Hepatic and Extrahepatic Colorectal Metastases Have Discordant Responses to Systemic Therapy. Pathology Data from Patients Undergoing Simultaneous Resection of Multiple Tumor Sites

Predictors of poor outcome following liver biopsy for the investigation of new hepatic space occupying lesion/s

BACKGROUND

Ultrasound-guided percutaneous liver biopsy (UPLB) is currently performed mainly to determine if new hepatic space occupying lesions (SOL) represent benign, primary malignant, or metastatic disease. This study sought to investigate the outcome of UPLB in this setting.

METHODS

In a retrospective study, patients with a new hepatic SOL who underwent UPLB during 1/2006-12/2016 were included and followed to 12/2018. Clinical data and pathology reports were reviewed. Mortality within 60 days and no change in patients' management following UPLB were defined as medically futile.

RESULTS

Included 140 patients, 50% male, mean age 68.8 ± 11.5 years; 112 patients died, all of malignant disease. 32 patients (23%) died within 60 days of UPLB. Median post-UPLB survival was 151 days. Survival was significantly shorter in patients with >1 hepatic lesion (n = 108) or an extrahepatic malignant lesion (n = 77) (p = 0.0082, p = 0.0301, respectively). On Cox Proportional Hazards analysis, significant predictors of mortality within 60 days of UPLB were: age as a continuous variable, (HR 1.070, 95% CI 1.011-1.131, p = 0.018), serum albumin <2.9 g/dL, (HR 4.822 95% CI 1.335-17.425, p = 0.016) and serum LDH >1500 U/L (HR 9.443, 95% CI 3.404-26.197, p < 0.0001).

CONCLUSIONS

In patients with these features or with disseminated disease, liver biopsy should be carefully reconsidered.

Effect of 5-Fluoro-Uracile + Oxaliplatin chemotherapy on the histological response of PEritoneal and hePatIc corectal metasTases in a mOuse model: PEPITO experimental study

BACKGROUND

The histological responses (HRs) after systemic chemotherapy should be used to determine the optimal management of patients with peritoneal and liver metastasis from colorectal cancer (cPM, cLM), in curative intent. We aimed to compare HRs of cPM and cLM in metastatic mice model after chemotherapy.

METHODS

Colon carcinoma CT26-luc cells were transplanted into syngeneic BALB/c mice by intraperitoneal (leading to cPM), intrasplenic (leading to cLM), or intraperitoneal + intrasplenic (leading to cPM cLM) injections and follow up using bioluminescence during 21 days. Bi-chemotherapeutic treatment (5-fluorouracil at D11, D17, and D20, and oxaliplatin at D13 and D19) was administered. The peritoneal cancer index (PCI) and HRs using Peritoneal Regression Grading Score (PRGS) and Tumor Regression Grade (TRG) classifications were analyzed at day 21.

RESULTS

Unlike bioluminescence rate, PCI was reduced after chemotherapy in all treated groups with cPM comparatively to controls (33 ± 9.5 vs. 19.8 ± 5, p = 0.002 for cPM groups; 37.7 ± 3.6 vs. 25.2 ± 10.8, p = 0.0003 for the cPM + cLM groups). The complete or major HR rates were higher in all treated groups compared to the non-treated mice (cPM, 2.29 ± 0.55 vs. 3.56 ± 1.01; cLM, 2.43 ± 1.89 vs. 4.86 ± 0.378; cPM + cLM, 2.73 ± 1.03 and 2.2 ± 0.65 vs. 3.79 ± 0.75 and 4.36 ± 0.43). The complete or major HR rates after chemotherapy were similar across the metastatic sites in 60% for cPM + cLM group.

CONCLUSIONS

The efficacy of chemotherapeutic treatment did not differ between the metastatic sites. Murine models are suitable in histological analyses to study tumor development and regression but clinical study will be performed to confirm these results.

