Impact of Size and Location of Metastases on Early Tumor Shrinkage and Depth of Response in Patients With Metastatic Colorectal Cancer: Subgroup Findings of the Randomized, Open-Label Phase 3 Trial FIRE-3/AIO KRK-0306

Treatment outcome comparisons of first-line targeted therapy in patients with KRAS wild-type metastatic colorectal cancer: A nationwide database study

BACKGROUND

The first-line systemic therapy for metastatic colorectal cancer (mCRC) is a combination of one targeted therapy agent and a chemotherapy doublet. Whether bevacizumab or anti-epidermal growth factor receptor (anti-EGFR) monoclonal antibody (mAb) is the more effective addition to a chemotherapy doublet as the first-line treatment for inoperable KRAS wild-type mCRC remains controversial in prior clinical trials. Moreover, the association between the sidedness of primary tumors and the efficacy of anti-EGFR mAb needs to be addressed.

METHODS

We established a cohort of patients with KRAS wild-type mCRC who were treated with first-line targeted therapy plus doublet chemotherapy between 2013 and 2018 using Taiwan's National Health Insurance Research Database. Secondary surgery was defined as either resection of primary tumors, liver metastases, lung metastases, or radiofrequency ablation.

RESULTS

A total of 6482 patients were included; bevacizumab and anti-EGFR mAb were the first-line targeted therapies in 3334 (51.4%) and 3148 (48.6%) patients, respectively. Compared with those who received bevacizumab, patients who received anti-EGFR mAb exhibited significantly longer overall survival (OS; median, 23.1 vs. 20.2 months, p = 0.012) and time to treatment failure (TTF; median, 11.3 vs. 10 months, p < 0.001). Among left-sided primary tumors, the OS and TTF benefits of anti-EGFR mAb remained. Among right-sided primary tumors, the OS and TTF were similar regardless of the type of targeted therapy. In multivariate analyses, first-line anti-EGFR mAb therapy remained an independent predictor of longer OS and TTF for left-sided primary tumors. Patients who received anti-EGFR mAb were more likely to receive secondary surgery (29.6% vs. 22.6%, p < 0.0001) than patients who received bevacizumab.

CONCLUSION

For patients who received first-line doublet chemotherapy for KRAS wild-type mCRC, adding anti-EGFR mAb was associated with significantly longer OS and TTF, especially for left-sided primary tumors.

Depth of response of induction therapy and consecutive maintenance treatment in patients with RAS wild-type metastatic colorectal cancer: An analysis of the PanaMa trial (AIO KRK 0212)

BACKGROUND

In patients with RAS wild-type metastatic colorectal cancer, depth of response (DpR) has gained importance as a novel end-point in clinical trials. We investigated the overall DpR, as well as the prognostic and predictive impact of DpR to induction therapy (six cycles of 5-fluorouracil, leucovorin [FU/FA], oxaliplatin [FOLFOX] and panitumumab [Pmab]) on consecutive maintenance therapy (FU/FA plus Pmab or FU/FA alone) in patients treated within the PanaMa trial.

METHODS

Central radiological assessment was performed according to RECIST 1.1. DpR was defined as percentage change in tumour diameter within defined time intervals (induction therapy, maintenance therapy, total course of therapy). For prognostic and predictive analyses, median DpR (</≥) served as threshold.

RESULTS

Out of 248 patients receiving maintenance therapy, 211 were evaluable for DpR analyses (FU/FA + Pmab, n = 106; FU/FA alone, n = 105). The overall DpR in all patients was 56.5%. DpR of induction therapy (42.5%) accounted for the largest proportion (75.2%) of the overall DpR. While greater DpR to induction therapy was significantly associated with prolonged PFS (HR 0.70, 95% CI 0.52-0.93, p = 0.013) and OS (HR 0.38, 95% CI 0.28-0.51, p < 0.001), there was no significant correlation of DpR and maintenance treatment arm.

