Complete Response To Tislelizumab In A Muscle-Invasive Urothelial Carcinoma After Surgery Associated With Tumor Mutational Burden High: A Case Report

Muscle-invasive urothelial carcinoma (MIUC) is a highly aggressive urothelial carcinoma. Radical cystectomy (RC) is standard of treatment, but still more than 50% patients with cancer invading the muscularis propria or involving the regional lymph nodes will have metastatic recurrence. In CheckMate274 study, programmed cell death-1 (PD-1) inhibitor nivolumab as adjuvant treatment has shown effective for patients with MIUC. Tislelizumab is an anti-human PD-1 monoclonal IgG4 antibody which was specifically engineered to minimize FcɣR macrophage binding to abrogate antibody-dependent phagocytosis. But there is no report of tislelizumab as adjuvant treatment in MIUC currently. Here, we report a case of MIUC in a patient with PD-L1-negative, microsatellite stable (MSS), high tumor mutational burden (TMB-H) obtained complete response (CR) receiving tislelizumab therapy after surgery. Progression-free survival (PFS) exceeded 6 months since tislelizumab treatment. To our knowledge, this is the first reported case of MIUC patient with PD-L1-negative, MSS and TMB-H who responded well to tislelizumab as adjuvant treatment. However, we still need more studies to assess the efficacy of tislelizumab as adjuvant treatment in MIUC and to confirm that TMB is a predicted biomarker of tislelizumab for efficacy.

Machine Learning to Discern Interactive Clusters of Risk Factors for Late Recurrence of Metastatic Breast Cancer

Background: Risk of metastatic recurrence of breast cancer after initial diagnosis and treatment depends on the presence of a number of risk factors. Although most univariate risk factors have been identified using classical methods, machine-learning methods are also being used to tease out non-obvious contributors to a patient’s individual risk of developing late distant metastasis. Bayesian-network algorithms can identify not only risk factors but also interactions among these risks, which consequently may increase the risk of developing metastatic breast cancer. We proposed to apply a previously developed machine-learning method to discern risk factors of 5-, 10- and 15-year metastases. Methods: We applied a previously validated algorithm named the Markov Blanket and Interactive Risk Factor Learner (MBIL) to the electronic health record (EHR)-based Lynn Sage Database (LSDB) from the Lynn Sage Comprehensive Breast Center at Northwestern Memorial Hospital. This algorithm provided an output of both single and interactive risk factors of 5-, 10-, and 15-year metastases from the LSDB. We individually examined and interpreted the clinical relevance of these interactions based on years to metastasis and reliance on interactivity between risk factors. Results: We found that, with lower alpha values (low interactivity score), the prevalence of variables with an independent influence on long-term metastasis was higher (i.e., HER2, TNEG). As the value of alpha increased to 480, stronger interactions were needed to define clusters of factors that increased the risk of metastasis (i.e., ER, smoking, race, alcohol usage). Conclusion: MBIL identified single and interacting risk factors of metastatic breast cancer, many of which were supported by clinical evidence. These results strongly recommend the development of further large data studies with different databases to validate the degree to which some of these variables impact metastatic breast cancer in the long term.

Machine-Learning to Discern Interactive Clusters of Risk Factors for Late Recurrence of Metastatic Breast Cancer

Background: Risk of metastatic recurrence of breast cancer after initial diagnosis and treatment depends on the presence of a number of risk factors. Although most univariate risk factors have been identified using classical methods, machine-learning methods are also being conducted to tease out non-obvious contributors to a patient’s individual risk of developing late distant metastasis. Bayesian-network algorithms may predict not only risk factors but also interactions among these risks, which consequently lead to metastatic breast cancer. We proposed to apply a previously developed machine-learning method to predict risk factors of 5-, 10- and 15-year metastasis. Methods: We applied a previously validated algorithm named the Markov Blanket and Interactive risk factor Learner (MBIL) on the electronic health record (EHR)-based Lynn Sage database (LSDB) from the Lynn Sage Comprehensive Breast Cancer at Northwestern Memorial Hospital. This algorithm provided an output of both single and interactive risk factors of 5-, 10-, and 15-year metastasis from LSDB. We individually examined and interpreted the clinical relevance of these interactions based on years to metastasis and the reliance on interactivity between risk factors. Results: We found that with lower alpha values (low interactivity score), the prevalence of variables with an independent influence on long term metastasis was higher (i.e., HER2, TNEG). As the value of alpha increased to 480, stronger interactions were needed to define clusters of factors that increased the risk of metastasis (i.e., ER, smoking, race, alcohol usage). Conclusion: MBIL identified single and interacting risk factors of metastatic breast cancer, many of which were supported by clinical evidence. These results strongly recommend the development of further large data studies with different databases to validate the degree to which some of these variables impact metastatic breast cancer in the long term.

