SOPHiA GENETICS is excited to be a part of the American Society of Clinical Oncology 2022 annual meeting, held in person and virtually in Chicago, IL from June 3rd-7th.
You will have the chance to chat with our experts at our booth #19153 and schedule a demo of the universal SOPHiA DDM™ platform. Stay tuned for more information on our activities at ASCO 2022.
Unlocking the Promise of Data-Driven Medicine in Cancer Care, Together
Translating complex multimodal data into actional insights
SOPHiA GENETICS & GE Healthcare Innovation Symposium
Monday, June 6th
6:30-8:00pm CDT
Hyatt Regency McCormick Place
An overview of the four accepted SOPHiA GENETICS abstracts at ASCO 2022 are included below. The full abstracts will be published in the Meeting Proceedings, an online supplement of the Journal of Clinical Oncology.
Individualized prediction of post-surgical pathologic T3a (pT3a) upstaging risk in localized renal tumors undergoing nephrectomy (UroCCR 15 study) (Abstract # 4547, Poster # 38)
Overview: UroCCR is a French national network of 37 multidisciplinary teams for kidney cancer management that collects longitudinal data on the routine clinical care of its patients. For the study, a retrospective cohort of 4,395 cases of clinical T1-T2 kidney tumors was analyzed. The study suggests that machine learning applied to pre-surgical multimodal data can predict the risk of pT3a upstaging of a localized kidney tumor and inform long-term outcomes at the individual patient level. The results have been validated on an external cohort of 1,759 patients with data from the clinical routine.
This abstract has been accepted for poster presentation in the “Genitourinary Cancer-Kidney and Bladder” session on June 4, 2022, 13:15-16:15 CDT.
Multimodal machine learning model prediction of complete pathological response to neoadjuvant chemotherapy in triple-negative breast cancer (Abstract # 601, Poster # 372)
Overview: Triple negative breast cancer (TNBC) is a biologically and clinically heterogenous disease, associated with poorer outcomes when compared with other subtypes of breast cancer. A retrospective cohort of 57 patients with early-stage TNBC treated with neoadjuvant chemotherapy was analyzed. The study suggests that machine learning applied to baseline multi-modal data can help predict pCR status after neoadjuvant chemotherapy for TNBC at the individual patient level, as well as stratify patients to inform long-term outcomes. Patients that would be predicted as non-pCR could benefit from concomitant treatment with immunotherapy, or dose intensification.
This abstract has been accepted for poster presentation in the “Breast Cancer-Local/Regional/Adjuvant” session on June 6, 2022, 08:00-11:00 CDT.
Multimodal prediction of response to neoadjuvant nivolumab and chemotherapy for surgically resectable stage IIIA non-small cell lung cancer (Abstract # 8542, Poster # 169)
Overview: The NADIM trial (NCT03081689), led by the Spanish Lung Cancer Group, assessed the antitumor activity and safety of neoadjuvant chemoimmunotherapy for resectable stage IIIA NSCLC. This study is, to our knowledge, the first to offer a multimodal analysis of the response to neoadjuvant treatment for surgically resectable stage IIIANSCLC and is a proof of concept that a machine learning algorithm can be used to predict the pCR in this context. These preliminary results are being confirmed in the ongoing NADIM II trial (NCT03838159).
This abstract has been accepted for poster presentation in the “Lung Cancer-Non-Small Cell Local-Regional/Small Cell/Other Thoracic Cancers” session on June 6, 2022, 08:00-11:00 CDT.
Multimodal machine learning model prediction of “individual” response to immunotherapy in 1L stage IV NSCLC (Abstract # e21151)
Overview: Immunotherapy (IO) is the standard of care in 1L stage IV non-small cell lung cancer (NSCLC) cases that are not eligible for targeted therapies. A retrospective 1-year cohort of 63 patients with advanced NSCLC, PD-L1 expression > 50%, and treated with 1L pembrolizumab monotherapy was analyzed. This proof-of-concept study suggests that machine learning applied to baseline multi-modal data can help predict response to IO at the individual patient level, as well as stratify patients to inform long-term outcomes. This algorithm is being improved and validated through a large real-world multicentric international observational study including more than 4000 patients (DEEP-Lung-IV study, NCT04994795).
This abstract has been accepted for online publication
