Monoclonal antibodies (mAbs) targeting the epidermal growth factor receptor (EGFR), including cetuximab and panitumumab, have been engineered for the treatment of metastatic colorectal cancer (mCRC). Nevertheless, the therapeutic advantage of these anti-EGFR mAbs has been confined to a subset of treatment-refractory mCRC patients, with cetuximab showing a 0% response rate in mCRC with KRASmutations and a 17% response rate in KRAS wild-type (WT) mCRC; similarly, panitumumab demonstrated a 10.8% response rate across mCRC patients. Therefore, a deeper understanding of CRC biology, derived from thorough investigation, remains an unfulfilled requirement for tailoring personalized medical treatments. Cancer cells often change how they process glutamine, a shift that could help them grow even when anti-EGFR therapies are used. This insight suggests that targeting this glutamine process could improve treatment outcomes.  Positron Emission Tomography (PET) imaging, known for its detailed and quantitative analysis, allows us to study tumor metabolism non-invasively. Our work focused on finding genetic and PET imaging markers that can predict how well patients respond to treatments that block both EGFR and glutamine metabolism.

As a co-clinical trial, we evaluated the efficacy of combined EGFR and glutaminolysis blockade treatment in patients with KRAS WT mCRC and patient-derived xenografts (PDX). Based on the treatment outcomes (RECIST1.1) in the clinical trial (NCT03263429), responders were assigned to the “clinical benefit Yes” group, while non-responders were placed in the “clinical benefit No” group (Figure 1A).  The clinical benefit was observed in 7 out of 16 patients (43.8%) and in 3 out of 6 PDX models (50%). We confirmed that our combined treatment regimen significantly benefits patients in terms of overall survival (OS) and progression-free survival (PFS), enhancing survival outcomes. Specifically, the improvement in OS was statistically significant with a log-rank p-value of 0.0099 (Figure 1B), and for PFS, the enhancement was even more pronounced, with a log-rank p-value of 0.00034 (Figure 1C). The median OS for the “Yes” group was 13.1 months, significantly longer than the 4.4 months observed in the “No” group. In addition, the median PFS for the “Yes” group was 5.91 months, which was much higher than the 1.69 months recorded for the “No” group. Our bioinformatic data mining analyses utilizing bulk RNA-seq data of the 16 patients identified a 4-gene signature linked to how well patients respond to treatment (Figure 1D), which, after further investigations using multiple public databases (Supporting Information Figure S1-3), seems connected to B cell activation. Applying the gene signature (Bscore) enabled accurate estimation of treatment response in PDX (Figure 2). Additionally, we looked into using PET tracers such as 18F-4-Fluoro-glutamine (¹⁸F-Gln) or l-[5-11C]-glutamine (¹¹C-Gln) and (4S)-4-(3-[18F]fluoropropyl)-L-glutamate (¹⁸F-FSPG) to track changes in how tumors process nutrients before and after treatment with CB-839 and EGFR inhibitors. In examining both PDX and patient tumors, we observed a tendency for decreased FSPG-PET uptake following treatment when a response was evident (Figure 3A, B). As for the glutamine PET analysis, clear patterns were not discernible in either of the two trials, suggesting a need for additional research.

In conclusion, our results indicate a novel connection between the immune system’s B cells and better outcomes from treatments that target both glutaminolysis and EGFR in patients with KRAS WT mCRC. Combining Bscore with PET scans to watch how tumors process glutamine presents a challenge that we still need to solve. Larger prospective clinical trials in the future are crucial to confirm our early findings and could help us tailor treatments more precisely to each patient’s cancer.

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Figure 1. A. A waterfall chart illustrates the diverse response to the combined treatment of CB- 839 and αEGFR. The samples are categorized into three response groups: progression disease (PD), stable disease (SD) and partial response (PR). Kaplan-Meier curves of overall survival (B) and progression-free survival (C). D. A heatmap summarizes the gene expression in each patient and according to the 4-gene signature and the pattern of the gene signature (GS). E. The proportion of immune cells in each patient estimated by CibersortX. F. The correlation between infiltrating B cells and the GS.

Figure 2. A heatmap summarizes the gene expression in each patient PDX and according to the 4-gene signature and the pattern of the gene signature (Bscore).

Figure 3. A. Exemplary 18F-FSPG PET/CT images of two representative two patients. B. Exemplary 18F-FSPG PET images of two representative PDX mouse models.

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