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John Le Quesne



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    MA09 - Lung Cancer Surgical and Molecular Pathology (ID 908)

    • Event: WCLC 2018
    • Type: Mini Oral Abstract Session
    • Track: Pathology
    • Presentations: 2
    • Moderators:
    • Coordinates: 9/24/2018, 15:15 - 16:45, Room 202 BD
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      MA09.03 - Multiple Pathological Variables Predict Efficacy of Adjuvant Chemotherapy in Primary Lung Adenocarcinoma (ID 13761)

      15:25 - 15:30  |  Presenting Author(s): John Le Quesne

      • Abstract
      • Presentation
      • Slides

      Background

      Adjuvant chemotherapy has become established as a vital complement to surgery over the last decade, and improves survival by targeting micrometastatic disease which is clinically inaparrent at the time of surgery. However, in comparison to other common malignancies, the guidelines for the administration of adjuvant chemotherapy in lung cancer are rudimentary, being based solely upon clinical stage II and above at the time of surgery. We set out to discover pathological factors with the potential to better identify patients who are likely to benefit from this vital therapy.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      662 cases of primary lung adenocarcinoma treated with surgery with curative intent were identified from 2005-2014; 109 received adjuvant chemotherapy. Comprehensive survival/recurrence data, pathological data, and treatment history data were collected. Detailed histopathological data (growth pattern, vascular invasion, pleural stage) were collected by review of scanned histopathological images.

      Multivariate Cox regression survival models were used to identify interactions between clinicopathological variables and adjuvant chemotherapy. A propensity score matching approasch was used to reduce selection biases in the data.

      4c3880bb027f159e801041b1021e88e8 Result

      The existing stage criteria for the recommendation of adjuvant chemotherapy are stage pN1/2 and size>40mm; only nodal invasion interacts with chemotherapy in an OS model (interaction term HR=0.67 P=0.017). However, signficant interactions are seen with predominant growth pattern (HR=0.47 P=0.001 ), pleural stage (HR=0.62 P=0.002 ), and vascular invasion (HR=0.56 P=0.033).

      We reduced selection bias by balancing treated and untreated groups by propensity matching for all prognostic variables. In the matched dataset, patients with predominantly in situ tumours experience no benefit of chemotherapy (HR=1.81 P=0.18), while higher-grade cases show substantial benefit (HR=0.53 P=0.01). Similar benefits were seen for patients with increasing pleural stage and vascular invasion.

      In a multivariate model designed to identify which variable(s) had the most ability to predict treatment efficacy, only tumour growth pattern showed a significant interaction with chemotherapy treatment (HR=0.51 P=0.01 ).

      8eea62084ca7e541d918e823422bd82e Conclusion

      We find that the existing stage-based criteria for adjuvant chemotherapy can be much improved. Low-grade cases experienced only negative effects of chemotherapy, while higher-grade cases showed a benefit. Pleural stage and vascular invasion were also significantly predictive. We suggest that the current criteria may be leading to substantial over- and under-treatment. A nuanced algorithm for the identification of patients likely to benefit from chemotherapy, which includes these additional pathoogical measures, may significantly improve patient outcomes. This would be especially impactful to the majority of surgical patients for whom no personalised therapy is as yet available.

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      MA09.05 - Can We Predict Radiosensitivity in Non-Small Cell Lung Cancer? (ID 13835)

      15:45 - 15:50  |  Author(s): John Le Quesne

      • Abstract
      • Presentation
      • Slides

      Background

      Patients with lung cancer receive different treatments depending on their detailed clinical-pathological context. However, over 70% of patients are treated with radiotherapy, which is of varying efficacy. Rather surprisingly, no biomarkers are currently used to predict tumour response and to aid with radiotherapy dosing or regimen. The aim of this study is to identify histopathological features which may predict tumour radiosensitivity in patients with NSCLC.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      We have identified a set of 67 NSCLC cases with a history of radiotherapy for which pre-treatment archival tissue and CT imaging follow-up is available from the period 2009 to 2014. Digital images of archival diagnostic tissue sections were examined to derive morphological measures with the potential to predict radiosensitivity. Quantitative radiological measures of response up to 6 months after radiotherapy were derived. Since radiographic measurements were taken at variable time-points, we standardised by inferring the fractional maximum diameter of the tumour 100 days after radiotherapy (FRT100)

