Mapping lesion-specific response and progression dynamics and inter-organ variability in metastatic colorectal cancer

Abstract

Achieving systemic tumor control across metastases is vital for long-term patient survival but remains intractable in many patients. High lesion-level response heterogeneity persists, conferring many dissociated responses across metastatic lesions. Most studies of metastatic disease focus on tumor molecular and cellular features, which are crucial to elucidating the mechanisms underlying lesion-level variability. However, our understanding of lesion-specific heterogeneity on the macroscopic level, such as lesion dynamics in growth, response, and progression during treatment, remains rudimentary. This study investigates lesion-specific response heterogeneity through analyzing 116,542 observations of 40,612 lesions in 4,308 metastatic colorectal cancer (mCRC) patients. Despite significant differences in their response and progression dynamics, metastatic lesions converge on four phenotypes that vary with anatomical site. Importantly, we find that organ-level progression sequence is closely associated with patient long-term survival, and that patients with the first lesion progression in the liver often have worse survival. In conclusion, our study provides insights into lesion-specific response and progression heterogeneity in mCRC and creates impetus for metastasis-specific therapeutics.

Incorporating lesion-to-lesion heterogeneity into early oncology decision making

“…In addition, we developed mathematical models to investigate lesion-specific heterogeneity in terms of their dynamics in growth, response, and progression during treatment. We found that organ-level progression sequence is closely associated with long-term survival; in addition, patients with metastatic colorectal cancer whose first lesion-level progression occurs in the liver often have worse survival ( 15 , 34 ).…”

“…Other groups have also pointed out the limitations of RECIST for classifying heterogeneous responses to immunotherapy (sometimes calling it 'dissociated response') and called for better ways to evaluate patient response (12)(13)(14)(15)(16). Although new guidelines (like iRECIST) have been developed to improve and complement RECISTv1.1 in cancer immunotherapy trials, the lesion-to-lesion heterogeneity remains largely underappreciated.…”

Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development

“…Another challenge of determining the optimal dosage during oncology drug development is the constantly evolving nature of cancer as a disease 12–15 . Cancer cells undergo continuous mutations and genetic changes, leading to time‐dependent variations in response to treatments and tumor progression 16–18 . This dynamic variation makes cancer a highly heterogeneous group of diseases, where treatment responses are heavily influenced by the tumor's genetic makeup both before and during the treatment process.…”

“…The algorithm learns from each dose level and adjusts the subsequent doses based on the response to previous doses, ultimately identifying the MTD and the RP2D. Machine learning has also been applied to predict the long‐term survival of patients with CRC based on their metastatic locations and heterogeneity of response across metastatic lesions 18,91 . These analyses could be potentially applied to extend our methods for better patient stratification when assessing E–R relationships and finding optimal doses.…”

“… 12 , 13 , 14 , 15 Cancer cells undergo continuous mutations and genetic changes, leading to time‐dependent variations in response to treatments and tumor progression. 16 , 17 , 18 This dynamic variation makes cancer a highly heterogeneous group of diseases, where treatment responses are heavily influenced by the tumor's genetic makeup both before and during the treatment process. As a result, early efficacy readouts may not always consistently translate to long‐term clinical outcome or survival benefit.…”