CONCLUSIONS

In the PanaMa trial, the overall DpR was similar to DpR achieved by other epidermal growth factor receptor-based regimens. DpR to induction therapy accounted for three quarters of the total tumour shrinkage potentially suggesting that FOLFOX plus Pmab can be de-escalated following induction without substantially compromising efficacy. DpR to induction therapy was prognostic but not predictive for efficacy of consecutive maintenance therapy.

CLINICAL TRIAL INFORMATION

NCT01991873.

The Phase II Study of Panitumumab in Chemotherapy-Naïve Frail or Elderly Patients with RAS Wild-type Colorectal Cancer: OGSG 1602 Final Results

BACKGROUND

We previously reported the response rate of a phase II OGSG1602 study on panitumumab in chemotherapy-naive frail or elderly patients with RAS wild-type unresectable colorectal cancer (CRC) [Terazawa T, Kato T, Goto M, et al. Oncologist. 2021;26(1):17]. Herein, we report a survival analysis.

METHODS

Patients aged ≥65 years and considered unsuitable for intensive chemotherapy or aged ≥76 years were enrolled. Primary tumors located from the cecum to the transverse colon were considered right-sided tumors (RSTs); those located from the splenic flexure to the rectum were considered left-sided tumors (LSTs).

RESULTS

Among the 36 enrolled patients, 34 were included in the efficacy analysis, with 26 and 8 having LSTs and RSTs, respectively. The median progression-free survival (PFS) and overall survival (OS) were 6.0 [95% CI, 5.4-10.0] and 17.5 months (95% CI, 13.8-24.3), respectively. Although no significant differences existed in PFS between patients with LST and RST {6.6 (95% CI, 5.4-11.5) vs. 4.9 months [95% CI, 1.9-not available (NA), P = .120]}, there were significant differences in OS [19.3 (95% CI, 14.2-NA) vs.12.3 months (95% CI, 9.9-NA), P = .043].

CONCLUSION

Panitumumab showed favorable OS in frail or elderly patients with RAS wild-type CRC and no prior exposure to chemotherapy. Panitumumab may be optimal for patients with LSTs (UMIN Clinical Trials Registry Number UMIN000024528).

The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization

Simple Summary

Modern, personalized therapy approaches are increasingly changing advanced cancer into a chronic disease. Compared to imaging, novel omics methodologies in molecular biology have already achieved an individual characterization of cancerous lesions. With quantitative imaging biomarkers, analyzed by radiomics or deep learning, an imaging-based assessment of tumoral biology can be brought into clinical practice. Combining these with other non-invasive methods, e.g., liquid profiling, could allow for more individual decision making regarding therapies and applications.

Abstract

Similar to the transformation towards personalized oncology treatment, emerging techniques for evaluating oncologic imaging are fostering a transition from traditional response assessment towards more comprehensive cancer characterization via imaging. This development can be seen as key to the achievement of truly personalized and optimized cancer diagnosis and treatment. This review gives a methodological introduction for clinicians interested in the potential of quantitative imaging biomarkers, treating of radiomics models, texture visualization, convolutional neural networks and automated segmentation, in particular. Based on an introduction to these methods, clinical evidence for the corresponding imaging biomarkers—(i) dignity and etiology assessment; (ii) tumoral heterogeneity; (iii) aggressiveness and response; and (iv) targeting for biopsy and therapy—is summarized. Further requirements for the clinical implementation of these imaging biomarkers and the synergistic potential of personalized molecular cancer diagnostics and liquid profiling are discussed.

Impact of early tumor shrinkage on quality of life in patients treated with first-line cetuximab plus chemotherapy for unresectable metastatic colorectal cancer: results of Phase II QUACK trial

Purpose

Although early tumor shrinkage (ETS) is a predictor of improved overall survival (OS), the association between ETS and health-related quality of life (HRQOL) remains unclear for patients with metastatic colorectal cancer (mCRC) treated with first-line cetuximab plus chemotherapy.

