AI Helps Determine Which Patients Benefit Most From Colorectal Cancer Treatment

Using a machine learning approach, researchers found the application of FOLFOXai, an artificial intelligence (AI)–based predictor of response to FOLFOX chemotherapy in metastatic colorectal cancer (mCRC), may lead to improvement of treatment outcomes for some patients with mCRC and other cancers. Specifically, the technology was able to predict which patients would have less benefits from oxaliplatin-containing regimens and may in turn benefit from alternative regimens.

FOLFOX is a chemotherapy regimen that includes the drugs leucovorin calcium (folinic acid), fluorouracil, and oxaliplatin. “FOLFOX, FOLFIRI, or FOLFOXIRI chemotherapy in combination with bevacizumab (BV) are considered standard [first-]line treatment options for patients with mCRC,” the researchers wrote. However, based on clinical factors, it is unclear whether any of these combination regimens are superior in individual patients with mCRC.

Use of oxaliplatin is also a first-line option as part of a FOLFOX regimen for esophageal and gastric cancer. “Because oxaliplatin has little activity as monotherapy, it is used exclusively in combination with fluoropyrimidines so a biomarker for FOLFOX (as opposed to oxaliplatin alone) would be of pertinent clinical value,” the authors explained. Oxaliplatin-associated neuropathy develops by treatment month 4 or 5 in most patients, over time resulting in chemotherapy-resistant tumors. Thus, initial therapeutic impact is critical in these populations.

Results from the TRIBE2 phase 3 study “demonstrated that the upfront triple-combination of 5-FU, oxaliplatin, and irinotecan (FOLFOXIRI) with BV followed by the reintroduction of the same regimen after disease progression resulted in improved overall survival compared to the sequential administration of chemotherapy doublets (FOLFOX followed by FOLFIRI), in combination with BV.” But improved outcomes were achieved at the cost of increased toxicity, limiting the approach’s applicability.

To determine patients’ likelihood of benefit from first-line treatment FOLFOX followed by FOLFIRI vs FOLFIRI followed by FOLFOX, researchers leveraged AI algorithms and comprehensive molecular profiling data to develop a machine learning approach.

Real-world data were gleaned from insurance claims of more than 10,000 physicians and the Caris Life Sciences Precision Oncology Alliance registry, and additional data were collected from a subset of cases of the TRIBE2 study. The training cohort consisted of individuals diagnosed with mCRC who were treated with FOLFOX-based combination therapy, had completed at least 1 full cycle of therapy, and completed next-generation DNA analysis of at least 1 CRC sample using a 592-gene panel.

Two additional validation cohorts were also generated. Individuals in these cohorts had a diagnosis of mCRC, received first-line FOLFOX/BV treatment (FOLFOX/BV cohort) or first-line FOLFIRI-based treatment (FOLFIRI cohort), completed at least 1 full cycle of therapy, completed next-generation DNA analysis of at least 1 CRC sample, and switched to an irinotecan-containing regimen (FOLFOX/BV cohort) or to FOLFOX (FOLFIRI cohort).

For algorithm training, a time to next treatment (TTNT) of 270 days was chosen to define whether a patient benefitted from receiving first-line FOLFOX. A total of 105 patients with mCRC were included in the training cohort. The first validation cohort included 412 patients treated with FOLFOX/BV and 55 patients who received FOLFIRI as first-line treatments. Additional validation cohorts included samples from the TRIBE2 randomized phase 3 study.

Overall, researchers found:

• A 67 gene signature was cross-validated in a training cohort (n = 105) that demonstrated the ability of FOLFOXai to distinguish patients with mCRC treated with FOLFOX with increased benefit from those with decreased benefit.
• The signature was predictive of TTNT and overall survival (OS) in an independent real-world evidence (RWE) data set of 412 patients who had received FOLFOX/BV in the first line and inversely predictive of survival in RWE data from 55 patients who had received first-line FOLFIRI.
• Blinded analysis of TRIBE2 samples confirmed that FOLFOXai was predictive of OS in both oxaliplatin-containing arms (FOLFOX: HR, 0.629; P = .04; FOLFOXIRI: HR, 0.483; P = .02) leading to a clinically relevant increase in OS of 6 months (FOLFOX/BV arm) or 14.9 months (FOLFOXIRI/BV arm).
• FOLFOXai was also predictive of treatment benefit from oxaliplatin-containing regimens in advanced esophageal/gastroesophageal junction cancers, as well as pancreatic ductal adenocarcinoma.

The study marks the development of the “first clinically validated machine-learning powered molecular predictor of chemotherapy efficacy in these diseases with immediate relevance for the initial therapeutic decision-making process,” the authors concluded.

Based on the results, they proposed that a clinical decision algorithm incorporating FOLFOXai should be used to prioritize FOLFOX or FOLFIRI as first-line chemotherapy in patients not eligible for the triple-agent FOLFOXIRI regimen. However, a prospective registry study should be carried out to test whether patients predicted to have decreased benefit from FOLFOX will derive greater benefit from FOLFIRI.

“Using this AI predictor, we saw a 71% difference in OS in patients treated with FOLFOX-FOLFIRI in whom we predicted would benefit from FOLFOX-FOLFIRI chemotherapy compared to those predicted to benefit from FOLFIRI-FOLFOX,” said Heinz-Josef Lenz, MD, associate director of clinical science at USC Norris Comprehensive Cancer Center.

“The results of these analyses demonstrate that precision medicine powered by AI has the potential to change how clinicians approach treatment for metastatic colorectal cancer and other cancers, given the selection of initial therapy has implications for improved treatment outcomes and disease progression,” said W. Michael Korn, MD, chief medical officer at Caris Life Sciences.

Reference

Abraham JP, Magee D, Cremolini C, et al. Clinical validation of a machine-learning derived signature predictive of outcomes from first-line oxaliplatin-based chemotherapy in advanced colorectal cancer. Clin Cancer Res. Published online December 3, 2020. doi:10.1158/1078-0432.CCR-20-3286