      4c3880bb027f159e801041b1021e88e8 Result

      The density of multipolar mitoses seen microscopically is related to radiosensitivity (regression against FRT100: R2 = 0.14, p=0.005*) and a trend toward a negative relationship with neuroendocrine differentiation (R2 =0.06, p=0.058). The presence of multipolar mitoses was further associated with poor overall survival ( Univariate Cox p= 0.02*). Patients with radiological evidence of good response (ie low FRT100) showed a time-dependent survival benefit (p=0.02*), while after 2 years tendency of both groups was similar. Patients showing squamous differentiation had a poor prognosis, with no overall survival after 4 years, while 21.8% of the ACA were still alive after 4 years (p= 0.04*)

      8eea62084ca7e541d918e823422bd82e Conclusion

      Multipolar mitoses and neuroendocrine differentiation may be predictive histological markers of radiosensitivity in NSCLC. More samples are being gathered, and immhunohistochemical and DNA sequence biomarkers of radiosensitvity are currently being assessed.

      6f8b794f3246b0c1e1780bb4d4d5dc53

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    P1.09 - Pathology (Not CME Accredited Session) (ID 941)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/24/2018, 16:45 - 18:00, Exhibit Hall
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      P1.09-29 - In Situ Growth Pattern in Lung Adenocarcinoma Is Divisible into Distinct Categories with Divergent Biological and Survival Implications (ID 13872)

      16:45 - 18:00  |  Presenting Author(s): John Le Quesne

      • Abstract

      Background

      The morphological stepwise progression of lung adenocarcinomas is well established, but little is known about the molecular events that underlie this. In particular, in situ patterns of growth are frequently seen in adenocarcinomas, and often it is not clear if this truly always represents early-stage preinvasive disease. Therefore we set out to better characterise in situ tumour growth, and to identify molecular and biological correlates of tumour invasion.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      We constructed a retrospective cohort of 964 locally held adenocarcinomas, with patient data and tissue microarrays (TMAs). Immunohistochemistry for Ki67 and epithelial-mesenchymal transition markers was applied to TMAs and quantified.

      In situ and adjacent invasive areas of 23 early tumours were subjected to further in situ assays and laser capture microdissection. Genomic DNA was extracted and driver genes were panel sequenced.

      4c3880bb027f159e801041b1021e88e8 Result

      We morphologically identified two distinct types of in situ tumour growth in early mixed pattern tumours: a low-grade precursor ('C1'- after the classic Noguchi classification) associated with higher-grade invasive disease, and a high-grade lepidic outgrowth ('C2') associated with invasive growth of similar cytological grade.

      C1 and C2-type in situ tumour growth are sharply separated by their proliferation rate (P=0.005) and their propensity for nodal metastasis (P=0.03), suggesting that this distinction is likely to be important in future grading/growth pattern classification. Furthermore, molecular analysis supports the classification; invasive areas of C1 tumours show driver mutations which are absent from neighbouring in situ disease (4/18 cases), indicating molecular progression, while in 5 sequenced C2 cases no evidence of molecular progression was seen.

      The difference between low-grade precursor and high-grade in situ patterns was further investigated in our full set of 964 tumours. We find that the prognostic power of proliferation rate (Ki67) is driven almost entirely by its effects in in situ areas of tumour growth. Proliferation rate in invasive tumour areas at most weakly predictive of patient outcome.

      8eea62084ca7e541d918e823422bd82e Conclusion

      We make several key findings:

      i) In situ disease in lung adenocaricnoma is divided into two biologically and prognostically distinct groups, with implications for our understanding of stepwise progression in lung cancer

      ii) These two groups can easily be separated on the basis of cellular proliferation rate

      ii) We identify mutations in key driver genes that are explicitly asociated with the transition from in situ to invasive growth

      iii) Proliferation rate is a potentially valuable prognostic marker, but this may be restricted to its ability to separate these two key biologically distinct growth patterns

      6f8b794f3246b0c1e1780bb4d4d5dc53