A Personalized Neoantigen Vaccine in Combination with Platinum-Based Chemotherapy Induces a T-Cell Response Coinciding with a Complete Response in Endometrial Carcinoma

Endometrial cancer (EC) is a common gynecological malignancy and the fourth most common malignancy in European and North American women. Amongst EC, the advanced serous, p53-mutated, and pMMR subtypes have the highest risk of relapse despite optimal standard of care therapy. At present, there is no standard of care maintenance treatment to prevent relapse among these high-risk patients. Vaccines are a form of immunotherapy that can potentially increase the immunogenicity of pMMR, serous, and p53-mutated tumors to render them responsive to check point inhibitor-based immunotherapy. We demonstrate, for the first time, the feasibility of generating a personalized dendritic cell vaccine pulsed with peptide neoantigens in a patient with pMMR, p53-mutated, and serous endometrial adenocarcinoma (SEC). The personalized vaccine was administered in combination with systemic chemotherapy to treat an inoperable metastatic recurrence. This treatment association demonstrated the safety and immunogenicity of the personalized dendritic cell vaccine. Interestingly, a complete oncological response was obtained with respect to both radiological assessment and the tumor marker CA-125.

Multidisciplinary management of solitary hypervascular metastatic recurrence of renal cell carcinoma presenting with pathological femoral fracture

Renal cell carcinoma (RCC) frequently presents with osseous metastasis, predominantly lytic and prone to pathological fracture. The metastatic lesion in the extremity presents with local swelling, pain and immobility due to pathological fracture. The solitary or oligometastatic lesions should be treated with curative intent, which can help the patient to lead a more prolonged and disability-free life. The RCCs and their metastases are hypervascular with an exuberant arterial supply. Surgery can lead to uncontrolled life-threatening haemorrhage. Preoperative transarterial embolisation reduces tumour vascularity significantly and reduces intraoperative blood loss. We present a 46-year-old male patient with solitary hypervascular metastatic recurrence of RCC with a pathological femoral fracture with an infeasible initial surgery due to profuse haemorrhage. He was successfully treated by preoperative transarterial embolisation, followed by surgical resection and implantation of a megaprosthesis. Multidisciplinary management reduces patient morbidity and mortality with successful treatment in solitary hypervascular metastasis from RCC.

Postoperative Natural Killer Cell Dysfunction: The Prime Suspect in the Case of Metastasis Following Curative Cancer Surgery

Surgical resection is the foundation for the curative treatment of solid tumors. However, metastatic recurrence due to the difficulty in eradicating micrometastases remain a feared outcome. Paradoxically, despite the beneficial effects of surgical removal of the primary tumor, the physiological stress resulting from surgical trauma serves to promote cancer recurrence and metastasis. The postoperative environment suppresses critical anti-tumor immune effector cells, including Natural Killer (NK) cells. The literature suggests that NK cells are critical mediators in the formation of metastases immediately following surgery. The following review will highlight the mechanisms that promote the formation of micrometastases by directly or indirectly inducing NK cell suppression following surgery. These include tissue hypoxia, neuroendocrine activation, hypercoagulation, the pro-inflammatory phase, and the anti-inflammatory phase. Perioperative therapeutic strategies designed to prevent or reverse NK cell dysfunction will also be examined for their potential to improve cancer outcomes by preventing surgery-induced metastases.

The 31-gene expression profile stratifies recurrence and metastasis risk in patients with cutaneous melanoma

Aim: Sentinel node biopsy is a prognostic indicator of melanoma recurrence. We hypothesized that adding the primary melanoma molecular signature from the 31-gene expression profile (31-GEP) test could refine the risk of recurrence prognosis for patients with stage I–III melanoma. Materials & methods: Four hundred thirty-eight patients with stage I–III melanoma consecutively tested with the 31-GEP were retrospectively analyzed. The 31-GEP stratified patients as low-risk (Class 1A), intermediate-risk (Class 1B/2A) or high risk (Class 2B) of recurrence or metastasis. Results: The 31-GEP significantly stratified patient risk for recurrence-free survival (p < 0.001), distant metastasis-free survival (p < 0.001) and melanoma-specific survival (p < 0.001) and was a significant, independent predictor of metastatic recurrence (hazard ratio: 5.38; p = 0.014). Conclusion: The 31-GEP improves prognostic accuracy in stage I–III melanoma.