Recurrent

Statement of the Problem: Cancer develops in a sequenced manner. Patches of lung cells gain the ability to multiply faster than their neighboring normal cells by acquiring mutations. These patches of cells are called “premalignant lesions" (PMLs). Some of these lesions may eventually become cancer. Knowledge about how some PMLs may develop into lung cancer is central to developing tools for Cancer Interception—interrupting the cancer development process and blocking progression to advanced metastatic disease. However, we lack effective lung cancer interception approaches due to our incomplete understanding of the earliest molecular events associated with the development of lung cancer, as well as the challenge in developing personalized tools for early detection and prevention.

Proposed Solution: The Dream Team will use a holistic approach to understand the genetics, immunology, radiological (imaging) profile, and treatment response of patients who show evidence of abnormal lung tissue that puts them at high risk to develop lung cancer. The Team has access to unique groups of patient populations (adenocarcinoma—ADC and squamous cell carcinoma—SCC) with signs of lung cancer risk, and has assembled a collection of blood and tissue specimens from these individuals. The main objective of the Team is to understand how lung cancer develops and test methods that will block the development of cancer. Their three-pronged approach to solve this problem will include:

• Creation of a molecular pre-cancer genome atlas (PCGA) of the lung with the goal of identifying which types of precancerous lung tissue require aggressive treatment and which treatments will block the development of these abnormal lesions to invasive lung cancer.
• Development of two diagnostic tools that can be directly applied in the clinic for simple, yet accurate, detection of early lung cancer: 1) the use of nasal swabs, blood tests, and imaging to confirm whether lung abnormalities found on chest imaging are lung cancer or benign lung disease. 2) blood tests that can identify patients at the earliest stages of lung cancer recurrence, thus enabling their timely and effective intervention with therapies such as those that boost the immune system.
• Development of tests to identify which individuals are most likely to benefit from a number of treatment strategies to intercept lung cancer, including emerging immunotherapies.

Statement of the Problem: Currently, low dose CT (LDCT) screening for lung cancer is recommended for high-risk individuals, defined as current or former smokers (quit within the past 15 years) with a 30-or-more pack-year smoking history, 55 to 74 years of age, and with no evidence of lung cancer. However, uptake of LDCT screening has been impeded by high rates of false-positive results, which in turn add significant physical and emotional stress to patients as well as additional healthcare costs. Since LDCT is now commonly available, lung nodules detected in LDCT scans are plentiful, but imprecise guidelines for care make it a challenge to ensure quality follow-up and expedited diagnoses for those with lung cancer. Furthermore, screening is not available for those who don’t meet the screening criteria (younger age or low smoking history) when they are the exact population among whom lung cancer rates are rising.

Proposed Solution: Leveraging expertise of academic research centers (Massachusetts General Hospital, Broad Institute, and Stanford University) and a non-profit managed-care consortium (Kaiser Permanente), the Dream Team will develop a test called the Lung Cancer Interception Assay (LCIA) to complement LDCT screening.

The proposal is novel in that it will develop complex computational algorithms to combine high-sensitivity/high-accuracy blood-based molecular biomarkers with image-based biomarkers to create the  LCIA assay. At the end of the award period, a streamlined product, the LCIA, will be ready to use in population-level definitive trials for the early detection of lung cancer.

Small cell lung cancer (SCLC) is a common, aggressive, and exceptionally lethal malignancy that is usually metastatic at the time of diagnosis. Newly diagnosed SCLC is remarkably sensitive to chemotherapy, with response rates of over 60% to cisplatin and etoposide even in patients with extensive-stage disease. These chemotherapy responses are often both rapid and deep, but are never curative: extensive-stage disease recurs universally, in most cases within a few months after completion of first-line therapy. Recurrent SCLC is remarkably refractory to treatment. The median overall survival of patients with extensive-stage SCLC is about nine months from the time of initial diagnosis, and the 5-year survival is less than 1%. Defining the basis of acquired chemotherapy resistance in SCLC could have clear and important ramifications for clinical management of this disease. Targeting the key determinants of acquired chemoresistance could be integrated into first-line treatment, and/or could drive rational targeted therapeutic studies for recurrent disease. We now have in hand multiple technical approaches that could comprehensively define the genomic and epigenetic changes that drive acquired resistance in SCLC. In this award application, we propose two parallel approaches to analyzing acquired resistance in SCLC: analysis of primary samples and patient-derived xenografts before and after acquired resistance.

Approximately 30% of patients with stage 1 non-small cell lung cancer (NSCLC) will die from recurrent disease despite surgical resection . Currently, there is a lack of reliable molecular predictors being routinely used in the clinic for identifying patients at high risk for developing recurrent disease and therefore may benefit from more aggressive therapies. To date several groups have applied microarray-based methods for global gene expression profiling of frozen tumor tissues in an attempt to identify prognostic ‘signatures’ in patients with early stage NSCLC.  Building upon this, through a meta-analysis of seven independent microarray studies Govindan et al. were able to identify a 64-gene signature that is highly predictive of survival in patients with stage I NSCLC. However, recent advances in high-throughput sequencing have enabled an unbiased view of the transcriptome including novel transcriptional events such as gene fusions, splicing variants, and novel transcripts. This offers an opportunity to incorporate additional markers, which may have eluded previous technologies, but could serve for improving a predictive signature of survival in non-small cell lung cancer (NSCLC). However, despite the potential of RNA-Seq to improve existing predictive signatures, we are still faced with the challenge of translating these findings into a platform where they can be reliably applied to formalin fixed paraffin embedded (FFPE) clinical tissue specimens routinely collected in tertiary referral centers. Therefore, this proposal focuses on refining the 64-gene predictive signature using transcriptome sequencing and developing a NanoString nCounter assay to reliably predict survival of patients with resected stage I NSCLC, using RNA derived from FFPE tumor blocks. To accomplish this we have proposed the following specific aims: (1) use transcriptome sequencing to detect molecular predictors of outcome in patients with stage I NSCLC, (2) validate the technical performance of the NanoString nCounter using an RNA-Seq predictive signature across FFPE tumor blocks of resected stage I NSCLC, and (3) clinically validate the prognostic power of the predictive signature across an independent sample set. Overall, if the proposed aims are met, this research could lead to an improved assay that could be translated into future prospective studies for risk-stratifying stage I NSCLC patients.