Lung cancer

The lungs are the most common source of cerebral metastases and these are usually multiple. The lung primary may be so small as torender it occult.

Necropsy demonstrates cerebral metastases in up to 50 % of patients with Small-cell lung cancer and non squamous, non-Small cell lung cancer ¹⁾.

Lung cancer is the leading cause of cancer mortality worldwide, accounting for 1.38 million annual deaths, representing 18.2% of total deaths from cancer ²⁾.

see Lung cancer classification.

Historically lung cancer is the most frequent neoplasm associated with sensory neuropathy.

The current procedure necessitates an invasive tissue biopsy for diagnosis and molecular subtyping, which presents patients with risk, morbidity, anxiety, and high false-positive rates. The high-risk diagnostic approach has highlighted the need to search for a reliable, low-risk noninvasive diagnostic approach to capture lung cancer heterogeneity precisely. The immune interaction profile of lung cancer is driven by immune cells' distinctive, precise interactions with the tumor microenvironment. Here, we hypothesize that immune cells, particularly T cells, can be used for accurate lung cancer diagnosis by exploiting the distinctive immune-tumor interaction by detecting the immune-diagnostic signature. We have developed an ultrasensitive T-sense nanosensor to probe these specific diagnostic signatures using the physical synthesis process of multiphoton ionization. Our research employed predictive in vitro models of lung cancers, cancer-associated T cells (PCAT, MCAT), and CSC-associated T cells (PCSCAT, MCSCAT), from primary and metastatic lung cancer patients to reveal the immune-diagnostic signature and uncover the molecular, functional, and phenotypic separation between patient-derived T cells (PDT) and healthy samples. We demonstrated this by adopting a machine learning model trained with SERS data obtained using cocultured T cells with preclinical models (CAT, CSCAT) of primary (H69AR) and metastatic lung cancer (H1915). Interrogating these distinct signatures with PDT captured the complexity and diversity of the tumor-associated T-cell signature across the patient population, exposing the clinical feasibility of immune diagnosis in an independent cohort of patient samples. Thus, our predictive approach using T cells from the patient peripheral blood showed a highly accurate diagnosis with a specificity and sensitivity of 94.1% and 100%, respectively, for primary lung cancer and 97.9% and 94.4% for metastatic lung cancer. Our results prove that the immune-diagnostic signature developed in this study could be used as a clinical technology for cancer diagnosis and determine the course of clinical management with T cells ³⁾.

A lung lesion should be biopsied to rule out SCLC before obtaining tissue from the cerebral mass.

PET Scan can detect small malignancies.

Useful in NSCLC.

Not useful in the initial evaluation of SCLC.

Chest CT: usually includes adrenals and liver (thus abdomen and pelvis CT not necessary)

Bone scan

Brain: CT or MRI

Lung cancer treatment remains a challenge for clinical practice and new therapeutic approaches are urgently needed. Loss of functional WEE1 kinase causes DNA replication stress, DNA damage and unscheduled mitotic entry due to elevated CDK activity. The selective WEE1 inhibitor MK-1775 synergize with DNA-damaging agent to inhibit cancer cell growth. Here we report that inhibition of Sirt1 deacetylase through small interfering RNA or selective inhibitor Ex527 greatly enhances MK-1775-induced growth inhibition and apoptosis in human lung cancer cells. We further demonstrate that Sirt1 interacts and deacetylates homologous recombination (HR) repair machinery proteins, including NBS1 and Rad51. Inhibition of Sirt1 impairs HR repair activity, which causes unrepairable damage when combining MK-1775 and Ex527. Meanwhile, combination of MK-1775 and Ex527 induces cooperative antitumor activity in lung cancer xenograft model in vivo. Thus, a study provides a novel therapeutic strategy to optimize MK-1775 treatment efficiency in lung cancers ⁴⁾.

see Lung cancer metastases