Virtual Library

Start Your Search

Colin Lindsay



Author of

  • +

    MA25 - Precision Medicine in Advanced NSCLC (ID 352)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Advanced NSCLC
    • Presentations: 1
    • Now Available
    • +

      MA25.08 - Characterisation of Tumor Aetiology Using Mutational Signatures from the Non-Small Cell Lung Cancer Genome (Now Available) (ID 2667)

      14:30 - 16:00  |  Presenting Author(s): Colin Lindsay

      • Abstract
      • Presentation
      • Slides

      Background

      Somatic genome and exome analyses in cancer are currently dominated by a search for actionable mutations that inform new treatments for stage IV patients. Tumour mutational signatures, originally described by the Sanger centre, offer the potential to understand cancer cure and prevention strategies by using the genome/exome to define aetiological contributions to cancer from both environmental and endogenous sources.

      Method

      132 NSCLC samples were resected from 131 Greater Manchester patients and submitted to the UK 100,000 Genomes Project (Genomics England). A 5×5×5 mm fresh tumour sample was taken from the surgical specimen and stored at -80°C before undergoing genomic testing. To determine the neoplastic cell count, an additional tumour biopsy was taken for routine histological assessment. Germline DNA for comparable whole genome analysis was extracted from peripheral blood lymphocytes from a paired whole blood sample.Whole genome sequencing (WGS) was performed on tumor specimens and matched blood samples. Through the 100,000 Genomes Project pipeline, coverage was calculated from high-quality, non-overlapping bases present on well-mapped reads, as defined by SAMtools v1.1. Whole genome sequencing analysis was undertaken with the Illumina North Star pipeline v2.6.53.23. Data were then mined for tumour mutational burden (TMB) and mutational signature profiles. Signatures were extracted if they accounted for >5% of the mutations per sample. Clinical characteristics including tumor size, nodal status and stage were documented. Mann-Whitney and Fisher’s exact tests were used for statistical comparisons.

      Result

      Signature 8 (unknown aetiology) was the most prevalent mutational process overall (122/132 samples, 92.4%), while smoking signature 4 was the main mutational process in 86/131 (65.6%) of NSCLC cases. SIgnature 4 contributed as a principal or secondary mutational process to a total of 105/131 (80.2%) cancers; 104/105 (99%) of these patients were annotated as smokers or ex-smokers. Signature 5 (unknown aetiology) was the second most common driving signature (24/131, 18.3% cancers), contributing to an additional 19 cancers as a secondary mutational process (43/131, 32.8% of cancers overall). Median number of signatures contributing to signature 4 NSCLC was four, whilst non-smoking mediated NSCLC had contributions from a median of 5.5 mutational signatures (range 2-8). A median of four signatures contributed to both adenocarcinomas and squamous cancers, with 61/88 (69.3%) adenocarcinomas and 25/41 (61%) squamous cancers associated with signature 4 as their main mutational process. More results will follow on duration of signature 4 persistence following discontinuation of smoking, as well as prevalence of each signature according to common molecular subtypes of NSCLC.

      Conclusion

      Tumor mutational signatures have the potential to inform cancer prevention by offering a new level of genetic detail that reflects environmental and endogenous carcinogenesis. As expected, signature 4 offers the main contribution to NSCLC although a number of other aetiological factors are involved in its carcinogenesis. In particular, signatures 5 and 8, both currently of unknown aetiology, significantly contribute to the NSCLC genome. Along with that reported by the Sanger centre, this work lays the foundations for characterisation and identification of new carcinogens.

      Only Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login, select "Add to Cart" and proceed to checkout. If you would like to become a member of IASLC, please click here.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.

  • +

    P1.04 - Immuno-oncology (ID 164)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Immuno-oncology
    • Presentations: 1
    • Now Available
    • Moderators:
    • Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
    • +

      P1.04-44 - Radiomics for Predicting Response to First-Line Anti-PD1 Therapy in Advanced NSCLC (Now Available) (ID 2172)

      09:45 - 18:00  |  Author(s): Colin Lindsay

      • Abstract
      • Slides

      Background

      Radiomics is the high-throughput extraction of quantitative imaging features from medical images that can reflect underlying tumour pathophysiology. Imaging biomarkers have the potential to improve disease characterisation and predict patient outcomes. In this study, the utility of radiomic features to predict response and survival to first-line immune check-point inhibition with pembrolizumab in advanced non-small cell lung cancer (NSCLC) was explored.

      Method

      Patients with Stage IIIB/IV NSCLC treated with first-line pembrolizumab and PD-L1 ≥50% were retrospectively identified and stratified by Best Overall Response (BOR) by RECIST 1.1. Patients with the primary tumour in situ and a contrast-enhanced CT thorax/abdomen (minimum 5mm CT slice thickness) at baseline were included. The single largest thoracic lesion was segmented in the diagnostic image using the Pinnacle radiotherapy treatment planning system. All tumour delineations were supervised by a highly experienced certified senior radiologist. Lesions <1cm, inflammatory and indeterminate lesions were excluded from delineation. A total of 47 radiomic features including shape, first-order and texture features were extracted from the segmented tumour using PyRadiomics. No pre-processing of the images was performed. Highly correlated features (r>0.85) were removed from further analysis. Least Absolute Shrinkage and Selection Operator (LASSO) feature selection was performed to find informative features that could predict either best overall response or overall survival. Univariate logistic regression and cox proportional hazard models were then used for an initial assessment of the potential of these features in predicting response and survival respectively.

      Result

      Sixteen patients with evaluable best overall response (partial response n=9, progressive disease n=7) were selected for the initial discovery-cohort. Mean age was 68 years with 63% adenocarcinoma histology. From the 47 features extracted, 32 were highly correlated to each other and were removed from further analysis. For predicting best overall response, LASSO selected 5 features with univariate logistic regression suggesting that tumour surface area to volume ratio might be informative (p=0.057, AUC of 0.83 (95% CI 0.61-1.0)). With respect to overall survival, LASSO selected 3 features with univariate cox regression suggesting the first-order feature skewness might be predictive (HR = 0.27, 95% CI 0.08-0.88, p=0.03). When split on the median skewness value the Kaplan-Meier plot showed a significant survival difference between high and low risk patients (p=0.007).

      Conclusion

      Radiomic features extracted from baseline contrast-enhanced CT scans may have the potential to predict response and survival in patients treated with first-line pembrolizumab in advanced NSCLC. We emphasize the exploratory nature of these results given the very limited number of patients in the study. We are expanding this discovery cohort to further investigate and validate these results. Updated results will be presented at the meeting.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.