Future direction of total neoadjuvant therapy for locally advanced rectal cancer

Abstract

Despite therapeutic advancements, disease-free survival and overall survival of patients with locally advanced rectal cancer have not improved in most trials as a result of distant metastases. For treatment decision-making, both long-term oncologic outcomes and impact on quality-of-life indices should be considered (for example, bowel function). Total neoadjuvant therapy (TNT), comprised of chemotherapy and radiotherapy or chemoradiotherapy, is now a standard treatment approach in patients with features of high-risk disease to prevent local recurrence and distant metastases. In selected patients who have a clinical complete response, subsequent surgery might be avoided through non-operative management, but patients who do not respond to TNT have a poor prognosis. Refined molecular characterization might help to predict which patients would benefit from TNT and non-operative management. Specifically, integrated analysis of spatiotemporal multi-omics using artificial intelligence and machine learning is promising. Three prospective trials of TNT and non-operative management in Japan, the USA and Germany are collaborating to better understand drivers of response to TNT. Here, we address the future direction for TNT.

Figure 1.

Mapping of collaborating clinical trials of TNT for LARC. ENSEMBLE compares doublet and triplet chemotherapy after SCRT. The Janus Rectal Cancer trial compares couplet and triplet chemotherapy after LCCRT. ACO/ARO/AIO-18.1 compares LCCRT followed by doublet chemotherapy with SCRT followed by doublet chemotherapy. Results from these trials can work together in developing the ideal treatment regimen. AI, artificial intelligence; CNCT, consolidation chemotherapy; CRT, chemoradiotherapy; INCT, induction chemotherapy; LCCRT, long-course chemoradiation therapy; NOM, non-operative management; pCR, pathological complete response; SCRT, short-course radiotherapy; QOL, quality of life; TNT, total neoadjuvant therapy.

Figure 2.

Development of AI models for predicting TNT treatment response utilizing spatio-temporal multi-omics analysis. Genomics, transcriptomics, and pathomics from biopsies before the start of treatment, as well as radiomics from CT, MRI, and endoscopic images, and genomics, transcriptomics from liquid biopsies and QOL over time during treatment will be accumulated in real time. The multi-omics data will be analysed by AI/ML on a supercomputer to develop an AI model for predicting treatment response, and early detection of recurrence and/or regrowth. AI, artificial intelligence; EORTC, European Organization for Research and Treatment of Cancer; HE, hemotoxin eosin; LARS, low anterior resection syndrome; ML, machine learning; NOM, non-operative management; QOL, quality of life; SF-36, MOS Short-Form 36-Item Health Survey; TC, transcriptome; TME, total mesorectal excision; TNT, total neoadjuvant therapy; WES, whole-exome sequencing; WGS, whole-genome sequencing; WTS, whole-transcriptome sequencing.

Figure 3.

Future treatment strategy for LARC with AI guide. In patients with dMMR tumours, chemotherapy with immune checkpoint inhibitors is used for organ preservation, and in patients with pMMR tumours, an AI model with multi-omics analysis proposes the optimal treatment (surgery, preoperative chemotherapy, preoperative radiation therapy and/or TNT.AI, artificial intelligence; cCR, clinical complete response; dMMR, deficient mismatch repair; Chemo, chemotherapy; CRT, chemoradiation therapy; ICI, immune-checkpoint inhibitor; iCR, incomplete clinical response; LARC, locally advanced rectal cancer; nCR, near-complete clinical response; NOM, nonoperative management; pMMR, proficient mismatch repair; SCRT, short-course radiation therapy; TME, total mesorectal excision; TNT, total neoadjuvant therapy.