Antibodies against tumor-related antigens are produced in humans with a variety of cancers. Although several methods exist for profiling antibodies, none are able to comprehensively characterize the antibodies produced in an immune response and to then rationally identify those likely to be functional-i.e., those that are key to the containment or pathogenesis of cancer. To address this challenge, we are developing technology that uses DNA barcoding for the large-scale sequencing of paired heavy- (HC) and light-chain (LC) antibody genes from individual B cells. This technology enables sequencing of the paired HC+LC antibody genes from hundreds to thousands of individual B cells in each experiment, thereby yielding antibody sequence datasets that enable bioinformatic generation of phylogenetic trees representing the antibody repertoire, as well as rational selection of key antibodies for recombinant expression, characterization of their antigen targets, and use as diagnostics or therapeutics. In cancer, we hypothesize that in-depth characterization of the antibody repertoire produced by circulating plasmablasts will uncover functional anti-tumor antibody responses. We detected high levels of plasmablasts (activated B cells) in the blood of individuals with metastatic lung adenocarcinoma whose cancer had not progressed several years after therapy. We applied our antibody repertoire capture (ARC) technology to plasmablasts isolated from a lung adenocarcinoma patient, and used bioinformatics to generate an phylogenetic tree of the antibody response and to select antibodies from large clonal families for recombinant expression. We identified three recombinant antibodies that bound in immunohistochemical analyses to >80% of lung adenocarcinomas derived from other patients. This application comprises four aims: In Aim 1, we will (i) develop a microfluidic front end that increases the throughput and depth of sequencing of ARC by an order of magnitude, and (ii) technically validate ARC for sequencing anti- cancer antibody responses. In Aim 2, we will use ARC to profile and compare the antibody responses in "non- progressors" and "progressors" with lung adenocarcinoma. In Aim 3, we will clone and express rationally selected, affinity-matured antibodies of plasmablasts from individuals with lung adenocarcinoma and identify their tumor antigen targets. In Aim 4, we will evaluate the potential of select anti-tumor antibodies andtumor antigens to serve as diagnostic/prognostic biomarkers or therapeutics for lung adenocarcinoma. Success of the proposed studies would transform cancer research by technically refining and validating ARC technology as a tool for the analysis of anti-cancer antibody responses-one that would advance our understanding of anti-cancer antibody responses and facilitate development of antibody-based diagnostics and therapeutics.