Factors affecting the engraftment success rates of primary breast cancer

The clinicopathological characteristics of the primary breast cancer patient population, including both chemo-naive and NAC groups, in relation to the PDX engraftment are summarized in Table 1, and the detailed PDX engraftment success rates across multi-passages are described in Additional file 1 and Additional file 2: Fig. 2. In the cohort of 353 primary breast cancer patients, the mean age in the engraftment success group was 45.8 ± 11.0 years, significantly younger than the 50.9 ± 12.2 years observed in the engraftment failure group (p = 0.003). A higher prevalence of TNBC was observed in the success group, accounting for 88.1% (52/59) compared to 32.3% (95/294) in the failure group (p < 0.001). Ki-67LI (%) was significantly elevated in the success group, with a mean value of 73.2 ± 14.9, compared to 39.0 ± 29.1 in the failure group (p < 0.001). In terms of NAC treatment, 79.7% (47/59) of the success group was from the NAC group, which was significantly higher than the 37.1% (109/294) in the failure group (p < 0.001). Tumor size was also significantly different, with the success group averaging 4.1 ± 2.6 cm and the failure group averaging 3.3 ± 2.2 cm (p = 0.019). The histologic grade was also notably associated with PDX engraftment success, with 91.5% (54/59) of successful cases being histologic grade 3 compared to 48.0% (141/294) in the failure group (p < 0.001). Other variables, including diagnosis, LVI, number of positive LNs, TIL%, and AJCC stages showed no significant differences between the success and failure groups.

Table 1 Characteristics of the primary breast cancer patient population based on the success of PDX engraftment

AI-assessed morphometric features in the cohort of 320 primary breast cancer patients, including both chemo-naive and NAC group patients, were also analyzed, and significant differences were observed between the failure (n = 270) and success groups (n = 50) (Table 2). The success group exhibited a significantly lower average AP (p = 0.006). Conversely, the success group had a significantly higher NP compared to the failure group (p < 0.001), along with a significantly lower TDLUP (p < 0.001). Similarly, the success group had a lower SP than the failure group (p = 0.007). Although the ICP was higher in the success group, statstical significance was not reached (p = 0.096). There were no significant differences in TILP between the groups.

Both univariate and multivariate logistic regression analyses were conducted, incorporating a range of clinicopathological variables as well as AI-analyzed morphometric features (Table 3). In univariate analysis, several clinicopathologic factors were found to be significantly related to successful PDX engraftment, including younger age (OR 0.96, CI 0.94–0.99, p = 0.005), higher Ki-67LI (OR 1.06, CI 1.04–1.08, p < 0.001), TNBC subtype (OR 9.78, CI 1.27–75.23, p = 0.028), histologic grade 3 (OR 16.62, CI 5.05–54.71, p < 0.001), larger invasive tumor size (OR 1.23, CI 1.09–1.38, p < 0.001) and more positive metastatic LNs (OR 1.04, CI 1.00–1.08, p = 0.031).

Table 2 AI-analyzed intratumoral image patch proportions and PDX engraftment success in primary breast cancersTable 3 Logistic regression analyses of clinicopathologic factors and AI-analyzed image data impacting PDX engraftment

 Also in the univariate logistic regression analyses, several morphological attributes showed statistical significance. A 0.1% increase in NP increased the odds of PDX engraftment by 58% (NP: OR 1.58, CI 1.39–1.80, p < 0.001). Conversely, a 0.1% increase in AP was associated with a 39% decrease in the odds of PDX engraftment (OR 0.61, CI 0.47–0.79, p = 0.035). A 0.1% increase in TDLUP resulted in an 82% decrease in PDX engraftment odds (OR 0.18, CI 0.08–0.41, p = 0.006), and a 0.1% increase in SP led to a 37% reduction in engraftment odds (OR 0.63, CI 0.46–0.86, p = 0.007).

