Biological pathways reflect the key cellular mechanisms that dictate disease states, drug response and altered cellular function. The local areas of pathways are defined as subpathways (SPs), whose dysfunction has been reported to be associated with the occurrence and development of cancer. With the development of high-throughput sequencing technology, identifying dysfunctional SPs by using multi-omics data has become possible. Moreover, the SPs are not isolated in the biological system but interact with each other. Here, we propose a network-based calculated method, CNA2Subpathway, to identify dysfunctional SPs is driven by somatic copy number alterations (CNAs) in cancer through integrating pathway topology information, multi-omics data and SP crosstalk. This provides a novel way of SP analysis by using the SP interactions in the system biological level. Using data sets from breast cancer and head and neck cancer, we validate the effectiveness of CNA2Subpathway in identifying cancer-relevant SPs driven by the somatic CNAs, which are also shown to be associated with cancer immune and prognosis of patients. We further compare our results with five pathway or SP analysis methods based on CNA and gene expression data without considering SP crosstalk. With these analyses, we show that CNA2Subpathway could help to uncover dysfunctional SPs underlying cancer via the use of SP crosstalk. CNA2Subpathway is developed as an R-based tool, which is freely available on GitHub (https://github.com/hanjunwei-lab/CNA2Subpathway).