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Background <p>Practice guidelines recommend panitumumab with chemotherapy to treat KRAS wild-type (WT) metastatic colorectal cancer (mCRC) patients. However, not all patients respond to this therapy. We propose a score termed “PROCC” to identify likely panitumumab responders.</p> Methods <p>The training (TRDS) and validation (VALDS) datasets included KRAS WT mCRC patients treated with panitumumab (P) plus chemotherapy (TRDS, FOLFOX4; VALDS, FOLFIRI). TRDS included 36 diverse features used to generate synthetic representations analyzed via machine learning (ML) to identify patient subgroups, which were correlated with PFS and OS. A multivariable logistic regression model identified independent predictors to develop a predictive score.</p> Results <p>ML identified two subpopulations in TRDS: SP-A (<i>n</i> = 162) and SP-B (<i>n</i> = 298). Only SP-A patients under P/FOLFOX4 showed improved OS versus FOLFOX4 (HR 0.68; <i>p</i> = 0.04). CEA, ALP, LDH, and platelets at baseline were used to create a predictive score (“PROCC”). For TRDS, the score had an area under the curve of 0.81. PROCC ≥8.5 correlated with lower risks of progression (HR 0.67; <i>p</i> = 0.03) and death (HR 0.65; <i>p</i> = 0.04) for P/FOLFOX4 versus FOLFOX4. Validation in VALDS confirmed similar results with FOLFIRI.</p> Conclusions <p>Based on four baseline parameters, PROCC may guide the selection of KRAS WT mCRC patients most likely to benefit from panitumumab.</p>

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PROCC: a predictive score to identify KRAS wild type metastatic colorectal cancer patients who are likely to obtain survival benefit from panitumumab treatment

  • 期刊论文

  • 发表日期：

    2025年8月20日

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  • Carlos María Galmarini,
  • Rafael Zamora,
  • Pablo Gómez del Campo,
  • José del Castillo-Izquierdo,
  • José Antonio De All,
  • Juan Manuel Domínguez

摘要

Background

Practice guidelines recommend panitumumab with chemotherapy to treat KRAS wild-type (WT) metastatic colorectal cancer (mCRC) patients. However, not all patients respond to this therapy. We propose a score termed “PROCC” to identify likely panitumumab responders.

Methods

The training (TRDS) and validation (VALDS) datasets included KRAS WT mCRC patients treated with panitumumab (P) plus chemotherapy (TRDS, FOLFOX4; VALDS, FOLFIRI). TRDS included 36 diverse features used to generate synthetic representations analyzed via machine learning (ML) to identify patient subgroups, which were correlated with PFS and OS. A multivariable logistic regression model identified independent predictors to develop a predictive score.

Results

ML identified two subpopulations in TRDS: SP-A (n = 162) and SP-B (n = 298). Only SP-A patients under P/FOLFOX4 showed improved OS versus FOLFOX4 (HR 0.68; p = 0.04). CEA, ALP, LDH, and platelets at baseline were used to create a predictive score (“PROCC”). For TRDS, the score had an area under the curve of 0.81. PROCC ≥8.5 correlated with lower risks of progression (HR 0.67; p = 0.03) and death (HR 0.65; p = 0.04) for P/FOLFOX4 versus FOLFOX4. Validation in VALDS confirmed similar results with FOLFIRI.

Conclusions

Based on four baseline parameters, PROCC may guide the selection of KRAS WT mCRC patients most likely to benefit from panitumumab.