In multivariate logistic regression analysis of primary breast cancer patients, including the chemo-naïve and NAC groups, variables such as age, Ki-67LI, NAC status, tumor size, histologic grade, NP, ICP, and SP were selected using the stepwise elimination method to achieve the optimal AIC. In the clinicopathologic analysis, significant factors for PDX engraftment included younger age (OR 0.96, CI 0.92–1.00, p = 0.032), higher Ki-67LI (OR 1.05, CI 1.02–1.07, p < 0.001), NAC status (OR 3.27, CI 1.41–7.60, p = 0.006), larger tumor size (OR 1.20, CI 1.02–1.41, p = 0.029), and histologic grade 3 (OR 4.34, CI 1.08–17.53, p = 0.039). In the analysis of morphometric features, a 0.1% increase in NP increased the odds of success by 92.7% (OR 1.927, CI 1.077–3.449, p = 0.027), and a 0.1% increase in ICP increased the odds of success by 82.0% (OR 1.820, CI 1.028–3.223, p = 0.040). A bootstrap analysis was conducted to validate the multivariate logistic regression analysis predictive model with the generation of 1000 replicates to assess its reliability. The initial AUC was 0.905, with a bias of 0.0079 and a standard error of 0.0184. The 95% BCa confidence interval for the AUC ranged from 0.8337 to 0.9296. The optimal cutoff point, determined by Youden's J statistic, was 0.129. At this cutoff, the PPV was 0.40 and the NPV was 0.99 (Fig. 2A).

Fig. 2

Receiver-operated curves for predictive models of engraftment success. A. Multivariate logistic regression of primary breast cancer patients (chemo-naïve and neoadjuvant chemotherapy (NAC)-treated groups), incorporating the selected variables from both AI-evaluated morphometric features and clinicopathological findings. B. Pruned decision tree prediction model using clinicopathological and AI-derived morphometric features for primary breast cancer patients (chemo-naïve and NAC-treated groups). C. Multivariate logistic regression model for the NAC-treated group. incorporating the selected variables from both AI-evaluated morphometric features and clinicopathological findings. D. Pruned decision tree prediction model in the NAC group, utilizing both clinicopathological and AI-derived morphometric features

We also generated a decision tree model using the RPART algorithm by incorporating both AI-analyzed morphometric and clinicopathological variables for the primary breast cancer group. The model was pruned at various CPs, and the optimal CP was selected based on the minimized cross-validation error.

In terms of variable importance, cancer subtype was the most significant at 20%, followed by tumor size at 14%, NP at 13%, Ki-67LI at 12%, and patient age at 7%. Additional variables such as SP, metastatic LNs, and histologic grade each contributed 6% to the model (Additional file 3: Table 1 and Fig. 3A).

Fig. 3

Pruned decision tree analyses. A Primary breast cancer, including both chemo-naive and NAC groups (n = 320) and B NAC-treated group (n = 131)

After bootstrapping with 1000 replicates, the pruned decision tree model yielded an AUC of 0.8304, accompanied by a bias of 0.0694 and a standard error of 0.041. The 95% confidence interval for the AUC, calculated using the BCa method, ranged from 0.6815 to 0.8511. At the optimal cutoff point determined by Youden's J statistic, which was 0.125, the model yielded a PPV of 0.398 and an NPV of 0.985 (Fig. 2B).

Factors affecting engraftment success rates of NAC-treated primary breast cancer

Separate statistical analyses were carried out for the NAC group with additional inclusion of unique variables, including the Miller-Payne grade, RCB class, and RCB score (Table 1). In the NAC group, samples from 47 of total 156 patients led to successful PDX engraftment (30.1%, 47/156). A significant difference was noted in the mean age between the failure and success groups, with younger age at diagnosis significantly related to PDX engraftment (50.4 ± 10.7 vs. 45.2 ± 11.0 years; p = 0.007). The proportion of the TNBC subtype was higher in the success group (87.2%) compared to the failure group (33.9%; p < 0.001). Ki-67LI (%) displayed a significant elevation in the success group, registering at 74.0 ± 15.1 compared to 33.9 ± 32.3 in the failure group (p < 0.001). The histologic grade also displayed a significant association with PDX engraftment success (p < 0.001). Specifically, the successful cases featured a high prevalence of histologic grade 3, accounting for 91.5% (43 out of 47), in contrast to 49.5% (54 out of 109) in the failure group. No significant differences were detected in variables such as diagnosis, LVI, the number of positive LNs, TIL%, tumor size, or AJCC stages between the success and failure groups. The Miller-Payne grade was significantly associated with PDX success (p < 0.001). While the RCB score did not show a significant difference, the RCB class showed a statistical trend toward success (p = 0.052).

A cohort of 131 NAC-treated primary breast cancers were analyzed using AI-detected morphometric features. These morphometric features were compared between the failure (n = 93) and success (n = 38) groups (Table 2). A notable statistical significance was observed for NP, which was considerably higher in the success group (1642.8 ± 1358.9 vs. 901.3 ± 1101.4; p = 0.001). TDLUP showed a significant decrease in the success group compared to the failure group (201.6 ± 213.9 vs. 475.2 ± 730.2; p = 0.001). Similarly, SP was significantly lower in the success group (2855.7 ± 1180.3 vs. 3512.4 ± 1523.4; p = 0.019).

