than never smokers (58 % vs 19 %; p = 0.03). Current smoking was associated with higher response even after adjusting for level of PD-L1 TPS expression (adjusted odds ratio 5.9, 95 % CI 1.6-25.0, p = 0.03). Exploratory analysis demonstrated higher 1-year survival rates in smokers compared to never smokers (p = 0.003)., Conclusions: Smoking remains an important factor associated with response to anti-PD-1 monotherapy. Advanced NSCLC patients with positive PD-L1 expression are more likely to respond to anti-PD-1 monotherapy if they are current smokers compared to never smokers., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Osimertinib is an irreversible EGFR-tyrosine kinase inhibitor initially approved for treatment of EGFR-positive patients exhibiting a T790 M resistance mutation in the second line setting and now emerging as the new standard of care for all EGFR positive patients as first-line treatment. Despite its efficacy, resistance to osimertinib inevitably develops and mechanisms of resistance can be grouped broadly in two categories: on-target EGFR-dependent and off-target EGFR-independent mechanisms. EGFR-dependent resistance typically is associated with additional EGFR-mutations disrupting the osimertinib binding through changes in the binding site by allosteric/ conformational transitions; EGFR-independent mechanisms are related mostly to alternate pathway activation or aberrant downstream signalling but also to lineage plasticity leading to small cell transformation. MET amplification is the most frequent off-target mechanisms of resistance to osimertinib treatment and recently published early trials show promising results for combination of MET-inhibitors with osimertinib upon development of resistance. This review will summarize mechanisms of resistance overall and in different treatment settings and will focus on potential new treatment options targeting specific acquired alterations after osimertinib failure., (Copyright © 2020. Published by Elsevier B.V.)