Abstract:Immunosurveillance plays an important role in breast cancer (BC) prognosis and progression, and can be geared by immunogenic chemotherapy. In a cohort of 1023 BC patients treated with neoadjuvant chemotherapy (NAC), 40% of the individuals took comedications mostly linked to aging and comorbidities. We systematically analyzed the off-target effects of 1178 concurrent comedications (classified according to the Anatomical Therapeutic Chemical (ATC) Classification System) on the density of tumorinfiltrating lympho… Show more
“…The contracted pool of naive T and B cells, subclinical inflammation, and accumulating senescent lymphocytes limit the ability of elderly subjects to counter neoantigens and predispose them to autoimmunity [171]. Concurrent COPD, comorbidities and comedications ,which are more frequent in lung compared to other cancer patients [172], further aggravate immunologic dysfunction [173, 174]. Since immunosenescence involves different T-cell subsets and differentiation pathways than cancer-related T-cell exhaustion (KLRG1 + /CD57 + vs.KLRG lo /PD-1 + /IR + , respectively) [175, 176], coexistence of the 2 conditions has additive effects.…”
Section: Immunopathogenesis Of Lung Cancermentioning
Cancer immunotherapy represents the most dynamic field of biomedical research currently, with thoracic immuno-oncology as a forerunner. PD-(L)1 inhibitors are already part of standard first-line treatment for both non-small-cell and small-cell lung cancer, while unprecedented 5-year survival rates of 15–25% have been achieved in pretreated patients with metastatic disease. Evolving strategies are mainly aiming for improvement of T-cell function, increase of immune activation in the tumor microenvironment (TME), and supply of tumor-reactive lymphocytes. Several novel therapeutics have demonstrated preclinical efficacy and are increasingly used in rational combinations within clinical trials. Two overarching trends dominate: extension of immunotherapy to earlier disease stages, mainly as neoadjuvant treatment, and a shift of focus towards multivalent, individualized, mutatome-based antigen-specific modalities, mainly adoptive cell therapies and cancer vaccines. The former ensures ample availability of treated and untreated patient samples, the latter facilitates deeper mechanistic insights, and both in combination build an overwhelming force that is accelerating progress and driving the greatest revolution cancer medicine has seen so far. Today, immune modulation represents the most potent therapeutic modality in oncology, the most important topic in clinical and translational cancer research, and arguably our greatest, meanwhile justified hope for achieving cure of pulmonary neoplasms and other malignancies in the next future.
“…The contracted pool of naive T and B cells, subclinical inflammation, and accumulating senescent lymphocytes limit the ability of elderly subjects to counter neoantigens and predispose them to autoimmunity [171]. Concurrent COPD, comorbidities and comedications ,which are more frequent in lung compared to other cancer patients [172], further aggravate immunologic dysfunction [173, 174]. Since immunosenescence involves different T-cell subsets and differentiation pathways than cancer-related T-cell exhaustion (KLRG1 + /CD57 + vs.KLRG lo /PD-1 + /IR + , respectively) [175, 176], coexistence of the 2 conditions has additive effects.…”
Section: Immunopathogenesis Of Lung Cancermentioning
Cancer immunotherapy represents the most dynamic field of biomedical research currently, with thoracic immuno-oncology as a forerunner. PD-(L)1 inhibitors are already part of standard first-line treatment for both non-small-cell and small-cell lung cancer, while unprecedented 5-year survival rates of 15–25% have been achieved in pretreated patients with metastatic disease. Evolving strategies are mainly aiming for improvement of T-cell function, increase of immune activation in the tumor microenvironment (TME), and supply of tumor-reactive lymphocytes. Several novel therapeutics have demonstrated preclinical efficacy and are increasingly used in rational combinations within clinical trials. Two overarching trends dominate: extension of immunotherapy to earlier disease stages, mainly as neoadjuvant treatment, and a shift of focus towards multivalent, individualized, mutatome-based antigen-specific modalities, mainly adoptive cell therapies and cancer vaccines. The former ensures ample availability of treated and untreated patient samples, the latter facilitates deeper mechanistic insights, and both in combination build an overwhelming force that is accelerating progress and driving the greatest revolution cancer medicine has seen so far. Today, immune modulation represents the most potent therapeutic modality in oncology, the most important topic in clinical and translational cancer research, and arguably our greatest, meanwhile justified hope for achieving cure of pulmonary neoplasms and other malignancies in the next future.
“…Several retrospective studies have reported this association, with the use of statins 44 , NSAIDs 45 , or beta-blockers 46 found to have indirect anticarcinogenic effects. It has recently been suggested that these non-oncological treatments may have immunomodulatory and chemosensitizing effects 47 . Fig.…”
Despite unprecedented amount of information now available in medical records, health data remain underexploited due to their heterogeneity and complexity. Simple charts and hypothesis-driven statistics can no longer apprehend the content of information-rich clinical data. There is, therefore, a clear need for powerful interactive visualization tools enabling medical practitioners to perceive the patterns and insights gained by state-of-the-art machine learning algorithms. Here, we report an interactive graphical interface for use as the front end of a machine learning causal inference server (MIIC), to facilitate the visualization and comprehension by clinicians of relationships between clinically relevant variables. The widespread use of such tools, facilitating the interactive exploration of datasets, is crucial both for data visualization and for the generation of research hypotheses. We demonstrate the utility of the MIIC interactive interface, by exploring the clinical network of a large cohort of breast cancer patients treated with neoadjuvant chemotherapy (NAC). This example highlights, in particular, the direct and indirect links between post-NAC clinical responses and patient survival. The MIIC interactive graphical interface has the potential to help clinicians identify actionable nodes and edges in clinical networks, thereby ultimately improving the patient care pathway.
“…While several extrinsic risk factors have been identified for different cancers, no single one can account for their risk proportions, suggesting complex mechanisms for their etiologies. These factors include factors related to the characteristics of the host (gender, age, body mass index) [16][17][18][19][20], toxins (tobacco, alcohol consumption) [21,22], exogenous exposures (hormonal treatments, chronically used medications) [23,24], and lifestyle (nutritional factors, diet, physical activity, sleep duration) [25,26], as well as environmental factors, such as pollution [27], sunlight exposure, UV radiation, ionizing radiation [28], weather/temperature, and the seasons [29]. About 20% to 40% of cancer cases and almost half of cancer deaths can be potentially prevented through lifestyle and environmental changes [30][31][32].…”
Breast cancer (BC) is the most common cancer in women worldwide. Neoadjuvant chemotherapy (NAC) makes it possible to monitor in vivo response to treatment. Several studies have investigated the impact of the seasons on the incidence and detection of BC, on tumor composition, and on the prognosis of BC. However, no evidence is available on their association with immune infiltration and the response to treatment. The objective of this study was to analyze pre- and post-NAC immune infiltration as assessed by TIL levels, the response to treatment as assessed by pathological complete response (pCR) rates, and oncological outcomes as assessed by relapse-free survival (RFS) or overall survival (OS) according to the seasonality of BC diagnoses in a clinical cohort of patients treated with neoadjuvant chemotherapy. Out of 1199 patients, the repartition of the season at BC diagnosis showed that 27.2% were diagnosed in fall, 25.4% in winter, 24% in spring, and 23.4% in summer. Baseline patient and tumor characteristics, including notable pre-NAC TIL levels, were not significantly different in terms of the season of BC diagnosis. Similarly, the pCR rates were not different. No association for oncological outcome was identified. Our data do not support the idea that the seasonality of diagnoses has a major impact on the natural history of BC treated with NAC.
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