In 2016 a total of 224,390 patients in the US were diagnosed with non-small cell lung cancer (NSCLC) and 16% of these patients (35,902) were diagnosed as early stage (I and II) and eligible for adjuvant cytotoxic chemotherapy (adj chemo). However, more than 50% of these patients may have low risk disease and hence may not receive added benefit from adj chemo, while suffering its side-effects. From an economic standpoint, unnecessary adj chemo for early stage NSCLC results in a loss of over $35,000 for each quality- adjusted life year lost. With increased lung cancer screening, we can expect an increase in diagnosis of early stage NSCLC. Two large completed randomized clinical trials of NSCLC (International Adjuvant Lung Cancer Trial (IALT) and JBR10) involving surgery with and without adj chemo, only found survival benefit in higher stage patients (>=Stage III). Unfortunately there are currently no validated predictive companion diagnostic (CDx) tools to identify (1) which stage II NSCLC are at a lower risk for disease recurrence and hence will not receive additional benefit from adj chemo and (2) which stage 1A, 1B patients are at elevated risk and hence will benefit? Extant genomic assays have only been shown to be prognostic (i.e. they predict mortality or recurrence) in early stage NSCLC, 1?5, but this does not imply they are predictive (i.e. they do not predict treatment response). Recently, our group validated the computerized histologic risk predictor (CHiRP), an approach that relies solely on computer extracted morphologic measurements (e.g. cellular orientation, texture, shape, architecture) from standard H&E tissue slide images to predict early recurrence in early stage NSCLC. CHiRP has been shown to be prognostic with an accuracy>85% in three independent clinical cohorts (N=290); higher compared to what has been previously reported for molecular based prognostic tests. However, to show that CHiRP is predictive, we need access to randomized clinical trial data involving early stage NSCLC patients treated with surgery and surgery+ adj chemo. The only two trials that fit these criteria are IALT and JBR10. Since molecular tests are tissue destructive, validation is more difficult compared to a tissue non-destructive approach like CHiRP; clinical trial groups are often reluctant to share tissue blocks since it is a valuable resource. For this study we have obtained preliminary approval for use of the slide images from IALT and JBR10 to establish CHiRP as a predictive Affordable Precision Medicine (APM) solution. This Academic-Industry partnership will leverage long-standing collaborations between (1) the Madabhushi group at Case Western who bring expertise in computational histomorphometric imaging, (2) the Velcheti group at the Cleveland Clinic (CCF) with clinical expertise in treatment and management of early stage NSCLC, and (3) Inspirata Inc., a cancer diagnostics company which has recently licensed a number of histomorphometry based technologies from the Madabhushi group and who will bring quality management systems and production software standards to help create a pre-commercial CHiRP test.
Even though many early stage non-small cell lung cancer (NSCLC) patients will be treated with chemotherapy following surgical resection of the tumor, the vast majority of these early stage NSCLC patients will not receive additional benefit from this adjuvant chemotherapy, thus unnecessarily suffering the deleterious effects of treatment. We aim to develop and validate the first predictive companion diagnostic assay - computerized histologic risk predictor (CHiRP) - to identify which early stage NSCLC patients, following surgery, will receive additional benefit from adjuvant chemotherapy.
|Corredor, Germán; Wang, Xiangxue; Zhou, Yu et al. (2018) Spatial architecture and arrangement of tumor-infiltrating lymphocytes for predicting likelihood of recurrence in early-stage non-small cell lung cancer. Clin Cancer Res :|
|Beig, Niha; Khorrami, Mohammadhadi; Alilou, Mehdi et al. (2018) Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas. Radiology :180910|
|Orooji, Mahdi; Alilou, Mehdi; Rakshit, Sagar et al. (2018) Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography. J Med Imaging (Bellingham) 5:024501|
|Cruz-Roa, Angel; Gilmore, Hannah; Basavanhally, Ajay et al. (2018) High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection. PLoS One 13:e0196828|
|Shiradkar, Rakesh; Ghose, Soumya; Jambor, Ivan et al. (2018) Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings. J Magn Reson Imaging 48:1626-1636|
|Nirschl, Jeffrey J; Janowczyk, Andrew; Peyster, Eliot G et al. (2018) A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue. PLoS One 13:e0192726|
|Peyster, Eliot G; Madabhushi, Anant; Margulies, Kenneth B (2018) Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection. Transplantation 102:1230-1239|
|Penzias, Gregory; Singanamalli, Asha; Elliott, Robin et al. (2018) Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings. PLoS One 13:e0200730|
|Whitney, Jon; Corredor, German; Janowczyk, Andrew et al. (2018) Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer. BMC Cancer 18:610|
|Carleton, Neil M; Lee, George; Madabhushi, Anant et al. (2018) Advances in the computational and molecular understanding of the prostate cancer cell nucleus. J Cell Biochem 119:7127-7142|
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