While cancer immunotherapy has gained much deserved attention in recent years, many areas regarding the optimization of such modalities remain unexplored, including the development of novel approaches and the strategic combination of therapies that target multiple aspects of the cancer-immunity cycle. Our own work involves the use of gene transfer technology to promote cell death and immune stimulation. Such immunogenic cell death, mediated by the combined transfer of the alternate reading frame (p14ARF in humans and p19Arf in mice) and the interferon-β cDNA in our case, was shown to promote an antitumor immune response in mouse models of melanoma and lung carcinoma. With these encouraging results, we are now setting out on the road toward translational and preclinical development of our novel immunotherapeutic approach. Here, we outline the perspectives and challenges that we face, including the use of human tumor and immune cells to verify the response seen in mouse models and the incorporation of clinically relevant models, such as patient-derived xenografts and spontaneous tumors in animals. In addition, we seek to combine our immunotherapeutic approach with other treatments, such as chemotherapy or checkpoint blockade, with the goal of reducing dosage and increasing efficacy. The success of any translational research requires the cooperation of a multidisciplinary team of professionals involved in laboratory and clinical research, a relationship that is fostered at the Cancer Institute of Sao Paulo.

Cancer-associated fibroblasts (CAF) influence tumor development at primary as well as in metastatic sites, but there have been no direct comparisons of the transcriptional profiles of stromal cells from different tumor sites. In this study, we used customized cDNA microarrays to compare the gene expression profile of stromal cells from primary tumor (CAF, n = 4), lymph node metastasis (N+, n = 3) and bone marrow (BM, n = 4) obtained from breast cancer patients. Biological validation was done in another 16 samples by RT-qPCR. Differences between CAF vs N+, CAF vs BM and N+ vs BM were represented by 20, 235 and 245 genes, respectively (SAM test, FDR < 0.01). Functional analysis revealed that genes related to development and morphogenesis were overrepresented. In a biological validation set, NOTCH2 was confirmed to be more expressed in N+ (vs CAF) and ADCY2, HECTD1, HNMT, LOX, MACF1, SLC1A3 and USP16 more expressed in BM (vs CAF). Only small differences were observed in the transcriptional profiles of fibroblasts from the primary tumor and lymph node of breast cancer patients, whereas greater differences were observed between bone marrow stromal cells and the other two sites. These differences may reflect the activities of distinct differentiation programs.

OBJETIVO: Na tentativa de melhorar a acurácia dos modelos preditivos de resposta à quimioterapia neoadjuvante em câncer de mama, utilizou-se a tecnologia de cDNA microarray para determinar o perfil transcricional dos tumores. A avaliação de assinaturas gênicas, associadas à predição de resposta à quimioterapia neoadjuvante, é o objeto desta revisão. MÉTODOS: Foi realizada busca no banco de dados eletrônico http://www.ncbi.nlm.nih.gov/pubmed/, usando as palavras "breast cancer" AND "neoadjuvant/primary chemotherapy" AND "gene expression profile/microarray". Recuperaram-se 279 publicações, excluindo-se as repetições, selecionando-se para exposição aquelas consideradas mais relevantes pelos autores. RESULTADOS: O número de publicações acerca desse assunto vem crescendo ao longo dos anos, chegando a mais de 50 em 2010, abordando resposta a diferentes quimioterápicos como antraciclinas, taxanos, isoladamente ou em associação. Os primeiros estudos são do início da década passada e utilizaram plataformas de microarray produzidas pelos pesquisadores. Trabalhos mais recentes utilizam plataformas de microarray comerciais, cujos dados são depositados em bancos públicos, permitindo análise de um número maior de amostras. Foram identificados vários perfis transcricionais associados à resposta patológica completa. Outros autores utilizaram como desfecho a resposta clínica ao tratamento, determinando, nesse caso, um painel preditivo de resistência ao esquema quimioterápico em questão. Essa questão também é fundamental, pois pode contribuir para individualizar o tratamento, permitindo que pacientes resistentes a determinado agente quimioterápico sejam submetidos a outro esquema terapêutico. CONCLUSÃO: A identificação de pacientes responsivos à quimioterapia é de fundamental interesse e, apesar de passos importantes terem sido dados, o assunto merece estudos adicionais em vista de sua complexidade.

OBJECTIVE: To improve the accuracy predictive models of response to neoadjuvante chemotherapy in breast cancer, cDNA microarray technology was used to study tumor transcriptional profile. Gene signatures associated with predicting the response to neoadjuvante chemotherapy are the subject of this review. METHODS: The data base http://www.ncbi.nlm.nih.gov/pubmed/ search was conducted by using the words "breast cancer" AND "neoadjuvante/primary chemotherapy" AND "gene expression profile/microarray". After excluding the repeats and selecting the publications considered most relevant by the authors to be presented, 279 publications were retrieved. RESULTS: The number of publications regarding this subject has been increasing over the years, reaching over 50 in 2010, including the response to different chemotherapeutic drugs, such as anthracyclines and taxanes either alone or in combination. The first studies are from early last decade and used microarray platforms produced by the investigators. Recent studies have used commercial microarray platforms whose data have been stored in public databases, allowing for the analysis of a higher number of samples. Several transcriptional profiles associated with the complete pathological response were identified. Other authors used the clinical response to treatment as an endpoint, and, in this case, a predictive panel of resistance to the chemotherapeutic regimen at issue was determined. This is also a key issue, as it can contribute to individualize treatment, allowing patients resistant to a certain chemotherapeutic agent to be offered another therapeutic regimen. CONCLUSION: Identifying patients responsive to chemotherapy is of essential interest and despite major steps have been taken, the issue warrants further studies in view of its complexity.