Abstract 2594
Background
Identifying low risk of disease recurrence in localised ccRCC is key for gatekeeping in the adjuvant therapy enrolment. Uncertainty increases the number of patients required for accrual to achieve statistical power. Current scoring systems are good at identifying very low and very high risk cohorts, but have not been proved to be as effective at accurately predicting disease recurrence in intermediate groups. These patients are perhaps those likely to benefit from intervention in addition to surgery, but many may be treated unnecessarily. Using digital pathology, image analysis and machine learning we sought to stratify for risk in this intermediate category.
Methods
Definiens Tissue studio and Developer XD were utilised for object thresholding to measure tumour cell nuclear morphological features on H&E digitised images from 120 ccRCC training set from UK and 217 ccRCC validation set from Singapore. Multiplexed immunofluorescence (mIF) was performed on 120 cases to co-detect neo-vasculature and pan-T cells. An algorithm was derived to measure spatial relationships between blood vessels and T cells. A statistical model was developed by generalised linear model with spatially adaptive local smoothing algorithm, having specificity prefixed (0.8-1) plus cross validation.
Results
Replacing manual nuclear grade with AI aided tumour cell nuclear morphological features improved the specificity of Leibovich score (LS) from 0.76 to 0.86 and from 0.84 to 0.94 in training and validation sets, respectively. Moreover, tumour microenvironment (TM) parameters significantly improved the specificity up to 0.93 in the training set. The negative predictive values of both LS 5 and 6 were zero, but by applying the algorithm the specificity for LS 5 and 6 cases became 0.93 and 0.40 respectively.
Conclusions
By applying image analysis it is possible to identify lower risk for recurrence patients in a conventionally identified intermediate risk group based on routine ccRCC H&E images, and multiplexed TM features. This approach to pathology should help refine selection of patients for clinical trials and form the basis of future AI-enabled prognostic and predictive algorithms in ccRCC.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
Authors.
Funding
Renal Cancer Research Fund and NHS Lothian.
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
5007 - Functional systemic CD4 immunity is required for clinical responses to PD-L1/PD-1 blockade therapy
Presenter: Miren Zuazo
Session: Poster Display session 3
Resources:
Abstract
5760 - Landscape of PD-L1 expression status in Chinese solid tumor patients.
Presenter: Yi Zhong
Session: Poster Display session 3
Resources:
Abstract
3733 - Anti-cancer and immunomodulatory effects of cobimetinib in triple negative breast cancer
Presenter: Chun-Yu Liu
Session: Poster Display session 3
Resources:
Abstract
4426 - Differential expression of immunoregulatory molecules and highly-associated cancer genes may provide novel insights into strategic trial design for therapeutics
Presenter: Jacob Adashek
Session: Poster Display session 3
Resources:
Abstract
2752 - Insights into the Tumor Immune Microenvironment using Tissue Phenomics to Drive Cancer Immunotherapy
Presenter: Martin Groher
Session: Poster Display session 3
Resources:
Abstract
5713 - Immune competent somatic mosaic model of colorectal cancer
Presenter: Stefania Napolitano
Session: Poster Display session 3
Resources:
Abstract
1898 - Genomic correlates of response to anti-PDL1 Atezolizumab in non-small-cell lung cancer OAK and POPLAR trials
Presenter: Hari Singhal
Session: Poster Display session 3
Resources:
Abstract
3246 - Erdafitinib (erda) versus available therapies in advanced urothelial cancer: A matching adjusted indirect comparison
Presenter: Yohann Loriot
Session: Poster Display session 3
Resources:
Abstract
3311 - High level of activity of Nivolumab anti-PD-1 immunotherapy and favorable outcome in metastatic/refractory MSI-H non-colorectal cancer: Results of the MSI cohort from the French AcSé program
Presenter: Christophe Tournigand
Session: Poster Display session 3
Resources:
Abstract
2314 - TP53 and ATM Co-mutation Predicts Response to Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer
Presenter: Yu Chen
Session: Poster Display session 3
Resources:
Abstract