Interpretation of coefficients of glmnet - LASSO/Cox model?

I have done a LASSO / Cox model run for a large dataset of 10K observations which has 1200 Variables.

fit    <- glmnet(   x, Surv(time, status), alpha=1, family='cox')
cv.fit <- cv.glmnet(x, Surv(time, status), alpha=1, family='cox')


After CV the model selected 56 variables which have non-zero coefficients, some of the coefficients have negative values and some have positive. I would like to know whether we say something about their significance with respect to the coefficient values of the variables?

What we can say about coefficients with negative value and coefficient with positive value?

Some Variables  and its Coefficients Values
CSI_SUPPORT               -2.51E-19
Power.Glass.Moonroof       0.046261522
FLOOR_PLAN_SUPPORT        -0.005169085
R.Design.Nubuck.Off.Black  0.254841459
TOTAL_AMOUNT              -6.19E-05
K36100                    -0.062819229
K36100                    -0.237663697
Textile.Off.Black.seats    0.159802697
Design.Leather.Black      -0.401298769
MARKETING_SUPPORT         -0.000182012

• Is it possible to have a link or a reference to justify that LASSO-Cox coefficients have a roughly similar interpretation as in a standard Cox model? Thank you in advance, Estelle – Estelle Aug 21 '18 at 15:03