High Dual Expression of the Biomarkers CD44v6/α2β1 and CD44v6/PD-L1 Indicate Early Recurrence after Colorectal Hepatic Metastasectomy

Considering the biology of CRC, distant metastases might support the identification of high-risk patients for early recurrence and targeted therapy. Expression of a panel of druggable, metastasis-related biomarkers was immunohistochemically analyzed in 53 liver (LM) and 15 lung metastases (LuM) and correlated with survival. Differential expression between LM and LuM was observed for the growth factor receptors IGF1R (LuM 92.3% vs. LM 75.8%, p = 0.013), EGFR (LuM 68% vs. LM 41.5%, p = 0.004), the cell adhesion molecules CD44v6 (LuM 55.7% vs. LM 34.9%, p = 0.019) and α2β1 (LuM 88.3% vs. LM 58.5%, p = 0.001) and the check point molecule PD-L1 (LuM 6.1% vs. LM 3.3%, p = 0.005). Contrary, expression of HGFR, Hsp90, Muc1, Her2/neu, ERα and PR was comparable in LuM and LM. In the LM cohort (n = 52), a high CD44v6 expression was identified as an independent factor of poor prognosis (PFS: HR 2.37, 95% CI 1.18-4.78, p = 0.016). High co-expression of CD44v6/α2β1 (HR 4.14, 95% CI 1.65-10.38, p = 0.002) and CD44v6/PD-L1 (HR 2.88, 95% CI 1.21-6.85, p = 0.017) indicated early recurrence after hepatectomy, in a substantial number of patients (CD44v6/α2β1: 11 (21.15%) patients; CD44v6/PD-L1: 12 (23.1%) patients). Dual expression of druggable protein biomarkers may refine prognostic prediction and stratify high-risk patients for new therapeutic concepts, depending on the metastatic location.

Is precision medicine for colorectal liver metastases still a utopia? New perspectives by modern biomarkers, radiomics, and artificial intelligence

The management of patients with liver metastases from colorectal cancer is still debated. Several therapeutic options and treatment strategies are available for an extremely heterogeneous clinical scenario. Adequate prediction of patients’ outcomes and of the effectiveness of chemotherapy and loco-regional treatments are crucial to reach a precision medicine approach. This has been an unmet need for a long time, but recent studies have opened new perspectives. New morphological biomarkers have been identified. The dynamic evaluation of the metastases across a time interval, with or without chemotherapy, provided a reliable assessment of the tumor biology. Genetics have been explored and, thanks to their strong association with prognosis, have the potential to drive treatment planning. The liver-tumor interface has been identified as one of the main determinants of tumor progression, and its components, in particular the immune infiltrate, are the focus of major research. Image mining and analyses provided new insights on tumor biology and are expected to have a relevant impact on clinical practice. Artificial intelligence is a further step forward. The present paper depicts the evolution of clinical decision-making for patients affected by colorectal liver metastases, facing modern biomarkers and innovative opportunities that will characterize the evolution of clinical research and practice in the next few years.

Temporal evolution of metastatic disease: part II-a novel proposal for subcategorization of metastatic disease from non-neural solid tumors with diverse histologies and locations

Tumor spread is a continuous process and metastases can further disseminate. Currently, metastatic disease from most primary tumors is subcategorized as M0 if absent and M1 if present. However, metastatic disease in different locations may have different prognostic implications, even if it is from the same primary tumor. The current staging systems for metastatic disease have not evolved to match our understanding of the disease's evolution or the evolving treatment paradigms. Primary tumor-specific subcategorization of metastatic disease is currently available for a few tumors, but not all of them imply further remote spread of tumor, similar to tumor (T) and N (node) subcategorizations of the TNM staging, nor are they applicable to wide spectrum of other tumors. In this era of precision medicine, tumor-type agnostic therapies based on common biomarkers rather than primary tumor sites are emerging, but a subcategorization system applicable to metastatic disease from diverse primary tumor locations and with diverse histologies is not available. In this article, we discuss the need to further classify the metastatic disease and present a subcategorization applicable to metastatic disease from non-neural solid tumors from different primary tumor sites and with different histologies, which is based on the temporal spread of metastatic disease. Our proposed subcategorization scheme for metastatic disease into M0, M1, M2 and M3, is universally applicable to a diverse spectrum of non-neural solid tumors, and increasing M subcategorization represents further remote spread of tumor.