Methods

The data were collected from a prospective trial that assessed HRQOL using the EORTC QLQ-C30. The impact of ETS on HRQOL was estimated using a linear mixed-effects model for repeated measures.

Results

ETS was achieved in 82 (64.1%) of 128 mCRC patients treated with first-line cetuximab plus chemotherapy, and these patients had a significantly longer OS than those without ETS (HR, 0.38; 95% CI, 0.20–0.72; P = .002). Asymptomatic patients with ETS had a favorable OS, while symptomatic patients without ETS had a worse OS (2-year OS rates, 77.8% vs. 42.5%). Symptomatic patients with ETS had similar outcomes as asymptomatic patients without ETS (2-year OS rates, 64.1% vs. 67.0%). For symptomatic patients, ETS was associated with improved HRQOL scores between baseline and 8 weeks: the mean changes for patients with and without ETS were 5.86 and -4.94 for global health status (GHS)/QOL, 26.73 and 3.79 for physical functioning, and 13.58 and -3.10 for social functioning, respectively. The improved HRQOL was comparable to that of asymptomatic patients without ETS. For asymptomatic patients, ETS showed a decreased deterioration in HRQOL.

Conclusion

Our findings highlight the importance of ETS for HRQOL and prognostic estimates, and assessing ETS may provide clinically useful information for physicians and patients to make more informed decisions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09811-x.

Identification of CT Imaging Phenotypes of Colorectal Liver Metastases from Radiomics Signatures-Towards Assessment of Interlesional Tumor Heterogeneity

(1) Background: Tumoral heterogeneity (TH) is a major challenge in the treatment of metastatic colorectal cancer (mCRC) and is associated with inferior response. Therefore, the identification of TH would be beneficial for treatment planning. TH can be assessed by identifying genetic alterations. In this work, a radiomics-based approach for assessment of TH in colorectal liver metastases (CRLM) in CT scans is demonstrated. (2) Methods: In this retrospective study, CRLM of mCRC were segmented and radiomics features extracted using pyradiomics. Unsupervised k-means clustering was applied to features and lesions. Feature redundancy was evaluated by principal component analysis and reduced by Pearson correlation coefficient cutoff. Feature selection was conducted by LASSO regression and visual analysis of the clusters by radiologists. (3) Results: A total of 47 patients’ (36% female, median age 64) CTs with 261 lesions were included. Five clusters were identified, and the categories small disseminated (n = 31), heterogeneous (n = 105), homogeneous (n = 64), mixed (n = 59), and very large type (n = 2) were assigned based on visual characteristics. Further statistical analysis showed correlation (p < 0.01) of clusters with sex, primary location, T- and N-status, and mutational status. Feature reduction and selection resulted in the identification of four features as a final set for cluster definition. (4) Conclusions: Radiomics features can characterize TH in liver metastases of mCRC in CT scans, and may be suitable for a better pretherapeutic classification of liver lesion phenotypes.

The Constantly Evolving Role of Medical Image Processing in Oncology: From Traditional Medical Image Processing to Imaging Biomarkers and Radiomics

The role of medical image computing in oncology is growing stronger, not least due to the unprecedented advancement of computational AI techniques, providing a technological bridge between radiology and oncology, which could significantly accelerate the advancement of precision medicine throughout the cancer care continuum. Medical image processing has been an active field of research for more than three decades, focusing initially on traditional image analysis tasks such as registration segmentation, fusion, and contrast optimization. However, with the advancement of model-based medical image processing, the field of imaging biomarker discovery has focused on transforming functional imaging data into meaningful biomarkers that are able to provide insight into a tumor’s pathophysiology. More recently, the advancement of high-performance computing, in conjunction with the availability of large medical imaging datasets, has enabled the deployment of sophisticated machine learning techniques in the context of radiomics and deep learning modeling. This paper reviews and discusses the evolving role of image analysis and processing through the lens of the abovementioned developments, which hold promise for accelerating precision oncology, in the sense of improved diagnosis, prognosis, and treatment planning of cancer.