SOPHiA GENETICS Announces Three Poster Presentations and One Online Publication Accepted at the 2022 American Society of Clinical Oncology Annual (ASCO) Meeting

Press Release

Published on 06/01/2022

8 min read

BOSTON, United States and LAUSANNE, Switzerland – May 31, 2022 – SOPHiA GENETICS (Nasdaq: SOPH), a leader in data-driven medicine, today announced three abstracts accepted for poster presentation and one for online publication at the 2022 American Society of Clinical Oncology (ASCO) Annual Meeting taking place June 3-7 in Chicago. SOPHiA GENETICS and GE Healthcare will also be hosting an Innovation Symposium on Monday, June 6th from 6:30 – 8:00 pm to present how the companies are working together to deliver on the promise of integrated cancer medicine by bringing global insights across multiple diagnostic modalities to clinical and biopharma customers.

“These ASCO contributions from SOPHiA GENETICS and collaborators demonstrate how our multimodal technology and solutions help drive novel insights and enhance oncology discoveries,” said Dr. Philippe Menu, Chief Medical Officer at SOPHiA GENETICS. “By utilizing our data-driven medicine approach and by applying our AI and machine learning algorithms to real-world multimodal data sets, SOPHiA GENETICS has the potential to help inform treatment decisions at the individual patient level for cancer patients globally. I am really excited to attend ASCO to share how our mission to democratize data-driven medicine is helping transform cancer care.”

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

About SOPHiA GENETICS

SOPHiA GENETICS (Nasdaq: SOPH) is a health care technology company dedicated to establishing the practice of data-driven medicine as the standard of care and for life sciences research. It is the creator of the SOPHiA DDM™ Platform, a cloud-based SaaS platform capable of analyzing data and generating insights from complex multimodal data sets and different diagnostic modalities. The SOPHiA DDM™ Platform and related solutions, products and services are currently used by more than 790 hospital, laboratory, and biopharma institutions globally. For more information, visit SOPHiAGENETICS.COM, or connect on Twitter, LinkedIn and Instagram. Where others see data, we see answers.

SOPHiA GENETICS products are for Research Use Only and not for use in diagnostic procedures, unless specified otherwise. The information in this press release is about products that may or may not be available in different countries and, if applicable, may or may not have received approval or market clearance by a governmental regulatory body for different indications for use. Please contact [email protected] to obtain the appropriate product information for your country of residence.

SOPHiA GENETICS Forward-Looking Statements:

This press release contains statements that constitute forward-looking statements. All statements other than statements of historical facts contained in this press release, including statements regarding our future results of operations and financial position, business strategy, products and technology, as well as plans and objectives of management for future operations, are forward-looking statements. Forward-looking statements are based on our management’s beliefs and assumptions and on information currently available to our management. Such statements are subject to risks and uncertainties, and actual results may differ materially from those expressed or implied in the forward-looking statements due to various factors, including those described in our filings with the U.S. Securities and Exchange Commission. No assurance can be given that such future results will be achieved. Such forward-looking statements contained in this press release speak only as of the date hereof . We expressly disclaim any obligation or undertaking to update these forward-looking statements contained in this press release to reflect any change in our expectations or any change in events, conditions, or circumstances on which such statements are based, unless required to do so by applicable law. No representations or warranties (expressed or implied) are made about the accuracy of any such forward-looking statements.

Media Contacts:
Don Granese
[email protected]

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