Univariate and multivariate logistic regression analyses were conducted, incorporating both clinicopathologic factors and AI-analyzed intratumoral image patch data from 131 NAC-treated primary breast cancers (Table 3). In univariate analysis, higher Ki-67LI, histologic grade 3, and lower Miller-Payne grade were found to be significantly associated with PDX engraftment, with odds ratios (ORs) of 1.06 (95% CI 1.03–1.09, p < 0.001), 9.20 (95% CI 2.64–32.05, p < 0.001), and 0.34 (95% CI 0.20–0.57, p < 0.001), respectively. For the morphometric features, higher NP, lesser TDLUP, and lesser SP were significantly associated with PDX engraftment. Specifically, a 0.1% increase in NP was associated with higher odds of successful engraftment (OR 1.60, CI 1.38–1.85, p = 0.003), while 0.1% increase in, TDLUP and SP indicated a decreased chance of engraftment (OR 0.22, CI 0.18–0.27, p = 0.034 and OR 0.71, CI 0.60–0.84, p = 0.022, respectively).

In multivariate logistic regression analysis for the NAC group, the variables TDLUP, Ki-67LI, and Miller-Payne grades were selected as the variables that optimized the AIC. A higher Ki-67LI and lower Miller-Payne grade resulted in successful PDX engraftements with ORs of 1.068 (95% CI 1.034–1.102, p < 0.001) and 0.303 (95% CI 0.158–0.577, p < 0.001), respectively. A lesser TDLUP was associated with PDX success, with 0.1% increase in TDLUP yielding an OR of 0.998 (95% CI 0.033–1.000, p = 0.062), showinga statistical trend.

To evaluate the robustness of the logistic regression model, a bootstrap analysis was conducted using 1000 samples. The original AUC was 0.889, with an associated 95% BCa confidence interval ranging from 0.8204 to 0.9348. The optimal cutoff value for the model, determined by Youden's J statistic, was 0.2863. At this cutoff, the PPV was 0.6034 and the NPV was 0.9589 (Fig. 2C).

In the decision tree for the NAC group incorporating both AI-derived and clinicopathological factors, the tree was pruned at two different CPs, with the optimal CP being 0.0395 as determined by the lowest cross-validation error. The root node error was evaluated at 0.2901, based on 93 failures and 38 successes among the observations (Fig. 3B).

When assessing variable importance, Ki-67LI emerged as the most influential factor, contributing 22% to the model's predictive power. This was followed by Miller-Payne grade (13%), subtype (12%), SP (11%), and histologic grade (9%). Other variables like NP, size, RCB score, and ICP each contributed less than or equal to 5%, whereas variables like LVI, metastatic LNs, TILP, TIL, and age had minimal impact (Additional file 3: Table 2).

The decision tree model for the NAC group demonstrated an AUC of 0.8967, with a bias of 0.0065 and a standard error of 0.0419. The BCa 95% confidence interval for the AUC ranged between 0.7680 and 0.9524 after bootstrapping with 1000 replicates. The recommended cutoff point based on Youden's J statistic was 0.2863, at which the model yielded a PPV of 0.6034 and an NPV of 0.9589 (Fig. 2D).

Engraftment success across sequential PDX passages and associated clinicopathological factors: metastatectomy cases (n = 19)

The characteristics of the metastatic breast cancers in relation to the success of PDX engraftment are summarized in Additional file 3: Table 3, and Additional file 1. A statistically significant difference was observed in the distribution of cancer subtypes between PDX engraftment success and failure groups (p = 0.013). Specifically, all successful engraftments occurred for tumors with the TNBC subtype (4 out of 4, 100%) while none of the HR + cases (0 out of 11, 0%) or HR + /HER2 + cases (0 out of 1, 0%) were successful.

The distribution of histologic grade between the unsuccessful and successful PDX groups, although not reaching statistical significance (p = 0.134), demonstrated a higher prevalence of grade 3 tumors in the success group at 75% (3/4) compared to 20% (3/15) in the failure group. Although the anatomical site of metastatectomy did not significantly impact PDX engraftment (p = 0.207), the successful group displayed a more diverse distribution of metastasis sites: bone at 25% (1/4), axillary lymph nodes at 50% (2/4), and lung at 25% (1/4).