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Katie Baker
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MA19 - Looking at PROs in Greater Detail - What Patients Actually Want and Expect (ID 147)
- Event: WCLC 2019
- Type: Mini Oral Session
- Track: Treatment in the Real World - Support, Survivorship, Systems Research
- Presentations: 1
- Now Available
- Moderators:Joaquim Bosch-Barrera, Virginia Calvo De Juan
- Coordinates: 9/10/2019, 11:30 - 13:00, Interlaken (1988)
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MA19.09 - Assessing Clinical Frailty in Advanced Lung Cancer Patients - An Opportunity to Improve Patient Outcomes? (Now Available) (ID 2363)
11:30 - 13:00 | Author(s): Katie Baker
- Abstract
- Presentation
Background
The median age of non-small cell lung cancer (NSCLC) diagnosis in England is 73 years. At that age, 40% of the general population has some degree of clinical frailty which may impact survival, quality of life, anti-cancer treatment tolerability and access to clinical trials. However, clinical frailty is often not addressed or managed at the time of anti-cancer treatments. This project was designed to integrate frailty assessments and build frailty pathways within an advanced cancer care setting in order to better support patients and improve outcomes.
Method
This quality improvement project that used Plan-Do-Study-Act (PDSA) methodology. Phase one of the project focused on establishing a multidisciplinary team to integrate a frailty screening tool, the Rockwood Clinical Frailty Scale (CFS), into standard clinical practice. The primary aim was to implement and screen ≥80% of all new lung cancer patients at a high-volume tertiary cancer centre. The secondary aim was to explore the correlation of CFS with age, performance status (PS), treatment selection and systemic anti-cancer treatment (SACT) tolerability. Specialised training was provided to the clinical team and the CFS was integrated from 26/11/2018 on an electronic form routinely completed by clinicians. A digital dashboard was set-up to monitor real-time data and the frail group was defined as CFS score >3. Data cut-off for this analysis was 29-03-2019.
Result
335 lung cancer patients were screened using CSF by a team of 20 clinicians with a compliance rate of 89%. There was a strong correlation between PS and CFS (r= 0.77, p<0.01). The distribution of both CFS and PS correlated with ageing (r= 0.2 and r= 0.17, respectively; p<0.01). Patients ≥70 years were more likely to be frail (56% vs 40%; OR 1.4, 95%CI 1.2-1.7; p<0.01). Frailty reduced the likelihood of receiving any anti-cancer treatment by 20%. Amongst those who started SACT, patients classed as frail were less likely to go beyond the first cycle of treatment (64% vs 91%; OR 0.7, 95%CI 0.5-0.9; p<0.01).
Conclusion
CFS screening is feasible within a busy clinical practice when incorporated as a digital tool. CFS helps to identify patients who may potentially benefit from specialised frailty assessment and management. This could ultimately be used to better inform on treatment selection, and support requirements during treatment, to improve outcomes for patients in the future.
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MA25 - Precision Medicine in Advanced NSCLC (ID 352)
- Event: WCLC 2019
- Type: Mini Oral Session
- Track: Advanced NSCLC
- Presentations: 1
- Now Available
- Moderators:Sebastian Defranchi, Karen Kelly
- Coordinates: 9/10/2019, 14:30 - 16:00, Dublin (1997)
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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 | Author(s): Katie Baker
- Abstract
- Presentation
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.
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