Guiding the Next Wave of Precision Oncology Innovation

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Strata Oncology CEO and co-founder Dan Rhodes, PhD, describes how molecular testing of tumors as it exists today is insufficient to realize the full promise of precision cancer care, and how Strata has developed a new category of tests that provides comprehensive treatment selection across therapeutic modalities.

When you look at the current state of precision oncology, where do you see the biggest opportunity to increase impact on patient care?

Roughly a decade ago, the emergence of comprehensive genomic profiling (CGP) was a radical step forward in precision oncology.

With the ability to sequence hundreds of genes simultaneously and use that data to guide mutation-targeted therapy, a patient’s tumor could now be characterized and treated based on its biology, not just its tissue of origin.

But this progress has not benefited all patients with cancer, or even most. It’s estimated that only about 20% of patients with cancer will receive CGP. Of those, only about a quarter will have a CGP-based molecular profile that matches them to an FDA-approved mutation-targeted therapy¹.

We must do better.

Since only a minority of patients derive benefit from CGP, how do we increase the actionability of molecular profiling for patients with cancer?

Many of the most exciting new cancer therapies, such as immunotherapies, angiogenesis inhibitors, and antibody-drug conjugates (ADCs), are expression-based.

We need biomarkers that can optimize and expand the use of this next wave of oncology treatment in order to ensure every patient receives their best possible therapy.

At Strata Oncology we’re leading the way in bringing expression-based therapies into the precision era with highly quantitative RNA and multivariate predictive treatment selection algorithms. This is made possible by our integrated testing platform that combines targeted, DNA and quantitative RNA sequencing on a single small tumor tissue sample.

We believe this new category of tests can more than double the number of patients who will have a biomarker-matched treatment option¹.

What is the key differentiator of your platform that enables Strata’s highly quantitative RNA and multivariate predictive treatment selection algorithms?

We know that gene and protein expression can be predictive of response for a wide range of therapies, such as immunotherapy, chemotherapy, endocrine therapy, angiogenesis inhibitors, and antigen-directed antibody therapy^2-6. In some cases, it is as simple as measuring the drug target, but in other cases, multivariate algorithms combining multiple expression markers are required to capture relevant biology and optimize treatment response prediction.

So, the question is: How best to accurately quantify expression across a wide range of potentially important genes in routine clinical specimens?

Immunohistochemistry (IHC), the most common method for target expression analysis, and quantitative PCR, the gold-standard for gene expression quantification in terms of sensitivity and specificity, both lack the throughput required for analysis of multiple targets simultaneously and require additional tissue samples on top of what is used for CGP.

Whole transcriptome sequencing using hybrid capture technologies is a powerful research tool but can be challenged by small degraded RNA input, which is common in routine clinical specimens. We have also noted a lack of dynamic range when characterizing expression of low or medium expressed genes, which may be required when setting clinical diagnostic thresholds or developing multivariate algorithms. Notably, whole transcriptome sequencing can identify novel genomic fusions relevant to some targeted therapies.

These challenges of sensitivity, specificity, and accuracy can be overcome by targeted RNA sequencing of select genes of interest using a PCR amplicon library preparation approach (vs. hybrid capture). This is the approach we take with our molecular profiling platform.

We use PCR-based sequencing technology to combine quantitative targeted RNA analysis with standard genomic profiling in a single sample. We thereby simultaneously enable insights on potential targeted therapies (including those targeting gene fusions) and predictive treatment selection for expression-based therapies based on our RNA and multivariate algorithms.

An added bonus is that the sensitivity of targeted PCR-based sequencing allows analysis of small, challenging samples, thus increasing the total number of patients who can access molecular profiling.

How are you gathering the data necessary to bring this new category of tests into routine clinical practice?

Through previous clinical trials, Strata Oncology has collected quantitative RNA profiles together with clinical outcome data from tens of thousands of patients. We’ve used this real-world data to both develop and analytically and clinically validate our RNA and multivariate predictive treatment selection algorithms. We are now further evaluating the clinical utility of these algorithms in an interventional clinical trial we are sponsoring ourselves—Strata PATH⁷.

For immunotherapy treatment selection, we’ve developed a predictive test that integrates tumor mutational burden (TMB) with immune gene expression, including PD-L1 and PD-1, among other tumor microenvironment markers. This test can predict treatment benefit better than the usual biomarkers for these therapies (PD-L1 expression by IHC or TMB alone)¹.

Our algorithm also identifies a significant number of patients who are likely to benefit from immunotherapy who otherwise would not be classified as eligible with current biomarkers¹. Specific validation data on this biomarker will be published soon.

For ADCs, we’ve developed a quantitative gene expression index that allows us to determine a patient’s likelihood of response based on precise measurement of target expression. We’ve shown high correlation between our quantitative RNA biomarkers and expression by immunohistochemistry, the current gold standard for clinical target expression evaluation¹.

Can you provide a specific example of how your predictive treatment selection algorithms could impact patient care?

Let’s take one of the therapies addressed by our quantitative gene expression index as an example: enfortumab vedotin-ejfv (PACDEV^®). This ADC targets Nectin-4, an immunoglobulin-like transmembrane protein. It is approved for previously treated locally advanced or metastatic urothelial cancer⁸.

While enfortumab vedotin has shown significant improvements in progression-free survival and overall survival in an unselected patient population, the overall response rates are 40.6% and 51% in the third and second line, respectively⁸.

Based on the fact that Nectin-4 is significantly over-expressed in bladder cancer relative to other tumor types, we hypothesize that the level of Nectin-4 expression may be an indicator of the likelihood of response. We thus classify patients across all tumor types as having “high” expression if the level is greater than the midpoint of all bladder cancers, and “low” expression if less than the midpoint.

We provide this information (for Nectin-4 and similarly for other ADC targets) as supplemental information on our reports today, so that physicians can have this additional insight.

This insight may help clinicians optimize the use of Nectin-4 therapy in bladder cancer but may also identify patients outside of bladder cancer that have equally high Nectin-4 expression and may thus also benefit from Nectin-4 therapy. We are studying this important hypothesis through the Strata PATH trial⁷.

From retrospective pan-tumor data, we predict that approximately 7% of patients outside of bladder cancer will be Nectin-4 “high” and match to the enfortumab vedotin arm of Strata PATH¹. Overall, we estimate more than 30% of patients tested via our platform will match to an RNA-based arm in Strata PATH¹.

How will this new category of molecular profiling tests change cancer care?

At Strata, everything we do is driven by our belief that advances in understanding the molecular underpinnings of cancer mean nothing until they become actionable for the physicians and patients we serve.

By developing tests that can optimize and expand the use of all major therapeutic classes for better patient outcomes, I believe we’ll increase cost-effectiveness for the healthcare system, while at the same time—and most importantly—delivering the highest quality care possible to patients with cancer.

References

1. Strata Oncology internal data.
2. Bardia et al. Ann Oncol. 2021;32(9):1148-1156.
3. Vera-Badillo et al. JCO. 2020;38:649-651.
4. Lu et al. JAMA Oncol. 2019;5(8):1195-1204.
5. Wolff et al. J. Oncol. Pract. 2018;14(7):437-441.
6. Coates et al. Cancer Discov. 2021;11(10):2436-2445.
7. Strata PATH™ (Precision Indications for Approved Therapies);ClinicalTrials.gov Identifier: NCT05097599.
8. FDA approval of enfortumab vedotin-ejfv (Accessed 09-09-22).

For additional information: www.strataoncology.com