|Dr. Lin: While identifying the MYC regulator, we learned that analyzing multimodal data provides a more comprehensive understanding of mechanistic disease biology than using a single modality. Studying the effects of drugging a transcriptional factor like MYC, which regulates hundreds to thousands of genes, requires a multidimensional phenotype readout because the perturbation attempts to selectively alter the tumor’s gene expression program. Understanding how this approach works—and differentiates itself from non-selectively killing cells or pan-transcriptionally inhibiting them—is crucial. With Tempus’ cohort of RNAseq data, we were able to conduct longitudinal analyses of biomarkers and predictively model how we select patients for clinical trials.
As the field continues to evolve from non-specific agents like cytotoxic chemotherapy into more targeted therapies, we can now learn how to predict the potential behavior of cancers and stay a step ahead of it. For example, when looking to validate the findings from studies in which patients were stratified into responders vs. non-responders, multimodal data can provide insights into mechanistic tumor biology, which then helps oncology research teams to systematically probe factors like protein interactions, drug binding kinetics, etc.
Dr. Razavi: Using RWD, we’ve learned more about the biology of breast cancer and translated these learnings to improve clinical trial design. For example, we can identify which patient subgroups are most likely to benefit from a therapy versus those that wouldn’t or decide which patients would benefit from escalating or de-escalating medications.
Let’s consider patients with estrogen receptor-positive (ER+) breast cancers treated with CDK4/6 inhibitors like Palbociclib and Abemaciclib. Although these medications are transformative therapeutics, they have been clinically administered without any biomarker analysis and with the understanding that 10%-20% of patients didn’t respond well and another 10%-20% barely achieved a median response post-treatment. At MSK, we questioned whether we could identify CDK4/6 inhibitor non-responders, and in our initial analysis, we observed a loss of RB1, which was already biologically validated but also implicated in the Hippo pathway. These findings have since resulted in the development of therapies targeting the Hippo pathway in breast cancer.
And we have expanded our clinical program based on the patients who developed CDK4/6 resistance. With thousands of patient data points, we can identify multiple resistance mechanisms, such as PTEN loss. Additionally, multimodal data helps cross-validate findings from different RWD analyses. For instance, Tempus helped validate our finding that BRCA1/2 germline variants result in CDK4/6 resistance, which is now driving the development of PARP inhibitors in this setting.
Traditionally, oncologists study whether specific pathways implicated in cancer types are amenable to being targeted, or preclude patients from benefiting from certain therapies—such as KRAS mutant colorectal cancers. Multimodal data is changing the course of oncology care by enabling oncologists to improve their predictions of cancer outcomes and the mechanisms by which these cancers progress across patient subgroups.
For instance, we now know that not every ER+ breast cancer patient receiving endocrine therapy will develop estrogen receptor 1 (ESR1) resistance mutations, even after years of treatment; only about 40% of treated breast cancer patients develop them. Understanding these patterns is crucial to designing clinical trials and determining the appropriate dose change or sequence of interventions.